使用磁共振成像和深度学习神经网络评估直肠癌新辅助放化疗反应。

Evaluation of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Using MR Images and Deep Learning Neural Networks.

机构信息

Department of Radiology, Bagcilar Training and Research Hospital, Istanbul, Turkey.

Department of Biomedical Engineering, Yeditepe University, Istanbul, Turkey.

出版信息

Curr Med Imaging. 2024;20:e15734056309748. doi: 10.2174/0115734056309748240509072222.

DOI:10.2174/0115734056309748240509072222

PMID:38874041

Abstract

INTRODUCTION

The aim of the study was to develop deep-learning neural networks to guide treatment decisions and for the accurate evaluation of tumor response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using magnetic resonance (MR) images.

METHODS

Fifty-nine tumors with stage 2 or 3 rectal cancer that received nCRT were retrospectively evaluated. Pathological tumor regression grading was carried out using the Dworak (Dw-TRG) guidelines and served as the ground truth for response predictions. Imaging-based tumor regression grading was performed according to the MERCURY group guidelines from pre-treatment and post-treatment para-axial T2-weighted MR images (MR-TRG). Tumor signal intensity signatures were extracted by segmenting the tumors volumetrically on the images. Normalized histograms of the signatures were used as input to a deep neural network (DNN) housing long short-term memory (LSTM) units. The output of the network was the tumor regression grading prediction, DNN-TRG.

RESULTS

In predicting complete or good response, DNN-TRG demonstrated modest agreement with Dw-TRG (Cohen's kappa= 0.79) and achieved 84.6% sensitivity, 93.9% specificity, and 89.8% accuracy. MR-TRG revealed 46.2% sensitivity, 100% specificity, and 76.3% accuracy. In predicting a complete response, DNN-TRG showed slight agreement with Dw-TRG (Cohen's kappa= 0.75) with 71.4% sensitivity, 97.8% specificity, and 91.5% accuracy. MR-TRG provided 42.9% sensitivity, 100% specificity, and 86.4% accuracy. DNN-TRG benefited from higher sensitivity but lower specificity, leading to higher accuracy than MR-TRG in predicting tumor response.

CONCLUSION

The use of deep LSTM neural networks is a promising approach for evaluating the tumor response to nCRT in rectal cancer.

摘要

介绍

本研究旨在开发深度学习神经网络，利用磁共振（MR）图像指导治疗决策，并准确评估直肠癌新辅助放化疗（nCRT）后的肿瘤反应。

方法

回顾性评估了 59 例接受 nCRT 的 2 或 3 期直肠癌肿瘤。采用 Dworak（Dw-TRG）标准进行病理肿瘤消退分级，并作为反应预测的基准。根据 MERCURY 组的指南，在治疗前和治疗后轴旁 T2 加权 MR 图像（MR-TRG）上进行基于影像学的肿瘤消退分级。通过对图像进行容积分割提取肿瘤信号强度特征。将特征的归一化直方图作为长短期记忆（LSTM）单元的深度神经网络（DNN）的输入。网络的输出是肿瘤消退分级预测，即 DNN-TRG。

结果

在预测完全或良好反应方面，DNN-TRG 与 Dw-TRG 具有中等一致性（Cohen's kappa=0.79），其敏感性为 84.6%，特异性为 93.9%，准确性为 89.8%。MR-TRG 的敏感性为 46.2%，特异性为 100%，准确性为 76.3%。在预测完全反应方面，DNN-TRG 与 Dw-TRG 具有轻微一致性（Cohen's kappa=0.75），敏感性为 71.4%，特异性为 97.8%，准确性为 91.5%。MR-TRG 提供了 42.9%的敏感性、100%的特异性和 86.4%的准确性。DNN-TRG 受益于更高的敏感性，但特异性较低，在预测肿瘤反应方面，其准确性高于 MR-TRG。