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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., Methods: CT/MRI image pairs from 99 prostate cancer patients across three different centers were used. A comparison between supervised and unsupervised conditional Generative Adversarial Networks (cGAN) was conducted. Unsupervised training incorporates a style transfer method with. Content and Style Representation for Enhanced Perceptual synthesis (CREPs) loss. For dose evaluation, the photon prescription dose was 60 Gy delivered in volumetric modulated arc therapy (VMAT). Imaging endpoint for sCT evaluation was Mean Absolute Error (MAE). Dosimetric endpoints included absolute dose differences and gamma analysis between CT and sCT dose calculations., Results: The unsupervised paired network exhibited the highest accuracy for the body with a MAE at 33.6 HU, the highest MAE was 45.5 HU obtained with unsupervised unpaired learning. All architectures provided clinically acceptable results for dose calculation with gamma pass rates above 94 % (1 % 1 mm 10 %)., Conclusions: This study shows that multicenter data can produce accurate sCTs via unsupervised learning, eliminating CT-MRI registration. The sCTs not only matched HU values but also enabled precise dose calculations, suggesting their potential for wider use in MRI-only radiotherapy workflows., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Cédric Hémon benefits from a PhD scholarship granted by Elekta AB., (© 2024 The Author(s).)

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. This work has analysed the effects of controlled errors introduced in CT scans on the delivered radiation dose for prostate cancer patients. Spearman correlation coefficient has been computed, and a global sensitivity analysis performed following the Morris screening method. This allows the classification of different error factors according to their impact on the dose at the isocentre. sCT HU estimation errors in the bladder appeared to be the least influential factor, and sCT quality assessment should not only focus on organs surrounding the radiation target, as errors in other soft tissue may significantly impact the dose in the target volume. This methodology links dose and intensity-based metrics, and is the first step to define a threshold of acceptability of HU uncertainties for accurate dose planning., (© 2023. The Author(s).)

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., Materials and Methods: In total, 90 patients from three centers (30 CT-MR prostate pairs/center) underwent treatment using volumetric modulated arc therapy for prostate cancer (PCa) (60 Gy in 20 fractions). T2 MRI images were acquired in addition to computed tomography (CT) images for treatment planning. The DL model was a 2D supervised conditional generative adversarial network (Pix2Pix). Patient images underwent preprocessing steps, including nonrigid registration. Seven different supervised models were trained, incorporating patients from one, two, or three centers. Each model was trained on 24 CT-MR prostate pairs. A generic model was trained using patients from all three centers. To compare sCT and CT, the mean absolute error in Hounsfield units was calculated for the entire pelvis, prostate, bladder, rectum, and bones. For dose analysis, mean dose differences of D 99% for CTV, V 95% for PTV, D max for rectum and bladder, and 3D gamma analysis (local, 1%/1 mm) were calculated from CT and sCT. Furthermore, Wilcoxon tests were performed to compare the image and dose results obtained with the generic model to those with the other trained models., Results: Considering the image results for the entire pelvis, when the data used for the test comes from the same center as the data used for training, the results were not significantly different from the generic model. Absolute dose differences were less than 1 Gy for the CTV D 99% for every trained model and center. The gamma analysis results showed nonsignificant differences between the generic and monocentric models., Conclusion: The accuracy of sCT, in terms of image and dose, is equivalent to whether MRI images are generated using the generic model or the monocentric model. The generic model, using only eight MRI-CT pairs per center, offers robust sCT generation, facilitating PCa MRI-only radiotherapy for routine clinical use., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Tahri, Texier, Nunes, Hemon, Lekieffre, Collot, Chourak, Le Guevelou, Greer, Dowling, Acosta, Bessieres, Marage, Boue-Rafle, De Crevoisier, Lafond and Barateau.)

