Department of Radiation Oncology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangzhou, China.
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Lancet Digit Health. 2022 Jan;4(1):e8-e17. doi: 10.1016/S2589-7500(21)00215-6.
Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated model to predict pathological complete response in patients with locally advanced rectal cancer using pretreatment MRI and haematoxylin and eosin (H&E)-stained biopsy slides.
In this multicentre observational study, eligible participants who had undergone neoadjuvant chemoradiotherapy followed by radical surgery were recruited, with their pretreatment pelvic MRI (T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging) and whole slide images of H&E-stained biopsy sections collected for annotation and feature extraction. The RAdioPathomics Integrated preDiction System (RAPIDS) was constructed by machine learning on the basis of three feature sets associated with pathological complete response: radiomics MRI features, pathomics nucleus features, and pathomics microenvironment features from a retrospective training cohort. The accuracy of RAPIDS for the prediction of pathological complete response in locally advanced rectal cancer was verified in two retrospective external validation cohorts and further validated in a multicentre, prospective observational study (ClinicalTrials.gov, NCT04271657). Model performances were evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
Between Sept 25, 2009, and Nov 3, 2017, 303 patients were retrospectively recruited in the training cohort, 480 in validation cohort 1, and 150 in validation cohort 2; 100 eligible patients were enrolled in the prospective study between Jan 10 and June 10, 2020. RAPIDS had favourable accuracy for the prediction of pathological complete response in the training cohort (AUC 0·868 [95% CI 0·825-0·912]), and in validation cohort 1 (0·860 [0·828-0·892]) and validation cohort 2 (0·872 [0·810-0·934]). In the prospective validation study, RAPIDS had an AUC of 0·812 (95% CI 0·717-0·907), sensitivity of 0·888 (0·728-0·999), specificity of 0·740 (0·593-0·886), NPV of 0·929 (0·862-0·995), and PPV of 0·512 (0·313-0·710). RAPIDS also significantly outperformed single-modality prediction models (AUC 0·630 [0·507-0·754] for the pathomics microenvironment model, 0·716 [0·580-0·852] for the radiomics MRI model, and 0·733 [0·620-0·845] for the pathomics nucleus model; all p<0·0001).
RAPIDS was able to predict pathological complete response to neoadjuvant chemoradiotherapy based on pretreatment radiopathomics images with high accuracy and robustness and could therefore provide a novel tool to assist in individualised management of locally advanced rectal cancer.
National Natural Science Foundation of China; Youth Innovation Promotion Association of the Chinese Academy of Sciences.
准确预测肿瘤对新辅助放化疗的反应，能够为局部晚期直肠癌患者提供个性化的围手术期治疗。本研究旨在开发和验证一种人工智能放射组学整合模型，利用局部晚期直肠癌患者的术前磁共振成像（T2 加权成像、增强 T1 加权成像和弥散加权成像）和苏木精和伊红（H&E）染色活检切片，预测病理完全缓解。
在这项多中心观察性研究中，招募了接受新辅助放化疗后行根治性手术的患者，采集其盆腔磁共振成像（T2 加权成像、增强 T1 加权成像和弥散加权成像）和 H&E 染色活检全片图像进行标注和特征提取。基于与病理完全缓解相关的三个特征集，即放射组学 MRI 特征、病理组学核特征和病理组学微环境特征，通过机器学习构建 RAdioPathomics Integrated preDiction System（RAPIDS）。在两个回顾性外部验证队列中验证 RAPIDS 对局部晚期直肠癌病理完全缓解的预测准确性，并在一项多中心前瞻性观察性研究（ClinicalTrials.gov，NCT04271657）中进一步验证。通过曲线下面积（AUC）、敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）评估模型性能。
2009 年 9 月 25 日至 2017 年 11 月 3 日，回顾性队列纳入 303 例患者，验证队列 1 纳入 480 例患者，验证队列 2 纳入 150 例患者；2020 年 1 月 10 日至 6 月 10 日，前瞻性研究纳入 100 例符合条件的患者。RAPIDS 对训练队列（AUC 0.868 [95%CI 0.825-0.912]）和验证队列 1（0.860 [0.828-0.892]）和验证队列 2（0.872 [0.810-0.934]）的病理完全缓解预测具有良好的准确性。在前瞻性验证研究中，RAPIDS 的 AUC 为 0.812（95%CI 0.717-0.907），敏感性为 0.888（0.728-0.999），特异性为 0.740（0.593-0.886），NPV 为 0.929（0.862-0.995），PPV 为 0.512（0.313-0.710）。RAPIDS 还显著优于单模态预测模型（病理组学微环境模型 AUC 0.630 [0.507-0.754]、放射组学 MRI 模型 AUC 0.716 [0.580-0.852]和病理组学核模型 AUC 0.733 [0.620-0.845]；均 p<0.0001）。
RAPIDS 能够基于术前放射组学图像，以较高的准确性和稳健性预测新辅助放化疗的病理完全缓解，因此可为局部晚期直肠癌的个体化治疗提供新的工具。
国家自然科学基金；中国科学院青年创新促进会。
