Automatic and standardized surgical reporting for central nervous system tumors

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%, 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. The 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.

Shapes of direct cortical responses vs. short-range axono-cortical evoked potentials: The effects of direct electrical stimulation applied to the human brain

Objective: Direct cortical responses (DCR) and axono-cortical evoked potentials (ACEP) are generated by electrically stimulating the cortex either directly or indirectly through white matter pathways, potentially leading to different electrogenic processes. For ACEP, the slow conduction velocity of axons (median around 4 m.s$^{-1}$) is anticipated to induce a delay. For DCR, direct electrical stimulation (DES) of the cortex is expected to elicit additional cortical activity involving smaller and slower non-myelinated axons. We tried to validate these hypotheses. Methods: DES was administered either directly on the cortex or to white matter fascicles within the resection cavity, while recording DCR or ACEP at the cortical level in nine patients. Results: Short but significant delays (around 2 ms) were measurable for ACEP immediately following the initial component (around 7 ms). Subsequent activities (around 40 ms) exhibited notable differences between DCR and ACEP, suggesting the presence of additional cortical activities for DCR. Conclusion: Distinctions between ACEPs and DCRs can be made based on a delay at the onset of early components and the dissimilarity in the shape of the later components >40 ms after the DES artifact). Significance: The comparison of different types of evoked potentials allows to better understand the effects of DES.

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

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.