A machine learning-based method toward the prognostic analysis of CD38 with a pathomics model in cutaneous melanoma
A machine learning-based method toward the prognostic analysis of CD38 with a pathomics model in cutaneous melanoma
CD38 possesses an essential role in cancer evolution, particularly in cutaneous melanoma. Herein, we aimed to evaluate the prognostic value of CD38 in patients with cutaneous melanoma (CM) and to propose a HE-based pathomics model for its expression prediction.
dlvr.it
October 30, 2026 at 7:33 PM
A machine learning-based method toward the prognostic analysis of CD38 with a pathomics model in cutaneous melanoma
The #NIDDK Central Repository webinar, “Pixels to Phenotypes – Whole Slide Imaging Pathomics,” features an expert presentation on scalable and explainable tools for whole slide imaging #pathomics. More information: dknet.org/about/dknetn...
https://dknet.org/about/dknetnews/2955
dknet.org
October 29, 2026 at 9:47 PM
The #NIDDK Central Repository webinar, “Pixels to Phenotypes – Whole Slide Imaging Pathomics,” features an expert presentation on scalable and explainable tools for whole slide imaging #pathomics. More information: dknet.org/about/dknetn...
📢 October Issue!! Available now — Open Access!
"Pathomic analysis of 5-year surveillance biopsies as predictors of kidney allograft loss" by Pinaki Sarder et al
buff.ly/IfYyiqY
#Pathomics #KidneyTransplant
"Pathomic analysis of 5-year surveillance biopsies as predictors of kidney allograft loss" by Pinaki Sarder et al
buff.ly/IfYyiqY
#Pathomics #KidneyTransplant
October 26, 2026 at 12:03 PM
📢 October Issue!! Available now — Open Access!
"Pathomic analysis of 5-year surveillance biopsies as predictors of kidney allograft loss" by Pinaki Sarder et al
buff.ly/IfYyiqY
#Pathomics #KidneyTransplant
"Pathomic analysis of 5-year surveillance biopsies as predictors of kidney allograft loss" by Pinaki Sarder et al
buff.ly/IfYyiqY
#Pathomics #KidneyTransplant
13/ Can Pathomics Replace MEST-C?
🔬 Without clinical variables, pathomics outperformed MEST-C
📊 C-statistic:
🔹 Pathomics: 0.74
🔹 MEST-C: 0.69
📈 However, adding clinical variables reversed this advantage:
🔹 Pathomics: 0.76
🔹 MEST-C: 0.79
🔬 Without clinical variables, pathomics outperformed MEST-C
📊 C-statistic:
🔹 Pathomics: 0.74
🔹 MEST-C: 0.69
📈 However, adding clinical variables reversed this advantage:
🔹 Pathomics: 0.76
🔹 MEST-C: 0.79
October 17, 2026 at 12:09 PM
13/ Can Pathomics Replace MEST-C?
🔬 Without clinical variables, pathomics outperformed MEST-C
📊 C-statistic:
🔹 Pathomics: 0.74
🔹 MEST-C: 0.69
📈 However, adding clinical variables reversed this advantage:
🔹 Pathomics: 0.76
🔹 MEST-C: 0.79
🔬 Without clinical variables, pathomics outperformed MEST-C
📊 C-statistic:
🔹 Pathomics: 0.74
🔹 MEST-C: 0.69
📈 However, adding clinical variables reversed this advantage:
🔹 Pathomics: 0.76
🔹 MEST-C: 0.79
15/ Does Pathomics Improve Risk Prediction?
📈 Adding pathomics improved the International IgAN Prediction Tool
📊 C-statistic (without ethnicity):
🔹 Standard model: 0.81
🔹 + Pathomics: 0.83
🎯 Improvement: +0.024 (95% CI 0.019–0.028)
📈 Adding pathomics improved the International IgAN Prediction Tool
📊 C-statistic (without ethnicity):
🔹 Standard model: 0.81
🔹 + Pathomics: 0.83
🎯 Improvement: +0.024 (95% CI 0.019–0.028)
October 17, 2026 at 12:09 PM
15/ Does Pathomics Improve Risk Prediction?
