Abstract

In developed countries, colorectal cancer is the second cause of cancer-related mortality. Chemotherapy is considered a standard treatment for colorectal liver metastases (CLM). Among patients who develop CLM, the assessment of patient response to chemotherapy is often required to determine the need for second-line chemotherapy and eligibility for surgery. However, while FOLFOX-based regimens are typically used for CLM treatment, the identification of responsive patients remains elusive. Computer-aided diagnosis systems may provide insight in the classification of liver metastases identified on diagnostic images. In this paper, we propose a fully automated framework based on deep convolutional neural networks (DCNN) which first differentiates treated and untreated lesions to identify new lesions appearing on CT scans, followed by a fully connected neural networks to predict from untreated lesions in pre-treatment computed tomography (CT) for patients with CLM undergoing chemotherapy, their response to a FOLFOX with Bevacizumab regimen as first-line of treatment. The ground truth for assessment of treatment response was histopathology-determined tumor regression grade. Our DCNN approach trained on 444 lesions from 202 patients achieved accuracies of 91% for differentiating treated and untreated lesions, and 78% for predicting the response to FOLFOX-based chemotherapy regimen. Experimental results showed that our method outperformed traditional machine learning algorithms and may allow for the early detection of non-responsive patients.

中文翻译：

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在基线 CT 上预测未经治疗的肝转移对基于 FOLFOX 的化疗方案的反应：深度神经网络方法

摘要

在发达国家，结直肠癌是癌症相关死亡的第二大原因。化疗被认为是结直肠肝转移 (CLM) 的标准治疗方法。在发生 CLM 的患者中，通常需要评估患者对化疗的反应，以确定是否需要二线化疗和手术资格。然而，虽然基于 FOLFOX 的方案通常用于 CLM 治疗，但仍难以识别有反应的患者。计算机辅助诊断系统可以提供对诊断图像上识别的肝转移分类的深入了解。在本文中，我们提出了一个基于深度卷积神经网络 (DCNN) 的全自动框架，该框架首先区分已治疗和未治疗的病变，以识别 CT 扫描中出现的新病变，随后是一个完全连接的神经网络，以预测接受化疗的 CLM 患者在治疗前计算机断层扫描 (CT) 中未治疗的病变，他们对以贝伐珠单抗方案作为一线治疗的 FOLFOX 的反应。评估治疗反应的基本事实是组织病理学确定的肿瘤消退等级。我们的 DCNN 方法对 202 名患者的 444 个病灶进行了训练，区分治疗和未治疗病灶的准确率达到 91%，预测对基于 FOLFOX 的化疗方案的反应的准确率达到 78%。实验结果表明，我们的方法优于传统的机器学习算法，并且可以及早发现无反应的患者。