Background:The RANO Resect group published the updated RANO categories for extent of resection in glioblastoma in 2023. Our aim was to provide a population-based validation of these categories. Methods:This population based retrospective cohort study included 470 consecutive patients treated at two centers in Norway and Sweden. None of the hospitals had a strategy to aim for supramarginal resections. Postoperative contrast-enhanced T1 and FLAIR volumes were used to group the patients according to the RANO categories. Cox models and Kaplan-Meier plots were used for survival analyses. We performed subgroup analyses of patients where the Stupp protocol was initiated, MGMT methylated tumors, and elderly patients (≥70 years). Results:Median overall survival was 16, 15, 11, and 6 months for the respective categories of supramaximal (Class 1), maximal (Class 2), submaximal resection (Class 3), and biopsy only (Class 4). Thirty-five patients (7.5%) were classified as Class 1, despite not aiming for resection beyond the contrast-enhancing tumor. This class had a median tumor volume of only 6 mL, smaller preoperative FLAIR volumes and was associated with improved survival compared to Class 2 (HR = 0.6, P < .0001) in the adjusted analysis. There was no significant difference between classes 3 and 4. Discussion and Conclusion:The RANO categories were associated with survival, but survival was shorter and differences across categories were less than in the original study. Maximal and supramaximal resections were associated with longer survival. One fourth of complete resections were incidentally classified as supramaximal resections, and these patients had a favorable prognosis.

Background: Glioblastoma (GBM) shows marked heterogeneity in overall survival (OS), yet robust radiological prognostic markers remain limited. The prognostic value of peritumoral edema and tumor location remains uncertain. Objective: To evaluate whether peritumoral edema, tumor burden, and spatial tumor distribution predict OS using automated MRI analysis and Brain-Grid-based topographical mapping. Methods: In this retrospective study, preoperative T1-contrast-enhanced and T2-FLAIR MRI sequences from 271 patients with IDH-wildtype GBM were analyzed using automated segmentation (Raidionics). Tumor and edema volumes, edema-to-tumor ratio (ETR), and voxel-wise infiltration patterns were extracted. Location was mapped using the Brain-Grid system. Survival was assessed using Kaplan-Meier and multivariable Cox regression adjusted for age and tumor volume, with correction for multiple testing. Results: In multivariable analysis, only age remained a conventional independent predictor of OS (HR 1.03, p < 0.001). Tumor volume, edema volume, and ETR were not associated with survival. In contrast, Brain-Grid analysis identified two centrally located subcortical voxel regions that remained significantly associated with shorter OS after full adjustment and multiple testing correction. Total number of infiltrated voxels showed no prognostic value. Conclusions: Spatially defined subcortical infiltration patterns, rather than global tumor or edema burden, independently stratify survival in GBM. These findings highlight the prognostic relevance of voxel-level tumor topography and support further validation of Brain-Grid-based imaging biomarkers.

Quantification of the residual tumor from early post-operative magnetic resonance imaging (MRI) is essential in follow-up and treatment planning for glioblastoma patients. Residual tumor segmentation from early post-operative MRI is particularly challenging compared to the closely related task of pre-operative segmentation, as the tumor lesions are small, fragmented, and easily confounded with noise in the resection cavity. Recently, several studies successfully trained deep learning models for early post-operative segmentation, yet with subpar performances compared to the analogous task pre-operatively. In this study, the impact of image and annotation quality on model training and performance in early post-operative glioblastoma segmentation was assessed. A dataset consisting of early post-operative MRI scans from 423 patients and two hospitals in Norway and Sweden was assembled, for which image and annotation qualities were evaluated by expert neurosurgeons. The Attention U-Net architecture was trained with five-fold cross-validation on different quality-based subsets of the dataset in order to evaluate the impact of training data quality on model performance. Including low-quality images in the training set did not deteriorate performance on high-quality images. However, models trained on exclusively high-quality images did not generalize to low-quality images. Models trained on exclusively high-quality annotations reached the same performance level as the models trained on the entire dataset, using only two-thirds of the dataset. Both image and annotation quality had a significant impact on model performance. In dataset curation, images should ideally be representative of the quality variations in the real-world clinical scenario, and efforts should be made to ensure exact ground truth annotations of high quality.

