Diversity in Renal Mass Data Cohorts: Implications for Urology AI Researchers.

Objective We examine the heterogeneity and distribution of the cohort populations in two publicly used radiological image cohorts, Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCIA TCGA KIRC) collection and 2019 MICCAI Kidney Tumor Segmentation Challenge (KiTS19), and deviations in real world population renal cancer data from National Cancer Database (NCDB) Participant User Data File (PUF) and tertiary center data. PUF data is used as an anchor for prevalence rate bias assessment. Specific gene expression and therefore biology of RCC differ by self-reported race especially between the African American and Caucasian population. AI algorithms learn from datasets, but if the dataset misrepresents the population, reinforcing bias may occur. Ignoring these demographic features may lead to inaccurate downstream effects, thereby limiting the translation of these analyses to clinical practice. Consciousness of model training biases is vital to patient care decisions when using models in clinical settings. Method Data evaluated included the gender, demographic and reported pathologic grading and cancer staging. American Urological Association risk levels were used. Poisson regression was used to estimate the population-based and sample specific estimation for prevalence rate and corresponding 95% confidence interval. SAS 9.4 was used for data analysis. Result Compared to PUF, KiTS19 and TCGA KIRC over sampled Caucasian by 9.5% (95% CI, -3.7% to 22.7%) and 15.1% (95% CI, 1.5% to 28.8%), under sampled African American by -6.7% (95% CI, -10% to -3.3%), -5.5% (95% CI, -9.3% to -1.8%). Tertiary also under sampled African American by -6.6% (95% CI, -8.7% to -4.6%). The tertiary cohort largely under sampled aggressive cancers by -14.7% (95% CI, -20.9% to -8.4%). No statistically significant difference was found among PUF, TCGA, and KiTS19 in aggressive rate, however heterogeneities in risk are notable. Conclusion Heterogeneities between cohorts need to be considered in future AI training and cross-validation for renal masses.

Oncology. 2023 Dec 15 [Epub ahead of print]

Harmony Selena Cen, Siddhartha Dandamudi, Xiaomeng Lei, Chris Weight, Mihir Desai, Inderbir Gill, Vinay Duddalwar