Background and Purpose: 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. To overcome this limitation, recent studies have proposed approaches, such as multicenter training . Material and methods: The purpose of this work was to propose a multicenter sCT synthesis by DL, using a 2D cycle-GAN on 128 prostate cancer patients, from four different centers. Four cases were compared: monocenter cases, monocenter training and test on another center, multicenter trainings and a test on a center not included in the training and multicenter trainings with an included center in the test. Trainings were performed using 20 patients. sCT accuracy evaluation was performed using Mean Absolute Error, Mean Error and Peak-Signal-to-Noise-Ratio. Dose accuracy was assessed with gamma index and Dose Volume Histogram comparison. Results: Qualitative, quantitative and dose results show that the accuracy of sCTs for monocenter trainings and multicenter trainings using a seen center in the test did not differ significantly. However, when the test involved an unseen center, the sCT quality was inferior. Conclusions: The aim of this work was to propose generalizable multicenter training for MR-to-CT synthesis. It was shown that only a few data from one center included in the training cohort allows sCT accuracy equivalent to a monocenter study., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

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. The VoxelMorph method was investigated using anatomical scans (VMorph_Sc) or label images (VMorph_Msk), or the combination of both (VMorph_Sc_Msk). Accumulated doses were compared with the planning dose., Results: The DSC ranges, averaged for prostate, rectum and bladder, were 0.60-0.71, 0.67-0.79, 0.93-0.98, and 0.89-0.96 for the FFD, VMorph_Sc, VMorph_Msk, and VMorph_Sc_Msk methods, respectively. When including both anatomical and label images, VoxelMorph estimated more complex deformations resulting in heterogeneous determinant of Jacobian and higher percentage of deformation vector field (DVF) folding (up to a mean value of 1.90% in the prostate). Large differences were observed between DL-based methods regarding estimation of the accumulated dose, showing systematic overdosage and underdosage of the bladder and rectum, respectively. The difference between planned mean dose and accumulated mean dose with VMorph_Sc_Msk reached a median value of +6.3 Gy for the bladder and -5.1 Gy for the rectum., Conclusion: The estimation of the deformations using DL-based approach is feasible for male pelvic anatomy but requires the inclusion of anatomical contours to improve organ correspondence. High variability in the estimation of the accumulated dose depending on the deformable strategy suggests further investigation of DL-based techniques before clinical deployment., (© 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

Objective. 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. Approach. The quantification was based on the segmentation of the main structures of interest in the CBCT images (clinical target volume [CTV], rectum, bladder, and bowel bag) using a deep learning model. Then, the PoD was selected from the treatment plan library according to the geometrical coverage of the CTV. For the evaluation, the resulting PoD was compared to the one obtained considering reference CBCT delineations. Main results. In experiments on a database of 23 patients with 272 CBCT images, the proposed method obtained an agreement between the reference PoD and the automatically identified PoD for 91.5% of treatment fractions (99.6% when considering a 5% margin on CTV coverage). Significance. The proposed automatic workflow automatically selected PoD for ART using deep-learning methods. The results showed the ability of the proposed process to identify the optimal PoD in a treatment plan library., (© 2022 Institute of Physics and Engineering in Medicine.)

Purpose: The first aim was to generate and compare synthetic-CT (sCT) images using a conditional generative adversarial network (cGAN) method (Pix2Pix) for MRI-only prostate radiotherapy planning by testing several generators, loss functions, and hyper-parameters. The second aim was to compare the optimized Pix2Pix model with five other architectures (bulk-density, atlas-based, patch-based, U-Net, and GAN)., Methods: For 39 patients treated by VMAT for prostate cancer, T2-weighted MRI images were acquired in addition to CT images for treatment planning. sCT images were generated using the Pix2Pix model. The generator, loss function, and hyper-parameters were tuned to improve sCT image generation (in terms of imaging endpoints). The final evaluation was performed by 3-fold cross-validation. This method was compared to five other methods using the following imaging endpoints: the mean absolute error (MAE) and mean error (ME) between sCT and reference CT images (rCT) of the whole pelvis, bones, prostate, bladder, and rectum. For dose planning analysis, the dose-volume histogram metric differences and 3D gamma analysis (local, 1 %/1 mm) were calculated using the sCT and reference CT images., Results: Compared with the other architectures, Pix2Pix with Perceptual loss function and generator ResNet 9 blocks showed the lowest MAE (29.5, 107.7, 16.0, 13.4, and 49.1 HU for the whole pelvis, bones, prostate, bladder, and rectum, respectively) and the highest gamma passing rates (99.4 %, using the 1 %/1mm and 10 % dose threshold criterion). Concerning the DVH points, the mean errors were -0.2% for the planning target volume V 95% , 0.1 % for the rectum V 70Gy , and -0.1 % for the bladder V 50Gy ., Conclusion: The sCT images generated from MRI data with the Pix2Pix architecture had the lowest image errors and similar dose uncertainties (in term of gamma pass-rate and dose-volume histogram metric differences) than other deep learning methods., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Ltd.)

