Multicenter privacy-preserving model training for deep learning brain metastases autosegmentation.

OBJECTIVES: This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without forgetting (LWF), to improve model generalizability without sharing raw data. MATERIALS AND METHODS: A total of six BM datasets from University Hospital Erlangen (UKER), University Hospital Zurich (USZ), Stanford, UCSF, New York University (NYU), and BraTS Challenge 2023 were used. First, the performance of the DeepMedic network for BM autosegmentation was established for exclusive single-center training and mixed multicenter training, respectively. Subsequently privacy-preserving bilateral collaboration was evaluated, where a pretrained model is shared to another center for further training using transfer learning (TL) either with or without LWF. RESULTS: For single-center training, average F1 scores of BM detection range from 0.625 (NYU) to 0.876 (UKER) on respective single-center test data. Mixed multicenter training notably improves F1 scores at Stanford and NYU, with negligible improvement at other centers. When the UKER pretrained model is applied to USZ, LWF achieves a higher average F1 score (0.839) than naive TL (0.570) and single-center training (0.688) on combined UKER and USZ test data. Naive TL improves sensitivity and contouring accuracy, but compromises precision. Conversely, LWF demonstrates commendable sensitivity, precision and contouring accuracy. When applied to Stanford, similar performance was observed. CONCLUSION: Data heterogeneity (e.g., variations in metastases density, spatial distribution, and image spatial resolution across centers) results in varying performance in BM autosegmentation, posing challenges to model generalizability. LWF is a promising approach to peer-to-peer privacy-preserving model training.

Automatized self-supervised learning for skin lesion screening.

Melanoma, the deadliest form of skin cancer, has seen a steady increase in incidence rates worldwide, posing a significant challenge to dermatologists. Early detection is crucial for improving patient survival rates. However, performing total body screening (TBS), i.e., identifying suspicious lesions or ugly ducklings (UDs) by visual inspection, can be challenging and often requires sound expertise in pigmented lesions. To assist users of varying expertise levels, an artificial intelligence (AI) decision support tool was developed. Our solution identifies and characterizes UDs from real-world wide-field patient images. It employs a state-of-the-art object detection algorithm to locate and isolate all skin lesions present in a patient's total body images. These lesions are then sorted based on their level of suspiciousness using a self-supervised AI approach, tailored to the specific context of the patient under examination. A clinical validation study was conducted to evaluate the tool's performance. The results demonstrated an average sensitivity of 95% for the top-10 AI-identified UDs on skin lesions selected by the majority of experts in pigmented skin lesions. The study also found that the tool increased dermatologists' confidence when formulating a diagnosis, and the average majority agreement with the top-10 AI-identified UDs reached 100% when assisted by our tool. With the development of this AI-based decision support tool, we aim to address the shortage of specialists, enable faster consultation times for patients, and demonstrate the impact and usability of AI-assisted screening. Future developments will include expanding the dataset to include histologically confirmed melanoma and validating the tool for additional body regions.

Magnetic resonance imaging radiomic features stability in brain metastases: Impact of image preprocessing, image-, and feature-level harmonization.

Background and purpose: Magnetic resonance imaging (MRI) scans are highly sensitive to acquisition and reconstruction parameters which affect feature stability and model generalizability in radiomic research. This work aims to investigate the effect of image pre-processing and harmonization methods on the stability of brain MRI radiomic features and the prediction performance of radiomic models in patients with brain metastases (BMs). Materials and methods: Two T1 contrast enhanced brain MRI data-sets were used in this study. The first contained 25 BMs patients with scans at two different time points and was used for features stability analysis. The effect of gray level discretization (GLD), intensity normalization (Z-score, Nyul, WhiteStripe, and in house-developed method named N-Peaks), and ComBat harmonization on features stability was investigated and features with intraclass correlation coefficient >0.8 were considered as stable. The second data-set containing 64 BMs patients was used for a classification task to investigate the informativeness of stable features and the effects of harmonization methods on radiomic model performance. Results: Applying fixed bin number (FBN) GLD, resulted in higher number of stable features compare to fixed bin size (FBS) discretization (10 ± 5.5 % higher). `Harmonization in feature domain improved the stability for non-normalized and normalized images with Z-score and WhiteStripe methods. For the classification task, keeping the stable features resulted in good performance only for normalized images with N-Peaks along with FBS discretization. Conclusions: To develop a robust MRI based radiomic model we recommend using an intensity normalization method based on a reference tissue (e.g N-Peaks) and then using FBS discretization.