IntroductionMagnetic resonance (MR) imaging is essential for diagnosing central nervous system (CNS) tumors, guiding surgical planning, treatment decisions, and assessing postoperative outcomes and complications. While recent work has advanced automated tumor segmentation and report generation, most efforts have focused on preoperative data, with limited attention to postoperative imaging analysis.MethodsThis study introduces a comprehensive pipeline for standardized postsurgical reporting in CNS tumors. Using the Attention U-Net architecture, segmentation models were trained, independently targeting the preoperative tumor core, non-enhancing tumor core, postoperative contrast-enhancing residual tumor, and resection cavity. In the process, the influence of varying MR sequence combinations was assessed. Additionally, MR sequence classification and tumor type identification for contrast-enhancing lesions were explored using the DenseNet architecture. The models were integrated seamlessly into an automated and standardized reporting pipeline, following the RANO 2.0 guidelines. Training was conducted on multicentric datasets comprising 2000 to 7000 patients, incorporating both private and public data, using a 5-fold cross-validation.ResultsEvaluation included patient-, voxel-, and object-wise metrics, with benchmarking against the latest BraTS challenge results. The segmentation models achieved average voxel-wise Dice scores of 87%, 66%, 70%, and 77% for the tumor core, non-enhancing tumor core, contrast-enhancing residual tumor, and resection cavity, respectively. Classification models reached 99.5% balanced accuracy in MR sequence classification and 80% in tumor type classification.DiscussionThe pipeline presented in this study enables robust, automated segmentation, MR sequence classification, and standardized report generation aligned with RANO 2.0 guidelines, enhancing postoperative evaluation and clinical decision-making. The proposed models and methods were integrated into Raidionics, open-source software platform for CNS tumor analysis, now including a dedicated module for postsurgical analysis.

Fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) scans are important for diagnosis, treatment planning, and monitoring of various brain tumors. Depending on the tumor type, the FLAIR hyperintensity volume is an important measure to assess the tumor volume, surrounding vasogenic edema, or treatment induced changes, such as gliosis. Automatic segmentation would therefore be valuable in the clinic and in clinical trials. In this study, around 5000 FLAIR images of various brain tumors types and acquisition time points, from different neurosurgical centers, were used to train a unified FLAIR hyperintensity segmentation model using an Attention U-Net architecture. The performance was compared against dataset-specific models and was validated on different tumor types, acquisition time points, and against BraTS. The unified model achieved an average Dice score of 88.65% for pre-operative meningiomas, 80.08% for pre-operative metastases, 90.92% for pre-operative and 84.60% for post-operative gliomas from BraTS, and 84.47% for pre-operative and 61.27% for post-operative lower grade gliomas. In addition, the results showed that the unified model achieved comparable segmentation performance to the dataset-specific models on their respective datasets. The documented generalization across tumor types and acquisition time points is a strong indicator for efficient deployment in a clinical setting. The model has been integrated into Raidionics, an open-source software for CNS tumor analysis.

OBJECTIVE The extent of resection (EOR) and postoperative residual tumor (RT) volume are prognostic factors in glioblastoma. Calculations of EOR and RT rely on accurate tumor segmentations. Raidionics is an open-access software that enables automatic segmentation of preoperative and early postoperative glioblastoma using pretrained deep learning models. The aim of this study was to compare the prognostic value of manually versus automatically assessed volumetric measurements in glioblastoma patients. METHODS Adult patients who underwent resection of histopathologically confirmed glioblastoma were included from 12 different hospitals in Europe and North America. Patient characteristics and survival data were collected as part of local tumor registries or were retrieved from patient medical records. The prognostic value of manually and automatically assessed EOR and RT volume was compared using Cox regression models. RESULTS Both manually and automatically assessed RT volumes were a negative prognostic factor for overall survival (manual vs automatic: HR 1.051, 95% CI1.034-1.067 [p < 0.001] vs HR 1.019, 95% CI1.007-1.030 [p = 0.001]). Both manual and automatic EOR models showed that patients with gross-total resection have significantly longer overall survival compared with those with subtotal resection (manual vs automatic: HR 1.580, 95% CI1.291-1.932 [p < 0.001] vs HR 1.395, 95% CI1.160-1.679 [p < 0.001]), but no significant prognostic difference of gross-total compared with near-total (90%-99%) resection was found. According to the Akaike information criterion and the Bayesian information criterion, all multivariable Cox regression models showed similar goodness-of-fit. CONCLUSIONS Automatically and manually measured EOR and RT volumes have comparable prognostic properties. Automatic segmentation with Raidionics can be used in future studies in patients with glioblastoma.

