Department of Radiology, The Second Affiliated Hospital of Soochow University, No.1055 Sanxiang Road, Gusu District, Suzhou, 215004, Jiangsu, China.
Department of Radiology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China.
BMC Cancer. 2022 May 9;22(1):524. doi: 10.1186/s12885-022-09584-3.
Preoperative prediction of microsatellite instability (MSI) status in colorectal cancer (CRC) patients is of great significance for clinicians to perform further treatment strategies and prognostic evaluation. Our aims were to develop and validate a non-invasive, cost-effective reproducible and individualized clinic-radiomics nomogram method for preoperative MSI status prediction based on contrast-enhanced CT (CECT)images.
A total of 76 MSI CRC patients and 200 microsatellite stability (MSS) CRC patients with pathologically confirmed (194 in the training set and 82 in the validation set) were identified and enrolled in our retrospective study. We included six significant clinical risk factors and four qualitative imaging data extracted from CECT images to build the clinics model. We applied the intra-and inter-class correlation coefficient (ICC), minimal-redundancy-maximal-relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) for feature reduction and selection. The selected independent prediction clinical risk factors, qualitative imaging data and radiomics features were performed to develop a predictive nomogram model for MSI status on the basis of multivariable logistic regression by tenfold cross-validation. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots and Hosmer-Lemeshow test were performed to assess the nomogram model. Finally, decision curve analysis (DCA) was performed to determine the clinical utility of the nomogram model by quantifying the net benefits of threshold probabilities.
Twelve top-ranked radiomics features, three clinical risk factors (location, WBC and histological grade) and CT-reported IFS were finally selected to construct the radiomics, clinics and combined clinic-radiomics nomogram model. The clinic-radiomics nomogram model with the highest AUC value of 0.87 (95% CI, 0.81-0.93) and 0.90 (95% CI, 0.83-0.96), as well as good calibration and clinical utility observed using the calibration plots and DCA in the training and validation sets respectively, was regarded as the candidate model for identification of MSI status in CRC patients.
The proposed clinic-radiomics nomogram model with a combination of clinical risk factors, qualitative imaging data and radiomics features can potentially be effective in the individualized preoperative prediction of MSI status in CRC patients and may help performing further treatment strategies.
术前预测结直肠癌（CRC）患者的微卫星不稳定性（MSI）状态对于临床医生制定进一步的治疗策略和预后评估具有重要意义。我们的目的是开发和验证一种基于增强 CT（CECT）图像的非侵入性、经济高效、可重复和个体化的临床放射组学列线图方法，用于术前 MSI 状态预测。
本回顾性研究共纳入 76 例 MSI CRC 患者和 200 例经病理证实的微卫星稳定（MSS）CRC 患者（训练集中 194 例，验证集中 82 例）。我们纳入了 6 个显著的临床危险因素和 4 个从 CECT 图像中提取的定性影像学数据，以构建临床模型。我们应用组内和组间相关系数（ICC）、最小冗余最大相关性（mRMR）和最小绝对收缩和选择算子（LASSO）进行特征降维和选择。基于多变量逻辑回归的十折交叉验证，对选择的独立预测临床危险因素、定性影像学数据和放射组学特征进行分析，建立用于 MSI 状态预测的列线图模型。通过接受者操作特征（ROC）曲线下面积（AUC）、校准图和 Hosmer-Lemeshow 检验评估列线图模型。最后，通过量化阈值概率的净收益，进行决策曲线分析（DCA）以确定列线图模型的临床实用性。
最终选择了 12 个排名最高的放射组学特征、3 个临床危险因素（部位、白细胞和组织学分级）和 CT 报告的 IFD，构建放射组学、临床和联合临床放射组学列线图模型。在训练集和验证集中，基于临床危险因素、定性影像学数据和放射组学特征构建的联合临床放射组学列线图模型具有最高 AUC 值（分别为 0.87[95%CI，0.81-0.93]和 0.90[95%CI，0.83-0.96]），且校准图和 DCA 显示出良好的校准度和临床实用性，被认为是识别 CRC 患者 MSI 状态的候选模型。
该研究提出的联合临床危险因素、定性影像学数据和放射组学特征的临床放射组学列线图模型，可有效预测 CRC 患者的 MSI 状态，有助于制定进一步的治疗策略。
