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Background: Direction Modulated Brachytherapy (DMBT) enables conformal dose distributions. However, clinicians may face challenges in creating viable treatment plans within a fast‐paced clinical setting, especially for a novel technology like DMBT, where cumulative clinical experience is limited. Deep learning‐based dose prediction methods have emerged as effective tools for enhancing efficiency. Purpose: To develop a voxel‐wise dose prediction model using an attention‐gating mechanism and a 3D UNET for cervical cancer high‐dose‐rate (HDR) brachytherapy treatment planning with DMBT six‐groove tandems with ovoids or ring applicators. Methods: A multi‐institutional cohort of 122 retrospective clinical HDR brachytherapy plans treated to a prescription dose in the range of 4.8–7.0 Gy/fraction was used. A DMBT tandem model was constructed and incorporated onto a research version of BrachyVision Treatment Planning System (BV‐TPS) as a 3D solid model applicator and retrospectively re‐planned all cases by seasoned experts. Those plans were randomly divided into 64:16:20 as training, validating, and testing cohorts, respectively. Data augmentation was applied to the training and validation sets to increase the size by a factor of 4. An attention‐gated 3D UNET architecture model was developed to predict full 3D dose distributions based on high‐risk clinical target volume (CTVHR) and organs at risk (OARs) contour information. The model was trained using the mean absolute error loss function, Adam optimization algorithm, a learning rate of 0.001, 250 epochs, and a batch size of eight. In addition, a baseline UNET model was trained similarly for comparison. The model performance was evaluated on the testing dataset by analyzing the outcomes in terms of mean dose values and derived dose‐volume‐histogram indices from 3D dose distributions and comparing the generated dose distributions against the ground‐truth dose distributions using dose statistics and clinically meaningful dosimetric indices. Results: The proposed attention‐gated 3D UNET model showed competitive accuracy in predicting 3D dose distributions that closely resemble the ground‐truth dose distributions. The average values of the mean absolute errors were 1.82 ± 29.09 Gy (vs. 6.41 ± 20.16 Gy for a baseline UNET) in CTVHR, 0.89 ± 1.25 Gy (vs. 0.94 ± 3.96 Gy for a baseline UNET) in the bladder, 0.33 ± 0.67 Gy (vs. 0.53 ± 1.66 Gy for a baseline UNET) in the rectum, and 0.55 ± 1.57 Gy (vs. 0.76 ± 2.89 Gy for a baseline UNET) in the sigmoid. The results showed that the mean absolute error (MAE) for the bladder, rectum, and sigmoid were 0.22 ± 1.22 Gy (3.62%) (p = 0.015), 0.21 ± 1.06 Gy (2.20%) (p = 0.172), and ‐0.03 ± 0.54 Gy (1.13%) (p = 0.774), respectively. The MAE for D90, V100%, and V150% of the CTVHR were 0.46 ± 2.44 Gy (8.14%) (p = 0.018), 0.57 ± 11.25% (5.23%) (p = 0.283), and ‐0.43 ± 19.36% (4.62%) (p = 0.190), respectively. The proposed model needs less than 5 s to predict a full 3D dose distribution of 64 × 64 × 64 voxels for any new patient plan, thus making it sufficient for near real‐time applications and aiding with decision‐making in the clinic. Conclusions: Attention gated 3D‐UNET model demonstrated a capability in predicting voxel‐wise dose prediction, in comparison to 3D UNET, for DMBT intracavitary brachytherapy planning. The proposed model could be used to obtain dose distributions for near real‐time decision‐making before DMBT planning and quality assurance. This will guide future automated planning, making the workflow more efficient and clinically viable. [ABSTRACT FROM AUTHOR]

Purpose: This study presents a novel and comprehensive framework for evaluating magnetic resonance guided radiotherapy (MRgRT) workflow by integrating the Failure Modes and Effects Analysis (FMEA) approach with Time‐Driven Activity‐Based Costing (TDABC). We assess the workflow for safety, quality, and economic implications, providing a holistic understanding of the MRgRT implementation. The aim is to offer valuable insights to healthcare practitioners and administrators, facilitating informed decision‐making regarding the 0.35T MRIdian MR‐Linac system's clinical workflow. Methods: For FMEA, a multidisciplinary team followed the TG‐100 methodology to assess the MRgRT workflow's potential failure modes. Following the mitigation of primary failure modes and workflow optimization, a treatment process was established for TDABC analysis. The TDABC was applied to both MRgRT and computed tomography guided RT (CTgRT) for typical five‐fraction stereotactic body RT (SBRT) treatments, assessing total workflow and costs associated between the two treatment workflows. Results: A total of 279 failure modes were identified, with 31 categorized as high‐risk, 55 as medium‐risk, and the rest as low‐risk. The top 20% risk priority numbers (RPN) were determined for each radiation oncology care team member. Total MRgRT and CTgRT costs were assessed. Implementing technological advancements, such as real‐time multi leaf collimator (MLC) tracking with volumetric modulated arc therapy (VMAT), auto‐segmentation, and increasing the Linac dose rate, led to significant cost savings for MRgRT. Conclusion: In this study, we integrated FMEA with TDABC to comprehensively evaluate the workflow and the associated costs of MRgRT compared to conventional CTgRT for five‐fraction SBRT treatments. FMEA analysis identified critical failure modes, offering insights to enhance patient safety. TDABC analysis revealed that while MRgRT provides unique advantages, it may involve higher costs. Our findings underscore the importance of exploring cost‐effective strategies and key technological advancements to ensure the widespread adoption and financial sustainability of MRgRT in clinical practice. [ABSTRACT FROM AUTHOR]

This systematic review study aims to provide comprehensive data on different radiobiological models, parameters, and endpoints used for calculating the normal tissue complication probability (NTCP) based on clinical data from head and neck cancer patients treated with conformal radiotherapy. A systematic literature search was carried out according to the PRISMA guideline for the identification of relevant publications in six electronic databases of Embase, PubMed, Scopus, and Google Scholar to July 2022 using specific keywords in the paper's title and abstract. The initial search resulted in 1368 articles for all organs for the review article about the NTCP parameters. One hundred and seventy‐eight articles were accepted for all organs with complete parameters for the mentioned models and finally, 20 head and neck cancer articles were accepted for review. Analysis of the studies shows that the Lyman‐Kutcher‐Burman (LKB) model properly links the NTCP curve parameters to the postradiotherapy endpoints. In the LKB model for esophagus, the minimum, and maximum corresponding parameters were reported as TD50 = 2.61 Gy with grade ≥3 radiation‐induced esophagitis endpoints as the minimum TD50 and TD50 = 68 Gy as the maximum ones. nmin = 0.06, nmax = 1.04, mmin = 0.1, and mmax = 0.65, respectively. Unfortunately, there was not a wide range of published articles on other organs at risk like ear or cauda equina except Burman et al. (Fitting of normal tissue tolerance data to an analytic function. Int J Radiat Oncol Biol Phys Ther. 1991;21:123–135). Findings suggest that the validation of different radiobiological models and their corresponding parameters need to be investigated in vivo and in vitro for developing a more accurate NTCP model to be used for radiotherapy treatment planning optimization. [ABSTRACT FROM AUTHOR]