Background:Accurate prognosis of glioblastoma is crucial for better-informed treatment decisions, potentially leading to improved disease management. We investigated whether clinical variables, tumor size, and location, can serve as prognostic factors. Methods:A retrospective, multicenter study enrolled 1318 adult patients with histopathologically confirmed glioblastoma undergoing first-time surgery, with survival censored for 188 patients. Pre-operative brain MRIs were used to compute tumor size and derive advanced radiological features describing tumor location, later refined by expert-based opinion. Post-operative MRIs were used to measure the enhancing residual tumor volume. The prognostic quality of all variables, measurements, and features was assessed as inputs of three survival regression models (CoxPH, Random Survival Forests, DeepSurv) to predict overall survival, under five timepoints of patient treatment: onset presentation, assessment by multidisciplinary board, intervention planning, post-intervention evaluation, and chemoradiotherapy planning. Model evaluation was performed with the C-index, Brier Score over Time, and Integrated Brier Score. Results:Multivariable Cox analysis identified most clinical variables and tumor size as strong predictors of patient survival, with varying hazard ratios across timepoints. DeepSurv was consistently the top performing model under all possible inputs and at all timepoints, yielding mean test C-index scores ranging from 61.71% to 70.29%, and mean Integrated Brier Scores ranging from 8.57% to 7.63%. Conclusion:Clinical variables, tumor size, and location carry prognostic value for the overall survival of patients with glioblastoma. The best predictive performance was observed under a Deep Survival model using all variables at the stage of chemoradiotherapy planning.

Magnetic resonance (MR) imaging is essential for evaluating central nervous system (CNS) tumors, guiding surgical planning, treatment decisions, and assessing postoperative outcomes and complication risks. While recent work has advanced automated tumor segmentation and report generation, most efforts have focused on preoperative data, with limited attention to postoperative imaging analysis. This study introduces a comprehensive pipeline for standardized postsurtical reporting in CNS tumors. Using the Attention U-Net architecture, segmentation models were trained for the preoperative (non-enhancing) tumor core, postoperative contrast-enhancing residual tumor, and resection cavity. Additionally, MR sequence classification and tumor type identification for contrast-enhancing lesions were explored using the DenseNet architecture. The models were integrated into a reporting pipeline, following the RANO 2.0 guidelines. Training was conducted on multicentric datasets comprising 2000 to 7000 patients, using a 5-fold cross-validation. Evaluation included patient-, voxel-, and object-wise metrics, with benchmarking against the latest BraTS challenge results. The segmentation models achieved average voxel-wise Dice scores of 87

To improve the prognosis of patients suffering from pulmonary diseases, such as lung cancer, early diagnosis and treatment are crucial. The analysis of CT images is invaluable for diagnosis, whereas high quality segmentation of the airway tree are required for intervention planning and live guidance during bronchoscopy. Recently, the Multi-domain Airway Tree Modeling (ATM'22) challenge released a large dataset, both enabling training of deep-learning based models and bringing substantial improvement of the state-of-the-art for the airway segmentation task. The ATM'22 dataset includes a large group of COVID'19 patients and a range of other lung diseases, however, relatively few patients with severe pathologies affecting the airway tree anatomy was found. In this study, we introduce a new public benchmark dataset (AeroPath), consisting of 27 CT images from patients with pathologies ranging from emphysema to large tumors, with corresponding trachea and bronchi annotations. Second, we present a multiscale fusion design for automatic airway segmentation. Models were trained on the ATM'22 dataset, tested on the AeroPath dataset, and further evaluated against competitive open-source methods. The same performance metrics as used in the ATM'22 challenge were used to benchmark the different considered approaches. Lastly, an open web application is developed, to easily test the proposed model on new data. The results demonstrated that our proposed architecture predicted topologically correct segmentations for all the patients included in the AeroPath dataset. The proposed method is robust and able to handle various anomalies, down to at least the fifth airway generation. In addition, the AeroPath dataset, featuring patients with challenging pathologies, will contribute to development of new state-of-the-art methods. The AeroPath dataset and the web application are made openly available.

