ISSN 1004-4140
CN 11-3017/P
李曼曼, 符益纲, 肖勇, 等. CT影像组学列线图预测结直肠癌肿瘤沉积和预后[J]. CT理论与应用研究(中英文), xxxx, x(x): 1-10. DOI: 10.15953/j.ctta.2024.055.
引用本文: 李曼曼, 符益纲, 肖勇, 等. CT影像组学列线图预测结直肠癌肿瘤沉积和预后[J]. CT理论与应用研究(中英文), xxxx, x(x): 1-10. DOI: 10.15953/j.ctta.2024.055.
LI M M, FU Y G, XIAO Y, et al. CT Radiomics Nomogram Prediction for Tumor Deposits and Prognosis in Colorectal Cancer[J]. CT Theory and Applications, xxxx, x(x): 1-10. DOI: 10.15953/j.ctta.2024.055. (in Chinese).
Citation: LI M M, FU Y G, XIAO Y, et al. CT Radiomics Nomogram Prediction for Tumor Deposits and Prognosis in Colorectal Cancer[J]. CT Theory and Applications, xxxx, x(x): 1-10. DOI: 10.15953/j.ctta.2024.055. (in Chinese).

CT影像组学列线图预测结直肠癌肿瘤沉积和预后

CT Radiomics Nomogram Prediction for Tumor Deposits and Prognosis in Colorectal Cancer

  • 摘要: 目的:建立CT影像组学列线图术前预测结直肠癌(CRC)患者肿瘤沉积(TD)和无复发生存(RFS)。方法:回顾性研究321例经手术病理证实的CRC患者,患者以6∶4分为训练集和验证集。从门静脉CT图像中提取基于肿瘤原发灶的影像组学特征,使用最小绝对收缩和选择算子筛选与TD相关的影像组学特征。临床-影像组学列线图是根据筛选的影像组学特征和最具预测性的临床因素开发的。采用单、多因素Cox回归分析筛选3年无复发生存(RFS)的独立危险因素。结果:在训练集、验证集中,影像组学模型的曲线下面积(AUC)分别为0.80和0.79。结合影像组学特征和临床预测因子(CEA,CA199,CT报告的淋巴结状态)构建列线图以术前预测TD,列线图在训练集和验证集AUC分别为0.85和0.85。此外,列线图预测的TD是RFS的独立危险因素,TD阳性组的RFS差于TD阴性组。结论:CT影像组学列线图能够有效术前预测CRC患者TD和预后。

     

    Abstract: Objective: To establish CT radiomics nomogram for preoperative prediction of tumor deposits (TD) and recurrence-free survival (RFS) in patients with colorectal cancer (CRC). Methods: A retrospective study was conducted on 321 CRC patients confirmed by surgical pathology. The patients' data were divided were divided into a training set and a validation set at a ratio of 6:4, respectively. Radiomics features based on the primary tumor site were extracted from portal venous phase CT images, and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was employed to select radiomics features associated with tumor deposits (TD). The least absolute shrinkage and selection operator (LASSO) regression algorithm was applied to choose radiomics features related to TD. A clinical-radiomics nomogram was developed based on the selected radiomics features and the most predictive clinical factors. Univariate and multivariate Cox regression analyses identified independent risk factors for a 3-year RFS. Results: The radiomics model achieved an area under the curve (AUC) of 0.80 in the training set and 0.79 in the validation set. By integrating radiomics features with clinical predictors (CEA, CA199, and CT-reported lymph node status), a nomogram was developed for the preoperative prediction of TD. The nomogram achieved an AUC of 0.85 in the training and validation sets. Furthermore, TD predicted by the nomogram was an independent risk factor for RFS, with poorer RFS observed in the TD-positive group compared to the TD-negative group. Conclusion: CT radiomics nomogram can effectively preoperatively predict TD and prognosis in CRC patients.

     

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