Background: The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas. Methods: Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data. Results: A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62–0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65–0.85). Conclusions: CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions.
背景：术前准确鉴别子宫平滑肌肉瘤与平滑肌瘤目前仍具挑战。目前诊断依赖于病理学家对切除肿瘤的检查。本研究旨在开发一种利用对比增强计算机断层扫描（CECT）图像提取的影像组学数据，能够准确区分平滑肌肉瘤与平滑肌瘤的机器学习算法。 方法：研究采用经组织学确诊为平滑肌瘤或平滑肌肉瘤并接受手术患者的术前CECT图像进行感兴趣区域识别和影像组学特征提取。使用PyRadiomics库进行特征提取，构建了三种特征选择方法分别与广义线性模型（GLM）、随机森林（RF）和支持向量机（SVM）分类器相结合的模型，并针对良恶性二分类任务进行训练与测试。同时，由放射科医师在有无临床数据辅助两种情况下进行诊断评估。 结果：研究共纳入30例平滑肌肉瘤患者（平均年龄59岁）和35例平滑肌瘤患者（平均年龄48岁），分别包含30个和51个病灶。在九种机器学习模型中，三种特征选择方法与GLM和RF分类器的组合表现出良好性能，其预测曲线下面积（AUC）、敏感性和特异性范围分别为0.78-0.97、0.78-1.00和0.67-0.93。而经验丰富的放射科医师在盲法评估临床资料时（AUC=0.73，95%CI=0.62-0.84）及参考临床数据时（AUC=0.75，95%CI=0.65-0.85）的诊断效能均低于机器学习模型。 结论：结合影像组学分析的CECT图像在鉴别子宫平滑肌瘤与平滑肌肉瘤方面具有巨大潜力。该工具可用于降低平滑肌肉瘤手术中肿瘤扩散的风险，并为良性子宫病变患者提供更安全的保留生育功能治疗方案。
肿瘤(癌症)患者之家