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Background The need for quantitative assessment of interstitial lung involvement on thin-section computed tomography (CT) has arisen in interstitial lung diseases including connective tissue disease (CTD). Purpose To evaluate the capability of machine learning (ML)-based CT texture analysis for disease severity and treatment response assessments in comparison with qualitatively assessed thin-section CT for patients with CTD. Material and Methods A total of 149 patients with CTD-related ILD (CTD-ILD) underwent initial and follow-up CT scans (total 364 paired serial CT examinations), pulmonary function tests, and serum KL-6 level tests. Based on all follow-up examination results, all paired serial CT examinations were assessed as “Stable” (n = 188), “Worse” (n = 98) and “Improved” (n = 78). Next, quantitative index changes were determined by software, and qualitative disease severity scores were assessed by consensus of two radiologists. To evaluate differences in each quantitative index as well as in disease severity score between paired serial CT examinations, Tukey's honestly significant difference (HSD) test was performed among the three statuses. Stepwise regression analyses were performed to determine changes in each pulmonary functional parameter and all quantitative indexes between paired serial CT scans. Results Δ% normal lung, Δ% consolidation, Δ% ground glass opacity, Δ% reticulation, and Δdisease severity score showed significant differences among the three statuses ( P < 0.05). All differences in pulmonary functional parameters were significantly affected by Δ% normal lung, Δ% reticulation, and Δ% honeycomb (0.16 ≤r2 ≤0.42; P < 0.05). Conclusion ML-based CT texture analysis has better potential than qualitatively assessed thin-section CT for disease severity assessment and treatment response evaluation for CTD-ILD.

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BackgroundComputed diffusion‐weighted imaging (cDWI) is a mathematical computation technique that generates DWIs for any b‐value by using actual DWI (aDWI) data with at least two different b‐values and may improve differentiation of metastatic from nonmetastatic lymph nodes.PurposeTo determine the appropriate b‐value for cDWI to achieve a better diagnostic capability for lymph node staging (N‐staging) in non‐small cell lung cancer (NSCLC) patients compared to aDWI, short inversion time (TI) inversion recovery (STIR) imaging, or positron emission tomography with 2‐[fluorine‐18] fluoro‐2‐deoxy‐d‐glucose combined with computed tomography (FDG‐PET/CT).Study TypeProspective.SubjectsA total of 245 (127 males and 118 females; mean age 72 years) consecutive histopathologically confirmed NSCLC patients.Field Strength/SequenceA 3 T, half‐Fourier single‐shot turbo spin‐echo sequence, electrocardiogram (ECG)‐triggered STIR fast advanced spin‐echo (FASE) sequence with black blood and STIR acquisition and DWI obtained by FASE with b‐values of 0 and 1000 sec/mm2.AssessmentFrom aDWIs with b‐values of 0 and 1000 (aDWI1000) sec/mm2, cDWI using 400 (cDWI400), 600 (cDWI600), 800 (cDWI800), and 2000 (cDWI2000) sec/mm2 were generated. Then, 114 metastatic and 114 nonmetastatic nodes (mediastinal and hilar lymph nodes) were selected and evaluated with a contrast ratio (CR) for each cDWI and aDWI, apparent diffusion coefficient (ADC), lymph node‐to‐muscle ratio (LMR) on STIR, and maximum standard uptake value (SUVmax).Statistical TestsReceiver operating characteristic curve (ROC) analysis, Youden index, and McNemar's test.ResultsArea under the curve (AUC) of CR600 was significantly larger than the CR400, CR800, CR2000, aCR1000, and SUVmax. Comparison of N‐staging accuracy showed that CR600 was significantly higher than CR400, CR2000, ADC, aCR1000, and SUVmax, although there were no significant differences with CR800 (P = 0.99) and LMR (P = 0.99).Data ConclusioncDWI with b‐value at 600 sec/mm2 may have potential to improve N‐staging accuracy as compared with aDWI, STIR, and PET/CT.Evidence Level2Technical EfficacyStage 2