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Background Determining failure to anti-angiogenic therapy in recurrent GBM (rGBM) remains a challenge. The purpose of the study was to assess treatment response to bevacizumab-based therapy in patients with rGBM using MR spectroscopy (MRS). Methods We performed longitudinal MRI/MRS in 33 patients with rGBM to investigate whether changes in N-acetylaspartate (NAA)/Choline (Cho) and Lactate (Lac)/NAA from baseline to subsequent time points after treatment can predict early failures to bevacizumab-based therapies. Results After stratifying based on 9 month survival, longer-term survivors had increased NAA/Cho and decreased Lac/NAA levels compared to shorter-term survivors. ROC analyses for intratumoral NAA/Cho correlated with survival at 1 day, 2 weeks, 8 weeks, and 16 weeks. Intratumoral Lac/NAA ROC analyses were predictive of survival at all time points tested. At the 8 week time point, 88% of patients with decreased NAA/Cho did not survive 9 months; furthermore, 90% of individuals with an increased Lac/NAA from baseline did not survive at 9 months. No other metabolic ratios tested significantly predicted survival. Conclusions Changes in metabolic levels of tumoral NAA/Cho and Lac/NAA can serve as early biomarkers for predicting treatment failure to anti-angiogenic therapy as soon as 1 day after bevacizumab-based therapy. The addition of MRS to conventional MR methods can provide better insight into how anti-angiogenic therapy affects tumor microenvironment and predict patient outcomes.

Purpose Reduced stress commonly occurs in talkers with Parkinson's disease (PD), whereas excessive and equal stress is frequently associated with dysarthria of talkers with amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS). This study sought to identify articulatory impairment patterns that underlie these two impaired stress patterns. We further aimed to determine if talkers with the same stress pattern disturbance but different diseases (ALS and MS) exhibit disease-specific articulatory deficits. Method Fifty-seven talkers participated in the study—33 talkers with dysarthria and 24 controls. Talkers with dysarthria were grouped based on their medical diagnosis: PD ( n = 15), ALS ( n = 10), MS ( n = 8). Participants repeated target words embedded in a carrier phrase. Kinematic data were recorded using electromagnetic articulography. Duration, displacement, peak speed, stiffness, time-to-peak speed, and parameter c were extracted for the initial lower lip opening stroke of each target word, which was either stressed or unstressed. Results Stress effects were significant for all kinematic measures across groups except for stiffness and time-to-peak speed, which were nonsignificant in ALS. For comparisons with controls, more kinematic measures significantly differed in the ALS group than in the PD and MS groups. Additionally, ALS and MS showed mostly similar articulatory impairment patterns. Conclusions In general, significant stress effects were observed in talkers with dysarthria. However, stress-specific between-group differences in articulatory performance, particularly displacement, may explain the perceptual impression of disturbed stress patterns. Furthermore, similar findings for ALS and MS suggest that articulatory deficits underlying similar stress pattern disturbances are not disease-specific.

Although researchers have recognized the need to better account for the heterogeneous perceptual speech characteristics among talkers with the same disease, guidance on how to best establish such dysarthria subgroups is currently lacking. Therefore, we compared subgroup decisions of two data-driven approaches based on a cohort of talkers with Huntington’s disease (HD): (1) a statistical clustering approach (STATCLUSTER) based on perceptual speech characteristic profiles and (2) an auditory free classification approach (FREECLASS) based on listeners’ similarity judgments. We determined the amount of overlap across the two subgrouping decisions and the perceptual speech characteristics driving the subgrouping decisions of each approach. The same speech samples produced by 48 talkers with HD were used for both grouping approaches. The STATCLUSTER approach had been conducted previously. The FREECLASS approach was conducted in the present study. Both approaches yielded four dysarthria subgroups, which overlapped between 50% to 78%. In both grouping approaches, overall bizarreness and speech rate characteristics accounted for the grouping decisions. In addition, voice abnormalities contributed to the grouping decisions in the FREECLASS approach. These findings suggest that apart from overall bizarreness ratings, indexing dysarthria severity, speech rate and voice characteristics may be important features to establish dysarthria subgroups in HD.