Publications by authors named "P A Kapur"
Neoadjuvant systemic treatment strategies have improved outcomes in several solid tumour types. This success has not yet been replicated in renal cell carcinoma (RCC). A consensus and international collaboration are urgently needed for the development of adaptive perioperative immunotherapy strategies for patients with RCC at high risk of recurrence.
View Article and Find Full Text PDFPurpose: A reliable and comprehensive cancer prognosis model for clear cell renal cell carcinoma (ccRCC) could better assist in personalizing treatment. In this work, we developed a multi-modal ensemble model (MMEM) which integrates pretreatment clinical information, multi-omics data, and histopathology whole slide image (WSI) data to learn complementary information to predict overall survival (OS) and disease-free survival (DFS) for patients with ccRCC.
Methods And Materials: We collected 226 patients from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma dataset (TCGA-KIRC).
Cardiovascular diseases are a major global health concern, with statin therapy playing a significant role in primary and secondary prevention. Statin-associated muscle symptoms typically occur early in treatment, but severe rhabdomyolysis is a rare complication. We present an unprecedented case of a patient who developed severe rhabdomyolysis with acute kidney injury after 10 years of uninterrupted statin therapy.
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- The study aimed to evaluate the effectiveness of combining arterial embolization with various embolic agents and microwave ablation (MWA) in reducing the size of renal tumor ablation zones in pigs.
- Researchers conducted experiments on 32 tumors, using different embolic materials before applying MWA, and then assessed the changes in ablation zones through gross and histologic analysis.
- Results showed that the use of smaller particle sizes for embolization significantly increased the size of the ablation zones compared to MWA alone, with the most effective particle sizes being between 50-150 μm.
Purpose: The management of renal cell carcinoma (RCC) relies on clinical and histopathological features for treatment decisions. Recently, radiomics, which involves the extraction and analysis of quantitative imaging features, has shown promise in improving RCC management. This review evaluates the current application and limitations of radiomics for predicting treatment and oncological outcomes in RCC.
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