Stereotactic Radiotherapy for penile metastasis: case report and systematic literature review.

Introduction Penile metastases (PM) are a rare clinical presentation mainly related to advanced stages of disease. Considering the low incidence, an optimal treatment approach has not yet been defined; surgery, chemotherapy, and radiotherapy are different options used in the vast majority with palliative intent. The advances in modern RT can represent an innovative tool in PM management and a curative option. This paper aims to report the case of a PM patient treated with Stereotactic Body Radiotherapy (SBRT) and perform a systematic literature review of current evidence on the RT approach to PM. Case report We reported the case of an 80-year-old patient with PM from primary bladder cancer. Following the surgical approach for the primary tumor, evidence of PM was shown, and the patient was admitted to SBRT treatment on PM after an adjuvant RT course on the pelvis. A 25 Gy in 5 fractions SBRT treatment was performed, and a complete clinical response was shown at the first follow-up. Methods A Pubmed/MEDLINE and Embase systematic review was carried out. The search strategy terms were [('penile metastasis'/exp OR 'penile metastasis' OR (penile AND ('metastasis'/exp OR metastasis))) AND ('radiotherapy'/exp OR radiotherapy)] and only original articles up to the 24.10.2023 were considered. Results A total of 174 studies were obtained using the previously mentioned search strategy, and the analysis was performed on 15 papers obtained following the complete selection process. All reported evidence was focused on the palliative approach of PM showing good results in terms of symptom control. Discussion The potential role of modern RT in the management of PM has yet to be defined. The reported case showed the feasibility and the clinical impact of SBRT in PM treatment.

A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.

OBJECTIVE: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer. METHODS: From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered. RESULTS: The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set. CONCLUSIONS: The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

Coping with modesty during radiotherapy for breast cancer: a multicentric study.

PURPOSE: Breast cancer is the most frequently diagnosed tumour, representing nearly 30% of all new cases in women. Radiotherapy (RT) plays a crucial role in the management of breast cancer. The objective of this study is to assess modesty in patients undergoing RT for breast cancer and take their suggestions and ideas into consideration to enhance the quality of treatment in this regard. METHODS: The study enrolled 555 breast cancer patients undergoing adjuvant RT in three Italian centres. Patients completed a self-test questionnaire assessing their comfort level concerning modesty during therapy and their relationship with strangers and healthcare professionals. The impact of religious views and potential changes in sexuality were also examined. RESULTS: Results showed that modesty was a common concern across the overall cohort of patients, with discomfort in being undressed during RT correlating with discomfort experienced in other daily life situations. Most patients felt more at ease with same sex healthcare workers. Age was also a major factor with younger patients generally feeling more comfortable with healthcare workers of the same age group. Interestingly, the surgical technique used (mastectomy vs. quadrantectomy) did not significantly influence modesty perceptions. Patients provided valuable suggestions to improve privacy and modesty during RT. CONCLUSION: This study demonstrates that modesty is an important issue for women undergoing RT, which can be influenced by personal characteristics and hospital-related factors. A reflection about the need to address modesty concerns and to incorporate dedicated interventions for protecting patients' physical and emotional well-being is warranted. Initiatives to improve communication, involvement, and body image support should also be integrated into the care path of patients to better their overall therapeutic experience. This study paves the way for broader research and interventions in daily cancer care.

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.

Impact of data transfer between treatment planning systems on dosimetric parameters.

