Personalised biomechanical modelling towards the optimisation of high dose-rate brachytherapy planning and treatment against prostate cancer.

High dose-rate brachytherapy presents a promising therapeutic avenue for prostate cancer management, involving the temporary implantation of catheters which deliver radioactive sources to the cancerous site. However, as catheters puncture and penetrate the prostate, tissue deformation is evident which may affect the accuracy and efficiency of the treatment. In this work, a data-driven modelling procedure is proposed to simulate brachytherapy while accounting for prostate biomechanics.

Serum-Free and Protein-Free Media for the Cultivation of Recombinant Chinese Hamster Ovary (CHO) Cell Lines.

We describe the preparation of two cell culture media formulations for the culture in suspension of Chinese hamster ovary (CHO) cell lines. The first medium, Cell growth SFM Medium, is a serum-free medium designed to maintain cell growth with high-viability profiles. The second corresponds to a protein-free version optimized to increase CHO recombinant protein production (Production PFM Medium).

Planning Benchmark Study for Stereotactic Body Radiation Therapy of Pancreas Carcinomas With Simultaneously Integrated Boost and Protection: Results of the DEGRO/DGMP Working Group on Stereotactic Radiation Therapy and Radiosurgery.

Purpose: The proximity or overlap of planning target volume (PTV) and organs-at-risk (OARs) poses a major challenge in stereotactic body radiation therapy (SBRT) of pancreatic cancer (PACA). This international treatment planning benchmark study investigates whether simultaneous integrated boost (SIB) and simultaneous integrated protection (SIP) concepts in PACA SBRT can lead to improved and harmonized plan quality.

Methods And Materials: A multiparametric specification of desired target doses (gross target volume [GTV], GTV, PTV, and PTV) with 2 prescription doses of GTV = 5 × 9.

The impact of multicentric datasets for the automated tumor delineation in primary prostate cancer using convolutional neural networks on F-PSMA-1007 PET.

Purpose: Convolutional Neural Networks (CNNs) have emerged as transformative tools in the field of radiation oncology, significantly advancing the precision of contouring practices. However, the adaptability of these algorithms across diverse scanners, institutions, and imaging protocols remains a considerable obstacle. This study aims to investigate the effects of incorporating institution-specific datasets into the training regimen of CNNs to assess their generalization ability in real-world clinical environments.

A clinical evaluation of the performance of five commercial artificial intelligence contouring systems for radiotherapy.

Purpose/objectives: Auto-segmentation with artificial intelligence (AI) offers an opportunity to reduce inter- and intra-observer variability in contouring, to improve the quality of contours, as well as to reduce the time taken to conduct this manual task. In this work we benchmark the AI auto-segmentation contours produced by five commercial vendors against a common dataset.

Methods And Materials: The organ at risk (OAR) contours generated by five commercial AI auto-segmentation solutions (Mirada (Mir), MVision (MV), Radformation (Rad), RayStation (Ray) and TheraPanacea (Ther)) were compared to manually-drawn expert contours from 20 breast, 20 head and neck, 20 lung and 20 prostate patients.