Missed Diagnosis of Clear Cell Renal Cell Carcinoma on Noncontrast Computed Tomography: The Potential Role of Artificial Intelligence in Reducing Radiological Diagnostic Error.

Background: Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of malignant renal neoplasms, with an increasing incidence in Western countries. Early diagnosis is a critical determinant of prognosis, as advanced-stage disease with metastatic dissemination entails a significantly reduced median survival. Characterizing complex cystic renal lesions presents a recognized diagnostic challenge on noncontrast computed tomography (CT), which often leads to underestimation and missed diagnoses. Artificial intelligence (AI) applied to diagnostic imaging is emerging as a supplementary decision-support tool, but its integration must be critically evaluated to understand its potential to reduce diagnostic errors as well as its limitations. Case Presentation: We report the case of a 73-year-old male physician with a history of myocardial infarction who presented with left flank pain. An initial noncontrast CT of the urinary tract on January 15 revealed a 37-mm left upper-pole renal cyst and a 5-mm suspected calculus. The cyst was characterized as simple, and the report offered only a generic follow-up recommendation. Over the next 4 months, the patient's persistent pain was attributed entirely to presumed ureteric stone disease, leading to a ureteroscopy and ureteral stent placement, without characterizing the known renal cyst with contrast. On June 5 (5 months after the initial CT), a contrast-enhanced CT revealed a 6-cm heterogeneous, exophytic left renal mass. Subsequent staging confirmed pulmonary and cerebral metastases from ccRCC. The patient died in mid-August, 7 months after the initial CT. Retrospective re-evaluation of the baseline CT images, both by a senior radiologist and through an AI platform, confirmed that the lesion already exhibited features suspicious for neoplasia at the time of the first scan, warranting further investigation with contrast-enhanced CT or magnetic resonance imaging in accordance with international guidelines. Simulated retrospective analysis via a web-based AI platform not only flagged the mass as suspicious but also generated several critical errors (e.g., hallucinating contrast enhancement on the unenhanced scan, mislocalizing the mass to the mid-to-lower pole, and claiming it was "new" without comparison data). Conclusions: This case demonstrates how a complex cystic renal lesion may be underestimated in the absence of contrast medium and an adequate level of clinical-radiological suspicion. Re-evaluation by AI produced a result consistent with that of the expert radiologist, suggesting that systematic integration of AI tools into the radiological workflow could contribute to reducing diagnostic errors, particularly in settings where interobserver variability represents a clinically significant risk factor. AI can act as an effective supplementary decision-support tool to reduce perceptual errors; clinicians and radiologists must remain highly vigilant regarding AI-generated factual inconsistencies. Proper adherence to imaging guidelines and the use of contrast medium remain paramount.