Objectives The 2019 ACR/EULAR classification criteria for IgG4-related disease (IgG4-RD) have exclusion criteria including positive disease-specific autoantibodies, and these have been documented to have a high specificity. This study aimed to further validate these criteria as well as identify characteristics of patients showing false-negative results. Methods We retrospectively analysed 162 IgG4-RD patients and 130 mimickers. The sensitivity, specificity and fulfilment rates for each criterion were calculated, and intergroup comparisons were performed to characterize the false-negative cases. Results Both the IgG4-RD patients and mimickers were aged ≥65 years with male predominance. The final diagnoses of mimickers were mainly malignancy, vasculitis, sarcoidosis and aneurysm. The classification criteria had a sensitivity of 72.8% and specificity of 100%. Of the 44 false-negative cases, one did not fulfil the entry criteria, 20 fulfilled one exclusion criterion and 27 did not achieve sufficient inclusion criteria scores. The false-negative cases had fewer affected organs, lower serum IgG4 levels, and were less likely to have received biopsies than the true-positive cases. Notably, positive disease-specific autoantibodies were the most common exclusion criterion fulfilled in 18 patients, only two of whom were diagnosed with a specific autoimmune disease complicated by IgG4-RD. In addition, compared with the true-positive cases, the 18 had comparable serum IgG4 levels, number of affected organs, and histopathology and immunostaining scores despite higher serum IgG and CRP levels. Conclusions The ACR/EULAR classification criteria for IgG4-RD have an excellent diagnostic specificity in daily clinical practice. Positive disease-specific autoantibodies may have limited clinical significance for the diagnosis of IgG4-RD.

In this study, the relationship between ground‐glass opacity (GGO) visibility and physical detectability index in low‐dose computed tomography (LDCT) for lung cancer screening was investigated. An anthropomorphic chest phantom that included synthetic GGOs with CT numbers of ‐630 Hounsfield units (HU; high attenuation GGO: HGGO) and ‐800 HU (low attenuation GGO: LGGO), and three phantoms for physical measurements were employed. The phantoms were scanned using 12 CT systems located in 11 screening centers in Japan. The slice thicknesses and CT dose indices (CTDIvol) varied over 1.0–5.0 mm and 0.85–3.30 mGy, respectively, and several reconstruction kernels were used. Physical detectability index values were calculated from measurements of resolution, noise, and slice thickness properties for all image sets. Five radiologists and one thoracic surgeon, blind to one another's observations, evaluated GGO visibility using a five‐point scoring system. The physical detectability index correlated reasonably well with the GGO visibility (R2=0.709,p<0.01 for 6 mm HGGO and R2=0.646,p<0.01 for 10 mm LGGO), and was nearly proportional to the CTDIvol. Consequently, the CTDIvol also correlated reasonably well with the GGO visibility (R2=0.701,p<0.01 for 6 mm HGGO and R2=0.680,p<0.01 for 10 mm LGGO). As a result, the CTDIvol was nearly dominant in the GGO visibility for image sets with different reconstruction kernels and slice thicknesses, used in this study.PACS numbers: 81.70.Tx, 87.57.Q‐

Background This study investigated the characteristics of hypertrophic pachymeningitis (HP) in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), using information from a multicenter study in Japan. Methods We analyzed the clinical information of 663 Asian patients with AAV (total AAV), including 558 patients with newly diagnosed AAV and 105 with relapsed AAV. Clinical findings were compared between patients with and without HP. To elucidate the relevant manifestations for HP development, multivariable logistic regression analyses were additionally performed. Results Of the patients with AAV (mean age, 70.2 ± 13.5 years), HP was noted in 30 (4.52%), including 20 (3.58%) with newly diagnosed AAV and 10 (9.52%) with relapsed AAV. Granulomatosis with polyangiitis (GPA) was classified in 50% of patients with HP. A higher prevalence of GPA was significantly observed in patients with HP than in those without HP in total AAV and newly diagnosed AAV (p < 0.001). In newly diagnosed AAV, serum proteinase 3 (PR3)-ANCA positivity was significantly higher in patients with HP than in those without HP (p = 0.030). Patients with HP significantly had ear, nose, and throat (ENT) (odds ratio [OR] 1.48, 95% confidence interval [CI] 1.03–2.14, p = 0.033) and mucous membrane/eye manifestations (OR 5.99, 95% CI 2.59–13.86, p < 0.0001) in total AAV. Moreover, they significantly had conductive hearing loss (OR 11.6, 95% CI 4.51–29.57, p < 0.0001) and sudden visual loss (OR 20.9, 95% CI 5.24–85.03, p < 0.0001). Conclusion GPA was predominantly observed in patients with HP. Furthermore, in newly diagnosed AAV, patients with HP showed significantly higher PR3-ANCA positivity than those without HP. The ear and eye manifestations may be implicated in HP development.

