Details

Autor(en) / Beteiligte

• Li, Wen
• Partridge, Savannah C
• Newitt, David C
• Steingrimsson, Jon
• Marques, Helga S
• Bolan, Patrick J
• Hirano, Michael
• Bearce, Benjamin Aaron
• Kalpathy-Cramer, Jayashree
• Boss, Michael A
• Teng, Xinzhi
• Zhang, Jiang
• Cai, Jing
• Kontos, Despina
• Cohen, Eric A
• Mankowski, Walter C
• Liu, Michael
• Ha, Richard
• Pellicer-Valero, Oscar J
• Maier-Hein, Klaus
• Rabinovici-Cohen, Simona
• Tlusty, Tal
• Ozery-Flato, Michal
• Parekh, Vishwa S
• Jacobs, Michael A
• Yan, Ran
• Sung, Kyunghyun
• Kazerouni, Anum S
• DiCarlo, Julie C
• Yankeelov, Thomas E
• Chenevert, Thomas L
• Hylton, Nola M

Titel

Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge

Ist Teil von

• Radiology. Imaging cancer, 2024-01, Vol.6 (1), p.e230033

Ort / Verlag

United States: Radiological Society of North America

Erscheinungsjahr

2024

Quelle

MEDLINE

Beschreibungen/Notizen

• Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 MRI, Breast, Tumor Response . © RSNA, 2024.