Correlation of CT-based radiomics analysis with pathological cellular infiltration in fibrosing interstitial lung diseases

• Published in: Japanese journal of radiology 2024-10, Vol.42 (10), p.1157-1167
• Main Authors: Haga, Akira, Iwasawa, Tae, Misumi, Toshihiro, Okudela, Koji, Oda, Tsuneyuki, Kitamura, Hideya, Saka, Tomoki, Matsushita, Shoichiro, Baba, Tomohisa, Natsume-Kitatani, Yayoi, Utsunomiya, Daisuke, Ogura, Takashi
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Biopsy; Cell culture; Classification; Computed tomography; Imaging; Infiltration; Lung diseases; Medicine; Medicine & Public Health; Nuclear Medicine; Original; Original Article; Radiology; Radiomics; Radiotherapy; Surgical equipment
• ISSN: 1867-1071; 1867-108X; 1867-108X
• DOI: 10.1007/s11604-024-01607-2

Purpose We aimed to identify computed tomography (CT) radiomics features that are associated with cellular infiltration and construct CT radiomics models predictive of cellular infiltration in patients with fibrotic ILD. Materials and methods CT images of patients with ILD who underwent surgical lung biopsy (SLB) were analyzed. Radiomics features were extracted using artificial intelligence-based software and PyRadiomics. We constructed a model predicting cell counts in histological specimens, and another model predicting two classifications of higher or lower cellularity. We tested these models using external validation. Results Overall, 100 patients (mean age: 62 ± 8.9 [standard deviation] years; 61 men) were included. The CT radiomics model used to predict cell count in 140 histological specimens predicted the actual cell count in 59 external validation specimens (root-mean-square error: 0.797). The two-classification model’s accuracy was 70% and the F1 score was 0.73 in the external validation dataset including 30 patients. Conclusion The CT radiomics-based model developed in this study provided useful information regarding the cellular infiltration in the ILD with good correlation with SLB specimens.