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Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors
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 f...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007877/ https://www.ncbi.nlm.nih.gov/pubmed/35007352 http://dx.doi.org/10.1002/path.5864 |
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author | Kumar, Neeraj Verma, Ruchika Chen, Chuheng Lu, Cheng Fu, Pingfu Willis, Joseph Madabhushi, Anant |
author_facet | Kumar, Neeraj Verma, Ruchika Chen, Chuheng Lu, Cheng Fu, Pingfu Willis, Joseph Madabhushi, Anant |
author_sort | Kumar, Neeraj |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9007877 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley & Sons, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-90078772022-10-14 Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors Kumar, Neeraj Verma, Ruchika Chen, Chuheng Lu, Cheng Fu, Pingfu Willis, Joseph Madabhushi, Anant J Pathol Original Articles 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. John Wiley & Sons, Ltd 2022-02-22 2022-05 /pmc/articles/PMC9007877/ /pubmed/35007352 http://dx.doi.org/10.1002/path.5864 Text en © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Articles Kumar, Neeraj Verma, Ruchika Chen, Chuheng Lu, Cheng Fu, Pingfu Willis, Joseph Madabhushi, Anant Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors |
title | Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors |
title_full | Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors |
title_fullStr | Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors |
title_full_unstemmed | Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors |
title_short | Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors |
title_sort | computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage ii and iv colon tumors |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007877/ https://www.ncbi.nlm.nih.gov/pubmed/35007352 http://dx.doi.org/10.1002/path.5864 |
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