MOREOVER: multiomics MR-guided radiotherapy optimization in locally advanced rectal cancer.

BACKGROUND: Complete response prediction in locally advanced rectal cancer (LARC) patients is generally focused on the radiomics analysis of staging MRI. Until now, omics information extracted from gut microbiota and circulating tumor DNA (ctDNA) have not been integrated in composite biomarkers-based models, thereby omitting valuable information from the decision-making process. In this study, we aim to integrate radiomics with gut microbiota and ctDNA-based genomics tracking during neoadjuvant chemoradiotherapy (nCRT). METHODS: The main hypothesis of the MOREOVER study is that the incorporation of composite biomarkers with radiomics-based models used in the THUNDER-2 trial will improve the pathological complete response (pCR) predictive power of such models, paving the way for more accurate and comprehensive personalized treatment approaches. This is due to the inclusion of actionable omics variables that may disclose previously unknown correlations with radiomics. Aims of this study are: - to generate longitudinal microbiome data linked to disease resistance to nCRT and postulate future therapeutic strategies in terms of both type of treatment and timing, such as fecal microbiota transplant in non-responding patients. - to describe the genomics pattern and ctDNA data evolution throughout the nCRT treatment in order to support the prediction outcome and identify new risk-category stratification agents. - to mine and combine collected data through integrated multi-omics approaches (radiomics, metagenomics, metabolomics, metatranscriptomics, human genomics, ctDNA) in order to increase the performance of the radiomics-based response predictive model for LARC patients undergoing nCRT on MR-Linac. EXPERIMENTAL DESIGN: The objective of the MOREOVER project is to enrich the phase II THUNDER-2 trial (NCT04815694) with gut microbiota and ctDNA omics information, by exploring the possibility to enhance predictive performance of the developed model. Longitudinal ctDNA genomics, microbiome and genomics data will be analyzed on 7 timepoints: prior to nCRT, during nCRT on a weekly basis and prior to surgery. Specific modelling will be performed for data harvested, according to the TRIPOD statements. DISCUSSION: We expect to find differences in fecal microbiome, ctDNA and radiomics profiles between the two groups of patients (pCR and not pCR). In addition, we expect to find a variability in the stability of the considered omics features over time. The identified profiles will be inserted into dedicated modelling solutions to set up a multiomics decision support system able to achieve personalized treatments.

Uterus motion analysis for radiotherapy planning optimization. The innovative contribution of on-board hybrid MR imaging.

Introduction: Organ motion (OM) and volumetric changes pose challenges in radiotherapy (RT) for locally advanced cervical cancer (LACC). Magnetic Resonance-guided Radiotherapy (MRgRT) combines improved MRI contrast with adaptive RT plans for daily anatomical changes. Our goal was to analyze cervico-uterine structure (CUS) changes during RT to develop strategies for managing OM. Materials and methods: LACC patients received chemoradiation by MRIdian system with a simultaneous integrated boost (SIB) protocol. Prescription doses of 55-50.6 Gy at PTV1 and 45-39.6 Gy at PTV2 were given in 22 and 25 fractions. Daily MRI scans were co-registered with planning scans and CUS changes were assessed.Six PTVs were created by adding 0.5, 0.7, 1, 1.3, 1.5, and 2 cm margins to the CUS, based on the simulation MRI. Adequate margins were determined to include 95 % of the CUSs throughout the entire treatment in 95 % of patients. Results: Analysis of 15 LACC patients and 372 MR scans showed a 31 % median CUS volume decrease. Asymmetric margins of 2 cm cranially, 0.5 cm caudally, 1.5 cm posteriorly, 2 cm anteriorly, and 1.5 cm on both sides were optimal for PTV, adapting to CUS variations. Post-14th fraction, smaller margins of 0.7 cm cranially, 0.5 cm caudally, 1.3 cm posteriorly, 1.3 cm anteriorly, and 1.3 cm on both sides sufficed. Conclusion: CUS mobility varies during RT, suggesting reduced PTV margins after the third week. MRgRT with adaptive strategies optimizes dose delivery, emphasizing the importance of streamlined IGRT with reduced PTV margins using a tailored MRgRT workflow with hybrid MRI-guided systems.

Challenges and opportunities in the development and clinical implementation of artificial intelligence based synthetic computed tomography for magnetic resonance only radiotherapy.

Synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) can serve as a substitute for planning CT in radiation therapy (RT), thereby removing registration uncertainties associated with multi-modality imaging pairing, reducing costs and patient radiation exposure. CE/FDA-approved sCT solutions are nowadays available for pelvis, brain, and head and neck, while more complex deep learning (DL) algorithms are under investigation for other anatomic sites. The main challenge in achieving a widespread clinical implementation of sCT lies in the absence of consensus on sCT commissioning and quality assurance (QA), resulting in variation of sCT approaches across different hospitals. To address this issue, a group of experts gathered at the ESTRO Physics Workshop 2022 to discuss the integration of sCT solutions into clinics and report the process and its outcomes. This position paper focuses on aspects of sCT development and commissioning, outlining key elements crucial for the safe implementation of an MRI-only RT workflow.

Evaluation of the extent of resection of intracranial tumors with virtual intraoperative MRI: a case series.

OBJECTIVE: Intraoperative MRI (iMRI) is the gold-standard technique for intraoperative evaluation of the extent of resection in brain tumor surgery. Unfortunately, it is currently available at only a few neurosurgical centers. A commercially available software, Virtual iMRI Cranial, provides an elastic fusion between preoperative MRI and intraoperative CT (iCT). The aim of this study was to evaluate the accuracy of this software in determining the presence of residual tumor. METHODS: Virtual iMRI was performed in patients who underwent iCT after intracranial tumor resection. The results of the software in terms of presence or absence of tumor residual were then compared with postoperative MRI performed within 48 hours after surgery to evaluate the diagnostic accuracy of virtual iMRI. RESULTS: Sixty-six patients were included in the present study. The virtual iMRI findings were concordant with the postoperative MRI data in 35 cases (53%) in the detection of tumor residual (p = 0.006). No false-negative findings (i.e., presence of residual on postoperative MRI and absence of residual on virtual iMRI) were encountered. Virtual iMRI had a sensitivity of 1 (95% CI 0.86-1), specificity of 0.26 (95% CI 0.14-0.42), positive predictive value of 0.44 (95% CI 0.3-0.58), and negative predictive value of 1 (95% CI 0.72-1). Subgroup analysis revealed that the virtual iMRI findings were concordant with postoperative MRI findings in all cases (n = 9) of lower-grade glioma (LGG) with a sensitivity of 1 (95% CI 0.59-1) and a specificity of 1 (95% CI 0.16-1) (p = 0.003); a statistically significant association was also found for grade 4 gliomas with a sensitivity of 1 (95% CI 0.69-1) and a specificity of 0.33 (95% CI 0.08-0.7) (p = 0.046) (19 patients). No significant association was found when considering meningiomas or metastases. CONCLUSIONS: The commercially available virtual iMRI can predict the presence or absence of tumor residual with high sensitivity. The diagnostic accuracy of this method was higher in LGGs and much lower for meningiomas or metastases; these findings must be evaluated in prospective studies in a larger population.

Impact of bias field correction on 0.35 T pelvic MR images: evaluation on generative adversarial network-based OARs' auto-segmentation and visual grading assessment.

Purpose: Magnetic resonance imaging (MRI)-guided radiotherapy enables adaptive treatment plans based on daily anatomical changes and accurate organ visualization. However, the bias field artifact can compromise image quality, affecting diagnostic accuracy and quantitative analyses. This study aims to assess the impact of bias field correction on 0.35 T pelvis MRIs by evaluating clinical anatomy visualization and generative adversarial network (GAN) auto-segmentation performance. Materials and methods: 3D simulation MRIs from 60 prostate cancer patients treated on MR-Linac (0.35 T) were collected and preprocessed with the N4ITK algorithm for bias field correction. A 3D GAN architecture was trained, validated, and tested on 40, 10, and 10 patients, respectively, to auto-segment the organs at risk (OARs) rectum and bladder. The GAN was trained and evaluated either with the original or the bias-corrected MRIs. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95th) were computed for the segmented volumes of each patient. The Wilcoxon signed-rank test assessed the statistical difference of the metrics within OARs, both with and without bias field correction. Five radiation oncologists blindly scored 22 randomly chosen patients in terms of overall image quality and visibility of boundaries (prostate, rectum, bladder, seminal vesicles) of the original and bias-corrected MRIs. Bennett's S score and Fleiss' kappa were used to assess the pairwise interrater agreement and the interrater agreement among all the observers, respectively. Results: In the test set, the GAN trained and evaluated on original and bias-corrected MRIs showed DSC/HD95th of 0.92/5.63 mm and 0.92/5.91 mm for the bladder and 0.84/10.61 mm and 0.83/9.71 mm for the rectum. No statistical differences in the distribution of the evaluation metrics were found neither for the bladder (DSC: p = 0.07; HD95th: p = 0.35) nor for the rectum (DSC: p = 0.32; HD95th: p = 0.63). From the clinical visual grading assessment, the bias-corrected MRI resulted mostly in either no change or an improvement of the image quality and visualization of the organs' boundaries compared with the original MRI. Conclusion: The bias field correction did not improve the anatomy visualization from a clinical point of view and the OARs' auto-segmentation outputs generated by the GAN.

