Li Jiatong, Cui Nan, Jiang Zhiyun, Li Wei, Liu Wei, Wang Shuai, Wang Kezheng
PET-CT/MRI Department, Harbin Medical University Cancer Hospital, Harbin Medical University, 150 Haping Road, Harbin, 150081, Heilongjiang, China.
Radiology Department, Harbin Medical University Cancer Hospital, Harbin Medical University, 150 Haping Road, Harbin, 150081, Heilongjiang, China.
J Cancer Res Clin Oncol. 2023 Nov;149(15):14101-14112. doi: 10.1007/s00432-023-05054-w. Epub 2023 Aug 8.
Clinical feasibility nomograms were developed to facilitate the differentiation between thymic epithelial tumors (TETs) and mediastinal lymphomas (MLs), aiming to minimize the occurrence of non-therapeutic thymectomy.
A total of 255 patients diagnosed with TETs or MLs underwent pre-treatment F-FDG PET/CT. Comprehensive clinical and imaging data were collected, including age, gender, lactate dehydrogenase (LDH) level, pathological results, presence of myasthenia gravis symptoms, B symptoms, mass size, location, morphology, margins, density, and metabolic parameters derived from PET/CT. Radiomic features were extracted from the region of interest (ROI) of the primary lesion. Feature selection techniques were employed to identify the most discriminative subset of features. Machine learning methods were utilized to build candidate models, which were subsequently evaluated based on their area under the curve (AUC). Finally, nomograms were constructed using the optimal model to provide a clinical tool for improved diagnostic accuracy. The performance of the radiomic models was evaluated by their calibration, discrimination, and clinical utility.
Several independent risk factors were identified for distinguishing TETs from MLs, including average standardized uptake value (SUVavg), LDH, age, mass size, and radiomic score (rad-score). Significance was observed in differentiating the two types of tumors based on these factors. The best clinical efficacy was demonstrated by the combined model, with an impressive AUC of 0.954. Decision curve analysis and calibration curves indicated that the combined model was clinically advantageous for discriminating TETs from MLs. Besides, the results of external validation showed a sensitivity of 0.8 and a specificity of 0.78.
Preoperatively, the differentiation of TETs from MLs can be facilitated by the utilization of the combined clinical information and radiomics model. This approach holds promise in minimizing the occurrence of unnecessary anterior mediastinal surgeries.
开发临床可行性列线图以促进胸腺上皮肿瘤（TETs）与纵隔淋巴瘤（MLs）的鉴别，旨在尽量减少非治疗性胸腺切除术的发生。
共有255例诊断为TETs或MLs的患者接受了治疗前F-FDG PET/CT检查。收集了全面的临床和影像数据，包括年龄、性别、乳酸脱氢酶（LDH）水平、病理结果、重症肌无力症状的存在情况、B症状、肿块大小、位置、形态、边界、密度以及从PET/CT得出的代谢参数。从原发灶的感兴趣区域（ROI）提取了影像组学特征。采用特征选择技术来识别最具鉴别力的特征子集。利用机器学习方法构建候选模型，随后根据其曲线下面积（AUC）对模型进行评估。最后，使用最优模型构建列线图，以提供提高诊断准确性的临床工具。通过校准、鉴别和临床实用性来评估影像组学模型的性能。
确定了几个区分TETs与MLs的独立危险因素，包括平均标准化摄取值（SUVavg）、LDH、年龄、肿块大小和影像组学评分（rad-score）。基于这些因素在区分这两种类型的肿瘤方面具有显著性。联合模型显示出最佳的临床疗效，AUC高达0.954。决策曲线分析和校准曲线表明联合模型在鉴别TETs与MLs方面具有临床优势。此外，外部验证结果显示敏感性为0.8，特异性为0.78。
术前，利用联合临床信息和影像组学模型可促进TETs与MLs的鉴别。这种方法有望尽量减少不必要的前纵隔手术的发生。
