International Consensus Statement on Allergy and Rhinology: Sinonasal Tumors.

BACKGROUND: Sinonasal neoplasms, whether benign and malignant, pose a significant challenge to clinicians and represent a model area for multidisciplinary collaboration in order to optimize patient care. The International Consensus Statement on Allergy and Rhinology: Sinonasal Tumors (ICSNT) aims to summarize the best available evidence and presents 48 thematic and histopathology-based topics spanning the field. METHODS: In accordance with prior International Consensus Statement on Allergy and Rhinology documents, ICSNT assigned each topic as an Evidence-Based Review with Recommendations, Evidence-Based Review, and Literature Review based on the level of evidence. An international group of multidisciplinary author teams were assembled for the topic reviews using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses format, and completed sections underwent a thorough and iterative consensus-building process. The final document underwent rigorous synthesis and review prior to publication. RESULTS: The ICSNT document consists of four major sections: general principles, benign neoplasms and lesions, malignant neoplasms, and quality of life and surveillance. It covers 48 conceptual and/or histopathology-based topics relevant to sinonasal neoplasms and masses. Topics with a high level of evidence provided specific recommendations, while other areas summarized the current state of evidence. A final section highlights research opportunities and future directions, contributing to advancing knowledge and community intervention. CONCLUSION: As an embodiment of the multidisciplinary and collaborative model of care in sinonasal neoplasms and masses, ICSNT was designed as a comprehensive, international, and multidisciplinary collaborative endeavor. Its primary objective is to summarize the existing evidence in the field of sinonasal neoplasms and masses.

Brief Report: High Levels of CD47 Expression in Thymic Epithelial Tumors.

Introduction: CD47 is a tumor antigen that inhibits phagocytosis leading to immune evasion. Anti-CD47 therapy is a promising new immunotherapy across numerous tumor types, but it has not been tested in thymic epithelial tumors (TETs): thymomas and thymic carcinomas. TETs are rare tumors that are difficult to treat, especially with programmed cell death protein 1/programmed death-ligand 1 checkpoint inhibitors, owing to the excessive rates of immune-related adverse events. This study investigated the levels of CD47 expression in TETs to explore the possibility of anti-CD47 therapy. Methods: A total of 67 thymic tumors (63 thymomas and 4 thymic carcinomas) and 14 benign thymus controls and their clinical data were included. Samples were stained for CD47 expression (rabbit monoclonal antibody SP279, Abcam, Waltham, MA) and scored for both intensity and H-score (intensity multiplied by the percentage of tumor involved). Intensity was defined as follows: 0 = none, 1 = weak, 2 = moderate, and 3 = strong. H-scores ranged from 0 to 300. Samples with an intensity score below 2 or an H-score below 150 were considered CD47low, whereas the rest were CD47high. Results: Compared with normal thymic tissues, TETs were more frequently CD47 positive and had significantly higher levels of CD47 expression. CD47 was positive in 79.1% of TETs compared with 57.1% of normal thymus. The level of CD47 expression was 16-fold higher in TETs (mean H-score 75.0 versus 4.6, p = 0.003). Multivariate analysis adjusted for age, sex, stage, resection status, and performance status revealed that CD47-high tumors were highly correlated with WHO histology type (p = 0.028). The most frequent CD47high tumors, in contrast to CD47low tumors, were types A (28.6% versus 7.5%) and AB (57.1% versus 13.2%), and the least frequent were B1 (7.1% versus 24.5%), B2 (0% versus 35.8%), B3 (7.1% versus 11.3%), and C (0% versus 7.5%). Conclusions: In contrast to normal thymus, TETs had significantly higher levels of CD47 expression. Tumor samples with high CD47 expression were mostly WHO types A and AB. This is the first study to explore CD47 expression in thymic cancers and lends support for ongoing investigation of anti-CD47 macrophage checkpoint inhibitor therapy in these tumors.

Artificial intelligence for dermatopathology: Current trends and the road ahead.

Artificial intelligence (AI), including deep learning methods that leverage neural network-based algorithms, hold significant promise for dermatopathology and other areas of diagnostic pathology in research and clinical practice. There has been significant progress over past several years in applying AI to analyzing digital histopathology images for diagnosis. While much work in AI analysis of histopathology data remains investigational, recent regulatory agency approval in Europe and United States of AI-assisted tools for clinical use in histopathologic diagnosis of prostate and breast cancer herald broader movement of AI into the clinical diagnostic realm of anatomic pathology, including dermatopathology. However, significant challenges remain in translating AI from research into clinical practice, including algorithmic real-world performance, robustness to variation in data sets and practice settings, effective integration into clinical workflows, and cost effectiveness. This review introduces core concepts and terminology in AI, and assesses current progress and challenges in applying AI to dermatopathology.