PURPOSE: In radiotherapy it is often necessary to transfer a patient's DICOM (Digital Imaging and COmmunications in Medicine) dataset from one system to another for re-treatment, plan-summation or registration purposes. The aim of the study is to evaluate effects of dataset transfer between treatment planning systems. MATERIALS AND METHODS: Twenty-five patients treated in a 0.35T MR-Linac (MRidian, ViewRay) for locally-advanced pancreatic cancer were enrolled. For each patient, a nominal dose distribution was optimized on the planning MRI. Each plan was daily re-optimized if needed to match the anatomy and exported from MRIdian-TPS (ViewRay Inc.) to Eclipse-TPS (Siemens-Varian). A comparison between the two TPSs was performed considering the PTV and OARs volumes (cc), as well as dose coverages and clinical constraints. RESULTS: From the twenty-five enrolled patients, 139 plans were included in the data comparison. The median values of percentage PTV volume variation are 10.8 % for each fraction, while percentage differences of PTV coverage have a mean value of -1.4 %. The median values of the percentage OARs volume variation are 16.0 %, 7.0 %, 10.4 % and 8.5 % for duodenum, stomach, small and large bowel, respectively. The percentage variations of the dose constraints are 41.0 %, 52.7 % and 49.8 % for duodenum, stomach and small bowel, respectively. CONCLUSIONS: This study has demonstrated a non-negligible variation in size and dosimetric parameters when datasets are transferred between TPSs. Such variations should be clinically considered. Investigations are focused on DICOM structure algorithm employed by the TPSs during the transfer to understand the cause of such variations.

REPeated mAgnetic resonance Image-guided stereotactic body Radiotherapy (MRIg-reSBRT) for oligometastatic patients: REPAIR, a mono-institutional retrospective study.

BACKGROUND: Oligo-progression or further recurrence is an open issue in the multi-integrated management of oligometastatic disease (OMD). Re-irradiation with stereotactic body radiotherapy (re-SBRT) technique could represent a valuable treatment option to improve OMD clinical outcomes. MRI-guided allows real-time visualization of the target volumes and online adaptive radiotherapy (oART). The aim of this retrospective study is to evaluate the efficacy and toxicity profile of MRI-guided repeated SBRT (MRIg-reSBRT) in the OMD setting and propose a re-SBRT classification. METHODS: We retrospectively analyzed patients (pts) with recurrent liver metastases or abdominal metastatic lesions between 1 and 5 centimeters from liver candidate to MRIg-reSBRT showing geometric overlap between the different SBRT courses and assessing whether they were in field (type 1) or not (type 2). RESULTS: Eighteen pts completed MRIg-reSBRT course for 25 metastatic hepatic/perihepatic lesions from July 2019 to January 2020. A total of 20 SBRT courses: 15 Type 1 re-SBRT (75%) and 5 Type 2 re-SBRT (25%) was delivered. Mean interval between the first SBRT and MRIg-reSBRT was 8,6 months. Mean prescribed dose for the first treatment was 43 Gy (range 24-50 Gy, mean BEDα/ß10=93), while 41 Gy (range 16-50 Gy, mean BEDα/ß10=92) for MRIg-reSBRT. Average liver dose was 3,9 Gy (range 1-10 Gy) and 3,7 Gy (range 1,6-8 Gy) for the first SBRT and MRIg-reSBRT, respectively. No acute or late toxicities were reported at a median follow-up of 10,7 months. The 1-year OS and PFS was 73,08% and 50%, respectively. Overall Clinical Benefit was 54%. CONCLUSIONS: MRIg-reSBRT could be considered an effective and safe option in the multi-integrated treatment of OMD.

A distributed feature selection pipeline for survival analysis using radiomics in non-small cell lung cancer patients.

Predictive modelling of cancer outcomes using radiomics faces dimensionality problems and data limitations, as radiomics features often number in the hundreds, and multi-institutional data sharing is ()often unfeasible. Federated learning (FL) and feature selection (FS) techniques combined can help overcome these issues, as one provides the means of training models without exchanging sensitive data, while the other identifies the most informative features, reduces overfitting, and improves model interpretability. Our proposed FS pipeline based on FL principles targets data-driven radiomics FS in a multivariate survival study of non-small cell lung cancer patients. The pipeline was run across datasets from three institutions without patient-level data exchange. It includes two FS techniques, Correlation-based Feature Selection and LASSO regularization, and Cox Proportional-Hazard regression with Overall Survival as endpoint. Trained and validated on 828 patients overall, our pipeline yielded a radiomic signature comprising "intensity-based energy" and "mean discretised intensity". Validation resulted in a mean Harrell C-index of 0.59, showcasing fair efficacy in risk stratification. In conclusion, we suggest a distributed radiomics approach that incorporates preliminary feature selection to systematically decrease the feature set based on data-driven considerations. This aims to address dimensionality challenges beyond those associated with data constraints and interpretability concerns.

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.

