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480 results on '"Mann, Ritse"'

3. Re-attendance in supplemental breast MRI screening rounds of the DENSE trial for women with extremely dense breasts

Author

Veenhuizen, Stefanie G. A., van Grinsven, Sophie E. L., Laseur, Isabelle L., Bakker, Marije F., Monninkhof, Evelyn M., de Lange, Stéphanie V., Pijnappel, Ruud M., Mann, Ritse M., Lobbes, Marc B. I., Duvivier, Katya M., de Jong, Mathijn D. F., Loo, Claudette E., Karssemeijer, Nico, van Diest, Paul J., Veldhuis, Wouter B., and van Gils, Carla H.

Published
2024
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8. Joint EANM-SNMMI guideline on the role of 2-[18F]FDG PET/CT in no special type breast cancer: (endorsed by the ACR, ESSO, ESTRO, EUSOBI/ESR, and EUSOMA)

Author

Vaz, Sofia C., Woll, John Patrick Pilkington, Cardoso, Fatima, Groheux, David, Cook, Gary J. R., Ulaner, Gary A., Jacene, Heather, Rubio, Isabel T., Schoones, Jan W., Peeters, Marie-Jeanne Vrancken, Poortmans, Philip, Mann, Ritse M., Graff, Stephanie L., Dibble, Elizabeth H., and de Geus-Oei, Lioe-Fee

Published
2024
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11. Preoperative breast MRI positively impacts surgical outcomes of needle biopsy–diagnosed pure DCIS: a patient-matched analysis from the MIPA study

Author

Cozzi, Andrea, Di Leo, Giovanni, Houssami, Nehmat, Gilbert, Fiona J., Helbich, Thomas H., Álvarez Benito, Marina, Balleyguier, Corinne, Bazzocchi, Massimo, Bult, Peter, Calabrese, Massimo, Camps Herrero, Julia, Cartia, Francesco, Cassano, Enrico, Clauser, Paola, de Lima Docema, Marcos F., Depretto, Catherine, Dominelli, Valeria, Forrai, Gábor, Girometti, Rossano, Harms, Steven E., Hilborne, Sarah, Ienzi, Raffaele, Lobbes, Marc B. I., Losio, Claudio, Mann, Ritse M., Montemezzi, Stefania, Obdeijn, Inge-Marie, Aksoy Ozcan, Umit, Pediconi, Federica, Pinker, Katja, Preibsch, Heike, Raya Povedano, José L., Rossi Saccarelli, Carolina, Sacchetto, Daniela, Scaperrotta, Gianfranco P., Schlooz, Margrethe, Szabó, Botond K., Taylor, Donna B., Ulus, Sila Ö., Van Goethem, Mireille, Veltman, Jeroen, Weigel, Stefanie, Wenkel, Evelyn, Zuiani, Chiara, and Sardanelli, Francesco

Published
2024
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15. Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis.

Author

Yoon, Jung, Strand, Fredrik, Baltzer, Pascal, Conant, Emily, Gilbert, Fiona, Lehman, Constance, Mullen, Lisa, Nishikawa, Robert, Sharma, Nisha, Vejborg, Ilse, Moy, Linda, Mann, Ritse, and Morris, Elizabeth

Subjects
Female, Humans, Artificial Intelligence, Breast Neoplasms, Early Detection of Cancer, Mammography, Breast, Retrospective Studies
Abstract

Background There is considerable interest in the potential use of artificial intelligence (AI) systems in mammographic screening. However, it is essential to critically evaluate the performance of AI before it can become a modality used for independent mammographic interpretation. Purpose To evaluate the reported standalone performances of AI for interpretation of digital mammography and digital breast tomosynthesis (DBT). Materials and Methods A systematic search was conducted in PubMed, Google Scholar, Embase (Ovid), and Web of Science databases for studies published from January 2017 to June 2022. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) values were reviewed. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Comparative (QUADAS-2 and QUADAS-C, respectively). A random effects meta-analysis and meta-regression analysis were performed for overall studies and for different study types (reader studies vs historic cohort studies) and imaging techniques (digital mammography vs DBT). Results In total, 16 studies that include 1 108 328 examinations in 497 091 women were analyzed (six reader studies, seven historic cohort studies on digital mammography, and four studies on DBT). Pooled AUCs were significantly higher for standalone AI than radiologists in the six reader studies on digital mammography (0.87 vs 0.81, P = .002), but not for historic cohort studies (0.89 vs 0.96, P = .152). Four studies on DBT showed significantly higher AUCs in AI compared with radiologists (0.90 vs 0.79, P < .001). Higher sensitivity and lower specificity were seen for standalone AI compared with radiologists. Conclusion Standalone AI for screening digital mammography performed as well as or better than radiologists. Compared with digital mammography, there is an insufficient number of studies to assess the performance of AI systems in the interpretation of DBT screening examinations. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Scaranelo in this issue.