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. Then, the voxel-based process was applied on two sCT generation methods: a bulk-density method and a generative adversarial network. The CT and MRI pairs of 39 patients treated by radiotherapy for prostate cancer were used for sCT generation, and 26 of them with delineated structures were selected for analysis. Voxel-wise errors in sCT compared to CT were assessed for image intensities and dose calculation, and a population-based statistical test was applied to identify the regions where discrepancies were significant. The cumulative histograms of the mean absolute dose error per volume of tissue were computed to give a quantitative indication of the error for each generation method. Accurate interpatient registration was achieved, with mean Dice scores higher than 0.91 for all organs. The proposed method produces three-dimensional maps that precisely show the location of the major discrepancies for both sCT generation methods, highlighting the heterogeneity of image and dose errors for sCT generation methods from MRI across the pelvic anatomy. Hence, this method provides additional information that will assist with both sCT development and quality control for MRI-based planning radiotherapy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chourak, Barateau, Tahri, Cadin, Lafond, Nunes, Boue-Rafle, Perazzi, Greer, Dowling, de Crevoisier and Acosta.)

During the joint SFRO/SFPM session of the 2019 congress, a state of the art of adaptive radiotherapy announced a strong impact in our clinical practice, in particular with the availability of treatment devices coupled to an MRI system. Three years later, it seems relevant to take stock of adaptive radiotherapy in practice, and especially the "online" strategy because it is indeed more and more accessible with recent hardware and software developments, such as coupled accelerators to a three-dimensional imaging device and algorithms based on artificial intelligence. However, the deployment of this promising strategy is complex because it contracts the usual time scale and upsets the usual organizations. So what do we need to deliver adapted treatment plans with an "online" strategy?, (Copyright © 2022. Published by Elsevier Masson SAS.)

Purpose: In radiotherapy, MRI is used for target volume and organs-at-risk delineation for its superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the electron density of tissue necessary for dose calculation. Several methods of synthetic-CT (sCT) generation from MRI data have been developed for radiotherapy dose calculation. This work reviewed deep learning (DL) sCT generation methods and their associated image and dose evaluation, in the context of MRI-based dose calculation., Methods: We searched the PubMed and ScienceDirect electronic databases from January 2010 to March 2021. For each paper, several items were screened and compiled in figures and tables., Results: This review included 57 studies. The DL methods were either generator-only based (45% of the reviewed studies), or generative adversarial network (GAN) architecture and its variants (55% of the reviewed studies). The brain and pelvis were the most commonly investigated anatomical localizations (39% and 28% of the reviewed studies, respectively), and more rarely, the head-and-neck (H&N) (15%), abdomen (10%), liver (5%) or breast (3%). All the studies performed an image evaluation of sCTs with a diversity of metrics, with only 36 studies performing dosimetric evaluations of sCT., Conclusions: The median mean absolute errors were around 76 HU for the brain and H&N sCTs and 40 HU for the pelvis sCTs. For the brain, the mean dose difference between the sCT and the reference CT was <2%. For the H&N and pelvis, the mean dose difference was below 1% in most of the studies. Recent GAN architectures have advantages compared to generator-only, but no superiority was found in term of image or dose sCT uncertainties. Key challenges of DL-based sCT generation methods from MRI in radiotherapy is the management of movement for abdominal and thoracic localizations, the standardization of sCT evaluation, and the investigation of multicenter impacts., (Copyright © 2021 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

