462 results on '"Chiappa V."'
Search Results
2. Added value of serum proteins to clinical and ultrasound information in predicting the risk of malignancy in ovarian tumors
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Coosemans, A, primary, Ceusters, J, additional, Landolfo, C, additional, Baert, T, additional, Froyman, W, additional, Heremans, R, additional, Thirion, G, additional, Claes, S, additional, Oosterlynck, J, additional, Wouters, R, additional, Vankerckhoven, A, additional, Moro, F, additional, Mascilini, F, additional, Neumann, A, additional, Van Rompuy, AS, additional, Schols, D, additional, Billen, J, additional, Van Gorp, T, additional, Vergote, I, additional, Bourne, T, additional, Van Holsbeke, C, additional, Chiappa, V, additional, Scambia, G, additional, Testa, A, additional, Fischerova, D, additional, Timmerman, D, additional, and Van Calster, B, additional
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- 2024
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3. Using rADioMIcs and machine learning with ultrasonography for the differential diagnosis of myometRiAL tumors (the ADMIRAL pilot study). Radiomics and differential diagnosis of myometrial tumors
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Chiappa, V., Interlenghi, M., Salvatore, C., Bertolina, F., Bogani, G., Ditto, A., Martinelli, F., Castiglioni, I., and Raspagliesi, F.
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- 2021
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4. Benign descriptors and ADNEX in two-step strategy to estimate risk of malignancy in ovarian tumors: retrospective validation on IOTA 5 multicenter cohort
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Landolfo, C, Bourne, T, Froyman, W, Van Calster, B, Ceusters, J, Testa, A, Wynants, L, Sladkevicius, P, Van Holsbeke, C, Domali, E, Fruscio, R, Epstein, E, Franchi, D, Kudla, M, Chiappa, V, Alcazar, J, Leone, F, Buonomo, F, Coccia, M, Guerriero, S, Deo, N, Jokubkiene, L, Savelli, L, Fischerova, D, Czekierdowski, A, Kaijser, J, Coosemans, A, Scambia, G, Vergote, I, Timmerman, D, Valentin, L, Landolfo C., Bourne T., Froyman W., Van Calster B., Ceusters J., Testa A. C., Wynants L., Sladkevicius P., Van Holsbeke C., Domali E., Fruscio R., Epstein E., Franchi D., Kudla M. J., Chiappa V., Alcazar J. L., Leone F. P. G., Buonomo F., Coccia M. E., Guerriero S., Deo N., Jokubkiene L., Savelli L., Fischerova D., Czekierdowski A., Kaijser J., Coosemans A., Scambia G., Vergote I., Timmerman D., Valentin L., Landolfo, C, Bourne, T, Froyman, W, Van Calster, B, Ceusters, J, Testa, A, Wynants, L, Sladkevicius, P, Van Holsbeke, C, Domali, E, Fruscio, R, Epstein, E, Franchi, D, Kudla, M, Chiappa, V, Alcazar, J, Leone, F, Buonomo, F, Coccia, M, Guerriero, S, Deo, N, Jokubkiene, L, Savelli, L, Fischerova, D, Czekierdowski, A, Kaijser, J, Coosemans, A, Scambia, G, Vergote, I, Timmerman, D, Valentin, L, Landolfo C., Bourne T., Froyman W., Van Calster B., Ceusters J., Testa A. C., Wynants L., Sladkevicius P., Van Holsbeke C., Domali E., Fruscio R., Epstein E., Franchi D., Kudla M. J., Chiappa V., Alcazar J. L., Leone F. P. G., Buonomo F., Coccia M. E., Guerriero S., Deo N., Jokubkiene L., Savelli L., Fischerova D., Czekierdowski A., Kaijser J., Coosemans A., Scambia G., Vergote I., Timmerman D., and Valentin L.
- Abstract
Objective: Previous work suggested that the ultrasound-based benign Simple Descriptors can reliably exclude malignancy in a large proportion of women presenting with an adnexal mass. We aim to validate a modified version of the Benign Simple Descriptors (BD), and we introduce a two-step strategy to estimate the risk of malignancy: if the BDs do not apply, the ADNEX model is used to estimate the risk of malignancy. Methods: This is a retrospective analysis using the data from the 2-year interim analysis of the IOTA5 study, in which consecutive patients with at least one adnexal mass were recruited irrespective of subsequent management (conservative or surgery). The main outcome was classification of tumors as benign or malignant, based on histology or on clinical and ultrasound information during one year of follow-up. Multiple imputation was used when outcome based on follow-up was uncertain according to predefined criteria. Results: 8519 patients were recruited at 36 centers between 2012 and 2015. We included all masses that were not already in follow-up at recruitment from 17 centers with good quality surgical and follow-up data, leaving 4905 patients for statistical analysis. 3441 (70%) tumors were benign, 978 (20%) malignant, and 486 (10%) uncertain. The BDs were applicable in 1798/4905 (37%) tumors, and 1786 (99.3%) of these were benign. The two-step strategy based on ADNEX without CA125 had an area under the receiver operating characteristic curve (AUC) of 0.94 (95% CI, 0.91-0.95). The risk of malignancy was slightly underestimated, but calibration varied between centers. A sensitivity analysis in which we expanded the definition of uncertain outcome resulted in 1419 (29%) tumors with uncertain outcome and an AUC of the two-step strategy without CA125 of 0.93 (95% CI, 0.91-0.95). Conclusion: A large proportion of adnexal masses can be classified as benign by the BDs. For the remaining masses the ADNEX model can be used to estimate the risk of malignancy. This
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- 2023
5. EP26.08: Imaging in gynecological disease: clinical and ultrasound characteristics of uterine malignant mixed Müllerian tumours
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Chiappa, V., primary, Palladino, S., additional, Ciccarone, F., additional, Testa, A. C., additional, De Meis, L., additional, Frühauf, F., additional, Fischerová, D., additional, Fruscio, R., additional, Pascual, M., additional, Epstein, E., additional, Sladkevicius, P., additional, Savelli, L., additional, Biscione, A., additional, Valentin, L., additional, and Franchi, D., additional
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- 2023
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6. OC19.01: Prediction of outcome in endometrial cancer improved by combining traditional with sonographic and demographic parameters: IETA4 cohort follow‐up
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Green, R. W., primary, Heremans, R., additional, Fischerová, D., additional, Yan, J., additional, Eriksson, L., additional, Mascilini, F., additional, Testa, A. C., additional, Franchi, D., additional, Sladkevicius, P., additional, Valentin, L., additional, Opolskiene, G., additional, Haak, L. A., additional, Fruscio, R., additional, Chiappa, V., additional, Van Holsbeke, C., additional, Alcazar, J., additional, Cibula, D., additional, Guerriero, S., additional, Frühauf, F., additional, Carlson, J., additional, Mestdagh, W., additional, Bourne, T., additional, Timmerman, D., additional, Van den Bosch, T., additional, and Epstein, E., additional
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- 2023
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7. OC12.08: Observer‐reality agreement for tumour spread and prediction non‐resectability in ovarian cancer (ISAAC study, imaging study on advanced ovarian cancer)
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Fischerová, D., primary, Pesta, M., additional, Blazko, M., additional, Moruzzi, M., additional, Hundarova, K., additional, Scovazzi, U., additional, de Sousa, N., additional, Ambrosio, M., additional, Testa, A. C., additional, Franchi, D., additional, Chiappa, V., additional, Alcazar, J., additional, Pinto, P., additional, and Cibula, D., additional
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- 2023
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8. EP29.08: Anamnestic and ultrasound findings of uterine perivascular epithelioid cell neoplasms
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Doglioli, M., primary, Ferla, S., additional, De Meis, L., additional, Chiappa, V., additional, Casadio, P., additional, Del Forno, S., additional, Raspagliesi, F., additional, and Seracchioli, R., additional
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- 2023
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9. OP12.05: Patient satisfaction with ultrasound, CT and WB‐DWI/MRI for preoperative ovarian cancer staging: a multicentre prospective survey
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Pinto, P., primary, Borcinova, M., additional, Wiesnerova, M., additional, Frühauf, F., additional, Burgetova, A., additional, Mašek, M., additional, Lambert, L., additional, Chiappa, V., additional, Franchi, D., additional, Testa, A. C., additional, Moro, F., additional, Avesani, G., additional, Panico, C., additional, Alessi, S., additional, Pricolo, P., additional, Vigorito, R., additional, Calareso, G., additional, Kocian, R., additional, Slama, J., additional, Fagotti, A., additional, Urbinati, A. M. Vidal, additional, Signorelli, M., additional, Valentin, L., additional, Cibula, D., additional, and Fischerová, D., additional
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- 2023
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10. OC03.03: Adnexal masses and risk of malignancy by radiomics: the future is now
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Chiappa, V., primary, Interlenghi, M., additional, Bascio, L. Spanò, additional, Salvatore, C., additional, Fruscio, R., additional, Ferrero, S., additional, Rosati, F., additional, De Meis, L., additional, Rolla, M., additional, Ficarelli, S., additional, Pino, I., additional, Franchi, D., additional, Mor, E., additional, Maggiore, U. Leone Roberti, additional, Bogani, G., additional, Raspagliesi, F., additional, and Castiglioni, I., additional
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- 2023
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11. Benign descriptors and ADNEX in two-step strategy to estimate risk of malignancy in ovarian tumors: retrospective validation in IOTA5 multicenter cohort
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Landolfo, C, Bourne, T, Froyman, W, Van Calster, B, Ceusters, J, Testa, Antonia Carla, Wynants, L, Sladkevicius, P, Van Holsbeke, C, Domali, E, Fruscio, R, Epstein, E, Franchi, D, Kudla, M J, Chiappa, V, Alcazar, J L, Leone, F P G, Buonomo, F, Coccia, M E, Guerriero, Silvia, Deo, N, Jokubkiene, L, Savelli, L, Fischerova, D, Czekierdowski, A, Kaijser, J, Coosemans, A, Scambia, Giovanni, Vergote, I, Timmerman, D, Valentin, L, Testa, A C (ORCID:0000-0003-2217-8726), Guerriero, S, Scambia, G (ORCID:0000-0003-2758-1063), Landolfo, C, Bourne, T, Froyman, W, Van Calster, B, Ceusters, J, Testa, Antonia Carla, Wynants, L, Sladkevicius, P, Van Holsbeke, C, Domali, E, Fruscio, R, Epstein, E, Franchi, D, Kudla, M J, Chiappa, V, Alcazar, J L, Leone, F P G, Buonomo, F, Coccia, M E, Guerriero, Silvia, Deo, N, Jokubkiene, L, Savelli, L, Fischerova, D, Czekierdowski, A, Kaijser, J, Coosemans, A, Scambia, Giovanni, Vergote, I, Timmerman, D, Valentin, L, Testa, A C (ORCID:0000-0003-2217-8726), Guerriero, S, and Scambia, G (ORCID:0000-0003-2758-1063)
- Abstract
