Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, China.
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Hepatopancreatobiliary Surgery Department I, Peking University Cancer Hospital & Institute, Beijing, China.
Int J Cancer. 2021 Apr 1;148(7):1717-1730. doi: 10.1002/ijc.33427. Epub 2020 Dec 29.
Accurate evaluation of tumor response to preoperative chemotherapy is crucial for assigning appropriate patients with colorectal liver metastases (CRLM) to surgery or conservative therapy. However, there is no well-recognized method for predicting pathological response before surgery. Our study constructed and validated a deep learning algorithm using prechemotherapy and postchemotherapy magnetic resonance imaging (MRI) to predict pathological response in CRLM. CRLM patients from center one who had ≤5 lesions and were scheduled to receive preoperative chemotherapy followed by liver resection between January 2013 and November 2016, were included prospectively and chronologically divided into a training cohort (80% of patients) and a testing cohort (20% of patients). Patients from center two were included January 2017 and December 2018 as an external validation cohort. MRI-based models were constructed to discriminate according to pathology tumor regression grade (TRG) between the response (TRG1/2) and nonresponse (TRG3/4/5) groups at the lesion level. From center one, 155 patients (328 lesions) were included; chronologically, 101 (264 lesions) in the training cohort and 54 (64 lesions) in the testing cohort. The model achieved better accuracy (0.875 vs 0.578) and AUC (0.849 vs 0.615) than RECIST for discriminating response; it also distinguished the survival outcomes after hepatectomy better than the RECIST criteria. Evaluations of the external validation cohort (25 patients, 61 lesions) also showed good ability with an AUC of 0.833. In conclusion, the MRI-based deep learning model provided accurate prediction of pathological tumor response to preoperative chemotherapy in patients with CRLM and may inform individualized treatment.
术前化疗后结直肠癌肝转移（CRLM）患者肿瘤反应的准确评估对于将适当的患者分配至手术或保守治疗至关重要。然而，目前尚缺乏一种术前预测病理反应的公认方法。我们的研究构建并验证了一种基于术前和化疗后磁共振成像（MRI）的深度学习算法，以预测 CRLM 患者的病理反应。前瞻性地按时间顺序将 2013 年 1 月至 2016 年 11 月在中心 1 接受≤5 个病灶且计划接受术前化疗加肝切除术的 CRLM 患者纳入研究，分为训练队列（80%的患者）和测试队列（20%的患者）。2017 年 1 月至 2018 年 12 月，中心 2 的患者纳入外部验证队列。构建 MRI 模型，以根据病理学肿瘤消退分级（TRG）在病灶水平上区分反应（TRG1/2）和非反应（TRG3/4/5）组。中心 1 纳入 155 例患者（328 个病灶），按时间顺序，训练队列中 101 例（264 个病灶），测试队列中 54 例（64 个病灶）。该模型在区分反应方面的准确性（0.875 比 0.578）和 AUC（0.849 比 0.615）均优于 RECIST，且在肝切除术后的生存结果区分方面优于 RECIST 标准。外部验证队列（25 例患者，61 个病灶）的评估也显示出良好的能力，AUC 为 0.833。总之，基于 MRI 的深度学习模型能够准确预测 CRLM 患者术前化疗的病理肿瘤反应，可能为个体化治疗提供依据。
