Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
J Healthc Eng. 2023 Feb 6;2023:8964676. doi: 10.1155/2023/8964676. eCollection 2023.
Lung cancer is the leading cause of cancer-related death in many countries, and an accurate histopathological diagnosis is of great importance in subsequent treatment. The aim of this study was to establish the random forest (RF) model based on radiomic features to automatically classify and predict lung adenocarcinoma (ADC), lung squamous cell carcinoma (SCC), and small cell lung cancer (SCLC) on unenhanced computed tomography (CT) images. Eight hundred and fifty-two patients (mean age: 61.4, range: 29-87, male/female: 536/316) with preoperative unenhanced CT and postoperative histopathologically confirmed primary lung cancers, including 525 patients with ADC, 161 patients with SCC, and 166 patients with SCLC, were included in this retrospective study. Radiomic features were extracted, selected, and then used to establish the RF classification model to analyse and classify primary lung cancers into three subtypes, including ADC, SCC, and SCLC according to histopathological results. The training (446 ADC, 137 SCC, and 141 SCLC) and testing cohorts (79 ADC, 24 SCC, and 25 SCLC) accounted for 85% and 15% of the whole datasets, respectively. The prediction performance of the RF classification model was evaluated by F1 scores and the receiver operating characteristic (ROC) curve. On the testing cohort, the areas under the ROC curve (AUC) of the RF model in classifying ADC, SCC, and SCLC were 0.74, 0.77, and 0.88, respectively. The F1 scores achieved 0.80, 0.40, and 0.73 in ADC, SCC, and SCLC, respectively, and the weighted average F1 score was 0.71. In addition, for the RF classification model, the precisions were 0.72, 0.64, and 0.70; the recalls were 0.86, 0.29, and 0.76; and the specificities were 0.55, 0.96, and 0.92 in ADC, SCC, and SCLC. The primary lung cancers were feasibly and effectively classified into ADC, SCC, and SCLC based on the combination of RF classification model and radiomic features, which has the potential for noninvasive predicting histological subtypes of primary lung cancers.
肺癌是许多国家癌症相关死亡的主要原因，准确的组织病理学诊断对后续治疗至关重要。本研究旨在建立基于放射组学特征的随机森林（RF）模型，以便在未增强 CT 图像上自动分类和预测肺腺癌（ADC）、肺鳞癌（SCC）和小细胞肺癌（SCLC）。本回顾性研究纳入了 852 例术前未增强 CT 及术后经组织病理学证实的原发性肺癌患者（平均年龄：61.4 岁，范围：29-87 岁，男/女：536/316 例），包括 525 例 ADC、161 例 SCC 和 166 例 SCLC。提取、选择放射组学特征，然后建立 RF 分类模型，根据组织病理学结果分析和分类原发性肺癌为 ADC、SCC 和 SCLC 三种亚型。训练集（446 例 ADC、137 例 SCC 和 141 例 SCLC）和测试集（79 例 ADC、24 例 SCC 和 25 例 SCLC）分别占整个数据集的 85%和 15%。使用 F1 分数和受试者工作特征（ROC）曲线评估 RF 分类模型的预测性能。在测试集上，RF 模型在分类 ADC、SCC 和 SCLC 中的 ROC 曲线下面积（AUC）分别为 0.74、0.77 和 0.88。在 ADC、SCC 和 SCLC 中，RF 模型的 F1 评分分别为 0.80、0.40 和 0.73，加权平均 F1 评分为 0.71。此外，对于 RF 分类模型，在 ADC、SCC 和 SCLC 中，准确率分别为 0.72、0.64 和 0.70；召回率分别为 0.86、0.29 和 0.76；特异性分别为 0.55、0.96 和 0.92。基于 RF 分类模型和放射组学特征的组合，可有效地将原发性肺癌分类为 ADC、SCC 和 SCLC，这为非侵入性预测原发性肺癌的组织学亚型提供了潜力。
