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Item An Open Source Platform for Computational Histopathology(IEEE, 2021) Yu, Xiaxia; Zhao, Bingshuai; Huang, Haofan; Tian, Mu; Zhang, Sai; Song, Hongping; Li, Zengshan; Huang, Kun; Gao, Yi; Biostatistics and Health Data Science, School of MedicineComputational histopathology is a fast emerging field which converts the traditional glass slide based department to a new examination platform. Such a paradigm shift also brings the in silico computation to the field. Much research have been presented in the past decades on the algorithm development for pathology image analysis. On the other hand, a comprehensive software platform with advanced visualization and computation capability, large developer community, flexible plugin mechanism, and friendly transnational license, would be extremely beneficial for the entire community. In this work, we present SlicerScope: an open platform for whole slide histopathology image computing based on the highly successful 3D Slicer. We present rationale on the choice of such an architecture, introducing new modules/tools for giga-pixel whole slide image viewing, and four specific analytical modules for qualitative presentation, nucleus level analysis, tissue scale computation, and 3D pathology. The entire software is publicly available at https://slicerscope.github.io/ , facilitating the algorithmic, clinical, and transnational researches.Item Artificial intelligence-based multi-class histopathologic classification of kidney neoplasms(Elsevier, 2023-02-16) Gondim, Dibson D.; Al-Obaidy, Khaleel I.; Idrees, Muhammad T.; Eble, John N.; Cheng, Liang; Pathology and Laboratory Medicine, School of MedicineArtificial intelligence (AI)-based techniques are increasingly being explored as an emerging ancillary technique for improving accuracy and reproducibility of histopathological diagnosis. Renal cell carcinoma (RCC) is a malignancy responsible for 2% of cancer deaths worldwide. Given that RCC is a heterogenous disease, accurate histopathological classification is essential to separate aggressive subtypes from indolent ones and benign mimickers. There are early promising results using AI for RCC classification to distinguish between 2 and 3 subtypes of RCC. However, it is not clear how an AI-based model designed for multiple subtypes of RCCs, and benign mimickers would perform which is a scenario closer to the real practice of pathology. A computational model was created using 252 whole slide images (WSI) (clear cell RCC: 56, papillary RCC: 81, chromophobe RCC: 51, clear cell papillary RCC: 39, and, metanephric adenoma: 6). 298,071 patches were used to develop the AI-based image classifier. 298,071 patches (350 × 350-pixel) were used to develop the AI-based image classifier. The model was applied to a secondary dataset and demonstrated that 47/55 (85%) WSIs were correctly classified. This computational model showed excellent results except to distinguish clear cell RCC from clear cell papillary RCC. Further validation using multi-institutional large datasets and prospective studies are needed to determine the potential to translation to clinical practice.Item Cutaneous Manifestations of Rheumatoid Arthritis: Diagnosis and Treatment(MDPI, 2023-10-10) Diaz, Michael J.; Natarelli, Nicole; Wei, Aria; Rechdan, Michaela; Botto, Elizabeth; Tran, Jasmine T.; Forouzandeh, Mahtab; Plaza, Jose A.; Kaffenberger, Benjamin H.; Medicine, School of MedicineRheumatoid arthritis (RA) is a chronic, systemic autoimmune disorder characterized by inflammatory arthritis and periarticular structural damage. Available evidence suggests that RA results from complex interactions between genetic susceptibility (e.g., HLA-DRB1), environmental factors (e.g., smoking), and immune dysregulation. Alongside joint-related symptoms, individuals with RA may also experience a wide array of skin issues, including the development of nodules, neutrophilic dermatoses, vasculitis, and vasculopathy. Treatment strategies for these manifestations vary but routinely involve corticosteroids, disease-modifying anti-rheumatic drugs, and biologics, with individualized approaches guided by disease severity. In this review, we provide comprehensive insights into the skin-related issues associated with RA, outlining their clinical characteristics and histopathological findings. Our aim is