Sex-specific impact of patterns of imageable tumor growth on survival of primary glioblastoma patients

Abstract

Background: Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences. Methods: Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females). Results: Among males, tumor (T1Gd) radius was a predictor of overall survival (HR=1.027, p=0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR=1.011, p<0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p=0.010 t-test), but tumor size was not correlated with female overall survival (p=0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p=0.004, F p=0.001, t-test). Additionally, extent of resection, tumor laterality, and IDH1 mutation status were also found to have sex-specific effects on overall survival. Conclusion: Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes.

Deep Learning Characterization of Brain Tumours With Diffusion Weighted Imaging

“…This could reduce the computational expense and potentially lead to more accurate predictions. Finally, recent works [18,44] have noted differences in gliomas based on patient sex and have called for the inclusion of this information in analysis. For our work, it would be interesting to observe the differences in parameter predictions for male and female patients, though this would require a much larger dataset than we have used in order to be meaningful.…”

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

“…Additionally, medical imaging provides non-invasive, serial observations of the spatiotemporal variations in the physical state of the patient, which can be employed as the observational data of a patient's digital twin. We [11,29,[43][44][45][46] and others [47][48][49][50][51] have demonstrated that mathematical models can be initialized and personalized by medical imaging data collected from patients. Once personalized, the image-guided mathematical model parameters serve as a digital representation of that patient's tumor where treatment can be optimized and response can be forecasted.…”