Purpose: Lung disease assessment in precapillary pulmonary hypertension (PH) is essential for appropriate patient management. This study aims to develop an artificial intelligence (AI) deep learning model for lung texture classification in CT Pulmonary Angiography (CTPA), and evaluate its correlation with clinical assessment methods. Materials and Methods: In this retrospective study with external validation, 122 patients with pre-capillary PH were used to train (n=83), validate (n=17) and test (n=10 internal test, n=12 external test) a patch based DenseNet-121 classification model. "Normal", "Ground glass", "Ground glass with reticulation", "Honeycombing", and "Emphysema" were classified as per the Fleishner Society glossary of terms. Ground truth classes were segmented by two radiologists with patches extracted from the labelled regions. Proportion of lung volume for each texture was calculated by classifying patches throughout the entire lung volume to generate a coarse texture classification mapping throughout the lung parenchyma. AI output was assessed against diffusing capacity of carbon monoxide (DLCO) and specialist radiologist reported disease severity. Results: Micro-average AUCs for the validation, internal test, and external test were 0.92, 0.95, and 0.94, respectively. The model had consistent performance across parenchymal textures, demonstrated strong correlation with diffusing capacity of carbon monoxide (DLCO), and showed good correspondence with disease severity reported by specialist radiologists. Conclusion: The classification model demonstrates excellent performance on external validation. The clinical utility of its output has been demonstrated. This objective, repeatable measure of disease severity can aid in patient management in adjunct to radiological reporting.

翻译：目的：毛细血管前肺动脉高压（PH）的肺疾病评估对患者管理至关重要。本研究旨在开发一种人工智能（AI）深度学习模型，用于CT肺血管造影（CTPA）中的肺纹理分类，并评估其与临床评估方法的相关性。材料与方法：在这项带有外部验证的回顾性研究中，122例毛细血管前PH患者被用于训练（n=83）、验证（n=17）和测试（n=10例内部测试，n=12例外部测试）基于补丁的DenseNet-121分类模型。根据Fleishner学会术语表，将纹理分类为"正常"、"磨玻璃"、"磨玻璃伴网格影"、"蜂窝状影"和"肺气肿"。真实标签由两名放射科医师对标注区域进行补丁提取后分割得到。通过在整个肺体积中分类补丁，计算每种纹理占肺体积的比例，从而生成全肺实质的粗纹理分类映射。将AI输出与一氧化碳弥散量（DLCO）及专科放射科医师报告的疾病严重程度进行对比评估。结果：验证集、内部测试集和外部测试集的微平均AUC分别为0.92、0.95和0.94。该模型在各类肺实质纹理上表现一致，与一氧化碳弥散量（DLCO）呈强相关性，并与专科放射科医师报告的疾病严重程度具有良好对应性。结论：该分类模型在外部验证中表现出色，其输出的临床实用性已得到验证。这种客观、可重复的疾病严重程度测量可作为放射学报告的辅助手段，帮助患者管理。