Purpose To identify and evaluate textural quantitative imaging signatures (QISes) for tumors occurring within the central gland (CG) and peripheral zone (PZ) of the prostate, respectively, as seen on in vivo 3 Tesla endorectal T2-weighted (T2w) Magnetic Resonance Imaging (MRI). Materials and Methods This study utilized 22 pre-operative prostate MRI datasets (16 PZ, 6 CG) acquired from men with confirmed prostate cancer (CaP) and scheduled for radical prostatectomy (RP). The prostate region-of-interest (ROI) was automatically delineated on T2w MRI, following which it was corrected for intensity-based acquisition artifacts. An expert pathologist manually delineated the dominant tumor regions on ex vivo sectioned and stained RP specimens as well as identified each of the studies as either a CG or PZ CaP. A non-linear registration scheme was employed to spatially align and then map CaP extent from the ex vivo RP sections onto the corresponding MRI slices. 110 texture features were then extracted on a per-voxel basis from all T2w MRI datasets. An information theoretic feature selection procedure was then applied to identify QISes comprising T2w MRI textural features specific to CG and PZ CaP, respectively. The QISes for CG and PZ CaP were evaluated via Quadratic Discriminant Analysis (QDA) on a per-voxel basis against the ground truth for CaP on T2w MRI, mapped from corresponding histology. Results The QDA classifier yielded an area under the Receiver Operating characteristic curve of 0.86 for the CG CaP studies, and 0.73 for the PZ CaP studies over 25 runs of randomized 3-fold cross-validation. By comparison, the accuracy of the QDA classifier was significantly lower when (a) using all 110 texture features (with no feature selection applied), as well as (b) a randomly selected combination of texture features. Conclusion CG and PZ prostate cancers have significantly differing textural quantitative imaging signatures on T2w endorectal in vivo MRI.

Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high-level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity.

Rationale and Objectives To develop a computerized data integration framework (MaWERiC) for quantitatively combining structural and metabolic information from different Magnetic Resonance (MR) imaging modalities. Materials and Methods In this paper, we present a novel computerized support system that we call Multimodal Wavelet Embedding Representation for data Combination (MaWERiC) which (1) employs wavelet theory and dimensionality reduction for providing a common, uniform representation of the different imaging (T2-w) and non-imaging (spectroscopy) MRI channels, and (2) leverages a random forest classifier for automated prostate cancer detection on a per voxel basis from combined 1.5 Tesla in vivo MRI and MRS. Results A total of 36 1.5 T endorectal in vivo T2-w MRI, MRS patient studies were evaluated on a per-voxel via MaWERiC, using a three-fold cross validation scheme across 25 iterations. Ground truth for evaluation of the results was obtained via ex-vivo whole-mount histology sections which served as the gold standard for expert radiologist annotations of prostate cancer on a per-voxel basis. The results suggest that MaWERiC based MRS-T2-w meta-classifier (mean AUC, μ = 0.89 ± 0.02) significantly outperformed (i) a T2-w MRI (employing wavelet texture features) classifier (μ = 0.55± 0.02), (ii) a MRS (employing metabolite ratios) classifier (μ= 0.77 ± 0.03), (iii) a decision-fusion classifier, obtained by combining individual T2-w MRI and MRS classifier outputs (μ = 0.85 ± 0.03) and (iv) a data combination scheme involving combination of metabolic MRS and MR signal intensity features (μ = 0.66± 0.02). Conclusion A novel data integration framework, MaWERiC, for combining imaging and non-imaging MRI channels was presented. Application to prostate cancer detection via combination of T2-w MRI and MRS data demonstrated significantly higher AUC and accuracy values compared to the individual T2-w MRI, MRS modalities and other data integration strategies.

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Studying early response to cancer treatment is significant for patient treatment stratification and follow-up. Although recent advances in positron emission tomography (PET) and magnetic resonance imaging (MRI) allow for evaluation of tumor response, a quantitative objective assessment of treatment-related effects offers localization and quantification of structural and functional changes in the tumor region. Radiomics, the process of computerized extraction of features from radiographic images, is a new strategy for capturing subtle changes in the tumor region that works by quantifying subvisual patterns which might escape human identification. The goal of this study was to demonstrate feasibility for performing radiomics analysis on integrated PET/MRI to characterize early treatment response in metastatic renal cell carcinoma (RCC) undergoing sunitinib therapy. Two patients with advanced RCC were imaged using an integrated PET/MRI scanner. [18 F] fluorothymidine (FLT) was used as the PET radiotracer, which can measure the degree of cell proliferation. Image acquisitions included test/retest scans before sunitinib treatment and one scan 3 weeks into treatment using [18 F] FLT-PET, T2-weighted (T2w), and diffusion-weighted imaging (DWI) protocols, where DWI yielded an apparent diffusion coefficient (ADC) map. Our framework to quantitatively characterize treatment-related changes involved the following analytic steps: 1) intraacquisition and interacquisition registration of protocols to allow voxel-wise comparison of changes in radiomic features, 2) correction and pseudoquantification of T2w images to remove acquisition artifacts and examine tissue-specific response, 3) characterization of information captured by T2w MRI, FLT-PET, and ADC via radiomics, and 4) combining multiparametric information to create a map of integrated changes from PET/MRI radiomic features. Standardized uptake value (from FLT-PET) and ADC textures ranked highest for reproducibility in a test/retest evaluation as well as for capturing treatment response, in comparison to high variability seen in T2w MRI. The highest-ranked radiomic feature yielded a normalized percentage change of 63% within the RCC region and 17% in a spatially distinct normal region relative to its pretreatment value. By comparison, both the original and postprocessed T2w signal intensity appeared to be markedly less sensitive and specific to changes within the tumor. Our preliminary results thus suggest that radiomics analysis could be a powerful tool for characterizing treatment response in integrated PET/MRI.