The aim of this study is to investigate the role of Delta Radiomics analysis in the prediction of one-year local control (1yLC) in patients affected by locally advanced pancreatic cancer (LAPC) and treated using Magnetic Resonance guided Radiotherapy (MRgRT). A total of 35 patients from two institutions were enrolled: A 0.35 Tesla T2*/T1 MR image was acquired for each case during simulation and on each treatment fraction. Physical dose was converted in biologically effective dose (BED) to compensate for different radiotherapy schemes. Delta Radiomics analysis was performed considering the gross tumour volume (GTV) delineated on MR images acquired at BED of 20, 40, and 60 Gy. The performance of the delta features in predicting 1yLC was investigated in terms of Wilcoxon Mann–Whitney test and area under receiver operating characteristic (ROC) curve (AUC). The most significant feature in predicting 1yLC was the variation of cluster shade calculated at BED = 40 Gy, with a p-value of 0.005 and an AUC of 0.78 (0.61–0.94). Delta Radiomics analysis on low-field MR images might play a promising role in 1yLC prediction for LAPC patients: further studies including an external validation dataset and a larger cohort of patients are recommended to confirm the validity of this preliminary experience.

Background: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer variability are main issues in manual OAR delineation, mainly in the head and neck (H & N) district. Deep-learning based auto-segmentation is a promising strategy to improve OARs contouring in radiotherapy departments. A comparison of deep-learning-generated auto-contours (AC) with manual contours (MC) was performed by three expert radiation oncologists from a single center. Methods: Planning computed tomography (CT) scans of patients undergoing RT treatments for H&N cancers were considered. CT scans were processed by Limbus Contour auto-segmentation software, a commercial deep-learning auto-segmentation based software to generate AC. H&N protocol was used to perform AC, with the structure set consisting of bilateral brachial plexus, brain, brainstem, bilateral cochlea, pharyngeal constrictors, eye globes, bilateral lens, mandible, optic chiasm, bilateral optic nerves, oral cavity, bilateral parotids, spinal cord, bilateral submandibular glands, lips and thyroid. Manual revision of OARs was performed according to international consensus guidelines. The AC and MC were compared using the Dice similarity coefficient (DSC) and 95% Hausdorff distance transform (DT). Results: A total of 274 contours obtained by processing CT scans were included in the analysis. The highest values of DSC were obtained for the brain (DSC 1.00), left and right eye globes and the mandible (DSC 0.98). The structures with greater MC editing were optic chiasm, optic nerves and cochleae. Conclusions: In this preliminary analysis, deep-learning auto-segmentation seems to provide acceptable H&N OAR delineations. For less accurate organs, AC could be considered a starting point for review and manual adjustment. Our results suggest that AC could become a useful time-saving tool to optimize workload and resources in RT departments.

Background: Prostate cancer (PCa) is the second most common cancer in men, and radiotherapy (RT) is one of the main treatment options. Although effective, RT can cause toxic side effects. The accurate prediction of dosimetric parameters, enhanced by advanced technologies and AI-based predictive models, is crucial to optimize treatments and reduce toxicity risks. This study aims to explore current methodologies for predictive dosimetric parameters associated with RT toxicity in PCa patients, analyzing both traditional techniques and recent innovations. Methods: A systematic review was conducted using the PubMed, Scopus, and Medline databases to identify dosimetric predictive parameters for RT in prostate cancer. Studies published from 1987 to April 2024 were included, focusing on predictive models, dosimetric data, and AI techniques. Data extraction covered study details, methodology, predictive models, and results, with an emphasis on identifying trends and gaps in the research. Results: After removing duplicate manuscripts, 354 articles were identified from three databases, with 49 shortlisted for in-depth analysis. Of these, 27 met the inclusion criteria. Most studies utilized logistic regression models to analyze correlations between dosimetric parameters and toxicity, with the accuracy assessed by the area under the curve (AUC). The dosimetric parameter studies included Vdose, Dmax, and Dmean for the rectum, anal canal, bowel, and bladder. The evaluated toxicities were genitourinary, hematological, and gastrointestinal. Conclusions: Understanding dosimetric parameters, such as DVH, Dmax, and Dmean, is crucial for optimizing RT and predicting toxicity. Enhanced predictive accuracy improves treatment effectiveness and reduces side effects, ultimately improving patients’ quality of life. Emerging artificial intelligence and machine learning technologies offer the potential to further refine RT in PCa by analyzing complex data, and enabling more personalized treatment approaches.

Objective: Magnetic resonance–guided radiotherapy (MRgRT) represents an innovative approach for personalized radiotherapy treatments and its applications are being explored in various anatomical sites to fully understand its potential advantages. This study describes the first clinical experience of MRgRT application in patients with locally advanced cervical cancer (LACC) undergoing neoadjuvant chemoradiotherapy. The feasibility of the technique is evaluated and its toxicity profile and clinical outcomes are reported. Methods: Patients with LACC (International Federation of Gynecology and Obstetrics stage IIA–IVA) undergoing neoadjuvant chemoradiotherapy (CRT) on a 0.35T Tri-60-Co hybrid unit (ViewRay) were retrospectively compared with randomly selected patients treated with a standard linear accelerator. Total prescribed dose was 50.6 Gy (2.3 Gy/fraction) to planning target volume 1 (PTV1) and 39.6 Gy (1.8 Gy/fraction) to PTV2, delivered using a simultaneous integrated boost. Surgery was performed 8 weeks after the end of CRT. The effect of magnetic resonance guidance on replanning approaches, treatment-related toxicities, and pathologic response were assessed for each patient. Patient outcomes were noted and dosimetric comparisons performed between the 2 arms. Results: Nine patients with LACC treated from May 2018 to November 2018 were retrospectively enrolled and their records compared with the records of an equivalent cohort of randomly selected patients. Five replanning cases were performed in the MRgRT group and 0 in the linear accelerator group. Acute G1–G2 gastrointestinal toxicities were observed in 33.3% of MRgRT patients and in 55.5% of linear accelerator patients; acute G1–G2 genitourinary toxicities in 22.2% and 33.3%, respectively. No G3 toxicity was found except for neutropenia in 2 patients. No differences were observed in pathologic response between the 2 groups. Conclusions: Despite the retrospective nature of the observations and the low number of enrolled patients, the application of MRgRT in LACC appears to be safe and feasible with a favorable toxicity profile and response rates comparable to gold standard, supporting the setup of larger prospective studies to investigate the potentialities of this new technology.