Published
2023

22. Screening and diagnostic breast MRI: how do they impact surgical treatment? Insights from the MIPA study

Author

Cozzi, Andrea, Di Leo, Giovanni, Houssami, Nehmat, Gilbert, Fiona J., Helbich, Thomas H., Álvarez Benito, Marina, Balleyguier, Corinne, Bazzocchi, Massimo, Bult, Peter, Calabrese, Massimo, Camps Herrero, Julia, Cartia, Francesco, Cassano, Enrico, Clauser, Paola, de Lima Docema, Marcos F., Depretto, Catherine, Dominelli, Valeria, Forrai, Gábor, Girometti, Rossano, Harms, Steven E., Hilborne, Sarah, Ienzi, Raffaele, Lobbes, Marc B. I., Losio, Claudio, Mann, Ritse M., Montemezzi, Stefania, Obdeijn, Inge-Marie, Ozcan, Umit A., Pediconi, Federica, Pinker, Katja, Preibsch, Heike, Raya Povedano, José L., Rossi Saccarelli, Carolina, Sacchetto, Daniela, Scaperrotta, Gianfranco P., Schlooz, Margrethe, Szabó, Botond K., Taylor, Donna B., Ulus, Özden S., Van Goethem, Mireille, Veltman, Jeroen, Weigel, Stefanie, Wenkel, Evelyn, Zuiani, Chiara, and Sardanelli, Francesco

Published
2023
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27. European Society of Breast Imaging (EUSOBI) guidelines on the management of axillary lymphadenopathy after COVID-19 vaccination: 2023 revision

Author

Schiaffino, Simone, Pinker, Katja, Cozzi, Andrea, Magni, Veronica, Athanasiou, Alexandra, Baltzer, Pascal A. T., Camps Herrero, Julia, Clauser, Paola, Fallenberg, Eva M., Forrai, Gabor, Fuchsjäger, Michael H., Gilbert, Fiona J., Helbich, Thomas, Kilburn-Toppin, Fleur, Kuhl, Christiane K., Lesaru, Mihai, Mann, Ritse M., Panizza, Pietro, Pediconi, Federica, Sardanelli, Francesco, Sella, Tamar, Thomassin-Naggara, Isabelle, Zackrisson, Sophia, and Pijnappel, Ruud M.

Published
2023
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30. Third International Consensus Conference on lesions of uncertain malignant potential in the breast (B3 lesions)

Author

Elfgen, Constanze, Leo, Cornelia, Kubik-Huch, Rahel A., Muenst, Simone, Schmidt, Noemi, Quinn, Cecily, McNally, Sorcha, van Diest, Paul J., Mann, Ritse M, Bago-Horvath, Zsuzsanna, Bernathova, Maria, Regitnig, Peter, Fuchsjäger, Michael, Schwegler-Guggemos, Daniela, Maranta, Martina, Zehbe, Sabine, Tausch, Christoph, Güth, Uwe, Fallenberg, Eva Maria, Schrading, Simone, Kothari, Ashutosh, Sonnenschein, Martin, Kampmann, Gert, Kulka, Janina, Tille, Jean-Christophe, Körner, Meike, Decker, Thomas, Lax, Sigurd F., Daniaux, Martin, Bjelic-Radisic, Vesna, Kacerovsky-Strobl, Stephanie, Condorelli, Rosaria, Gnant, Michael, and Varga, Zsuzsanna

Published
2023
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31. The Lancet Breast Cancer Commission