Artificial intelligence for prediction of donor liver allograft steatosis and early post-transplantation graft failure.

BACKGROUND: Donor livers undergo subjective pathologist review of steatosis before transplantation to mitigate the risk for early allograft dysfunction (EAD). We developed an objective, computer vision artificial intelligence (CVAI) platform to score donor liver steatosis and compared its capability for predicting EAD against pathologist steatosis scores. METHODS: Two pathologists scored digitized donor liver biopsy slides from 2014 to 2019. We trained four CVAI platforms with 1:99 training:prediction split. Mean intersection-over-union (IU) characterized CVAI model accuracy. We defined EAD using liver function tests within 1 week of transplantation. We calculated separate EAD logistic regression models with CVAI and pathologist steatosis and compared the models' discrimination and internal calibration. RESULTS: From 90 liver biopsies, 25,494 images trained CVAI models yielding peak mean IU = 0.80. CVAI steatosis scores were lower than pathologist scores (median 3% vs 20%, P < 0.001). Among 41 transplanted grafts, 46% developed EAD. The median CVAI steatosis score was higher for those with EAD (2.9% vs 1.9%, P = 0.02). CVAI steatosis was independently associated with EAD after adjusting for donor age, donor diabetes, and MELD score (aOR = 1.34, 95%CI = 1.03-1.75, P = 0.03). CONCLUSION: The CVAI steatosis EAD model demonstrated slightly better calibration than pathologist steatosis, meriting further investigation into which modality most accurately and reliably predicts post-transplantation outcomes.

Impact of a deep learning assistant on the histopathologic classification of liver cancer.

Artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, while their actual impact on human diagnosticians, when incorporated into clinical workflows, remains relatively unexplored. In this study, we developed a deep learning-based assistant to help pathologists differentiate between two subtypes of primary liver cancer, hepatocellular carcinoma and cholangiocarcinoma, on hematoxylin and eosin-stained whole-slide images (WSI), and evaluated its effect on the diagnostic performance of 11 pathologists with varying levels of expertise. Our model achieved accuracies of 0.885 on a validation set of 26 WSI, and 0.842 on an independent test set of 80 WSI. Although use of the assistant did not change the mean accuracy of the 11 pathologists (p = 0.184, OR = 1.281), it significantly improved the accuracy (p = 0.045, OR = 1.499) of a subset of nine pathologists who fell within well-defined experience levels (GI subspecialists, non-GI subspecialists, and trainees). In the assisted state, model accuracy significantly impacted the diagnostic decisions of all 11 pathologists. As expected, when the model's prediction was correct, assistance significantly improved accuracy (p = 0.000, OR = 4.289), whereas when the model's prediction was incorrect, assistance significantly decreased accuracy (p = 0.000, OR = 0.253), with both effects holding across all pathologist experience levels and case difficulty levels. Our results highlight the challenges of translating AI models into the clinical setting, and emphasize the importance of taking into account potential unintended negative consequences of model assistance when designing and testing medical AI-assistance tools.

Detection and Surveillance of Bladder Cancer Using Urine Tumor DNA.

Current regimens for the detection and surveillance of bladder cancer are invasive and have suboptimal sensitivity. Here, we present a novel high-throughput sequencing (HTS) method for detection of urine tumor DNA (utDNA) called utDNA CAPP-Seq (uCAPP-Seq) and apply it to 67 healthy adults and 118 patients with early-stage bladder cancer who had urine collected either prior to treatment or during surveillance. Using this targeted sequencing approach, we detected a median of 6 mutations per patient with bladder cancer and observed surprisingly frequent mutations of the PLEKHS1 promoter (46%), suggesting these mutations represent a useful biomarker for detection of bladder cancer. We detected utDNA pretreatment in 93% of cases using a tumor mutation-informed approach and in 84% when blinded to tumor mutation status, with 96% to 100% specificity. In the surveillance setting, we detected utDNA in 91% of patients who ultimately recurred, with utDNA detection preceding clinical progression in 92% of cases. uCAPP-Seq outperformed a commonly used ancillary test (UroVysion, P = 0.02) and cytology and cystoscopy combined (P ≤ 0.006), detecting 100% of bladder cancer cases detected by cytology and 82% that cytology missed. Our results indicate that uCAPP-Seq is a promising approach for early detection and surveillance of bladder cancer. SIGNIFICANCE: This study shows that utDNA can be detected using HTS with high sensitivity and specificity in patients with early-stage bladder cancer and during post-treatment surveillance, significantly outperforming standard diagnostic modalities and facilitating noninvasive detection, genotyping, and monitoring.This article is highlighted in the In This Issue feature, p. 453.

