Evidence map›Paper›PMID 36170366›Full record

ArticleScience advances2022

Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker-driven learning framework.

Sabrina H Rossi, Izzy Newsham, Sara Pita, Kevin Brennan, Gahee Park, Christopher G Smith, Radoslaw P Lach, Thomas Mitchell, Junfan Huang, Anne Babbage and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. DNA Methylation-Based Classification of Kidney Neoplasms.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025
    Article
  3. Article
  4. Article
  5. Article
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

16 authors.

Sabrina H RossiDepartment of Oncology, University of Cambridge, Hutchison-MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK.ORCID 0000-0001-7048-7158
Izzy NewshamMRC Cancer Unit, University of Cambridge, Hutchison-MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK.ORCID 0000-0003-0816-4045
Sara PitaDepartment of Oncology, University of Cambridge, Hutchison-MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK.ORCID 0000-0002-5163-7306
Kevin BrennanStanford Centre for Biomedical Informatics Research, Department of Medicine and Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Gahee ParkDepartment of Oncology, University of Cambridge, Hutchison-MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK.ORCID 0000-0002-9599-687X
Christopher G SmithCancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.ORCID 0000-0001-7357-2737
Radoslaw P LachDepartment of Oncology, University of Cambridge, Hutchison-MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK.
Thomas MitchellDepartment of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge Biomedical Campus, Cambridge, UK.ORCID 0000-0003-0761-9503
Junfan HuangMRC Cancer Unit, University of Cambridge, Hutchison-MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK.ORCID 0000-0002-3282-1722
Anne BabbageDepartment of Oncology, University of Cambridge, Hutchison-MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK.
Anne Y WarrenDepartment of Histopathology, University of Cambridge, Addenbrooke's Hospital, Cambridge Biomedical Campus, Cambridge, UK.ORCID 0000-0002-1170-7867
John T LeppertDepartment of Urology, Stanford University School of Medicine, Stanford University, Stanford, CA, USA.ORCID 0000-0001-9980-3863
Grant D StewartDepartment of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge Biomedical Campus, Cambridge, UK.ORCID 0000-0003-3188-9140
Olivier GevaertStanford Centre for Biomedical Informatics Research, Department of Medicine and Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.ORCID 0000-0002-9965-5466
Charles E MassieDepartment of Oncology, University of Cambridge, Hutchison-MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK.ORCID 0000-0003-2314-4843
Shamith A SamarajiwaMRC Cancer Unit, University of Cambridge, Hutchison-MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK.ORCID 0000-0003-1046-0601

Funding

Cancer Research UK 27176Medical Research Council MC_UU_12022/10Wellcome Trust
6 · The paper itself

Abstract

Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney. The prediction accuracy in the testing set was 0.960, with class-wise ROC AUCs >0.988 for all classes. External validation was performed on >500 samples from four independent datasets, achieving AUCs >0.89 for all classes and average accuracies of 0.824, 0.703, 0.875, and 0.894 for the four datasets. Furthermore, consistent classification of multiregion samples (

Identifiers

PMID36170366
PMCPMC9519038

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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.