Treatment Response Prediction in Major Depressive Disorder Using Multimodal MRI and Clinical Data: Secondary Analysis of a Randomized Clinical Trial.

OBJECTIVE: Response to antidepressant treatment in major depressive disorder varies substantially between individuals, which lengthens the process of finding effective treatment. The authors sought to determine whether a multimodal machine learning approach could predict early sertraline response in patients with major depressive disorder. They assessed the predictive contribution of MR neuroimaging and clinical assessments at baseline and after 1 week of treatment. METHODS: This was a preregistered secondary analysis of data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a multisite double-blind, placebo-controlled randomized clinical trial that included 296 adult outpatients with unmedicated recurrent or chronic major depressive disorder. MR neuroimaging and clinical data were collected before and after 1 week of treatment. Performance in predicting response and remission, collected after 8 weeks, was quantified using balanced accuracy (bAcc) and area under the receiver operating characteristic curve (AUROC) scores. RESULTS: A total of 229 patients were included in the analyses (mean age, 38 years [SD=13]; 66% female). Internal cross-validation performance in predicting response to sertraline (bAcc=68% [SD=10], AUROC=0.73 [SD=0.03]) was significantly better than chance. External cross-validation on data from placebo nonresponders (bAcc=62%, AUROC=0.66) and placebo nonresponders who were switched to sertraline (bAcc=65%, AUROC=0.68) resulted in differences that suggest specificity for sertraline treatment compared with placebo treatment. Finally, multimodal models outperformed unimodal models. CONCLUSIONS: The study results confirm that early sertraline treatment response can be predicted; that the models are sertraline specific compared with placebo; that prediction benefits from integrating multimodal MRI data with clinical data; and that perfusion imaging contributes most to these predictions. Using this approach, a lean and effective protocol could individualize sertraline treatment planning to improve psychiatric care.

A Reflectivity Measure to Quantify Bruch's Membrane Calcification in Patients with Pseudoxanthoma Elasticum Using Optical Coherence Tomography.

Purpose: Progressive calcification of Bruch's membrane (BM) causes considerable visual morbidity in patients with pseudoxanthoma elasticum (PXE). Since calcification is hyperreflective on optical coherence tomography (OCT), our aim was to measure BM calcification with OCT imaging. Methods: Case-control study with 45 patients with PXE under 40 years (range, 11-39) and 25 controls (range, 14-39). Spectralis HRA-OCT imaging consisted of seven macular B-scans with 250-µm spacing. Retinal segmentation was performed with the IOWA Reference Algorithms. MATLAB was used to extract and average z-axis reflectivity profiles. Layer reflectivities were normalized to the ganglion cell and inner plexiform layers. Both median and peak layer reflectivities were compared between patients with PXE and controls. The discriminative value of the retinal pigment epithelium (RPE)-BM peak reflectivity was analyzed using receiver operating characteristic analysis. Results: The reflectivity profile of patients with PXE differed from controls in the outer retinal layers. The normalized median RPE-BM reflectivity was 41.1 (interquartile range [IQR], 26.3-51.9) in patients with PXE, compared with 22.5 (IQR, 19.3-29.5) in controls (P = 2.09 × 10-3). The normalized RPE-BM peak reflectivity was higher in patients with PXE (67.5; IQR, 42.1-84.2) than in controls (32.7; IQR, 25.7-38.9; P = 2.43 × 10-5) and had a high discriminative value with an area under the curve of 0.85 (95% confidence interval, 0.76-0.95). In patients with PXE under 40 years, increasing age did not have a statistically significant effect on the RPE-BM peak reflectivity (patients under 20 years: 44.2 [IQR, 40.5-74.6]; 20-30 years: 66.0 [IQR, 45.1-83.8]; 30-40 years: 70.8 [IQR, 49.0-88.0], P = 0.47). Conclusions: BM calcification can be measured as increased RPE-BM reflectivity in young patients with PXE and has a high discriminative value. Translational Relevance: In patients with PXE, the OCT reflectivity of Bruch's membrane may be the first biomarker for Bruch's membrane calcification and a valuable ophthalmologic endpoint in clinical trials.

Physics-informed Deep Learning for Dual-Energy Computed Tomography Image Processing.

Dual-energy CT (DECT) was introduced to address the inability of conventional single-energy computed tomography (SECT) to distinguish materials with similar absorbances but different elemental compositions. However, material decomposition algorithms based purely on the physics of the underlying attenuation process have several limitations, leading to low signal-to-noise ratio (SNR) in the derived material-specific images. To overcome these, we trained a convolutional neural network (CNN) to develop a framework to reconstruct non-contrast SECT images from DECT scans. We show that the traditional physics-based decomposition algorithms do not bring to bear the full information content of the image data. A CNN that leverages the underlying physics of the DECT image generation process as well as the anatomic information gleaned via training with actual images can generate higher fidelity processed DECT images.