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Quantitative Pathologic Analysis of Digitized Images of Colorectal Carcinoma Improves Prediction of Recurrence-Free Survival.
Pai, Reetesh K; Banerjee, Imon; Shivji, Sameer; Jain, Suchit; Hartman, Douglas; Buchanan, Daniel D; Jenkins, Mark A; Schaeffer, David F; Rosty, Christophe; Como, Julia; Phipps, Amanda I; Newcomb, Polly A; Burnett-Hartman, Andrea N; Le Marchand, Loic; Samadder, Niloy J; Patel, Bhavik; Swallow, Carol; Lindor, Noralane M; Gallinger, Steven J; Grant, Robert C; Westerling-Bui, Thomas; Conner, James; Cyr, David P; Kirsch, Richard; Pai, Rish K.
Afiliação
  • Pai RK; Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Banerjee I; Department of Radiology and Machine Intelligence in Medicine and Imaging Center (MI-2), Mayo Clinic Arizona, Phoenix, Arizona.
  • Shivji S; Department of Pathology, Mount Sinai Hospital, Toronto, Ontario, Canada.
  • Jain S; Department of Radiology and Machine Intelligence in Medicine and Imaging Center (MI-2), Mayo Clinic Arizona, Phoenix, Arizona.
  • Hartman D; Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Buchanan DD; Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia; University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia; Genomic Medicine and Family Cancer Clinic, Royal Me
  • Jenkins MA; University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
  • Schaeffer DF; Department of Pathology and Laboratory Medicine, Vancouver General Hospital, Vancouver, British Columbia, Canada.
  • Rosty C; Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia; Envoi Specialist Pathologists, Brisbane, QLD, Australia; Faculty of Medicine, The University of QLD, Brisbane, QLD, Australia.
  • Como J; Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia; University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia.
  • Phipps AI; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington; Department of Epidemiology, University of Washington, Seattle, Washington.
  • Newcomb PA; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington; Department of Epidemiology, University of Washington, Seattle, Washington.
  • Burnett-Hartman AN; Department of Epidemiology, University of Washington, Seattle, Washington; Institute for Health Research, Kaiser Permanente Colorado, Denver, Colorado.
  • Le Marchand L; Department of Epidemiology, University of Hawaii, Honolulu, Hawaii.
  • Samadder NJ; Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Phoenix, Arizona.
  • Patel B; Department of Radiology and Machine Intelligence in Medicine and Imaging Center (MI-2), Mayo Clinic Arizona, Phoenix, Arizona.
  • Swallow C; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada; Department of Surgical Oncology, Princess Margaret Cancer Centre and Mount Sinai Hospital, Toronto, Ontario, Canada; Division of General Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Can
  • Lindor NM; Department of Health Sciences Research Mayo Clinic, Scottsdale, Arizona.
  • Gallinger SJ; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada; Ontario Institute for Cancer Research, Toronto, Ontario, Canada; Hepatobiliary/Pancreatic Surgical Oncology Program, University Health Network, Toronto, Ontario, Canada.
  • Grant RC; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada; Vector Institute, Toronto, Ontario, Canada; Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada.
  • Westerling-Bui T; Aiforia Inc, Cambridge, Massachusetts.
  • Conner J; Department of Pathology, Mount Sinai Hospital, Toronto, Ontario, Canada.
  • Cyr DP; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada; Department of Surgical Oncology, Princess Margaret Cancer Centre and Mount Sinai Hospital, Toronto, Ontario, Canada; Division of General Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Can
  • Kirsch R; Department of Pathology, Mount Sinai Hospital, Toronto, Ontario, Canada.
  • Pai RK; Department of Pathology and Laboratory Medicine, Mayo Clinic Arizona, Scottsdale, Arizona. Electronic address: pai.rish@mayo.edu.
Gastroenterology ; 163(6): 1531-1546.e8, 2022 12.
Article em En | MEDLINE | ID: mdl-35985511
ABSTRACT
BACKGROUND &

AIMS:

To examine whether quantitative pathologic analysis of digitized hematoxylin and eosin slides of colorectal carcinoma (CRC) correlates with clinicopathologic features, molecular alterations, and prognosis.

METHODS:

A quantitative segmentation algorithm (QuantCRC) was applied to 6468 digitized hematoxylin and eosin slides of CRCs. Fifteen parameters were recorded from each image and tested for associations with clinicopathologic features and molecular alterations. A prognostic model was developed to predict recurrence-free survival using data from the internal cohort (n = 1928) and validated on an internal test (n = 483) and external cohort (n = 938).

RESULTS:

There were significant differences in QuantCRC according to stage, histologic subtype, grade, venous/lymphatic/perineural invasion, tumor budding, CD8 immunohistochemistry, mismatch repair status, KRAS mutation, BRAF mutation, and CpG methylation. A prognostic model incorporating stage, mismatch repair, and QuantCRC resulted in a Harrell's concordance (c)-index of 0.714 (95% confidence interval [CI], 0.702-0.724) in the internal test and 0.744 (95% CI, 0.741-0.754) in the external cohort. Removing QuantCRC from the model reduced the c-index to 0.679 (95% CI, 0.673-0.694) in the external cohort. Prognostic risk groups were identified, which provided a hazard ratio of 2.24 (95% CI, 1.33-3.87, P = .004) for low vs high-risk stage III CRCs and 2.36 (95% CI, 1.07-5.20, P = .03) for low vs high-risk stage II CRCs, in the external cohort after adjusting for established risk factors. The predicted median 36-month recurrence rate for high-risk stage III CRCs was 32.7% vs 13.4% for low-risk stage III and 15.8% for high-risk stage II vs 5.4% for low-risk stage II CRCs.

CONCLUSIONS:

QuantCRC provides a powerful adjunct to routine pathologic reporting of CRC. A prognostic model using QuantCRC improves prediction of recurrence-free survival.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Testiculares / Neoplasias Colorretais Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans / Male Idioma: En Revista: Gastroenterology Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Testiculares / Neoplasias Colorretais Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans / Male Idioma: En Revista: Gastroenterology Ano de publicação: 2022 Tipo de documento: Article