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. This study aims to evaluate the accuracy of a DLM and to compare this method with three existing methods of dose calculation from CBCT in H&N cancer radiotherapy., Methods: Forty-four patients received VMAT for H&N cancer (70-63-56 Gy). For each patient, reference CT (Bigbore, Philips) and CBCT images (XVI, Elekta) were acquired. The DLM was based on a generative adversarial network. The three compared methods were: (a) a method using a density to Hounsfield Unit (HU) relation from phantom CBCT image (HU-D curve method), (b) a water-air-bone density assignment method (DAM), and iii) a method using deformable image registration (DIR). The imaging endpoints were the mean absolute error (MAE) and mean error (ME) of HU from pCT and reference CT (CT ref ). The dosimetric endpoints were dose discrepancies and 3D gamma analyses (local, 2%/2 mm, 30% dose threshold). Dose discrepancies were defined as the mean absolute differences between DVHs calculated from the CT ref and pCT of each method., Results: In the entire body, the MAEs and MEs of the DLM, HU-D curve method, DAM, and DIR method were 82.4 and 17.1 HU, 266.6 and 208.9 HU, 113.2 and 14.2 HU, and 95.5 and -36.6 HU, respectively. The MAE obtained using the DLM differed significantly from those of other methods (Wilcoxon, P ≤ 0.05). The DLM dose discrepancies were 7 ± 8 cGy (maximum = 44 cGy) for the ipsilateral parotid gland D mean and 5 ± 6 cGy (max = 26 cGy) for the contralateral parotid gland mean dose (D mean ). For the parotid gland D mean , no significant dose difference was observed between the DLM and other methods. The mean 3D gamma pass rate ± standard deviation was 98.1 ± 1.2%, 91.0 ± 5.3%, 97.9 ± 1.6%, and 98.8 ± 0.7% for the DLM, HU-D method, DAM, and DIR method, respectively. The gamma pass rates and mean gamma results of the HU-D curve method, DAM, and DIR method differed significantly from those of the DLM., Conclusions: For H&N radiotherapy, DIR method and DLM appears as the most appealing CBCT-based dose calculation methods among the four methods in terms of dose accuracy as well as calculation time. Using the DIR method or DLM with CBCT images enables dose monitoring in the parotid glands during the treatment course and may be used to trigger replanning., (© 2020 American Association of Physicists in Medicine.)

Background: 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. The aim of this study was to quantify the SRR dose sparing by using the AMD model in the planning, while preserving the dose to the prostate. Material and Methods: Three-dimensional volumetric modulated arc therapy (VMAT) planning dose distributions for 60 patients were computed following four different strategies, delivering 78 Gy to the prostate, while meeting the genitourinary group dose constraints to the OAR: (i) a standard plan corresponding to the standard practice for rectum sparing (STD pl ), (ii) a plan adding constraints to SRR (SRR pl ), (iii) a plan using the AMD model applied to the rectum only (AMD&#95;RECT pl ), and (iv) a final plan using the AMD model applied to both the rectum and the SRR (AMD&#95;RECT&#95;SRR pl ). After PTV dose normalization, plans were compared with regard to dose distributions, quality, and estimated risk of RB using a normal tissue complication probability model. Results: AMD&#95;RECT&#95;SRR pl showed the largest SRR dose sparing, with significant mean dose reductions of 7.7, 3, and 2.3 Gy, with respect to the STD pl , SRR pl , and AMD&#95;RECT pl , respectively. AMD&#95;RECT&#95;SRR pl also decreased the mean rectal dose by 3.6 Gy relative to STD pl and by 3.3 Gy relative to SRR pl . The absolute risk of grade ≥1 RB decreased from 22.8% using STD pl planning to 17.6% using AMD&#95;RECT&#95;SRR pl considering SRR volume. AMD&#95;RECT&#95;SRR pl plans, however, showed slightly less dose homogeneity and significant increase of the number of monitor units, compared to the three other strategies. Conclusion: Compared to a standard prostate planning, applying dose constraints to a patient-specific SRR by using the achievable mean dose model decreased the mean dose by 7.7 Gy to the SRR and may decrease the relative risk of RB by 22%., (Copyright © 2020 Lafond, Barateau, N'Guessan, Perichon, Delaby, Simon, Haigron, Mylona, Acosta and de Crevoisier.)

