Quantitative CT and machine learning classification of fibrotic interstitial lung diseases

Objectives To evaluate quantitative computed tomography (QCT) features and QCT feature-based machine learning (ML) models in classifying interstitial lung diseases (ILDs). To compare QCT-ML and deep learning (DL) models’ performance. Methods We retrospectively identified 1085 patients with pathologi...
Ausführliche Beschreibung

Objectives To evaluate quantitative computed tomography (QCT) features and QCT feature-based machine learning (ML) models in classifying interstitial lung diseases (ILDs). To compare QCT-ML and deep learning (DL) models’ performance. Methods We retrospectively identified 1085 patients with pathologically proven usual interstitial pneumonitis (UIP), nonspecific interstitial pneumonitis (NSIP), and chronic hypersensitivity pneumonitis (CHP) who underwent peri-biopsy chest CT. Kruskal-Wallis test evaluated QCT feature associations with each ILD. QCT features, patient demographics, and pulmonary function test (PFT) results trained eXtreme Gradient Boosting (training/validation set n = 911) yielding 3 models: M1 = QCT features only; M2 = M1 plus age and sex; M3 = M2 plus PFT results. A DL model was also developed. ML and DL model areas under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs) were compared for multiclass (UIP vs. NSIP vs. CHP) and binary (UIP vs. non-UIP) classification performances. Results The majority (69/78 [88%]) of QCT features successfully differentiated the 3 ILDs (adjusted p ≤ 0.05). All QCT-ML models achieved higher AUC than the DL model (multiclass AUC micro-averages 0.910, 0.910, 0.925, and 0.798 and macro-averages 0.895, 0.893, 0.925, and 0.779 for M1, M2, M3, and DL respectively; binary AUC 0.880, 0.899, 0.898, and 0.869 for M1, M2, M3, and DL respectively). M3 demonstrated statistically significant better performance compared to M2 (∆AUC: 0.015, CI: [0.002, 0.029]) for multiclass prediction. Conclusions QCT features successfully differentiated pathologically proven UIP, NSIP, and CHP. While QCT-based ML models outperformed a DL model for classifying ILDs, further investigations are warranted to determine if QCT-ML, DL, or a combination will be superior in ILD classification. Key Points • Quantitative CT features successfully differentiated pathologically proven UIP, NSIP, and CHP. • Our quantitative CT-based machine learning models demonstrated high performance in classifying UIP, NSIP, and CHP histopathology, outperforming a deep learning model. • While our quantitative CT-based machine learning models performed better than a DL model, additional investigations are needed to determine whether either or a combination of both approaches delivers superior diagnostic performance. Ausführliche Beschreibung

Autor*in:	Koo, Chi Wan [verfasserIn] Williams, James M. Liu, Grace Panda, Ananya Patel, Parth P. Frota Lima, Livia Maria M. Karwoski, Ronald A. Moua, Teng Larson, Nicholas B. Bratt, Alex

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Machine learning Interstitial lung disease Usual interstitial pneumonitis Nonspecific interstitial pneumonitis Chronic hypersensitivity pneumonitis

Anmerkung:	© The Author(s), under exclusive licence to European Society of Radiology 2022

Übergeordnetes Werk:	Enthalten in: European radiology - Berlin : Springer, 1991, 32(2022), 12 vom: 09. Juni, Seite 8152-8161
Übergeordnetes Werk:	volume:32 ; year:2022 ; number:12 ; day:09 ; month:06 ; pages:8152-8161

Links:	Volltext

DOI / URN:	10.1007/s00330-022-08875-4

Katalog-ID:	SPR048740578