DeepEOR: automated perioperative volumetric assessment of variable grade gliomas using deep learning

Abstract

Purpose Volumetric assessments, such as extent of resection (EOR) or residual tumor volume, are essential criterions in glioma resection surgery. Our goal is to develop and validate segmentation machine learning models for pre- and postoperative magnetic resonance imaging scans, allowing us to assess the percentagewise tumor reduction after intracranial surgery for gliomas. Methods For the development of the preoperative segmentation model (U-Net), MRI scans of 1053 patients from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2021 as well as from patients who underwent surgery at the University Hospital in Zurich were used. Subsequently, the model was evaluated on a holdout set containing 285 images from the same sources. The postoperative model was developed using 72 scans and validated on 45 scans obtained from the BraTS 2015 and Zurich dataset. Performance is evaluated using Dice Similarity score, Jaccard coefficient and Hausdorff 95%. Results We were able to achieve an overall mean Dice Similarity Score of 0.59 and 0.29 on the pre- and postoperative holdout sets, respectively. Our algorithm managed to determine correct EOR in 44.1%. Conclusion Although our models are not suitable for clinical use at this point, the possible applications are vast, going from automated lesion detection to disease progression evaluation. Precise determination of EOR is a challenging task, but we managed to show that deep learning can provide fast and objective estimates.

I Asked a ChatGPT to Write an Editorial About How We Can Incorporate Chatbots Into Neurosurgical Research and Patient Care…

“…fforts to apply artificial intelligence (AI) and machine learning in the medical field and specifically in neurosurgery are actively underway. [1][2][3][4][5][6][7] Currently, imaging-based diagnosis, disease diagnosis, and methods of prediction using clinical data and genomic data are fields that receive the most attention. [3][4][5][6][8][9][10][11] However, AI technologies associated with natural language processing are also being investigated.…”

“…[1][2][3][4][5][6][7] Currently, imaging-based diagnosis, disease diagnosis, and methods of prediction using clinical data and genomic data are fields that receive the most attention. [3][4][5][6][8][9][10][11] However, AI technologies associated with natural language processing are also being investigated. 12 Conversational AI is an application of natural language processing that refers to AI technologies capable of communicating with people.…”

Augmented surgical decision-making for glioblastoma: integrating AI tools into education and practice

“…Zanier et al ( 18 ) developed and validated a ML model for segmentation on MRI scans, enabling the assessment of percentage-wise tumor reduction post-intracranial surgery for gliomas. The preoperative segmentation model (U-Net) utilized MRI scans from 1,053 patients from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2021 and those who underwent surgery at the University Hospital in Zurich.…”