Department of Gastroenterology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, China.
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510120, China.
BMC Cancer. 2024 Oct 4;24(1):1226. doi: 10.1186/s12885-024-12951-x.
Colon cancer, a frequently encountered malignancy, exhibits a comparatively poor survival prognosis. Perineural invasion (PNI), highly correlated with tumor progression and metastasis, is a substantial effective predictor of stage II-III colon cancer. Nonetheless, the lack of effective and facile predictive methodologies for detecting PNI prior operation in colon cancer remains a persistent challenge.
Pre-operative computer tomography (CT) images and clinical data of patients diagnosed with stage II-III colon cancer between January 2015 and December 2023 were obtained from two sub-districts of Sun Yat-sen Memorial Hospital (SYSUMH). The LASSO/RF/PCA filters were used to screen radiomics features and LR/SVM models were utilized to construct radiomics model. A comprehensive model, shown as nomogram finally, combining with radiomics score and significant clinical features were developed and validated by area under the curve (AUC) and decision curve analysis (DCA).
The total cohort, comprising 426 individuals, was randomly divided into a development cohort and a validation cohort as a 7:3 ratio. Radiomics scores were extracted from LASSO-SVM models with AUC of 0.898/0.726 in the development and validation cohorts, respectively. Significant clinical features (CA199, CA125, T-stage, and N-stage) were used to establish combining model with radiomics scores. The combined model exhibited superior reliability compared to single radiomics model in AUC value (0.792 vs. 0.726, p = 0.003) in validation cohorts. The radiomics-clinical model demonstrated an AUC of 0.918/0.792, a sensitivity of 0.907/0.813 and a specificity of 0.804/0.716 in the development and validation cohorts, respectively.
The study developed and validated a predictive nomogram model combining radiomics scores and clinical features, and showed good performance in predicting PNI pre-operation in stage II-III colon cancer patients.
结肠癌是一种常见的恶性肿瘤，其预后较差。神经周围侵犯（PNI）与肿瘤的进展和转移密切相关，是预测 II 期-III 期结肠癌的重要有效指标。然而，在结肠癌术前检测 PNI 方面，缺乏有效的简便预测方法仍然是一个持续存在的挑战。
从中山大学孙逸仙纪念医院（SYSUMH）的两个分区获取 2015 年 1 月至 2023 年 12 月期间诊断为 II 期-III 期结肠癌的患者的术前计算机断层扫描（CT）图像和临床数据。使用 LASSO/RF/PCA 滤波器筛选放射组学特征，并利用 LR/SVM 模型构建放射组学模型。最后，结合放射组学评分和有意义的临床特征，开发并通过曲线下面积（AUC）和决策曲线分析（DCA）验证综合模型。
总队列包括 426 名患者，按照 7:3 的比例随机分为开发队列和验证队列。从 LASSO-SVM 模型中提取放射组学评分，在开发和验证队列中的 AUC 分别为 0.898/0.726。利用有意义的临床特征（CA199、CA125、T 期和 N 期）建立联合模型与放射组学评分。与单一放射组学模型相比，在验证队列中，联合模型的 AUC 值（0.792 对 0.726，p=0.003）更可靠。放射组学-临床模型在开发和验证队列中的 AUC 值分别为 0.918/0.792，灵敏度分别为 0.907/0.813，特异性分别为 0.804/0.716。
本研究开发并验证了一种结合放射组学评分和临床特征的预测列线图模型，在预测 II 期-III 期结肠癌患者术前 PNI 方面具有良好的性能。
