Dong Yumeng, Yang Siyu, Jing Xiaoke, Hu Xiaoqing, Liang Yun, Wang Jun, Liang Gang, He Sheng, Jiang Zengyu
Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan 030001, China.
College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China.
Eur J Radiol Open. 2025 May 16;14:100659. doi: 10.1016/j.ejro.2025.100659. eCollection 2025 Jun.
To investigate the value of applying habitat imaging (HI) radiomics on venous-phase computed tomography (CT) images from laryngeal squamous cell carcinoma (LSCC) patients, as part of a nomogram to predict Ki-67 positivity, an indicator of poorer LSCC prognoses.
Clinical and CT imaging data from 128 LSCC patients, divided into training (89) and testing (39) groups, were analyzed. Conventional and HI radiomics features were extracted from enhanced venous phase images, either from the entire tumor (conventional) or 3 sub-regions (HI). Radiomics models were established, based on 5 machine learning algorithms, while clinical characteristics were analyzed by both uni- and multi-variate logistic regression analyses for their associations with Ki-67 positivity. Afterwards, a predictive nomogram was constructed by combining clinical characteristics, conventional radiomics, and HI radiomics.
The only clinical characteristic strongly predictive for Ki-67-positivity is the degree of differentiation (low/medium vs. high). Additionally, HI radiomics was significantly more accurate than conventional for predicting Ki-67-positivity. The most accurate model, though, was the predictive nomogram, with areas under the curve of 0.945 (training) and 0.871 (testing), which was significantly higher than for clinical characteristics, conventional radiomics and HI radiomics models alone; it also had the highest net benefit, and thus greatest clinical utility under decision curve analysis.
HI radiomics features were more accurate for predicting Ki-67-positivity in LSCC than conventional radiomics. However, the combination of those features with conventional radiomics and the degree of differentiation in a predictive nomogram yields the most accurate model for Ki-67-positivity.
研究在喉鳞状细胞癌（LSCC）患者的静脉期计算机断层扫描（CT）图像上应用瘤周成像（HI）放射组学的价值，作为预测Ki-67阳性的列线图的一部分，Ki-67阳性是LSCC预后较差的一个指标。
分析了128例LSCC患者的临床和CT成像数据，这些患者被分为训练组（89例）和测试组（39例）。从增强静脉期图像中提取常规和HI放射组学特征，要么从整个肿瘤（常规）提取，要么从3个子区域（HI）提取。基于5种机器学习算法建立放射组学模型，同时通过单变量和多变量逻辑回归分析临床特征与Ki-67阳性的相关性。之后，通过结合临床特征、常规放射组学和HI放射组学构建预测列线图。
对Ki-67阳性具有强烈预测作用的唯一临床特征是分化程度（低/中与高）。此外，在预测Ki-67阳性方面，HI放射组学比常规放射组学显著更准确。然而，最准确的模型是预测列线图，其训练组曲线下面积为0.945，测试组为0.871，显著高于单独的临床特征、常规放射组学和HI放射组学模型；在决策曲线分析下，它也具有最高的净效益，因此具有最大的临床实用性。
在预测LSCC的Ki-67阳性方面，HI放射组学特征比常规放射组学更准确。然而，将这些特征与常规放射组学以及预测列线图中的分化程度相结合，可得出用于Ki-67阳性的最准确模型。
