Department of Medical Imaging, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Department of PET-CT, Harbin Medical University Cancer Hospital, Harbin, China.
BMC Med Imaging. 2024 Aug 1;24(1):197. doi: 10.1186/s12880-024-01367-5.
This study was designed to develop a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas based on contrast-enhanced computed tomography (CE-CT) images.
The clinical and CT data of 178 patients with thymoma (100 patients with low-risk thymomas and 78 patients with high-risk thymomas) collected in our hospital from March 2018 to July 2023 were retrospectively analyzed. The patients were randomly divided into a training set (n = 125) and a validation set (n = 53) in a 7:3 ratio. Qualitative radiological features were recorded, including (a) tumor diameter, (b) location, (c) shape, (d) capsule integrity, (e) calcification, (f) necrosis, (g) fatty infiltration, (h) lymphadenopathy, and (i) enhanced CT value. Radiomics features were extracted from each CE-CT volume of interest (VOI), and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select the optimal discriminative ones. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The differentiating efficacy was determined using receiver operating characteristic (ROC) analysis.
Only one clinical factor (incomplete capsule) and seven radiomics features were found to be independent predictors and were used to establish the radiomics nomogram. In differentiating low-risk thymomas (types A, AB, and B1) from high-risk ones (types B2 and B3), the nomogram demonstrated better diagnostic efficacy than any single model, with the respective area under the curve (AUC), accuracy, sensitivity, and specificity of 0.974, 0.921, 0.962 and 0.900 in the training cohort, 0.960, 0.892, 0923 and 0.897 in the validation cohort, respectively. The calibration curve showed good agreement between the prediction probability and actual clinical findings.
The nomogram incorporating clinical factors and radiomics features provides additional value in differentiating the risk categorization of thymomas, which could potentially be useful in clinical practice for planning personalized treatment strategies.
本研究旨在基于增强 CT（CE-CT）图像，开发一种联合放射组学列线图，用于术前预测胸腺瘤的风险分类。
回顾性分析了 2018 年 3 月至 2023 年 7 月我院收治的 178 例胸腺瘤患者（低危胸腺瘤 100 例，高危胸腺瘤 78 例）的临床和 CT 资料。患者按 7:3 的比例随机分为训练集（n=125）和验证集（n=53）。记录定性影像学特征，包括（a）肿瘤直径、（b）位置、（c）形状、（d）包膜完整性、（e）钙化、（f）坏死、（g）脂肪浸润、（h）淋巴结病和（i）增强 CT 值。从每个 CE-CT 感兴趣区（VOI）提取放射组学特征，采用最小绝对收缩和选择算子（LASSO）算法选择最佳鉴别特征。进一步基于临床因素和放射组学评分建立联合放射组学列线图。采用受试者工作特征（ROC）分析确定鉴别效能。
仅发现一个临床因素（包膜不完整）和七个放射组学特征是独立预测因子，并用于建立放射组学列线图。在区分低危胸腺瘤（A、AB 和 B1 型）和高危胸腺瘤（B2 和 B3 型）时，与任何单一模型相比，该列线图具有更好的诊断效能，在训练队列中的曲线下面积（AUC）、准确性、敏感性和特异性分别为 0.974、0.921、0.962 和 0.900，在验证队列中分别为 0.960、0.892、0.923 和 0.897。校准曲线显示预测概率与实际临床结果吻合良好。
纳入临床因素和放射组学特征的列线图在区分胸腺瘤的风险分类方面提供了附加价值，这可能对制定个性化治疗策略的临床实践具有潜在意义。