Author

Arreola-Ornelas, Hector, Bhadelia, Afsan, Boughey, Judy C, Chatterjee, Sanjoy, Dodwell, David, Doubova, Svetlana, Du Plooy, Dorothy, Essue, Beverley, Goel, Neha, Gralow, Julie, Hawley, Sarah, Kiely, Belinda, Mann, Ritse, Mertz, Shirley, Palmieri, Carlo, Poortmans, Philip, Spanic, Tanja, Stephen, Lesley, Symmans, Fraser, Towns, Catherine, Verhoeven, Didier, Vinnicombe, Sarah, Watkins, David, Yip, Cheng-Har, Zikmund-Fisher, Brian, Coles, Charlotte E, Earl, Helena, Anderson, Benjamin O, Barrios, Carlos H, Bienz, Maya, Bliss, Judith M, Cameron, David A, Cardoso, Fatima, Cui, Wanda, Francis, Prudence A, Jagsi, Reshma, Knaul, Felicia Marie, McIntosh, Stuart A, Phillips, Kelly-Anne, Radbruch, Lukas, Thompson, Mareike K, André, Fabrice, Abraham, Jean E, Bhattacharya, Indrani S, Franzoi, Maria Alice, Drewett, Lynsey, Fulton, Alexander, Kazmi, Farasat, Inbah Rajah, Dharrnesha, Mutebi, Miriam, Ng, Dianna, Ng, Szeyi, Olopade, Olufunmilayo I, Rosa, William E, Rubasingham, Jeffrey, Spence, Dingle, Stobart, Hilary, Vargas Enciso, Valentina, Vaz-Luis, Ines, and Villarreal-Garza, Cynthia

Published
2024
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32. MRI-guided optimisation of neoadjuvant chemotherapy duration in stage II–III HER2-positive breast cancer (TRAIN-3): a multicentre, single-arm, phase 2 study

Author

van Leeuwen-Stok, E., van Leeuwen, L., de Graaf, H., van Riel, J.M.G.H., Houtsma, D., Vrijaldenhoven, S., van der Velden, A.W.G., Peerdeman, A.L., van den Brink - Schimmel, R.J., Drooger, J.C., Imholz, A.L.Th., Dercksen, M.W., Oulad Hadj, J., Bakker, S.D., van der Wouw, A.J., Kerver, E.D., Bouma, G., van Schaik, C., de Boer, M., Mandigers, C.M.P.W., Koornstra, R.H.T., Smals, A., Rietbroek, R.C., van der Padt - Pruijsten, A., den Boer, M.O., Bos, H., Schiphorst, P.P.J.B.M., Vriens, B.E.P.J., van Rossum, Q.C., Tol, J., Hoogwerf - Kluft, D., Kruijtzer - Schimmel, C.M.F., Oosterkamp, H.M., Baas, I.O., Dietvorst, A.H.P., Davidis - van Schoonhoven, M., van Dijk, M.A., van der Voort, Anna, Louis, Fleur M, van Ramshorst, Mette S, Kessels, Rob, Mandjes, Ingrid A, Kemper, Inge, Agterof, Mariette J, van der Steeg, Wim A, Heijns, Joan B, van Bekkum, Marlies L, Siemerink, Ester J, Kuijer, Philomeen M, Scholten, Astrid, Wesseling, Jelle, Vrancken Peeters, Marie-Jeanne T F D, Mann, Ritse M, and Sonke, Gabe S

Published
2024
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35. Computer-aided diagnosis of masses in breast computed tomography imaging: deep learning model with combined handcrafted and convolutional radiomic features.

Author

Caballo, Marco, Hernandez, Andrew M, Lyu, Su Hyun, Teuwen, Jonas, Mann, Ritse M, van Ginneken, Bram, Boone, John M, and Sechopoulos, Ioannis

Subjects
breast cancer, breast computed tomography, computer-aided diagnosis, deep learning, radiomics, Breast Cancer, Networking and Information Technology R&D (NITRD), Cancer, Biomedical Imaging, Good Health and Well Being
Abstract

Purpose: A computer-aided diagnosis (CADx) system for breast masses is proposed, which incorporates both handcrafted and convolutional radiomic features embedded into a single deep learning model. Approach: The model combines handcrafted and convolutional radiomic signatures into a multi-view architecture, which retrieves three-dimensional (3D) image information by simultaneously processing multiple two-dimensional mass patches extracted along different planes through the 3D mass volume. Each patch is processed by a stream composed of two concatenated parallel branches: a multi-layer perceptron fed with automatically extracted handcrafted radiomic features, and a convolutional neural network, for which discriminant features are learned from the input patches. All streams are then concatenated together into a final architecture, where all network weights are shared and the learning occurs simultaneously for each stream and branch. The CADx system was developed and tested for diagnosis of breast masses ( N=284 ) using image datasets acquired with independent dedicated breast computed tomography systems from two different institutions. The diagnostic classification performance of the CADx system was compared against other machine and deep learning architectures adopting handcrafted and convolutional approaches, and three board-certified breast radiologists. Results: On a test set of 82 masses (45 benign, 37 malignant), the proposed CADx system performed better than all other model architectures evaluated, with an increase in the area under the receiver operating characteristics curve (AUC) of 0.05±0.02 , and achieving a final AUC of 0.947, outperforming the three radiologists ( AUC=0.814-0.902 ). Conclusions: In conclusion, the system demonstrated its potential usefulness in breast cancer diagnosis by improving mass malignancy assessment.

