Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
Department of Radiology, Chengdu Seventh People's Hospital, Chengdu, Sichuan Province, China.
Med Phys. 2022 Sep;49(9):5943-5952. doi: 10.1002/mp.15789. Epub 2022 Jul 4.
Pulmonary cryptococcosis (PC) is an invasive pulmonary fungal disease, and nodule/mass-type PC may mimic lung cancer (LC) in imaging appearance. Thus, an accurate diagnosis of nodule/mass-type PC is beneficial for appropriate management. However, the differentiation of nodule/mass-type PC from LC through computed tomography (CT) is still challenging.
To develop and externally test a CT-based radiomics model for differentiating nodule/mass-type PC from LC.
In this retrospective study, patients with nodule/mass-type PC or LC who underwent non-enhanced chest CT were included: Institution 1 was for the training set, and institutions 2 and 3 were for the external test set. Large quantities of radiomics features were extracted. The radiomics score (Rad-score) was calculated using the linear discriminant analysis, and a subsequent fivefold cross-validation was performed. A combined model was developed by incorporating Rad-score and clinical factors. Finally, the models were tested with an external test set and compared using the area under the receiver operating characteristic curve (AUC).
A total of 168 patients (45 with PC and 123 with LC) were in the training set, and 72 (36 with PC and 36 with LC) were in the external test set. Of the 81 patients with PC, 30 were immunocompromised (37%). Rad-score, comprising 18 features, had an AUC of 0.844 after fivefold cross-validation, which was lower than that (AUC = 0.943, p = 0.003) of the combined model integrating Rad-score, age, lobulation, pleural retraction, and patches. In the external test set, Rad-score and the combined model obtained good predictive performance (AUC = 0.824 for Rad-score, and 0.869 for the combined model). Moreover, the combined model outperformed the clinical model in the cross-validation and external test (0.943 vs. 0.810, p <0.001; 0.869 vs. 0.769, p = 0.011).
The proposed combined model exhibits a good differential diagnostic performance between nodule/mass-type PC and LC. The CT-based radiomics analysis has the potential to serve as an effective tool for the differentiation of nodule/mass-type PC from LC in clinical practice.
肺隐球菌病（PC）是一种侵袭性肺部真菌感染，结节/肿块型 PC 在影像学表现上可能与肺癌（LC）相似。因此，准确诊断结节/肿块型 PC 有助于进行适当的管理。然而，通过计算机断层扫描（CT）区分结节/肿块型 PC 与 LC 仍然具有挑战性。
建立并外部验证一种基于 CT 的放射组学模型，用于区分结节/肿块型 PC 与 LC。
本回顾性研究纳入了经非增强胸部 CT 检查诊断为结节/肿块型 PC 或 LC 的患者：机构 1 为训练集，机构 2 和机构 3 为外部测试集。提取大量放射组学特征。使用线性判别分析计算放射组学评分（Rad-score），并进行五次交叉验证。通过纳入 Rad-score 和临床因素，建立联合模型。最后，使用外部测试集对模型进行测试，并通过受试者工作特征曲线下面积（AUC）进行比较。
共有 168 例患者（PC 组 45 例，LC 组 123 例）纳入训练集，72 例（PC 组 36 例，LC 组 36 例）纳入外部测试集。81 例 PC 患者中，30 例为免疫功能低下（37%）。Rad-score 包含 18 个特征，经五次交叉验证后 AUC 为 0.844，低于联合模型（AUC = 0.943，p = 0.003），后者整合了 Rad-score、年龄、分叶、胸膜回缩和斑片。在外部测试集中，Rad-score 和联合模型均具有良好的预测性能（Rad-score 的 AUC 为 0.824，联合模型的 AUC 为 0.869）。此外，联合模型在交叉验证和外部测试中的表现优于临床模型（0.943 比 0.810，p <0.001；0.869 比 0.769，p = 0.011）。
所提出的联合模型在区分结节/肿块型 PC 和 LC 方面具有良好的鉴别诊断性能。基于 CT 的放射组学分析有可能成为临床实践中区分结节/肿块型 PC 与 LC 的有效工具。