Radiomics-enhanced early regression index for predicting treatment response in rectal cancer: a multi-institutional 0.35 T MRI-guided radiotherapy study.

PURPOSE: The accurate prediction of treatment response in locally advanced rectal cancer (LARC) patients undergoing MRI-guided radiotherapy (MRIgRT) is essential for optimising treatment strategies. This multi-institutional study aimed to investigate the potential of radiomics in enhancing the predictive power of a known radiobiological parameter (Early Regression Index, ERITCP) to evaluate treatment response in LARC patients treated with MRIgRT. METHODS: Patients from three international sites were included and divided into training and validation sets. 0.35 T T2*/T1-weighted MR images were acquired during simulation and at each treatment fraction. The biologically effective dose (BED) conversion was used to account for different radiotherapy schemes: gross tumour volume was delineated on the MR images corresponding to specific BED levels and radiomic features were then extracted. Multiple logistic regression models were calculated, combining ERITCP with other radiomic features. The predictive performance of the different models was evaluated on both training and validation sets by calculating the receiver operating characteristic (ROC) curves. RESULTS: A total of 91 patients was enrolled: 58 were used as training, 33 as validation. Overall, pCR was observed in 25 cases. The model showing the highest performance was obtained combining ERITCP at BED = 26 Gy with a radiomic feature (10th percentile of grey level histogram, 10GLH) calculated at BED = 40 Gy. The area under ROC curve (AUC) of this combined model was 0.98 for training set and 0.92 for validation set, significantly higher (p = 0.04) than the AUC value obtained using ERITCP alone (0.94 in training and 0.89 in validation set). CONCLUSION: The integration of the radiomic analysis with ERITCP improves the pCR prediction in LARC patients, offering more precise predictive models to further personalise 0.35 T MRIgRT treatments of LARC patients.

Auto-segmentation of pelvic organs at risk on 0.35T MRI using 2D and 3D Generative Adversarial Network models.

PURPOSE: Manual recontouring of targets and Organs At Risk (OARs) is a time-consuming and operator-dependent task. We explored the potential of Generative Adversarial Networks (GAN) to auto-segment the rectum, bladder and femoral heads on 0.35T MRIs to accelerate the online MRI-guided-Radiotherapy (MRIgRT) workflow. METHODS: 3D planning MRIs from 60 prostate cancer patients treated with 0.35T MR-Linac were collected. A 3D GAN architecture and its equivalent 2D version were trained, validated and tested on 40, 10 and 10 patients respectively. The volumetric Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95th) were computed against expert drawn ground-truth delineations. The networks were also validated on an independent external dataset of 16 patients. RESULTS: In the internal test set, the 3D and 2D GANs showed DSC/HD95th of 0.83/9.72 mm and 0.81/10.65 mm for the rectum, 0.92/5.91 mm and 0.85/15.72 mm for the bladder, and 0.94/3.62 mm and 0.90/9.49 mm for the femoral heads. In the external test set, the performance was 0.74/31.13 mm and 0.72/25.07 mm for the rectum, 0.92/9.46 mm and 0.88/11.28 mm for the bladder, and 0.89/7.00 mm and 0.88/10.06 mm for the femoral heads. The 3D and 2D GANs required on average 1.44 s and 6.59 s respectively to generate the OARs' volumetric segmentation for a single patient. CONCLUSIONS: The proposed 3D GAN auto-segments pelvic OARs with high accuracy on 0.35T, in both the internal and the external test sets, outperforming its 2D equivalent in both segmentation robustness and volume generation time.

Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects.

MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms.

Motion and dosimetric criteria for selecting gating technique for apical lung lesions in magnetic resonance guided radiotherapy.

Introduction: Patients treatment compliance increases during free-breathing (FB) treatment, taking generally less time and fatigue with respect to deep inspiration breath-hold (DIBH). This study quantifies the gross target volume (GTV) motion on cine-MRI of apical lung lesions undergoing a SBRT in a MR-Linac and supports the patient specific treatment gating pre-selection. Material and methods: A total of 12 patients were retrospectively enrolled in this study. During simulation and treatment fractions, sagittal 0.35 T cine-MRI allows real-time GTV motion tracking. Cine-MRI has been exported, and an in-house developed MATLAB script performed image segmentation for measuring GTV centroid position on cine-MRI frames. Motion measurements were performed during the deep inspiration phase of DIBH patient and during all the session for FB patient. Treatment plans of FB patients were reoptimized using the same cost function, choosing the 3 mm GTV-PTV margin used for DIBH patients instead of the original 5 mm margin, comparing GTV and OARs DVH for the different TP. Results: GTV centroid motion is <2.2 mm in the antero-posterior and cranio-caudal direction in DIBH. For FB patients, GTV motion is lower than 1.7 mm, and motion during the treatment was always in agreement with the one measured during the simulation. No differences have been observed in GTV coverage between the TP with 3-mm and 5-mm margins. Using a 3-mm margin, the mean reduction in the chest wall and trachea-bronchus Dmax was 2.5 Gy and 3.0 Gy, respectively, and a reduction of 1.0 Gy, 0.6 Gy, and 2.3% in Dmax, Dmean, and V5Gy, respectively, of the homolateral lung and 1.7 Gy in the contralateral lung Dmax. Discussions: Cine-MRI allows to select FB lung patients when GTV motion is <2 mm. The use of narrower PTV margins reduces OARs dose and maintains target coverage.

Prior knowledge based deep learning auto-segmentation in magnetic resonance imaging-guided radiotherapy of prostate cancer.

Background and purpose: Automation is desirable for organ segmentation in radiotherapy. This study compared deep learning methods for auto-segmentation of organs-at-risk (OARs) and clinical target volume (CTV) in prostate cancer patients undergoing fractionated magnetic resonance (MR)-guided adaptive radiation therapy. Models predicting dense displacement fields (DDFMs) between planning and fraction images were compared to patient-specific (PSM) and baseline (BM) segmentation models. Materials and methods: A dataset of 92 patients with planning and fraction MR images (MRIs) from two institutions were used. DDFMs were trained to predict dense displacement fields (DDFs) between the planning and fraction images, which were subsequently used to propagate the planning contours of the bladder, rectum, and CTV to the daily MRI. The training was performed either with true planning-fraction image pairs or with planning images and their counterparts deformed by known DDFs. The BMs were trained on 53 planning images, while to generate PSMs, the BMs were fine-tuned using the planning image of a given single patient. The evaluation included Dice similarity coefficient (DSC), the average (HDavg) and the 95th percentile (HD95) Hausdorff distance (HD). Results: The DDFMs with DSCs for bladder/rectum of 0.76/0.76 performed worse than PSMs (0.91/0.90) and BMs (0.89/0.88). The same trend was observed for HDs. For CTV, DDFM and PSM performed similarly yielding DSCs of 0.87 and 0.84, respectively. Conclusions: DDFMs were found suitable for CTV delineation after rigid alignment. However, for OARs they were outperformed by PSMs, as they predicted only limited deformations even in the presence of substantial anatomical changes.

THeragnostic utilities for neoplastic DisEases of the rectum by MRI guided radiotherapy (THUNDER 2) phase II trial: interim safety analysis.

