Evidence map›Paper›PMID 37301916›Full record

ArticleNPJ precision oncology2023

Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data.

Melissa Zhao, Mai Chan Lau, Koichiro Haruki, Juha P Väyrynen, Carino Gurjao, Sara A Väyrynen, Andressa Dias Costa, Jennifer Borowsky, Kenji Fujiyoshi, Kota Arima and 15 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ precision oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Density of T-cell Subsets in Colorectal Cancer in Relation to Disease-Specific Survival.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025
    Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Non-Contrasted CT Radiomics for SAH Prognosis Prediction.Bioengineering (Basel, Switzerland) · 2023
    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

25 authors.

Melissa ZhaoProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. mzhao11@bwh.harvard.edu.ORCID http://orcid.org/0000-0002-5190-3635
Mai Chan LauProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Koichiro HarukiProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Juha P VäyrynenProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-8683-2996
Carino GurjaoProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Sara A VäyrynenProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Andressa Dias CostaDepartment of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0003-1046-4899
Jennifer BorowskyProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Kenji FujiyoshiProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Kota ArimaProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Tsuyoshi HamadaProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Jochen K LennerzDepartment of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0003-2434-4978
Charles S FuchsGenentech/Roche, South San Francisco, CA, USA.
Reiko NishiharaProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Andrew T ChanClinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Kimmie NgDepartment of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0003-0631-1494
Xuehong ZhangChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-8260-8508
Jeffrey A MeyerhardtDepartment of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
Mingyang SongDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Molin WangDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Marios GiannakisDepartment of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0001-9012-6982
Jonathan A Nowak *Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Kun-Hsing Yu *Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0001-9892-8218
Tomotaka Ugai *Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Shuji Ogino *Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. sogino@bwh.harvard.edu.ORCID http://orcid.org/0000-0002-3909-2323

Funding

Statistical MethodsP01CA087969 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIASSEN, A. HEATHER, TAMIMI, RULLA M · 2000 to 2019
$77.8M
Validity of Diet and Activity Measures in WomenP01CA055075 · NCI · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI RIMM, ERIC B · 1991 to 2009
$41.8M
Long Term Multidisciplinary Study of Cancer in Women: The Nurses Health StudyUM1CA186107 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIASSEN, A. HEATHER, STAMPFER, MEIR · 2014 to 2023
$22.3M
Cancer Epidemiology Cohort in Male Health ProfessionalsU01CA167552 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Lorelei Mucci, Walter C. Willett · 2017 to 2026
$17.0M
Cancer Epidemiology Cohort in Male Health ProfessionalsUM1CA167552 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI WILLETT, WALTER C. · 2012 to 2016
$11.5M
Spatial Immunopathological Epidemiology of Colorectal Adenoma-Carcinoma SpectrumR01CA248857 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Jonathan Andrew Nowak, Shuji Ogino · 2020 to 2026
$6.6M
Inflammation and Colorectal NeoplasiaR01CA137178 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI CHAN, ANDREW T · 2009 to 2019
$6.5M
Accelerating Transdisciplinary Epidemiology of Colorectal CancerR35CA197735 · NCI · DANA-FARBER CANCER INST · PI OGINO, SHUJI · 2015 to 2021
$6.0M
Novel randomized controlled trials of vitamin D supplementation in patients with colorectal cancer: Impact on survival and biologyR01CA205406 · NCI · DANA-FARBER CANCER INST · PI Kimmie Ng · 2017 to 2026
$4.9M
Illuminating the evolutionary history of colorectal cancer metastasis: basic principles and clinical applicationsR37CA225655 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI NAXEROVA, KAMILA · 2018 to 2024
$3.0M
Epigenetic Events and Colorectal Cancer EpidemiologyR01CA151993 · NCI · DANA-FARBER CANCER INST · PI OGINO, SHUJI · 2010 to 2014
$2.6M
Robust, Generalizable, and Fair Machine Learning Models for BiomedicineR35GM142879 · NIGMS · HARVARD MEDICAL SCHOOL · PI YU, KUN-HSING · 2021 to 2025
$2.4M
NCI NIH HHS K07 CA188126NCI NIH HHS P01 CA055075NCI NIH HHS P01 CA087969NCI NIH HHS R01 CA137178NCI NIH HHS R01 CA151993NCI NIH HHS R01 CA205406NCI NIH HHS R01 CA248857NCI NIH HHS R21 CA252962NCI NIH HHS R35 CA197735NCI NIH HHS R37 CA225655NCI NIH HHS U01 CA167552NCI NIH HHS UM1 CA167552NCI NIH HHS UM1 CA186107NHLBI NIH HHS R01 HL174679NIDDK NIH HHS K24 DK098311NIGMS NIH HHS R35 GM142879
6 · The paper itself

Abstract

Routine tumor-node-metastasis (TNM) staging of colorectal cancer is imperfect in predicting survival due to tumor pathobiological heterogeneity and imprecise assessment of tumor spread. We leveraged Bayesian additive regression trees (BART), a statistical learning technique, to comprehensively analyze patient-specific tumor characteristics for the improvement of prognostic prediction. Of 75 clinicopathologic, immune, microbial, and genomic variables in 815 stage II-III patients within two U.S.-wide prospective cohort studies, the BART risk model identified seven stable survival predictors. Risk stratifications (low risk, intermediate risk, and high risk) based on model-predicted survival were statistically significant (hazard ratios 0.19-0.45, vs. higher risk; P < 0.0001) and could be externally validated using The Cancer Genome Atlas (TCGA) data (P = 0.0004). BART demonstrated model flexibility, interpretability, and comparable or superior performance to other machine-learning models. Integrated bioinformatic analyses using BART with tumor-specific factors can robustly stratify colorectal cancer patients into prognostic groups and be readily applied to clinical oncology practice.

Identifiers

PMID37301916
PMCPMC10257677

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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.