Decidual granulomatous reaction in a placenta from a preeclamptic pregnancy: a case report and review of the literature.

We report a case of decidual perivascular non-necrotizing granulomas in a placenta from a pregnancy complicated by severe preeclampsia with no evidence of infection. The mother was a 20-year-old primigravida with severe preeclampsia diagnosed in the third trimester with subsequent delivery of a healthy baby boy at 37 weeks 5 days gestation. Pathologic examination of the placenta showed scattered non-necrotizing granulomas in decidua, often adjacent to remodeled decidual arteries without fibrinoid necrosis. These were well-formed, non-necrotizing granulomas with scant lymphoid cuffs. Polarization microscopy did not show foreign material. There were no histopathologic or clinical findings suggestive of maternal-fetal infection or systemic vasculitis at the time of delivery, and the mother had no other reported conditions associated with granulomatous inflammation. Our case demonstrates that granulomatous reaction may be seen in the placenta from a pregnancy complicated by severe preeclampsia, although work-up for infection may be indicated.

Does the method of radiologic surveillance affect survival after resection of stage I non-small cell lung cancer?

OBJECTIVES: Controversy persists regarding appropriate radiographic surveillance strategies after lung cancer resection. We compared the impact of surveillance computed tomography scan versus chest radiography in patients who underwent resection for stage I lung cancer. METHODS: A retrospective analysis was performed of all patients undergoing resection for pathologic stage I lung cancer from January 2000 to April 2013. After resection, follow-up included routine history and physical examination in conjunction with chest radiography or computed tomography at the discretion of the treating physician. Identification of successive lung malignancy (ie, recurrence at any new site or new primary) and survival were recorded. RESULTS: There were 554 evaluable patients, with 232 receiving routine postoperative computed tomography and 322 receiving routine chest radiography. Postoperative 5-year survival was 67.8% in the computed tomography group versus 74.8% in the chest radiography group (P = .603). Successive lung malignancy was found in 27% (63/232) of patients receiving computed tomography versus 22% (72/322) receiving chest radiography (P = .19). The mean time from surgery to diagnosis of successive malignancy was 1.93 years for computed tomography versus 2.56 years for chest radiography (P = .046). For the computed tomography group, 41% (26/63) of successive malignancies were treated with curative intent versus 40% (29/72) in the chest radiography group (P = .639). Cox proportional hazard analysis indicated imaging modality (computed tomography vs chest radiography) was not associated with survival (P = .958). CONCLUSIONS: Surveillance computed tomography may result in earlier diagnosis of successive malignancy versus chest radiography in stage I lung cancer, although no difference in survival was demonstrated. A randomized trial would help determine the impact of postoperative surveillance strategies on survival.

Quantitative analysis of the effects of photoswitchable distance constraints on the structure of a globular protein.

Photoswitchable distance constraints in the form of photoisomerizable chemical cross-links offer a general approach to the design of reversibly photocontrolled proteins. To apply these effectively, however, one must have guidelines for the choice of cross-linker structure and cross-linker attachment sites. Here we investigate the effects of varying cross-linker structure on the photocontrol of folding of the Fyn SH3 domain, a well-studied model protein. We develop a theoretical framework based on an explicit-chain model of protein folding, modified to include detailed model linkers, that allows prediction of the effect of a given linker on the free energy of folding of a protein. Using this framework, we were able to quantitatively explain the experimental result that a longer, but somewhat flexible, cross-linker is less destabilizing to the folded state than a shorter more rigid cross-linker. The models also suggest how misfolded states may be generated by cross-linking, providing a rationale for altered dynamics seen in nuclear magnetic resonance analyses of these proteins. The theoretical framework is readily portable to any protein of known folded state structure and thus can be used to guide the design of photoswitchable proteins generally.