BACKGROUND: The THUNDER-2 phase II single institutional trial investigates the benefits of MRI-guided radiotherapy (MRIgRT) in treating locally advanced rectal cancer (LARC). This study focuses on evaluating the impact of escalating radiation therapy dose in non-responder patients using the Early Tumour Regression Index (ERI) for predicting complete response (CR). The trial's primary endpoint is to increase the CR rate in non-responders by 10% and assess the feasibility of the delta radiomics-based MRIgRT predictive model. This interim analysis assesses the feasibility and safety of the proposed MRIgRT dose escalation strategy in terms of acute toxicity (gastrointestinal, genitourinary and haematological) and treatment adherence. METHODS: Stage cT2-3, N0-2, or cT4 patients with anal sphincter involvement, N0-2a, M0, but without high-risk features were enrolled. MRIgRT treatment consisted of a standard dose of 55 Gy to the Gross Tumor Volume (GTV) and mesorectum, and 45 Gy to the mesorectum and drainage nodes in 25 fractions with concomitant chemotherapy. 0.35 T MRI was used for simulation imaging and daily alignment. ERI was calculated at the 10th fraction. Non-responders with an ERI above 13.1 received intensified dose escalation from the 11th fraction, resulting in a total dose of 60.1 Gy. Acute toxicity was assessed using the CTCAE v.5 scale. RESULTS: From March 2021 to November 2022, 33 out of the total number of 63 patients to be enrolled (52.4%) were included, with one withdrawal unrelated to treatment. Sixteen patients (50%) underwent dose escalation. Treatment was well tolerated, with only one patient (3.1%) in the standard treatment group experiencing acute Grade 3 diarrhea, proctitis, and cystitis. No significant differences in toxicity were observed between the two groups (p = 0.5463). CONCLUSIONS: MRIgRT treatment with dose escalation up to 60.1 Gy is well tolerated in LARC patients predicted as non-responders by ERI, confirming the feasibility and safety of this approach. The THUNDER-2 trial's primary and secondary endpoints will be fully analyzed when all planned patients will be enrolled.

Delta Radiomic Analysis of Mesorectum to Predict Treatment Response and Prognosis in Locally Advanced Rectal Cancer.

BACKGROUND: The aim of this study is to evaluate the delta radiomics approach based on mesorectal radiomic features to develop a model for predicting pathological complete response (pCR) and 2-year disease-free survival (2yDFS) in locally advanced rectal cancer (LARC) patients undergoing neoadjuvant chemoradiotherapy (nCRT). METHODS: Pre- and post-nCRT MRIs of LARC patients treated at a single institution from May 2008 to November 2016 were retrospectively collected. Radiomic features were extracted from the GTV and mesorectum. The Wilcoxon-Mann-Whitney test and area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the features in predicting pCR and 2yDFS. RESULTS: Out of 203 LARC patients, a total of 565 variables were evaluated. The best performing pCR prediction model was based on two GTV features with an AUC of 0.80 in the training set and 0.69 in the validation set. The best performing 2yDFS prediction model was based on one GTV and two mesorectal features with an AUC of 0.79 in the training set and 0.70 in the validation set. CONCLUSIONS: The results of this study suggest a possible role for delta radiomics based on mesorectal features in the prediction of 2yDFS in patients with LARC.

Dose-response Analysis in Hepatic Tumors Treated with 90Y-TARE According to a Personalized Dosimetric Workflow: Preliminary Results.

BACKGROUND: Transarterial Radioembolization (TARE) is a widespread radiation therapy for unresectable hepatic lesions, but a clear understanding of the dose-response link is still missing. The aim of this preliminary study is to investigate the role of both dosimetric and clinical parameters as classifiers or predictors of response and survival for TARE in hepatic tumors and to present possible response cut-off. METHODS: 20 patients treated with glass or resin microspheres according to a personalized workflow were included. Dosimetric parameters were extracted from personalized absorbed dose maps obtained from the convolution of 90Y PET images with 90Y voxel S-values. RESULTS: D95 ≥ 104 Gy and tumor mean absorbed dose MADt ≥ 229 Gy were found to be optimal cut-off values for complete response, while D30 ≥ 180 Gy and MADt ≥ 117 Gy were selected as cut-off values for at least partial response and predicted better survival. Clinical parameters Alanine Transaminase (ALT) and Model for End-Stage Liver Disease (MELD) didn't show sufficient classification capability for response or survival. CONCUSION: These preliminary results highlight the importance of an accurate dosimetric evaluation and suggest a cautious approach when considering clinical indicators. Dosimetric cut-off values could be a support tool in both planning and post-treatment phases. Larger multi-centric randomized trials, with standardized methods regarding patient selection, response criteria, Regions of Interest definition, dosimetric approach and activity planning are needed to confirm these promising results.

