Dr. Zakharia will be chairing "Going Beyond RECIST: Radiomics in Kidney Cancer" at IKCS 2024. Here is a preview of the event.
What are the highlights that attendees should take away from your presentation?
The currently available radiology images modality and the RECIST criteria used in renal cancer have limitations in the clinical trial setting. They do not account for the frequent necrosis often seen with tyrosine kinase inhibitors (TKIs). By blocking the vasculature of the tumor with TKIs, it is not uncommon at all to have the best response as necrotic lesions (without shrinkage in size, or even occasionally bigger due to inflammation), that per RECIST criteria we use in clinical trials response evaluation, those lesions are mistakenly called stable or progressive disease when indeed those fully necrotic lesions could represent an excellent response to treatment. On multiple occasions resecting them shows pathologic complete remission. On top of that, the usual FDG PET CT scan, commonly used in other malignancies, lacked the sensitivity and specificity in renal cancer.
Pseudo progression — albeit an uncommon phenomenon in metastatic renal cell carcinoma (RCC) — pauses another challenge in clinical trial settings, especially when using immunotherapy. In certain circumstances this might lead to an early discontinuation of a potentially effective treatment option.
There is an unmet need for better imaging modality and better criteria than RECIST to utilize in managing clear cell RCC (ccRCC), both in and outside clinical trial settings.
One of the exciting new modalities in RCC imaging is the 89Zr-DFO-girentuximab a monoclonal antibody that targets carbonic anhydrase IX (CAIX), an enzyme highly expressed in ccRCC. In the phase 3 ZIRCON clinical trial, 300 patients; with ≤7cm renal masse (tumor stage cT1) undergoing partial nephrectomy; were enrolled. The coprimary objectives of sensitivity and specificity of this PET modality were 86% [80%, 90%] and 87% [79%, 92%] respectively. This offers a noninvasive option to identify ccRCC.
We currently see the tip of the iceberg when it comes to utilizing artificial intelligence (AI) in detecting kidney cancer, tumor volume assessment and the use of such models for assessing treatment responses.
How do these findings and/or conclusions potentially impact clinical practice?
At present, RECIST v1.1 is still the main endpoint for clinical trials. However, changes in tumor size alone are inadequate for assessing response in the context of immune checkpoint inhibitors and anti-angiogenic agents.
Various imaging biomarkers and response criteria have been proposed specifically for immune checkpoint inhibitors and anti-angiogenic agents. However, it is still unclear which of these should be adopted for routine use.
What else would you like attendees to know about your presentation?
Limitations of Current Imaging and Criteria: Existing imaging modalities and RECIST criteria for assessing renal cancer often fail to accurately evaluate treatment responses, particularly in cases involving necrosis due to tyrosine kinase inhibitors. This can lead to misclassification of tumor status, impacting clinical trial outcomes.
Need for Improved Modalities: There is a critical need for more effective imaging techniques and response criteria for managing ccRCC, as current methods do not adequately capture treatment efficacy.
Emerging Imaging Techniques: The new imaging modality 89Zr-DFO-girentuximab shows promising sensitivity and specificity for detecting ccRCC, providing a noninvasive diagnostic option.
Role of AI: The integration of AI in kidney cancer management is in its early stages, but it holds potential for enhancing detection, tumor volume assessment, and treatment response evaluation.
What are 3–5 questions you would ask attendees about the topic of your presentation to spark an engaging conversation?
What are the specific challenges associated with the detection of small renal tumors using CT scans, and how do these limitations affect clinical outcomes?
How do issues such as radiation exposure and contrast-related complications impact the overall safety and effectiveness of CT scans in monitoring renal cancer progression?
How can AI algorithms improve the accuracy of early detection and diagnosis of renal cancer compared to traditional imaging techniques?
In what ways can AI enhance treatment planning and personalization for patients with renal cancer, particularly in predicting responses to different therapies?
What are the potential ethical considerations and challenges associated with implementing AI technologies in renal cancer management, particularly regarding patient data privacy and decision-making?
Dr. Zakharia has no conflicts of interest to report.
Illustration by April Brust
