A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI

Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adult patients with a median survival of around one year. Prediction of survival outcomes in GBM patients could represent a huge step in treatment personalization. The objective of this study was to develop machine learning (ML) algorithms for survival prediction of GBM patient. We identified a radiomic signature on a training-set composed of data from the 2019 BraTS challenge (210 patients) from MRI retrieved at diagnosis. Then, using this signature along with the age of the patients for training classification models, we obtained on test-sets AUCs of 0.85, 0.74 and 0.58 (0.92, 0.88 and 0.75 on the training-sets) for survival at 9-, 12- and 15-months, respectively. This signature was then validated on an independent cohort of 116 GBM patients with confirmed disease relapse for the prediction of patients surviving less or more than the median OS of 22 months. Our model insured an AUC of 0.71 (0.65 on train). The Kaplan–Meier method showed significant OS difference between groups (log-rank p = 0.05). These results suggest that radiomic signatures may improve survival outcome predictions in GBM thus creating a solid clinical tool for tailoring therapy in this population.

A pilot project informing the design of a web-based dynamic nomogram in order to predict survival one year after hip fracture surgery (Preprint)

BACKGROUND Hip fracture is associated with high mortality. Identification of individual risk informs anesthetic and surgical decision making and can reduce the risk of death. However, interpretation of data, and application of research findings can be difficult, and there is a need to simplify risk indices for clinicians and lay-people alike. Results Twenty-four (7.3%) patients died within 30 days, 65 (19.8%) within 120 days and 94 (28.6%) within 365 days of surgery. Independent predictors of mortality common to all models were admission Age, BMI, and creatinine, lactate and their combination. Age and BMI inversely correlated with mortality. Presentation with a creatinine level of 90 mol.L-1 increased the odds of death OR 2.9 (1.4 - 6.0) 365 days after surgery compared to an admission level of 60 mol. L-1 Presentation with a plasma lactate level of 2 mmol. L-1 increased the odds of death OR 2.2 (1.1 - 4.5) 365 days after surgery compared to a plasma lactate level of 1 mmol. L-1. Patients presenting to hospital with a BMI of 30 kg.m-2 were less likely to die within 365 days OR 0.41 (0.17 - 0.99) after surgery compared to patients with a BMI of 20 kg.m-2. We presented four models in Shiny. Data entry created Kaplan-Meier graphs and outcome measures (95%CI). Conclusion We developed easy to read and interpretable web-based nomograms for prediction of survival after hip fracture surgery. OBJECTIVE Our primary objective was to develop a web-based nomogram for prediction of survival 365 days after fracture hip surgery. METHODS We collected data from 329 patients up to 365 days after hip fracture surgery and built four models using packages in RStudio. A global Cox Proportional Hazards Model was developed from all covariates. Covariates included sex, age, BMI, white cell count, lactate, creatinine, hemoglobin, C-reactive protein, ASA status, socio-economic status, duration of surgery, total time in the operating room, side of surgery and procedure urgency. We also developed a Cox proportional hazards model (CPH). a logistic regression model (LRM), and a generalized linear model (GLM) for binomial response data using iterative data reduction and elimination. We wrote an app in Shiny in order to present the models in a user-friendly way. The app consists of a drop-down box for model selection, horizontal sliders for data entry, model summaries, and prediction and survival plots. A slider selects patient follow-up over 365 days. RESULTS Twenty-four (7.3%) patients died within 30 days, 65 (19.8%) within 120 days and 94 (28.6%) within 365 days of surgery. Independent predictors of mortality common to all models were admission Age, BMI, and creatinine, lactate and their combination. Age and BMI inversely correlated with mortality. Presentation with a creatinine level of 90 mol.L-1 increased the odds of death OR 2.9 (1.4 - 6.0) 365 days after surgery compared to an admission level of 60 mol. L-1 Presentation with a plasma lactate level of 2 mmol. L-1 increased the odds of death OR 2.2 (1.1 - 4.5) 365 days after surgery compared to a plasma lactate level of 1 mmol. L-1. Patients presenting to hospital with a BMI of 30 kg.m-2 were less likely to die within 365 days OR 0.41 (0.17 - 0.99) after surgery compared to patients with a BMI of 20 kg.m-2. We presented four models in Shiny. Data entry created Kaplan-Meier graphs and outcome measures (95%CI). CONCLUSIONS We developed easy to read and interpretable web-based nomograms for prediction of survival after hip fracture surgery. CLINICALTRIAL Nil