Hyo Suk Nam

Background and purpose Cerebral infarction in the basal ganglia may cause secondary and delayed neuronal degeneration in the substantia nigra (SN). However, the clinical significance of SN degeneration remains poorly understood. Methods This retrospective observational study included patients with acute ischemic stroke in the basal ganglia on initial diffusion‐weighted imaging who underwent follow‐up diffusion‐weighted imaging between 4 and 30 days after symptom onset. SN degeneration was defined as a hyperintensity lesion in the SN observed on diffusion‐weighted imaging. We compared functional outcomes at 3 months between patients with and without SN degeneration. A poor outcome was defined as a score of 3–6 (functional dependence or death) on the modified Rankin Scale. Results Of 350 patients with basal ganglia infarction (median age = 74.0 years, 53.7% male), 125 (35.7%) had SN degeneration. The proportion of functional dependence or death was 79.2% (99/125 patients) in patients with SN degeneration, which was significantly higher than that in those without SN degeneration (56.4%, 127/225 patients, p < 0.001). SN degeneration was more frequent in patients with functional dependence or death (99/226 patients, 43.8%) than in those with functional independence (26/124 patients, 21.0%, p < 0.001). Multivariable logistic regression analysis showed a significant association between SN degeneration and functional dependence or death (odds ratio = 2.91, 95% confidence interval = 1.17–7.21, p = 0.021). Conclusions The study showed that patients with degeneration of SN were associated with functional dependence or death at 3 months, suggesting that secondary degeneration is a predictor of poor stroke outcomes and a potential therapeutic target.

BACKGROUND: We aimed to develop and validate machine learning models to diagnose patients with ischemic stroke with cancer through the analysis of histopathologic images of thrombi obtained during endovascular thrombectomy. METHODS: This was a retrospective study using a prospective multicenter registry which enrolled consecutive patients with acute ischemic stroke from South Korea who underwent endovascular thrombectomy. This study included patients admitted between July 1, 2017 and December 31, 2021 from 6 academic university hospitals. Whole-slide scanning was performed for immunohistochemically stained thrombi. Machine learning models were developed using transfer learning with image slices as input to classify patients into 2 groups: cancer group or other determined cause group. The models were developed and internally validated using thrombi from patients of the primary center, and external validation was conducted in 5 centers. The model was also applied to patients with hidden cancer who were diagnosed with cancer within 1 month of their index stroke. RESULTS: The study included 70 561 images from 182 patients in both internal and external datasets (119 patients in internal and 63 in external). Machine learning models were developed for each immunohistochemical staining using antibodies against platelets, fibrin, and erythrocytes. The platelet model demonstrated consistently high accuracy in classifying patients with cancer, with area under the receiver operating characteristic curve of 0.986 (95% CI, 0.983–0.989) during training, 0.954 (95% CI, 0.937–0.972) during internal validation, and 0.949 (95% CI, 0.891–1.000) during external validation. When applied to patients with occult cancer, the model accurately predicted the presence of cancer with high probabilities ranging from 88.5% to 99.2%. CONCLUSIONS: Machine learning models may be used for prediction of cancer as the underlying cause or detection of occult cancer, using platelet-stained immunohistochemical slide images of thrombi obtained during endovascular thrombectomy.