📈 Adding pathomics improved the International IgAN Prediction Tool
📊 C-statistic (without ethnicity):
🔹 Standard model: 0.81
🔹 + Pathomics: 0.83
🎯 Improvement: +0.024 (95% CI 0.019–0.028)
📈 Adding pathomics improved the International IgAN Prediction Tool
📊 C-statistic (without ethnicity):
🔹 Standard model: 0.81
🔹 + Pathomics: 0.83
🎯 Improvement: +0.024 (95% CI 0.019–0.028)
14/ How Well Does Pathomics Stratify Risk?
📊 Both MEST-C and pathomics models stratified patients into 4 risk groups
📉 Kaplan–Meier curves showed progressively worse kidney outcomes with increasing risk
🔬 Pathomics provided comparable risk stratification
📊 Both MEST-C and pathomics models stratified patients into 4 risk groups
📉 Kaplan–Meier curves showed progressively worse kidney outcomes with increasing risk
🔬 Pathomics provided comparable risk stratification
October 17, 2026 at 12:09 PM
14/ How Well Does Pathomics Stratify Risk?
📊 Both MEST-C and pathomics models stratified patients into 4 risk groups
📉 Kaplan–Meier curves showed progressively worse kidney outcomes with increasing risk
🔬 Pathomics provided comparable risk stratification
📊 Both MEST-C and pathomics models stratified patients into 4 risk groups
📉 Kaplan–Meier curves showed progressively worse kidney outcomes with increasing risk
🔬 Pathomics provided comparable risk stratification
18/ Take-Home Messages
🫘 Pathomics offers quantitative biopsy assessment
🔬 It may complement, but not replace, Oxford MEST-C
📈 Adding pathomics modestly improves IgAN risk prediction
🚀 AI may support more personalized nephrology!
🫘 Pathomics offers quantitative biopsy assessment
🔬 It may complement, but not replace, Oxford MEST-C
📈 Adding pathomics modestly improves IgAN risk prediction
🚀 AI may support more personalized nephrology!
October 17, 2026 at 12:09 PM
18/ Take-Home Messages
🫘 Pathomics offers quantitative biopsy assessment
🔬 It may complement, but not replace, Oxford MEST-C
📈 Adding pathomics modestly improves IgAN risk prediction
🚀 AI may support more personalized nephrology!
🫘 Pathomics offers quantitative biopsy assessment
🔬 It may complement, but not replace, Oxford MEST-C
📈 Adding pathomics modestly improves IgAN risk prediction
🚀 AI may support more personalized nephrology!
7/ What Can Pathomics Add?
🔬 Pathomics enables quantitative tissue assessment.
📊 Hölscher et al. (Nat Commun, 2023) identified 5 morphometric features linked to IgAN progression.
❓ Can these improve risk prediction?
🔗https://www.nature.com/articles/s41467-023-36173-0
🔬 Pathomics enables quantitative tissue assessment.
📊 Hölscher et al. (Nat Commun, 2023) identified 5 morphometric features linked to IgAN progression.
❓ Can these improve risk prediction?
🔗https://www.nature.com/articles/s41467-023-36173-0
October 17, 2026 at 12:09 PM
7/ What Can Pathomics Add?
🔬 Pathomics enables quantitative tissue assessment.
📊 Hölscher et al. (Nat Commun, 2023) identified 5 morphometric features linked to IgAN progression.
❓ Can these improve risk prediction?
🔗https://www.nature.com/articles/s41467-023-36173-0
🔬 Pathomics enables quantitative tissue assessment.
📊 Hölscher et al. (Nat Commun, 2023) identified 5 morphometric features linked to IgAN progression.
❓ Can these improve risk prediction?
🔗https://www.nature.com/articles/s41467-023-36173-0
1/ Tweetorial Alert 🚨
🆕 Hey #NephTwitter!
Welcome to a new #tweetorial brought to you by @KIReports!
🫘 Can artificial intelligence improve risk prediction in IgA nephropathy?
🔬 Let's explore how pathomics could reshape kidney biopsy interpretation!
🆕 Hey #NephTwitter!
Welcome to a new #tweetorial brought to you by @KIReports!
🫘 Can artificial intelligence improve risk prediction in IgA nephropathy?
🔬 Let's explore how pathomics could reshape kidney biopsy interpretation!
October 17, 2026 at 12:09 PM
1/ Tweetorial Alert 🚨
🆕 Hey #NephTwitter!
