Discussion
SAH, hemorrhage, TBI, EDH, hematoma
SUBDURAL HEMORRHAGE, HEMORRHAGE, HEMATOMA, SDH
In this study, we quantified the ability of volume, spatial location and type of brain lesions observed on admission CT-scans to predict the therapeutic intensity level of TBI patients within the first week in ICU. Volumes of 7 different lesions on 11 structural zones were able to predict this outcome with a mean AUC of 89% and a standard deviation of 17%. Although the small sample size limits the conclusions of this work, the most influential metrics are the volume of lesions located in brain lobes, and especially the volume of subdural hemorrhage. This result is coherent with the medical experience about the large impact of SDH, often located in the frontal and parietal lobes, on medical careWe also highlighted the low predictive power of BLAST-CT to automatically predict our TILsum-based outcome. This lack of prediction could be explained by the poor segmentation obtained using BLAST-CT on our brain CT-scans. Indeed, although BLAST-CT was developed on large multicentric datasets (n = 839) the DICE obtained using BLAST-CT on our patients were lower than those published by Monteiro et al.Finally, in order to link our segmentation work to a clinically relevant issue, we validated our results by using the improved segmentations to predict our clinical outcome on the test sub-dataset of Dataset 1. We showed that our improved segmentations predict the TILsum based criteria as the manual ones do. Segmentation and classification were then validated on a new external dataset (Dataset2) leading, as obtained on the internal validation, to better results with transfer learning approaches, and a prediction accuracy of 83% with automatic segmentation (10/12), better than the one obtained with manual segmentation (8/12), which is counterintuivite. This former result could be explained by the unperfect manual segmentation, as illustrated on the Supplementary Fig. Compared to recent automated hemorrhage segmentation literature, most of the studies do not discriminate SDH from others hemorrhage. While Yao et al. only focused on hematoma volume estimation, Monteiro et al. merged SDH with SAH and EDH. To our knowledge, the study of Farzaneh et al. is the only study to segment SDH, which is crucial to discriminate against short term evolution, as shown by our classification study. Farzaneh et al. study reached a Dice score of more than 0.75 by combining deep learning and classical image processing methods, outperforming our external validation SDH Dice score of 0.46. While differences in patients' inclusion and statistical evaluation methods might explain part of this score, it is probable that non deep learning post processing might improve deep learning segmentation by making them closer to neuroradiologists segmentations.To our knowledge, we developed the first automatic tool to predict the intensity level of medical care from CT-scans of brain-injured patients, linking image processing to clinical care. In order to share our CT-scan quantification tool described on Fig. Of course, this study has some limitations. First, our datasets are small, leading to unstable classification performances and all these results must be validated on larger and multicentric cohorts before any further use in clinical practice. Secondly, since this is the first study to predict the therapeutic intensity level, we cannot compare ourselves to the literature and evaluate the quality of our CT-scans quantification. To overcome this limitation, one will soon evaluate the prediction of the 6-months mortality to be able to compare our classification results with the ones of the most similar study conducted by Rosnati et al.To conclude, we believe that the automatic quantification of CT-scans to predict short-term outcome of TBI patients has the potential to bring reproducible and reliable information that can help improve clinical care. One must multiply the research studies in this way but also investigate the lesions evolution on repeated CT-scans, that might contain crucial information currently unused.