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26 results on '"Mann, Ritse"'

1. 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

5. Reducing False-Positive Screening MRI Rate in Women with Extremely Dense Breasts Using Prediction Models Based on Data from the DENSE Trial

Author

den Dekker, Bianca M, Bakker, Marije F, de Lange, Stéphanie V, Veldhuis, Wouter B, van Diest, Paul J, Duvivier, Katya M, Lobbes, Marc B I, Loo, Claudette E, Mann, Ritse M, Monninkhof, Evelyn M, Veltman, Jeroen, Pijnappel, Ruud M, van Gils, Carla H, Duvivier, KM, de Graaf, P, Beeldvorming, RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, MUMC+: DA BV Medisch Specialisten Radiologie (9), Pathology, Radiology and nuclear medicine, and CCA - Imaging and biomarkers

Subjects
Dense connective tissue, medicine.medical_specialty, Breast imaging, Breast Neoplasms, Sensitivity and Specificity, Breast cancer screening, MAMMOGRAPHY, Breast cancer, All institutes and research themes of the Radboud University Medical Center, Interquartile range, Biopsy, medicine, Humans, Radiology, Nuclear Medicine and imaging, False Positive Reactions, FAMILIAL RISK, Breast, Prospective Studies, Aged, Breast Density, Netherlands, medicine.diagnostic_test, Receiver operating characteristic, business.industry, Reproducibility of Results, Middle Aged, medicine.disease, CANCER, Magnetic Resonance Imaging, Women's cancers Radboud Institute for Health Sciences [Radboudumc 17], Clinical trial, Female, Radiology, business
Abstract

Background High breast density increases breast cancer risk and lowers mammographic sensitivity. Supplemental MRI screening improves cancer detection but increases the number of false-positive screenings. Thus, methods to distinguish true-positive MRI screening results from false-positive ones are needed. Purpose To build prediction models based on clinical characteristics and MRI findings to reduce the rate of false-positive screening MRI findings in women with extremely dense breasts. Materials and Methods Clinical characteristics and MRI findings in Dutch breast cancer screening participants (age range, 50-75 years) with positive first-round MRI screening results (Breast Imaging Reporting and Data System 3, 4, or 5) after a normal screening mammography with extremely dense breasts (Volpara density category 4) were prospectively collected within the randomized controlled Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial from December 2011 through November 2015. In this secondary analysis, prediction models were built using multivariable logistic regression analysis to distinguish true-positive MRI screening findings from false-positive ones. Results Among 454 women (median age, 52 years; interquartile range, 50-57 years) with a positive MRI result in a first supplemental MRI screening round, 79 were diagnosed with breast cancer (true-positive findings), and 375 had false-positive MRI results. The full prediction model (area under the receiver operating characteristics curve [AUC], 0.88; 95% CI: 0.84, 0.92), based on all collected clinical characteristics and MRI findings, could have prevented 45.5% (95% CI: 39.6, 51.5) of false-positive recalls and 21.3% (95% CI: 15.7, 28.3) of benign biopsies without missing any cancers. The model solely based on readily available MRI findings and age had a comparable performance (AUC, 0.84; 95% CI: 0.79, 0.88; P = .15) and could have prevented 35.5% (95% CI: 30.4, 41.1) of false-positive MRI screening results and 13.0% (95% CI: 8.8, 18.6) of benign biopsies. Conclusion Prediction models based on clinical characteristics and MRI findings may be useful to reduce the false-positive first-round screening MRI rate and benign biopsy rate in women with extremely dense breasts. Clinical trial registration no. NCT01315015 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Imbriaco in this issue.

Published
2021

6. Interval Cancer Detection Using a Neural Network and Breast Density in Women with Negative Screening Mammograms

Author

Wanders, Alexander J. T., primary, Mees, Willem, additional, Bun, Petra A.M., additional, Janssen, Natasja, additional, Rodríguez-Ruiz, Alejandro, additional, Dalmış, Mehmet Ufuk, additional, Karssemeijer, Nico, additional, van Gils, Carla H., additional, Sechopoulos, Ioannis, additional, Mann, Ritse M., additional, and van Rooden, Cornelis Jan, additional

Published
2022
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17. Reducing False-Positive Screening MRI Rate in Women with Extremely Dense Breasts Using Prediction Models Based on Data from the DENSE Trial

Author

Dekker, Bianca M. den, Bakker, Marije F., Lange, Stéphanie V. de, Veldhuis, Wouter B., Diest, Paul J. van, Duvivier, Katya M., Lobbes, Marc B. I., Loo, Claudette E., Mann, Ritse M., Monninkhof, Evelyn M., Veltman, Jeroen, Pijnappel, Ruud M., and Gils, Carla H. van

Abstract

Prediction models based on clinical characteristics and MRI findings may be useful to reduce the false-positive rate of first-round supplemental screening MRI in women with extremely dense breasts.

