Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes.

Rationale: Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. Objectives: Apply multiple instance learning (MIL) to develop an explainable deep learning algorithm for prediction of UIP from CT and validate its performance in independent cohorts. Methods: We trained an MIL algorithm using a pooled dataset (n = 2,143) and tested it in three independent populations: data from a prior publication (n = 127), a single-institution clinical cohort (n = 239), and a national registry of patients with pulmonary fibrosis (n = 979). We tested UIP classification performance using receiver operating characteristic analysis, with histologic UIP as ground truth. Cox proportional hazards and linear mixed-effects models were used to examine associations between MIL predictions and survival or longitudinal FVC. Measurements and Main Results: In two cohorts with biopsy data, MIL improved accuracy for histologic UIP (area under the curve, 0.77 [n = 127] and 0.79 [n = 239]) compared with visual assessment (area under the curve, 0.65 and 0.71). In cohorts with survival data, MIL-UIP classifications were significant for mortality (n = 239, mortality to April 2021: unadjusted hazard ratio, 3.1; 95% confidence interval [CI], 1.96-4.91; P < 0.001; and n = 979, mortality to July 2022: unadjusted hazard ratio, 3.64; 95% CI, 2.66-4.97; P < 0.001). Individuals classified as UIP positive by the algorithm had a significantly greater annual decline in FVC than those classified as UIP negative (-88 ml/yr vs. -45 ml/yr; n = 979; P < 0.01), adjusting for extent of lung fibrosis. Conclusions: Computerized assessment using MIL identifies clinically significant features of UIP on CT. Such a method could improve confidence in radiologic assessment of patients with interstitial lung disease, potentially enabling earlier and more precise diagnosis.

Updates on grading mesothelioma.

Mesothelioma is a rare disease with an historically poor prognosis. Over the past decade, a grading system has been developed that is a powerful prognostic tool in epithelioid mesothelioma. Grading of epithelioid mesothelioma is now required or strongly recommended by expert consensus, the College of American Pathologists, the World Health Organization, and the International Mesothelioma Interest Group. The original nuclear grading system for epithelioid mesothelioma, developed in the United States, split epithelioid mesotheliomas into three prognostic groups with marked differences in survival. Now, this three-tiered nuclear grading system has been combined with the presence or absence of necrosis to form the currently recommended two-tiered grading system of low- and high-grade epithelioid mesothelioma. This review will focus on the development of this grading system in mesothelioma, the grading system's shortcomings, and the application of the grading system to cytology specimens and other extra-pleural sites. Lastly, this review will briefly discuss alternative grading systems and future considerations.

Nonmesothelial Spindle Cell Tumors of Pleura and Pericardium.

Spindle cell lesions of the pleura and pericardium are rare. Distinction from sarcomatoid mesothelioma, which has a range of morphologic patterns, can be difficult, but accurate diagnosis matters. This article provides practical guidance for the diagnosis of pleural spindle cell neoplasms, focusing on primary lesions.

Hypersensitivity Pneumonitis on Thin-Section Chest CT Scans: Diagnostic Performance of the ATS/JRS/ALAT versus ACCP Imaging Guidelines.

Purpose To compare the diagnostic performance of the American Thoracic Society, Japanese Respiratory Society, and Asociación Latinoamericana del Tórax (ATS/JRS/ALAT) versus the American College of Chest Physicians (ACCP) imaging classifications for hypersensitivity pneumonitis (HP). Materials and Methods Patients in the institutional review board-approved Interstitial Lung Disease (ILD) registry referred for multidisciplinary discussion (MDD) at the authors' institution (January 1, 2006-April 1, 2021) were included in this retrospective study when ILD was diagnosed at MDD. MDD diagnoses included HP, connective tissue disease-ILD, and idiopathic pulmonary fibrosis. Retrospective review of thin-section CT images was performed in consensus by two cardiothoracic radiologists blinded to the diagnosis. Diagnostic patterns were determined for thin-section CT images using both classifications. Discordance rates were determined. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were assessed using MDD diagnosis as the reference standard. Results A total of 297 patients were included in the study: 200 (67%) with HP, 49 (16%) with connective tissue disease-ILD, and 48 (16%) with idiopathic pulmonary fibrosis at MDD. The discordance rate between the two classifications was 21%. Assuming low HP prevalence (10%), ATS/JRS/ALAT classification outperformed ACCP classification, with greater accuracy (92.3% vs 87.6%) and greater positive predictive value (60.7% vs 42.9%). Assuming high prevalence (50%), accuracy and negative predictive value were superior using ACCP classification (81.7% vs 79.7% and 77.7% vs 72.6%, respectively), and positive predictive value was superior using ATS/JRS/ALAT classification (93.3% vs 87.1%). Conclusion Accuracy of the ATS/JRS/ALAT and ACCP HP classifications was greater in settings with low and high HP prevalence, respectively. Diagnostic performance of both classifications was discordant in a minority of cases. Keywords: CT, Thorax, Hypersensitivity Pneumonitis, Interstitial Lung Disease Supplemental material is available for this article. © RSNA, 2024.

Guidelines for Pathologic Diagnosis of Mesothelioma.

CONTEXT.­: Mesothelioma is an uncommon tumor that can be difficult to diagnose. OBJECTIVE.­: To provide updated, practical guidelines for the pathologic diagnosis of mesothelioma. DATA SOURCES.­: Pathologists involved in the International Mesothelioma Interest Group and others with expertise in mesothelioma contributed to this update. Reference material includes peer-reviewed publications and textbooks. CONCLUSIONS.­: There was consensus opinion regarding guidelines for (1) histomorphologic diagnosis of mesothelial tumors, including distinction of epithelioid, biphasic, and sarcomatoid mesothelioma; recognition of morphologic variants and patterns; and recognition of common morphologic pitfalls; (2) molecular pathogenesis of mesothelioma; (3) application of immunohistochemical markers to establish mesothelial lineage and distinguish mesothelioma from common morphologic differentials; (4) application of ancillary studies to distinguish benign from malignant mesothelial proliferations, including BAP1 and MTAP immunostains; novel immunomarkers such as Merlin and p53; fluorescence in situ hybridization (FISH) for homozygous deletion of CDKN2A; and novel molecular assays; (5) practical recommendations for routine reporting of mesothelioma, including grading epithelioid mesothelioma and other prognostic parameters; (6) diagnosis of mesothelioma in situ; (7) cytologic diagnosis of mesothelioma, including use of immunostains and molecular assays; and (8) features of nonmalignant peritoneal mesothelial lesions.