Use of secondary air kerma from dose area product and fluoroscopy time for preventive effective dose estimation in interventional radiology procedures for staff.

INTRODUCTION: interventional radiology workers are potentially exposed to high levels of ionizing radiation, therefore preventive dose estimation is mandatory for the correct risk classification of staff. Effective dose (ED) is a radiation protection quantity strictly related to the secondary air kerma (KS), using appropriate multiplicative conversion factors (ICRP 106). The aim of this work is to evaluate the accuracy ofKSestimated from physically measurable quantities such as dose-area product (DAP) or fluoroscopy time (FT). METHODS: radiological units (n= 4) were characterized in terms of primary beam air kerma and DAP-meter response, consequently defining a DAP-meter correction factor (CF) for each unit.KS, scattered from an anthropomorphic phantom and measured by a digital multimeter, was then compared with the value estimated from DAP and FT. Different combinations of tube voltages, field sizes, current and scattering angles were used to simulate the variation of working conditions. Further measurements were performed to estimate the couch transmission factor for different phantom placements on the operational couch, defining a CF as the mean transmission factor. RESULTS: when no CFs were applied, the measuredKSshowed a median percentage difference of between 33.8% and 115.7% with respect toKSevaluated from DAP, and between -46.3% and 101.8% forKSevaluated from FT. By contrast, when previously defined CFs were applied to the evaluatedKS, the median percentage difference between the measuredKSand the value evaluated from DAP ranged from between -7.94% and 15.0%, and between -66.2% and 17.2% for that evaluated from FT. CONCLUSION: when appropriate CF are applied, the preventive ED estimation from the median DAP value seems to be more conservative and easier to obtain with respect to the one obtained from the FT value. Further measurements should be performed with a personal dosimeter during routine activities to assess the properKSto ED conversion factor.

Phase II Trial of LINAC-Based STereotactic Arrhythmia Radioablation (STAR) for Paroxysmal Atrial Fibrillation in Elderly: Planning and Dosimetric Point of View.

Purpose: Approaching treatment for elderly patients with atrial fibrillation is difficult. A prospective phase II trial evaluating LINAC-based stereotactic arrhythmia radioablation (STAR) safety in this population started in 2021. Dosimetric and planning data were reported. Materials and Methods: A vac-lock bag was used for immobilization in the supine position and a computed tomography (CT, 1 mm) was performed. The clinical target volume (CTV) was defined as the area around the pulmonary veins. An internal target volume (ITV) was added to the CTV to compensate heart and respiratory movement. The planning target volume (PTV) was defined by adding 0-3 mm to the ITV. STAR was performed during free-breathing with a PTV prescription total dose (Dp) of 25 Gy/1 fraction. Flattening filter-free volumetric-modulated arc therapy plans were generated, optimized, and delivered by TrueBeamTM. Image-guided radiotherapy with cone-beam CT and surface-guided radiotherapy with Align-RT (Vision RT) were employed. Results: From May 2021 to March 2022, 10 elderly patients were treated. Mean CTVs, ITVs, and PTVs were 23.6 cc, 44.32 cc, and 62.9 cc, respectively; the mean prescription isodose level and D2% were 76.5% and 31.2 Gy, respectively. The average heart and left anterior descending artery (LAD) Dmean were 3.9 and 6.3 Gy, respectively; the mean Dmax for LAD, spinal cord, left and right bronchus, and esophagus were 11.2, 7.5, 14.3, 12.4, and 13.6 Gy, respectively. The overall treatment time (OTT) was 3 min. Conclusions: The data showed an optimal target coverage, sparing surrounding tissue, in 3 min of OTT. LINAC-based STAR for AF could represent a valid non-invasive alternative for elderly patients who were excluded from catheter ablation.

The role of psychopathological symptoms in lumbar stenosis: A prediction model of disability after lumbar decompression and fusion.