ObjectivePrevious work has suggested that the ultrasound-based benign simple descriptors (BDs) can reliably exclude malignancy in a large proportion of women presenting with an adnexal mass. This study aimed to validate a modified version of the BDs and to validate a two-step strategy to estimate the risk of malignancy, in which the modified BDs are followed by the Assessment of Different NEoplasias in the adneXa (ADNEX) model if modified BDs do not apply. MethodsThis was a retrospective analysis using data from the 2-year interim analysis of the International Ovarian Tumor Analysis (IOTA) Phase-5 study, in which consecutive patients with at least one adnexal mass were recruited irrespective of subsequent management (conservative or surgery). The main outcome was classification of tumors as benign or malignant, based on histology or on clinical and ultrasound information during 1 year of follow-up. Multiple imputation was used when outcome based on follow-up was uncertain according to predefined criteria. ResultsA total of 8519 patients were recruited at 36 centers between 2012 and 2015. We excluded patients who were already in follow-up at recruitment and all patients from 19 centers that did not fulfil our criteria for good-quality surgical and follow-up data, leaving 4905 patients across 17 centers for statistical analysis. Overall, 3441 (70%) tumors were benign, 978 (20%) malignant and 486 (10%) uncertain. The modified BDs were applicable in 1798/4905 (37%) tumors, of which 1786 (99.3%) were benign. The two-step strategy based on ADNEX without CA125 had an area under the receiver-operating-characteristics curve (AUC) of 0.94 (95% CI, 0.92-0.96). The risk of malignancy was slightly underestimated, but calibration varied between centers. A sensitivity analysis in which we expanded the definition of uncertain outcome resulted in 1419 (29%) tumors with uncertain outcome and an AUC of the two-step strategy without CA125 of 0.93 (95% CI, 0.91-0.95). ConclusionA large
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- 2023
12. External Validation of the Ovarian-Adnexal Reporting and Data System (O-RADS) Lexicon and the International Ovarian Tumor Analysis 2-Step Strategy to Stratify Ovarian Tumors Into O-RADS Risk Groups
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Timmerman, S, Valentin, L, Ceusters, J, Testa, A, Landolfo, C, Sladkevicius, P, Van Holsbeke, C, Domali, E, Fruscio, R, Epstein, E, Franchi, D, Kudla, M, Chiappa, V, Alcazar, J, Leone, F, Buonomo, F, Coccia, M, Guerriero, S, Deo, N, Jokubkiene, L, Kaijser, J, Scambia, G, Andreotti, R, Timmerman, D, Bourne, T, Van Calster, B, Froyman, W, Timmerman, Stefan, Valentin, Lil, Ceusters, Jolien, Testa, Antonia C, Landolfo, Chiara, Sladkevicius, Povilas, Van Holsbeke, Caroline, Domali, Ekaterini, Fruscio, Robert, Epstein, Elisabeth, Franchi, Dorella, Kudla, Marek J, Chiappa, Valentina, Alcazar, Juan L, Leone, Francesco P G, Buonomo, Francesca, Coccia, Maria Elisabetta, Guerriero, Stefano, Deo, Nandita, Jokubkiene, Ligita, Kaijser, Jeroen, Scambia, Giovanni, Andreotti, Rochelle, Timmerman, Dirk, Bourne, Tom, Van Calster, Ben, Froyman, Wouter, Timmerman, S, Valentin, L, Ceusters, J, Testa, A, Landolfo, C, Sladkevicius, P, Van Holsbeke, C, Domali, E, Fruscio, R, Epstein, E, Franchi, D, Kudla, M, Chiappa, V, Alcazar, J, Leone, F, Buonomo, F, Coccia, M, Guerriero, S, Deo, N, Jokubkiene, L, Kaijser, J, Scambia, G, Andreotti, R, Timmerman, D, Bourne, T, Van Calster, B, Froyman, W, Timmerman, Stefan, Valentin, Lil, Ceusters, Jolien, Testa, Antonia C, Landolfo, Chiara, Sladkevicius, Povilas, Van Holsbeke, Caroline, Domali, Ekaterini, Fruscio, Robert, Epstein, Elisabeth, Franchi, Dorella, Kudla, Marek J, Chiappa, Valentina, Alcazar, Juan L, Leone, Francesco P G, Buonomo, Francesca, Coccia, Maria Elisabetta, Guerriero, Stefano, Deo, Nandita, Jokubkiene, Ligita, Kaijser, Jeroen, Scambia, Giovanni, Andreotti, Rochelle, Timmerman, Dirk, Bourne, Tom, Van Calster, Ben, and Froyman, Wouter
- Abstract
Importance: Correct diagnosis of ovarian cancer results in better prognosis. Adnexal lesions can be stratified into the Ovarian-Adnexal Reporting and Data System (O-RADS) risk of malignancy categories with either the O-RADS lexicon, proposed by the American College of Radiology, or the International Ovarian Tumor Analysis (IOTA) 2-step strategy. Objective: To investigate the diagnostic performance of the O-RADS lexicon and the IOTA 2-step strategy. Design, Setting, and Participants: Retrospective external diagnostic validation study based on interim data of IOTA5, a prospective international multicenter cohort study, in 36 oncology referral centers or other types of centers. A total of 8519 consecutive adult patients presenting with an adnexal mass between January 1, 2012, and March 1, 2015, and treated either with surgery or conservatively were included in this diagnostic study. Twenty-five patients were excluded for withdrawal of consent, 2777 were excluded from 19 centers that did not meet predefined data quality criteria, and 812 were excluded because they were already in follow-up at recruitment. The analysis included 4905 patients with a newly detected adnexal mass in 17 centers that met predefined data quality criteria. Data were analyzed from January 31 to March 1, 2022. Exposures: Stratification into O-RADS categories (malignancy risk <1%, 1% to <10%, 10% to <50%, and ≥50%). For the IOTA 2-step strategy, the stratification is based on the individual risk of malignancy calculated with the IOTA 2-step strategy. Main Outcomes and Measures: Observed prevalence of malignancy in each O-RADS risk category, as well as sensitivity and specificity. The reference standard was the status of the tumor at inclusion, determined by histology or clinical and ultrasonographic follow-up for 1 year. Multiple imputation was used for uncertain outcomes owing to inconclusive follow-up information. Results: Median age of the 4905 patients was 48 years (IQR, 36-62 years).
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- 2023
13. SENTINEL NODE MAPPING IN ENDOMETRIAL CANCER: ANALYSIS OF MORE THAN 200 CASES OF HYSTEROSCOPIC INJECTION OF TRACERS: EP571
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Martinelli, F, Ditto, A, Bogani, G, Roberti Maggiore, Leone U, Chiappa, V, Evangelista, M, Liberale, V, Zanaboni, F, and Raspagliesi, F
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- 2019
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14. THE IMPACT OF GENE-SPECIFIC GERMLINE PATHOGENIC VARIANTS ON CLINICAL PRESENTATION OF ENDOMETRIAL CANCER IN LYNCH SYNDROME: EP480
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Bogani, G, Chiappa, V, Ditto, A, and Raspagliesi, F
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- 2019
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15. SENTINEL NODE MAPPING VS. LYMPHADENECTOMY IN ENDOMETRIAL CANCER: A SYSTEMATIC REVIEW AND META-ANALYSIS: EP481
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Bogani, G, Ditto, A, Martinelli, F, Chiappa, V, Murgia, F, Roberti Maggiore, Leone U, and Raspagliesi, F
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- 2019
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16. PRIMARY CONIZATION OVERCOMES THE RISK OF LOCAL RECURRENCE FOLLOWING LAPAROSCOPIC RADICAL HYSTERECTOMY IN EARLY-STAGE CERVICAL CANCER: EP262
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Bogani, G, Ditto, A, Chiappa, V, and Raspagliesi, F
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- 2019
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17. LAPAROSCOPIC SURGERY IMPROVES SHORT- AND MEDIUM-TERM OUTCOMES OF NERVE-SPARING RADICAL HYSTERECTOMY: A PROPENSITY-MATCHED ANALYSIS WITH OPEN ABDOMINAL PROCEDURES: EP263
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Bogani, G, Chiappa, V, Ditto, A, and Raspagliesi, F
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- 2019
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18. 165 A retrospective study showing the safety of minimally invasive surgery of cervical cancer
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Ditto, A, Leone Roberti Maggiore, U, Martinelli, F, Bogani, G, Evangelista, MT, Chiappa, V, Murgia, F, Sonetto, C, Maltese, G, Zanaboni, F, Raspagliesi, F, and Raspagliesi, F
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- 2019
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19. 37 Reducing overtreatment of early stage ovarian cancer: strategies implementation by a mirna-driven prognostic assessment
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Ditto, A, Raspagliesi, F, Leone Roberti Maggiore, U, Chiappa, V, Bogani, G, Liberale, V, Evangelista, MT, Sonetto, C, Maltese, G, Zanaboni, F, Carcangiu, ML, Paolini, B, Mezzanzanica, D, Alberti, P, Murgia, F, Tomassetti, A, De Cecco, L, Devecchi, A, and Bagnoli, M
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- 2019
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20. 41 Sentinel node mapping vs. lymphadenectomy in endometrial cancer: a systematic review and meta-analysis
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Bogani, G, Ditto, A, Murgia, F, Chiappa, V, Leone, U, and Raspagliesi, F
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- 2019
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21. Benign descriptors and ADNEX in two‐step strategy to estimate risk of malignancy in ovarian tumors: retrospective validation in IOTA5 multicenter cohort
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Landolfo, C., primary, Bourne, T., additional, Froyman, W., additional, Van Calster, B., additional, Ceusters, J., additional, Testa, A. C., additional, Wynants, L., additional, Sladkevicius, P., additional, Van Holsbeke, C., additional, Domali, E., additional, Fruscio, R., additional, Epstein, E., additional, Franchi, D., additional, Kudla, M. J., additional, Chiappa, V., additional, Alcazar, J. L., additional, Leone, F. P. G., additional, Buonomo, F., additional, Coccia, M. E., additional, Guerriero, S., additional, Deo, N., additional, Jokubkiene, L., additional, Savelli, L., additional, Fischerova, D., additional, Czekierdowski, A., additional, Kaijser, J., additional, Coosemans, A., additional, Scambia, G., additional, Vergote, I., additional, Timmerman, D., additional, and Valentin, L., additional
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- 2023
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22. OC04.03: Preoperative assessment of non‐resectability in ovarian cancer according to ESGO criteria: a prospective multicentre study using imaging (ISAAC study).
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Moro, F., Pinto, P., Chiappa, V., Testa, A., Alcazar, J., Franchi, D., Frühauf, F., Burgetova, A., Mašek, M., Lambert, L., Arraiza, M., Altmanova, D., Avesani, G., Panico, C., Alessi, S., Pricolo, P., Vigorito, R., Calareso, G., Kocian, R., and Slama, J.
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DIFFUSION magnetic resonance imaging ,RECEIVER operating characteristic curves ,COMPUTED tomography ,WHOLE body imaging ,GYNECOLOGIC oncology ,CANCER prognosis - Abstract
The article titled "OC04.03: Preoperative assessment of non-resectability in ovarian cancer according to ESGO criteria: a prospective multicentre study using imaging (ISAAC study)" presents the findings of a study conducted in five European gynecological oncology centers between 2018 and 2022. The study aimed to compare the effectiveness of abdominopelvic ultrasound, computed tomography (CT), and whole-body diffusion-weighted Magnetic Resonance Imaging (WB-DWI/MRI) in predicting surgical outcomes for patients with tubo-ovarian cancer. The results showed that ultrasound was non-inferior to CT and WB-DWI/MRI in predicting non-resectability when performed by experienced examiners. The study highlights the potential of ultrasound as a reliable imaging method for preoperative assessment in patients with tubo-ovarian cancer. [Extracted from the article]
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- 2024
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23. OC04.01: Prediction of non‐resectability in ovarian cancer patients using peritoneal cancer index: a prospective multicentre study using imaging (ISAAC study).
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Pinto, P., Moro, F., Chiappa, V., Testa, A.C., Franchi, D., Alcazar, J., Frühauf, F., Burgetova, A., Mašek, M., Lambert, L., Altmanova, D., Arraiza, M., Avesani, G., Panico, C., Alessi, S., Pricolo, P., Vigorito, R., Calareso, G., Kocian, R., and Slama, J.
