Colligiani Leonardo, Marzi Chiara, Uggenti Vincenzo, Colantonio Sara, Tavanti Laura, Pistelli Francesco, Alì Greta, Neri Emanuele, Romei Chiara
Department of Translational Research, Academic Radiology, University of Pisa, 56126, Pisa, Italy.
Division of Radiology, Pisa University Hospital, 56126, Pisa, Italy.
Radiol Med. 2025 Aug 22. doi: 10.1007/s11547-025-02067-y.
To differentiate interstitial lung diseases (ILDs) with fibrotic and inflammatory patterns using high-resolution computed tomography (HRCT) and a radiomics-based artificial intelligence (AI) pipeline.
This single-center study included 84 patients: 50 with idiopathic pulmonary fibrosis (IPF)-representative of fibrotic pattern-and 34 with cellular non-specific interstitial pneumonia (NSIP) secondary to connective tissue disease (CTD)-as an example of mostly inflammatory pattern. For a secondary objective, we analyzed 50 additional patients with COVID-19 pneumonia. We performed semi-automatic segmentation of ILD regions using a deep learning model followed by manual review. From each segmented region, 103 radiomic features were extracted. Classification was performed using an XGBoost model with 1000 bootstrap repetitions and SHapley Additive exPlanations (SHAP) were applied to identify the most predictive features.
The model accurately distinguished a fibrotic ILD pattern from an inflammatory ILD one, achieving an average test set accuracy of 0.91 and AUROC of 0.98. The classification was driven by radiomic features capturing differences in lung morphology, intensity distribution, and textural heterogeneity between the two disease patterns. In differentiating cellular NSIP from COVID-19, the model achieved an average accuracy of 0.89. Inflammatory ILDs exhibited more uniform imaging patterns compared to the greater variability typically observed in viral pneumonia.
Radiomics combined with explainable AI offers promising diagnostic support in distinguishing fibrotic from inflammatory ILD patterns and differentiating inflammatory ILDs from viral pneumonias. This approach could enhance diagnostic precision and provide quantitative support for personalized ILD management.
使用高分辨率计算机断层扫描（HRCT）和基于放射组学的人工智能（AI）管道区分具有纤维化和炎症模式的间质性肺疾病（ILDs）。
这项单中心研究纳入了84例患者：50例特发性肺纤维化（IPF）——代表纤维化模式——以及34例继发于结缔组织病（CTD）的细胞性非特异性间质性肺炎（NSIP）——作为主要为炎症模式的一个例子。作为次要目标，我们分析了另外50例新冠肺炎患者。我们使用深度学习模型对ILD区域进行半自动分割，随后进行人工复查。从每个分割区域中提取103个放射组学特征。使用具有1000次自助重复的XGBoost模型进行分类，并应用SHapley加性解释（SHAP）来识别最具预测性的特征。
该模型准确地区分了纤维化ILD模式和炎症性ILD模式，平均测试集准确率达到0.91，曲线下面积（AUROC）为0.98。分类由放射组学特征驱动，这些特征捕捉了两种疾病模式在肺形态、强度分布和纹理异质性方面的差异。在区分细胞性NSIP和新冠肺炎时，该模型的平均准确率达到0.89。与通常在病毒性肺炎中观察到的更大变异性相比，炎症性ILDs表现出更均匀的成像模式。
放射组学与可解释的AI相结合，在区分纤维化和炎症性ILD模式以及区分炎症性ILDs和病毒性肺炎方面提供了有前景的诊断支持。这种方法可以提高诊断精度，并为个性化ILD管理提供定量支持。
