Defining the potential for sexual structures-sparing for prostate cancer external beam radiotherapy: A dosimetric study.

Purpose: The purpose of the study was to evaluate the dosimetric impact of sexual-sparing radiotherapy for prostate cancer, with magnetic resonance-only treatment planning.

Material And Methods: Fifteen consecutive patients receiving prostate cancer radiotherapy were selected. A synthetic CT was generated with a deep learning method from each T2-weighted MRI performed at the time of treatment planning.

3D Unsupervised deep learning method for magnetic resonance imaging-to-computed tomography synthesis in prostate radiotherapy.

Background And Purpose: Magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis is essential in MRI-only radiotherapy workflows, particularly through deep learning techniques known for their accuracy. However, current supervised methods are limited to specific center's learnings and depend on registration precision. The aim of this study was to evaluate the accuracy of unsupervised and supervised approaches in the context of prostate MRI-to-CT generation for radiotherapy dose calculation.

The dosimetric parameters impact on local recurrence in stereotactic radiotherapy for brain metastases.

Objectives: Stereotactic radiotherapy (SRT) for brain metastases (BM) allows very good local control (LC). However, approximately 20%-30% of these lesions will recur. The objective of this retrospective study was to evaluate the impact of dosimetric parameters on LC in cerebral SRT.

A deep learning model to generate synthetic CT for prostate MR-only radiotherapy dose planning: a multicenter study.

Introduction: For radiotherapy based solely on magnetic resonance imaging (MRI), generating synthetic computed tomography scans (sCT) from MRI is essential for dose calculation. The use of deep learning (DL) methods to generate sCT from MRI has shown encouraging results if the MRI images used for training the deep learning network and the MRI images for sCT generation come from the same MRI device. The objective of this study was to create and evaluate a generic DL model capable of generating sCTs from various MRI devices for prostate radiotherapy.

Computed tomography synthesis from magnetic resonance imaging using cycle Generative Adversarial Networks with multicenter learning.

Addressing the need for accurate dose calculation in MRI-only radiotherapy, the generation of synthetic Computed Tomography (sCT) from MRI has emerged. Deep learning (DL) techniques, have shown promising results in achieving high sCT accuracies. However, existing sCT synthesis methods are often center-specific, posing a challenge to their generalizability.

Determination of acceptable Hounsfield units uncertainties via a sensitivity analysis for an accurate dose calculation in the context of prostate MRI-only radiotherapy.

Radiation therapy is moving from CT based to MRI guided planning, particularly for soft tissue anatomy. An important requirement of this new workflow is the generation of synthetic-CT (sCT) from MRI to enable treatment dose calculations. Automatic methods to determine the acceptable range of CT Hounsfield Unit (HU) uncertainties to avoid dose distribution errors is thus a key step toward safe MRI-only radiotherapy.

Contour-guided deep learning based deformable image registration for dose monitoring during CBCT-guided radiotherapy of prostate cancer.

Purpose: To evaluate deep learning (DL)-based deformable image registration (DIR) for dose accumulation during radiotherapy of prostate cancer patients.

Methods And Materials: Data including 341 CBCTs (209 daily, 132 weekly) and 23 planning CTs from 23 patients was retrospectively analyzed. Anatomical deformation during treatment was estimated using free-form deformation (FFD) method from Elastix and DL-based VoxelMorph approaches.

Practical and technical key challenges in head and neck adaptive radiotherapy: The GORTEC point of view.

Anatomical variations occur during head and neck (H&N) radiotherapy (RT) treatment. These variations may result in underdosage to the target volume or overdosage to the organ at risk. Replanning during the treatment course can be triggered to overcome this issue.

Automatic segmentation for plan-of-the-day selection in CBCT-guided adaptive radiation therapy of cervical cancer.

Plan-of-the-day (PoD) adaptive radiation therapy (ART) is based on a library of treatment plans, among which, at each treatment fraction, the PoD is selected using daily images. However, this strategy is limited by PoD selection uncertainties. This work aimed to propose and evaluate a workflow to automatically and quantitatively identify the PoD for cervix cancer ART based on daily CBCT images.

Quality assurance for MRI-only radiation therapy: A voxel-wise population-based methodology for image and dose assessment of synthetic CT generation methods.

The quality assurance of synthetic CT (sCT) is crucial for safe clinical transfer to an MRI-only radiotherapy planning workflow. The aim of this work is to propose a population-based process assessing local errors in the generation of sCTs and their impact on dose distribution. For the analysis to be anatomically meaningful, a customized interpatient registration method brought the population data to the same coordinate system.

