Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.
Department of Urology.
Curr Opin Urol. 2024 Jan 1;34(1):1-7. doi: 10.1097/MOU.0000000000001144. Epub 2023 Nov 1.
This review aims to highlight the integration of artificial intelligence-powered radiomics in urologic oncology, focusing on the diagnostic and prognostic advancements in the realm of managing prostate, kidney, and bladder cancers.
As artificial intelligence continues to shape the medical imaging landscape, its integration into the field of urologic oncology has led to impressive results. For prostate cancer diagnostics, machine learning has shown promise in refining clinically-significant lesion detection, with some success in deciphering ambiguous lesions on multiparametric MRI. For kidney cancer, radiomics has emerged as a valuable tool for better distinguishing between benign and malignant renal masses and predicting tumor behavior from CT or MRI scans. Meanwhile, in the arena of bladder cancer, there is a burgeoning emphasis on prediction of muscle invasive cancer and forecasting disease trajectory. However, many studies showing promise in these areas face challenges due to limited sample sizes and the need for broader external validation.
Radiomics integrated with artificial intelligence offers a pioneering approach to urologic oncology, ushering in an era of enhanced diagnostic precision and reduced invasiveness, guiding patient-tailored treatment plans. Researchers must embrace broader, multicentered endeavors to harness the full potential of this field.
本文旨在强调人工智能驱动的影像组学在泌尿肿瘤学中的整合应用，重点介绍在前列腺癌、肾癌和膀胱癌管理领域的诊断和预后进展。
随着人工智能继续塑造医学影像领域，其在泌尿肿瘤学领域的整合取得了令人瞩目的成果。在前列腺癌诊断方面，机器学习在优化临床显著病变检测方面显示出了潜力，在解读多参数 MRI 上的模棱两可病变方面取得了一定的成功。对于肾癌，影像组学已成为一种有价值的工具，可更好地区分良性和恶性肾肿瘤，并从 CT 或 MRI 扫描中预测肿瘤行为。同时，在膀胱癌领域，人们越来越关注预测肌肉浸润性癌症和预测疾病轨迹。然而，许多在这些领域显示出前景的研究由于样本量有限以及需要更广泛的外部验证，面临着挑战。
人工智能与影像组学的整合为泌尿肿瘤学提供了一种开创性的方法，带来了更高的诊断精度和更低的侵袭性，为患者量身定制治疗计划。研究人员必须开展更广泛的多中心研究，以充分发挥这一领域的潜力。
