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Predicting treatment response from longitudinal images using multi-task deep learning
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DOI：10.1038/s41467-021-22188-y.png)
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摘要
En 中文
Radiographic imaging is routinely used to evaluate treatment response in solid tumors. Current imaging response metrics do not reliably predict the underlying biological response. Here, we present a multi-task deep learning approach that allows simultaneous tumor segmentation and response prediction. We design two Siamese subnetworks that are joined at multiple layers, which enables integration of multi-scale feature representations and in-depth comparison of pre-treatment and post-treatment images. The network is trained using 2568 magnetic resonance imaging scans of 321 rectal cancer patients for predicting pathologic complete response after neoadjuvant chemoradiotherapy. In multi-institution validation, the imaging-based model achieves AUC of 0.95 (95% confidence interval: 0.91-0.98) and 0.92 (0.87-0.96) in two independent cohorts of 160 and 141 patients, respectively. When combined with blood-based tumor markers, the integrated model further improves prediction accuracy with AUC 0.97 (0.93-0.99). Our approach to capturing dynamic information in longitudinal images may be broadly used for screening, treatment response evaluation, disease monitoring, and surveillance. Radiographic imaging is routinely used to evaluate treatment response in solid tumors. Here, the authors present a multi-task deep learning approach that allows simultaneous tumor segmentation and response prediction from longitudinal images in a multi-center study on rectal cancer.
Keyword:
PATHOLOGICAL COMPLETE RESPONSE
RECTAL-CANCER
NEOADJUVANT CHEMORADIATION
RADIOMICS
WATCH
MULTICENTER
ALGORITHM
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期刊
IF:
15.7
论文数:
9.4W
被引数:
91.2W
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引用论文
MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy.png)
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