Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC)

Gurbani, Sidharth; Morgan, Dane; Jog, Varun; Dreyfuss, Leo D.; Shen, Mei‐Hua; Das, Arighno; Abel, E. Jason; Lubner, Meghan G.

doi:10.1007/s00261-021-03083-y

Cited by 10 publications

(5 citation statements)

References 51 publications

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“…Based on MRI, Purkayastha et al 12 developed a non-invasive VB model to differentiate between low- and high-grade RCC, yielding an AUC of 0.59. Also, Gurbani et al 65 were able to discriminate kidney cancer grade on CT scans, with an AUC of 0.67 in the internal validation set. Further analysis should be carried out on multicentric data sets and by combining clinical and/or pathological features in the ML model.…”

Section: Resultsmentioning

confidence: 99%

Virtual biopsy in abdominal pathology: where do we stand?

et al. 2023

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In recent years researchers have explored new ways to obtain information from pathological tissues, also exploring non-invasive techniques, such as virtual biopsy (VB). VB can be defined as a test that provides promising outcomes compared to traditional biopsy by extracting quantitative information from radiological images not accessible through traditional visual inspection. Data is processed in such a way that it can be correlated with the patient’s phenotypic expression, or with molecular patterns and mutations, creating a bridge between traditional radiology, pathology, genomics, and artificial intelligence (AI). Radiomics is the backbone of VB, since it allows the extraction and selection of features from radiological images, feeding them into AI models in order to derive lesions' pathological characteristics and molecular status. Presently the output of VB provides only a gross approximation of the findings of tissue biopsy. However, in the future, with the improvement of imaging resolution and processing techniques, VB could partially substitute the classical surgical or percutaneous biopsy with the advantage of being non-invasive, comprehensive, accounting for lesion heterogeneity, and low cost. In this review, we investigate the concept of VB in abdominal pathology, focusing on its pipeline development and potential benefits.

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Section: Resultsmentioning

confidence: 99%

Virtual biopsy in abdominal pathology: where do we stand?

et al. 2023

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“…Notably, Zheng et al developed and validated a CT-based nomogram in a cohort of 258 patients for preoperatively predicting nuclear grades of ccRCC with impressive predictive accuracy (AUC of 0.929 and 0.878 in their training and validation sets, respectively) [47]. This outperformed a similar CT radiomics approach by Gurbani et al , which featured a relatively weaker predictive capability (AUC values ranging from 0.58 to 0.69) [48]. Meanwhile, Demirjian et al explored CT radiomics-augmented machine learning to predict both tumor grade and TNM staging in ccRCC.…”

Section: Stratification Of Renal Cell Carcinoma Tumor Gradementioning

confidence: 99%

AI-powered radiomics: revolutionizing detection of urologic malignancies

Gelikman,

Rais-Bahrami,

Pinto

et al. 2023

Current Opinion in Urology

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Purpose of Review 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. Recent Findings 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. Summary 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.

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“…Additionally, advanced imaging techniques like CT-based volumetric radiomics have been crucial in guiding therapeutic decisions by predicting outcomes and treatment responses in RCC patients. The use of ML algorithms, particularly the XG Boost model, has allowed for the identification of associations with aggressive tumor characteristics, providing clinicians with crucial information on the potential behavior of large RCCs [22].…”

Section: Summary Of Critical Research Outcomesmentioning

confidence: 99%

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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Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC)

Cited by 10 publications

References 51 publications

Virtual biopsy in abdominal pathology: where do we stand?

Virtual biopsy in abdominal pathology: where do we stand?

AI-powered radiomics: revolutionizing detection of urologic malignancies

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

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