Jonathan M Hanna1, Danielle Temares2, Fahmeed Hyder3, Douglas L Rothman3, Robert K Fulbright4, Veronica L Chiang5, Daniel Coman6. 1. Yale School of Medicine, 333 Cedar St, New Haven, CT 06510, USA. Electronic address: Jonathan.hanna@yale.edu. 2. Department of Biomedical Engineering, Yale University, New Haven, CT, USA. 3. Yale School of Medicine, 333 Cedar St, New Haven, CT 06510, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Magnetic Resonance Research Center (MRRC), Yale University, 300 Cedar St, New Haven, CT 06519, USA; Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA. 4. Yale School of Medicine, 333 Cedar St, New Haven, CT 06510, USA; Magnetic Resonance Research Center (MRRC), Yale University, 300 Cedar St, New Haven, CT 06519, USA; Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA. 5. Yale School of Medicine, 333 Cedar St, New Haven, CT 06510, USA; Department of Neurosurgery, Yale University, 800 Howard Ave, New Haven, CT 06519, USA. 6. Yale School of Medicine, 333 Cedar St, New Haven, CT 06510, USA; Magnetic Resonance Research Center (MRRC), Yale University, 300 Cedar St, New Haven, CT 06519, USA; Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA. Electronic address: daniel.coman@yale.edu.
Abstract
BACKGROUND AND PURPOSE: Given increasing interest in laser interstitial thermotherapy (LITT) to treat brain tumor patients, we explored if examining multiple MRI contrasts per brain tumor patient undergoing surgery can impact predictive accuracy of survival post-LITT. MATERIALS AND METHODS: MRI contrasts included fluid-attenuated inversion recovery (FLAIR), T1 pre-gadolinium (T1pre), T1 post-gadolinium (T1Gd), T2, diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), susceptibility weighted images (SWI), and magnetization-prepared rapid gradient-echo (MPRAGE). The latter was used for MRI data registration across preoperative to postoperative scans. Two ROIs were identified by thresholding preoperative FLAIR (large ROI) and T1Gd (small ROI) images. For each MRI contrast, a numerical score was assigned based on changing image intensity of both ROIs (vs. a normal ROI) from preoperative to postoperative stages. The fully-quantitative method was based on changing image intensity across scans at different stages without any human intervention, whereas the semi-quantitative method was based on subjective criteria of cumulative trends across scans at different stages. A fully-quantitative/semi-quantitative score per patient was obtained by averaging scores for each MRI contrast. A standard neuroradiological reading score per patient was obtained from radiological interpretation of MRI data. Scores from all 3 methods per patient were compared against patient survival, and re-examined for comorbidity and pathology effects. RESULTS: Patient survival correlated best with semi-quantitative scores obtained from T1Gd, ADC, and T2 data, and these correlations improved when biopsy and comorbidity were included. CONCLUSION: These results suggest interfacing neuroradiological readings with semi-quantitative image analysis can improve predictive accuracy of patient survival.
BACKGROUND AND PURPOSE: Given increasing interest in laser interstitial thermotherapy (LITT) to treat brain tumorpatients, we explored if examining multiple MRI contrasts per brain tumorpatient undergoing surgery can impact predictive accuracy of survival post-LITT. MATERIALS AND METHODS: MRI contrasts included fluid-attenuated inversion recovery (FLAIR), T1 pre-gadolinium (T1pre), T1 post-gadolinium (T1Gd), T2, diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), susceptibility weighted images (SWI), and magnetization-prepared rapid gradient-echo (MPRAGE). The latter was used for MRI data registration across preoperative to postoperative scans. Two ROIs were identified by thresholding preoperative FLAIR (large ROI) and T1Gd (small ROI) images. For each MRI contrast, a numerical score was assigned based on changing image intensity of both ROIs (vs. a normal ROI) from preoperative to postoperative stages. The fully-quantitative method was based on changing image intensity across scans at different stages without any human intervention, whereas the semi-quantitative method was based on subjective criteria of cumulative trends across scans at different stages. A fully-quantitative/semi-quantitative score per patient was obtained by averaging scores for each MRI contrast. A standard neuroradiological reading score per patient was obtained from radiological interpretation of MRI data. Scores from all 3 methods per patient were compared against patient survival, and re-examined for comorbidity and pathology effects. RESULTS:Patient survival correlated best with semi-quantitative scores obtained from T1Gd, ADC, and T2 data, and these correlations improved when biopsy and comorbidity were included. CONCLUSION: These results suggest interfacing neuroradiological readings with semi-quantitative image analysis can improve predictive accuracy of patient survival.