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DIFFUSION magnetic resonance imaging ,COMPUTED tomography ,WHOLE body imaging ,PERITONEAL cancer ,CANCER patients ,NEOADJUVANT chemotherapy - Abstract
This article presents the findings of a European multicentric observational cohort study that aimed to evaluate the performance of the peritoneal cancer index (PCI) using different imaging methods in assessing peritoneal carcinomatosis and predicting non-resectability in advanced tubo-ovarian cancer patients. The study compared the use of ultrasound, computed tomography (CT), and whole-body diffusion-weighted magnetic resonance imaging (WB-DWI/MRI) in calculating the PCI score. The results showed that ultrasound was non-inferior to CT in predicting non-resectability, and it was the best imaging method for determining preoperative PCI when performed by experienced examiners in dedicated oncological centers. [Extracted from the article]
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- 2024
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24. Imaging in gynecological disease: clinical and ultrasound characteristics of ovarian carcinosarcomas
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Ciccarone, F, Biscione, A, Moro, F, Fischerova, D, Savelli, L, Munaretto, M, Jokubkiene, L, Sladkevicius, P, Chiappa, V, Fruscio, R, Franchi, D, Epstein, E, Timmerman, D, Froyman, W, Valentin, L, Scambia, G, Testa, A C, Ciccarone, F, Biscione, A, Moro, F, Fischerova, D, Savelli, L, Munaretto, M, Jokubkiene, L, Sladkevicius, P, Chiappa, V, Fruscio, R, Franchi, D, Epstein, E, Timmerman, D, Froyman, W, Valentin, L, Scambia, G, and Testa, A
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diagnostic imaging ,pattern recognition ,carcinosarcoma ,ultrasonography ,ovarian neoplasm - Abstract
OBJECTIVE: To describe the clinical and ultrasound characteristics of ovarian carcinosarcoma. METHODS: This was a retrospective multicenter study. Patients with a histological diagnosis of ovarian carcinosarcoma, who had undergone preoperative ultrasound examination between 2010 and 2019, were identified from the International Ovarian Tumor Analysis (IOTA) database. Additional patients who were examined outside of the IOTA study were identified from the databases of the participating centers. The masses were described using the terms and definitions of the IOTA group. Additionally, two experienced ultrasound examiners reviewed all available images to identify typical ultrasound features using pattern recognition. RESULTS: Ninety-one patients with ovarian carcinosarcoma who had undergone ultrasound examination were identified, of whom 24 were examined within the IOTA studies and 67 were examined outside of the IOTA studies. Median age at diagnosis was 66 (range, 33-91) years and 84/91 (92.3%) patients were postmenopausal. Most patients (67/91, 73.6%) were symptomatic, with the most common complaint being pain (51/91, 56.0%). Most tumors (67/91, 73.6%) were International Federation of Gynecology and Obstetrics (FIGO) Stage III or IV. Bilateral lesions were observed on ultrasound in 46/91 (50.5%) patients. Ascites was present in 38/91 (41.8%) patients. The median largest tumor diameter was 100 (range, 18-260) mm. All ovarian carcinosarcomas contained solid components, and most were described as solid (66/91, 72.5%) or multilocular-solid (22/91, 24.2%). The median diameter of the largest solid component was 77.5 (range, 11-238) mm. Moderate or rich vascularization was found in 78/91 (85.7%) cases. Retrospective analysis of ultrasound images and videoclips using pattern recognition in 73 cases revealed that all tumors had irregular margins and inhomogeneous echogenicity of the solid components. Forty-seven of 73 (64.4%) masses appeared as a solid tumor with cystic areas. Cooked appearance of the solid tissue was identified in 28/73 (38.4%) tumors. No pathognomonic ultrasound sign of ovarian carcinosarcoma was found. CONCLUSIONS: Ovarian carcinosarcomas are usually diagnosed in postmenopausal women and at an advanced stage. The most common ultrasound appearance is a large solid tumor with irregular margins, inhomogeneous echogenicity of the solid tissue and cystic areas. The second most common pattern is a large multilocular-solid mass with inhomogeneous echogenicity of the solid tissue.
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- 2022
25. Developing and validating ultrasound‐based radiomics models for predicting high‐risk endometrial cancer
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Moro, F., primary, Albanese, M., additional, Boldrini, L., additional, Chiappa, V., additional, Lenkowicz, J., additional, Bertolina, F., additional, Mascilini, F., additional, Moroni, R., additional, Gambacorta, M. A., additional, Raspagliesi, F., additional, Scambia, G., additional, Testa, A. C., additional, and Fanfani, F., additional
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- 2022
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26. Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study
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Van Calster, B, Valentin, L, Froyman, W, Landolfo, C, Ceusters, J, Testa, A, Wynants, L, Sladkevicius, P, Van Holsbeke, C, Domali, E, Fruscio, R, Epstein, E, Franchi, D, Kudla, M, Chiappa, V, Alcazar, J, Leone, F, Buonomo, F, Coccia, M, Guerriero, S, Deo, N, Jokubkiene, L, Savelli, L, Fischerova, D, Czekierdowski, A, Kaijser, J, Coosemans, A, Scambia, G, Vergote, I, Bourne, T, Timmerman, D, Van Calster B., Valentin L., Froyman W., Landolfo C., Ceusters J., Testa A. C., Wynants L., Sladkevicius P., Van Holsbeke C., Domali E., Fruscio R., Epstein E., Franchi D., Kudla M. J., Chiappa V., Alcazar J. L., Leone F. P. G., Buonomo F., Coccia M. E., Guerriero S., Deo N., Jokubkiene L., Savelli L., Fischerova D., Czekierdowski A., Kaijser J., Coosemans A., Scambia G., Vergote I., Bourne T., Timmerman D., Van Calster, B, Valentin, L, Froyman, W, Landolfo, C, Ceusters, J, Testa, A, Wynants, L, Sladkevicius, P, Van Holsbeke, C, Domali, E, Fruscio, R, Epstein, E, Franchi, D, Kudla, M, Chiappa, V, Alcazar, J, Leone, F, Buonomo, F, Coccia, M, Guerriero, S, Deo, N, Jokubkiene, L, Savelli, L, Fischerova, D, Czekierdowski, A, Kaijser, J, Coosemans, A, Scambia, G, Vergote, I, Bourne, T, Timmerman, D, Van Calster B., Valentin L., Froyman W., Landolfo C., Ceusters J., Testa A. C., Wynants L., Sladkevicius P., Van Holsbeke C., Domali E., Fruscio R., Epstein E., Franchi D., Kudla M. J., Chiappa V., Alcazar J. L., Leone F. P. G., Buonomo F., Coccia M. E., Guerriero S., Deo N., Jokubkiene L., Savelli L., Fischerova D., Czekierdowski A., Kaijser J., Coosemans A., Scambia G., Vergote I., Bourne T., and Timmerman D.
- Abstract
OBJECTIVE: To evaluate the performance of diagnostic prediction models for ovarian malignancy in all patients with an ovarian mass managed surgically or conservatively. DESIGN: Multicentre cohort study. SETTING: 36 oncology referral centres (tertiary centres with a specific gynaecological oncology unit) or other types of centre. PARTICIPANTS: Consecutive adult patients presenting with an adnexal mass between January 2012 and March 2015 and managed by surgery or follow-up. MAIN OUTCOME MEASURES: Overall and centre specific discrimination, calibration, and clinical utility of six prediction models for ovarian malignancy (risk of malignancy index (RMI), logistic regression model 2 (LR2), simple rules, simple rules risk model (SRRisk), assessment of different neoplasias in the adnexa (ADNEX) with or without CA125). ADNEX allows the risk of malignancy to be subdivided into risks of a borderline, stage I primary, stage II-IV primary, or secondary metastatic malignancy. The outcome was based on histology if patients underwent surgery, or on results of clinical and ultrasound follow-up at 12 (±2) months. Multiple imputation was used when outcome based on follow-up was uncertain. RESULTS: The primary analysis included 17 centres that met strict quality criteria for surgical and follow-up data (5717 of all 8519 patients). 812 patients (14%) had a mass that was already in follow-up at study recruitment, therefore 4905 patients were included in the statistical analysis. The outcome was benign in 3441 (70%) patients and malignant in 978 (20%). Uncertain outcomes (486, 10%) were most often explained by limited follow-up information. The overall area under the receiver operating characteristic curve was highest for ADNEX with CA125 (0.94, 95% confidence interval 0.92 to 0.96), ADNEX without CA125 (0.94, 0.91 to 0.95) and SRRisk (0.94, 0.91 to 0.95), and lowest for RMI (0.89, 0.85 to 0.92). Calibration varied among centres for all models, however the ADNEX models and SRRisk were th
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- 2020
27. Imaging in gynecological disease (23): clinical and ultrasound characteristics of ovarian carcinosarcoma
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Ciccarone, F., Biscione, A., Moro, Francesca, Fischerova, D., Savelli, L., Munaretto, M., Jokubkiene, L., Sladkevicius, P., Chiappa, V., Fruscio, R., Franchi, D., Epstein, E., Timmerman, D., Froyman, W., Valentin, L., Scambia, Giovanni, Testa, Antonia Carla, Moro F., Scambia G. (ORCID:0000-0003-2758-1063), Testa A. C. (ORCID:0000-0003-2217-8726), Ciccarone, F., Biscione, A., Moro, Francesca, Fischerova, D., Savelli, L., Munaretto, M., Jokubkiene, L., Sladkevicius, P., Chiappa, V., Fruscio, R., Franchi, D., Epstein, E., Timmerman, D., Froyman, W., Valentin, L., Scambia, Giovanni, Testa, Antonia Carla, Moro F., Scambia G. (ORCID:0000-0003-2758-1063), and Testa A. C. (ORCID:0000-0003-2217-8726)
- Abstract
Objective: To describe the clinical and ultrasound characteristics of ovarian carcinosarcoma. Methods: This was a retrospective multicenter study. Patients with a histological diagnosis of ovarian carcinosarcoma, who had undergone preoperative ultrasound examination between 2010 and 2019, were identified from the International Ovarian Tumor Analysis (IOTA) database. Additional patients who were examined outside of the IOTA study were identified from the databases of the participating centers. The masses were described using the terms and definitions of the IOTA group. Additionally, two experienced ultrasound examiners reviewed all available images to identify typical ultrasound features using pattern recognition. Results: Ninety-one patients with ovarian carcinosarcoma who had undergone ultrasound examination were identified, of whom 24 were examined within the IOTA studies and 67 were examined outside of the IOTA studies. Median age at diagnosis was 66 (range, 33–91) years and 84/91 (92.3%) patients were postmenopausal. Most patients (67/91, 73.6%) were symptomatic, with the most common complaint being pain (51/91, 56.0%). Most tumors (67/91, 73.6%) were International Federation of Gynecology and Obstetrics (FIGO) Stage III or IV. Bilateral lesions were observed on ultrasound in 46/91 (50.5%) patients. Ascites was present in 38/91 (41.8%) patients. The median largest tumor diameter was 100 (range, 18–260) mm. All ovarian carcinosarcomas contained solid components, and most were described as solid (66/91, 72.5%) or multilocular-solid (22/91, 24.2%). The median diameter of the largest solid component was 77.5 (range, 11–238) mm. Moderate or rich vascularization was found in 78/91 (85.7%) cases. Retrospective analysis of ultrasound images and videoclips using pattern recognition in 73 cases revealed that all tumors had irregular margins and inhomogeneous echogenicity of the solid components. Forty-seven of 73 (64.4%) masses appeared as a solid tumor with cystic areas.
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- 2022
28. Developing and validating ultrasound-based radiomics models for predicting high-risk endometrial cancer
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Moro, Francesca, Albanese, M, Boldrini, Luca, Chiappa, V, Lenkowicz, Jacopo, Bertolina, F, Mascilini, F, Moroni, R, Gambacorta, Maria Antonietta, Raspagliesi, F, Scambia, Giovanni, Testa, Antonia Carla, Fanfani, Francesco, Moro, F, Boldrini, L, Lenkowicz, J, Gambacorta, M A (ORCID:0000-0001-5455-8737), Scambia, G (ORCID:0000-0003-2758-1063), Testa, A C (ORCID:0000-0003-2217-8726), Fanfani, F (ORCID:0000-0003-1991-7284), Moro, Francesca, Albanese, M, Boldrini, Luca, Chiappa, V, Lenkowicz, Jacopo, Bertolina, F, Mascilini, F, Moroni, R, Gambacorta, Maria Antonietta, Raspagliesi, F, Scambia, Giovanni, Testa, Antonia Carla, Fanfani, Francesco, Moro, F, Boldrini, L, Lenkowicz, J, Gambacorta, M A (ORCID:0000-0001-5455-8737), Scambia, G (ORCID:0000-0003-2758-1063), Testa, A C (ORCID:0000-0003-2217-8726), and Fanfani, F (ORCID:0000-0003-1991-7284)
- Abstract
n/a
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- 2022
29. Imaging in gynecological disease (23): clinical and ultrasound characteristics of ovarian carcinosarcoma
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Ciccarone, F., primary, Biscione, A., additional, Moro, F., additional, Fischerova, D., additional, Savelli, L., additional, Munaretto, M., additional, Jokubkiene, L., additional, Sladkevicius, P., additional, Chiappa, V., additional, Fruscio, R., additional, Franchi, D., additional, Epstein, E., additional, Timmerman, D., additional, Froyman, W., additional, Valentin, L., additional, Scambia, G., additional, and Testa, A. C., additional
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- 2022
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30. On the Origin of Multiple Melting Endotherms of Poly(Ethylene) Single Crystals
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Alfonso, G. C., Ceschina, M., Chiappa, V., Pedemonte, E., and Dosière, Marcel, editor
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- 1993
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31. EP23.15: Diagnostic performance of the IOTA two‐step strategy in an oncological referral centre.