IntroductionPostoperative hematomas that require reoperation are a serious, but uncommon complication to glioma surgery. However, smaller blood volumes are frequently observed, but their clinical significance is less known.Research questionWhat are the incidence rates, risk factors, and patient-reported outcomes of all measurable blood in or near the resection cavity on postoperative MRI in diffuse glioma patients?Material and MethodsWe manually segmented intradural and extradural blood from early postoperative MRI of 292 diffuse glioma resections. Potential associations between blood volume and tumor characteristics, demographics, and perioperative factors were explored using non-parametric methods. The assessed outcomes were generic and disease-specific patient-reported HRQoL.ResultsOut of the 292 MRI scans included, 184 (63%) had intradural blood, and 212 (73%) had extradural blood in or near the resection cavity. The median blood volumes were 0.4 mL and 3.0 mL, respectively. Intradural blood volume was associated with tumor volume, intraoperative blood loss, and EOR. Extradural blood volume was associated with age and tumor volume. Greater intradural blood volume was associated with less headache and cognitive improvement, but not after adjustments for tumor volume.Discussion and ConclusionsPostoperative blood on early postoperative MRI is common. Intradural blood volumes tend to be larger in patients with larger tumors, more intraoperative blood loss, or undergoing subtotal resections. Extradural blood volumes tend to be larger in younger patients with larger tumors. Postoperative blood in or near the resection cavity that does not require reoperation does not seem to affect HRQoL in diffuse glioma patients.

For patients suffering from brain tumors, prognosis estimation and treatment decisions are made by a multidisciplinary team of medical doctors based on a set of MR scans. Currently, the lack of automatic, standardized, and robust methods for tumor characterization represents a major hurdle for use in clinical practice. This paper describes our contribution to the BraTS 2023 Continuous Evaluation challenge for the segmentation of all tumor types, using our single-stage AGU-Net architecture and various training strategies. Performance over the training sets were reported using our custom pixel-wise, patient-wise, and lesion-wise metrics. For the tumor core, an average lesion-wise Dice score of 85

Extent of resection, MGMT promoter methylation status, age, functional level, and residual tumor volume are established prognostic factors for overall survival in glioblastoma patients. Preoperative tumor volume has also been investigated, but the results have been inconclusive. We hypothesized that the surface area and the shape were more representative of the tumor’s infiltrative capacities, and thus, the purpose of this study was to assess the prognostic value of tumor size and shape in patients with glioblastoma. In total, 271 patients with primary, unifocal glioblastoma were included from two centers in Norway and Sweden, respectively. All tumors were automatically segmented on preoperative MRI scans and manually validated. Tumor volume was used as a measurement of size, whereas sphericity index and area-to-volume ratio defined the shape complexity of the tumor. Contact surface area of the tumor was considered a measurement of both size and shape. Multivariable Cox proportional hazards models were used to assess the prognostic value of the respective tumor measurements, with previously established prognostic factors as covariates. There were no associations between preoperative tumor volume and overall survival. Contact surface area (HR = 1.013, p = 0.002) and sphericity index (HR = 2.223, p = 0.001) were both significant independent prognostic factors for survival in the multivariable Cox models. Contact surface area was also associated with MGMT promoter methylation (p = 0.039) and extent of resection (p = 0.017). Tumor shape complexity appears to be an independent prognostic factor in glioblastoma patients and may also be associated with MGMT promoter methylation status and extent of surgical resection.

