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Simple Summary: Glioblastoma is the most aggressive type of brain cancer, with patients typically surviving less than 15 months after diagnosis. Accurate prediction of survival time is crucial for tailoring treatment plans to individual patients. This study develops a new computer-based method that analyzes brain scans to forecast survival in glioblastoma patients. By examining detailed features from magnetic resonance imaging (MRI) scans, our approach aims to provide more precise and personalized survival estimates than current methods. We tested our model on a large group of patients from two different hospitals, demonstrating its ability to predict survival accurately at various time points. If validated in future studies, this tool could help doctors make more informed decisions about patient care, potentially improving outcomes for those diagnosed with this challenging disease. (1) Background: Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with an aggressive disease course that requires accurate prognosis for individualized treatment planning. This study aims to develop and evaluate a radiomics-based machine learning (ML) model to estimate overall survival (OS) for patients with GBM using pre-treatment multi-parametric magnetic resonance imaging (MRI). (2) Methods: The MRI data of 865 patients with GBM were assessed, comprising 499 patients from the UPENN-GBM dataset and 366 patients from the UCSF-PDGM dataset. A total of 14,598 radiomic features were extracted from T1, T1 with contrast, T2, and FLAIR MRI sequences using PyRadiomics. The UPENN-GBM dataset was used for model development (70%) and internal validation (30%), while the UCSF-PDGM dataset served as an external test set. The NGBoost Survival model was developed to generate continuous probability estimates as well as predictions for 6-, 12-, 18-, and 24-month OS. (3) Results: The NGBoost Survival model successfully predicted survival, achieving a C-index of 0.801 on internal validation and 0.725 on external validation. For 6-month OS, the model attained an AUROC of 0.791 (95% CI: 0.742–0.832) and 0.708 (95% CI: 0.654–0.748) for internal and external validation, respectively. (4) Conclusions: The radiomics-based ML model demonstrates potential to improve the prediction of OS for patients with GBM. [ABSTRACT FROM AUTHOR]

Background and Purpose: Early and reliable prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) is crucial for treatment decisions and early intervention. The purpose of this study was to conduct a systematic review and meta‐analysis on the performance of artificial intelligence (AI) and machine learning (ML) models that utilize neuroimaging to predict HT. Methods: A systematic search of PubMed, EMBASE, and Web of Science was conducted until February 19, 2024. Inclusion criteria were as follows: patients with AIS who received reperfusion therapy; AI/ML algorithm using imaging to predict HT; or presence of sufficient data on the predictive performance. Exclusion criteria were as follows: articles with less than 20 patients; articles lacking algorithms that operate solely on images; or articles not detailing the algorithm used. The quality of eligible studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies‐2 and Checklist for Artificial Intelligence in Medical Imaging. Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated using a random‐effects model, and a summary receiver operating characteristic curve was constructed using the Reitsma method. Results: We identified six eligible studies, which included 1640 patients. Aside from an unclear risk of bias regarding flow and timing identified in two of the studies, all studies showed low risk of bias and applicability concerns in all categories. Pooled sensitivity, specificity, and DOR were.849,.878, and 45.598, respectively. Conclusion: AI/ML models can reliably predict the occurrence of HT in AIS patients. More prospective studies are needed for subgroup analyses and higher clinical certainty and usefulness. [ABSTRACT FROM AUTHOR]

Study Design: A retrospective machine learning (ML) classification study for prognostic modeling after anterior cervical corpectomy (ACC). Purpose: To evaluate the effectiveness of ML in predicting ACC outcomes and develop an accessible, user-friendly tool for this purpose. Overview of Literature: Based on our literature review, no study has examined the capability of ML algorithms to predict major short-term ACC outcomes, such as prolonged length of hospital stay (LOS), non-home discharge, and major complications. Methods: The American College of Surgeons' National Surgical Quality Improvement Program database was used to identify patients who underwent ACC. Prolonged LOS, non-home discharges, and major complications were assessed as the outcomes of interest. ML models were developed with the TabPFN algorithm and integrated into an open-access website to predict these outcomes. Results: The models for predicting prolonged LOS, non-home discharges, and major complications demonstrated mean areas under the receiver operating characteristic curve (AUROC) of 0.802, 0.816, and 0.702, respectively. These findings highlight the discriminatory capacities of the models: fair (AUROC >0.7) for differentiating patients with major complications from those without, and good (AUROC >0.8) for distinguishing between those with and without prolonged LOS and non-home discharges. According to the SHapley Additive Explanations analysis, single- versus multiple-level surgery, age, body mass index, preoperative hematocrit, and American Society of Anesthesiologists physical status repetitively emerged as the most important variables for each outcome. Conclusions: This study has considerably enhanced the prediction of postoperative results after ACC surgery by implementing advanced ML techniques. A major contribution is the creation of an accessible web application, highlighting the practical value of the developed models. Our findings imply that ML can serve as an invaluable supplementary tool to stratify patient risk for this procedure and can predict diverse postoperative adverse outcomes. [ABSTRACT FROM AUTHOR]

Background and Objectives: Retrieve data from the National Cancer Database (NCDB) to examine information on the epidemiological prevalence, treatment strategies, and survival outcomes of pediatric vertebral, sacral and pelvic osteosarcomas. Methods: We reviewed NCDB data from 2008 to 2018, concentrating on vertebral, sacral, and pelvic osteosarcomas in children 0 to 21 years. Our analysis involved logistic and Poisson regression, Kaplan-Meier survival estimates, and Cox proportional hazards models. Results: The study population included 207 patients. For vertebral osteosarcomas, 62.5% of patients were female, and 78.1% were white. Regional lymph node involvement predicted 80 times higher mortality hazard (p = 0.021). Distant metastasis predicted 25 times higher mortality hazard (p = 0.027). For sacral and pelvic osteosarcomas, 58.3% of patients were male, and 72% were white. Patients with residual tumor were 4 times more likely to have prolonged LOS (p = 0.031). No residual tumor (HR = 0.53, p = 0.03) and radiotherapy receipt (HR = 0.46, p = 0.034) were associated with lower mortality hazards. Distant metastasis predicted 3 times higher mortality hazard (p < 0.001). Hispanic ethnicity was linked to lower resection odds (OR = 0.342, p = 0.043), possibly due to language barriers affecting patient understanding and care decisions. Conclusions: In conclusion, our examination of NCDB offers a thorough exploration of demographics, treatment patterns, and results, highlighting the importance of personalized approaches to enhance patient outcomes. [ABSTRACT FROM AUTHOR]