Lohmann, Philipp

Lay Summary Accurately differentiating actual tumor progression from treatment-related changes, such as pseudoprogression or radiation-induced injury, remains one of the most critical diagnostic challenges in neuro-oncology for both primary and secondary brain tumors. Conventional MRI often lacks specificity because it primarily reflects blood-brain barrier disruption rather than viable tumor tissue. Consequently, advanced techniques like perfusion-weighted MRI and amino acid PET have gained significant attention. While perfusion-weighted MRI provides essential insights into blood flow and is widely available, accumulating evidence suggests that amino acid PET can be more reliable in identifying treatment-related changes. Since both modalities offer complementary information, there is an ongoing debate regarding their relative roles and the optimal way to integrate them into clinical practice. Combining these tools aims to provide more reliable diagnoses, ultimately preventing unnecessary interventions and ensuring that true tumor growth is detected as early as possible.

Abstract BACKGROUND Volumetric analysis of brain metastatic lesions is of great relevance in neuro-oncology. Since manual annotation of a lesion is a laborious and time-consuming task, utilizing artificial intelligence (AI) tools for automated segmentation of brain metastases has shown promising potential, but published algorithms are not incorporated into clinical practice because of poor generalizability across institutions. Large publicly available and well-annotated datasets from across the world are critical for further clinical translation. MATERIAL AND METHODS The Brain Tumor Segmentation - Metastases (BraTS-METS) 2025 Lighthouse Challenge provides a competitive environment where scientists from various disciplines can benchmark their AI-based algorithms for automatic segmentation of pre- and post-treatment brain metastases on a well-curated publicly available brain metastasis dataset comprising 1,778 multiparametric brain MRI cases. Each case consists of T1-weighed, T1 post-contrast and FLAIR sequences. The cases were retrospectively obtained from 8 institutions in the USA and overseas, and underwent 3D nnU-Net pre-segmentation, followed by manual refinements performed by medical students and neuroradiologists, and a final approval by two independent board-certified neuroradiologists. A testing dataset was prepared through four rounds of annotation: two completed ‘from scratch’ and two following 3D nnU-Net pre-segmentation, all conducted by two independent neuroradiologists, with the segmentation process screen recorded for future analysis. This allowed us to establish inter- and intra-rater variability in brain metastases segmentation and create a reference standard dataset that will be publicly available after the conclusion of the challenge. The model performance of the participating teams in the challenge will be assessed via the following metrics: Dice Similarity Coefficient (DSC), Normalized Surface Distance (NSD), sensitivity, and precision of the model. RESULTS A multi-annotator testing dataset of 75 MRI cases of brain metastases and 300 segmentations (150 created ‘from scratch’ and 150 from 3D nnU-Net pre-segmentations of the cases) was developed as a new reference standard. The mean intra- and inter-rater DSC for the segmentations ‘from scratch’ were 0.68 and 0.59, respectively. The mean intra- and inter-rater DSC for the segmentations built upon 3D nnU-Net pre-segmentations were 0.88 and 0.77, respectively. CONCLUSION The BraTS-METS 2025 Lighthouse Challenge is a key initiative designed to advance the use of AI to improve characterization of brain metastases by providing algorithms for automatic segmentation of all lesions and peritumoral edema that are measured against a novel reference standard based on rigorous multi-annotator brain MRI segmentations.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well- annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied environments and providing the tools for future development of personalized volumetric reports to patients undergoing BM treatment.

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps.

Abstract Brain tumor diagnostics have significantly evolved with the use of positron emission tomography (PET) and advanced magnetic resonance imaging (MRI) techniques. In addition to anatomical MRI, these modalities may provide valuable information for several clinical applications such as differential diagnosis, delineation of tumor extent, prognostication, differentiation between tumor relapse and treatment-related changes, and the evaluation of response to anticancer therapy. In particular, joint recommendations of the Response Assessment in Neuro-Oncology (RANO) Group, the European Association of Neuro-oncology, and major European and American Nuclear Medicine societies highlighted that the additional clinical value of radiolabeled amino acids compared to anatomical MRI alone is outstanding and that its widespread clinical use should be supported. For advanced MRI and its steadily increasing use in clinical practice, the Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition provided more recently an updated acquisition protocol for the widely used dynamic susceptibility contrast perfusion MRI. Besides amino acid PET and perfusion MRI, other PET tracers and advanced MRI techniques (e.g. MR spectroscopy) are of considerable clinical interest and are increasingly integrated into everyday clinical practice. Nevertheless, these modalities have shortcomings which should be considered in clinical routine. This comprehensive review provides an overview of potential challenges, limitations, and pitfalls associated with PET imaging and advanced MRI techniques in patients with gliomas or brain metastases. Despite these issues, PET imaging and advanced MRI techniques continue to play an indispensable role in brain tumor management. Acknowledging and mitigating these challenges through interdisciplinary collaboration, standardized protocols, and continuous innovation will further enhance the utility of these modalities in guiding optimal patient care.

Abstract Radiographic response assessment in neuro-oncology is critical in clinical practice and trials. Conventional criteria, such as the MacDonald and response assessment in neuro-oncology (RANO) criteria, rely on bidimensional (2D) measurements of a single tumor cross-section. Although RANO criteria are established for response assessment in clinical trials, there is a critical need to address the complexity of brain tumor treatment response with multiple new approaches being proposed. These include volumetric analysis of tumor compartments, structured MRI reporting systems like the Brain Tumor Reporting and Data System, and standardized approaches to advanced imaging techniques to distinguish tumor response from treatment effects. In this review, we discuss the strengths and limitations of different neuro-oncology response criteria and summarize current research findings on the role of novel response methods in neuro-oncology clinical trials and practice.