Published
2021

37. Multi‐marker quantitative radiomics for mass characterization in dedicated breast CT imaging

Author

Caballo, Marco, Pangallo, Domenico R, Sanderink, Wendelien, Hernandez, Andrew M, Lyu, Su Hyun, Molinari, Filippo, Boone, John M, Mann, Ritse M, and Sechopoulos, Ioannis

Subjects
Medical and Biological Physics, Engineering, Physical Sciences, Biomedical Engineering, Biomedical Imaging, Cancer, Breast Cancer, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Good Health and Well Being, Algorithms, Breast, Breast Neoplasms, Humans, ROC Curve, Tomography, X-Ray Computed, breast cancer, breast CT, computer&#8208, aided diagnosis, precision medicine, radiomics, computer-aided diagnosis, Other Physical Sciences, Oncology and Carcinogenesis, Nuclear Medicine & Medical Imaging, Biomedical engineering, Medical and biological physics
Abstract

PurposeTo develop and evaluate the diagnostic performance of an algorithm for multi-marker radiomic-based classification of breast masses in dedicated breast computed tomography (bCT) images.MethodsOver 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, and margin sharpness were developed and implemented. These included well-established texture and shape feature descriptors, which were supplemented with additional approaches for contour irregularity quantification, spicule and lobe detection, characterization of degree of infiltration, and differences in peritumoral compartments. All descriptors were extracted from a training set of 202 bCT masses (133 benign and 69 malignant), and their individual diagnostic performance was investigated in terms of area under the receiver operating characteristics (ROC) curve (AUC) of single-feature-based linear discriminant analysis (LDA) classifiers. Subsequently, the most relevant descriptors were selected through a multiple-step feature selection process (including stability analysis, statistical significance, evaluation of feature interaction, and dimensionality reduction), and used to develop a final LDA radiomic model for classification of benign and malignant masses, which was then tested on an independent test set of 82 cases (45 benign and 37 malignant).ResultsThe majority of the individual radiomic descriptors showed, on the training set, an AUC value deriving from a linear decision boundary higher than 0.65, with the lower limit of the associated 95% confidence interval (C.I.) not overlapping with random chance (AUC = 0.5). The final LDA radiomic model resulted in a test set AUC of 0.90 (95% C.I. 0.80-0.96).ConclusionsThe proposed multi-marker radiomic approach achieved high diagnostic accuracy in bCT mass classification, using a radiomic signature based on different feature types. While future studies with larger datasets are needed to further validate these results, quantitative radiomics applied to bCT shows potential to improve the breast cancer diagnosis pipeline.

Published
2021

41. Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI)

Author

Mann, Ritse M., Athanasiou, Alexandra, Baltzer, Pascal A. T., Camps-Herrero, Julia, Clauser, Paola, Fallenberg, Eva M., Forrai, Gabor, Fuchsjäger, Michael H., Helbich, Thomas H., Killburn-Toppin, Fleur, Lesaru, Mihai, Panizza, Pietro, Pediconi, Federica, Pijnappel, Ruud M., Pinker, Katja, Sardanelli, Francesco, Sella, Tamar, Thomassin-Naggara, Isabelle, Zackrisson, Sophia, Gilbert, Fiona J., and Kuhl, Christiane K.

Published
2022
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43. Magnetic resonance imaging before breast cancer surgery: results of an observational multicenter international prospective analysis (MIPA)