Dynamic anthropomorphic thorax phantom for quality assurance of motion management in radiotherapy.

Background and purpose: Motion management techniques are important to spare the healthy tissue adequately. However, they are complex and need dedicated quality assurance. The aim of this study was to create a dynamic phantom designed for quality assurance and to replicate a patient's size, anatomy, and tissue density. Materials and methods: A computed tomography (CT) scan of a cancer patient was used to create molds for the lungs, heart, ribs, and vertebral column via additive manufacturing. A pump system and software were developed to simulate respiratory dynamics. The extent of respiratory motion was quantified using a 4DCT scan. End-to-end tests were conducted to evaluate two motion management techniques for lung stereotactic body radiotherapy (SBRT). Results: The chest wall moved between 4 mm and 13 mm anteriorly and 2 mm to 7 mm laterally during the breathing. The diaphragm exhibited superior-inferior movement ranging from 5 mm to 16 mm in the left lung and 10 mm to 36 mm in the right lung. The left lung tumor displaced ± 7 mm superior-inferiorly and anterior-posteriorly. The CT numbers were for lung: -716 ± 108 HU (phantom) and -713 ± 70 HU (patient); bone: 460 ± 20 HU (phantom) and 458 ± 206 HU (patient); soft tissue: 92 ± 9 HU (phantom) and 60 ± 25 HU (patient). The end-to-end testing showed an excellent agreement between the measured and the calculated dose for ion chamber and film dosimetry. Conclusions: The phantom is recommended for quality assurance, evaluating the institution's specific planning and motion management strategies either through end-to-end testing or as an external audit phantom.

Validation of echo planar imaging based diffusion-weighted magnetic resonance imaging on a 0.35 T MR-Linac.

Background and Purpose: The feasibility of acquiring diffusion-weighted imaging (DWI) images on an MR-Linac for quantitative response assessment during radiotherapy was explored. DWI data obtained with a Spin Echo Echo Planar Imaging sequence adapted for a 0.35 T MR-Linac were examined and compared with DWI data from a conventional 3 T scanner. Materials and Methods: Apparent diffusion coefficient (ADC) measurements and a distortion correction technique were investigated using DWI-calibrated phantoms and in the brains of seven volunteers. All DWI utilized two phase-encoding directions for distortion correction and off-resonance field estimation. ADC maps in the brain were analyzed for automatically segmented normal tissues. Results: Phantom ADC measurements on the MR-Linac were within a 3 % margin of those recorded by the 3 T scanner. The maximum distortion observed in the phantom was 2.0 mm prior to correction and 1.1 mm post-correction on the MR-Linac, compared to 6.0 mm before correction and 3.6 mm after correction at 3 T. In vivo, the average ADC values for gray and white matter exhibited variations of 14 % and 4 %, respectively, for different selections of b-values on the MR-Linac. Distortions in brain images before correction, estimated through the off-resonance field, reached 2.7 mm on the MR-Linac and 12 mm at 3 T. Conclusion: Accurate ADC measurements are achievable on a 0.35 T MR-Linac, both in phantom and in vivo. The selection of b-values significantly influences ADC values in vivo. DWI on the MR-Linac demonstrated lower distortion levels, with a maximum distortion reduced to 1.1 mm after correction.

Characterization of spatial integrity with active and passive implants in a low-field magnetic resonance linear accelerator scanner.