Welcome to a new #tweetorial brought to you by @KIReports!
🫘 Can artificial intelligence improve risk prediction in IgA nephropathy?
🔬 Let's explore how pathomics could reshape kidney biopsy interpretation!
🆕 Hey #NephTwitter!
Welcome to a new #tweetorial brought to you by @KIReports!
🫘 Can artificial intelligence improve risk prediction in IgA nephropathy?
🔬 Let's explore how pathomics could reshape kidney biopsy interpretation!
2/
👩⚕️ Our author: Andreea @manoleaandreea.bsky.social (Nephrology resident)
🔬 Our topic: Incorporating Pathomics Into IgAN Risk Prediction
🫘 Can AI-derived biopsy features improve risk prediction in IgA nephropathy?
🔗https://www.sciencedirect.com/science/article/pii/S2468024926029402
👩⚕️ Our author: Andreea @manoleaandreea.bsky.social (Nephrology resident)
🔬 Our topic: Incorporating Pathomics Into IgAN Risk Prediction
🫘 Can AI-derived biopsy features improve risk prediction in IgA nephropathy?
🔗https://www.sciencedirect.com/science/article/pii/S2468024926029402
October 17, 2026 at 12:09 PM
2/
👩⚕️ Our author: Andreea @manoleaandreea.bsky.social (Nephrology resident)
🔬 Our topic: Incorporating Pathomics Into IgAN Risk Prediction
🫘 Can AI-derived biopsy features improve risk prediction in IgA nephropathy?
🔗https://www.sciencedirect.com/science/article/pii/S2468024926029402
👩⚕️ Our author: Andreea @manoleaandreea.bsky.social (Nephrology resident)
🔬 Our topic: Incorporating Pathomics Into IgAN Risk Prediction
🫘 Can AI-derived biopsy features improve risk prediction in IgA nephropathy?
🔗https://www.sciencedirect.com/science/article/pii/S2468024926029402
9/ Methods – Study Design
🔬 Retrospective analysis of 555 patients with IgAN from the European VALIGA cohort
🖥️ Deep learning extracted quantitative features from digitized kidney biopsies
🎯 Pathomics was compared with Oxford MEST-C and the International IgAN Prediction Tool
🔬 Retrospective analysis of 555 patients with IgAN from the European VALIGA cohort
🖥️ Deep learning extracted quantitative features from digitized kidney biopsies
🎯 Pathomics was compared with Oxford MEST-C and the International IgAN Prediction Tool
October 17, 2026 at 12:09 PM
9/ Methods – Study Design
🔬 Retrospective analysis of 555 patients with IgAN from the European VALIGA cohort
🖥️ Deep learning extracted quantitative features from digitized kidney biopsies
🎯 Pathomics was compared with Oxford MEST-C and the International IgAN Prediction Tool
🔬 Retrospective analysis of 555 patients with IgAN from the European VALIGA cohort
🖥️ Deep learning extracted quantitative features from digitized kidney biopsies
🎯 Pathomics was compared with Oxford MEST-C and the International IgAN Prediction Tool
8/ Our #tweetorial is based on a recent publication by Hölscher et al. in @KIReports:
🔬 Incorporating Pathomics Into IgAN Risk Prediction
Examining whether AI-derived biopsy features improve existing risk prediction tools in IgA nephropathy.
🔬 Incorporating Pathomics Into IgAN Risk Prediction
Examining whether AI-derived biopsy features improve existing risk prediction tools in IgA nephropathy.
October 17, 2026 at 12:09 PM
8/ Our #tweetorial is based on a recent publication by Hölscher et al. in @KIReports:
🔬 Incorporating Pathomics Into IgAN Risk Prediction
Examining whether AI-derived biopsy features improve existing risk prediction tools in IgA nephropathy.
🔬 Incorporating Pathomics Into IgAN Risk Prediction
Examining whether AI-derived biopsy features improve existing risk prediction tools in IgA nephropathy.
19/ What Do You Think? 💭
🔬 Based on these findings, could pathomics become part of routine IgAN risk assessment?
🫘 Would you consider AI-derived biopsy features alongside traditional histopathology?
💬 Share your thoughts!
🔬 Based on these findings, could pathomics become part of routine IgAN risk assessment?