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Albertini, G.C., Ortelli, G., Bogani, G., Palladino, S., Maggiore, U. Leone Roberti, Raspagliesi, F., and Chiappa, V.
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BENIGN tumors ,MEDIAN (Mathematics) ,DATA analysis ,HISTOLOGY ,RETROSPECTIVE studies - Abstract
This article, titled "Diagnostic performance of the IOTA two-step strategy in an oncological referral centre," provides an external validation of the "IOTA two-step strategy" in women with adnexal masses referred to the Fondazione IRCCS Istituto Nazionale dei Tumori. The study analyzed data from 1,200 patients with adnexal masses, excluding those already in follow-up and those without good-quality follow-up data. The main outcome was the classification of tumors as benign or malignant based on histology or clinical and ultrasound information during at least one year of conservative follow-up. The study found that the "IOTA two-step strategy" had good diagnostic performance in predicting malignancy in this high-risk population. [Extracted from the article]
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- 2024
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32. OC05.06: Clinical and ultrasound characteristics of ovarian cancers arising from mature cystic teratoma.
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Giudice, M., Pozzati, F., Urbinati, A., Franchi, D., Landolfo, C., Savelli, L., Guerriero, S., Buonomo, F., Chiappa, V., Valentin, L., Froyman, W., Timmerman, D., Bourne, T., Testa, A., and Moro, F.
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OVARIAN tumors ,SQUAMOUS cell carcinoma ,TERATOMA ,OVARIAN cancer ,CARCINOID - Abstract
This article, titled "OC05.06: Clinical and ultrasound characteristics of ovarian cancers arising from mature cystic teratoma," provides a retrospective multicenter study on the clinical and ultrasound features of malignant tumors arising from mature cystic teratoma (MCT). The study analyzed 22 patients with histologically confirmed malignancies arising from MCT between 2015 and 2023. The results showed that the majority of these tumors were large unilateral adnexal masses, with a cystic component and a solid component. The solid component exhibited inhomogeneous echogenicity and moderate vascularization. This study provides valuable information on the ultrasonographic appearance of cancers arising from MCT. [Extracted from the article]
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- 2024
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33. OC04.02: Prediction of non‐resectability in ovarian cancer using the predictive index value score: a prospective multicentre study using imaging (ISAAC study).
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Moro, F., Pinto, P., Chiappa, V., Testa, A., Alcazar, J., Franchi, D., Frühauf, F., Burgetova, A., Mašek, M., Lambert, L., Arraiza, M., Altmanova, D., Avesani, G., Panico, C., Alessi, S., Pricolo, P., Vigorito, R., Calareso, G., Roman, K., and Slama, J.
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DIFFUSION magnetic resonance imaging ,RECEIVER operating characteristic curves ,COMPUTED tomography ,MAGNETIC resonance imaging ,SMALL intestine ,CONTRAST-enhanced magnetic resonance imaging - Abstract
This article discusses a study conducted in European gynecological oncology centers to evaluate the performance of different imaging methods in predicting non-resectability in patients with suspected tubo-ovarian cancer. The study used a laparoscopy-based scoring system called the Predictive Index Value (PIV) score, which assesses the presence of disease in six predefined intra-abdominal sites. The imaging methods evaluated were ultrasound (US), contrast-enhanced computed tomography (CT), and whole-body magnetic resonance imaging with diffusion-weighted sequence (WB-DWI/MRI). The results showed that all three imaging methods had similar performance in predicting non-resectable tumors, with US being a viable alternative to MRI or CT when performed by experienced examiners in dedicated oncological centers. [Extracted from the article]
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- 2024
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34. Imaging in gynecological disease: ultrasound features of malignant ovarian yolk sac tumors (endodermal sinus tumors)
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Anfelter, P, Testa, A, Chiappa, V, Froyman, W, Fruscio, R, Guerriero, S, Alcazar, J L, Mascillini, F, Pascual, M A, Sibal, M, Savelli, L, Zannoni, G F, Timmerman, D, Epstein, E, Anfelter, P, Testa, A, Chiappa, V, Froyman, W, Fruscio, R, Guerriero, S, Alcazar, J, Mascillini, F, Pascual, M, Sibal, M, Savelli, L, Zannoni, G, Timmerman, D, and Epstein, E
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ultrasound feature ,malignant germ cell tumor ,endodermal sinus tumor ,yolk sac tumor - Abstract
Objectives To describe the clinical and sonographic characteristics of malignant ovarian yolk sac tumors. Methods In this retrospective multicenter‐study we included 21 patients with a histological diagnosis of ovarian yolk sac tumor where still images and/or videoclips were available. Ten patients collected from the IOTA‐studies, had undergone a standardized preoperative ultrasound examination by an experienced ultrasound examiner between 1999 and 2016. The remaining eleven were identified through medical files, where images were retrieved from local image work stations and PACs systems. All tumors were described using IOTA terminology. The collected images and video clips were used for additional characterization by two observers. Results All cases were pure yolk sac tumors except for one, that was a mixed tumor (80% yolk sac tumor and 20% embryonic carcinoma). Median age at diagnosis was 25 (Interquartile range, IQR 19.5‐30.5) years. Seventy‐six percent (16/21) were FIGO stage I‐II when diagnosed. 58% (11/19) women felt pain during examination and one patient presented with ovarian torsion. Median S‐AFP level was 4755 μg/L (IQR, 1071 ‐ 25303) and CA‐125 126 kU/L (IQR, 35‐227). On ultrasound assessment 95% (20/21) of tumors were unilateral. The median of the maximal tumor diameter was 157 mm (IQR 107‐181), and the largest solid component 110 mm (IQR 66‐159). Tumors were classified either as multilocular‐solid (10/21, 48%), or as solid (11/21, 52%). Papillary projections were found in 10% (2/21) of the cases. Most tumors (20/21, 95%) were well vascularized (color score 3‐4) and none of the tumors had acoustic shadowing. Malignancy was suspected in all cases except for the patient with torsion, which presented with a color score of 1 and was classified as probably benign. Image and video clip quality was considered as adequate in 18/21 cases. Reviewing the images and videoclips we found that all tumors contained both solid components and cystic spaces, and that 89% (16/18) of the tumors had an irregular, still fine‐textured, and slightly hyperechoic solid tissue, giving them a characteristic appearance. Conclusion Malignant ovarian yolk sac tumors are often detected at an early stage, in young women usually in the second or third decade of life, presenting with pain and markedly elevated S‐AFP. On ultrasound yolk sac tumors are mostly unilateral, large, multilocular‐solid or solid, with fine‐textured slightly hyperechoic solid tissue, and rich vascularization.
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- 2020
35. 364 RadiOmics and molecular classification in endometrial cancer (the ROME study): a step forward to a simplified precision medicine
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Bogani, G, primary, Castiglioni, I, additional, Chiappa, V, additional, and Raspagliesi, F, additional
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- 2021
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36. 387 Developing and validating ultrasound-based radiomics models for predicting high-risk endometrial cancer
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Moro, F, primary, Albanese, M, additional, Boldrini, L, additional, Chiappa, V, additional, Lenkowicz, J, additional, Bertolina, F, additional, Mascilini, F, additional, Moroni, R, additional, Gambacorta, MA, additional, Raspagliesi, F, additional, Scambia, G, additional, Testa, AC, additional, and Fanfani, F, additional
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- 2021
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37. 101 Hysteroscopic compared to cervical injection for sentinel node detection in endometrial cancer: a multicenter prospective randomized controlled trial
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Ditto, A, primary, Roberti Maggiore, U Leone, additional, Martinelli, F, additional, Bogani, G, additional, Signorelli, M, additional, Chiappa, V, additional, Evangelista, MT, additional, Casarin, J, additional, Ghezzi, F, additional, Perrone, AM, additional, De Iaco, P, additional, Scollo, P, additional, Scibilia, G, additional, Giorda, G, additional, Paolini, B, additional, Lo Vullo, S, additional, Mariani, L, additional, and Raspagliesi, F, additional
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- 2021
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38. OC10.01: *Developing and validating ultrasound‐based radiomics models for predicting high‐risk category in endometrial cancer patients
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Moro, F., primary, Albanese, M., additional, Boldrini, L., additional, Chiappa, V., additional, Lenkowicz, J., additional, Bertolina, F., additional, Mascilini, F., additional, Moroni, R., additional, Gambacorta, M., additional, Raspagliesi, F., additional, Scambia, G., additional, Testa, A.C., additional, and Fanfani, F., additional
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- 2021
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39. 319 Gynecologic malignancies in the era of precision medicine
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Mantiero, M, primary, Ducceschi, M, additional, Bini, M, additional, Lopez, S, additional, Duca, M, additional, Damian, S, additional, Ditto, A, additional, Martinelli, F, additional, Roberti Maggiore, U Leone, additional, Bogani, G, additional, Signorelli, M, additional, Bertolina, F, additional, Chiappa, V, additional, Paolini, B, additional, Agnelli, L, additional, and Raspagliesi, F, additional
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- 2021
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40. 100 Uterine serous carcinoma: role of surgery, risk factors and oncologic outcomes. Experience of a tertiary center
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Ditto, A, primary, Lopez, S, additional, Martinelli, F, additional, Bogani, G, additional, Lo Vullo, S, additional, Roberti Maggiore, U Leone, additional, Brusadelli, C, additional, Paolini, B, additional, Ducceschi, M, additional, Mantiero, M, additional, Chiappa, V, additional, Signorelli, M, additional, Evangelista, MT, additional, Mariani, L, additional, and Raspagliesi, F, additional
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- 2021
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41. Agreement of two-dimensional and three-dimensional transvaginal ultrasound with magnetic resonance imaging in assessment of parametrial infiltration in cervical cancer
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Chiappa, V., Di Legge, A., Valentini, A. L., Gui, B., Miccò, M., Ludovisi, M., Giansiracusa, C., Testa, A. C., and Valentin, L.
