ArticleJournal of pathology informatics2022
Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer.
Article in Journal of pathology informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 32 citations in OpenAlex.
- STGBench: sequencing-level spatial DNA-RNA simulation for multimodal and virtual cell-oriented benchmarking of genomic alterations.Briefings in bioinformatics · 2026Article
- Multimodal AI in high-grade serous ovarian cancer: integrated prediction and clinical decision-making.Frontiers in oncology · 2026Review
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- T cell subsets of urine-derived lymphocytes (UDLs) serve as an indicator of TILs and reflect immunological sex differences in bladder cancer.Journal for immunotherapy of cancer · 2025Article
- Deep learning for endometrial cancer subtyping and predicting tumor mutational burden from histopathological slides.NPJ precision oncology · 2024Article
- A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.Nature cancer · 2024Article
- Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond.Molecular cancer · 2024Review
- A guide to artificial intelligence for cancer researchers.Nature reviews. Cancer · 2024Review
- Cancer drug sensitivity prediction from routine histology images.NPJ precision oncology · 2024Article
- Immunotherapy and radiotherapy for older patients with invasive bladder cancer unfit for surgery or chemotherapy: practical proposal by the international geriatric radiotherapy group.Frontiers in oncology · 2024Article
- Focalizing regions of biomarker relevance facilitates biomarker prediction on histopathological images.iScience · 2023Article
- Privacy risks of whole-slide image sharing in digital pathology.Nature communications · 2023Article
- Which data subset should be augmented for deep learning? a simulation study using urothelial cell carcinoma histopathology images.BMC bioinformatics · 2023Article
- Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning.BME frontiers · 2022Article
- Computational pathology in bladder cancer: A scoping review.Bladder cancer (Amsterdam, Netherlands)Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
Abstract
Background: High tumor mutation burden (TMB-H) could result in an increased number of neoepitopes from somatic mutations expressed by a patient's own tumor cell which can be recognized and targeted by neighboring tumor-infiltrating lymphocytes (TILs). Deeper understanding of spatial heterogeneity and organization of tumor cells and their neighboring immune infiltrates within tumors could provide new insights into tumor progression and treatment response. Methods: Here we first developed computational approaches using whole slide images (WSIs) to predict bladder cancer patients' TMB status and TILs across tumor regions, and then investigate spatial heterogeneity and organization of regions harboring TMB-H tumor cells and TILs within tumors, as well as their prognostic utility. Results: In experiments using WSIs from The Cancer Genome Atlas (TCGA) bladder cancer (BLCA), our findings show that computational pathology can reliably predict patient-level TMB status and delineate spatial TMB heterogeneity and co-organization with TILs. TMB-H patients with low spatial heterogeneity enriched with high TILs show improved overall survival. Conclusions: Computational approaches using WSIs have the potential to provide rapid and cost-effective TMB testing and TILs detection. Survival analysis illuminates potential clinical utility of spatial heterogeneity and co-organization of TMB and TILs as a prognostic biomarker in BLCA which warrants further validation in future studies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.