A Novel Superpixel Approach to the Tumoral Microenvironment in Colorectal Cancer
Colorectal cancer (CRC) is the most common malignancy of the gastrointestinal tract. The stroma and the tumoral microenvironment (TME) represent ecosystem-like biological networks and are new frontiers in CRC. The present study demonstrates the use of a novel machine learning-based superpixel approa...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2022-01-01
|
Series: | Journal of Pathology Informatics |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353922000098 |
_version_ | 1797977021851107328 |
---|---|
author | Sean M. Hacking Dongling Wu Claudine Alexis Mansoor Nasim |
author_facet | Sean M. Hacking Dongling Wu Claudine Alexis Mansoor Nasim |
author_sort | Sean M. Hacking |
collection | DOAJ |
description | Colorectal cancer (CRC) is the most common malignancy of the gastrointestinal tract. The stroma and the tumoral microenvironment (TME) represent ecosystem-like biological networks and are new frontiers in CRC. The present study demonstrates the use of a novel machine learning-based superpixel approach for whole slide images to unravel this biology. Findings of significance include the association of low proportionated stromal area, high immature stromal percentage, and high myxoid stromal ratio (MSR) with worse prognostic outcomes in CRC. Overall, stromal computational markers outperformed all others at predicting clinical outcomes. MSR may be able to prognosticate patients independent of pathological stage, representing an optimal way to effectively prognosticate CRC patients which circumvents the need for more extensive molecular and/or computational profiling. The superpixel approaches to the TME demonstrated here can be performed by a trained pathologist and recorded during synoptic cancer reporting with appropriate quality assurance. Future clinical trials will have the ultimate say in determining whether we can better tailor the need for adjuvant therapy in patients with CRC. |
first_indexed | 2024-04-11T05:00:20Z |
format | Article |
id | doaj.art-e549277c3b244aa184cae17362bcaa01 |
institution | Directory Open Access Journal |
issn | 2153-3539 |
language | English |
last_indexed | 2024-04-11T05:00:20Z |
publishDate | 2022-01-01 |
publisher | Elsevier |
record_format | Article |
series | Journal of Pathology Informatics |
spelling | doaj.art-e549277c3b244aa184cae17362bcaa012022-12-26T04:07:58ZengElsevierJournal of Pathology Informatics2153-35392022-01-0113100009A Novel Superpixel Approach to the Tumoral Microenvironment in Colorectal CancerSean M. Hacking0Dongling Wu1Claudine Alexis2Mansoor Nasim3Department of Pathology and Laboratory Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 2200 Northern Blvd, Suite 104, Greenvale, NY 11548, USA; Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Warren Alpert Medical School of Brown University, 593 Eddy St, APC 12, Providence, RI 02903, USA; Corresponding author.Department of Pathology and Laboratory Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 2200 Northern Blvd, Suite 104, Greenvale, NY 11548, USADepartment of Pathology and Laboratory Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 2200 Northern Blvd, Suite 104, Greenvale, NY 11548, USADepartment of Pathology and Laboratory Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 2200 Northern Blvd, Suite 104, Greenvale, NY 11548, USA; Department of Pathology, Renaissance School of Medicine, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USAColorectal cancer (CRC) is the most common malignancy of the gastrointestinal tract. The stroma and the tumoral microenvironment (TME) represent ecosystem-like biological networks and are new frontiers in CRC. The present study demonstrates the use of a novel machine learning-based superpixel approach for whole slide images to unravel this biology. Findings of significance include the association of low proportionated stromal area, high immature stromal percentage, and high myxoid stromal ratio (MSR) with worse prognostic outcomes in CRC. Overall, stromal computational markers outperformed all others at predicting clinical outcomes. MSR may be able to prognosticate patients independent of pathological stage, representing an optimal way to effectively prognosticate CRC patients which circumvents the need for more extensive molecular and/or computational profiling. The superpixel approaches to the TME demonstrated here can be performed by a trained pathologist and recorded during synoptic cancer reporting with appropriate quality assurance. Future clinical trials will have the ultimate say in determining whether we can better tailor the need for adjuvant therapy in patients with CRC.http://www.sciencedirect.com/science/article/pii/S2153353922000098Superpixel SegmentationTumoral MicroenvironmentColorectal CancerFrontiers Beyond the MicroscopeStromal Differentiation |
spellingShingle | Sean M. Hacking Dongling Wu Claudine Alexis Mansoor Nasim A Novel Superpixel Approach to the Tumoral Microenvironment in Colorectal Cancer Journal of Pathology Informatics Superpixel Segmentation Tumoral Microenvironment Colorectal Cancer Frontiers Beyond the Microscope Stromal Differentiation |
title | A Novel Superpixel Approach to the Tumoral Microenvironment in Colorectal Cancer |
title_full | A Novel Superpixel Approach to the Tumoral Microenvironment in Colorectal Cancer |
title_fullStr | A Novel Superpixel Approach to the Tumoral Microenvironment in Colorectal Cancer |
title_full_unstemmed | A Novel Superpixel Approach to the Tumoral Microenvironment in Colorectal Cancer |
title_short | A Novel Superpixel Approach to the Tumoral Microenvironment in Colorectal Cancer |
title_sort | novel superpixel approach to the tumoral microenvironment in colorectal cancer |
topic | Superpixel Segmentation Tumoral Microenvironment Colorectal Cancer Frontiers Beyond the Microscope Stromal Differentiation |
url | http://www.sciencedirect.com/science/article/pii/S2153353922000098 |
work_keys_str_mv | AT seanmhacking anovelsuperpixelapproachtothetumoralmicroenvironmentincolorectalcancer AT donglingwu anovelsuperpixelapproachtothetumoralmicroenvironmentincolorectalcancer AT claudinealexis anovelsuperpixelapproachtothetumoralmicroenvironmentincolorectalcancer AT mansoornasim anovelsuperpixelapproachtothetumoralmicroenvironmentincolorectalcancer AT seanmhacking novelsuperpixelapproachtothetumoralmicroenvironmentincolorectalcancer AT donglingwu novelsuperpixelapproachtothetumoralmicroenvironmentincolorectalcancer AT claudinealexis novelsuperpixelapproachtothetumoralmicroenvironmentincolorectalcancer AT mansoornasim novelsuperpixelapproachtothetumoralmicroenvironmentincolorectalcancer |