Factors influencing local control after MR-guided stereotactic body radiotherapy (MRgSBRT) for adrenal metastases.

Purpose: Stereotactic body radiotherapy (SBRT) is an effective treatment for adrenal gland metastases, but it is technically challenging and there are concerns about toxicity. We performed a multi-institutional pooled retrospective analysis to study clinical outcomes and toxicities after MR-guided SBRT (MRgSBRT) using for adrenal gland metastases. Methods and Materials: Clinical and dosimetric data of patients treated with MRgSBRT on a 0.35 T MR-Linac at 11 institutions between 2016 and 2022 were analyzed. Local control (LC), local progression-free survival (LPFS), distant progression-free survival (DPFS) and overall survival (OS) were estimated using Kaplan-Meier method and log-rank test. Results: A total of 255 patients (269 adrenal metastases) were included. Metastatic pattern was solitary in 25.9 % and oligometastatic in 58.0 % of patients. Median total dose was 45 Gy (range, 16-60 Gy) in a median of 5 fractions, and the median BED10 was 100 Gy (range, 37.5-132.0 Gy). Adaptation was done in 87.4 % of delivered fractions based on the individual clinicians' judgement. The 1- and 2- year LPFS rates were 94.0 % (95 % CI: 90.7-97.3 %) and 88.3 % (95 % CI: 82.4-94.2 %), respectively and only 2 patients (0.8 %) experienced grade 3 + toxicity. No local recurrences were observed after treatment to a total dose of BED10 > 100 Gy, with single fraction or fractional dose of > 10 Gy. Conclusions: This is a large retrospective multi-institutional study to evaluate the treatment outcomes and toxicities with MRgSBRT in over 250 patients, demonstrating the need for frequent adaptation in 87.4 % of delivered fractions to achieve a 1- year LPFS rate of 94 % and less than 1 % rate of grade 3 + toxicity. Outcomes analysis in 269 adrenal lesions revealed improved outcomes with delivery of a BED10 > 100 Gy, use of single fraction SBRT and with fraction doses > 10 Gy, providing benchmarks for future clinical trials.

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.

Evaluation of clinical parallel workflow in online adaptive MR-guided Radiotherapy: A detailed assessment of treatment session times.

Introduction: Advancements in MRI-guided radiotherapy (MRgRT) enable clinical parallel workflows (CPW) for online adaptive planning (oART), allowing medical physicists (MPs), physicians (MDs), and radiation therapists (RTTs) to perform their tasks simultaneously. This study evaluates the impact of this upgrade on the total treatment time by analyzing each step of the current 0.35T-MRgRT workflow. Methods: The time process of the workflow steps for 254 treatment fractions in 0.35 MRgRT was examined. Patients have been grouped based on disease site, breathing modality (BM) (BHI or FB), and fractionation (stereotactic body RT [SBRT] or standard fractionated long course [LC]). The time spent for the following workflow steps in Adaptive Treatment (ADP) was analyzed: Patient Setup Time (PSt), MRI Acquisition and Matching (MRt), MR Re-contouring Time (RCt), Re-Planning Time (RPt), Treatment Delivery Time (TDt). Also analyzed was the timing of treatments that followed a Simple workflow (SMP), without the online re-planning (PSt + MRt + TDt.). Results: The time analysis revealed that the ADP workflow (median: 34 min) is significantly (p < 0.05) longer than the SMP workflow (19 min). The time required for ADP treatments is significantly influenced by TDt, constituting 40 % of the total time. The oART steps (RCt + RPt) took 11 min (median), representing 27 % of the entire procedure. Overall, 79.2 % of oART fractions were completed in less than 45 min, and 30.6 % were completed in less than 30 min. Conclusion: This preliminary analysis, along with the comparative assessment against existing literature, underscores the potential of CPW to diminish the overall treatment duration in MRgRT-oART. Additionally, it suggests the potential for CPW to promote a more integrated multidisciplinary approach in the execution of oART.

The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.

Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO).

INTRODUCTION: The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT. METHODS: A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered. RESULTS: A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques. DISCUSSION: AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.

Sterotactic Ablative Radiotherapy in a Multicentric Series of Oligometastatic SCLC: The SAMOS Cohort.

