Critical assessment of knowledge-based models for craniospinal irradiation of paediatric patients.

Background And Purpose: Knowledge-Based Planning (KBP) is increasingly used to standardize and optimize radiotherapy planning. This study aims to develop, refine, and compare multicentric KBP models for craniospinal irradiation (CSI) in pediatric patients.

Materials And Methods: A total of 113 CSI treatments from three Italian centers were collected, comprising Computed Tomography scans, target and organ contours, and treatment plans.

Technical note: A software tool to extract complexity metrics from radiotherapy treatment plans.

Background: Complexity metrics are mathematical quantities designed to quantify aspects of radiotherapy treatment plans that may affect both their deliverability and dosimetric accuracy. Despite numerous studies investigating their utility, there remains a notable absence of shared tools for their extraction.

Purpose: This study introduces UCoMX (Universal Complexity Metrics Extractor), a software package designed for the extraction of complexity metrics from the DICOM-RT plan files of radiotherapy treatments.

On the necessity of specialized knowledge-based models for SBRT prostate treatments plans.

Purpose: Test whether a well-grounded KBP model trained on moderately hypo-fractionated prostate treatments can be used to satisfactorily drive the optimization of SBRT prostate treatments.

Materials And Methods: A KBP model (SBRT-model) was developed, trained and validated using the first forty-seven clinically treated VMAT SBRT prostate plans (42.7 Gy/7fx or 36.

Prediction models as decision-support tools for virtual patient-specific quality assurance of helical tomotherapy plans.

Background And Purpose: Prediction models may be reliable decision-support tools to reduce the workload associated with the measurement-based patient-specific quality assurance (PSQA) of radiotherapy plans. This study compared the effectiveness of three different models based on delivery parameters, complexity metrics and sinogram radiomics features as tools for virtual-PSQA (vPSQA) of helical tomotherapy (HT) plans.

Materials And Methods: A dataset including 881 RT plans created with two different treatment planning systems (TPSs) was collected.

Updating a clinical Knowledge-Based Planning prediction model for prostate radiotherapy.

Background And Purpose: Clinical knowledge-based planning (KBP) models dedicated to prostate radiotherapy treatment may require periodical updates to remain relevant and to adapt to possible changes in the clinic. This study proposes a paired comparison of two different update approaches through a longitudinal analysis.

Materials And Methods: A clinically validated KBP model for moderately hypofractionated prostate therapy was periodically updated using two approaches: one was targeted at achieving the biggest library size (M), while the other one at achieving the highest mean sample quality (R).

Quantitative assessment of helical tomotherapy plans complexity.

Purpose: An unnecessary amount of complexity in radiotherapy plans affects the efficiency of the treatments, increasing the uncertainty of dose deposition and its susceptibility to anatomical changes or setup errors. To date, tools for quantitatively assessing the complexity of tomotherapy plans are still limited. In this study, new metrics were developed to characterize different aspects of helical tomotherapy (HT) plans, and their actual effectiveness was investigated.

Analysis of clinical patient-specific pre-treatment quality assurance with the new helical tomotherapy platform, following the AAPM TG-218 report.

Purpose: This study presents patient-specific quality assurance (QA) results from the first 395 clinical cases for the new helical TomoTherapy® platform (Radixact) coupled with dedicated Precision TPS.

Methods: The passing rate of the Gamma Index (GP%) of 395 helical QA of patient-specific tomotherapy, acquired with ArcCHECK, is presented, analysed and correlated to various parameters of the plan. Following TG-218 recommendations, the clinic specific action limit (AL) and tolerance limit (TL) were calculated for our clinic and monitored during the analysed period.

Optimizing radiotherapy plans for cancer treatment with Tensor Networks.

We present a novel application of Tensor Network methods in cancer treatment as a potential tool to solve the dose optimization problem in radiotherapy. In particular, the intensity-modulated radiation therapy technique-that allows treating irregular and inhomogeneous tumors while reducing the radiation toxicity on healthy organs-is based on the optimization problem of the beamlets intensities that shall result in a maximal delivery of the therapy dose to cancer while avoiding the organs at risk of being damaged by the radiation. The resulting optimization problem is expressed as a cost function to be optimized.