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Abstract：
UNASSIGNED: This study developed and validated a deep learning (DL) model based on whole slide imaging (WSI) for predicting the treatment response to chemotherapy and radiotherapy (CRT) among patients with non-small cell lung cancer (NSCLC).
UNASSIGNED: We collected the WSI of 120 nonsurgical patients with NSCLC treated with CRT from three hospitals in China. Based on the processed WSI, two DL models were established: a tissue classification model which was used to select tumor-tiles, and another model which predicted the treatment response of the patients based on the tumor-tiles (predicting the treatment response of each tile). A voting method was employed, by which the label of tiles with the greatest quantity from 1 patient would be used as the label of the patient.
UNASSIGNED: The tissue classification model had a great performance (accuracy in the training set/internal validation set =0.966/0.956). Based on 181,875 tumor-tiles selected by the tissue classification model, the model for predicting the treatment response demonstrated strong predictive ability (accuracy of patient-level prediction in the internal validation set/external validation set 1/external validation set 2 =0.786/0.742/0.737).
UNASSIGNED: A DL model was constructed based on WSI to predict the treatment response of patients with NSCLC. This model can help doctors to formulate personalized CRT plans and improve treatment outcomes.
UNASSIGNED: We collected the WSI of 120 nonsurgical patients with NSCLC treated with CRT from three hospitals in China. Based on the processed WSI, two DL models were established: a tissue classification model which was used to select tumor-tiles, and another model which predicted the treatment response of the patients based on the tumor-tiles (predicting the treatment response of each tile). A voting method was employed, by which the label of tiles with the greatest quantity from 1 patient would be used as the label of the patient.
UNASSIGNED: The tissue classification model had a great performance (accuracy in the training set/internal validation set =0.966/0.956). Based on 181,875 tumor-tiles selected by the tissue classification model, the model for predicting the treatment response demonstrated strong predictive ability (accuracy of patient-level prediction in the internal validation set/external validation set 1/external validation set 2 =0.786/0.742/0.737).
UNASSIGNED: A DL model was constructed based on WSI to predict the treatment response of patients with NSCLC. This model can help doctors to formulate personalized CRT plans and improve treatment outcomes.
摘要：
■本研究开发并验证了基于全片成像（WSI）的深度学习（DL）模型，用于预测非小细胞肺癌（NSCLC）患者对化疗和放疗（CRT）的治疗反应。
■我们收集了来自中国三家医院接受CRT治疗的120例非手术NSCLC患者的WSI。基于处理后的WSI，建立了两个DL模型：一个用于选择肿瘤块的组织分类模型，和另一个模型，该模型基于肿瘤图块预测患者的治疗反应（预测每个图块的治疗反应）。采用了投票方法，由此,具有来自1个患者的最大量的瓦片的标签将被用作患者的标签。
■组织分类模型具有出色的性能（在训练集/内部验证集中的准确性=0.966/0.956）。基于组织分类模型选择的181,875个肿瘤块，预测治疗反应的模型显示出较强的预测能力(内部验证集/外部验证集1/外部验证集2中患者水平预测的准确性=0.786/0.742/0.737).
■基于WSI构建DL模型来预测NSCLC患者的治疗反应。该模型可以帮助医生制定个性化的CRT计划并改善治疗效果。
■我们收集了来自中国三家医院接受CRT治疗的120例非手术NSCLC患者的WSI。基于处理后的WSI，建立了两个DL模型：一个用于选择肿瘤块的组织分类模型，和另一个模型，该模型基于肿瘤图块预测患者的治疗反应（预测每个图块的治疗反应）。采用了投票方法，由此,具有来自1个患者的最大量的瓦片的标签将被用作患者的标签。
■组织分类模型具有出色的性能（在训练集/内部验证集中的准确性=0.966/0.956）。基于组织分类模型选择的181,875个肿瘤块，预测治疗反应的模型显示出较强的预测能力(内部验证集/外部验证集1/外部验证集2中患者水平预测的准确性=0.786/0.742/0.737).
■基于WSI构建DL模型来预测NSCLC患者的治疗反应。该模型可以帮助医生制定个性化的CRT计划并改善治疗效果。