Author

Sardanelli, Francesco, Trimboli, Rubina M., Houssami, Nehmat, Gilbert, Fiona J., Helbich, Thomas H., Álvarez Benito, Marina, Balleyguier, Corinne, Bazzocchi, Massimo, Bult, Peter, Calabrese, Massimo, Camps Herrero, Julia, Cartia, Francesco, Cassano, Enrico, Clauser, Paola, Cozzi, Andrea, de Andrade, Danúbia A., de Lima Docema, Marcos F., Depretto, Catherine, Dominelli, Valeria, Forrai, Gábor, Girometti, Rossano, Harms, Steven E., Hilborne, Sarah, Ienzi, Raffaele, Lobbes, Marc B. I., Losio, Claudio, Mann, Ritse M., Montemezzi, Stefania, Obdeijn, Inge-Marie, Ozcan, Umit A., Pediconi, Federica, Pinker, Katja, Preibsch, Heike, Raya Povedano, José L., Sacchetto, Daniela, Scaperrotta, Gianfranco P., Schiaffino, Simone, Schlooz, Margrethe, Szabó, Botond K., Taylor, Donna B., Ulus, Özden S., Van Goethem, Mireille, Veltman, Jeroen, Weigel, Stefanie, Wenkel, Evelyn, Zuiani, Chiara, and Di Leo, Giovanni

Published
2022
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44. An unsupervised automatic segmentation algorithm for breast tissue classification of dedicated breast computed tomography images

Author

Caballo, Marco, Boone, John M, Mann, Ritse, and Sechopoulos, Ioannis

Subjects
Medical and Biological Physics, Physical Sciences, Biomedical Imaging, Bioengineering, Cancer, Breast Cancer, Adipose Tissue, Breast, Cluster Analysis, Humans, Mammography, Pattern Recognition, Automated, Skin, Tomography, X-Ray Computed, Unsupervised Machine Learning, breast cancer, CAD, dedicated breast CT, image classification, segmentation, CAD, Other Physical Sciences, Biomedical Engineering, Oncology and Carcinogenesis, Nuclear Medicine & Medical Imaging, Biomedical engineering, Medical and biological physics
Abstract

PurposeTo develop and evaluate a new automatic classification algorithm to identify voxels containing skin, vasculature, adipose, and fibroglandular tissue in dedicated breast CT images.MethodsThe proposed algorithm combines intensity- and region-based segmentation methods with energy minimizing splines and unsupervised data mining approaches for classifying and segmenting the different tissue types. Breast skin segmentation is achieved by a region-growing method which uses constraints from the previously extracted skin centerline to add robustness to the model and to reduce the false positive rate. An energy minimizing active contour model is then used to classify adipose tissue voxels by including gradient flow and region-based features. Finally, blood vessels are separated from fibroglandular tissue by a k-means clustering algorithm based on automatically extracted shape-based features. To evaluate the accuracy of the algorithm, two sets of 15 different patient breast CT scans, each acquired with different breast CT systems and acquisition settings were obtained. Three slices from each scan were manually segmented under the supervision of an experienced breast radiologist and considered the gold standard. Comparisons with manual segmentation were quantified using five similarity metrics: Dice similarity coefficient (DSC), sensitivity, conformity coefficient, and two Hausdorff distance measures. To evaluate the robustness to image noise, the segmentation was repeated after separately adding Gaussian noise with increasing standard deviation (in four steps, from 0.01 to 0.04) to an additional 15 slices from the first dataset. In addition, to evaluate vasculature classification, three different pre- and postcontrast injection patient breast CT images were classified and compared. Finally, DSC was also used for quantitative comparisons with previously proposed approaches for breast CT tissue classification using 10 images from the first dataset.ResultsThe algorithm showed a high accuracy in classifying the different tissue types for both breast CT systems, with an average DSC of 95% and 90% for the first and second image dataset, respectively. Furthermore, it demonstrated to be robust to image noise with a robustness to image noise of 85%, 83%, 79%, and 71% for the images corrupted with the four increasing noise levels. Previous methods for breast tissues classification resulted, for the tested dataset, in an average global DSC of 87%, while our approach resulted in a global average DSC of 94.5%.ConclusionsThe proposed algorithm resulted in accurate and robust breast tissue classification, with no prior training or threshold setting. Potential applications include breast density quantification and tissue pattern characterization (both biomarkers of cancer development), simulation-based radiation dose analysis, and patient data-based phantom design, which could be used for further breast imaging research.

Published
2018

49. Patients' perceptions of targeted breast ultrasound and digital breast tomosynthesis in the diagnostic setting: A mixed methods study.

Author

Siebers, Carmen C. N., Appelman, Linda, Kočo, Lejla, Palm, Mette, Rainey, Linda, Broeders, Mireille J. M., Appelman, Peter T. M., Go, Shirley, Van Oirsouw, Marja C. J., and Mann, Ritse M.