Deep learning-based segmentation in prostate radiation therapy using Monte Carlo simulated cone-beam computed tomography.

Purpose: Segmenting organs in cone-beam CT (CBCT) images would allow to adapt the radiotherapy based on the organ deformations that may occur between treatment fractions. However, this is a difficult task because of the relative lack of contrast in CBCT images, leading to high inter-observer variability. Deformable image registration (DIR) and deep-learning based automatic segmentation approaches have shown interesting results for this task in the past years.

Planning With Patient-Specific Rectal Sub-Region Constraints Decreases Probability of Toxicity in Prostate Cancer Radiotherapy.

A rectal sub-region (SRR) has been previously identified by voxel-wise analysis in the inferior-anterior part of the rectum as highly predictive of rectal bleeding (RB) in prostate cancer radiotherapy. Translating the SRR to patient-specific radiotherapy planning is challenging as new constraints have to be defined. A recent geometry-based model proposed to optimize the planning by determining the achievable mean doses (AMDs) to the organs at risk (OARs), taking into account the overlap between the planning target volume (PTV) and OAR.

Comparison of CBCT-based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning.

Purpose: Anatomical variations occur during head and neck (H&N) radiotherapy treatment. kV cone-beam computed tomography (CBCT) images can be used for daily dose monitoring to assess dose variations owing to anatomic changes. Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) from CBCT to perform dose calculation.

Comparison of Deep Learning-Based and Patch-Based Methods for Pseudo-CT Generation in MRI-Based Prostate Dose Planning.

Purpose: Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for magnetic resonance imaging (MRI) based dose planning. This study aims to evaluate and compare DLMs (U-Net and generative adversarial network [GAN]) using various loss functions (L2, single-scale perceptual loss [PL], multiscale PL, weighted multiscale PL) and a patch-based method (PBM).

Methods And Materials: Thirty-nine patients received a volumetric modulated arc therapy for prostate cancer (78 Gy).

Pseudo-CT Generation for MRI-Only Radiation Therapy Treatment Planning: Comparison Among Patch-Based, Atlas-Based, and Bulk Density Methods.

Purpose: Methods have been recently developed to generate pseudo-computed tomography (pCT) for dose calculation in magnetic resonance imaging (MRI)-only radiation therapy. This study aimed to propose an original nonlocal mean patch-based method (PBM) and to compare this PBM to an atlas-based method (ABM) and to a bulk density method (BDM) for prostate MRI-only radiation therapy.

Materials And Methods: Thirty-nine patients received a volumetric modulated arc therapy for prostate cancer.

CBCT-guided evolutive library for cervical adaptive IMRT.

Purpose: In the context of adaptive radiation therapy (ART) for locally advanced cervical carcinoma (LACC), this study proposed an original cone-beam computed tomography (CBCT)-guided "Evolutive library" and evaluated it against four other known radiotherapy (RT) strategies.

Material And Methods: For 20 patients who underwent intensity-modulated radiation therapy (IMRT) for LACC, three planning CTs [with empty (EB), intermediate (IB), and full (FB) bladder volumes], a CT scan at 20 Gy and bi-weekly CBCTs for 5 weeks were performed. Five RT strategies were simulated for each patient: "Standard RT" was based on one IB planning CT; "internal target volume (ITV)-based RT" was an ITV built from the three planning CTs; "RT with one mid-treatment replanning (MidTtReplan)" corresponded to the standard RT with a replanning at 20 Gy; "Pretreatment library ART" using a planning library based on the three planning CTs; and the "Evolutive library ART", which was the "Pretreatment library ART" strategy enriched by including some CBCT anatomies into the library when the daily clinical target volume (CTV) shape differed from the ones in the library.

Dose calculation accuracy of different image value to density tables for cone-beam CT planning in head & neck and pelvic localizations.

Purpose: We aimed to identify the most accurate combination of phantom and protocol for image value to density table (IVDT) on volume-modulated arc therapy (VMAT) dose calculation based on kV-Cone-beam CT imaging, for head and neck (H&N) and pelvic localizations.

Methods: Three phantoms (Catphan(®)600, CIRS(®)062M (inner phantom for head and outer phantom for body), and TomoTherapy(®) "Cheese" phantom) were used to create IVDT curves of CBCT systems with two different CBCT protocols (Standard-dose Head and Standard Pelvis). Hounsfield Unit (HU) time stability and repeatability for a single On-Board-Imager (OBI) and compatibility of two distinct devices were assessed with Catphan(®)600.