Insights into Renal Cell Carcinoma with Novel Imaging Approaches

Schawkat, Khoschy; Krajewski, Katherine M.

doi:10.1016/j.hoc.2023.05.002

Cited by 4 publications

(7 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Radiomics enhances this landscape by offering an intricate analysis of medical images, identifying tumor patterns that escape the naked eye, thereby enriching our methodology in tumor characterization and affording a nuanced perspective of cancer behavior and progression [3]. Schawkat et al took an in-depth look at novel imaging approaches for evaluating renal masses and renal cell carcinoma [3].…”

Section: Introductionmentioning

confidence: 99%

“…Schawkat et al took an in-depth look at novel imaging approaches for evaluating renal masses and renal cell carcinoma [3]. They discussed the updated Bosniak classification and the clear cell carcinoma likelihood score, along with newer modalities like contrast-enhanced ultrasound, dual-energy CT, and molecular imaging [3]. The integration of radiomics and artificial intelligence (AI) techniques was also explored.…”

Section: Introductionmentioning

confidence: 99%

“…The integration of radiomics and artificial intelligence (AI) techniques was also explored. These contemporary diagnostic tools, combined with established methods, could address current limitations in renal mass characterization [3]. Evrimler et al in their study assessed the potential of machine learning (ML)-based CT radiomics in predicting histological variants of bladder urothelial carcinoma, which is crucial for management [4].…”

Section: Introductionmentioning

confidence: 99%

“…The research involved analyzing RFs and clinical data from 175 patients, using enhanced CT imaging and the LASSO algorithm for feature selection. The resulting radiomics nomogram integrated RFs and clinical predictors, demonstrating improved predictive accuracy for progression-free survival over clinical variables alone, highlighting its potential for Radiomics enhances this landscape by offering an intricate analysis of medical images, identifying tumor patterns that escape the naked eye, thereby enriching our methodology in tumor characterization and affording a nuanced perspective of cancer behavior and progression [3]. Schawkat et al took an in-depth look at novel imaging approaches for evaluating renal masses and renal cell carcinoma [3].…”

Section: Introductionmentioning

confidence: 99%

“…The resulting radiomics nomogram integrated RFs and clinical predictors, demonstrating improved predictive accuracy for progression-free survival over clinical variables alone, highlighting its potential for Radiomics enhances this landscape by offering an intricate analysis of medical images, identifying tumor patterns that escape the naked eye, thereby enriching our methodology in tumor characterization and affording a nuanced perspective of cancer behavior and progression [3]. Schawkat et al took an in-depth look at novel imaging approaches for evaluating renal masses and renal cell carcinoma [3]. They discussed the updated Bosniak classification and the clear cell carcinoma likelihood score, along with newer modalities like contrast-enhanced ultrasound, dual-energy CT, and molecular imaging [3].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Feretzakis,

Juliebø-Jones,

Tsaturyan

et al. 2024

Cancers

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Feretzakis,

Juliebø-Jones,

Tsaturyan

et al. 2024

Cancers

View full text Add to dashboard Cite

show abstract

Unveiling the Potential of Wearable Antennas and Microwave Technology in Kidney Cancer Detection

Sangeetha,

Mohanbabu,

Premkumar

et al. 2024

J. Inst. Eng. India Ser. B

View full text Add to dashboard Cite

Update on Renal Cell Carcinoma Diagnosis with Novel Imaging Approaches

Bellin,

Valente,

Bekdache

et al. 2024

Cancers

View full text Add to dashboard Cite

This review highlights recent advances in renal cell carcinoma (RCC) imaging. It begins with dual-energy computed tomography (DECT), which has demonstrated a high diagnostic accuracy in the evaluation of renal masses. Several studies have suggested the potential benefits of iodine quantification, particularly for distinguishing low-attenuation, true enhancing solid masses from hyperdense cysts. By determining whether or not a renal mass is present, DECT could avoid the need for additional imaging studies, thereby reducing healthcare costs. DECT can also provide virtual unenhanced images, helping to reduce radiation exposure. The review then provides an update focusing on the advantages of multiparametric magnetic resonance (MR) imaging performance in the histological subtyping of RCC and in the differentiation of benign from malignant renal masses. A proposed standardized stepwise reading of images helps to identify clear cell RCC and papillary RCC with a high accuracy. Contrast-enhanced ultrasound may represent a promising diagnostic tool for the characterization of solid and cystic renal masses. Several combined pharmaceutical imaging strategies using both sestamibi and PSMA offer new opportunities in the diagnosis and staging of RCC, but their role in risk stratification needs to be evaluated. Although radiomics and tumor texture analysis are hampered by poor reproducibility and need standardization, they show promise in identifying new biomarkers for predicting tumor histology, clinical outcomes, overall survival, and the response to therapy. They have a wide range of potential applications but are still in the research phase. Artificial intelligence (AI) has shown encouraging results in tumor classification, grade, and prognosis. It is expected to play an important role in assessing the treatment response and advancing personalized medicine. The review then focuses on recently updated algorithms and guidelines. The Bosniak classification version 2019 incorporates MRI, precisely defines previously vague imaging terms, and allows a greater proportion of masses to be placed in lower-risk classes. Recent studies have reported an improved specificity of the higher-risk categories and better inter-reader agreement. The clear cell likelihood score, which adds standardization to the characterization of solid renal masses on MRI, has been validated in recent studies with high interobserver agreement. Finally, the review discusses the key imaging implications of the 2017 AUA guidelines for renal masses and localized renal cancer.

show abstract

Insights into Renal Cell Carcinoma with Novel Imaging Approaches

Cited by 4 publications

References 48 publications

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

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

Unveiling the Potential of Wearable Antennas and Microwave Technology in Kidney Cancer Detection

Update on Renal Cell Carcinoma Diagnosis with Novel Imaging Approaches

Contact Info

Product

Resources

About