Background and Purpose: Standard imaging protocols can guarantee the spatial integrity of magnetic resonance (MR) images utilized in radiotherapy. However, the presence of metallic implants can significantly compromise this integrity. Our proposed method aims at characterizing the geometric distortions induced by both passive and active implants commonly encountered in planning images obtained from a low-field 0.35 T MR-linear accelerator (LINAC). Materials and Methods: We designed a spatial integrity phantom defining 1276 control points and covering a field of view of 20x20x20 cm3. This phantom was scanned in a water tank with and without different implants used in hip and shoulder arthroplasty procedures as well as with active cardiac stimulators. The images were acquired with the clinical planning sequence (balanced steady-state free-precession, resolution 1.5x1.5x1.5 mm3). Spatial integrity was assessed by the Euclidian distance between the control point detected on the image and their theoretical locations. A first plane free of artefact (FPFA) was defined to evaluate the spatial integrity beyond the larger banding artefact. Results: In the region extending up to 20 mm from the largest banding artefacts, the tested passive and active implants could cause distortions up to 2 mm and 3 mm, respectively. Beyond this region the spatial integrity was recovered and the image could be considered as unaffected by the implants. Conclusions: We characterized the impact of common implants on a low field MR-LINAC planning sequence. These measurements could support the creation of extra margin while contouring organs at risk and target volumes in the vicinity of implants.

MRI-based radiomics for predicting histology in malignant salivary gland tumors: methodology and "proof of principle".

Defining the exact histological features of salivary gland malignancies before treatment remains an unsolved problem that compromises the ability to tailor further therapeutic steps individually. Radiomics, a new methodology to extract quantitative information from medical images, could contribute to characterizing the individual cancer phenotype already before treatment in a fast and non-invasive way. Consequently, the standardization and implementation of radiomic analysis in the clinical routine work to predict histology of salivary gland cancer (SGC) could also provide improvements in clinical decision-making. In this study, we aimed to investigate the potential of radiomic features as imaging biomarker to distinguish between high grade and low-grade salivary gland malignancies. We have also investigated the effect of image and feature level harmonization on the performance of radiomic models. For this study, our dual center cohort consisted of 126 patients, with histologically proven SGC, who underwent curative-intent treatment in two tertiary oncology centers. We extracted and analyzed the radiomics features of 120 pre-therapeutic MRI images with gadolinium (T1 sequences), and correlated those with the definitive post-operative histology. In our study the best radiomic model achieved average AUC of 0.66 and balanced accuracy of 0.63. According to the results, there is significant difference between the performance of models based on MRI intensity normalized images + harmonized features and other models (p value < 0.05) which indicates that in case of dealing with heterogeneous dataset, applying the harmonization methods is beneficial. Among radiomic features minimum intensity from first order, and gray level-variance from texture category were frequently selected during multivariate analysis which indicate the potential of these features as being used as imaging biomarker. The present bicentric study presents for the first time the feasibility of implementing MR-based, handcrafted radiomics, based on T1 contrast-enhanced sequences and the ComBat harmonization method in an effort to predict the formal grading of salivary gland carcinoma with satisfactory performance.

Randomized self-controlled study comparing open-face vs. closed immobilization masks in fractionated cranial radiotherapy.

PURPOSE: To compare patient discomfort and immobilisation performance of open-face and closed immobilization masks in cranial radiotherapy. MATERIAL AND METHODS: This was a single-center randomized self-controlled clinical trial. At CT simulation, an open-face and closed mask was made for each patient and treatment plans with identical dose prescription were generated for each mask. Patients were randomised to start treatment with an open-face or closed mask. Masks were switched halfway through the treatment course; every patient was their own control. Patients self-reported discomfort, anxiety and pain using the visual analogue scale (VAS). Inter- and intrafraction set-up variability was measured with planar kV imaging and a surface guided radiotherapy (SGRT) system for the open-face masks. RESULTS: 30 patients with primary or metastatic brain tumors were randomized - 29 completed radiotherapy to a median total dose of 54 Gy (range 30-60 Gy). Mean discomfort VAS score was significantly lower with open-face masks (0.5, standard deviation 1.0) vs. closed masks (3.3, standard deviation 2.9), P < 0.0001. Anxiety and pain VAS scores were significantly lower with open-face masks (P < 0.0001). Closed masks caused more discomfort in infraorbital (P < 0.001) and maxillary (P = 0.02) areas. Two patients and 27 patients preferred closed or open-face masks, respectively. Interfraction longitudinal shifts and roll and yaw rotations were significantly smaller and lateral shifts were significantly larger with closed masks in combination with the laser system (P < 0.05) compared to open masks in combination with a SGRT system. Intrafraction variability did not differ between the masks. CONCLUSIONS: Open-face masks are associated with decreased patient discomfort without compromising patient positioning and immobilisation accuracy.