AIMS: SCLC is the most aggressive lung cancer histology with a 5-year OS <10%. At the diagnosis, almost two-thirds of the SCLC an Extended Disease presentation. Two randomized studies (CASPIAN and ImPower133) demonstrated an OS improvement, when immunotherapy was prescribed as maintenance therapy after standard chemotherapy. To date, SABR has had a limited indication in managing metastatic SCLC, although recent reports proposed it as a valid treatment option in selected patients. We propose a retrospective multicentric analysis of patients treated with SABR for oligometastatic SCLC. METHOD: Data of patients affected by oligometastatic-SCLC treated with SABR between 2017 and 2022 in 11 Italian centers were collected. Clinical and therapeutic variables together with OS and time to next treatment were analyzed. Univariate analysis with Kaplan-Meier curve were calculated, and log-rank test were applied. Cox proportional hazard model was used for multivariate analysis. RESULTS: Data from 93 patients and 132 metastatic lesions were analyzed. The median age was 64 years (36-86) and all but 1 had Performance Status 0 or 1. Fifty-two patients presented ED at diagnosis. The first line treatment was radiochemotherapy in 42%, CHT alone in 24% and CHT-IO in 28%, others treatment accounts for 4% and only 2% of patients underwent best supportive care. Of the 132 lesions treated with SBRT 55 were in brain, 27 in lung, 11 in liver, 10 in lymph nodes, 8 in bones and 20 in adrenal gland. Median OS was 14 months, 1 year-OS and 2 years OS were 53% and 27%, respectively. The median TtNT was 14 months for the entire population. Of all the analyzed variables only, the anatomical site of the metastases and their number showed statistical significance in the univariate analysist, confirmed in the subsequent multivariate. CONCLUSION: SABR seems to play a role in delaying further systemic lines in oligometastatic disease and to extend the use of ongoing treatment in oligoprogressive state. Prospective studies are needed to confirm these findings.

Rectal Cancer MRI Guided Radiotherapy: A Practical Review for the Physician.

MR-guided radiotherapy is a treatment approach that combines the advantages of magnetic resonance imaging (MRI) with the precision of radiation therapy. This practical review provides an overview of the current state-of-the-art of MR-guided radiotherapy for rectal cancer, including its technical aspects, clinical outcomes, and existing limitations. Even though some studies have demonstrated the feasibility and safety of this treatment modality, challenges remain in terms of patient selection, treatment planning optimization, and long-term follow-up. Despite these issues, MR-guided radiotherapy shows promise as a potentially valuable rectal cancer treatment approach.

Stereotactic MR-guided on-table adaptive radiation therapy (SMART) for borderline resectable and locally advanced pancreatic cancer: A multi-center, open-label phase 2 study.

BACKGROUND AND PURPOSE: Radiation dose escalation may improve local control (LC) and overall survival (OS) in select pancreatic ductal adenocarcinoma (PDAC) patients. We prospectively evaluated the safety and efficacy of ablative stereotactic magnetic resonance (MR)-guided adaptive radiation therapy (SMART) for borderline resectable (BRPC) and locally advanced pancreas cancer (LAPC). The primary endpoint of acute grade ≥ 3 gastrointestinal (GI) toxicity definitely related to SMART was previously published with median follow-up (FU) 8.8 months from SMART. We now present more mature outcomes including OS and late toxicity. MATERIALS AND METHODS: This prospective, multi-center, single-arm open-label phase 2 trial (NCT03621644) enrolled 136 patients (LAPC 56.6 %; BRPC 43.4 %) after ≥ 3 months of any chemotherapy without distant progression and CA19-9 ≤ 500 U/mL. SMART was delivered on a 0.35 T MR-guided system prescribed to 50 Gy in 5 fractions (biologically effective dose10 [BED10] = 100 Gy). Elective coverage was optional. Surgery and chemotherapy were permitted after SMART. RESULTS: Mean age was 65.7 years (range, 36-85), induction FOLFIRINOX was common (81.7 %), most received elective coverage (57.4 %), and 34.6 % had surgery after SMART. Median FU was 22.9 months from diagnosis and 14.2 months from SMART, respectively. 2-year OS from diagnosis and SMART were 53.6 % and 40.5 %, respectively. Late grade ≥ 3 toxicity definitely, probably, or possibly attributed to SMART were observed in 0 %, 4.6 %, and 11.5 % patients, respectively. CONCLUSIONS: Long-term outcomes from the phase 2 SMART trial demonstrate encouraging OS and limited severe toxicity. Additional prospective evaluation of this novel strategy is warranted.