Standard treatment of patients with glioblastoma includes surgical resection of the tumor. The extent of resection (EOR) achieved during surgery significantly impacts prognosis and is used to stratify patients in clinical trials. In this study, we developed a U-Net-based deep-learning model to segment contrast-enhancing tumor on post-operative MRI exams taken within 72 h of resection surgery and used these segmentations to classify the EOR as either maximal or submaximal. The model was trained on 122 multiparametric MRI scans from our institution and achieved a mean Dice score of 0.52 ± 0.03 on an external dataset (n = 248), a performance ­on par with the interrater agreement between expert annotators as reported in literature. We obtained an EOR classification precision/recall of 0.72/0.78 on the internal test dataset (n = 462) and 0.90/0.87 on the external dataset. Furthermore, Kaplan-Meier curves were used to compare the overall survival between patients with maximal and submaximal resection in the internal test dataset, as determined by either clinicians or the model. There was no significant difference between the survival predictions using the model's and clinical EOR classification. We find that the proposed segmentation model is capable of reliably classifying the EOR of glioblastoma tumors on early post-operative MRI scans. Moreover, we show that stratification of patients based on the model's predictions offers at least the same prognostic value as when done by clinicians.

Background:Knowledge about meningioma growth characteristics is needed for developing biologically rational follow-up routines. In this study of untreated meningiomas followed with repeated magnetic resonance imaging (MRI) scans, we studied growth dynamics and explored potential factors associated with tumor growth.Methods:In a single-center cohort study, we included 235 adult patients with radiologically suspected intracranial meningioma and at least 3 MRI scans during follow-up. Tumors were segmented using an automatic algorithm from contrast-enhanced T1 series, and, if needed, manually corrected. Potential meningioma growth curves were statistically compared: linear, exponential, linear radial, or Gompertzian. Factors associated with growth were explored.Results:In 235 patients, 1394 MRI scans were carried out in the median 5-year observational period. Of the models tested, a Gompertzian growth curve best described growth dynamics of meningiomas on group level. 59% of the tumors grew, 27% remained stable, and 14% shrunk. Only 13 patients (5%) underwent surgery during the observational period and were excluded after surgery. Tumor size at the time of diagnosis, multifocality, and length of follow-up were associated with tumor growth, whereas age, sex, presence of peritumoral edema, and hyperintense T2-signal were not significant factors.Conclusions:Untreated meningiomas follow a Gompertzian growth curve, indicating that increasing and potentially doubling subsequent follow-up intervals between MRIs seems biologically reasonable, instead of fixed time intervals. Tumor size at diagnosis is the strongest predictor of future growth, indicating a potential for longer follow-up intervals for smaller tumors. Although most untreated meningiomas grow, few require surgery.

Post-surgical evaluation and quantification of residual tumor tissue from magnetic resonance images (MRI) is a crucial step for treatment planning and follow-up in glioblastoma care. Segmentation of enhancing residual tumor tissue from early post-operative MRI is particularly challenging due to small and fragmented lesions, post-operative bleeding, and noise in the resection cavity. Although a lot of progress has been made on the adjacent task of pre-operative glioblastoma segmentation, more targeted methods are needed for addressing the specific challenges and detecting small lesions. In this study, a state-of-the-art architecture for pre-operative segmentation was used, trained on a large in-house multi-center dataset for early post-operative segmentation. Various pre-processing, data sampling techniques, and architecture variants were explored for improving the detection of small lesions. The models were evaluated on a dataset annotated by 8 novice and expert human raters, and the performance compared against the human inter-rater variability. Trained models' performance were shown to be on par with the performance of human expert raters. As such, automatic segmentation models have the potential to be a valuable tool in a clinical setting as an accurate and time-saving alternative, compared to the current standard manual method for residual tumor measurement after surgery.

