Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer.

IF 8.1 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Radiology-Artificial Intelligence Pub Date : 2024-11-06 DOI:10.1148/ryai.240124
Casey E Stowers, Chengyue Wu, Zhan Xu, Sidharth Kumar, Clinton Yam, Jong Bum Son, Jingfei Ma, Jonathan I Tamir, Gaiane M Rauch, Thomas E Yankeelov
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Abstract

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To combine deep learning and biology-based modeling to predict the response of locally advanced, triple negative breast cancer before initiating neoadjuvant chemotherapy (NAC). Materials and Methods In this retrospective study, a biology-based mathematical model of tumor response to NAC was constructed and calibrated on a patient-specific basis using imaging data from patients enrolled in the MD Anderson ARTEMIS trial (ClinicalTrials.gov, NCT02276443) between April 2018 and May 2021. To relate the calibrated parameters in the biology-based model and pretreatment MRI data, a convolutional neural network (CNN) was employed. The CNN predictions of the calibrated model parameters were used to estimate tumor response at the end of NAC. CNN performance in the estimations of total tumor volume (TTV), total tumor cellularity (TTC), and tumor status was evaluated. Model-predicted TTC and TTV measurements were compared with MRI-based measurements using the concordance correlation coefficient (CCC), and area under the receiver operating characteristic curve (for predicting pathologic complete response at the end of NAC). Results The study included 118 female patients (median age, 51 [range, 29-78] years). For comparison of CNN predicted to measured change in TTC and TTV over the course of NAC, the CCCs were 0.95 (95% CI: 0.90-0.98) and 0.94 (95% CI: 0.87-0.97), respectively. CNN-predicted TTC and TTV had an AUC of 0.72 (95% CI: 0.34-0.94) and 0.72 (95% CI: 0.40-0.95) for predicting tumor status at the time of surgery, respectively. Conclusion Deep learning integrated with a biology-based mathematical model showed good performance in predicting the spatial and temporal evolution of a patient's tumor during NAC using only pre-NAC MRI data. ©RSNA, 2024.

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结合生物学和磁共振成像数据驱动模型预测三阴性乳腺癌患者对新辅助化疗的反应
"刚刚接受 "的论文经过同行评审,已被接受在《放射学》上发表:人工智能》上发表。这篇文章在以最终版本发表之前,还将经过校对、排版和校对审核。请注意,在制作最终校对稿的过程中,可能会发现影响内容的错误。目的 结合深度学习和基于生物学的建模,在开始新辅助化疗(NAC)前预测局部晚期三阴性乳腺癌的反应。材料与方法 在这项回顾性研究中,利用2018年4月至2021年5月期间MD安德森ARTEMIS试验(ClinicalTrials.gov,NCT02276443)入组患者的成像数据,构建了基于生物学的NAC肿瘤反应数学模型,并在患者特异性的基础上进行了校准。为了将基于生物学的模型中的校准参数与治疗前核磁共振成像数据联系起来,采用了卷积神经网络(CNN)。CNN 对校准模型参数的预测用于估计 NAC 结束时的肿瘤反应。评估了 CNN 在估计肿瘤总体积(TTV)、肿瘤细胞总数(TTC)和肿瘤状态方面的性能。使用一致性相关系数(CCC)和接收者操作特征曲线下面积(用于预测 NAC 结束时的病理完全反应)将模型预测的 TTC 和 TTV 测量值与基于 MRI 的测量值进行比较。结果 研究纳入了 118 名女性患者(中位年龄 51 [范围 29-78] 岁)。比较 CNN 预测与测量的 TTC 和 TTV 在 NAC 疗程中的变化,CCC 分别为 0.95(95% CI:0.90-0.98)和 0.94(95% CI:0.87-0.97)。CNN 预测的 TTC 和 TTV 预测手术时肿瘤状态的 AUC 分别为 0.72(95% CI:0.34-0.94)和 0.72(95% CI:0.40-0.95)。结论 深度学习与基于生物学的数学模型相结合,在仅使用 NAC 前的 MRI 数据预测 NAC 期间患者肿瘤的空间和时间演变方面表现出色。©RSNA, 2024.
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CiteScore
16.20
自引率
1.00%
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0
期刊介绍: Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.
期刊最新文献
Integrated Deep Learning Model for the Detection, Segmentation, and Morphologic Analysis of Intracranial Aneurysms Using CT Angiography. RSNA 2023 Abdominal Trauma AI Challenge Review and Outcomes Analysis. SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans. Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer. Optimizing Performance of Transformer-based Models for Fetal Brain MR Image Segmentation.
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