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Most cohort studies are limited by sampling and accrual bias. The capability to detect specific lesions identified in radiological text reports could eliminate these biases and benefit patient care, clinical research, and trial recruitment. This study derived and internally validated text search algorithms to identify four common urological lesions (solid renal masses, complex renal cysts, adrenal masses, and simple renal cysts) using radiology text reports.

Derivation and validation of text search algorithms for renal and adrenal lesion identification in radiology text reports