arXiv subjects

Faraz Farhadi

Publications and source records attributed to Faraz Farhadi.

5 recordsLinked to original sources

arXiv2026

Medical Radioisotopes: Clinical Demand, Production Physics, and Untapped Pathways

Over 40 radioisotopes are currently in use or under development across virtually all fields of modern medicine. Their availability relies on a complex interplay of nuclear physics, radiochemistry, and nuclear medicine. This review provides an isotope-by-isotope map of medically important radioisotopes from the perspective of these disparate yet complementary fields. We address the landscape grouped into three tiers: the established clinical core, including, familiar workhorses such as Tc-99m, F-18, Lu-177; an under-leveraged middle tier including Re-186, Re-188, and Sm-153, and the emerging frontier, radioisotopes with demonstrated but underutilised clinical promise such as Ac-225, At-211, Cu-67, and Sc-47. We discuss the properties of these radioisotopes with respect to clinical applications and their production pathways, as well as the associated nuclear and radiochemical challenges. We synthesise these disparate perspectives to identify situations in which alleviating production-side bottlenecks may improve access and patient outcomes.

physics.med-ph↗

arXiv2026

Medical Radioisotopes: Supply-chain Resilience, Distributed Production, and Access

Over 50 million diagnostic procedures involving medical radioisotopes are performed per year and the number of therapeutic and theranostic agents continues to climb. Yet, the global supply chain is far from stable and many regions are underserved. This is the second of a Series of two review papers providing a comprehensive overview of the landscape of medically relevant radioisotopes. This paper focuses on the supply chain. It presents a critical overview of the 2025-2026 status quo, case studies of acute stressors and disruptions they caused, and methods in place to secure a stable supply. A literature-based analysis is paired with expert interviews. We argue that the ageing fleet of nuclear reactors currently used for the centralised production of many isotopes should be complemented by regional accelerator-based centres, alleviating single-point failures and cross-border logistics complications, and increasing access to short-lived isotopes in harder-to-reach regions and low- and middle-income countries.

physics.med-ph↗

arXiv2026

Cross-Modal Contrastive Learning from Histopathology and CT for Automated Renal Cell Carcinoma Grading

Background: Clear cell renal cell carcinoma (ccRCC) exhibits substantial clinical heterogeneity, and accurate grade assessment is essential for risk stratification and treatment planning. However, conventional grading requires invasive tissue sampling. We developed RCC-Align, a cross-modal contrastive learning framework that leverages paired histopathology and computed tomography (CT) data during training to improve noninvasive CT-based ccRCC grade prediction. Methods: RCC-Align aligns paired whole-slide histopathology images (WSIs) and CT scans through contrastive cross-modal objectives, transferring grade-discriminative information from microscopic tissue morphology to macroscopic radiologic representations. The framework was trained and evaluated on paired TCGA and CPTAC cohorts using patient-level five-fold cross-validation. Performance for low- versus high-grade ccRCC classification was compared against CT-only baselines (DINOv2-Base and DINOv2-Finetuned) and a WSI-based reference model (GigaPath-Finetuned). Cross-modal alignment was assessed using cosine similarity analysis. Results: RCC-Align achieved an AUC of 0.601 (95% CI, 0.524-0.673) and AUPRC of 0.599 (95% CI, 0.541-0.676), outperforming DINOv2-Finetuned (AUC 0.545; AUPRC 0.543) with significantly improved low-grade prediction (p = 0.004). RCC-Align also demonstrated stronger paired WSI-CT embedding alignment compared with baselines. The WSI-based GigaPath reference achieved an AUC of 0.719. Conclusion: Pathology-guided contrastive learning improves CT-based ccRCC grading while requiring only CT at inference. This approach may complement tissue diagnosis when biopsy is unsafe, infeasible, or limited by intratumoral heterogeneity. Validation in larger, multi-institutional cohorts with external testing is needed before clinical translation.

cs.CV↗

arXiv2025

Towards Integrated Clinical-Computational Nuclear Medicine

The field of Clinical-Computational Nuclear Medicine is rapidly advancing, fueled by AI, tracer kinetic modeling, radiomics, and integrated informatics. These technologies improve imaging quality, automate lesion detection, and enable personalized radiopharmaceutical therapy through physiologically based pharmacokinetic (PBPK) modeling and voxel-level dosimetry. Workflow automation and Natural Language Processing (NLP) further enhance operational efficiency. However, successful implementation and adoption of these tools require clinical oversight to ensure accuracy, interpretability, and patient safety. This paper highlights key computational innovations and emphasizes the critical role of clinician-guided evaluation in shaping the future of precision imaging and therapy.

physics.med-ph↗

arXiv2025

Predicting Targeted Therapy Resistance in Non-Small Cell Lung Cancer Using Multimodal Machine Learning

Lung cancer is the primary cause of cancer death globally, with non-small cell lung cancer (NSCLC) emerging as its most prevalent subtype. Among NSCLC patients, approximately 32.3% have mutations in the epidermal growth factor receptor (EGFR) gene. Osimertinib, a third-generation EGFR-tyrosine kinase inhibitor (TKI), has demonstrated remarkable efficacy in the treatment of NSCLC patients with activating and T790M resistance EGFR mutations. Despite its established efficacy, drug resistance poses a significant challenge for patients to fully benefit from osimertinib. The absence of a standard tool to accurately predict TKI resistance, including that of osimertinib, remains a critical obstacle. To bridge this gap, in this study, we developed an interpretable multimodal machine learning model designed to predict patient resistance to osimertinib among late-stage NSCLC patients with activating EGFR mutations, achieving a c-index of 0.82 on a multi-institutional dataset. This machine learning model harnesses readily available data routinely collected during patient visits and medical assessments to facilitate precision lung cancer management and informed treatment decisions. By integrating various data types such as histology images, next generation sequencing (NGS) data, demographics data, and clinical records, our multimodal model can generate well-informed recommendations. Our experiment results also demonstrated the superior performance of the multimodal model over single modality models (c-index 0.82 compared with 0.75 and 0.77), thus underscoring the benefit of combining multiple modalities in patient outcome prediction.

cs.LG↗