A Deep Learning Model to Predict the Response to Neoadjuvant Chemoradiotherapy by the Pretreatment Apparent Diffusion Coefficient Images of Locally Advanced Rectal Cancer.
A Deep Learning Model to Predict the Response to Neoadjuvant Chemoradiotherapy by the Pretreatment Apparent Diffusion Coefficient Images of Locally Advanced Rectal Cancer.
复制标题
通过局部晚期直肠癌治疗前表观扩散系数图像预测新辅助放化疗反应的深度学习模型
DOI：
10.3389/fonc.2020.574337
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发表时间：
2020
影响因子：
4.7
通讯作者：
Sun YS
中科院分区：
文献类型：
--
作者：
Zhu HT;Zhang XY;Shi YJ;Li XT;Sun YS
Background and Purpose Pretreatment prediction of the response to neoadjuvant chemoradiotherapy (NCRT) helps to determine the subsequent plans for the patients with locally advanced rectal cancer (LARC). If the good responders (GR) and non-good responders (non-GR) can be accurately predicted, they can choose to intensify the neoadjuvant chemoradiotherapy to decrease the risk of tumor progression during NCRT and increase the chance of organ preservation. Compared with radiomics methods, deep learning (DL) may adaptively extract features from the images without the need of feature definition. However, DL suffers from limited training samples and signal discrepancy among different scanners. This study aims to construct a DL model to predict GRs by training apparent diffusion coefficient (ADC) images from different scanners. Methods The study retrospectively recruited 700 participants, chronologically divided into a training group (n = 500) and a test group (n = 200). Deep convolutional neural networks were constructed to classify GRs and non-GRs. The networks were designed with a max-pooling layer parallelized by a center-cropping layer to extract features from both the macro and micro scale. ADC images and T2-weighted images were collected at 1.5 Tesla and 3.0 Tesla. The networks were trained by the image patches delineated by radiologists in ADC images and T2-weighted images, respectively. Pathological results were used as the ground truth. The deep learning models were evaluated on the test group and compared with the prediction by mean ADC value. Results Area under curve (AUC) of receiver operating characteristic (ROC) is 0.851 (95% CI: 0.789–0.914) for DL model with ADC images (DL_ADC), significantly larger (P = 0.018, Z = 2.367) than that of mean ADC with AUC = 0.723 (95% CI: 0.637–0.809). The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of DL_ADC model are 94.3%, 68.3%, 87.4% and 83.7%, respectively. The DL model with T2-weighted images (DL_T2) produces an AUC of 0.721 (95% CI: 0.640–0.802), significantly (P = 0.000, Z = 3.554) lower than that of DL_ADC model. Conclusion Deep learning model reveals the potential of pretreatment apparent diffusion coefficient images for the prediction of good responders to neoadjuvant chemoradiotherapy.
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影响因子：
19.7
作者：
Horvat, Natally;Veeraraghavan, Harini;Petkovska, Iva
通讯作者：
Petkovska, Iva
基于 MRI 的放射组学预测局部晚期直肠癌对新辅助放化疗的肿瘤反应
影响因子：
4.7
作者：
Yi, Xiaoping;Pei, Qian;Chen, Bihong T.
通讯作者：
Chen, Bihong T.
影响因子：
168.9
作者：
van der Valk, Maxime J. M.;Hilling, Denise E.;van de Velde, Cornelis J. H.
通讯作者：
van de Velde, Cornelis J. H.
影响因子：
45.3
作者：
Gerard, Jean-Pierre;Azria, David;Conroy, Thierry
通讯作者：
Conroy, Thierry
扩散加权成像的定量分析预测局部晚期直肠癌新辅助放化疗的病理良好反应
影响因子：
5.7
作者：
Tang, Zhenchao;Zhang, Xiao-Yan;Tian, Jie
通讯作者：
Tian, Jie