Pathomic model based on histopathological features and machine learning to predict IDO1 status and its association with breast cancer prognosis.

PURPOSE: To establish a pathomic model using histopathological image features for predicting indoleamine 2,3-dioxygenase 1 (IDO1) status and its relationship with overall survival (OS) in breast cancer. METHODS: A pathomic model was constructed using machine learning and histopathological images obtained from The Cancer Genome Atlas database to predict IDO1 expression. The model performance was evaluated based on the area under the curve, calibration curve, and decision curve analysis (DCA). Prediction scores (PSes) were generated from the model and applied to divide the patients into two groups. Survival outcomes, gene set enrichment, immune microenvironment, and tumor mutations were assessed between the two groups. RESULTS: Survival analysis followed by multivariate correction revealed that high IDO1 is a protective factor for OS. Further, the model was calibrated, and it exhibited good discrimination. Additionally, the DCA showed that the proposed model provided a good clinical net benefit. The Kaplan-Meier analysis revealed a positive correlation between high PS and improved OS. Univariate and multivariate Cox regression analyses demonstrated that PS is an independent protective factor for OS. Moreover, differentially expressed genes were enriched in various essential biological processes, including extracellular matrix receptor interaction, angiogenesis, transforming growth factor ß signaling, epithelial mesenchymal transition, cell junction, tryptophan metabolism, and heme metabolic processes. PS was positively correlated with M1 macrophages, CD8 + T cells, T follicular helper cells, and tumor mutational burden. CONCLUSION: These results indicate the potential ability of the proposed pathomic model to predict IDO1 status and the OS of breast cancer patients.

Added Value of Frequency of Imaging Markers for Prediction of Outcome After Intracerebral Hemorrhage: A Secondary Analysis of Existing Data.

BACKGROUND: Frequency of imaging markers (FIM) has been identified as an independent predictor of hematoma expansion in patients with intracerebral hemorrhage (ICH), but its impact on clinical outcome of ICH is yet to be determined. The aim of the present study was to investigate this association. METHODS: This study was a secondary analysis of our prior research. The data for this study were derived from six retrospective cohorts of ICH from January 2018 to August 2022. All consecutive study participants were examined within 6 h of stroke onset on neuroimaging. FIM was defined as the ratio of the number of imaging markers on noncontrast head tomography (i.e., hypodensities, blend sign, and island sign) to onset-to-neuroimaging time. The primary poor outcome was defined as a modified Rankin Scale score of 3-6 at 3 months. RESULTS: A total of 1253 patients with ICH were included for final analysis. Among those with available follow-up results, 713 (56.90%) exhibited a poor neurologic outcome at 3 months. In a univariate analysis, FIM was associated with poor prognosis (odds ratio 4.36; 95% confidence interval 3.31-5.74; p < 0.001). After adjustment for age, Glasgow Coma Scale score, systolic blood pressure, hematoma volume, and intraventricular hemorrhage, FIM was still an independent predictor of worse prognosis (odds ratio 3.26; 95% confidence interval 2.37-4.48; p < 0.001). Based on receiver operating characteristic curve analysis, a cutoff value of 0.28 for FIM was associated with 0.69 sensitivity, 0.66 specificity, 0.73 positive predictive value, 0.62 negative predictive value, and 0.71 area under the curve for the diagnosis of poor outcome. CONCLUSIONS: The metric of FIM is associated with 3-month poor outcome after ICH. The novel indicator that helps identify patients who are likely within the 6-h time window at risk for worse outcome would be a valuable addition to the clinical management of ICH.

Prediction of small intracranial aneurysm rupture status based on combined Clinical-Radiomics model.

Background: Accumulating small unruptured intracranial aneurysms are detected due to the improved quality and higher frequency of cranial imaging, but treatment remains controversial. While surgery or endovascular treatment is effective for small aneurysms with a high risk of rupture, such interventions are unnecessary for aneurysms with a low risk of rupture. Consequently, it is imperative to accurately identify small aneurysms with a low risk of rupture. The purpose of this study was to develop a clinically practical model to predict small aneurysm ruptures based on a radiomics signature and clinical risk factors. Methods: A total of 293 patients having an aneurysm with a diameter of less than 5 mm, including 199 patients (67.9 %) with a ruptured aneurysm and 94 patients (32.1 %) without a ruptured aneurysm, were included in this study. Digital subtraction angiography or surgical treatment was required in all cases. Data on the clinical risk factors and the features on computed tomography angiography images associated with the aneurysm rupture status were collected simultaneously. We developed a clinical-radiomics model to predict aneurysm rupture status using multivariate logistic regression analysis. The combined clinical-radiomics model was constructed by nomogram analysis. The diagnostic performance, clinical utility, and model calibration were evaluated by operating characteristic curve analysis, decision curve analysis, and calibration analysis. Results: A combined clinical-radiomics model (Area Under Curve [AUC], 0.85; 95 % confidence interval [CI], 0.757-0.947) showed effective performance in the operating characteristic curve analysis. In the validation cohort, the performance of the combined model was better than that of the radiomics model (AUC, 0.75; 95 % CI, 0.645-0.865; Delong's test p-value = 0.01) and the clinical model (AUC, 0.74; 95 % CI, 0.625-0.851; Delong's test p-value <0.01) alone. The results of the decision curve, nomogram, and calibration analyses demonstrated the clinical utility and good fitness of the combined model. Conclusion: Our study demonstrated the effectiveness of a clinical-radiomics model for predicting rupture status in small aneurysms.

