使用随机森林算法的基于放射学的骨盆软骨肉瘤鉴别诊断模型
A radiology-based differential diagnostic model for pelvic chondrosarcoma using random forest algorithms.
摘要(中文译文)
为开发一种基于临床和影像学特征的非侵入性骨盆软骨肉瘤诊断方法，旨在提高早期诊断准确性并减少对侵入性活检的依赖。共纳入238例患者（39.50%为骨盆软骨肉瘤；中位年龄42.87岁；53.78%为男性；均为中国人），并分配至训练队列（n = 167）和测试队列（n = 71）。评估了七项临床和影像学特征与骨盆软骨肉瘤诊断的潜在关联。采用随机森林算法构建预测模型，并在测试队列中进行验证，通过部分依赖图分析及其他准确性指标评估其性能。骨盆软骨肉瘤阳性诊断的可能性与年龄约50岁（p < 0.001）、T2加权磁共振成像上存在高信号（p = 0.021）、对比增强T1加权磁共振成像上的环弧状强化模式（p < 0.001）以及瘤内钙化（p < 0.001）显著相关。肿瘤位置也是关键决定因素（p = 0.009），髋臼肿瘤表现出更高的诊断概率。在所有变量中，随机森林模型确定环弧状强化模式和诊断时患者年龄为两个最具影响力的预测因子。该模型取得了优异的诊断性能，灵敏度为96.55%，特异度为90.48%，总体准确率为92.96%。我们基于随机森林的模型为骨盆软骨肉瘤提供了一种可靠且实用的非侵入性诊断工具，提高了诊断准确性，同时可能减少对侵入性活检操作的依赖，并支持临床决策。
Abstract
To develop a non-invasive diagnostic method for pelvic chondrosarcoma using clinical and radiological features, aiming to improve early diagnostic accuracy and reduce reliance on invasive biopsies. Two hundred and thirty-eight patients (39.50% with pelvic chondrosarcoma; median age, 42.87 years; 53.78% male; all Chinese) were enrolled and assigned to training (n = 167) and testing (n = 71) cohorts. Seven clinical and radiological features were evaluated for their potential associations with pelvic chondrosarcoma diagnosis. A predictive model was developed using a random forest algorithm and validated in the testing cohort, with performance assessed by partial dependence plot analysis and additional accuracy metrics. The likelihood of a positive diagnosis of pelvic chondrosarcoma was significantly associated with age around 50 years (p < 0.001), the presence of high-intensity signals on T2-weighted magnetic resonance imaging (p = 0.021), a ring-and-arc enhancement pattern on contrast-enhanced T1-weighted magnetic resonance imaging (p < 0.001), and intratumoral calcification (p < 0.001). Tumor location was also a critical determinant (p = 0.009), with acetabular tumors exhibiting higher diagnostic probability. Among all variables, the random forest model identified the ring-and-arc enhancement pattern and patient age at diagnosis as the two most influential predictors. The model achieved excellent diagnostic performance, with a sensitivity of 96.55%, specificity of 90.48%, and overall accuracy of 92.96%. Our random forest-based model provides a reliable and practical non-invasive diagnostic tool for pelvic chondrosarcoma, improving diagnostic accuracy while potentially reducing the reliance on invasive biopsy procedures and supporting clinical decision-making.
如何引用
AMA
Xiao Ma, Yutong Jiang, Nong Lin, Zhaoming Ye, Hengyuan Li. A radiology-based differential diagnostic model for pelvic chondrosarcoma using random forest algorithms.. Frontiers in oncology. 2026; doi:10.3389/fonc.2026.1818815.
APA
Xiao Ma, Yutong Jiang, Nong Lin, Zhaoming Ye, Hengyuan Li (2026). A radiology-based differential diagnostic model for pelvic chondrosarcoma using random forest algorithms.. Frontiers in oncology. https://doi.org/10.3389/fonc.2026.1818815
GB/T 7714
Xiao Ma, Yutong Jiang, Nong Lin, Zhaoming Ye, Hengyuan Li. A radiology-based differential diagnostic model for pelvic chondrosarcoma using random forest algorithms.[J]. Frontiers in oncology, 2026 doi:10.3389/fonc.2026.1818815.
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