Stereotactic body radiotherapy for oligoprogression with or without switch of systemic therapy.

Background: Oligoprogression is defined as cancer progression of a limited number of metastases under active systemic therapy. The role of metastasis-directed therapy, using stereotactic body radiotherapy (SBRT), is controversial as is the continuation versus switch of systemic therapy. We report outcomes of oligoprogressive patients after SBRT, and compare those patients that continued or switched their current line of systemic therapy. Material/Methods: We included patients who developed up to 5 progressive extracranial metastases under systemic therapy for any solid organ malignancy and were treated with SBRT to all lesions at our institution between 01/2014 and 12/2019. Overall survival (OS) and progression-free survival (PFS) were analyzed using the Kaplan-Meier method, and the interval to the next systemic therapy line determined using cumulative incidence functions. Multivariable Cox regression models were used to analyze the influence of baseline and post-progression variables on OS, PFS and survival with the next systemic therapy after SBRT. Results: Among 135 patients with oligoprogressive disease of which the most common primary tumor was lung cancer (n = 46, 34.1 %), 96 continued their current line of systemic therapy after oligoprogression. Among 39 who switched systemic therapy, 28 (71.8 %) paused or discontinued, while 11 (28.2 %) immediately started another systemic treatment. After a median follow-up of 27.2 months, patients that switched and those who continued systemic therapy after oligoprogression had comparable median OS (32.1 vs. 38.2 months, p = 0.47) and PFS (4.3 vs. 3.4 months, p = 0.6). The intervals to the next systemic therapy line were comparable between both cohorts (p = 0.6). An ECOG performance status of 2 and immediately starting a new systemic therapy after oligoprogression were associated with a poorer survival without next systemic therapy, while the de-novo OMD state was associated with better survival without next systemic therapy compared to the induced state. Conclusion: Oncological outcomes of patients that continued or switched systemic therapy after SBRT for oligoprogression were comparable, potentially indicating that further lines of treatment may be safely delayed in selected cases.

An in-silico planning study of stereotactic body radiation therapy for polymetastatic patients with more than ten extra-cranial lesions.

Background and purpose: Limited data is available about the feasibility of stereotactic body radiation therapy (SBRT) for treating more than five extra-cranial metastases, and almost no data for treating more than ten. The aim of this study was to investigate the feasibility of SBRT in this polymetatstatic setting. Materials and methods: Consecutive metastatic melanoma patients with more than ten extra-cranial metastases and a maximum lesion diameter below 11 cm were selected from a single-center prospective registry for this in-silico planning study. For each patient, SBRT plans were generated to treat all metastases with a prescribed dose of 5x7Gy, and dose-limiting organs (OARs) were analyzed. A cell-kill based inverse planning approach was used to automatically determine the maximum deliverable dose to each lesion individually, while respecting all OARs constraints. Results: A total of 23 polymetastatic patients with a medium of 17 metastases (range, 11-51) per patient were selected. SBRT plans with sufficient target coverage and respected OARs dose constraints were achieved in 16 out of 23 patients. In the remaining seven patients, the lungs V5Gy < 80 % and the liver D700 cm3 < 15Gy were most frequently the dose-limiting constraints. The cell-kill based planning approach allowed optimizing the dose administration depending on metastases total volume and location. Conclusion: This retrospective planning study shows the feasibility of definitive SBRT for 70% of polymetastatic patients with more than ten extra-cranial lesions and proposes the cell-killing planning approach as an approach to individualize treatment planning in polymetastatic patients'.