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- 2015
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42. SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study
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COVIDSurg Collaborative, GlobalSurg Collaborative, A, M., Abdelhamid, M. H. S., Alhabdan, S., Nekoval, V., Nel, C., Nel, D., Nel, H., Nelihela, P., Nelissen, E., Nelli, T., Nelson, C., Nemeth, M., Nene, A., AlHADAD, M., Neo, E. L., Neo, E. N., Neri, C. M., Neshige, S., Nespoli, L. C., Nessa, A., Nessim, C., Neuerburg, C., Neves, F., Neves, M., Alhaddad, A., Newcomb, A., Newitt, L., Newman, J., Newman, M., Newman, S., Newton, L., Neykov, V., Nezerwa, Y., Nezi, G., Ng, B., Alhaddad, W., Ng, C. S. H., Ng, C. F., Ng, C. E., Ng, D., Ng, D. S. W., Ng, J. C. K., Ng, J. Y., Ng, J., Ng, K. K., Ng, K. C., Alhadeethi, A., Ng, M. Y., Ng, M., Ng, S., Ng We Yong, Y. J., Ng-Kamstra, J., Ngan, T. Y., Ngaserin, S., Ngeruka, M. L., Ngu, J., Alhadi, A., Ngulube, A., Nguyen, A., Nguyen, H. M. N., Nguyen, H., Nguyen, S., Ni Bhroin, O., Nia, A., Niazi, F. A. K., Niazi, M., Alhag, A., Niazi, S. U., Nichani, J., Nichita, M., Nicholas, E., Nicholson, K., Nicholus, M., Nicolaiciuc, S., Nicolas, B., Nicolás-López, T., Niculae, C., Alhaidari, A., Nidaw, M., Nielsen, M. F., Niemeier, A., Nieto Calvache, A. J., Nieto Rodriguez, J. M., Nieto-Moreno, A., Nieto-Moreno, E., Nigalye, N., Nikaj, H., Nikiteas, N., Alhaje, A., Nikolai, K., Nikolaieva, K., Nikolic, S., Nikoloudaki, Z., Nikolouzakis, T., Nikolovska, B., Nikoupour, H., Nikova, A., Nimako, B., Nimako-Mensah, J., Alhajji, Z., Nimale, S., Nimer, B., Ninan, J. E., Ninic, A., Ninkovic, M., Niño Corredor, R. A., Niquen-Jimenez, M., Nirgude, A., Niriella, S., Nisa, K., Abdeljalil, A., AlHajri, N., Nischwitz, S. P., Nishihara, N., Nishikawa, K., Niska, K., Nistor-Ciurba, C. C., Nitschke, C., Nixon, A., Niyongombwa, I., Nizami, M., Niziol, R., Alhakami, N., Njeze, N., Njim, T., Njuki, P. N., Nk, V., Nkole, T., Nkrumah, J., Nkubito, J. M. S., Nkunzimana, E., Nabugwu, I., Najiuba, H., Alhalal, H., Noah, K., Nocera, G., Noda, M., Noe, C., Noel, C., Noël, A., Nofal, S., Noguchi, H., Nogueiro, J., Noguera Roman, L., Alhamid, A., Nogués, A., Noguez Castillo, M., Noiret, B., Nolan, D., Noll, F., Nolte, P., Noltes, M., Noman, H., Nomura, T., Noone, A., Noor, A., Noor, D. F., Noor Mohamed, M. S., Noordin, S., Noori, E., Noori, F., Norawat, R., Nordberg, M., Noreen, D. Z., Noriega, D. C., Alhamid, M., Norman, L., Noro, S., Noronha, W. J., Norouzi, S., Norris, J., Norrish, A., Nortey, M., Norton, J., Norton, S., Norton, W., Alhammad, O., Notario, J., Noton, T., Notz, Q., Nouh, T., Noureen, D. S., Noureldin, Y., Novak, Z., Novello, S., Novembre, D., Novi, A., Alhammali, T., Novikov, D., Novikova, A., Novoa, B., Novotny, H. F. G., Novysedlák, R., Nowak, K., Nowar, M., Nowosielski, Y., Nsaful, J., Nshuti, S., AlHarazi, A., Ntaganda, E., Ntalouka, M., Ntege, A., Ntege, J., Ntezamizero, J., Ntirenganya, F., Ntoto, M. C. O., Ntziovara, A. M., Nubair, S., Nugent, T., Alharthi, B., Nugur, A., Nuh, A., Nunes, R., Nunes, S., Nunes Coelho, M., Nunez, J., Nuñez, J., Nuñez, R., Núñez, A., Núñez, B., Abdelkabir, M., Alharthi, M., Nuñez Del Barrio, H., Núñez Lucic, J., Núñez O'Sullivan, S., Nunez-Garcia, B., Núñez-González, RÁ, Nuño-Escobar, C., Nurgozhin, T., Nuseir, A., Nwabuoku, S. E., Nwachukwu, C., Alharthi, N., Nwachukwu, I., Nwafor, I., Nwagboso, C., Nwaorgu, O., Nwashilli, N., Nwegbu, C., Nwinee, H., Nwosu, D., Nyadu, B., Nyangoh Timoh, K., Alharthi, S., Nyarko, O. O., Nyarko Agyeman, Y., Nyeko-Lacek, M., Nygaard, R. M., Nyirahabakurama, D., Nyirahabimana, J., Nyirimodoka, A., O Connor, C., O'Banion, L. A., O'Brien, S., Alhasan, H., O'Connell, E., O'Connell, R., O'Connor, B., O'Connor, D. B., O'Connor, J., O'Connor, Z., O'Donnell, R., O'Donoghue, C., O'Donoghue, G., O'Farrell, E., ALhassan, A., O'Grady, M., O'Hagan, P., O'Kelly, J., O'Mara-Gardner, K., O'Neill, A., O'Neill, C., O'Neill, R., O'Shea, M., O'Sullivan, E., Obadan, I., Alhassan, B. A. B., Obahiagbon, O., Obaid, K., Obaid, M., Obara, S., Obateru, J., Obbeng, A., Obeidat, R., Obied, H., Obrist, C., Obuekwe, O., Alhassan, B., Obure, R., Ocampo, L., Ocampos, L., Ocampos Hernandez, M., Occhionorelli, S., Ochoa Carrillo, F. J., Ochoa Gaete, K., Ochoa Villalabeitia, B., Ocsan, R. J., Od, S., Alhassoun, T., Odai, E. D., Odedra, A., Odeh, A., Odejinmi, F., Odetto, D., Odeyemi, O., Odeyemi, P. O., Odicino, F., Odigie, P. V., Odonnell, E., Alhathal, N., Oduah, S., Odunafolabi, T., Oduola, O., Odutola, O., Offiong, R., Ofori, B., Ofori, E. O., Ofosu-Barko, B. B., Ogawa, M., Ogden, E., Alhattab, M., Ogenya, R., Ogihara, T., Ogu, R., Ogunade, O., Ogundele, I., Ogundoyin, O., Ogunkeyede, S. A., Ogunlaja, O., Ogunrewo, T., Abdelkader Salama, I., Alhawatmeh, M., Ogunsua, O., Oguttu, B., Oğuz, C., Ohama, S., Ohara, R., Ohazurike, E., Oikonomou, K., Ojeda Rojas, A. M., Ojeda Thies, C., Ojeda Villasboa, D. J., Alhazmi, A., Ojewola, R. W., Ojha, B. K., Ojo, O., Okada, H., Okada, R., Okada, S., Okada, T., Okafor, B., Alhazmi, B., Okail, M. H., Okamura, K., Okamura, Y., Okere, O., Okereke, C., Okhakhu, A., Okolie, L., Okonkwo, I., Okoro, K., Okoye, E., Alhazzaa, N., Okoye, O., Okpani, A., Okpani, C., Okugbo, S., Okunade, K., Okunlola, A., Okunowo, A., Okwara, B., Olabinjo, A., Oladejo, T., Alhefdhi, A., Oladiran, A., Olagunju, N., Olajide, A., Olajide, T. O., Olaogun, J., Olaomi, O., Olaosebikan, R., Olaoye, I., Olasehinde, O., Olatoke, S. A., Alhelal, B., Olave Montaño, V. D., Olawoye, O., Olayiwola, D. O., Olaywah, D., Olaywah, N., Oldani, M., Oldbury, M., Oldrà, G., Olea Vielba, C., Olearo, E., Alhelal, F., Olieman, A., Oliva, A. R., Oliva, J. 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P., Conci, S., Conde, O., Conde Monroy, D., Condon, M., Condron, S., Conesa, A., Adiamah, A., Confalonieri, M. G., Connelly, T., Connolly, M., Conroy, P., Conroy, S., Considine, S., Conso, C., Consorti, G., Constance, H. L., Constantino-Duarte, R., Adikaibe, V., Contador, M., Conti, A., Conti, L., Contis, I., Contreras Baquero, L. G., Contreras Saiz, E., Contu, L., Cooke, F., Cool, P., Adil, M. T., Coonar, A., Cooper, Z., Cope, D., Cope, J., Copelli, C., Coratti, F., Corbellini, C., Corbett, H., Corbetta Machado, M. J., Corbiere, L., Adisa, A., Corbin, S., Corcione, F., Córcoles, M., Cordera, F., Cordero Spencer, S., Cordingley, T., Cordova, A., Córdova García, D., Corella, F., Coretti, G., Aditianingsih, D., Corkill, J., Cormane Alfaro, V., Cornali, T., Cornford, P., Corona-Cruz, J., Coronas Soucheiron, M., Corpodean, A. A., Corr, P., Corral Rubio, E., Corralez Alvarez, J. A., Adjeso, T., Correa Bonito, A., Correia, A. 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M., Advani, R., Cubek, D., Cucinotta, E., Cucoreanu, C., Cuellar Martin, L. A., Cuenca, O., Cuende Diez, M., Cuesta Argos, M., Cuesta-González, F. J., Cueto, M. A. C., Cueva-Martinez, E., Adze, J., Cuevas, A., Cuevas Almando, G. A., Cuevas Lantigua, G., Cuevas López, M. J., Cujiño, I., Cuk, V., Cukier, M., Culcu, S., Čuljak Blagojević, I., Cullen, I., Aeed, M., Cullen, J., Cullinane, C., Cullis, P., Cunchuan, W., Cunha, M. F., Currow, C., Curry, T., Cursiefen, C., Curtis, A., Curtis, N., Afaque, S. N., Curtis Martínez, C., Cuthbert, R., Cvetkovic, A., Czako, L., Czok, V., Czyz, A., D'acapito, F., D'Addino, J. L., D'alessandro, V., D'Alessio, R., Afifi, A., D'Alimonte, L., D'Aloisio, G., D'ambrosio, G., D'amico, M., D'Andrea, G., D'Andrea, M., D'Andrea, V., D'Annunzio, S., D'Archi, S., D'Aulerio, G., Afifi, E., D'Auria, M., D'óleo García, C. S., D'Souza, C., D'Ugo, D., D'Ugo, S., D'ulisse, S., D'urso, A., da Cunha Viana Júnior, A., da Silva, C., da Silva, J. C., Afifi, N., Da Silva Neto, R. A., Daary, D., Dabalos, J. J., Dabbagh, E., Dabirmoghaddam, P., Dabis, J., Daboo Salifu, L., Daddi, N., Dadhich, S., Dadoe, D., Afify, A., Dafnios, N., Dagestani, H., Daghash, I. T., Daghriri, Y., Dagklis, T., Daham, L., Dahhan, H., Dai, N., Dainius, E., Dajani, K., Afolabi, A. O., Dajti, I., Daketsey, E., Dakpé, S., Dalal, A., Dalaleh, M., Dale, T., Daley, B., Dalgleish, S., Dallan, I., Dallaserra, M., Afolabi, A., Dalmar, K., Dalprà, F., Daly, C., Damah, M., Damara, F. A., Damasio Cotovio, M., Dambrauskas, Z., Damburacı, N., Damjanović, A., Dancilla, N., Abbas, A. S., Afolabi, B., Dandurand, C., Danelli, P., Danguy des Déserts, M., Danial, A., Danias, N., Danic Hadzibegovic, A., Daniel, J., Daniel, M., Daniel, T., Daniele, A., Afshar, S., Daniels, J. S., Danielsson, D., Daniyal, M. D., Daniyan, M., Danton, M., Danwaththa Liyanage, A. S., Daodu, I., Daood, M. 