Peritumoral Radiomics Strategy Based on Ensemble Learning for the Prediction of Gleason Grade Group of Prostate Cancer

Yang, Qian; Liu, Yunfan; Shu, Xin; Qiao, Xiaohong; Ai, Guang-Yong; He, Xiaojing

doi:10.1016/j.acra.2023.06.011

Cited by 6 publications

(3 citation statements)

References 45 publications

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“…Prostate cancer (PCa), being the most prevalent cancer in men, has witnessed significant advancements with the application of AI in MRI-based detection and management. Algorithms driven by AI have enhanced the precision in detecting clinically significant PCa, mitigating unnecessary biopsies, and facilitating informed treatment decisions [8,9]. Qiu et al [8] developed a peritumoral radiomic-based ML model to differentiate between low and high Gleason-grade group lesions.…”

Section: Introductionmentioning

confidence: 99%

“…Algorithms driven by AI have enhanced the precision in detecting clinically significant PCa, mitigating unnecessary biopsies, and facilitating informed treatment decisions [8,9]. Qiu et al [8] developed a peritumoral radiomic-based ML model to differentiate between low and high Gleason-grade group lesions. They included 175 patients and used MRI sequences to delineate original and peritumoral regions of interest [8].…”

Section: Introductionmentioning

confidence: 99%

“…Qiu et al [8] developed a peritumoral radiomic-based ML model to differentiate between low and high Gleason-grade group lesions. They included 175 patients and used MRI sequences to delineate original and peritumoral regions of interest [8]. The model, which combined peritumoral features, outperformed other models with an area under the curve (AUC) of 0.850 and an average accuracy of 0.950, showing greater efficacy in predicting peripheral zone lesions.…”

Section: Introductionmentioning

confidence: 99%

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Emerging Trends in AI and Radiomics for Bladder, Kidney, and Prostate Cancer: A Critical Review

Feretzakis,

Juliebø-Jones,

Tsaturyan

et al. 2024

Cancers

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This comprehensive review critically examines the transformative impact of artificial intelligence (AI) and radiomics in the diagnosis, prognosis, and management of bladder, kidney, and prostate cancers. These cutting-edge technologies are revolutionizing the landscape of cancer care, enhancing both precision and personalization in medical treatments. Our review provides an in-depth analysis of the latest advancements in AI and radiomics, with a specific focus on their roles in urological oncology. We discuss how AI and radiomics have notably improved the accuracy of diagnosis and staging in bladder cancer, especially through advanced imaging techniques like multiparametric MRI (mpMRI) and CT scans. These tools are pivotal in assessing muscle invasiveness and pathological grades, critical elements in formulating treatment plans. In the realm of kidney cancer, AI and radiomics aid in distinguishing between renal cell carcinoma (RCC) subtypes and grades. The integration of radiogenomics offers a comprehensive view of disease biology, leading to tailored therapeutic approaches. Prostate cancer diagnosis and management have also seen substantial benefits from these technologies. AI-enhanced MRI has significantly improved tumor detection and localization, thereby aiding in more effective treatment planning. The review also addresses the challenges in integrating AI and radiomics into clinical practice, such as the need for standardization, ensuring data quality, and overcoming the “black box” nature of AI. We emphasize the importance of multicentric collaborations and extensive studies to enhance the applicability and generalizability of these technologies in diverse clinical settings. In conclusion, AI and radiomics represent a major paradigm shift in oncology, offering more precise, personalized, and patient-centric approaches to cancer care. While their potential to improve diagnostic accuracy, patient outcomes, and our understanding of cancer biology is profound, challenges in clinical integration and application persist. We advocate for continued research and development in AI and radiomics, underscoring the need to address existing limitations to fully leverage their capabilities in the field of oncology.

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Section: Introductionmentioning

confidence: 99%