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.

Dosimetric Analysis of Proximal Bronchial Tree Subsegments to Assess The Risk of Severe Toxicity After Stereotactic Body Radiation Therapy of Ultra-central Lung Tumors.

•Stereotactic body radiation therapy (SBRT) for ultra-central lung tumors is associated with high toxicity rates.•To evaluate differences in radiosensitivity within the proximal bronchial tree (PBT), the PBT was sub-segmented into seven anatomical sections.•A risk-adapted SBRT regimen of EQD2_10 = 54.4 Gy in 8 or 10 fractions results in excellent local control and low rates of severe toxicity.•Data from a recent meta-analysis, the NORDIC Hilus trial and dosimetric data from this study were combined to create a NTCP model.•A dose threshold of EQD2_3 = 100 Gy to the PBT or any of its subsegments is expected to result in low rates of severe bronchial toxicity.

The Future of MR-Guided Radiation Therapy.

Magnetic resonance image guided radiation therapy (MRIgRT) is a relatively new technology that has already shown outcomes benefits but that has not yet reached its clinical potential. The improved soft-tissue contrast provided with MR, coupled with the immediacy of image acquisition with respect to the treatment, enables expansion of on-table adaptive protocols, currently at a cost of increased treatment complexity, use of human resources, and longer treatment slot times, which translate to decreased throughput. Many approaches are being investigated to meet these challenges, including the development of artificial intelligence (AI) algorithms to accelerate and automate much of the workflow and improved technology that parallelizes workflow tasks, as well as improvements in image acquisition speed and quality. This article summarizes limitations of current available integrated MRIgRT systems and gives an outlook about scientific developments to further expand the use of MRIgRT.

Intra- and inter-fraction breath-hold variations and margins for radiotherapy of abdominal targets.

Radiotherapy in expiration breath-hold (EBH) has the potential to reduce treatment volumes of abdominal targets compared to an internal target volume concept in free-breathing. The reproducibility of EBH and required safety margins were investigated to quantify this volumetric benefit. Pre- and post-treatment diaphragm position difference and the positioning variability were determined on computed tomography. Systematic and random errors for EBH position reproducibility and positioning variability were calculated, resulting in margins of 7 to 12 mm depending on the prescription isodose and fractionation. A reduced volume was shown for EBH for lesions with superior-inferior breathing motion above 4 to 8 mm.

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.

Efficacy and safety analysis in metastatic cancer patients treated with multiple courses of repeat radiation therapy.

Background and purpose: Due to advances in oncology, a growing proportion of patients is treated with repetitive courses of radiotherapy. The aim of this study is to analyze whether radiotherapy maintains its safety and efficacy profile in patients treated with multiple repeat courses of irradiation. Material and methods: All patients treated between 2011 and 2019 at our institution were screened for a minimum of five repeat irradiation courses, to analyze treatment characteristics, survival, safety and efficacy. The type of re-irradiation was classified according to ESTRO-EORTC consensus guidelines. Results: A total of n = 112 patients receiving n = 660 radiotherapy courses were included in this retrospective cohort study. The most frequent primary tumors were lung cancer in 41.9 % (n = 47) and malignant melanoma in 8.9 % (n = 10). The most frequent re-irradiation types were repeat irradiation and Type 2 re-irradiation in 309 (46.8 %) and 113 (17.1 %) cases, respectively. Median survival after the first course of radiotherapy was 3.6 (0.3-13.4) years. Response to radiotherapy was observed in 548 (83.0 %) cases and CTCAE toxicity grade ≥ 3 was observed in 21 (3.2 %) cases. An increasing number of RT courses (HR: 1.30, p=<0.0001), Type 1 re-irradiation (HR 3.50, p = 0.008) and KPS ≤ 80 % (HR: 2.02, p = 0.002) were associated with significantly worse treatment responses. Toxicity rates remained stable with increasing numbers of RT courses. Conclusion: Multiple courses of repeat radiotherapy maintain a favorable therapeutic ratio of high response combined with reasonable safety profile.