For patients suffering from central nervous system tumors, prognosis estimation, treatment decisions, and postoperative assessments are made from the analysis of a set of magnetic resonance (MR) scans. Currently, the lack of open tools for standardized and automatic tumor segmentation and generation of clinical reports, incorporating relevant tumor characteristics, leads to potential risks from inherent decisions’ subjectivity. To tackle this problem, the proposed Raidionics open-source software has been developed, offering both a user-friendly graphical user interface and stable processing backend. The software includes preoperative segmentation models for each of the most common tumor types (i.e., glioblastomas, lower grade gliomas, meningiomas, and metastases), together with one early postoperative glioblastoma segmentation model. Preoperative segmentation performances were quite homogeneous across the four different brain tumor types, with an average Dice around 85% and patient-wise recall and precision around 95%. Postoperatively, performances were lower with an average Dice of 41%. Overall, the generation of a standardized clinical report, including the tumor segmentation and features computation, requires about ten minutes on a regular laptop. The proposed Raidionics software is the first open solution enabling an easy use of state-of-the-art segmentation models for all major tumor types, including preoperative and postsurgical standardized reports.

Purpose: This study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable registration for abdominal imaging. Methods: Different training strategies, loss functions, and transfer learning schemes were considered. Furthermore, an augmentation layer which generates artificial training image pairs on-the-fly was proposed, in addition to a loss layer that enables dynamic loss weighting. Results: Guiding registration using segmentations in the training step proved beneficial for deep-learning-based image registration. Finetuning the pretrained model from the brain MRI dataset to the abdominal CT dataset further improved performance on the latter application, removing the need for a large dataset to yield satisfactory performance. Dynamic loss weighting also marginally improved performance, all without impacting inference runtime. Conclusion: Using simple concepts, we improved the performance of a commonly used deep image registration architecture, VoxelMorph. In future work, our framework, DDMR, should be validated on different datasets to further assess its value.

ABSTRACT As lung cancer evolves, the presence of potentially malignant lymph nodes must be assessed to properly estimate disease progression and select the best treatment strategy. A method for accurate and automatic segmentation is hence decisive for quantitatively describing lymph nodes. In this study, the use of 3D convolutional neural networks, either through slab-wise schemes or the leveraging of downsampled entire volumes, is investigated. As lymph nodes have similar attenuation values to nearby anatomical structures, we use the knowledge of other organs as prior information to guide the segmentation. To assess the performances, a 5-fold cross-validation strategy was followed over a dataset of 120 contrast-enhanced CT volumes. For the 1178 lymph nodes with a short-axis diameter mm, our best-performing approach reached a patient-wise recall of 92%, a false positive per patient ratio of 5 and a segmentation overlap of 80.5%. Fusing a slab-wise and a full volume approach within an ensemble scheme generated the best performances. The anatomical priors guiding strategy is promising, yet a larger set than four organs appears needed to generate an optimal benefit. A larger dataset is also mandatory given the wide range of expressions a lymph node can exhibit (i.e. shape, location and attenuation).

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.

Objective: Pre-clinical trials have obtained promising results that focused ultrasound (FUS) combined with micro-bubbles (MBs) increases tumor uptake and the therapeutic effect of drugs. The aims of the study described here were to investigate whether FUS and MBs could improve the effect of chemotherapy in patients with liver metas-tases from colorectal cancer and to investigate the safety and feasibility of using FUS + MBs.Methods: We included 17 patients with liver metastases from colorectal cancer, selected two lesions in each patient's liver and randomized the lesions for, respectively, treatment with FUS + MBs or control. After chemo-therapy (FOLFIRI or FOLFOXIRI), the lesions were treated with FUS (frequency = 1.67 MHz, mechanical index = 0.5, pulse repetition frequency = 0.33 Hz, 33 oscillations, duty cycle = 0.2%-0.4% and MBs (SonoVue) for 35 min). Nine boluses of MBs were injected intravenously at 3.5 min intervals. Patients were scheduled for four cycles of treatment. Changes in the size of metastases were determined from computed tomography images.Results: Treatment with FUS + MBs is safe at the settings used. There was considerable variation in treatment response between lesions and mixed response between lesions receiving only chemotherapy. There is a tendency toward larger-volume reduction in lesions treated with FUS + MBs compared with control lesions, but a mixed response to chemotherapy and lesion heterogeneity make it difficult to interpret the results.Conclusion: The combination of FUS and MBs is a safe, feasible and available strategy for improving the effect of chemotherapy in cancer patients. Therapeutic effect was not demonstrated in this trial. Multicenter trials with standardized protocols should be performed.