Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques

Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab

BACKGROUND

Bevacizumab is a humanized antibody against vascular endothelial growth factor approved for treatment of recurrent glioblastoma. There is a need to discover imaging biomarkers that can aid in the selection of patients who will likely derive the most survival benefit from bevacizumab.

METHODS

The aim of the study was to examine if pre- and posttherapy multimodal MRI features could predict progression-free survival and overall survival (OS) for patients with recurrent glioblastoma treated with bevacizumab. The patient population included 84 patients in a training cohort and 42 patients in a testing cohort, separated based on pretherapy imaging date. Tumor volumes of interest were segmented from contrast-enhanced T1-weighted and fluid attenuated inversion recovery images and were used to derive volumetric, shape, texture, parametric, and histogram features. A total of 2293 pretherapy and 9811 posttherapy features were used to generate the model.

RESULTS

Using standard radiographic assessment criteria, the hazard ratio for predicting OS was 3.38 (P < .001). The hazard ratios for pre- and posttherapy features predicting OS were 5.10 (P < .001) and 3.64 (P < .005) for the training and testing cohorts, respectively.

CONCLUSION

With the use of machine learning techniques to analyze imaging features derived from pre- and posttherapy multimodal MRI, we were able to develop a predictive model for patient OS that could potentially assist clinical decision making.

Imaging genomics in cancer research: limitations and promises

Recently, radiogenomics or imaging genomics has emerged as a novel high-throughput method of associating imaging features with genomic data. Radiogenomics has the potential to provide comprehensive intratumour, intertumour and peritumour information non-invasively. This review article summarizes the current state of radiogenomic research in tumour characterization, discusses some of its limitations and promises and projects its future directions. Semi-radiogenomic studies that relate specific gene expressions to imaging features will also be briefly reviewed.

Brain CT and MRI: differential diagnosis of imaging findings

Following a traditional approach, in Chapters 5 and 14-29 in the previous volume, diverse brain diseases are listed and their imaging findings described in detail. In this chapter the approach is from the imaging finding to the disease: for instance, what list of diseases can give rise to a contrast-enhancing mass in the cerebellopontine angle? Imaging findings that are reviewed in succession include the location of the lesion, its multiplicity and symmetry, its volume, ranging from atrophy to mass effect, its homogeneity, its density, measurable by computed tomography (CT), its appearance on T1, T2, and diffusion magnetic resonance imaging (MRI), and, finally, its characteristics after the infusion of intravenous contrast. A differential diagnosis for each finding is provided. While the approach adopted in this chapter is unconventional, we hope that it will be most helpful to anyone reading images. Furthermore, it could serve as the basis to create or complete image databases to guide in the interpretation of brain CT and MRI.