背景:微卫星不稳定性(MSI)状态是大肠癌免疫治疗反应的强预测因子。放射基因组学方法有望使用非侵入性常规临床图像深入了解潜在的肿瘤生物学。这项研究调查了肿瘤形态与MSI和微卫星稳定性(MSS)的状态之间的关联。在外部多中心队列上验证新的放射学组学签名。
方法:回顾性收集了来自三家医院的243例结直肠癌患者的术前计算机断层扫描,其MSI状态匹配:首尔国立大学医院(SNUH);荷兰癌症研究所(NKI);和FondazioneIRCCSIstitutoNazionaledeiTumori,意大利米兰(INT)。放射科医生在每次扫描中描绘了原发性肿瘤,从中提取放射学特征。在SNUH数据上训练以识别MSI肿瘤的机器学习模型使用NKI和INT图像进行外部验证。根据接收操作曲线下面积(AUROC)比较性能。
结果:我们鉴定了包含7个放射组学特征的放射组学特征,这些特征可预测MSS或MSI的肿瘤(AUROC0.69,95%置信区间[CI]0.54-0.84,p=0.018)。将影像组学和临床数据整合到算法中,可将预测性能提高到AUROC为0.78(95%CI0.60-0.91,p=0.002),并增强了预测的可靠性。
结论:可以使用放射基因组学方法检测肿瘤MSS或MSI之间放射组学形态学表型的差异。未来的研究涉及大规模多中心前瞻性研究,结合各种诊断数据是必要的,以完善和验证更可靠,潜在的肿瘤不可知MSI放射基因组模型。
结论:来自计算机断层扫描的非侵入性影像学特征可以预测结直肠癌的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。
结论:基于CT的无创影像组学预测了结直肠癌的MSI,加强分层。在多中心队列中,具有MSI和MSS的七个特征的影像组学特征分化肿瘤。整合影像组学和临床数据提高了算法的预测性能。
BACKGROUND: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.