Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas

BACKGROUND AND PURPOSE

Patient survival in high-grade glioma remains poor, despite the recent developments in cancer treatment. As new chemo-, targeted molecular, and immune therapies emerge and show promising results in clinical trials, image-based methods for early prediction of treatment response are needed. Deep learning models that incorporate radiomics features promise to extract information from brain MR imaging that correlates with response and prognosis. We report initial production of a combined deep learning and radiomics model to predict overall survival in a clinically heterogeneous cohort of patients with high-grade gliomas.

MATERIALS AND METHODS

Fifty patients with high-grade gliomas from our hospital and 128 patients with high-grade glioma from The Cancer Genome Atlas were included. For each patient, we calculated 348 hand-crafted radiomics features and 8192 deep features generated by a pretrained convolutional neural network. We then applied feature selection and Elastic Net-Cox modeling to differentiate patients into long- and short-term survivors.

RESULTS

In the 50 patients with high-grade gliomas from our institution, the combined feature analysis framework classified the patients into long- and short-term survivor groups with a log-rank test P value < .001. In the 128 patients from The Cancer Genome Atlas, the framework classified patients into long- and short-term survivors with a log-rank test P value of .014. For the mixed cohort of 50 patients from our institution and 58 patients from The Cancer Genome Atlas, it yielded a log-rank test P value of .035.

CONCLUSIONS

A deep learning model combining deep and radiomics features can dichotomize patients with high-grade gliomas into long- and short-term survivors.

Diffusional Kurtosis along the Corticospinal Tract in Adult Normal Pressure Hydrocephalus

BACKGROUND AND PURPOSE

Normal Pressure Hydrocephalus is a reversible form of dementia characterized by enlarged ventricles, which can deform and cause disruptions to adjacent white matter fibers. The purpose of this work was to examine how diffusion and kurtosis parameters vary along the corticospinal tract and determine where along this path microstructure is compromised in patients diagnosed with normal pressure hydrocephalus. We hypothesized that disruption of the corticospinal tract from ventricular enlargement can be measured using diffusion MR imaging and this will be quantified in periventricular regions.

MATERIALS AND METHODS

We developed a method to analyze diffusion parameters at discrete points along neural tracts. We then used diffusion MR imaging data from patients with Alzheimer disease and healthy controls to compare whether diffusion along the corticospinal tract differs from that of patients with normal pressure hydrocephalus.

RESULTS

We found that diffusion parameters can differentiate patients with normal pressure hydrocephalus from those with Alzheimer disease and healthy controls: Axial diffusion, axial kurtosis, and the axonal water fraction were found to differ significantly across groups (P < .05) in an area located close to the superior internal capsule and corona radiata but below the cortex.

CONCLUSIONS

A lower axonal water fraction indicates a lower axonal density in the corticospinal tract, which may indicate permanent damage. Lower axial kurtosis may imply that axons are being more aligned due to compression.