to facilitate early diagnosis and personalized treatment to improve the quality of life of affected individuals.Item Deep Learning-Based Classification of Epithelial-Mesenchymal Transition for Predicting Response to Therapy in Clear Cell Renal Cell Carcinoma(Frontiers Media, 2022) Chen, Qiwei; Kuai, Yue; Wang, Shujing; Zhu, Xinqing; Wang, Hongyu; Liu, Wenlong; Cheng, Liang; Yang, Deyong; Pathology and Laboratory Medicine, School of MedicineEpithelial–mesenchymal transition (EMT) profoundly impacts prognosis and immunotherapy of clear cell renal cell carcinoma (ccRCC). However, not every patient is tested for EMT status because this requires additional genetic studies. In this study, we developed an EMT gene signature to classify the H&E-stained slides from The Cancer Genome Atlas (TCGA) into epithelial and mesenchymal subtypes, then we trained a deep convolutional neural network to classify ccRCC which according to our EMT subtypes accurately and automatically and to further predict genomic data and prognosis. The clinical significance and multiomics analysis of the EMT signature was investigated. Patient cohorts from TCGA (n = 252) and whole slide images were used for training, testing, and validation using an algorithm to predict the EMT subtype. Our approach can robustly distinguish features predictive of the EMT subtype in H&E slides. Visualization techniques also detected EMT-associated histopathological features. Moreover, EMT subtypes were characterized by distinctive genomes, metabolic states, and immune components. Deep learning convolutional neural networks could be an extremely useful tool for predicting the EMT molecular classification of ccRCC tissue. The underlying multiomics information can be crucial in applying the appropriate and tailored targeted therapy to the patient.Item Histopathologic correlation of pancreatic fibrosis with pancreatic magnetic resonance imaging quantitative metrics and Cambridge classification(Springer, 2022) Tirkes, Temel; Saeed, Omer A.; Osuji, Vitalis C.; Kranz, Carsyn E.; Roth, Adam A.; Patel, Aashish A.; Zyromski, Nicholas J.; Fogel, Evan L.; Radiology and Imaging Sciences, School of MedicinePurpose: To determine the correlation of the T1-weighted signal intensity ratio (T1 SIR, or T1 Score) and arterial-to-delayed venous enhancement ratio (ADV ratio) of the pancreas with pancreatic fibrosis on histopathology. Methods: Sixty consecutive adult CP patients who had an MRI/MRCP study prior to pancreatic surgery were analyzed. Three blinded observers measured T1 SIR of pancreas to spleen (T1 SIR p/s), pancreas-to-paraspinal muscle (T1 SIR p/m), ADV ratio, and Cambridge grade. Histopathologic grades were given by a gastrointestinal pathologist using Ammann's fibrosis score. Statistical analysis included Spearman's correlation coefficient of the T1 SIR, ADV ratio, Cambridge grade with the fibrosis score, and weighted kappa for interobserver agreement. Results: The study population included 31 female and 29 male patients, with an average age of 52.1 (26-78 years). Correlations between fibrosis score and T1 SIR p/s, T1 SIR p/m, and ADV ratio were ρ = - 0.54 (p = 0.0001), ρ = - 0.19 (p = 0.19), and ρ = - 0.39 (p = 0.003), respectively. The correlation of Cambridge grade with fibrosis score was ρ = 0.26 (p = 0.07). There was substantial interobserver agreement (weighted kappa) for T1 SIR p/s (0.78), T1 SIR p/m (0.71), and ADV ratio (0.64). T1 SIR p/s of ≤ 1.20 provided a sensitivity of 74% and specificity of 50% (AUC: 0.74), while ADV ratio of ≤ 1.10 provided a sensitivity of 75% and specificity of 55% (AUC: 0.68) to detect a fibrosis score of ≥ 6. Conclusion: There is a moderate negative correlation between the T1 Score (SIR p/s) and ADV ratio with pancreatic fibrosis and a substantial interobserver agreement. These parenchymal metrics show a higher correlation than the Cambridge grade.Item Kidney Histopathology and Prediction of Kidney Failure: A Retrospective Cohort Study(Elsevier, 2020-09) Eadon, Michael T.; Schwantes-An, Tae-Hwi; Phillips, Carrie L.; Roberts, Anna R.; Greene, Colin V.; Hallab, Ayman; Hart, Kyle J.; Lipp, Sarah N.; Perez-Ledezma, Claudio; Omar, Khawaja O.; Kelly, Katherine J.; Moe, Sharon M.; Dagher, Pierre C.; El-Achkar, Tarek M.; Moorthi, Ranjani N.; Medical and Molecular Genetics, School of MedicineRationale & objective: The use of kidney histopathology