Evidence map›Paper›PMID 35007352›Full record

ArticleThe Journal of pathology2022

Computer-extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors.

Neeraj Kumar, Ruchika Verma, Chuheng Chen, Cheng Lu, Pingfu Fu, Joseph Willis, Anant Madabhushi

Abstract read
In one paragraph

Article in The Journal of pathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Review
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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

7 authors.

Neeraj KumarDepartment of Computing Science, University of Alberta and Alberta Machine Intelligence Institute, Edmonton, Canada.ORCID 0000-0002-3221-3831
Ruchika VermaDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
Chuheng ChenDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
Cheng LuDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
Pingfu FuDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
Joseph WillisDepartment of Pathology, Case Western Reserve University, Cleveland, OH, USA.
Anant MadabhushiDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.

Funding

Clinical and Translational Science Collaborative of ClevelandUL1TR002548 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI MCCOMSEY, GRACE A · 2018 to 2022
$35.5M
Clinical and Translational Science Collaborative of Northern Ohio, Catalyzing Linkages to Equity in Health (CLE Health)UM1TR004528 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI GRACE A MCCOMSEY · 2023 to 2026
$32.1M
Targeting 15-PGDH in Colon Cancer Prognosis, Prediction, Treatment and PreventionP50CA150964 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI MARKOWITZ, SANFORD D. · 2011 to 2022
$24.6M
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
Mechanisms of metabolic reprogramming by PIK3CA oncogenic mutationsR01CA196643 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI Zhenghe Wang · 2016 to 2026
$3.1M
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
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
Pathology Image Informatics Platform for visualization, analysis and managementU24CA199374 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI GURCAN, METIN NAFI, MADABHUSHI, ANANT · 2015 to 2019
$2.9M
Novel Radiomics for Predicting Response to Immunotherapy for Lung CancerR01CA257612 · NCI · EMORY UNIVERSITY · PI Anant Madabhushi, Vamsidhar Velcheti · 2021 to 2026
$2.7M
BLRD VA I01 BX004121BLRD VA IK6 BX006185NCATS NIH HHS UL1 TR002548NCATS NIH HHS UM1 TR004528NCI NIH HHS P50 CA150964NCI NIH HHS R01 CA196643NCI NIH HHS R01 CA202752NCI NIH HHS R01 CA208236NCI NIH HHS R01 CA216579NCI NIH HHS R01 CA220581NCI NIH HHS R01 CA249992NCI NIH HHS R01 CA257612NCI NIH HHS U01 CA239055NCI NIH HHS U01 CA248226NCI NIH HHS U24 CA199374NCI NIH HHS U54 CA254566NCRR NIH HHS C06 RR012463NIBIB NIH HHS R43 EB028736
6 · The paper itself

Abstract

We assessed the utility of quantitative features of colon cancer nuclei, extracted from digitized hematoxylin and eosin-stained whole slide images (WSIs), to distinguish between stage II and stage IV colon cancers. Our discovery cohort comprised 100 stage II and stage IV colon cancer cases sourced from the University Hospitals Cleveland Medical Center (UHCMC). We performed initial (independent) model validation on 51 (143) stage II and 79 (54) stage IV colon cancer cases from UHCMC (The Cancer Genome Atlas's Colon Adenocarcinoma, TCGA-COAD, cohort). Our approach comprised the following steps: (1) a fully convolutional deep neural network with VGG-18 architecture was trained to locate cancer on WSIs; (2) another deep-learning model based on Mask-RCNN with Resnet-50 architecture was used to segment all nuclei from within the identified cancer region; (3) a total of 26 641 quantitative morphometric features pertaining to nuclear shape, size, and texture were extracted from within and outside tumor nuclei; (4) a random forest classifier was trained to distinguish between stage II and stage IV colon cancers using the five most discriminatory features selected by the Wilcoxon rank-sum test. Our trained classifier using these top five features yielded an AUC of 0.81 and 0.78, respectively, on the held-out cases in the UHCMC and TCGA validation sets. For 197 TCGA-COAD cases, the Cox proportional hazards model yielded a hazard ratio of 2.20 (95% CI 1.24-3.88) with a concordance index of 0.71, using only the top five features for risk stratification of overall survival. The Kaplan-Meier estimate also showed statistically significant separation between the low-risk and high-risk patients, with a log-rank P value of 0.0097. Finally, unsupervised clustering of the top five features revealed that stage IV colon cancers with peritoneal spread were morphologically more similar to stage II colon cancers with no long-term metastases than to stage IV colon cancers with hematogenous spread. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Indexed as

Colonic NeoplasmsPulmonary Disease, Chronic ObstructiveComputersEosine Yellowish-(YS)HematoxylinHumansEosine Yellowish-(YS)Hematoxylincolon cancercomputational pathologyhematogenous spreadperitoneal spreadquantitative histomorphometric image analysis

Identifiers

PMID35007352
PMCPMC9007877

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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.