Qin Huan, Hu Xianling, Zhang Junfeng, Dai Haisu, He Yonggang, Zhao Zhiping, Yang Jiali, Xu Zhengrong, Hu Xiaofei, Chen Zhiyu
Institute of Hepatopancreatobiliary Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Communication NCO Academy, Army Engineering University of PLA, Chongqing, China.
Liver Int. 2021 Apr;41(4):837-850. doi: 10.1111/liv.14763. Epub 2020 Dec 25.
Up to 40%-65% of patients with perihilar cholangiocarcinoma (PHC) rapidly progress to early recurrence (ER) even after curative resection. Quantification of ER risk is difficult and a reliable prognostic prediction tool is absent. We developed and validated a multilevel model, integrating clinicopathology, molecular pathology and radiology, especially radiomics coupled with machine-learning algorithms, to predict the ER of patients after curative resection in PHC.
In total, 274 patients who underwent contrast-enhanced CT (CECT) and curative resection at 2 institutions were retrospectively identified and randomly divided into training (n = 167), internal validation (n = 70) and external validation (n = 37) sets. A machine-learning analysis of 18,120 radiomic features based on multiphase CECT and 48 clinico-radiologic characteristics was performed for the multilevel model.
Comprehensively, 7 independent factors (tumour differentiation, lymph node metastasis, pre-operative CA19-9 level, enhancement pattern, A-Shrink score, V-Shrink score and P-Shrink score) were built to the multilevel model and quantified the risk of ER. We benchmarked the gain in discrimination with the area under the curve (AUC) of 0.883, superior to the rival clinical and radiomic models (AUCs 0.792-0.805). The accuracy (ACC) of the multilevel model was 0.826, which was significantly higher than those of the conventional staging systems (AJCC 8th (0.641), MSKCC (0.617) and Gazzaniga (0.581)).
The radiomics-based multilevel model demonstrated superior performance to rival models and conventional staging systems, and could serve as a visual prognostic tool to plan surveillance of ER and guide post-operative individualized management in PHC.
高达40%-65%的肝门部胆管癌（PHC）患者即使在根治性切除术后也会迅速进展为早期复发（ER）。ER风险的量化困难，且缺乏可靠的预后预测工具。我们开发并验证了一个多层次模型，整合临床病理学、分子病理学和放射学，特别是结合机器学习算法的放射组学，以预测PHC患者根治性切除术后的ER。
回顾性确定了2家机构中274例行增强CT（CECT）和根治性切除的患者，并随机分为训练组（n = 167）、内部验证组（n = 70）和外部验证组（n = 37）。基于多期CECT的18120个放射组学特征和48个临床放射学特征进行了多层次模型的机器学习分析。
综合起来，7个独立因素（肿瘤分化、淋巴结转移、术前CA19-9水平、强化模式、A-Shrink评分、V-Shrink评分和P-Shrink评分）被纳入多层次模型并量化了ER风险。我们用曲线下面积（AUC）为0.883来衡量鉴别力的提高，优于竞争的临床和放射组学模型（AUC为0.792-0.805）。多层次模型的准确率（ACC）为0.826，显著高于传统分期系统（美国癌症联合委员会第8版（0.641）、纪念斯隆凯特琳癌症中心（0. MSKCC 617）和加扎尼加（0.581））。
基于放射组学的多层次模型表现出优于竞争模型和传统分期系统的性能，可作为一种可视化的预后工具，用于规划ER监测并指导PHC术后的个体化管理。