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.

A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging.

Purpose/objectives: An artificial intelligence-based pseudo-CT from low-field MR images is proposed and clinically evaluated to unlock the full potential of MRI-guided adaptive radiotherapy for pelvic cancer care. Materials and method: In collaboration with TheraPanacea (TheraPanacea, Paris, France) a pseudo-CT AI-model was generated using end-to-end ensembled self-supervised GANs endowed with cycle consistency using data from 350 pairs of weakly aligned data of pelvis planning CTs and TrueFisp-(0.35T)MRIs. The image accuracy of the generated pCT were evaluated using a retrospective cohort involving 20 test cases coming from eight different institutions (US: 2, EU: 5, AS: 1) and different CT vendors. Reconstruction performance was assessed using the organs at risk used for treatment. Concerning the dosimetric evaluation, twenty-nine prostate cancer patients treated on the low field MR-Linac (ViewRay) at Montpellier Cancer Institute were selected. Planning CTs were non-rigidly registered to the MRIs for each patient. Treatment plans were optimized on the planning CT with a clinical TPS fulfilling all clinical criteria and recalculated on the warped CT (wCT) and the pCT. Three different algorithms were used: AAA, AcurosXB and MonteCarlo. Dose distributions were compared using the global gamma passing rates and dose metrics. Results: The observed average scaled (between maximum and minimum HU values of the CT) difference between the pCT and the planning CT was 33.20 with significant discrepancies across organs. Femoral heads were the most reliably reconstructed (4.51 and 4.77) while anal canal and rectum were the less precise ones (63.08 and 53.13). Mean gamma passing rates for 1%1mm, 2%/2mm, and 3%/3mm tolerance criteria and 10% threshold were greater than 96%, 99% and 99%, respectively, regardless the algorithm used. Dose metrics analysis showed a good agreement between the pCT and the wCT. The mean relative difference were within 1% for the target volumes (CTV and PTV) and 2% for the OARs. Conclusion: This study demonstrated the feasibility of generating clinically acceptable an artificial intelligence-based pseudo CT for low field MR in pelvis with consistent image accuracy and dosimetric results.

Why we should care about gas pockets in online adaptive MRgRT: a dosimetric evaluation.

Introduction: Contouring of gas pockets is a time consuming step in the workflow of adaptive radiotherapy. We would like to better understand which gas pockets electronic densitiy should be used and the dosimetric impact on adaptive MRgRT treatment. Materials and methods: 21 CT scans of patients undergoing SBRT were retrospectively evaluated. Anatomical structures were contoured: Gross Tumour Volume (GTV), stomach (ST), small bowel (SB), large bowel (LB), gas pockets (GAS) and gas in each organ respectively STG, SBG, LBG. Average HU in GAS was converted in RED, the obtained value has been named as Gastrointestinal Gas RED (GIGED). Differences of average HU in GAS, STG, SBG and LBG were computed. Three treatment plans were calculated editing the GAS volume RED that was overwritten with: air RED (0.0012), water RED (1.000), GIGED, generating respectively APLAN, WPLAN and the GPLAN. 2-D dose distributions were analyzed by gamma analysis. Parameter called active gas volume (AGV) was calculated as the intersection of GAS with the isodose of 5% of prescription dose. Results: Average HU value contained in GAS results to be equal to -620. No significative difference was noted between the average HU of gas in different organ at risk. Value of Gamma Passing Rate (GPR) anticorrelates with the AGV for each plan comparison and the threshold value for GPR to fall below 90% is 41, 60 and 139 cc for WPLANvsAPLAN, GPLANvsAPLAN and WPLANvsGPLAN respectively. Discussions: GIGED is the right RED for Gastrointestinal Gas. Novel AGV is a useful parameter to evaluate the effect of gas pocket on dose distribution.