Published
2021
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18. BI-RADS Category Assignments by GPT-3.5, GPT-4, and Google Bard: A Multilanguage Study

Author

Cozzi, Andrea, Pinker, Katja, Hidber, Andri, Zhang, Tianyu, Bonomo, Luca, Lo Gullo, Roberto, Christianson, Blake, Curti, Marco, Rizzo, Stefania, Grande, Filippo Del, Mann, Ritse M., and Schiaffino, Simone

Abstract

Based on breast imaging reports written in three languages, GPT-3.5, GPT-4, and Google Bard achieved moderate agreement with human reader–assigned Breast Imaging Reporting and Data System categories but also yielded a high percentage of discordant category assignments that would negatively impact clinical management.

Published
2024
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21. Supplemental Breast MRI for Women with Extremely Dense Breasts: Results of the Second Screening Round of the DENSE Trial

Author

Veenhuizen, Stefanie G. A., de Lange, Stéphanie V., Bakker, Marije F., Pijnappel, Ruud M., Mann, Ritse M., Monninkhof, Evelyn M., Emaus, Marleen J., de Koekkoek-Doll, Petra K., Bisschops, Robertus H. C., Lobbes, Marc B. I., de Jong, Mathijn D. F., Duvivier, Katya M., Veltman, Jeroen, Karssemeijer, Nico, de Koning, Harry J., van Diest, Paul J., Mali, Willem P. T. M., van den Bosch, Maurice A. A. J., van Gils, Carla H., and Veldhuis, Wouter B.

Abstract

The cancer detection rate in an incident breast MRI screening round after a negative mammographic examination is 5.8 per 1000 screening examinations—compared with 16.5 per 1000 screening examinations in the prevalent round—which is accompanied by a strong reduction in the number of false positive results.

Published
2021
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23. MR Imaging as an Additional Screening Modality for the Detection of Breast Cancer in Women Aged 50–75 Years with Extremely Dense Breasts: The DENSE Trial Study Design

Author

Emaus, Marleen J., primary, Bakker, Marije F., additional, Peeters, Petra H. M., additional, Loo, Claudette E., additional, Mann, Ritse M., additional, de Jong, Mathijn D. F., additional, Bisschops, Robertus H. C., additional, Veltman, Jeroen, additional, Duvivier, Katya M., additional, Lobbes, Marc B. I., additional, Pijnappel, Ruud M., additional, Karssemeijer, Nico, additional, de Koning, Harry J., additional, van den Bosch, Maurice A. A. J., additional, Monninkhof, Evelyn M., additional, Mali, Willem P. Th. M., additional, Veldhuis, Wouter B., additional, and van Gils, Carla H., additional

Published
2015
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24. The Role of Imaging Specialists as Authors of Systematic Reviews on Diagnostic and Interventional Imaging and Its Impact on Scientific Quality: Report from the EuroAIM Evidence-based Radiology Working Group

Author

Sardanelli, Francesco, primary, Bashir, Humayun, additional, Berzaczy, Dominik, additional, Cannella, Guglielmo, additional, Espeland, Ansgar, additional, Flor, Nicola, additional, Helbich, Thomas, additional, Hunink, Myriam, additional, Malone, Dermot E., additional, Mann, Ritse, additional, Muzzupappa, Claudia, additional, Petersen, Lars J., additional, Riklund, Katrine, additional, Sconfienza, Luca M., additional, Serafin, Zbigniew, additional, Spronk, Sandra, additional, Stoker, Jaap, additional, van Beek, Edwin J. R., additional, Vorwerk, Dierk, additional, and Leo, Giovanni Di, additional

Published
2014
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25. Influence of Risk Category and Screening Round on the Performance of an MR Imaging and Mammography Screening Program in Carriers of the BRCAMutation and Other Women at Increased Risk

Author

Vreemann, Suzan, Gubern-Mérida, Albert, Schlooz-Vries, Margrethe S., Bult, Peter, van Gils, Carla H., Hoogerbrugge, Nicoline, Karssemeijer, Nico, and Mann, Ritse M.

Abstract

In real-life practice, the performance of a high-risk screening program is affected by risk category and the frequency of prophylactic mastectomies.

Published
2018
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