Jing Han-Hui, Hao Di, Liu Xue-Jun, Cui Ming-Juan, Xue Kui-Jin, Wang Dong-Sheng, Zhang Jun-Hao, Lu Yun, Tian Guang-Ye, Liu Shang-Long
Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
School of Control Science and Engineering, Shandong University, Jinan, People's Republic of China.
Eur Radiol. 2025 Jun;35(6):3409-3417. doi: 10.1007/s00330-024-11198-1. Epub 2024 Dec 2.
To compare the ability of a model based on CT radiomics features, a model based on clinical data, and a fusion model based on a combination of both radiomics and clinical data to predict the risk of liver metastasis after surgery for colorectal cancer.
Two hundred and twelve patients with pathologically confirmed colorectal cancer were divided into a training set (n = 148) and a validation set (n = 64). Radiomics features from the most recent CT scans and clinical data obtained before surgery were extracted. Random forest models were trained to predict tumors with clinical data and evaluated using the area under the receiver-operating characteristic curve (AUC) and other metrics on the validation set.
Fourteen features were selected to establish the radiomics model, which yielded an AUC of 0.751 for the training set and an AUC of 0.714 for the test set. The fusion model, based on a combination of the radiomics signature and clinical data, showed good performance in both the training set (AUC 0.952) and the test set (AUC 0.761).
We have developed a fusion model that integrates radiomics features with clinical data. This fusion model could serve as a non-invasive, reliable, and accurate tool for the preoperative prediction of liver metastases after surgery for colorectal cancer.
Question Can a radiomics and clinical fusion model improve the prediction of liver metastases in colorectal cancer and help optimize clinical decision-making? Findings The presented fusion model combining CT radiomics and clinical data showed superior accuracy in predicting colorectal cancer liver metastases compared to single models. Clinical relevance Our study provides a non-invasive, relatively accurate method for predicting the risk of liver metastasis, improving personalized treatment decisions, and enhancing preoperative planning and prognosis management in colorectal cancer patients.
比较基于CT影像组学特征的模型、基于临床数据的模型以及基于影像组学和临床数据相结合的融合模型预测结直肠癌术后肝转移风险的能力。
212例经病理证实的结直肠癌患者被分为训练集（n = 148）和验证集（n = 64）。提取最近CT扫描的影像组学特征和术前获得的临床数据。使用随机森林模型基于临床数据训练以预测肿瘤，并在验证集上使用受试者操作特征曲线下面积（AUC）和其他指标进行评估。
选择14个特征建立影像组学模型，训练集的AUC为0.751，测试集的AUC为0.714。基于影像组学特征和临床数据相结合的融合模型在训练集（AUC 0.952）和测试集（AUC 0.761）中均表现出良好性能。
我们开发了一种将影像组学特征与临床数据相结合的融合模型。该融合模型可作为结直肠癌术后肝转移术前预测的非侵入性、可靠且准确的工具。
问题 影像组学和临床融合模型能否改善结直肠癌肝转移的预测并有助于优化临床决策？ 研究结果 与单一模型相比，所呈现的结合CT影像组学和临床数据的融合模型在预测结直肠癌肝转移方面显示出更高的准确性。 临床意义 我们的研究提供了一种非侵入性、相对准确的方法来预测肝转移风险，改善个性化治疗决策，并加强结直肠癌患者的术前规划和预后管理。
