Proceedings Article | 16 March 2020
KEYWORDS: Tumors, Magnetic resonance imaging, Brain, Cancer, Feature extraction, Neuroimaging, Diagnostics, Biopsy, Radiation oncology
About a third of all cancer patients develop brain metastases. Stereotactic radiosurgery (SRS) is a common treatment for patients with brain metastases and offers excellent tumor control rates. However, the most significant clinical challenge for patients after SRS is reliably differentiating tumor progression from radiation necrosis, a side effect of SRS, which occurs in about 20% of all metastatic brain tumor patients. The current diagnostic accuracy in distinguishing radiation necrosis from tumor recurrence via visual inspection by expert radiologists on routine MRI scans is 50-60%. Invasive surgical intervention provides the only definitive means of diagnosis. Therefore, finding non-invasive markers to reliably differentiate between these diagnoses are urgently needed. In this work, we present a radiomic-pipeline that involves capturing texture features (Laws, Haralick, Gabor) from not just the lesion but also the peri-lesion environment (defined as “lesion habitat”) to distinguish radiation necrosis and tumor recurrence. We obtained a discovery dataset (n=37 studies) from Cleveland Clinic with post-Gd T1w, T2w, and FLAIR volumes. Patients were confirmed via histopathology (biopsy or resection) as having a recurrent tumor (n=17) or radiation necrosis (n=20). A separate set of 12 patients was kept blinded as the hold-out" set. Each patient's T2 and FLAIR volumes were co-registered onto Gd-T1w. Lesion habitat sub-compartments were annotated by an expert as enhancing tumor, necrosis, and perilesional edema. Following pre-processing, a total of 4,740 texture features (including Haralick, Gabor, Laws, CoLlAGe) were extracted from each sub-compartment of the lesion habitat across all three available MRI sequences. To identify the most discriminatory features, we used Wilcoxon rank-sum test to remove correlated features (correlation 0.75). This was followed by a 3-fold cross-validation scheme, within a random forest classifier using the training cohort. The most discriminatory features were then evaluated on the hold out test set. Within our discovery set, we obtained an average AUC of .9781 with a sensitivity of .91 and a specificity of .92. On the blinded hold out set, we obtained an accuracy of 91% in distinguishing radiation necrosis from tumor recurrence. Laws features (capturing edges, waves, ripple patterns) from the enhancing tumor region on Gd-T1w MRI were identified as being the most discriminatory in distinguishing radiation necrosis and tumor recurrence in METs patients.