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D. S. J., Mieog, S., Mifsud, C., Miftah, M., Migliore, M., Migliorelli, F., Miguel, T., Mihanovic, J., Mihnea Bogdan, B., Mijares Olivo, M. F., Mikalauskas, S., Alconchel, F., Mikdad, S., Mikhael, E., Mikhail, M. M., Mikhail, P., Miki, H., Mikru, F., Mikulic, I., Mikutaitis, V., Miladinov, M., Milan Moussa, R., Aldaghiri, K., Milana, F., Milani, M. S., Milanovic, M., Milas, T., Miles, S., Milia, F., Milićević, M., Milicic, B., Milisavljević, F., Militaru, G. M., AlDahash, H., Miljković, A., Millar, K., Miller, C., Miller, D., Miller, S., Millesi, M., Millikan, K., Mills, E., Millward, C. P., Milne, S., Aldahma, M., Milner, S., Milojevic, B., Milot, G., Milotoiu, I., Milovanovic, J., Milton, A., Milutinović, V., Mimica, X., Minafra, M., Minamoto, F., Abdelbaset, A., Aldakhil, A., Minasidou, M., Minaya Bravo, A. M., Ming, Y. J., Mingoli, A., Minguell-Monyart, J., Mínguez Ruiz, G., Minich, A., Minicozzi, A., Minlah Allah, S., Minni, F., Aldakhyel, L., Miotla, P., Mir, A., Mirabella, D., Miraj, S., Miralpeix, E., Mirambeaux, M., Miranda, I., Miranda Espinsa, N., Miranda Gómez, D. G., Miret Alomar, E., Aldakkan, A., Mirica, R. M., Mirnezami, R., Miron, A., Miron, S., Mironescu, A., Mirsal, T., Mirzaie, M., Mishra, A., Aldalal, S., Mishra, B., Mishra, N., Mishra, S., Mishra, T., Miskovic, J., Misra, B., Misra, G., Misra, S., Misrai, V., Aldalati, M. Z., Mistry, R., Mitchell, C., Mitrofan, C. G., Mitroudi, M., Mitrovic, N., Mittal, A., Mittal, R., Mittal, S., Aldana, J., Mittal, Y., Mitteregger, M., Mittermair, C., Mitu, T., Miyamoto, M., Miyazawa, N., Mizota, T., Mj Hassan Almukhariq, F., Mkandawire, C., Mladěnka, A., Aldebeyan, W., Mlakar, J., Mlambo, B., Moawad, N., Moazami Pour, A. M. P., Moazin, M., Mobarak, S., Moctezuma Velázquez, P., Moczko, T., Modabber, A., Modahi, N., Aldelensi Alzubi, A. S. M., Modak, G., Modolo, M. M., Moeckelmann, N., Moeen, A., Mofikoya, B., Mogbo, C., Moggia, E., Moghazy, M., Mogoanta, C. A., Mogoanta, S., Alderazi, A., Moguel, R., Moh Pauzi, S. F., Mohamad, B., Mohamad, R., Mohamed, A., Mohamed, E., Aldhaam, N., Mohamed, G., Mohamed, H., Mohamed, I., Mohamed, L., Mohamed, M., Abdeldayem, H., Aldhurais, A., Mohamed, M. H., Mohamed, O., Mohamed, S., Mohamed Ahmed, M., Mohamed Altukhy, A., Mohamed Elsaid, D., Mohamed Hussein, A., Mohamed Osman, S. I. O. M. O., Aldlyami, E., Mohamed Saad, A., Mohamed Salim, M. A., Mohamed Sayed, A., Mohamedahmed, A. Y. Y., Mohammad, A., Mohammad, M., Mohammad Azmi, M. A. F., Aldressi, W., Mohammad Mohammad, A., Mohammed, A., Mohammed, A. A., Mohammed, B., Mohammed, D., Mohammed, G., Mohammed, K., Mohammed, M. M., Aldridge, K., Mohammed, M., Mohammed, R., Mohammed, T., Mohammed Abdul, N., Mohammed Bello, U., Mohammed Fawaz Ashour, G., Mohammed Saeed, M., Mohammed-Ali, R., Mohammed-Durosinlorun, A., Aldrighetti, L. A., Mohammedali, S., Mohan, H., Mohan, P. V., Mohanty, S. R., Mohd Miswan, M. F., Mohd Zahid, A. Z., Mohd Zain, A., Mohindra, S., Mohmand, O., Mohorko, T., Alduba'ai, M., Mohseni, S., Mohsin, M., Mohsine, R., Mohyieldin, O., Moin, I., Moir, J., Moises, E. C. D., Moisiuk, P., Mok, C. W., Mok, C. Y., Aldulaijan, F., Mokarami, H. M., Mokhtar, A., Moktash, M., Moldovan, B., Molero, Á, Molfino, S., Moličnik, A., Molina, M., Molina, O., Molina-Corbacho, M., Aldumaini, H., Molinas, J., Molitvin, Y., Molla, M., Moller, C. C., Molyneux, S., Mombet, A., Momen, A., Momoh, M., Monaco, F., Mondino, J., Ale, O., Mondy, H., Monfort Mira, M., Mongardi, L., Mongelli, F., Monib, F. A., Monica, R., Monier, E., Monís, A., Monji, K., Monleón López, B., Alecci, A., Monnier, C., Monreal Clua, S., Monsalve, G., Montal, S., Montan, C., Montcusí, B., Monteith, C., Monteleone, M., Montemayor, J. A., Montenegro, A., Abdelfattah, M., Aleem, M., Montenegro, E., Montero Cruces, L., Montero Macías, R., Montes, F. R., Montes-Jovellar, L., Montes-Manrique, M., Montferrer Estruch, N., Montgomery, S., Monti, E., Monti, M., Alegbeleye, J. O., Montiel, G., Montoya Casella, A. J., Montrimaite, M., Montuori, M., Moore, A., Moore, E., Moore, H., Moores, T., Mora, S., Mora Oliver, I., ALeissa, M., Moraes, I., Morais, H., Morais, M., Moraleda Gudayol, I., Morales, S., Morales, V. V., Morales, X., Morales Ariza, V., Morales Chew, K. M., Morales Comas, C., Aleksandrova, E., Morales Palacios, N., Morales Valencia, E., Morales-Angulo, C., Morales-Garcia, D., Morales-Puebla, J. M., Morán Galaviz, R. E., Morandi, A., Morariu, D. S., Moratalla Charcos, L. M., Moreau, J., Aleksić, L., Moreira, C., Moreira, J., Moreira Alves, W. S., Morell, A., Morelli, L., Morello, A., Moreno, A., Moreno, E., Moreno, J., Moreno Mata, N., Alelwany, A., Moreno Portillo, M., Moreno Salazar, Y. T., Moreno Sierra, J., Moreno Suarez, M., Moreno Y Suárez, T., Moreno-Gutierrez, A., Moret, C., Moretto, G., Moretto, S., Morezzi, D., Alemán, M., Morgan, C., Morgan, E., Morgan, K., Morgan, R., Morgan, S., Morhason-Bello, J., Mori, K., Moriarty, C., Morić, T., Morichau-Beauchant, T., Alemanno, G., Moriche Carretero, M., Moris, D., Morita, T., Moriyama, M., Morla, A. A., Morley, H. L., Morna, M. T., Moro-Valdezate, D., Morochovič, R., Morote, M., Alemu, B., Morović, D., Morris, B., Morris, D., Morris, R., Morrison, J., Morrison-Jones, V., Morsy, A., Morsy, M. S., Mortada, H., Alemu, M., Mortazavi, S. M. J., Mortiers, X., Mortimer, B., Mortini, P., Morton, A., Mosabha, M., Moslemi, M. K., Mosley, F., Mosneaga, R., Mosorin, M. A., Abdelgadir, A., Alen Villamayor, V., Mosos, M., Mosquera Paz, M. S., Moss, J. L., Mossaad, M., Mosshammer, V., Mostafa, A. M. H. A. M., Mostafa, B., Mostafa, M., Mostafa, M. E., Mosteiro-Cadaval, A., Alesmail, A., Mota, C., Motallebzadeh, R., Motas, N., Motasaddi Zarandy, M., Motoyasu, A., Motter, D., Motwani, K., Moug, S., Mougahed, M., Mouhandes, A. E., Alexakis, N., Moujtahid, H., Moukoko, D., Moura, M., Mourad, M., Mouriaux, F., Mourmouris, P., Mousa, M., Moussa, A., Moustafa, E., Moutinho, L., Alexander, D., Mouw, T., Moya-Angeler, J., Moyana Muguto, V., Moyano, P., Moyo, A., Moyo, G., Moyo, N., Mozafari, M., Mozel, M., Mpagi Katassi, L., Alexander, G., Mpirimbanyi, C., Mrad, B., Mrad, M. A., Mraiyan, M., Mshelbwala, P., Msherghi, A., Mtimkulu, W., Muallem-Kalmovich, L., Mubambe, M., Alexander, P., Mubarek, K., Mubarik, A., Mubaydeen, T., Mubezi, I., Mucilli, F., Muduly, D., Muehlbacher, I., Mueller, L. K., Muftah, H., Mufth, M., Alexander, T., Muganga, D., Mughal, A., Mughal, S., Mughal, Z. U., Mugisa, D., Mugla, W., Muguerza, J. M., Muguwu, A., Muhammad, A. B., Muhammad, R., Muhammad, S., Muhammad Daneji, S., Muheilan, M., Muhemi, R., Muhmmed, K., Muhssein, H., Muhtaroğlu, A., Muhtaseb, M., Alexandrou, A., Muirhead, W., Muiru, C., Mujtaba, S. N., Mukase, S., Mukezamfura, R., Mukherjee, P., Mukherjee, S., Mukhtar, I. A., Alexe, V., Mulas Fernández, C., Mulchandani, M., Mulder, W. W., Muleta, L., Muleta, M. B., Mulita, F., Mulla, H., Mulla, O., Muller, B., Müller, A., Abdelgalel, K., Alezabi, B., Müller, K., Müller, S., Müller, T., Mulliri, A., Mulugeta, E., Mulugeta, G., Mumtaz, K., Munanzvi, K., Munarriz, M., AlFakhri, A., Munawwar, F., Muneza, S., Mungazi, S., Muniandy, M., Munini, M., Munir, M. M., Munoz, H., Munoz Abraham, A. S., Muñoz Botero, N. A., Muñoz Camarena, J. M., Alfarah, M., Muñoz de Nova, J. L., Muñoz Fernández, M. E., Muñoz Lindez, I., Muñoz Rodríguez, J. M., Muñoz Sornosa, E., Muñoz Vives, J. M., Muñoz-Bellvis, L., Munro, W., Munshi, F., Muntaka, A. M., Alfaro, H., Muntean, G. O., Muntean, M., Munteanu, A., Munton, H., Munyaneza, E., Munyif, H., Munyika, A., Mura, G., Murad, M., Muraglia, A., Alfaro-Goldaracena, A., Murakawa, T., Murali, S., Muralidharan, V., Muratore, A., Mureko, A., Murgese, A., Murgoitio, U., Muriel, M. E., Muriithi, F. G., Murillo, J., Alfarra, D., Murillo Castellanos, J. A., Murji, B., Murphy, B., Murphy, J., Murphy, M., Murphy, N., Murphy, S., Murray, D., Murray, E., Alfayez, F., Murray, I., Murray, J., Murtaza, G., Murugesan, A., Murugesan, K., Musa, D., Musa, E., Musa, R., Musa, S., Musa Adamu, D. K., Alfeqeeh, A., Musa Kirfi, A., Musaab, M., Musallam, N., Musana, O., Musawa, N., Muscat, E., Muscatello, L., Musetti, S., Musharraf, M. B., Mushawarima, T., Alferdaus, A., Mushiwokufa, W., Mushtaq, A., Mushtaq, H., Musina, A. M., Muslim, M., Muslumov, G., Musrati, F., Mustafa, A., Mustafa, D. F., Mustafa, I., Alfieri, A., Mustafa, N., Mustafa, S., Mustapha, M., Mustea, A., Musthafa, S., Mutabazi, E., Mutalima, N., Mutambanengwe, P. G. T., Mutarello, N., Abdelghafar, M., Alfieri, S., Muthkumaran, G., Muthu, S., Mutimamwiza, I., Mutsonziwa, N., Mutter, D., Mutyaba, F., Muwanga, T., Muwanguzi, P., Muzaffar, N., Muzaffar, R., Alfonso Carrillo, C., Mwachiro, M., Mwale, G., Mwangi, V., Mwaria, C., Myatt, R., Myers, J., Myint, Y., Myintmo, A., Myszkowski, S., N, R., Alford, S., N Mohammad, A., Na, H., Naabo, N., Naar, L., Nabawanuka, A., Nabi, M. S., Nabian, M. H., Nabil, A., Nabil, S., Alfraidy, D., Nachelleh, E., Nachum, E. R., Nacion, A. J. D., Nada, A., Nadal, L. R., Nadal Rodrigues, D., Nadeau, S., Nadeem, A., Nader, N. E., Nadia Maria, N., Älgå, A., Nadkarni, M., Nadreen, M., Naeem, M., Naeem, S., Naegele, F., Nael, A., Naem, A., Naem, J., Naffi, A. A., Nagachandra, G., Algadi, N., Nagamoto, S., Naganathar, S., Naganuma, H., Nagappa, M., Nagar, M., Nagarasa, P., Nagashima, F., Nagassima, K., Nageeb, A., Nageh, M. A., Algar-Yañez, B., Nageh, M., Naghibi, A., Nagiub, M., Nagorni, E. A., Nagpal, P., Nagra, R. N., Nagra, S., Nah, S. A., Nahal, E. A., Nahm, C., Algarni, M., Nahtomi Shick, O., Naieem, M., Naik, P., Nail, D., Naimy, G., Nair, A., Nair, A. K., Nair, D., Nair, G., Algarni, S., Nair, S., Nair, V. P., Naiya, H. I. I., Najafian Motahaver, N., Najera, R., Naji, N., Naji, O., Najjad, M. K. R., Najjar, M. S., Nakagawa, S., Alghamdi, A., Nakamura, Y., Nakanishi, R., Nakano, L., Nakanwagi, C., Nakas, A., Nakata, Y., Nakazawa, H., Nakirijja, T., Namikawa, T., Namrata, D., Abdelhafez, M., Namugenyi, C., Namutosi, E., Nana, G. R., Nanayakkara, A., Nankivell, P., Naoum, N., Napolitano, L., Nappi, F., Nappo, G., Naqi, S. A., Alghamdi, A. A., Naqui, Z., Naqvi, W., Naraen, A., Naranjo, S., Narasiman, V., Narasimhan, G., Narayan, S., Narayanan, V., Narayanasamy, K., Nardi, C., Naredla, P., Narendra, D., Narroway, H., Naseem, H., Naseem, M. Y., Naseer, M., Naseer, M. A., Nashed, E., Nashidengo, P. R., Nasir, A., Alghamdi, I., Nasir, M., Naskar, U., Nasreddin, M., Nasri, M. Z., Nassali, G., Nassanga, R., Nassar, A., Nassef, M. A., Alghamdi, R., Nasser, A., Nasser, N., Nasser, S., Nassif, M., Nassif, Y., Nassimu, M., Natal Álvarez, F., Nataraja, R., Nathan, M., Nathan, S. K., ALghamdi, S., Nathani, R., Nativ, H., Naumeri, F., Naunheim, M., Naureen, S., Nausheen, S., Navaratne, L., Navarrete-Peón, A., Navarro, A., Navarro, D., Alghamdi, S., Navarro, J. C., Navarro Casado, R., Navarro Martínez, S., Navarro-Alean, J., Navarro-Barrios, A., Navarro-Sánchez, A., Nawara, H., Nawawi, A., Nayak, P., Nayen, A., Alghazal, T., Nayyer, F., Naz, F., Nazal, M., Nazar, Z., Nazia, N., Nazimi, A. J., Nazir, S., Nazir, S. F., Nazir, U., Ncogoza, I., Alghazo, A., Ndabvonga, G., Ndatewapo, F., Ndayishyigikiye, M. D., Ndegbu, C., Ndizeye, O., Ndlovu, S., Ndong, A., Neagu, A., Neal, A., Neal, N., Alghuson, L., Neal-Smith, G., Neary, P., Negash, S., Negoi, I., Negreanu, L., Negrete