Evidence map›Paper›PMID 35944502›Full record

ArticleCancer cell2022

Pan-cancer integrative histology-genomic analysis via multimodal deep learning.

Richard J Chen, Ming Y Lu, Drew F K Williamson, Tiffany Y Chen, Jana Lipkova, Zahra Noor, Muhammad Shaban, Maha Shady, Mane Williams, Bumjin Joo and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer cell, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 309 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
309citing papers in PubMed, 4 pooled it
–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

309 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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249 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Richard J ChenDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Mass General Hospital, Harvard Medical School, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cancer Data Science Program, Dana-Farber/Harvard Cancer Institute, Boston, MA, USA.
Ming Y LuDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Mass General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cancer Data Science Program, Dana-Farber/Harvard Cancer Institute, Boston, MA, USA; Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA.
Drew F K WilliamsonDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Mass General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cancer Data Science Program, Dana-Farber/Harvard Cancer Institute, Boston, MA, USA.
Tiffany Y ChenDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cancer Data Science Program, Dana-Farber/Harvard Cancer Institute, Boston, MA, USA.
Jana LipkovaDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cancer Data Science Program, Dana-Farber/Harvard Cancer Institute, Boston, MA, USA.
Zahra NoorDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Muhammad ShabanDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Mass General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cancer Data Science Program, Dana-Farber/Harvard Cancer Institute, Boston, MA, USA.
Maha ShadyDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cancer Data Science Program, Dana-Farber/Harvard Cancer Institute, Boston, MA, USA.
Mane WilliamsDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Mass General Hospital, Harvard Medical School, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cancer Data Science Program, Dana-Farber/Harvard Cancer Institute, Boston, MA, USA.
Bumjin JooDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Faisal MahmoodDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Mass General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cancer Data Science Program, Dana-Farber/Harvard Cancer Institute, Boston, MA, USA; Harvard Data Sciences Initiative, Harvard University, Cambridge, MA, USA. Electronic address: faisalmahmood@bwh.harvard.edu.

Funding

RESEARCH TRAINING-MEDICAL INFORMATICS 90T15LM007092 · NLM · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Nils Gehlenborg · 1992 to 2026
$32.8M
Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
Multimodal and Generative AI for PathologyR35GM138216 · NIGMS · BROAD INSTITUTE, INC. · PI Faisal Mahmood · 2020 to 2026
$3.4M
Oncopathology Training ProgramT32CA251062 · NCI · DANA-FARBER CANCER INST · PI JON C. ASTER, KATHLEEN H BURNS · 2020 to 2026
$2.4M
NCI NIH HHS T32 CA251062NHGRI NIH HHS T32 HG002295NIGMS NIH HHS R35 GM138216NLM NIH HHS T15 LM007092
6 · The paper itself

Abstract

The rapidly emerging field of computational pathology has demonstrated promise in developing objective prognostic models from histology images. However, most prognostic models are either based on histology or genomics alone and do not address how these data sources can be integrated to develop joint image-omic prognostic models. Additionally, identifying explainable morphological and molecular descriptors from these models that govern such prognosis is of interest. We use multimodal deep learning to jointly examine pathology whole-slide images and molecular profile data from 14 cancer types. Our weakly supervised, multimodal deep-learning algorithm is able to fuse these heterogeneous modalities to predict outcomes and discover prognostic features that correlate with poor and favorable outcomes. We present all analyses for morphological and molecular correlates of patient prognosis across the 14 cancer types at both a disease and a patient level in an interactive open-access database to allow for further exploration, biomarker discovery, and feature assessment.

Indexed as

Deep LearningNeoplasmsAlgorithmsGenomicsHumansPrognosisartificial intelligencebiomarker discoverycancer prognosiscomputational pathologydata fusiondeep learningdigital pathologymultimodal integrationmultimodal prognostic modelspan-cancer

Identifiers

PMID35944502
PMCPMC10397370

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Registered trials

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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.