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

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.