Background: Evaluating muscle mass and function among stroke patients is important. However, evaluating muscle volume and function is not easy due to the disturbances of consciousness and paresis. Temporal muscle thickness (TMT) has been introduced as a novel surrogate marker for muscle mass, function, and nutritional status. We herein performed a narrative literature review on temporal muscle and stroke to understand the current meaning of TMT in clinical stroke practice. Methods: The search was performed in PubMed, last updated in October 2021. Reports on temporal muscle morphomics and stroke-related diseases or clinical entities were collected. Results: Four studies reported on TMT and subarachnoid hemorrhage, two studies on intracerebral hemorrhage, two studies on ischemic stroke, two studies on standard TMT values, and two studies on nutritional status. TMT was reported as a prognostic factor for several diseases, a surrogate marker for skeletal muscle mass, and an indicator of nutritional status. Computed tomography, magnetic resonance imaging, and ultrasonography were used to measure TMT. Conclusions: TMT is gradually being used as a prognostic factor for stroke or a surrogate marker for skeletal muscle mass and nutritional status. The establishment of standard methods to measure TMT and large prospective studies to further investigate the relationship between TMT and diseases are needed.

The surgical efficacy for supratentorial intracerebral hemorrhage (ICH) remains unknown. We compared the advantages of the widely practiced endoscopic hematoma removal under local anesthesia with that of craniotomy under general anesthesia for ICH. We also focused on our novel operative concept of intentional hematoma leaving technique to avoid further damage to the brain. We retrospectively analyzed 134 consecutive patients (66 endoscopies and 68 craniotomies) who were surgically treated for supratentorial ICH. The characteristics of the 134 patients were as follows: The median (interquartile range) age was 73 (61–82) years. The median Glasgow Coma Scale scores at admission, on day 7, and the median modified Rankin Scale (mRS) score at 6 months were 10 (7–13), 13 (10–14), and 4 (3–5) respectively. The statistical comparison revealed there were no differences in GCS score on day seven between the endoscopy 13 (12–14) and craniotomy group 12 (9–14). No differences were observed in mRS scores at 6 months between the endoscopy 4 (2–5) and craniotomy group 4 (3–5). However, the patients treated with our technique tended to have favorable outcomes. Multivariate analysis revealed the operative time was significantly decreased in the endoscopy group compared to the craniotomy group (p < 0.001).

Background: Skeletal muscle mass is an important factor for various diseases’ outcomes. As for its indicators, temporal muscle thickness (TMT) and temporal muscle area (TMA) on the head computed tomography are useful, and TMT and TMA were reported as potential prognostic factors for aneurysmal subarachnoid hemorrhage (SAH). We examined the clinical characteristics, including TMT and TMA, of SAH patients aged 75 or younger. Methods: We retrospectively investigated 127 SAH patients with all World Federation of Neurosurgical Societies (WFNS) grades and treated by clipping between 2009 and 2019. Clinical outcome was measured with the modified Rankin Scale (mRS) at 6 months, with favorable outcome defined as mRS 0–2. The associations between the clinical variables and the outcomes were analyzed. Results: The mean age was 60.6 (32–74) years, and 65% were women. The mean ± standard deviation of WFNS grade was 2.8 ± 1.4. TMT and TMA were larger in the favorable outcome group than the poor one. Multivariate analysis revealed that age, smoking, WFNS grade, and TMT or TMA were associated with favorable outcome. Receiver operating characteristic analysis found that the threshold of TMT was 4.9 mm in female and 6.7 mm in male, and that of TMA was 193 mm² in female and 333 mm² in male. Conclusion: The odds ratios for TMT and TMA related to clinical outcome were lower than for smoking and WFNS grade; however, on multivariate analysis they remained independent prognostic factors in SAH patients aged 75 or younger treated by clipping. Further studies are needed to confirm these findings.

Background: Reliable prediction models of intracerebral hemorrhage (ICH) outcomes are needed for decision-making of the treatment. Statistically making such prediction models needs a large number of samples and time-consuming statistical analysis. Deep learning (DL), one of the artificial intelligence, is attractive, but there were no reports on DL-based functional outcome prediction models for ICH outcomes after surgery. We herein made a functional outcome prediction model using DLframework, Prediction One (Sony Network Communications Inc., Tokyo, Japan), and compared it to original ICH score, ICH Grading Scale, and FUNC score.Methods: We used 140 consecutive hypertensive ICH patients’ data in our hospital between 2012 and 2019. All patients were surgically treated. Modified Rankin Scale 0–3 at 6 months was defined as a favorable outcome. We randomly divided them into 100 patients training dataset and 40 patients validation dataset. Prediction One made the prediction model using the training dataset with 5-fold cross-validation. We calculated area under the curves (AUCs) regarding the outcome using the DL-based model, ICH score, ICH Grading Scale, and FUNC score. The AUCs were compared.Results: The model made by Prediction One using 64 variables had AUC of 0.997 in the training dataset and that of 0.884 in the validation dataset. These AUCs were superior to those derived from ICH score, ICH Grading Scale, and FUNC score.Conclusion: We easily and quickly made prediction models using Prediction One, even with a small single-center dataset. The accuracy of the DL-based model was superior to those of previous statistically calculated models.

Background: Reliable prediction models of subarachnoid hemorrhage (SAH) outcomes are needed for decision-making of the treatment. SAFIRE score using only four variables is a good prediction scoring system. However, making such prediction models needs a large number of samples and time-consuming statistical analysis. Deep learning (DL), one of the artificial intelligence, is attractive, but there were no reports on prediction models for SAH outcomes using DL. We herein made a prediction model using DL software, Prediction One (Sony Network Communications Inc., Tokyo, Japan) and compared it to SAFIRE score.Methods: We used 153 consecutive aneurysmal SAH patients data in our hospital between 2012 and 2019. Modified Rankin Scale (mRS) 0–3 at 6 months was defined as a favorable outcome. We randomly divided them into 102 patients training dataset and 51 patients external validation dataset. Prediction one made the prediction model using the training dataset with internal cross-validation. We used both the created model and SAFIRE score to predict the outcomes using the external validation set. The areas under the curve (AUCs) were compared.Results: The model made by Prediction One using 28 variables had AUC of 0.848, and its AUC for the validation dataset was 0.953 (95%CI 0.900–1.000). AUCs calculated using SAFIRE score were 0.875 for the training dataset and 0.960 for the validation dataset, respectively.Conclusion: We easily and quickly made prediction models using Prediction One, even with a small single-center dataset. The accuracy of the model was not so inferior to those of previous statistically calculated prediction models.