High-resolution computed tomography assessment of connective tissue disease-associated interstitial lung disease: Advances in diagnosis and prognostic evaluation

Table 1 Comparison of high-resolution computed tomography-based assessment approaches in connective tissue disease-associated interstitial lung disease

Approach	Core characteristics	Main advantages	Main clinical applications	Current evidence	Main limitations
Visual interpretation	Direct radiologist assessment of recognizable HRCT patterns and abnormalities, including GGO, consolidation, reticulation, traction bronchiectasis, honeycombing, and OP-like changes; also allows integrated assessment of lesion distribution and dominant morphologic pattern	Widely available; rapid; does not require dedicated software; directly linked to established radiologic terminology and multidisciplinary clinical decision-making; preserves morphologic context that may be difficult to fully capture with numerical features	Initial detection and characterization of CTD-ILD; identification of dominant HRCT pattern; assessment of inflammatory vs fibrotic manifestations; qualitative evaluation of disease progression and treatment-related change; support for multidisciplinary diagnosis	Most established approach in routine practice and supported by extensive CTD-ILD literature across multiple disease subtypes. It remains the clinical reference framework against which many quantitative and computational methods are compared[39,40]	Dependent on reader experience and pattern definitions; subject to interobserver variability; subtle, diffuse, or mixed abnormalities may be difficult to assess consistently; small longitudinal changes can be challenging to recognize and quantify reproducibly
Semiquantitative scoring	Structured visual grading of disease extent or severity using predefined lobar, zonal, or whole-lung scoring systems for GGO, fibrosis, reticulation, honeycombing, or total ILD burden	More structured and reproducible than purely descriptive visual assessment; provides clinically interpretable estimates of disease burden; relatively easy to apply without complex computational infrastructure; facilitates baseline risk stratification and serial comparison	Quantification of baseline disease extent; severity assessment; risk stratification; evaluation of inflammatory and fibrotic burden; prognostic assessment; longitudinal monitoring and treatment-response evaluation	Well established in CTD-ILD, particularly in SSc-ILD and inflammatory myopathy-associated ILD, and frequently used in observational cohorts and clinical studies. Several scoring systems have demonstrated associations with pulmonary function and clinical outcomes[11,39,69]	Still relies on reader judgment; scoring definitions and anatomic sampling schemes differ among studies; ceiling/floor effects and coarse categorical scales may reduce sensitivity to subtle changes; reproducibility can decline in heterogeneous or mixed-pattern disease
Quantitative CT	Computer-assisted extraction of predefined numerical measures from HRCT, such as lung volume, attenuation distribution, density histogram parameters, texture burden, and the proportion or extent of specific parenchymal abnormalities	Provides continuous and more objective measurements of disease burden; may improve sensitivity to diffuse or subtle abnormalities; enables more standardized longitudinal comparison than visual estimation alone; can quantify structural change that may be difficult to appreciate visually	Objective measurement of ILD extent and severity; quantification of fibrosis, GGO, reticulation, and other parenchymal patterns; early disease detection; correlation with pulmonary function; prognostic assessment; longitudinal progression and treatment-response monitoring	Quantitative CT has accumulated an increasing body of evidence in SSc-ILD and has also been investigated in RA-ILD, inflammatory myopathy-associated ILD, and mixed CTD-ILD cohorts. It represents an emerging objective adjunct, although implementation remains variable across centers. Some quantitative metrics have also been evaluated in longitudinal cohorts and treatment studies[41,79,91,100]	Sensitive to scanner type, acquisition parameters, reconstruction algorithms, inspiratory effort, segmentation quality, and postprocessing software; results may not be directly comparable across institutions; platform-specific outputs and limited standardization can restrict reproducibility
Radiomics	High-dimensional extraction of quantitative features describing image intensity, shape, spatial distribution, and texture, usually followed by feature selection and construction of a radiomics score or integrated clinical-imaging model	Captures imaging heterogeneity beyond conventional visual and predefined quantitative measures; can identify subtle patterns not readily visible to the human eye; supports combination of imaging features with clinical, serologic, or functional variables for individualized prediction	Detection of CTD-ILD or subclinical lung involvement; disease phenotyping; classification of imaging patterns; assessment of disease severity; risk stratification; prediction of progression, rapidly progressive ILD, mortality, or other clinical outcomes	Promising CTD-ILD-specific evidence is available, including studies with internal and external validation in inflammatory myopathy-associated ILD and RA-ILD. However, the overall evidence base remains smaller and less standardized than that for conventional and semiquantitative assessment, and radiomics remains predominantly investigational[36,45,71,83]	Feature values are sensitive to acquisition, reconstruction, segmentation, and preprocessing choices; high-dimensional features increase the risk of overfitting in small datasets; feature reproducibility and interpretability may be limited; many studies remain retrospective and external validation is still inconsistent
Machine learning	Use of statistical or computational algorithms to learn relationships among imaging, clinical, physiologic, and serologic variables for classification or outcome prediction; commonly includes methods such as logistic regression-based learning, random forest, support vector machine, and gradient-boosting approaches	Can integrate multiple heterogeneous variables and model complex nonlinear relationships; allows development of combined clinical-imaging prediction tools; may improve individualized diagnostic or prognostic discrimination compared with single-variable approaches	Diagnostic classification; differentiation of CTD-ILD phenotypes; integrated risk prediction; prediction of progression, pulmonary function decline, rapidly progressive disease, mortality, or other clinically relevant outcomes	Several CTD-ILD studies have reported favorable diagnostic or prognostic performance, including multicenter analyses in selected settings. Nevertheless, many models are developed retrospectively, independent multicenter external validation remains limited across the broader CTD-ILD spectrum, and machine-learning approaches remain predominantly investigational[62,67,85]	Performance strongly depends on cohort size, variable quality, and model-development strategy; susceptible to overfitting and optimistic performance estimates in small or selected datasets; generalizability across institutions and CTD subtypes may be limited; implementation requires reproducible input variables and standardized workflows
Deep learning	Neural-network-based learning of hierarchical image representations directly from CT data, reducing reliance on predefined handcrafted features; commonly applied to automated segmentation, pattern recognition, quantitative mapping, and serial image analysis	Supports automated or semi-automated image analysis; can reduce manual segmentation burden; enables direct recognition of complex spatial patterns; particularly suitable for whole-lung or lesion segmentation, automated pattern classification, and longitudinal comparison	Automated lung and lesion segmentation; quantification of ILD extent and individual HRCT patterns; pattern classification; correlation of imaging burden with physiologic impairment; prognostic assessment; detection and quantification of longitudinal structural change	Feasibility and good performance have been demonstrated in SSc-ILD, inflammatory myopathy-associated ILD, and mixed CTD-ILD cohorts, including studies using independent test or validation datasets. However, validation remains variable, and deep-learning approaches remain predominantly investigational rather than established tools for routine clinical use[12,46,48,87]	Requires sufficiently large and well-annotated datasets and greater computational resources; performance may vary across scanners, protocols, and patient populations; training and maintenance can be resource intensive; external validation and standardized deployment remain insufficient for widespread routine use

