Structural brain differences in essential tremor and Parkinson's disease deep brain stimulation patients.

BACKGROUND: Essential tremor (ET) and Parkinson's disease (PD) are the most common tremor disorders and are common indications for deep brain stimulation (DBS). In some patients, PD and ET symptoms overlap and diagnosis can be challenging based on clinical criteria alone. The objective of this study was to identify structural brain differences between PD and ET DBS patients to help differentiate these disorders and improve our understanding of the different brain regions involved in these pathologic processes. METHODS: We included ET and PD patients scheduled to undergo DBS surgery in this observational study. Patients underwent 3T brain MRI while under general anesthesia as part of their procedure. Cortical thicknesses and subcortical volumes were quantified from T1-weighted images using automated multi-atlas segmentation. We used logistic regression analysis to identify brain regions associated with diagnosis of ET or PD. RESULTS: 149 ET and 265 PD patients were included. Smaller volumes in the pallidum and thalamus and reduced thickness in the anterior orbital gyrus, lateral orbital gyrus, and medial precentral gyrus were associated with greater odds of ET diagnosis. Conversely, reduced volumes in the caudate, amygdala, putamen, and basal forebrain, and reduced thickness in the orbital part of the inferior frontal gyrus, supramarginal gyrus, and posterior cingulate were associated with greater odds of PD diagnosis. CONCLUSIONS: These findings identify structural brain differences between PD and ET patients. These results expand our understanding of the different brain regions involved in these disorders and suggest that structural MRI may help to differentiate patients with these two disorders.

Towards Machine Learning Prediction of Deep Brain Stimulation (DBS) Intra-operative Efficacy Maps.

Deep brain stimulation (DBS) has the potential to improve the quality of life of people with a variety of neurological diseases. A key challenge in DBS is in the placement of a stimulation electrode in the anatomical location that maximizes efficacy and minimizes side effects. Pre-operative localization of the optimal stimulation zone can reduce surgical times and morbidity. Current methods of producing efficacy probability maps follow an anatomical guidance on magnetic resonance imaging (MRI) to identify the areas with the highest efficacy in a population. In this work, we propose to revisit this problem as a classification problem, where each voxel in the MRI is a sample informed by the surrounding anatomy. We use a patch-based convolutional neural network to classify a stimulation coordinate as having a positive reduction in symptoms during surgery. We use a cohort of 187 patients with a total of 2,869 stimulation coordinates, upon which 3D patches were extracted and associated with an efficacy score. We compare our results with a registration-based method of surgical planning. We show an improvement in the classification of intraoperative stimulation coordinates as a positive response in reduction of symptoms with AUC of 0.670 compared to a baseline registration-based approach, which achieves an AUC of 0.627 (p < 0.01). Although additional validation is needed, the proposed classification framework and deep learning method appear well-suited for improving pre-surgical planning and personalize treatment strategies.

Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results.

Pathologic complete response following neoadjuvant therapy (NAT) is used as a short-term surrogate marker of eventual outcome in patients with breast cancer. Analyzing voxel-level heterogeneity in MRI-derived parametric maps, obtained before and after the first cycle of NAT ([Formula: see text]), in conjunction with receptor status, may improve the predictive accuracy of tumor response to NAT. Toward that end, we incorporated two MRI-derived parameters, the apparent diffusion coefficient and efflux rate constant, with receptor status in a logistic ridge-regression model. The area under the curve (AUC) and Brier score of the model computed via 10-fold cross validation were 0.94 (95% CI: 0.85, 0.99) and 0.11 (95% CI: 0.06, 0.16), respectively. These two statistics strongly support the hypothesis that our proposed model outperforms the other models that we investigated (namely, models without either receptor information or voxel-level information). The contribution of the receptor information was manifested by an 8% to 15% increase in AUC and a 14% to 21% decrease in Brier score. These data indicate that combining multiparametric MRI with hormone receptor status has a high likelihood of improved prediction of pathologic response to NAT in breast cancer.

The Attenuation Distribution Across the Long Axis of Breast Cancer Liver Metastases at CT: A Quantitative Biomarker for Predicting Overall Survival.

OBJECTIVE: The objective of our study was to compare attenuation distribution across the long-axis (ADLA) measurements, Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1, and Choi criteria for predicting overall survival (OS) in patients with metastatic breast cancer treated with bevacizumab. MATERIALS AND METHODS: We obtained HIPAA-compliant data from a prospective, multisite, phase 3 trial of bevacizumab for the treatment of metastatic breast cancer. For patients with one or more liver metastases measuring 15 mm or larger at baseline, we evaluated up to two target liver lesions using RECIST, Choi criteria, and ADLA measurements, with the latter defined as the SD of the CT attenuation values of each pixel along the tumor long-axis diameter. The optimal percentage change threshold for defining an ADLA response was computed by cross-validation analysis in a Cox model. The log-rank test was applied to evaluate RECIST, Choi criteria, and ADLA for discriminating patients with superior OS. The predictive accuracies of all three techniques were compared using Brier scores and areas under the ROC curve (AUC). All analyses were performed separately using best overall response (BOR) and response at the first follow-up time point (FU1). RESULTS: One hundred sixty-four patients met the inclusion criteria. A 25% decrease in the ADLA measurement from baseline was the optimal ADLA response threshold for BOR and FU1. RECIST, Choi criteria, and ADLA successfully identified patients with superior OS when using BOR (RECIST, p = 0.02; Choi and ADLA, p < 0.001), but only Choi criteria and ADLA measurements were successful when using FU1 (RECIST, p = 0.43; Choi and ADLA, p < 0.001). In a direct comparison, ADLA measurements outperformed both RECIST and Choi criteria using BOR (95% CI for Brier score differences, ADLA-RECIST [-0.58 to -0.08] and ADLA-Choi [-0.55 to -0.06]; 95% CI for AUC differences, ADLA-RECIST [0.16-0.33] and ADLA-Choi [0.17-0.36]) as well as using FU1 (95% CI for Brier score differences, ADLA-RECIST [-0.77 to -0.08] and ADLA-Choi [-0.58 to -0.03]; 95% CI for AUC differences, ADLA-RECIST [0.22-0.39] and ADLA-Choi [0.01-0.22]). CONCLUSION: ADLA measurements may be a useful noninvasive indicator of cancer treatment response. Because ADLA measurements may be extracted relatively easily using existing radiologist workflows, further investigation of the ADLA technique is warranted.

Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis.

This meta-analysis assesses the prognostic value of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) performed during neoadjuvant therapy (NAT) of locally advanced breast cancer. A systematic literature search was conducted to identify studies of quantitative DCE-MRI and DW-MRI performed during breast cancer NAT that report the sensitivity and specificity for predicting pathological complete response (pCR). Details of the study population and imaging parameters were extracted from each study for subsequent meta-analysis. Metaregression analysis, subgroup analysis, study heterogeneity, and publication bias were assessed. Across 10 studies that met the stringent inclusion criteria for this meta-analysis (out of 325 initially identified studies), we find that MRI had a pooled sensitivity of 0.91 [95% confidence interval (CI), 0.80 to 0.96] and specificity of 0.81(95% CI, 0.68 to 0.89) when adjusted for covariates. Quantitative DCE-MRI exhibits greater specificity for predicting pCR than semiquantitative DCE-MRI ([Formula: see text]). Quantitative DCE-MRI and DW-MRI are able to predict, early in the course of NAT, the eventual response of breast tumors, with a high level of specificity and sensitivity. However, there is a high degree of heterogeneity in published studies highlighting the lack of standardization in the field.