Comron Hassanzadeh

Purpose: The Decipher Prostate Genomic Classifier (GC) and ArteraAI Multimodal Artificial Intelligence (MMAI) platform are widely used prognostic tools for localized prostate cancer, yet no direct comparison has been performed within the same patient cohort. Experimental Design: We sought to evaluate the concordance and prognostic value of GC and MMAI across multi-institutional cohorts totaling 688 patients with localized and oligometastatic prostate cancer. These included populations enriched for African American and East Asian patients. GC and MMAI scores were stratified into risk groups based on specimen type [biopsy or radical prostatectomy (RP)]. The primary endpoint was distant metastasis–free survival, measured from diagnosis to last follow-up or event. Results: GC and MMAI scores were significantly correlated across multiple cohorts. In Moffitt RP, biopsy, and National Cancer Centre Singapore cohorts, both MMAI and GC were prognostic alone and after adjusting for clinical variables. Multivariable analysis, including both biomarkers and clinical variables, showed that MMAI was significant even when accounting for GC in Moffitt RP and borderline in Moffitt biopsy. Analysis of concordant/discordant cases showed that patients who were high risk for both GC and MMAI did qualitatively worse. In pathway analysis, concordance with GC and MMAI was high for Moffitt RP and oligometastatic patients. Across both biopsy cohorts, although most biological pathways were concordant, there were consistent discordant signaling, metabolic, and DNA repair pathways. Conclusions: In the first direct patient-level comparison of GC and MMAI in prostate cancer, these biomarkers were found to demonstrate moderate correlation, and both biomarkers demonstrated prognostic value across diverse populations and disease states.

Background: Independent end-to-end verification of adaptive radiotherapy on MR-Linac systems is limited by the lack of patient-specific phantoms able to reproduce imaging and dosimetric properties from CT and MRI scanners. We present a method for automated generation of 4D, patient-specific, multi-material 3D-printable phantoms for quality assurance of adaptive radiotherapy on a 1.5T MR-Linac. Methods: Patient images were automatically segmented using a pretrained deep learning model. The segmented structures were converted into high-resolution 3D meshes and assembled into printable phantoms. A dosimeter holder was inserted at user-defined anatomical locations, with orientation optimized to avoid traversal across heterogeneous tissue interfaces. Physiological motion was incorporated by generating phantoms from images at different timepoints and interpolating deformation fields to create continuous 4D models. Multi-material organs designed by mixing a set of six polymers at various proportions were used to reproduce tissue-specific imaging properties. The properties of material mixtures were evaluated in a clinical CT simulator and a 1.5T MR-Linac. Results: The proposed workflow enables automated generation of anatomically realistic phantoms with several types of embedded dosimeters. A discrete search method was designed for placement and immobilization of OSLD, film, and ion chamber dosimeters. Calibration curves for Hounsfield units were derived through variations in radiopaque material content, while MR signal intensity was modulated by gel and tissue matrix mixtures. Patient-derived abdominal phantoms were fabricated at multiple scales while replicating internal anatomical detail. Multi-dimensional phantom generation enabled continuous representation of motion states with consistent mesh topology across phases. Conclusions: We demonstrate an end-to-end workflow for automated generation of 4D patient-specific phantoms for MR-Linac quality assurance. The method combines realistic anatomy, embedded dosimetry, multimodal imaging properties, and physiological motion within a single fabrication framework. This approachmay enable an improved validation of adaptive radiotherapy workflows in MR-guided treatment devices.

PURPOSE: Radiotherapy treatment planning is a resource-intensive process characterized by multiple manual steps that can contribute to treatment delays and interobserver variability. The Radiation Planning Assistant (RPA) is a Web-based platform designed to deliver automated contouring and planning approaches tailored to low-resource settings. This work expands the RPA to develop and clinically validate end-to-end, artificial intelligence–driven workflows for prostate and cervical cancers, designed to improve efficiency, consistency, and accessibility in low- and middle-income countries. MATERIALS AND METHODS: We developed deep learning–based auto-contouring models using nnU-Net and integrated them with knowledge-based planning models trained on curated data sets from over 1,000 prostate and 110 cervical cancer treatment plans. For prostate cancer, models were developed to accommodate prostate directed, prostate bed, and nodal treatment scenarios. Cervical cancer planning followed EMBRACE II guidelines and included pelvic and para-aortic nodal volumes. These tools were integrated into the RPA. Clinical acceptability of the auto-contours and plans was assessed retrospectively by radiation oncologists using a five-point Likert scale. RESULTS: In all, 50 test patients (40 prostate, 10 cervical) were evaluated end-to-end. For prostate cancer, 70% of target auto-contours and 73% of treatment plans were clinically acceptable without edits; for cervical cancer, these rates were 80% and 80%, respectively. For prostate cancer planning, 77% of target and 98% of organ-at-risk structures met all per-protocol compliance criteria. For cervical cancer planning, all EMBRACE II protocol hard constraint criteria were met. Bowel and vaginal contours demonstrated lower performance, but these did not compromise plan quality. CONCLUSION: We present validated, end-to-end radiotherapy planning workflows for prostate and cervical cancers that leverage the RPA's infrastructure to streamline treatment planning in a globally accessible platform and demonstrate high clinical acceptability.

