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Automated clear cell renal carcinoma grade classification with prognostic significance
by
Veta, Mitko
, Heng, Yujing J.
, Rubadue, Christopher A.
, Lin, Douglas I.
, Irshad, Humayun
, Tian, Katherine
, Pyle, Michael E.
in
Aged
/ Algorithms
/ Automation
/ Biology and Life Sciences
/ Cancer
/ Cancer genetics
/ Carcinoma
/ Carcinoma, Renal Cell - diagnosis
/ Carcinoma, Renal Cell - pathology
/ Care and treatment
/ Classification
/ Clear cell-type renal cell carcinoma
/ Computer and Information Sciences
/ Computer applications
/ Computer simulation
/ Engineering and Technology
/ Evaluation
/ Feature extraction
/ Female
/ Generalized linear models
/ Genetic aspects
/ Genomes
/ Genomics
/ Histopathology
/ Humans
/ Image processing
/ Image Processing, Computer-Assisted
/ Image segmentation
/ Informatics
/ Kaplan-Meier Estimate
/ Kidney cancer
/ Kidney Neoplasms - diagnosis
/ Kidney Neoplasms - pathology
/ Machine Learning
/ Male
/ Medical diagnosis
/ Medical prognosis
/ Medical research
/ Medical schools
/ Medicine and Health Sciences
/ Methods
/ Middle Aged
/ Morphology
/ Neoplasm Grading
/ Pathologists
/ Pathology
/ Physical Sciences
/ Practice
/ Prognosis
/ Quality
/ Renal cell carcinoma
/ Reviews
/ Segmentation
/ Statistical models
2019
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Automated clear cell renal carcinoma grade classification with prognostic significance
by
Veta, Mitko
, Heng, Yujing J.
, Rubadue, Christopher A.
, Lin, Douglas I.
, Irshad, Humayun
, Tian, Katherine
, Pyle, Michael E.
in
Aged
/ Algorithms
/ Automation
/ Biology and Life Sciences
/ Cancer
/ Cancer genetics
/ Carcinoma
/ Carcinoma, Renal Cell - diagnosis
/ Carcinoma, Renal Cell - pathology
/ Care and treatment
/ Classification
/ Clear cell-type renal cell carcinoma
/ Computer and Information Sciences
/ Computer applications
/ Computer simulation
/ Engineering and Technology
/ Evaluation
/ Feature extraction
/ Female
/ Generalized linear models
/ Genetic aspects
/ Genomes
/ Genomics
/ Histopathology
/ Humans
/ Image processing
/ Image Processing, Computer-Assisted
/ Image segmentation
/ Informatics
/ Kaplan-Meier Estimate
/ Kidney cancer
/ Kidney Neoplasms - diagnosis
/ Kidney Neoplasms - pathology
/ Machine Learning
/ Male
/ Medical diagnosis
/ Medical prognosis
/ Medical research
/ Medical schools
/ Medicine and Health Sciences
/ Methods
/ Middle Aged
/ Morphology
/ Neoplasm Grading
/ Pathologists
/ Pathology
/ Physical Sciences
/ Practice
/ Prognosis
/ Quality
/ Renal cell carcinoma
/ Reviews
/ Segmentation
/ Statistical models
2019
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Automated clear cell renal carcinoma grade classification with prognostic significance
by
Veta, Mitko
, Heng, Yujing J.
, Rubadue, Christopher A.
, Lin, Douglas I.
, Irshad, Humayun
, Tian, Katherine
, Pyle, Michael E.
in
Aged
/ Algorithms
/ Automation
/ Biology and Life Sciences
/ Cancer
/ Cancer genetics
/ Carcinoma
/ Carcinoma, Renal Cell - diagnosis
/ Carcinoma, Renal Cell - pathology
/ Care and treatment
/ Classification
/ Clear cell-type renal cell carcinoma
/ Computer and Information Sciences
/ Computer applications
/ Computer simulation
/ Engineering and Technology
/ Evaluation
/ Feature extraction
/ Female
/ Generalized linear models
/ Genetic aspects
/ Genomes
/ Genomics
/ Histopathology
/ Humans
/ Image processing
/ Image Processing, Computer-Assisted
/ Image segmentation
/ Informatics
/ Kaplan-Meier Estimate
/ Kidney cancer
/ Kidney Neoplasms - diagnosis
/ Kidney Neoplasms - pathology
/ Machine Learning
/ Male
/ Medical diagnosis
/ Medical prognosis
/ Medical research
/ Medical schools
/ Medicine and Health Sciences
/ Methods
/ Middle Aged
/ Morphology
/ Neoplasm Grading
/ Pathologists
/ Pathology
/ Physical Sciences
/ Practice
/ Prognosis
/ Quality
/ Renal cell carcinoma
/ Reviews
/ Segmentation
/ Statistical models
2019
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Automated clear cell renal carcinoma grade classification with prognostic significance
Journal Article
Automated clear cell renal carcinoma grade classification with prognostic significance
2019
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Overview
We developed an automated 2-tiered Fuhrman's grading system for clear cell renal cell carcinoma (ccRCC). Whole slide images (WSI) and clinical data were retrieved for 395 The Cancer Genome Atlas (TCGA) ccRCC cases. Pathologist 1 reviewed and selected regions of interests (ROIs). Nuclear segmentation was performed. Quantitative morphological, intensity, and texture features (n = 72) were extracted. Features associated with grade were identified by constructing a Lasso model using data from cases with concordant 2-tiered Fuhrman's grades between TCGA and Pathologist 1 (training set n = 235; held-out test set n = 42). Discordant cases (n = 118) were additionally reviewed by Pathologist 2. Cox proportional hazard model evaluated the prognostic efficacy of the predicted grades in an extended test set which was created by combining the test set and discordant cases (n = 160). The Lasso model consisted of 26 features and predicted grade with 84.6% sensitivity and 81.3% specificity in the test set. In the extended test set, predicted grade was significantly associated with overall survival after adjusting for age and gender (Hazard Ratio 2.05; 95% CI 1.21-3.47); manual grades were not prognostic. Future work can adapt our computational system to predict WHO/ISUP grades, and validating this system on other ccRCC cohorts.
Publisher
Public Library of Science,Public Library of Science (PLoS)
Subject
/ Cancer
/ Carcinoma, Renal Cell - diagnosis
/ Carcinoma, Renal Cell - pathology
/ Clear cell-type renal cell carcinoma
/ Computer and Information Sciences
/ Female
/ Genomes
/ Genomics
/ Humans
/ Image Processing, Computer-Assisted
/ Kidney Neoplasms - diagnosis
/ Kidney Neoplasms - pathology
/ Male
/ Medicine and Health Sciences
/ Methods
/ Practice
/ Quality
/ Reviews
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