🫘 Would you consider AI-derived biopsy features alongside traditional histopathology?
💬 Share your thoughts!
October 17, 2026 at 12:09 PM
19/ What Do You Think? 💭
🔬 Based on these findings, could pathomics become part of routine IgAN risk assessment?
🫘 Would you consider AI-derived biopsy features alongside traditional histopathology?
💬 Share your thoughts!
🔬 Based on these findings, could pathomics become part of routine IgAN risk assessment?
🫘 Would you consider AI-derived biopsy features alongside traditional histopathology?
💬 Share your thoughts!
16/ Why Do These Findings Matter?
🔬 Pathomics provides an objective, quantitative assessment of kidney biopsies
📊 It may complement conventional histopathology and modestly improve IgAN risk prediction
💡 A step toward more standardized biopsy assessment!
🔬 Pathomics provides an objective, quantitative assessment of kidney biopsies
📊 It may complement conventional histopathology and modestly improve IgAN risk prediction
💡 A step toward more standardized biopsy assessment!
October 17, 2026 at 12:09 PM
16/ Why Do These Findings Matter?
🔬 Pathomics provides an objective, quantitative assessment of kidney biopsies
📊 It may complement conventional histopathology and modestly improve IgAN risk prediction
💡 A step toward more standardized biopsy assessment!
🔬 Pathomics provides an objective, quantitative assessment of kidney biopsies
📊 It may complement conventional histopathology and modestly improve IgAN risk prediction
💡 A step toward more standardized biopsy assessment!
6/ Can AI Improve Histopathological Assessment?
🔬 Pathomics uses deep learning to extract quantitative features from digitized kidney biopsies
📊 It measures tissue structures, including glomeruli, tubules & arteries
🎯 Could it offer a more objective alternative to MEST-C?
🔬 Pathomics uses deep learning to extract quantitative features from digitized kidney biopsies
📊 It measures tissue structures, including glomeruli, tubules & arteries
🎯 Could it offer a more objective alternative to MEST-C?
October 17, 2026 at 12:09 PM
6/ Can AI Improve Histopathological Assessment?
🔬 Pathomics uses deep learning to extract quantitative features from digitized kidney biopsies
📊 It measures tissue structures, including glomeruli, tubules & arteries
🎯 Could it offer a more objective alternative to MEST-C?
🔬 Pathomics uses deep learning to extract quantitative features from digitized kidney biopsies
📊 It measures tissue structures, including glomeruli, tubules & arteries
🎯 Could it offer a more objective alternative to MEST-C?
Deep learning-based pathomics reveals renal tubular atrophy with inflammatory cell infiltration as a prognostic marker in IgA nephropathy SciReports
Deep learning-based pathomics reveals renal tubular atrophy with inflammatory cell infiltration as a prognostic marker in IgA nephropathy
Scientific Reports, Published online: 04 September 2026; doi:10.1038/s41598-026-62741-7Deep learning-based pathomics reveals renal tubular atrophy with inflammatory cell infiltration as a prognostic marker in IgA nephropathy
dlvr.it
September 4, 2026 at 4:39 AM
Deep learning-based pathomics reveals renal tubular atrophy with inflammatory cell infiltration as a prognostic marker in IgA nephropathy SciReports
RRIDs were included in this in Frontiers in Oncology paper. We value the author's support of reproducibility. #BetterScience #OpenResearch #RRID
Frontiers | Self-identified African ancestry and pathomics-derived tumor-infiltrating lymphocyte scores in colon and rectal adenocarcinoma whole-slide images: an exploratory multicohort whole-slide image study
IntroductionAI assisted computed higher tumor infiltrating lymphocyte% (TIL%) scores from hematoxylin and eosin (H&E) stained whole slide images (WSI) may se...