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). T 2 -weighted MRIs were acquired in addition to planning CTs. The pCTs were generated from the MRIs using 7 configurations: 4 GANs (L2, single-scale PL, multiscale PL, weighted multiscale PL), 2 U-Net (L2 and single-scale PL), and the PBM. The imaging endpoints were mean absolute error and mean error, in Hounsfield units, between the reference CT (CT ref ) and the pCT. Dose uncertainties were quantified as mean absolute differences between the dose volume histograms (DVHs) calculated from the CT ref and pCT obtained by each method. Three-dimensional gamma indexes were analyzed., Results: Considering the image uncertainties in the whole pelvis, GAN L2 and U-Net L2 showed the lowest mean absolute error (≤34.4 Hounsfield units). The mean errors were not different than 0 (P ≤ .05). The PBM provided the highest uncertainties. Very few DVH points differed when comparing GAN L2 or U-Net L2 DVHs and CT ref DVHs (P ≤ .05). Their dose uncertainties were ≤0.6% for the prostate planning target Volume V 95% , ≤0.5% for the rectum V 70Gy , and ≤0.1% for the bladder V 50Gy . The PBM, U-Net PL, and GAN PL presented the highest systematic dose uncertainties. The gamma pass rates were >99% for all DLMs. The mean calculation time to generate 1 pCT was 15 s for the DLMs and 62 min for the PBM., Conclusions: Generating pCT for MRI dose planning with DLMs and PBM provided low-dose uncertainties. In particular, the GAN L2 and U-Net L2 provided the lowest dose uncertainties together with a low computation time., (Crown Copyright © 2019. Published by Elsevier Inc. All rights reserved.)

Intensity modulated radiotherapy combined with image guided radiotherapy has led to increase the precision of external beam radiotherapy. However, intra or inter-fraction anatomical variations are frequent during the treatment course and can cause under-dosing of the target volume and/or over-dosing of the organs at risk. Several adaptive radiotherapy (ART) strategies can be defined to compensate these anatomical variations. The purpose of this article is to provide an overview of available ART strategies: offline, online, hybrid (library of treatment plans) or in real-time, while considering the arrival of MR-Linac devices in radiotherapy departments. The tools required to these ART strategies such as auto-segmentation, deformable image registration, calculation of the daily dose or dose accumulation, are also described. Implementing an ART strategy requires a rigorous quality assurance process, at each stage and on the entire workflow, as well as prior organization and training from of all the trades. A strong multidisciplinary involvement is finally required in order to ensure ART treatments., (Copyright © 2019 Société française de radiothérapie oncologique (SFRO). Published by Elsevier Masson SAS. All rights reserved.)

Adaptive radiotherapy (ART) is a complexe image-guided radiotherapy modality that comprises multiple planning to account for anatomical variations occurring during irradiation. Schematically, two strategies of RTA can be distinguished and combined according to tumor locations. One or more replanning can be proposed to correct systematic variations such as tumor shrinkage. A library of treatment plans with day-to-day plan selection from cone-beam CT imaging can also be proposed to correct random variations such as uterine motion or bladder/rectum volume changes. Because of strong anatomical variations occurring during irradiation, RTA appears therefore particularly justified in head and neck, lung, bladder, cervical and rectum and pancreas tumors, and to a lesser extent for prostate tumors and other digestive tumors. For these tumor locations, ART provides a fairly clear dosimetric benefit but a clinical benefit not yet formally demonstrated. ART cannot be proposed in a routine practice but must be evaluated medico-economically in the context of prospective trials. A rigorous quality control must be associated., (Copyright © 2019 Société française de radiothérapie oncologique (SFRO). Published by Elsevier Masson SAS. All rights reserved.)