Bladder preservation has emerged as an established treatment option for selected patients with muscle-invasive bladder cancer (MIBC), offering durable oncologic control with the potential to maintain native bladder function and quality of life. Over the past several decades, prospective trials and large institutional experiences have refined trimodality therapy (TMT)—maximal transurethral resection followed by definitive radiation therapy with concurrent radiosensitizing systemic therapy—and clarified principles of patient selection, treatment delivery, surveillance, and salvage. Randomized evidence supports combined-modality therapy as the backbone of bladder preservation, and contemporary comparative analyses suggest outcomes comparable to radical cystectomy in appropriately selected populations. This review synthesizes the clinical foundations of bladder preservation, including radiobiologic considerations, advances in radiation technique, and patterns of recurrence following TMT. We discuss outcomes in higher-risk populations, including locally advanced and node-positive disease, and examine the evolving integration of systemic therapies. The emergence of immune checkpoint inhibitors and antibody–drug conjugates in urothelial carcinoma has reshaped the systemic treatment landscape and raises important questions regarding patient selection, sequencing, and the potential expansion of organ-preserving strategies. Finally, we outline future directions—including response-adaptive approaches, advances in image-guided and adaptive radiotherapy, and ctDNA-enabled risk stratification—while emphasizing the need for prospective validation and multidisciplinary collaboration to refine and optimize bladder-preserving care.

Hypofractionated particle therapy (HPT) is an evolving treatment paradigm in radiation oncology, offering enhanced therapeutic ratios through both biological and dosimetric advantages. Proton therapy leverages the Bragg peak phenomenon to reduce the integral dose to normal tissues, a benefit amplified by delivering larger doses per fraction. Carbon ion radiotherapy (CIRT) further augments this approach through high linear energy transfer (LET) and greater relative biological effectiveness (RBE), potentially overcoming radioresistance mechanisms. This review synthesizes updated clinical evidence for HPT, including proton and CIRT across various disease sites. Clinical trial data on efficacy and toxicity are reviewed, including comparisons with photon-based radiotherapy. The clinical outcomes observed thus far highlight the safety and efficacy of biologically intensified HPT regimens, underscoring the need for continued, well-designed phase II/III trials to define optimal indications and fractionation strategies.

Background: Metastasis-directed therapy (MDT) for oligometastatic cancer is utilized for genitourinary malignancies including prostate and kidney cancers. Clinical research on MDT for urothelial carcinoma (UC) remains sparse, especially as systemic therapy advances have improved outcomes. Objective: We investigated the role of MDT, specifically radiotherapy, for patients with oligometastatic bladder or upper-tract UC. Methods: Data were collated on patients with metastatic UC with 5 or fewer metastatic sites undergoing MDT with ablative radiotherapy with or without preceding systemic therapy during January 2016 to July 2024. Endpoints were progression-free survival (PFS), and overall survival (OS). Cox proportional hazards analysis was conducted to determine the covariates associated with these endpoints. Results: Fifty-two patients were included. Most were men (67%). Median age was 68 years (interquartile range, 62–78). Most had bladder primary tumors (79%). Patients had a median of 1 metastatic site. Most received ≥2 lines of systemic therapy before MDT (60%), whereas 8% received no systemic therapy before MDT. MDT was delivered to all metastases in 71% of cases, whereas the remaining cases (29%) had MDT delivered to select sites. Median follow-up from the diagnosis of metastasis was 32 months (interquartile range, 23–42). Median PFS and OS were 19 months (95% CI, 15–24) and 42 months (95% CI, 24–60), respectively. Conclusions: MDT may serve as an effective adjunct to systemic therapy to improve outcomes of oligometastatic and oligoprogressive UC.