doi.org
August 27, 2026 at 7:02 AM
RRIDs were included in this in Frontiers in Oncology paper. We value the author's support of reproducibility. #BetterScience #OpenResearch #RRID
Incorporating #Pathomics Into #IgAN Risk #Prediction
#VisualAbstract by @DrPSVali
https://www.kireports.org/article/S2468-0249(26)02940-2/fulltext
#VisualAbstract by @DrPSVali
https://www.kireports.org/article/S2468-0249(26)02940-2/fulltext
August 26, 2026 at 1:00 AM
Incorporating #Pathomics Into #IgAN Risk #Prediction
#VisualAbstract by @DrPSVali
https://www.kireports.org/article/S2468-0249(26)02940-2/fulltext
#VisualAbstract by @DrPSVali
https://www.kireports.org/article/S2468-0249(26)02940-2/fulltext
Machine learning-based clinical, protemic and pathomics model to predict overall survival and therapeutic benefit in patients with cervical cancer #NeuroDegeneration 🧪🧠
https://www.researchsquare.com/article/rs-9465974/latest
https://www.researchsquare.com/article/rs-9465974/latest
August 18, 2026 at 7:03 AM
Machine learning-based clinical, protemic and pathomics model to predict overall survival and therapeutic benefit in patients with cervical cancer #NeuroDegeneration 🧪🧠
https://www.researchsquare.com/article/rs-9465974/latest
https://www.researchsquare.com/article/rs-9465974/latest
Deep Learning Pathomics Integrated with Single-Cell Transcriptomics Reveals MGAT4A-Mediated "Lock-and-Load" Dysfunction of CD8+ Tissue-Resident Memory T Cells in Endometrial Cancer #SingleCell 🧪🧬🖥️
https://www.researchsquare.com/article/rs-9428731/latest
https://www.researchsquare.com/article/rs-9428731/latest
May 30, 2026 at 7:00 AM
Deep Learning Pathomics Integrated with Single-Cell Transcriptomics Reveals MGAT4A-Mediated "Lock-and-Load" Dysfunction of CD8+ Tissue-Resident Memory T Cells in Endometrial Cancer #SingleCell 🧪🧬🖥️
https://www.researchsquare.com/article/rs-9428731/latest
https://www.researchsquare.com/article/rs-9428731/latest
Integrated Multi-Omics and Pathomics Analysis for Prognostic Modeling of Hepatocellular Carcinoma Reveals Oxidative Stress-Driven Immunometabolic Regulatory Mechanisms #SingleCell 🧪🧬🖥️
https://www.researchsquare.com/article/rs-7489138/latest
https://www.researchsquare.com/article/rs-7489138/latest
November 3, 2025 at 8:01 AM
Integrated Multi-Omics and Pathomics Analysis for Prognostic Modeling of Hepatocellular Carcinoma Reveals Oxidative Stress-Driven Immunometabolic Regulatory Mechanisms #SingleCell 🧪🧬🖥️
https://www.researchsquare.com/article/rs-7489138/latest
https://www.researchsquare.com/article/rs-7489138/latest
CellSymphony: Deciphering the molecular and phenotypic orchestration of cells with single-cell pathomics #SingleCell 🧪🧬🖥️
https://arxiv.org/abs/2508.10232
https://arxiv.org/abs/2508.10232
August 15, 2025 at 7:01 AM
CellSymphony: Deciphering the molecular and phenotypic orchestration of cells with single-cell pathomics #SingleCell 🧪🧬🖥️
https://arxiv.org/abs/2508.10232
https://arxiv.org/abs/2508.10232
Mark S. Keller, et al.: Interactive visualization of kidney micro-compartmental segmentations and associated pathomics on whole slide images https://arxiv.org/abs/2510.19499 https://arxiv.org/pdf/2510.19499 https://arxiv.org/html/2510.19499
October 23, 2025 at 6:50 AM
Mark S. Keller, et al.: Interactive visualization of kidney micro-compartmental segmentations and associated pathomics on whole slide images https://arxiv.org/abs/2510.19499 https://arxiv.org/pdf/2510.19499 https://arxiv.org/html/2510.19499
A causal machine learning framework predicts individualized treatment effects of corticosteroids for IgA nephropathy patients by integrating clinical variables, histopathological scores, and pathomics from digitized kidney biopsies.
🔗 Source: https://doi.org/10.1101/2025.10.08.25337548
🔗 Source: https://doi.org/10.1101/2025.10.08.25337548
July 31, 2026 at 5:50 AM
A causal machine learning framework predicts individualized treatment effects of corticosteroids for IgA nephropathy patients by integrating clinical variables, histopathological scores, and pathomics from digitized kidney biopsies.
🔗 Source: https://doi.org/10.1101/2025.10.08.25337548
🔗 Source: https://doi.org/10.1101/2025.10.08.25337548