Evidence map›Paper›PMID 41728332›Full record

ArticlemedRxiv : the preprint server for health sciences2026

UCSF RMaC: University of California San Francisco 3D Multi-Phase Renal Mass CT Dataset with Tumor Segmentations.

Sule Sahin, Ernesto Diaz, Abhejit Rajagopal, Mansour Abtahi, Samantha Jones, Qing Dai, Sage Kramer, Zhen Jane Wang, Peder Ez Larson

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Sule SahinUC Berkeley - UCSF Graduate Program in Bioengineering, 1700 4 St, San Francisco, CA, USA 94158.
Ernesto DiazRadiology and Biomedical Imaging, University of California, San Francisco, 1700 4 St, San Francisco, CA, USA 94158.
Abhejit RajagopalRadiology and Biomedical Imaging, University of California, San Francisco, 1700 4 St, San Francisco, CA, USA 94158.ORCID 0000-0003-0402-4643
Mansour AbtahiRadiology and Biomedical Imaging, University of California, San Francisco, 1700 4 St, San Francisco, CA, USA 94158.ORCID 0000-0001-7463-9470
Samantha JonesRadiology and Biomedical Imaging, University of California, San Francisco, 1700 4 St, San Francisco, CA, USA 94158.
Qing DaiRadiology and Biomedical Imaging, University of California, San Francisco, 1700 4 St, San Francisco, CA, USA 94158.
Sage KramerRadiology and Biomedical Imaging, University of California, San Francisco, 1700 4 St, San Francisco, CA, USA 94158.
Zhen Jane WangRadiology and Biomedical Imaging, University of California, San Francisco, 1700 4 St, San Francisco, CA, USA 94158.
Peder Ez LarsonUC Berkeley - UCSF Graduate Program in Bioengineering, 1700 4 St, San Francisco, CA, USA 94158.ORCID 0000-0003-4183-3634

Funding

Translating Hyperpolarized 13C Metabolic MRI to Predict Renal Tumor AggressivenessR01CA249909 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LARSON, PEDER ERIC ZUFALL, WANG, ZHEN JANE · 2021 to 2025
$3.2M
NCI NIH HHS R01 CA249909
6 · The paper itself

Abstract

Current standard of care imaging practices cannot reliably differentiate among certain renal tumors such as benign oncocytoma and clear cell renal cell carcinoma (RCC), and between low and high grade RCCs. Previous work has explored using deep learning, radiomics, and texture analysis to predict renal tumor subtypes and differentiate between low and high grade RCCs with mixed success. To further this work, large diverse datasets are needed to improve model performance and provide strong evaluation sets. In this work, a dataset of 831 multi-phase 3D CT exams was curated. Each exam contains up to three contrast-enhanced CT phases. Tumor outlines or bounding boxes were annotated and registered to the image volumes. The pathology results for each tumor and relevant patient metadata are also included.

Identifiers

PMID41728332
PMCPMC12919120

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.