{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T14:26:24Z","timestamp":1792074384775,"version":"4.4.0"},"reference-count":33,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T00:00:00Z","timestamp":1728172800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA251792"],"award-info":[{"award-number":["R01CA251792"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The I\u2010SPY 2 trial is a national\u2010wide, multi\u2010institutional clinical trial designed to evaluate multiple new therapeutic drugs for high\u2010risk breast cancer. Previous studies suggest that pathological complete response (pCR) is a viable indicator of long\u2010term outcomes of neoadjuvant chemotherapy for high\u2010risk breast cancer. While pCR can be assessed during surgery after the chemotherapy, early prediction of pCR before the completion of the chemotherapy may facilitate personalized treatment management to achieve an improved outcome. Notably, the acquisition of dynamic contrast\u2010enhanced magnetic resonance (DCEMR) images at multiple time points during the I\u2010SPY 2 trial opens up the possibility of achieving early pCR prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>In this study, we investigated the feasibility of the early prediction of pCR to neoadjuvant chemotherapy using multi\u2010time point DCEMR images and clinical data acquired in the I\u2010SPY2 trial. The prediction uncertainty was also quantified to allow physicians to make patient\u2010specific decisions on treatment plans based on the level of associated uncertainty.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The dataset used in our study included 624 patients with DCEMR images acquired at 3 time points before the completion of the chemotherapy: pretreatment (T0), after 3 cycles of treatment (T1), and after 12 cycles of treatment (T2). A convolutional long short\u2010term memory (LSTM) network\u2010based deep learning model, which integrated multi\u2010time point deep image representations with clinical data, including tumor subtypes, was developed to predict pCR. The performance of the model was evaluated via the method of nested 5\u2010fold cross validation. Moreover, we also quantified prediction uncertainty for each patient through test\u2010time augmentation. To investigate the relationship between predictive performance and uncertainty, the area under the receiver operating characteristic curve (AUROC) was assessed on subgroups of patients stratified by the uncertainty score.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>By integrating clinical data and DCEMR images obtained at three\u2010time points before treatment completion, the AUROC reached 0.833 with a sensitivity of 0.723 and specificity of 0.800. This performance was significantly superior (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.01) to models using only images (AUROC\u00a0=\u00a00.706) or only clinical data (AUROC\u00a0=\u00a00.746). After stratifying the patients into eight subgroups based on the uncertainty score, we found that group #1, with the lowest uncertainty, had a superior AUROC of 0.873. The AUROC decreased to 0.637 for group #8, which had the highest uncertainty.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The results indicate that our convolutional LSTM network\u2010based deep learning model can be used to predict pCR earlier before the completion of chemotherapy. By combining clinical data and multi\u2010time point deep image representations, our model outperforms models built solely on clinical or image data. Estimating prediction uncertainty may enable physicians to prioritize or disregard predictions based on their associated uncertainties. This approach could potentially enhance the personalization of breast cancer therapy.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17451","type":"journal-article","created":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T22:03:17Z","timestamp":1728252197000},"page":"9385-9393","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification"],"prefix":"10.1002","volume":"51","author":[{"given":"Bowen","family":"Jing","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology University of Texas Southwestern Medical Center  Dallas Texas USA"},{"name":"Advanced Imaging and Informatics for Radiation Therapy (AIRT) Lab University of Texas Southwestern Medical Center  Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Texas Southwestern Medical Center  Dallas Texas USA"},{"name":"Advanced Imaging and Informatics for Radiation Therapy (AIRT) Lab University of Texas Southwestern Medical Center  Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erich","family":"Schmitz","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Texas Southwestern Medical Center  Dallas Texas USA"},{"name":"Advanced Imaging and Informatics for Radiation Therapy (AIRT) Lab University of Texas Southwestern Medical Center  Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Texas Southwestern Medical Center  Dallas Texas USA"},{"name":"Advanced Imaging and Informatics for Radiation Therapy (AIRT) Lab University of Texas Southwestern Medical Center  Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunxiang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Texas Southwestern Medical Center  Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"You","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Texas Southwestern Medical Center  Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Texas Southwestern Medical Center  Dallas Texas USA"},{"name":"Advanced Imaging and Informatics for Radiation Therapy (AIRT) Lab University of Texas Southwestern Medical Center  Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,10,6]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/clpt.2009.68"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12609\u2010019\u201000334\u20102"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2020.2535"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41571\u2010019\u20100299\u20109"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41523\u2010020\u201000203\u20107ARTN63"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1158\/2159\u20108290.Cd\u201020\u20101557"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1158\/1078\u20100432.Ccr\u201020\u20102737"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24782"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010016\u20104565\u20102"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018180273"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.12110748"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015150013"},{"key":"e_1_2_8_14_1","unstructured":"AyhanMS BerensP.Test\u2010time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks. 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