Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Int J Cancer. 2023 Jan 1;152(1):31-41. doi: 10.1002/ijc.34053. Epub 2022 May 13.
Currently, the prognosis assessment of stage II colorectal cancer (CRC) remains a difficult clinical problem; therefore, more accurate prognostic predictors must be developed. In our study, we developed a prognostic prediction model for stage II CRC by fusing radiomics and deep-learning (DL) features of primary lesions and peripheral lymph nodes (LNs) in computed tomography (CT) scans. First, two CT radiomics models were built using primary lesion and LN image features. Subsequently, an information fusion method was used to build a fusion radiomics model by combining the tumor and LN image features. Furthermore, a transfer learning method was applied to build a deep convolutional neural network (CNN) model. Finally, the prediction scores generated by the radiomics and CNN models were fused to improve the prognosis prediction performance. The disease-free survival (DFS) and overall survival (OS) prediction areas under the curves (AUCs) generated by the fusion model improved to 0.76 ± 0.08 and 0.91 ± 0.05, respectively. These were significantly higher than the AUCs generated by the models using the individual CT radiomics and deep image features. Applying the survival analysis method, the DFS and OS fusion models yielded concordance index (C-index) values of 0.73 and 0.9, respectively. Hence, the combined model exhibited good predictive efficacy; therefore, it could be used for the accurate assessment of the prognosis of stage II CRC patients. Moreover, it could be used to screen out high-risk patients with poor prognoses, and assist in the formulation of clinical treatment decisions in a timely manner to achieve precision medicine.
目前，Ⅱ期结直肠癌（CRC）的预后评估仍然是一个临床难题，因此必须开发更准确的预后预测指标。在我们的研究中，我们通过融合 CT 扫描中原发灶和外周淋巴结（LNs）的放射组学和深度学习（DL）特征，建立了Ⅱ期 CRC 的预后预测模型。首先，使用原发灶和 LN 图像特征构建了两个 CT 放射组学模型。随后，使用信息融合方法，通过结合肿瘤和 LN 图像特征构建融合放射组学模型。此外，应用迁移学习方法构建深度卷积神经网络（CNN）模型。最后，融合放射组学和 CNN 模型生成的预测评分，以提高预后预测性能。融合模型生成的无病生存（DFS）和总生存（OS）预测曲线下面积（AUC）分别提高到 0.76±0.08 和 0.91±0.05，明显高于使用个体 CT 放射组学和深度图像特征的模型生成的 AUC。通过生存分析方法，DFS 和 OS 融合模型的一致性指数（C-index）值分别为 0.73 和 0.9。因此，联合模型具有良好的预测效果；因此，它可用于准确评估Ⅱ期 CRC 患者的预后。此外，它可以筛选出预后不良的高危患者，并及时协助制定临床治疗决策，实现精准医学。
