Duan Lijun, Liu Wenyun, Li Mingyang, Guo Liang, Ren Mengran, Dong Xin, Lu Xiaoqian, Cao Dianbo
Department of Radiology, The First Hospital of Jilin University, No. 1 Xinmin Street, Changchun, 130021, Jilin, China.
Department of Pathology, The First Hospital of Jilin University, Changchun, Author's email, 130021, Jilin, China.
Sci Rep. 2025 Apr 25;15(1):14434. doi: 10.1038/s41598-025-99465-z.
Per the 2021 World Health Organization (WHO) Classification of Thoracic Tumors, poorly differentiated invasive nonmucinous adenocarcinoma (INMA) demonstrates aggressive clinicopathological behavior characterized by lymphatic invasion, correlates with poor prognosis, and necessitates modifications to surgical planning even in early-stage adenocarcinoma. This study aimed to investigate the predictive value of radiomics features of intratumoral and peritumoral microenvironment in poorly differentiated INMA in the lung. A total of 451 patients with INMA were collected from three hospitals. They were divided into the train cohort (173 grade 1/2; 116 grade 3), internal test cohort (89 grade 1/2; 35 grade 3) and external test cohort (26 grade 1/2; 12 grade 3). The logistic regression analysis was used to establish the clinical and radiomic models. The receiver operating characteristic (ROC) curve and the decision curve analysis (DCA) were used to assess diagnostic performance of different models. The internal test dataset (124 patients) was used to evaluate the radiologists' performance without and with the assistance of optimal model. Nodule attenuation, consolidation size and consolidation-to-tumor ratio (CTR) meaning the ratio of maximum consolidation to tumor dimensions on axial imaging were independent predictors of poorly differentiated INMA (p < 0.05). In the internal test cohort, the area under the curve (AUC) values of the clinical model, the intratumor radiomic model, the combined 3 mm radiomic model, and the combined 5 mm radiomic model were 0.875(95%CI: 0.811-0.938), 0.882(95%CI: 0.807-0.957), 0.907(95%CI: 0.844-0.969) and 0.858(95%CI: 0.783-0.933), respectively. Corresponding results in the external test cohort showed moderate performance across all models: clinical model (AUC: 0.760, 95%CI: 0.603-0.916), intratumor radiomic model (AUC: 0.760, 95%CI: 0.580-0.939), and combined 3 mm/5 mm radiomic model(AUC: 0.772, 95%CI: 0.593-0.952; AUC: 0.766; 95%CIs: 0.580-0.953). Radiologists had higher diagnostic performance and confidence score with the aid of the combined 3 mm radiomic model. The combined 3 mm radiomic model of non-enhanced CT can identify poorly differentiated INMA with excellent performance and improve radiologists to achieve better diagnostic performance.
根据2021年世界卫生组织（WHO）胸肿瘤分类，低分化浸润性非黏液腺癌（INMA）表现出侵袭性的临床病理行为，其特征为淋巴侵犯，与预后不良相关，即使在早期腺癌中也需要对手术规划进行调整。本研究旨在探讨肿瘤内及肿瘤周围微环境的影像组学特征对肺低分化INMA的预测价值。从三家医院共收集了451例INMA患者。他们被分为训练队列（173例1/2级；116例3级）、内部测试队列（89例1/2级；35例3级）和外部测试队列（26例1/2级；12例3级）。采用逻辑回归分析建立临床和影像组学模型。采用受试者操作特征（ROC）曲线和决策曲线分析（DCA）评估不同模型的诊断性能。内部测试数据集（124例患者）用于评估放射科医生在无最佳模型辅助和有最佳模型辅助情况下的表现。结节衰减、实变大小和实变与肿瘤比值（CTR）（即轴位成像上最大实变与肿瘤尺寸的比值）是低分化INMA的独立预测因素（p<0.05）。在内部测试队列中，临床模型、肿瘤内影像组学模型、3mm联合影像组学模型和5mm联合影像组学模型的曲线下面积（AUC）值分别为0.875（95%CI：0.811-0.938）、0.882（95%CI：0.807-0.957）、0.907（95%CI：0.844-0.969）和0.858（95%CI：0.783-0.933）。外部测试队列中的相应结果显示所有模型的表现中等：临床模型（AUC：0.760，95%CI：0.603-0.916）、肿瘤内影像组学模型（AUC：0.760，95%CI：0.580-0.939）和3mm/5mm联合影像组学模型（AUC：0.772，95%CI：0.593-0.952；AUC：0.766；95%CI：0.580-0.953）。在3mm联合影像组学模型的辅助下，放射科医生具有更高的诊断性能和信心评分。非增强CT的3mm联合影像组学模型能够以优异的性能识别低分化INMA，并提高放射科医生的诊断性能。
