Download This Paper
Preprints with The Lancet is a collaboration between The Lancet Group of journals and SSRN to facilitate the open sharing of preprints for early engagement, community comment, and collaboration. Preprints available here are not Lancet publications or necessarily under review with a Lancet journal. These preprints are early-stage research papers that have not been peer-reviewed. The usual SSRN checks and a Lancet-specific check for appropriateness and transparency have been applied. The findings should not be used for clinical or public health decision-making or presented without highlighting these facts. For more information, please see the FAQs.
Differentiation of Low and High Grade Renal Cell Carcinoma on Routine MR with an Externally Validated Automatic Machine Learning Algorithm
23 Pages Posted: 17 Aug 2020
More...Abstract
Background: 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 MR imaging.
Methods: 482 pathologically confirmed renal cell carcinoma lesions from 2008 to 2019 in a multicenter cohort were retrospectively identified. 439 Fuhrman-graded lesions from 4 institutions were divided into training and validation sets with an 8:2 split for model development and internal validation, other 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 pipelines.
Findings: The best-performing hand-optimized pipeline was a Bayesian classifier with Fischer Score feature selection, achieving an external validation ROC AUC of 0.59 (95% CI: 0.49-0.68), accuracy of 0.77 (95% CI: 0.68-0.84), sensitivity of 0.38 (95% CI: 0.29-0.48), and specificity of 0.86 (95% CI: 0.78-0.92). The best-performing TPOT pipeline achieved an external validation ROC AUC of 0.60 (95% CI: 0.50-0.69), accuracy of 0.81 (95% CI: 0.72-0.88), sensitivity of 0.12 (95% CI: 0.14-0.30), and specificity of 0.97 (95% CI: 0.87-0.97).
Interpretations: Automated machine learning pipelines can perform equivalent to or better than hand-optimized pipelines on an external validation test non-invasively predicting histological grade of renal cell carcinoma using conventional MR imaging.
Funding Statement: This study was supported by RSNA fellow research grant (RF1802), National Institution of Health/National Cancer Institute Grant (R03CA249554) and SIR Foundation Radiology Resident Research Grant to HXB. This project was supported by a training grant from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under award number 5T32EB1680 and by the National Cancer Institute (NCI) of the National Institutes of Health under Award Number F30CA239407 to K. Chang.
Declaration of Interests: No conflicts of interests exist among all authors.
Ethics Approval Statement: The study was approved by the Institutional Review Boards of HUP, MAY, SXH, and PHH. With the agreement to use TCGA/TCIA data, the IRB approval of our study was waived for TCIA.
Keywords: renal cell carcinoma; Fuhrman grade; radiomics; automatic machine learning
Suggested Citation: Suggested Citation