Differentiation of low and high grade renal cell carcinoma on routine MRI with an externally validated automatic machine learning algorithm
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- @Article{Purkayastha:2020:SR,
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author = "Subhanik Purkayastha and Yijun Zhao and Jing Wu and
Rong Hu and Aidan McGirr and Sukhdeep Singh and
Ken Chang and Raymond Y. Huang and Paul J. Zhang and
Alvin Silva and Michael C. Soulen and
S. William Stavropoulos and Zishu Zhang and Harrison X. Bai",
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title = "Differentiation of low and high grade renal cell
carcinoma on routine {MRI} with an externally validated
automatic machine learning algorithm",
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journal = "Scientific Reports",
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year = "2020",
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volume = "10",
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pages = "Article number: 19503",
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month = "11 " # nov,
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keywords = "genetic algorithms, genetic programming, TPOT,
Diagnosis, Machine learning",
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URL = "https://rdcu.be/dabfG",
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DOI = "doi:10.1038/s41598-020-76132-z",
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abstract = "Pre-treatment determination of renal cell carcinoma
aggressiveness may help guide clinical decision-making.
We aimed to differentiate low-grade (Fuhrman I-II) from
high-grade (Fuhrman III-IV) renal cell carcinoma using
radiomics features extracted from routine MRI. 482
pathologically confirmed renal cell carcinoma lesions
from 2008 to 2019 in a multicenter cohort were
retrospectively identified. 439 lesions with
information on Fuhrman grade from 4 institutions were
divided into training and test sets with an 8:2 split
for model development and internal validation. Another
43 lesions from a separate institution were set aside
for independent external validation. The performance of
TPOT (Tree-Based Pipeline Optimization Tool), an
automatic machine learning pipeline optimizer, was
compared to hand-optimized machine learning pipeline.
The best-performing hand-optimized pipeline was a
Bayesian classifier with Fischer Score feature
selection, achieving an external validation ROC AUC of
0.59 (95percent CI 0.49-0.68), accuracy of 0.77
(95percent CI 0.68-0.84), sensitivity of 0.38
(95percent CI 0.29-0.48), and specificity of 0.86
(95percent CI 0.78-0.92). The best-performing TPOT
pipeline achieved an external validation ROC AUC of
0.60 (95percent CI 0.50-0.69), accuracy of 0.81
(95percent CI 0.72-0.88), sensitivity of 0.12
(95percent CI 0.14-0.30), and specificity of 0.97
(95percent CI 0.87-0.97). Automated machine learning
pipelines can perform equivalent to or better than
hand-optimized pipeline on an external validation test
non-invasively predicting Fuhrman grade of renal cell
carcinoma using conventional MRI.",
- }
Genetic Programming entries for
Subhanik Purkayastha
Yijun Zhao
Jing Wu
Rong Hu
Aidan J McGirr
Sukhdeep Singh
Ken Chang
Raymond Y Huang
Paul J Zhang
Alvin Silva
Michael C Soulen
S William Stavropoulos
Zishu Zhang
Harrison X Bai
Citations