for predicting kidney failure is not established. We hypothesized that the use of histopathologic features of kidney biopsy specimens would improve prediction of clinical outcomes made using demographic and clinical variables alone. Study design: Retrospective cohort study and development of a clinical prediction model. Setting & participants: All 2,720 individuals from the Biopsy Biobank Cohort of Indiana who underwent kidney biopsy between 2002 and 2015 and had at least 2 years of follow-up. New predictors & established predictors: Demographic variables, comorbid conditions, baseline clinical characteristics, and histopathologic features. Outcomes: Time to kidney failure, defined as sustained estimated glomerular filtration rate ≤ 10mL/min/1.73m2. Analytical approach: Multivariable Cox regression model with internal validation by bootstrapping. Models including clinical and demographic variables were fit with the addition of histopathologic features. To assess the impact of adding a histopathology variable, the amount of variance explained (r2) and the C index were calculated. The impact on prediction was assessed by calculating the net reclassification index for each histopathologic variable and for all combined. Results: Median follow-up was 3.1 years. Within 5 years of biopsy, 411 (15.1%) patients developed kidney failure. Multivariable analyses including demographic and clinical variables revealed that severe glomerular obsolescence (adjusted HR, 2.03; 95% CI, 1.51-2.03), severe interstitial fibrosis and tubular atrophy (adjusted HR, 1.99; 95% CI, 1.52-2.59), and severe arteriolar hyalinosis (adjusted HR, 1.53; 95% CI, 1.14-2.05) were independently associated with the primary outcome. The addition of all histopathologic variables to the clinical model yielded a net reclassification index for kidney failure of 5.1% (P < 0.001) with a full model C statistic of 0.915. Analyses addressing the competing risk for death, optimism, or shrinkage did not significantly change the results. Limitations: Selection bias from the use of clinically indicated biopsies and exclusion of patients with less than 2 years of follow-up, as well as reliance on surrogate indicators of kidney failure onset. Conclusions: A model incorporating histopathologic features from kidney biopsy specimens improved prediction of kidney failure and may be valuable clinically. Future studies will be needed to understand whether even more detailed characterization of kidney tissue may further improve prognostication about the future trajectory of estimated glomerular filtration rate.Item Predicting melanoma survival and metastasis with interpretable histopathological features and machine learning models(Frontiers Media, 2023-01-06) Couetil, Justin; Liu, Ziyu; Huang, Kun; Zhang, Jie; Alomari, Ahmed K.; Medical and Molecular Genetics, School of MedicineIntroduction: Melanoma is the fifth most common cancer in US, and the incidence is increasing 1.4% annually. The overall survival rate for early-stage disease is 99.4%. However, melanoma can recur years later (in the same region of the body or as distant metastasis), and results in a dramatically lower survival rate. Currently there is no reliable method to predict tumor recurrence and metastasis on early primary tumor histological images. Methods: To identify rapid, accurate, and cost-effective predictors of metastasis and survival, in this work, we applied various interpretable machine learning approaches to analyze melanoma histopathological H&E images. The result is a set of image features that can help clinicians identify high-risk-of-metastasis patients for increased clinical follow-up and precision treatment. We use simple models (i.e., logarithmic classification and KNN) and "human-interpretable" measures of cell morphology and tissue architecture (e.g., cell size, staining intensity, and cell density) to predict the melanoma survival on public and local Stage I-III cohorts as well as the metastasis risk on a local cohort. Results: We use penalized survival regression to limit features available to downstream classifiers and investigate the utility of convolutional neural networks in isolating tumor regions to focus morphology extraction on only the tumor region. This approach allows us to predict survival and metastasis with a maximum F1 score of 0.72 and 0.73, respectively, and to visualize