Ocampo, J. R., Negussie, A., Nehe, A., Neidert, N., Collaborative, Covidsurg, Collaborative Dmitri Nepogodiev, Globalsurg, F Simoes, Joana F, Li, Elizabeth, Glasbey, Jame, Picciochi, Maria, K Kamarajah, Sivesh, Gujjuri, Rohan, Bhangu, Aneel, A, Maryam, A Azab, Mohammed, A Janjua, Dratif, A Zahran, Mohammed, A Abdelaziz, Areej, Luglio, Gaetano, Surgery, Medicine and Pharmacy academic/administration, Basic (bio-) Medical Sciences, Clinical sciences, Surgical clinical sciences, Faculty of Medicine and Pharmacy, Medical Oncology, Gastroenterology, Department of Surgery, HUS Abdominal Center, Chiantera, V, Pecoraro, F, COVIDSurg Collaborative, GlobalSurg Collaborative, Paediatric Surgery, Amsterdam Gastroenterology Endocrinology Metabolism, AGEM - Endocrinology, metabolism and nutrition, Graduate School, Amsterdam Reproduction & Development (AR&D), Tytgat Institute for Liver and Intestinal Research, Institut de Recherche sur les Maladies Virales et Hépatiques (IVH), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Laura Alberici, Filippo Antonacci, Alessandro Arena, Angela Belvedere, Fabio Bernagozzi, Paolo Bernante, Pietro Bertoglio, Lorenzo Bianchi, Maria Bisulli, Barbara Bonfanti, Safia Boussedra, Jury Brandolini, Crescenzo Cacciapuoti, Stefano Cardelli, Riccardo Casadei, Matteo Cescon, Alessandro Cipolli, Riccardo Cipriani, Alfredo Conti, Luca Contu, Francesco Costa, Niccolo’ Daddi, Eugenia De Crescenzo, Pierandrea De Iaco, Alessandra De Palma, Massimo Del Gaudio, Anna Nunzia Della Gatta, Giampiero Dolci, Giulia Dondi, Matteo Droghetti, Sergio Nicola Forti Parri, Elena Garelli, Chiara Gelati, Giuliana Germinario, Federico A. Giorgini, Carlo Ingaldi, Elio Jovine, Kenji Kawamukai, Antonio Lanci Lanci, Raffaele Lombardi, Maria Elisa Lozano Miralles, Claudio Marchetti, Michele Masetti, Francesco Minni, Daniele Morezzi, Daniele Parlanti, Alice Pellegrini, Anna Myriam Perrone, Anna paola Pezzuto, Marco Pignatti, Gianluigi Pilu, Valentina Pinto, Gilberto Poggioli, Silvana Bernadetta Puglisi, Diego Raimondo, Matteo Ravaioli, Claudio Ricci, Sara Ricciardi, Francesco Ricotta, Roberta Rizzo, Angela Romano, Matteo Rottoli, Riccardo Schiavina, Renato Seracchioli, Matteo Serenari, Margherita Serra, Leonardo Solaini, Piergiorgio Solli, Gioia Sorbi, Mario Taffurelli, Marta Tanzanu, Achille Tarsitano, Marco Tesei, Gabriele Vago, Tommaso Violante, Simone Zanotti, Tommaso Frisoni, pignatti marco, ERSÖZ, CEVPER, GlobalSurg Collaborative, COVIDSurg Collaborative, Nepogodiev, Dmitri, Simoes, Joana F. F., Glasbey, James C., Alser, Osaid, Boccalatte, Luis A., Calvache, Jose A., Duran, Irani, Elhadi, Muhammed, Elmujtaba, Mohammed, Emile, Sameh, Harrison, Ewen M., Hutchinson, Peter, Allen Ingabire, J. C., Isik, Arda, Kamarajah, Sivesh K., Karandikar, Sharad, Kauppila, Joonas H., Kembuan, Gabriele, Lederhuber, Han, Löffler, Markus W., Mann, Harvinder, Marson, Ella J., Mclean, Kenneth A., Munyaneza, Emmanuel, Norman, Lisa, Omar, Omar M., Pius, Riinu, Pockney, Peter, Rutegård, Martin, Stewart, Grant D., Taylor, Elliott H., Venn, Mary L., Aizza, Giada, Bellio, Gabriele, Bogoni, Selene, Bortul, Marina, Casagranda, Biagio, Cortinovis, Sara, de Manzini, Nicolo', Drigo, Davide, Germani, Paola, Mastronardi, Manuela, Paiano, Lucia, Palmisano, Silvia, Nepogodiev, D, Bhangu, A, Gallo, G, Picciariello, P, Abushanab, M, Fruscio, R, Ornaghi, S, Iovino, C, Rossi, S, and Simonelli, F
- Subjects
Male ,COVID-19 Vaccine ,safe surgery ,vaccination modelling ,COVID-19 ,Vascular damage Radboud Institute for Health Sciences [Radboudumc 16] ,MULTICENTER ,Comorbidity ,030230 surgery ,0302 clinical medicine ,phase 3 clinical trial (topic) ,Case fatality rate ,Prospective Studies ,SARS-CoV-2 , Vaccination , Safe surgery ,COVID-19/epidemiology ,SARS-CoV-2 ,vaccination ,education.field_of_study ,case fatality rate ,Vaccination ,Adolescent ,Adult ,Aged ,COVID-19 Vaccines ,Elective Surgical Procedures ,Female ,Humans ,Middle Aged ,Postoperative Complications ,Preoperative Period ,Young Adult ,health care policy ,030220 oncology & carcinogenesis ,cancer ,outcome ,mortality ,infection ,modelling ,Cohort study ,prospective study ,Human ,medicine.medical_specialty ,Article ,03 medical and health sciences ,SARS-CoV-2, vaccination ,SDG 3 - Good Health and Well-being ,COVID-19 Vaccines/pharmacology ,Vaccination/methods ,Elective surgery ,education ,Science & Technology ,Elective Surgical Procedure ,adult ,aged ,cancer grading ,cancer surgery ,computer assisted tomography ,elective surgery ,female ,follow up ,human ,incidence ,infection rate ,infection risk ,major clinical study ,male ,middle aged ,outcome assessment ,preoperative care ,sensitivity analysis ,seroprevalence ,Severe acute respiratory syndrome coronavirus 2 ,young adult ,surgery ,Cura preoperatòria ,Prospective Studie ,Surgery ,Human medicine ,Postoperative Complication ,610 Medizin und Gesundheit ,AcademicSubjects/MED00910 ,Settore MED/18 - CHIRURGIA GENERALE ,Settore MED/29 - CHIRURGIA MAXILLOFACCIALE ,ESTUDOS PROSPECTIVOS ,Vacunació ,Prospective cohort study ,Incidence (epidemiology) ,covid ,Elective Surgical Procedures/methods ,COVID, vaccination ,Original Article ,AcademicSubjects/MED00010 ,Life Sciences & Biomedicine ,international prospective cohort study ,Postoperative Complications/prevention & control ,Population ,SARS-CoV-2/immunology ,NO ,medicine ,ddc:610 ,LS7_4 ,business.industry ,Number needed to vaccinate ,Preoperative care ,3126 Surgery, anesthesiology, intensive care, radiology ,Settore MED/18 ,Reconstructive and regenerative medicine Radboud Institute for Health Sciences [Radboudumc 10] ,Emergency medicine ,business ,[SDV.MHEP]Life Sciences [q-bio]/Human health and pathology - Abstract
Background Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18–49, 50–69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population., The aim of this study was to inform vaccination prioritization by modelling the impact of vaccination on elective inpatient surgery. The study found that patients aged at least 70 years needing elective surgery should be prioritized alongside other high-risk groups during early vaccination programmes. Once vaccines are rolled out to younger populations, prioritizing surgical patients is advantageous.
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- 2021
43. Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study
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Van Calster, B. Valentin, L. Froyman, W. Landolfo, C. Ceusters, J. Testa, A.C. Wynants, L. Sladkevicius, P. Van Holsbeke, C. Domali, E. Fruscio, R. Epstein, E. Franchi, D. Kudla, M.J. Chiappa, V. Alcazar, J.L. Leone, F.P.G. Buonomo, F. Coccia, M.E. Guerriero, S. Deo, N. Jokubkiene, L. Savelli, L. Fischerová, D. Czekierdowski, A. Kaijser, J. Coosemans, A. Scambia, G. Vergote, I. Bourne, T. Timmerman, D.
- Abstract
OBJECTIVE: To evaluate the performance of diagnostic prediction models for ovarian malignancy in all patients with an ovarian mass managed surgically or conservatively. DESIGN: Multicentre cohort study. SETTING: 36 oncology referral centres (tertiary centres with a specific gynaecological oncology unit) or other types of centre. PARTICIPANTS: Consecutive adult patients presenting with an adnexal mass between January 2012 and March 2015 and managed by surgery or follow-up. MAIN OUTCOME MEASURES: Overall and centre specific discrimination, calibration, and clinical utility of six prediction models for ovarian malignancy (risk of malignancy index (RMI), logistic regression model 2 (LR2), simple rules, simple rules risk model (SRRisk), assessment of different neoplasias in the adnexa (ADNEX) with or without CA125). ADNEX allows the risk of malignancy to be subdivided into risks of a borderline, stage I primary, stage II-IV primary, or secondary metastatic malignancy. The outcome was based on histology if patients underwent surgery, or on results of clinical and ultrasound follow-up at 12 (±2) months. Multiple imputation was used when outcome based on follow-up was uncertain. RESULTS: The primary analysis included 17 centres that met strict quality criteria for surgical and follow-up data (5717 of all 8519 patients). 812 patients (14%) had a mass that was already in follow-up at study recruitment, therefore 4905 patients were included in the statistical analysis. The outcome was benign in 3441 (70%) patients and malignant in 978 (20%). Uncertain outcomes (486, 10%) were most often explained by limited follow-up information. The overall area under the receiver operating characteristic curve was highest for ADNEX with CA125 (0.94, 95% confidence interval 0.92 to 0.96), ADNEX without CA125 (0.94, 0.91 to 0.95) and SRRisk (0.94, 0.91 to 0.95), and lowest for RMI (0.89, 0.85 to 0.92). Calibration varied among centres for all models, however the ADNEX models and SRRisk were the best calibrated. Calibration of the estimated risks for the tumour subtypes was good for ADNEX irrespective of whether or not CA125 was included as a predictor. Overall clinical utility (net benefit) was highest for the ADNEX models and SRRisk, and lowest for RMI. For patients who received at least one follow-up scan (n=1958), overall area under the receiver operating characteristic curve ranged from 0.76 (95% confidence interval 0.66 to 0.84) for RMI to 0.89 (0.81 to 0.94) for ADNEX with CA125. CONCLUSIONS: Our study found the ADNEX models and SRRisk are the best models to distinguish between benign and malignant masses in all patients presenting with an adnexal mass, including those managed conservatively. TRIAL REGISTRATION: ClinicalTrials.gov NCT01698632. © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. No commercial re-use. See rights and permissions. Published by BMJ.