HRCT: High-resolution computed tomography; GGO: Ground-glass opacity; OP: Organizing pneumonia; CTD-ILD: Connective tissue disease-associated interstitial lung disease; SSc-ILD: Systemic sclerosis-associated interstitial lung disease; RA-ILD: Rheumatoid arthritis-associated interstitial lung disease.

Table 2 Representative high-resolution computed tomography-based studies in connective tissue disease-associated interstitial lung disease by clinical application

Clinical application	Ref.	CTD subtype/population	HRCT approach	Main finding	Main limitation
Diagnosis	Frauenfelder et al[50]	SSc	Reduced 9-slice visual HRCT	Sensitivity: 88.3%; radiation dose decreased from 2.09 ± 1.34 mSv to 0.08 ± 0.06 mSv	SSc-specific reduced-slice screening may miss limited abnormalities
	Wang et al[51]	Juvenile DM-ILD	Semiquantitative CT scoring	CT score correlated strongly with serum KL-6 (r = 0.784, P < 0.01)	Pediatric disease-specific population limits generalizability
	Chen et al[49]	CTD-ILD	Dual-energy quantitative CT	Whole-lung monochromatic CT number identified extensive disease (AUC: 0.901; sensitivity: 82.1%; specificity: 85.4%)	Cross-sectional design; prognostic relevance not established
	Yang et al[62]	PM/DM	Quantitative CT + machine learning	Random forest achieved AUC 0.843 in the temporal test cohort for ILD detection.	Retrospective study; independent external validation is needed
	Xu et al[63]	IIM	Clinical + CT radiomics machine learning	Combined nomogram achieved AUCs of 0.877 and 0.898 in training and testing cohorts	Independent external validation and prospective assessment are needed
Prognostic assessment	Goh et al[69]	SSc-ILD	Visual HRCT extent + FVC	HRCT extent > 20% defined extensive disease; FVC 70% aided classification when HRCT extent was 10%-30%	Developed for SSc-ILD; generalizability to other CTD-ILD is uncertain
	Schniering et al[71]	SSc-ILD	Quantitative radiomic risk score	High-risk status predicted shorter progression-free survival and increased progression risk (HR: 5.14) after external validation	Radiomics workflow requires standardization and broader prospective validation
	Xu et al[83]	Anti-MDA5-positive DM-ILD	Radiomics + age + FVC	Rad-score plus model achieved C-indices of 0.88, 0.88, 0.83, and 0.84 across four datasets	Disease-specific model; generalizability beyond anti-MDA5-positive DM-ILD is uncertain
Longitudinal monitoring and treatment response	Goldin et al[91]	SSc-ILD	Volumetric QILD	Whole-lung QILD decreased after treatment and its change correlated with FVC, DLCO, and dyspnea score	Imaging change alone should not be interpreted as proof of treatment efficacy
	Occhipinti et al[100]	SSc-ILD	Quantitative CT during immunosuppressive therapy	Total lung-volume change predicted a composite FVC/DLCO endpoint (AUC: 0.74); individual texture changes had limited predictive ability	Small cohort; individual texture patterns had limited predictive ability
	Tian et al[114]	IIM-ILD	Quantitative fibrosis growth-rate modeling	Five longitudinal patterns were identified; rapid progression was significantly associated with mortality	Small exploratory cohort; the growth-rate model requires further validation

CTD: Connective tissue disease; HRCT: High-resolution computed tomography; SSc: Systemic sclerosis; DM-ILD: Dermatomyositis-associated interstitial lung disease; KL-6: Krebs von den Lungen-6; AUC: Area under the receiver operating characteristic curve; PM: Polymyositis; IIM: Idiopathic inflammatory myopathy; CT: Computed tomography; FVC: Forced vital capacity; HR: Hazard ratio; Anti-MDA5: Anti-melanoma differentiation-associated gene 5; QILD: Quantitative interstitial lung disease; DLCO: Diffusing capacity of the lung for carbon monoxide.