Introduction: Pre-operative psychological factors may influence outcome after spine surgery. The identification of patients at risk of persisting disability may be useful for patient selection and possibly to improve treatment outcome. Methods: Patients with neurogenic claudication associated with degenerative lumbar spinal stenosis (DLSS) performed a psychological assessment before lumbar decompression and fusion (LDF) surgery. The following tests were administrated: Visual Analogic Scale; Symptom Checklist-90 (SCL-90-R), Short Form-36 and Oswestry Disability Index (ODI). The primary outcome was ODI score lower than 20. A cross correlation matrix (CCM) was carried out with significant variables after univariate analysis and a linear logistic regression model was calculated considering the most significant variable. Results: 125 patient (61 men and 64 women) were included in the study. Seven parameters of the SCL-90-R scale showed statistical significance at the univariate analysis: obsessivity (p < 0.001), Current Symptom Index (p = 0.001), Global Severity Index (p < 0.001), depression (p < 0.001), positive Symptom Total (p = 0.002), somatization (p = 0.001) and anxiety (p = 0.036). Obsessivity was correlated with other significant parameters, except GSI (Pearson's correlation coefficient = 0.11).The ROC curve for the logistic model considering obsessivity as risk factor, has an area under the curve of 0.75. Conclusion: Pre-operative psychopathological symptoms can predict persistence of disability after LDF for DLSS. Future studies will evaluate the possibility of modifying post operative outcome through targeted treatment for psychological features emerged during pre-operative assessment.

An ESTRO-ACROP guideline on quality assurance and medical physics commissioning of online MRI guided radiotherapy systems based on a consensus expert opinion.

OBJECTIVE: The goal of this consensus expert opinion was to define quality assurance (QA) tests for online magnetic resonance image (MRI) guided radiotherapy (oMRgRT) systems and to define the important medical physics aspects for installation and commissioning of an oMRgRT system. MATERIALS AND METHODS: Ten medical physicists and two radiation oncologists experienced in oMRgRT participated in the survey. In the first round of the consensus expert opinion, ideas on QA and commissioning were collected. Only tests and aspects different from commissioning of a CT guided radiotherapy (RT) system were considered. In the following two rounds all twelve participants voted on the importance of the QA tests, their recommended frequency and their suitability for the two oMRgRT systems approved for clinical use as well as on the importance of the aspects to consider during medical physics commissioning. RESULTS: Twenty-four QA tests were identified which are potentially important during commissioning and routine QA on oMRgRT systems compared to online CT guided RT systems. An additional eleven tasks and aspects related to construction, workflow development and training were collected. Consensus was found for most tests on their importance, their recommended frequency and their suitability for the two approved systems. In addition, eight aspects mostly related to the definition of workflows were also found to be important during commissioning. CONCLUSIONS: A program for QA and commissioning of oMRgRT systems was developed to support medical physicists to prepare for safe handling of such systems.

Evaluation of real-time tumor contour prediction using LSTM networks for MR-guided radiotherapy.

BACKGROUND AND PURPOSE: Magnetic resonance imaging guided radiotherapy (MRgRT) with deformable multileaf collimator (MLC) tracking would allow to tackle both rigid displacement and tumor deformation without prolonging treatment. However, the system latency must be accounted for by predicting future tumor contours in real-time. We compared the performance of three artificial intelligence (AI) algorithms based on long short-term memory (LSTM) modules for the prediction of 2D-contours 500ms into the future. MATERIALS AND METHODS: Models were trained (52 patients, 3.1h of motion), validated (18 patients, 0.6h) and tested (18 patients, 1.1h) with cine MRs from patients treated at one institution. Additionally, we used three patients (2.9h) treated at another institution as second testing set. We implemented 1) a classical LSTM network (LSTM-shift) predicting tumor centroid positions in superior-inferior and anterior-posterior direction which are used to shift the last observed tumor contour. The LSTM-shift model was optimized both in an offline and online fashion. We also implemented 2) a convolutional LSTM model (ConvLSTM) to directly predict future tumor contours and 3) a convolutional LSTM combined with spatial transformer layers (ConvLSTM-STL) to predict displacement fields used to warp the last tumor contour. RESULTS: The online LSTM-shift model was found to perform slightly better than the offline LSTM-shift and significantly better than the ConvLSTM and ConvLSTM-STL. It achieved a 50% Hausdorff distance of 1.2mm and 1.0mm for the two testing sets, respectively. Larger motion ranges were found to lead to more substantial performance differences across the models. CONCLUSION: LSTM networks predicting future centroids and shifting the last tumor contour are the most suitable for tumor contour prediction. The obtained accuracy would allow to reduce residual tracking errors during MRgRT with deformable MLC-tracking.

Mesorectal motion evaluation in rectal cancer MR-guided radiotherapy: an exploratory study to quantify treatment margins.

