Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
Eur Radiol Exp. 2024 Aug 26;8(1):98. doi: 10.1186/s41747-024-00484-8.
Microsatellite instability (MSI) status is a strong predictor of response to immunotherapy of colorectal cancer. Radiogenomic approaches promise the ability to gain insight into the underlying tumor biology using non-invasive routine clinical images. This study investigates the association between tumor morphology and the status of MSI versus microsatellite stability (MSS), validating a novel radiomic signature on an external multicenter cohort.
Preoperative computed tomography scans with matched MSI status were retrospectively collected for 243 colorectal cancer patients from three hospitals: Seoul National University Hospital (SNUH); Netherlands Cancer Institute (NKI); and Fondazione IRCCS Istituto Nazionale dei Tumori, Milan Italy (INT). Radiologists delineated primary tumors in each scan, from which radiomic features were extracted. Machine learning models trained on SNUH data to identify MSI tumors underwent external validation using NKI and INT images. Performances were compared in terms of area under the receiving operating curve (AUROC).
We identified a radiomic signature comprising seven radiomic features that were predictive of tumors with MSS or MSI (AUROC 0.69, 95% confidence interval [CI] 0.54-0.84, p = 0.018). Integrating radiomic and clinical data into an algorithm improved predictive performance to an AUROC of 0.78 (95% CI 0.60-0.91, p = 0.002) and enhanced the reliability of the predictions.
Differences in the radiomic morphological phenotype between tumors MSS or MSI could be detected using radiogenomic approaches. Future research involving large-scale multicenter prospective studies that combine various diagnostic data is necessary to refine and validate more robust, potentially tumor-agnostic MSI radiogenomic models.
Noninvasive radiomic signatures derived from computed tomography scans can predict MSI in colorectal cancer, potentially augmenting traditional biopsy-based methods and enhancing personalized treatment strategies.
Noninvasive CT-based radiomics predicted MSI in colorectal cancer, enhancing stratification. A seven-feature radiomic signature differentiated tumors with MSI from those with MSS in multicenter cohorts. Integrating radiomic and clinical data improved the algorithm's predictive performance.
微卫星不稳定性（MSI）状态是预测结直肠癌免疫治疗反应的有力指标。放射基因组学方法有望通过非侵入性常规临床图像获得对潜在肿瘤生物学的深入了解。本研究调查了肿瘤形态学与 MSI 与微卫星稳定（MSS）之间的关系，在一个外部多中心队列中验证了一种新的放射组学特征。
从首尔国立大学医院（SNUH）、荷兰癌症研究所（NKI）和米兰意大利国家肿瘤研究所基金会（INT）的三家医院回顾性收集了 243 例结直肠癌患者的术前计算机断层扫描，这些患者的 MSI 状态均匹配。放射科医生在每个扫描中描绘了原发肿瘤，并从中提取了放射组学特征。在 SNUH 数据上训练的用于识别 MSI 肿瘤的机器学习模型在 NKI 和 INT 图像上进行了外部验证。比较了接受者操作特征曲线下的面积（AUROC）。
我们确定了一个由七个放射组学特征组成的放射组学特征，该特征可预测 MSS 或 MSI 肿瘤（AUROC 0.69，95%置信区间 [CI] 0.54-0.84，p=0.018）。将放射组学和临床数据集成到算法中可将预测性能提高到 0.78（95%CI 0.60-0.91，p=0.002），并提高预测的可靠性。
使用放射基因组学方法可以检测 MSS 或 MSI 肿瘤之间的放射组学形态表型差异。未来需要涉及大规模多中心前瞻性研究，结合各种诊断数据，以进一步完善和验证更强大的、潜在的肿瘤无关的 MSI 放射基因组学模型。
从计算机断层扫描中提取的非侵入性放射组学特征可以预测结直肠癌中的 MSI，可能增强传统的基于活检的方法并增强个性化治疗策略。
基于 CT 的非侵入性放射组学预测结直肠癌中的 MSI，增强分层。一个由七个特征组成的放射组学特征可区分多中心队列中具有 MSI 的肿瘤和具有 MSS 的肿瘤。整合放射组学和临床数据可提高算法的预测性能。