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- 2020
44. Ultrasound-based risk model for preoperative prediction of lymph-node metastases in women with endometrial cancer: model-development study
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Eriksson, L. S. E., Epstein, E., Testa, A. C., Fischerova, D., Valentin, L., Sladkevicius, P., Franchi, D., Fruhauf, F., Fruscio, R., Haak, L. A., Opolskiene, G., Mascilini, F., Alcazar, J. L., Van Holsbeke, C., Chiappa, V., Bourne, T., Lindqvist, P. G., Van Calster, B., Timmerman, D., Verbakel, J. Y., Van den Bosch, T., Wynants, L., Eriksson, L. S. E., Epstein, E., Testa, A. C., Fischerova, D., Valentin, L., Sladkevicius, P., Franchi, D., Fruhauf, F., Fruscio, R., Haak, L. A., Opolskiene, G., Mascilini, F., Alcazar, J. L., Van Holsbeke, C., Chiappa, V., Bourne, T., Lindqvist, P. G., Van Calster, B., Timmerman, D., Verbakel, J. Y., Van den Bosch, T., and Wynants, L.
- Abstract
Objective To develop a preoperative risk model, using endometrial biopsy results and clinical and ultrasound variables, to predict the individual risk of lymph-node metastases in women with endometrial cancer.Methods A mixed-effects logistic regression model for prediction of lymph-node metastases was developed in 1501 prospectively included women with endometrial cancer undergoing transvaginal ultrasound examination before surgery, from 16 European centers. Missing data, including missing lymph-node status, were imputed. Discrimination, calibration and clinical utility of the model were evaluated using leave-center-out cross validation. The predictive performance of the model was compared with that of risk classification from endometrial biopsy alone (high-risk defined as endometrioid cancer Grade 3/non-endometrioid cancer) or combined endometrial biopsy and ultrasound (high-risk defined as endometrioid cancer Grade 3/non-endometrioid cancer/deep myometrial invasion/cervical stromal invasion/extrauterine spread).Results Lymphadenectomy was performed in 691 women, of whom 127 had lymph-node metastases. The model for prediction of lymph-node metastases included the predictors age, duration of abnormal bleeding, endometrial biopsy result, tumor extension and tumor size according to ultrasound and undefined tumor with an unmeasurable endometrium. The model's area under the curve was 0.73 (95% CI, 0.68-0.78), the calibration slope was 1.06 (95% CI, 0.79-1.34) and the calibration intercept was 0.06 (95% CI, -0.15 to 0.27). Using a risk threshold for lymph-node metastases of 5% compared with 20%, the model had, respectively, a sensitivity of 98% vs 48% and specificity of 11% vs 80%. The model had higher sensitivity and specificity than did classification as high-risk, according to endometrial biopsy alone (50% vs 35% and 80% vs 77%, respectively) or combined endometrial biopsy and ultrasound (80% vs 75% and 53% vs 52%, respectively). The model's clinical
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- 2020
45. Ultrasound-based risk model for preoperative prediction of lymph-node metastases in women with endometrial cancer: model-development study
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Eriksson, L, Epstein, E, Testa, A, Fischerova, D, Valentin, L, Sladkevicius, P, Franchi, D, Frühauf, F, Fruscio, R, Haak, L, Opolskiene, G, Mascilini, F, Alcazar, J, Van Holsbeke, C, Chiappa, V, Bourne, T, Lindqvist, P, Van Calster, B, Timmerman, D, Verbakel, J, Van den Bosch, T, Wynants, L, Eriksson, Linda S E, Epstein, Elisabeth, Testa, Antonia C, Fischerova, Daniela, Valentin, Lil, Sladkevicius, Povilas, Franchi, Dorella, Frühauf, Filip, Fruscio, Robert, Haak, Lucia A, Opolskiene, Gina, Mascilini, Floriana, Alcazar, Juan Luis, Van Holsbeke, Caroline, Chiappa, Valentina, Bourne, Tom, Lindqvist, Pelle G, Van Calster, Ben, Timmerman, Dirk, Verbakel, Jan Y, Van den Bosch, Thierry, Wynants, Laure, Eriksson, L, Epstein, E, Testa, A, Fischerova, D, Valentin, L, Sladkevicius, P, Franchi, D, Frühauf, F, Fruscio, R, Haak, L, Opolskiene, G, Mascilini, F, Alcazar, J, Van Holsbeke, C, Chiappa, V, Bourne, T, Lindqvist, P, Van Calster, B, Timmerman, D, Verbakel, J, Van den Bosch, T, Wynants, L, Eriksson, Linda S E, Epstein, Elisabeth, Testa, Antonia C, Fischerova, Daniela, Valentin, Lil, Sladkevicius, Povilas, Franchi, Dorella, Frühauf, Filip, Fruscio, Robert, Haak, Lucia A, Opolskiene, Gina, Mascilini, Floriana, Alcazar, Juan Luis, Van Holsbeke, Caroline, Chiappa, Valentina, Bourne, Tom, Lindqvist, Pelle G, Van Calster, Ben, Timmerman, Dirk, Verbakel, Jan Y, Van den Bosch, Thierry, and Wynants, Laure
- Abstract
Objective: To develop a preoperative risk model, using endometrial biopsy results and clinical and ultrasound variables, to predict the individual risk of lymph-node metastases in women with endometrial cancer. Methods: A mixed-effects logistic regression model for prediction of lymph-node metastases was developed in 1501 prospectively included women with endometrial cancer undergoing transvaginal ultrasound examination before surgery, from 16 European centers. Missing data, including missing lymph-node status, were imputed. Discrimination, calibration and clinical utility of the model were evaluated using leave-center-out cross validation. The predictive performance of the model was compared with that of risk classification from endometrial biopsy alone (high-risk defined as endometrioid cancer Grade 3/non-endometrioid cancer) or combined endometrial biopsy and ultrasound (high-risk defined as endometrioid cancer Grade 3/non-endometrioid cancer/deep myometrial invasion/cervical stromal invasion/extrauterine spread). Results: Lymphadenectomy was performed in 691 women, of whom 127 had lymph-node metastases. The model for prediction of lymph-node metastases included the predictors age, duration of abnormal bleeding, endometrial biopsy result, tumor extension and tumor size according to ultrasound and undefined tumor with an unmeasurable endometrium. The model's area under the curve was 0.73 (95% CI, 0.68–0.78), the calibration slope was 1.06 (95% CI, 0.79–1.34) and the calibration intercept was 0.06 (95% CI, –0.15 to 0.27). Using a risk threshold for lymph-node metastases of 5% compared with 20%, the model had, respectively, a sensitivity of 98% vs 48% and specificity of 11% vs 80%. The model had higher sensitivity and specificity than did classification as high-risk, according to endometrial biopsy alone (50% vs 35% and 80% vs 77%, respectively) or combined endometrial biopsy and ultrasound (80% vs 75% and 53% vs 52%, respectively). The model's clinical utility was
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- 2020
46. An ultrasound-based risk model to predict lymph node metastases before surgery in women with endometrial cancer: a model development study
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Eriksson, LSE, Epstein, E, Testa, AC, Fischerova, D, Valentin, L, Sladkevicius, P, Franchi, D, Frühauf, F, Fruscio, R, Haak, LA, Opolskiene, G, Mascilini, F, Alcazar, JL, Van Holsbeke, C, Chiappa, V, Bourne, T, Lindqvist, PG, Van Calster, B, Timmerman, D, Verbakel, JY, Van den Bosch, T, and Wynants, L
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diagnostic imaging ,decision support model ,1114 Paediatrics and Reproductive Medicine ,endometrial neoplasm ,lymphatic metastasis ,ultrasonography ,neoplasm staging ,Obstetrics & Reproductive Medicine - Abstract
OBJECTIVES: To develop a pre-operative risk model using endometrial biopsy results, clinical and ultrasound variables to predict the individual risk of lymph node metastases in women with endometrial cancer. METHODS: A mixed effects logistic regression model was developed on 1501 prospectively included women with endometrial cancer subjected to transvaginal ultrasound examination before surgery. Missing data, including missing lymph node status, was imputed. Discrimination, calibration and clinical utility were evaluated using leave-center-out cross-validation. The predictive performance was compared with risk classification from endometrial biopsy alone (high-risk = endometrioid cancer grade 3/non-endometrioid cancer) or combined endometrial biopsy and ultrasound (high-risk = endometrioid cancer grade 3/non-endometrioid cancer/deep myometrial invasion/cervical stromal invasion/extrauterine spread). RESULTS: Lymphadenectomy was performed in 691 women, of which 127 had lymph node metastases. The model included the predictors age, duration of abnormal bleeding, endometrial biopsy result, tumor extension and tumor size according to ultrasound and "undefined tumor with an unmeasurable endometrium". The model's AUC was 0.73 (95% CI 0.68 to 0.78), calibration slope 1.06 (95% CI 0.79 to 1.34) and calibration intercept 0.06 (95% CI 0.15 to 0.27). Using risk thresholds for lymph node metastases 5% vs. 20% the model had sensitivity 98% vs. 48% and specificity 11% vs. 80%. The model had higher sensitivity and specificity than high-risk according to endometrial biopsy alone (50% vs. 35% and 80% vs. 77%) or combined endometrial biopsy and ultrasound (80% vs. 75% and 53% vs. 52%). The model's clinical utility was higher than that of endometrial biopsy alone or combined endometrial biopsy and ultrasound at any given risk threshold. CONCLUSIONS: Based on endometrial biopsy results, clinical and ultrasound characteristics, the individual risk of lymph node metastases in women with endometrial cancer can be reliably estimated before surgery. The model is superior to risk classification by endometrial biopsy alone or in combination with ultrasound. This article is protected by copyright. All rights reserved.
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- 2019
47. VP66.11: Clinical and ultrasound characteristics of ovarian carcinosarcomas
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Ciccarone, F., primary, Biscione, A., additional, Codecà, C., additional, Ettore, C., additional, Fischerová, D., additional, Savelli, L., additional, Valentin, L., additional, Fruscio, R., additional, Chiappa, V., additional, Franchi, D., additional, Timmerman, D., additional, Sladkevicius, P., additional, Jokubkiene, L., additional, Froyman, W., additional, Scambia, G., additional, and Testa, A.C., additional
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- 2020
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48. VP66.24: The adoption of radiomics and machine learning improves the diagnostic processes of women with ovarian masses (aroma study)
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Chiappa, V., primary, Bogani, G., additional, Bertolina, F., additional, Interlenghi, M., additional, Salvatore, C., additional, Signorelli, M., additional, Castiglioni, I., additional, and Raspagliesi, F., additional
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- 2020
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49. Ultrasound‐based risk model for preoperative prediction of lymph‐node metastases in women with endometrial cancer: model‐development study
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Eriksson, L. S. E., primary, Epstein, E., additional, Testa, A. C., additional, Fischerova, D., additional, Valentin, L., additional, Sladkevicius, P., additional, Franchi, D., additional, Frühauf, F., additional, Fruscio, R., additional, Haak, L. A., additional, Opolskiene, G., additional, Mascilini, F., additional, Alcazar, J. L., additional, Van Holsbeke, C., additional, Chiappa, V., additional, Bourne, T., additional, Lindqvist, P. G., additional, Van Calster, B., additional, Timmerman, D., additional, Verbakel, J. Y., additional, Van den Bosch, T., additional, and Wynants, L., additional
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- 2020
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50. Imaging in gynecological disease (17): ultrasound features of malignant ovarian yolk sac tumors (endodermal sinus tumors)
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Anfelter, P., primary, Testa, A., additional, Chiappa, V., additional, Froyman, W., additional, Fruscio, R., additional, Guerriero, S., additional, Alcazar, J. L., additional, Mascillini, F., additional, Pascual, M. A., additional, Sibal, M., additional, Savelli, L., additional, Zannoni, G. F., additional, Timmerman, D., additional, and Epstein, E., additional
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- 2020
- Full Text
- View/download PDF
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