Background and Purpose: During head-and-neck (H&N) radiotherapy, the parotid glands (PGs) may be overdosed; thus, a tool is required to monitor the delivered dose. This study aimed to assess the dose accuracy of a patient-specific density assignment method (DAM) for dose calculation to monitor the dose to PGs during treatment., Patients and Methods: Forty patients with H&N cancer received an intensity modulated radiation therapy (IMRT), among whom 15 had weekly CTs. Dose distributions were calculated either on the CTs (CT ref ), on one-class CTs (1C-CT, water), or on three-class CTs (3C-CT, water-air-bone). The inter- and intra-patient DAM uncertainties were evaluated by the difference between doses calculated on CT ref and 1C-CTs or 3C-CTs. PG mean dose (D mean ) and spinal cord maximum dose (D 2% ) were considered. The cumulated dose to the PGs was estimated by the mean D mean of the weekly CTs., Results: The mean (maximum) inter-patient DAM dose uncertainties for the PGs (in cGy) were 23 (75) using 1C-CTs and 12 (50) using 3C-CTs (p ≤ 0.001). For the spinal cord, these uncertainties were 118 (245) and 15 (67; p ≤ 0.001). The mean (maximum) DAM dose uncertainty between cumulated doses calculated on CTs and 3C-CTs was 7 cGy (45 cGy) for the PGs. Considering the difference between the planned and cumulated doses, 53% of the ipsilateral and 80% of the contralateral PGs were overdosed by +3.6 Gy (up to 8.2 Gy) and +1.9 Gy (up to 5.2 Gy), respectively., Conclusion: The uncertainty of the three-class DAM appears to be clinically non-significant (<0.5 Gy) compared with the PG overdose (up to 8.2 Gy). This DAM could therefore be used to monitor PG doses and trigger replanning.

In external beam radiotherapy, the dose planning is currently based on computed tomography (CT) images. A relation between Hounsfield numbers and electron densities (or mass densities) is necessary for dose calculation taking heterogeneities into account. In image-guided radiotherapy process, the cone beam CT is classically used for tissue visualization and registration. Cone beam CT for dose calculation is also attractive in dose reporting/monitoring perspectives and particularly in a context of dose-guided adaptive radiotherapy. The accuracy of cone beam CT-based dose calculation is limited by image characteristics such as quality, Hounsfield numbers consistency and restrictive sizes of volume acquisition. The analysis of the literature identifies three kinds of strategies for cone beam CT-based dose calculation: establishment of Hounsfield numbers versus densities curves, density override to regions of interest, and deformable registration between CT and cone beam CT images. Literature results show that discrepancies between the reference CT-based dose calculation and the cone beam CT-based dose calculation are often lower than 3%, regardless of the method. However, they can also reach 10% with unsuitable method. Even if the accuracy of the cone beam CT-based dose calculation is independent of the method, some strategies are promising but need improvements in the automating process for a routine implementation., (Copyright © 2017 Société française de radiothérapie oncologique (SFRO). Published by Elsevier SAS. All rights reserved.)

Proximity ligation assay (PLA) appears as a quick and easy technique to visualize within fixed cells the occurrence and in situ distribution of protein complexes. PLA has been validated to detect protein-protein interactions within the nuclear compartment. Here, we describe a protocol which allows the detection of interactions between A-type nuclear lamins and either LEM-domain proteins (such as emerin, integrated within the inner nuclear membrane, and LAP2α which accumulates within the nucleoplasm) or gene regulatory factors (e.g., the transcription factor SREBP1). The distinct amounts and patterns of PLA signals obtained for various complexes highlight the pertinence of using PLA to reveal in situ where and to which extent nuclear envelope proteins bind specific partners.