BACKGROUND: Mesorectal motion (MM) is a source of uncertainty during neoadjuvant chemoradiotherapy (nCRT) delivery for locally advanced rectal cancer (LARC). Previously published experiences using cone-beam computed tomography imaging have already described significant movement. Aim of this analysis is to assess inter-fraction MM using the higher tissue contrast provided by hybrid magnetic resonance imaging (MRI) in LARC patients (pts) treated with MRI guided radiation therapy (MRgRT). METHODS: The total mesorectum, its superior (Msup), middle (Mmid) and lower (Mlow) regions were contoured on the positioning MRIs acquired on simulation day and on each treatment day. Six PTVs were obtained adding 0.5, 0.7, 1, 1.3, 1.5 and 2 cm margin to the whole mesorectum, starting from the simulation MRI. Margins including 95% of the mesorectal structures during whole treatment in 95% of patients (pts) were considered adequate. RESULTS: A total number of 312 fractions of 12 consecutive pts was retrospectively analyzed. The different mesorectum regions show specific motion variability. In particular, Msup shows larger variability in left, right and anterior directions, while the Mlow in caudal and posterior ones. The anterior margin is significantly larger in the Msup than in the other regions. CONCLUSION: Different mesorectal regions move differently throughout the radiotherapy treatment, with the largest MM in the Msup anterior direction. Asymmetrical margins are recommended.

Patient-specific transfer learning for auto-segmentation in adaptive 0.35 T MRgRT of prostate cancer: a bi-centric evaluation.

BACKGROUND: Online adaptive radiation therapy (RT) using hybrid magnetic resonance linear accelerators (MR-Linacs) can administer a tailored radiation dose at each treatment fraction. Daily MR imaging followed by organ and target segmentation adjustments allow to capture anatomical changes, improve target volume coverage, and reduce the risk of side effects. The introduction of automatic segmentation techniques could help to further improve the online adaptive workflow by shortening the re-contouring time and reducing intra- and inter-observer variability. In fractionated RT, prior knowledge, such as planning images and manual expert contours, is usually available before irradiation, but not used by current artificial intelligence-based autocontouring approaches. PURPOSE: The goal of this study was to train convolutional neural networks (CNNs) for automatic segmentation of bladder, rectum (organs at risk, OARs), and clinical target volume (CTV) for prostate cancer patients treated at 0.35 T MR-Linacs. Furthermore, we tested the CNNs generalization on data from independent facilities and compared them with the MR-Linac treatment planning system (TPS) propagated structures currently used in clinics. Finally, expert planning delineations were utilized for patient- (PS) and facility-specific (FS) transfer learning to improve auto-segmentation of CTV and OARs on fraction images. METHODS: In this study, data from fractionated treatments at 0.35 T MR-Linacs were leveraged to develop a 3D U-Net-based automatic segmentation. Cohort C1 had 73 planning images and cohort C2 had 19 planning and 240 fraction images. The baseline models (BMs) were trained solely on C1 planning data using 53 MRIs for training and 10 for validation. To assess their accuracy, the models were tested on three data subsets: (i) 10 C1 planning images not used for training, (ii) 19 C2 planning, and (iii) 240 C2 fraction images. BMs also served as a starting point for FS and PS transfer learning, where the planning images from C2 were used for network parameter fine tuning. The segmentation output of the different trained models was compared against expert ground truth by means of geometric metrics. Moreover, a trained physician graded the network segmentations as well as the segmentations propagated by the clinical TPS. RESULTS: The BMs showed dice similarity coefficients (DSC) of 0.88(4) and 0.93(3) for the rectum and the bladder, respectively, independent of the facility. CTV segmentation with the BM was the best for intermediate- and high-risk cancer patients from C1 with DSC=0.84(5) and worst for C2 with DSC=0.74(7). The PS transfer learning brought a significant improvement in the CTV segmentation, yielding DSC=0.72(4) for post-prostatectomy and low-risk patients and DSC=0.88(5) for intermediate- and high-risk patients. The FS training did not improve the segmentation accuracy considerably. The physician's assessment of the TPS-propagated versus network-generated structures showed a clear advantage of the latter. CONCLUSIONS: The obtained results showed that the presented segmentation technique has potential to improve automatic segmentation for MR-guided RT.