Subjects
PATIENTS' attitudes, DIAGNOSTIC ultrasonic imaging, TOMOSYNTHESIS, BREAST ultrasound, BREAST exams, BREAST
Abstract

Background: Although DBT is the standard initial imaging modality for women with focal breast symptoms, the importance of ultrasound has grown rapidly in the past decades. Therefore, the Breast UltraSound Trial (BUST) focused on assessing the diagnostic value of ultrasound and digital breast tomosynthesis (DBT) for the evaluation of breast symptoms by reversing the order of breast imaging; first performing ultrasound followed by DBT. This side-study of the BUST evaluates patients' perceptions of ultrasound and DBT in a reversed setting. Methods: After imaging, 1181/1276 BUST participants completed a survey consisting of open and closed questions regarding both exams (mean age 47.2, ±11.74). Additionally, a different subset of BUST participants (n = 29) participated in six focus group interviews 18–24 months after imaging to analyze their imaging experiences in depth. Results: A total of 55.3% of women reported reluctance to undergoing DBT, primarily due of pain, while the vast majority also find bilateral DBT reassuring (87.3%). Thematic analysis identified themes related to 1) imaging reluctance (pain/burden, result, and breast harm) and 2) ultrasound and DBT perceptions. Regarding the latter, the theme comfort underscores DBT as burdensome and painful, while ultrasound is largely perceived as non-burdensome. Ultrasound is also particularly valued for its interactive nature, as highlighted in the theme interaction. Perceived effectiveness reflects women's interest in bilateral breast evaluation with DBT and the visibility of lesions, while they express more uncertainty about the reliability of ultrasound. Emotional impact portrays DBT as reassuring for many women, whereas opinions on the reassurance provided by ultrasound are more diverse. Additional themes include costs, protocols and privacy. Conclusions: Ultrasound is highly tolerated, and particularly valued is the interaction with the radiologist. Nearly half of women express reluctance towards DBT; nevertheless, a large portion report feeling more confident after undergoing bilateral DBT, reassuring them of the absence of abnormalities. Understanding patients' perceptions of breast imaging examinations is of great value when optimizing diagnostic pathways. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Joint EANM-SNMMI guideline on the role of 2-[18F]FDG PET/CT in no special type breast cancer: (endorsed by the ACR, ESSO, ESTRO, EUSOBI/ESR, and EUSOMA).

Author

Vaz, Sofia C., Woll, John Patrick Pilkington, Cardoso, Fatima, Groheux, David, Cook, Gary J. R., Ulaner, Gary A., Jacene, Heather, Rubio, Isabel T., Schoones, Jan W., Peeters, Marie-Jeanne Vrancken, Poortmans, Philip, Mann, Ritse M., Graff, Stephanie L., Dibble, Elizabeth H., and de Geus-Oei, Lioe-Fee

Subjects
POSITRON emission tomography, BREAST cancer, LITERATURE reviews, NUCLEAR medicine
Abstract

Introduction: There is much literature about the role of 2-[18F]FDG PET/CT in patients with breast cancer (BC). However, there exists no international guideline with involvement of the nuclear medicine societies about this subject. Purpose: To provide an organized, international, state-of-the-art, and multidisciplinary guideline, led by experts of two nuclear medicine societies (EANM and SNMMI) and representation of important societies in the field of BC (ACR, ESSO, ESTRO, EUSOBI/ESR, and EUSOMA). Methods: Literature review and expert discussion were performed with the aim of collecting updated information regarding the role of 2-[18F]FDG PET/CT in patients with no special type (NST) BC and summarizing its indications according to scientific evidence. Recommendations were scored according to the National Institute for Health and Care Excellence (NICE) criteria. Results: Quantitative PET features (SUV, MTV, TLG) are valuable prognostic parameters. In baseline staging, 2-[18F]FDG PET/CT plays a role from stage IIB through stage IV. When assessing response to therapy, 2-[18F]FDG PET/CT should be performed on certified scanners, and reported either according to PERCIST, EORTC PET, or EANM immunotherapy response criteria, as appropriate. 2-[18F]FDG PET/CT may be useful to assess early metabolic response, particularly in non-metastatic triple-negative and HER2+ tumours. 2-[18F]FDG PET/CT is useful to detect the site and extent of recurrence when conventional imaging methods are equivocal and when there is clinical and/or laboratorial suspicion of relapse. Recent developments are promising. Conclusion: 2-[18F]FDG PET/CT is extremely useful in BC management, as supported by extensive evidence of its utility compared to other imaging modalities in several clinical scenarios. [ABSTRACT FROM AUTHOR]

Published
2024
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