Bradykinin protects nucleus pulposus cells from tert-butyl hydroperoxide-induced damage and delays intervertebral disc degeneration.

Intervertebral disc degeneration (IVDD) is a leading cause of degenerative spinal disorders, involving complex biological processes. This study investigates the role of the kallikrein-kinin system (KKS) in IVDD, focusing on the protective effects of bradykinin (BK) on nucleus pulposus cells (NPCs) under oxidative stress. Clinical specimens were collected, and experiments were conducted using human and rat primary NPCs to elucidate BK's impact on tert-butyl hydroperoxide (TBHP)-induced oxidative stress and damage. The results demonstrate that BK significantly inhibits TBHP-induced NPC apoptosis and restores mitochondrial function. Further analysis reveals that this protective effect is mediated through the BK receptor 2 (B2R) and its downstream PI3K/AKT pathway. Additionally, BK/PLGA sustained-release microspheres were developed and validated in a rat model, highlighting their potential therapeutic efficacy for IVDD. Overall, this study sheds light on the crucial role of the KKS in IVDD pathogenesis and suggests targeting the B2R as a promising therapeutic strategy to delay IVDD progression and promote disc regeneration.

Combining Non-Contrast CT Signs With Onset-to-Imaging Time to Predict the Evolution of Intracerebral Hemorrhage.

OBJECTIVE: This study aimed to determine the predictive performance of non-contrast CT (NCCT) signs for hemorrhagic growth after intracerebral hemorrhage (ICH) when stratified by onset-to-imaging time (OIT). MATERIALS AND METHODS: 1488 supratentorial ICH within 6 h of onset were consecutively recruited from six centers between January 2018 and August 2022. NCCT signs were classified according to density (hypodensities, swirl sign, black hole sign, blend sign, fluid level, and heterogeneous density) and shape (island sign, satellite sign, and irregular shape) features. Multivariable logistic regression was used to evaluate the association between NCCT signs and three types of hemorrhagic growth: hematoma expansion (HE), intraventricular hemorrhage growth (IVHG), and revised HE (RHE). The performance of the NCCT signs was evaluated using the positive predictive value (PPV) stratified by OIT. RESULTS: Multivariable analysis showed that hypodensities were an independent predictor of HE (adjusted odds ratio [95% confidence interval] of 7.99 [4.87-13.40]), IVHG (3.64 [2.15-6.24]), and RHE (7.90 [4.93-12.90]). Similarly, OIT (for a 1-h increase) was an independent inverse predictor of HE (0.59 [0.52-0.66]), IVHG (0.72 [0.64-0.81]), and RHE (0.61 [0.54-0.67]). Blend and island signs were independently associated with HE and RHE (10.60 [7.36-15.30] and 10.10 [7.10-14.60], respectively, for the blend sign and 2.75 [1.64-4.67] and 2.62 [1.60-4.30], respectively, for the island sign). Hypodensities demonstrated low PPVs of 0.41 (110/269) or lower for IVHG when stratified by OIT. When OIT was ≤ 2 h, the PPVs of hypodensities, blend sign, and island sign for RHE were 0.80 (215/269), 0.90 (142/157), and 0.83 (103/124), respectively. CONCLUSION: Hypodensities, blend sign, and island sign were the best NCCT predictors of RHE when OIT was ≤ 2 h. NCCT signs may assist in earlier recognition of the risk of hemorrhagic growth and guide early intervention to prevent neurological deterioration resulting from hemorrhagic growth.

Is the frequency of imaging markers still a predictor for revised intracerebral hemorrhage expansion?

INTRODUCTION: Frequency of imaging markers (FIM) has been described as a novel predictor for hematoma expansion after intracerebral hemorrhage (ICH). A revised definition of hematoma expansion that incorporates intraventricular hemorrhage (IVH) growth, that is, revised hematoma expansion (RHE), has also been proposed. Nevertheless, the associations between FIM and IVH growth or RHE remains unexplored. The objective of this study was to assess the influence and performance of the FIM on two types. MATERIALS AND METHODS: Patient selection and variables were based on our published protocol. FIM was defined as the ratio of the number of imaging markers to the onset-to-neuroimaging time. The association between FIM and two definitions was tested by multivariate analysis. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the FIM on two definitions were also evaluated. RESULTS: There were 303 (20.36%) and 583 (39.18%) subjects in the IVH growth and RHE, respectively. Multivariate analysis demonstrated that FIM was associated with both IVH growth and RHE (odds ratio [OR] = 1.96, 95% confidence interval [CI] = 1.60-2.39; OR = 15.01, 95% CI = 10.51-21.43, respectively). The optimal cutoff points for FIM to predict IVH growth and RHE were 0.63 and 0.62, with AUC, sensitivity, specificity, PPV, and NPV of 0.66, 0.50, 0.78, 0.36, and 0.86 versus 0.80, 0.60, 0.93, 0.84, and 0.78, respectively. DISCUSSION AND CONCLUSION: FIM was not only a predictor of IVH growth, but also of RHE. These findings may have important clinical implications for decision-making based on risk stratification of patients with ICH.