Accurate Prediction of Metachronous Liver Metastasis in Stage I-III Colorectal Cancer Patients Using Deep Learning With Digital Pathological Images

作者：Chanchan Xiao, Meihua Zhou, Xihua Yang, Haoyun Wang, Zhen Tang, Zheng Zhou, Zeyu Tian, Qi Liu, Xiaojie Li, Wei Jiang, Jihui Luo · 发表于：Frontiers in Oncology · 年份：2022 · DOI：10.3389/fonc.2022.844067 · 被引用次数：14 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Hepatocellular Carcinoma Treatment and Prognosis、AI in cancer detection

Objectives: Metachronous liver metastasis (LM) significantly impacts the prognosis of stage I-III colorectal cancer (CRC) patients. An effective biomarker to predict LM after surgery is urgently needed. We aimed to develop deep learning-based models to assist in predicting LM in stage I-III CRC patients using digital pathological images. Methods: Six-hundred eleven patients were retrospectively included in the study and randomly divided into training (428 patients) and validation (183 patients) cohorts according to the 7:3 ratio. Digital HE images from training cohort patients were used to construct the LM risk score based on a 50-layer residual convolutional neural network (ResNet-50). An LM prediction model was established by multivariable Cox analysis and confirmed in the validation cohort. The performance of the integrated nomogram was assessed with respect to its calibration, discrimination, and clinical application value. Results: <0.001). Multivariable analysis revealed that the LM risk score, VELIPI, pT stage and pN stage were independent predictors of LM. Then, the prediction model was developed and presented as a nomogram to predict the 1-, 2-, and 3-year probability of LM. The integrated nomogram achieved satisfactory discrimination, with C-indexes of 0.807 (95% CI: 0.787, 0.827) and 0.812 (95% CI: 0.773, 0.850) and AUCs of 0.840 (95% CI: 0.795, 0.885) and 0.848 (95% CI: 0.766, 0.931) in the training and validation cohorts, respectively. Favorable calibration of th...