Browsing by Subject "Histopathology"
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Item 302 Diagnostic Evidence Gauge of Spatial Transcriptomics (DEGAS-ST): Using transfer learning to map clinical data to spatial transcriptomics in prostate cancer(Cambridge University Press, 2024-04-03) Couetil, Justin; Alomari, Ahmed K.; Zhang, Jie; Huang, Kun; Johnson, Travis S.; Medical and Molecular Genetics, School of MedicineOBJECTIVES/GOALS: The 'field effect' is a concept in pathology that pre-malignant tissue changes forecast health. Spatial transcriptomics could detect these changes earlier than histopathology, suggesting new early cancer screening methods. Knowing how normal tissue damage relates to cancer’s origin and progression may improve long-term outcomes. METHODS/STUDY POPULATION: We trained DEGAS, our machine learning framework, with prostate cancer data, combining both general cancer patterns and in-depth genetic information from individual tumors. The Tumor Cancer Genome Atlas (TCGA) shows how gene patterns in tumors relate to patient outcomes, emphasizing the differences between tumors from different patients (intertumor). On the other hand, spatial transcriptomics (ST) shows the genetic variety within a single tumor (intratumor) but has limited samples, making it hard to know which genetic differences are important for treatment. DEGAS bridges these areas by finding tissue sections that resemble those in TCGA profiles and are key indicators of patient survival. DEGAS serves as a valuable tool for generating clinically-important hypotheses. RESULTS/ANTICIPATED RESULTS: DEGAS identified benign-appearing glands in a normal prostate as being highly associated with poor progression-free survival. These glands have transcriptional signatures similar to high-grade prostate cancer. We confirmed this finding in a separate prostate cancer ST dataset. By integrating single cell (SC) data we demonstrated that cells annotated as cancerous in the SC data map to regions of benign glands in the ST dataset. We pinpoint several genes, chiefly Microseminoprotein-β (MSMB, PSP94), where reduced expression is highly correlated with poor progression-free survival. Cell type specific differential expression analysis further revealed that loss of MSMB expression associated with poor outcomes occurs specifically in luminal epithelia, the putative progenitor of prostate cancer. DISCUSSION/SIGNIFICANCE: DEGAS reveals that normal-appearing tissue can be highly-associated with tumor progression and underscores the importance of the 'field effect' in cancer research. Traditional analysis may miss such nuance, hiding key transitional cell states. Validating gene markers could boost early cancer detection and understanding of metastasis.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 Efficient transmission of human prion diseases to a glycan-free prion protein-expressing host(Oxford University Press, 2024) Cracco, Laura; Cali, Ignazio; Cohen, Mark L.; Aslam, Rabail; Notari, Silvio; Kong, Qingzhong; Newell, Kathy L.; Ghetti, Bernardino; Appleby, Brian S.; Gambetti, Pierluigi; Pathology and Laboratory Medicine, School of MedicineIt is increasingly evident that the association of glycans with the prion protein (PrP), a major post-translational modification, significantly impacts the pathogenesis of prion diseases. A recent bioassay study has provided evidence that the presence of PrP glycans decreases spongiform degeneration and disease-related PrP (PrPD) deposition in a murine model. We challenged (PRNPN181Q/197Q) transgenic (Tg) mice expressing glycan-free human PrP (TgGlyc-), with isolates from sporadic Creutzfeldt-Jakob disease subtype MM2 (sCJDMM2), sporadic fatal insomnia and familial fatal insomnia, three human prion diseases that are distinct but share histotypic and PrPD features. TgGlyc- mice accurately replicated the basic histotypic features associated with the three diseases but the transmission was characterized by high attack rates, shortened incubation periods and a greatly increased severity of the histopathology, including the presence of up to 40 times higher quantities of PrPD that formed prominent deposits. Although the engineered protease-resistant PrPD shared at least some features of the secondary structure and the presence of the anchorless PrPD variant with the wild-type PrPD, it exhibited different density gradient profiles of the PrPD aggregates and a higher stability index. The severity of the histopathological features including PrP deposition appeared to be related to the incubation period duration. These findings are clearly consistent with the protective role of the PrP glycans but also emphasize the complexity of the conformational changes that impact PrPD following glycan knockout. Future studies will determine whether these features apply broadly to other human prion diseases or are PrPD-type dependent.Item FUSION: A web-based application for in-depth exploration of multi-omics data with brightfield histology(bioRxiv, 2024-08-22) Border, Samuel; Ferreira, Ricardo Melo; Lucarelli, Nicholas; Manthey, David; Kumar, Suhas; Paul, Anindya; Mimar, Sayat; Naglah, Ahmed; Cheng, Ying-Hua; Barisoni, Laura; Ray, Jessica; Strekalova, Yulia; Rosenberg, Avi Z.; Tomaszewski, John E.; Hodgin, Jeffrey B.; HuBMAP consortium; El-Achkar, Tarek M.; Jain, Sanjay; Eadon, Michael T.; Sarder, Pinaki; Medicine, School of MedicineSpatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed FUSION (Functional Unit State IdentificatiON in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images. FUSION enables end-to-end analysis of functional tissue units (FTUs), automatically aggregating underlying molecular data to provide a histopathology-based medium for analyzing healthy and altered cell states and driving new discoveries using "pathomic" features. We demonstrate FUSION using 10x Visium spatial transcriptomics (ST) data from both formalin-fixed paraffin embedded (FFPE) and frozen prepared datasets consisting of healthy and diseased tissue. Through several use-cases, we demonstrate how users can identify spatial linkages between quantitative pathomics, qualitative image characteristics, and spatial --omics.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.