Potential of ChatGPT in facilitating research in radiation oncology?

PURPOSE: To evaluate the potential of the artificial intelligence (AI) chatbot ChatGPT in supporting young clinical scientists with scientific tasks in radio oncological research. MATERIALS AND METHODS: Seven scientific tasks were to be completed in 3 h by 8 radiation oncologists with different scientific experience working at a university hospital: creation of a scientific synopsis, creation of a research question and corresponding clinical trial hypotheses, writing of the first paragraph of a manuscript introduction, clinical trial sample size calculation, and clinical data analyses (multivariate analysis, boxplot and survival curve). No participant had prior experience with an AI chatbot. All participants were instructed in ChatGPT v3.5 and its use was provided for all tasks. Answers were scored independently by two blinded experts. The subjective value of ChatGPT was rated by each participant. Data were analyzed with regression-, t-test and Spearman correlation (p < 0.05). RESULTS: Participants completed tasks 1-3 with an average score of 50% and 4-7 with 56%. Scientific experience, number of original publications and of first/last authorships showed a positive correlation with overall scoring (p = 0.01-0.04). Participants with little to moderate scientific experience scored ChatGPT to be more helpful in solving tasks 4-7 compared to more experienced participants (p = 0.04), with simultaneously presenting lower scorings (p = 0.03). CONCLUSIONS: ChatGPT did not compensate for differences in scientific experience of young clinical scientists, with less experienced researchers believing false AI-generated scientific results.

External validation of three prognostic scores for brain metastasis velocity in patients treated with intracranial stereotactic radiotherapy.

BACKGROUND AND INTRODUCTION: Brain metastasis velocity (BMV) has been proposed as a prognostic factor for overall survival (OS) in patients with brain metastases (BMs). In this study, we conducted an external validation and comparative assessment of the performance of all three BMV scores. MATERIALS AND METHODS: Patients treated with intracranial stereotactic radiotherapy (SRT) for BM at a single center between 2014 and 2018 were identified. Where possible, all three BMV scores were calculated. Log-rank tests and linear, logistic and Cox regression analysis were used for validation and predictor identification of OS. RESULTS: For 333 of 384 brain metastasis patients, at least one BMV score could be calculated. In a sub-group of 187 patients, "classic" BMV was validated as categorical (p < 0.0001) and continuous variable (HR 1.02; 95% CI 1.02-1.03; p < 0.0001). In a sub-group of 284 patients, "initial" BMV was validated as categorical variable (high-risk vs. low-risk; p < 0.01), but not as continuous variable (HR 1.02; 95% CI 0.99-1.04; p = 0.224). "Volume-based" BMV could not be validated in a sub-group of 104 patients. On multivariable Cox regression analysis, iBMV (HR 1.85; 95% CI 1.01-3.38; p < 0.05) and cBMV (HR 2.32; 95% CI 1.15 4.68; p < 0.05) were predictors for OS for intermediate-risk patients after first SRT and first DBFs, respectively. cBMV proved to be the dominant predictor for OS for high-risk patients (HR 2.99; 95% CI 1.30-6.91; p < 0.05). CONCLUSION: This study externally validated cBMV and iBMV as prognostic scores for OS in patients treated with SRT for BMs whereas validation of vBMV was not achieved.

Dosimetric analysis of 17 cardiac Sub-structures, Toxicity, and survival in ultra central lung tumor patients treated with SBRT.

•Data on cardiac toxicity after SBRT for ultra-central lung tumors remains limited.•We analyzed the dose to 18 cardiac sub-structures and cardiovascular toxicity.•A SBRT regimen of 45 Gy in 8-10 fractions yields good local control and low toxicity.•The highest cardiac doses were observed in the pulmonary artery and left atrium.•Higher doses to the base of the heart seem to be associated with non-cancer deaths.