Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer. - Vesna Barros; Nour Abdallah; Michal Ozery-Flato; Avihu Dekel; Moshiko Raboh; Nicholas Heller; Simona Rabinovici-Cohen; Alex Golts; Amilcare Gentili; Daniel Lang; Suman Chaudhary; Varsha Satish; et al. (PLoS ONE)
We consider the problem of renal mass risk classification to support doctors in adjuvant treatment decisions following nephrectomy. Recommendation of adjuvant therapy based on the mass appearance poses two major challenges: first, morphologic patterns may sometimes overlap across subtypes of varying risks. Second, interobserver variability is large. These complexities encourage the use of computational models as accurate noninvasive tools to find relevant relationships between individual perioperative renal mass characteristics and patient risk. In addition, recent evidence highlights the importance of clinical context as a promising direction to inform treatment decisions post-nephrectomy. In this work, we aim to identify relevant clinical markers that can be predictive of renal cancer prognosis. As a starting point, we perform a clinical feature ablation study by training a logistic regression baseline model to predict renal cancer patients' eligibility for adjuvant therapy. The training dataset consisted of medical records of 300 individuals with renal tumors who underwent partial or radical nephrectomy between 2011 and 2020. In addition, we evaluate the same task using a transformer-based model pretrained on a much larger dataset of over 300,000 clinical records of individuals from the UK Biobank. Our findings demonstrate the pretrained model's efficacy in knowledge transfer across different populations, with radiographic data from preoperative cross-sectional imaging playing an important role in informing renal risk and treatment decisions.