Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.); Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.).
Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.).
Acad Radiol. 2023 Dec;30(12):2880-2893. doi: 10.1016/j.acra.2023.04.011. Epub 2023 May 22.
Bronchial arterial chemoembolization (BACE) was deemed as an effective and safe approach for advanced standard treatment-ineligible/rejected lung cancer patients. However, the therapeutic outcome of BACE varies greatly and there is no reliable prognostic tool in clinical practice. This study aimed to investigate the effectiveness of radiomics features in predicting tumor recurrence after BACE treatment in lung cancer patients.
A total of 116 patients with pathologically confirmed lung cancer who received BACE treatment were retrospectively recruited. All patients underwent contrast-enhanced CT within 2 weeks before BACE treatment and were followed up for more than 6 months. We conducted a machine learning-based characterization of each lesion on the preoperative contrast-enhanced CT images. In the training cohort, recurrence-related radiomics features were screened by least absolute shrinkage and selection operator (LASSO) regression. Three predictive radiomics signatures were built with linear discriminant analysis (LDA), support vector machine (SVM) and logistic regression (LR) algorithms, respectively. Univariate and multivariate LR analyses were performed to select the independent clinical predictors for recurrence. The radiomics signature with best predictive performance was integrated with the clinical predictors to form a combined model, which was visualized as a nomogram. The performance of the combined model was assessed by receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).
Nine recurrence-related radiomics features were screened out, and three radiomics signatures (Radscore, Radscore and Radscore) were built based on these features. Patients were classified into the low-risk and high-risk groups based on the optimal threshold of three signatures. Progression-free survival (PFS) analysis showed that patients of low-risk group achieved longer PFS than patients of high-risk group (P < 0.05). The combined model including Radscore and independent clinical predictors (tumor size, carcinoembryonic antigen and pro-gastrin releasing peptide) achieved the best predictive performance for recurrence after BACE treatment. It yields AUCs of 0.865 and 0.867 in the training and validation cohorts, with accuracy (ACC) of 0.804 and 0.750, respectively. Calibration curves indicated that the probability of recurrence predicted by the model fits well with the actual recurrence probability. DCA showed that the radiomics nomogram was clinically useful.
The radiomics and clinical predictors-based nomogram can predict tumor recurrence after BACE treatment effectively, which allowing oncologists to identify potential recurrence and enable better patient management and clinical decision-making.
支气管动脉化疗栓塞（BACE）被认为是一种对标准治疗不适用/拒绝的晚期肺癌患者有效的安全治疗方法。然而，BACE 的治疗效果差异很大，临床实践中尚无可靠的预后工具。本研究旨在探讨影像组学特征在预测肺癌患者 BACE 治疗后肿瘤复发中的作用。
回顾性纳入 116 例经病理证实的肺癌患者，均接受 BACE 治疗。所有患者在 BACE 治疗前 2 周内行增强 CT 检查，并随访超过 6 个月。我们对术前增强 CT 图像上的每个病灶进行基于机器学习的特征描述。在训练队列中，采用最小绝对收缩和选择算子（LASSO）回归筛选与复发相关的影像组学特征。采用线性判别分析（LDA）、支持向量机（SVM）和逻辑回归（LR）算法分别构建三个预测性影像组学特征签名。进行单因素和多因素 LR 分析以选择与复发相关的独立临床预测因素。选择预测性能最佳的影像组学特征与临床预测因素相结合，形成联合模型，并将其可视化作为诺模图。采用受试者工作特征曲线（ROC）、校准曲线和决策曲线分析（DCA）评估联合模型的性能。
筛选出 9 个与复发相关的影像组学特征，基于这些特征构建了三个影像组学特征签名（Radscore、Radscore 和 Radscore）。根据三个特征签名的最佳阈值将患者分为低风险组和高风险组。无进展生存期（PFS）分析显示，低风险组患者的 PFS 长于高风险组（P<0.05）。包含 Radscore 和独立临床预测因素（肿瘤大小、癌胚抗原和胃泌素释放肽）的联合模型在预测 BACE 治疗后复发方面具有最佳预测性能。在训练组和验证组中，该模型的 AUC 值分别为 0.865 和 0.867，准确性（ACC）分别为 0.804 和 0.750。校准曲线表明，模型预测的复发概率与实际复发概率吻合良好。DCA 表明，影像组学诺模图具有临床应用价值。
基于影像组学和临床预测因素的诺模图可以有效地预测 BACE 治疗后肿瘤复发，有助于肿瘤学家识别潜在的复发，并为患者管理和临床决策提供更好的指导。