several high-risk cell morphologies. Discussion: This lays the foundation for future work, which will focus on using our interpretable pipeline to predict metastasis in Stage I & II melanoma.Item Safety and feasibility of carotid revascularization in patients with cerebral embolic strokes associated with carotid webs and histopathology revisited(Sage, 2021) Mathew, S.; Davidson, Darrell D.; Tejada, J.; Martinez, M.; Kovoor, J.; Ophthalmology, School of MedicineIntroduction: Carotid web is increasingly recognized as the cause of ischemic embolic strokes in younger patients. The best way to treat carotid web is debatable and carotid artery stenting (CAS) has been reported as a treatment for the carotid web in only a few case series. In this study we evaluate the safety and feasibility of CAS in symptomatic carotid webs and examined the histopathology of a carotid web. Materials and methods: At our institution between 2017 and 2019, 10 consecutive patients with symptomatic carotid webs were treated. We retrospectively analyzed the data for patient demographics, clinical presentation, imaging, treatment methodology and follow up. Results: All the patients had presented with ipsilateral embolic stroke. The mean age at presentation was 50 years (range 37-71) with seven female and three male patients. All patients underwent CAS except one patient who underwent carotid endarterectomy (CEA). In one stented patient, there was significant hypotension in the post-procedural period lasting a week. The patients were followed for a mean of 5.5 months (range one day-12 months). No recurrent stroke or transient ischemic attack (TIA) occurred. Surgical pathological studies confirmed fibromuscular dysplasia in one specimen. Conclusion: In our experience CAS for carotid web is feasible and safe in patients presenting with ischemic embolic strokes.Item Substantial hepatic necrosis is prognostic in fulminant liver failure(Baishideng Publishing Group, 2017-06-21) Ndekwe, Paul; Ghabri, Marwan S.; Zang, Yong; Mann, Steven A.; Cummings, Oscar W.; Lin, Jingmei; Pathology and Laboratory Medicine, School of MedicineAIM: To evaluate if any association existed between the extent of hepatic necrosis in initial liver biopsies and patient survival. METHODS: Thirty-seven patients with fulminant liver failure, whose liver biopsy exhibited substantial necrosis, were identified and included in the study. The histological and clinical data was then analyzed in order to assess the relationship between the extent of necrosis and patient survival, with and without liver transplantation. The patients were grouped based on the etiology of hepatic necrosis. Each of the etiology groups were then further stratified according to whether or not they had received a liver transplant post-index biopsy, and whether or not the patient survived. RESULTS: The core tissue length ranged from 5 to 44 mm with an average of 23 mm. Causes of necrosis included 14 autoimmune hepatitis, 10 drug induced liver injury (DILI), 9 hepatitis virus infection, and 4 unknown origin. Among them, 11 showed submassive (26%-75% of the parenchymal volume) and 26 massive (76%-100%) necrosis. Transplant-free survival was worse in patients with a higher extent of necrosis (40%, 71.4% and 100% in groups with necrosis of 76%-100%, 51%-75% and 26%-50%, respectively). Additionally, transplant-free survival rates were 66.7%, 57.1%, and 25.0% in groups of autoimmune hepatitis, DILI, and viral hepatitis, respectively. Even after liver transplantation, the survival rate in patients as a result of viral hepatitis remained the lowest (80%, 100%, and 40% in groups of autoimmune hepatitis, DILI, and viral hepatitis, respectively). CONCLUSION: Adequate liver biopsy with more than 75% necrosis is associated with significant transplant-free mortality that is critical in predicting survival.Item The 5th Edition of the World Health Organization Classification of Tumours of the Eye and Orbit(Karger, 2023) Milman, Tatyana; Grossniklaus, Hans E.; Goldman-Levy, Gabrielle; Kivelä, Tero T.; Coupland, Sarah E.; White, Valerie A.; Mudhar, Hardeep Singh; Eberhart, Charles G.; Verdijk, Robert M.; Heegaard, Steffen; Gill, Anthony J.; Jager, Martine J.; Rodríguez-Reyes, Abelardo A.; Esmaeli, Bita; Hodge, Jennelle C.; Cree, Ian A.; WHO Classification of Tumours Editorial Board; Medical and Molecular Genetics, School of Medicine