Article in JHLT open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
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.
2 · The registry
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.
Eliot G PeysterCardiovascular Research Institute, University of Pennsylvania, Philadelphia, Pennsylvania.
Cai YuanDepartment of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia.
Sara ArabyarmohammadiDepartment of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia.
Priti LalDepartment of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Michael D FeldmanDepartment of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana.
Pingfu FuDepartment of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia.
Kenneth B MarguliesCardiovascular Research Institute, University of Pennsylvania, Philadelphia, Pennsylvania.
Anant MadabhushiDepartment of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia.
Funding
Pathology CoreU54CA254566 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI MADABHUSHI, ANANT · 2020 to 2024
$5.0M
Oral Cavity Quantitative Histomorphometric Risk Classifier (OHbIC) in Oral Cavity Squamous Cell Carcinoma (OC-SCC)R01CA249992 · NCI · EMORY UNIVERSITY · PI LEWIS, JAMES, MADABHUSHI, ANANT · 2021 to 2025
$3.2M
Computerized histologic image predictor of cancer outcomeR01CA202752 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI FELDMAN, MICHAEL D, GANESAN, SHRIDAR · 2016 to 2020
$3.1M
Quantitative Histomorphometric Risk Classifier (QuHbIC) in HPV + Oropharyngeal CarcinomaR01CA220581 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI KOYFMAN, SHLOMO, LEWIS, JAMES · 2018 to 2023
$3.1M
Computerized Histologic Risk Predictor (CHiRP) for Early Stage Lung CancersR01CA216579 · NCI · EMORY UNIVERSITY · PI FU, PINGFU, LLOYD, MARK · 2018 to 2023
$3.1M
Prognostic and Predictive Digital Tissue Image Assay for Prostate CancerR01CA268287 · NCI · EMORY UNIVERSITY · PI GUPTA, SHILPA, LAL, PRITI · 2022 to 2025
$3.0M
MR Fingerprinting and Computerized Decision Support for Prostate CancerR01CA208236 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GULANI, VIKAS, PONSKY, LEE EVAN · 2017 to 2022
$3.0M
Novel Radiomics for Predicting Response to Immunotherapy for Lung CancerR01CA257612 · NCI · EMORY UNIVERSITY · PI Anant Madabhushi, Vamsidhar Velcheti · 2021 to 2026
$2.7M
An AI-enabled Digital Pathology Platform for Multi-Cancer Diagnosis, Prognosis and Prediction of Therapeutic BenefitU01CA269181 · NCI · EMORY UNIVERSITY · PI Anant Madabhushi, tanuja shet · 2022 to 2026
$2.5M
RadxTools for assessing tumor treatment response on imagingU01CA248226 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI TIWARI, PALLAVI, VISWANATH, SATISH EASWAR · 2020 to 2022
$1.4M
HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identificationU01CA239055 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI JANOWCZYK, ANDREW ROBERT, MADABHUSHI, ANANT · 2019 to 2022
$1.0M
Personalized Rejection Risk Assessment in Cardiac TransplantationK08HL159344 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI PEYSTER, ELIOT · 2021 to 2024
Background: The hostile immune environment created by allotransplantation can accelerate pathologic tissue remodeling. Both overt and indolent inflammatory insults propel this remodeling, but there is a paucity of tools for monitoring the speed and severity of remodeling over time. Methods: This retrospective cohort consisted of Results: The top 5 features of pathologic stromal remodeling most associated with adverse allograft outcomes were also strongly associated with histories of both overt and indolent inflammatory events. Compared to controls, a history of high-grade or treated rejection was significantly associated with progressive pathologic remodeling and future adverse outcomes (32.9% vs 5.1%, Conclusions: A history of both traditionally treated and traditionally ignored alloimmune responses can predispose patients to pathologic allograft remodeling and adverse outcomes. Computational pathology analysis of allograft stroma yields translationally relevant biomarkers, identifying accelerated remodeling before adverse outcomes occur. Data Availability: The data that support the findings of this study are presented in the manuscript and extended data sections. Unprocessed raw data are available from the corresponding author upon reasonable request. Source code for the stromal feature analysis pipeline is hosted on GitHub and freely available: https://github.service.emory.edu/CYUAN31/Pathomics_StromalBioMarker_in_Myocardium.git.
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 Socratic graph.
Computational pathology assessments of cardiac stromal remodeling: Clinical correlates and prognostic implications in heart transplantation. · full record | Socratic