Objectives To investigate the association between decreased serum IgG levels caused by remission-induction immunosuppressive therapy of antineutrophil cytoplasmic antibody-associated vasculitis (AAV) and the development of severe infections. Methods We conducted a retrospective cohort study of patients with new-onset or severe relapsing AAV enrolled in the J-CANVAS registry, which was established at 24 referral sites in Japan. The minimum serum IgG levels up to 24 weeks and the incidence of severe infection up to 48 weeks after treatment initiation were evaluated. After multiple imputations for all explanatory variables, we performed the multivariate analysis using a Fine-Gray model to assess the association between low IgG (the minimum IgG levels &lt; 500 mg/dl) and severe infections. In addition, the association was expressed as a restricted cubic spline (RCS) and analysed by treatment subgroups. Results Of 657 included patients (microscopic polyangiitis, 392; granulomatosis with polyangiitis, 139; eosinophilic granulomatosis with polyangiitis, 126), 111 (16.9%) developed severe infections. The minimum serum IgG levels were measured in 510 patients, of whom 77 (15.1%) had low IgG. After multiple imputations, the confounder-adjusted hazard ratio of low IgG for the incidence of severe infections was 1.75 (95% confidence interval: 1.03–3.00). The RCS revealed a U-shaped association between serum IgG levels and the incidence of severe infection with serum IgG 946 mg/dl as the lowest point. Subgroup analysis showed no obvious heterogeneity between treatment regimens. Conclusion Regardless of treatment regimens, low IgG after remission-induction treatment was associated with the development of severe infections up to 48 weeks after treatment initiation.

Renal pathology is essential for diagnosing and assessing the severity and prognosis of kidney diseases. Deep learning-based approaches have developed rapidly and have been applied in renal pathology. However, methods for the automated classification of normal and abnormal renal tubules remain scarce. Using a deep learning-based method, we aimed to classify normal and abnormal renal tubules, thereby assisting renal pathologists in the evaluation of renal biopsy specimens. Consequently, we developed a U-Net-based segmentation model using randomly selected regions obtained from 21 renal biopsy specimens. Further, we verified its performance in multiclass segmentation by calculating the Dice coefficients (DCs). We used 15 cases of tubulointerstitial nephritis to assess its applicability in aiding routine diagnoses conducted by renal pathologists and calculated the agreement ratio between diagnoses conducted by two renal pathologists and the time taken for evaluation. We also determined whether such diagnoses were improved when the output of segmentation was considered. The glomeruli and interstitium had the highest DCs, whereas the normal and abnormal renal tubules had intermediate DCs. Following the detailed evaluation of the tubulointerstitial compartments, the proximal, distal, atrophied, and degenerated tubules had intermediate DCs, whereas the arteries and inflamed tubules had low DCs. The annotation and output areas involving normal and abnormal tubules were strongly correlated in each class. The pathological concordance for the glomerular count, t, ct, and ci scores of the Banff classification of renal allograft pathology remained high with or without the segmented images. However, in terms of time consumption, the quantitative assessment of tubulitis, tubular atrophy, degenerated tubules, and the interstitium was improved significantly when renal pathologists considered the segmentation output. Deep learning algorithms can assist renal pathologists in the classification of normal and abnormal tubules in renal biopsy specimens, thereby facilitating the enhancement of renal pathology and ensuring appropriate clinical decisions.