Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results.

(1) Background: Despite the complementarity between radiology and histopathology, both from a diagnostic and a prognostic perspective, quantitative analyses of these modalities are usually performed in disconnected silos. This work presents initial results for differentiating two major non-small cell lung cancer (NSCLC) subtypes by exploring cross-scale associations between Computed Tomography (CT) images and corresponding digitized pathology images. (2) Methods: The analysis comprised three phases, (i) a multi-resolution cell density quantification to identify discriminant pathomic patterns for differentiating adenocarcinoma (ADC) and squamous cell carcinoma (SCC), (ii) radiomic characterization of CT images by using Haralick descriptors to quantify tumor textural heterogeneity as represented by gray-level co-occurrences to discriminate the two pathological subtypes, and (iii) quantitative correlation analysis between the multi-modal features to identify potential associations between them.

Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study.

(1) : The relatively poor expert restaging accuracy of MRI in rectal cancer after neoadjuvant chemoradiation may be due to the difficulties in visual assessment of residual tumor on post-treatment MRI. In order to capture underlying tissue alterations and morphologic changes in rectal structures occurring due to the treatment, we hypothesized that radiomics texture and shape descriptors of the rectal environment (e.g.

Differentiating Cancerous and Non-cancerous Prostate Tissue Using Multi-scale Texture Analysis on MRI.

Prostate cancer (PCa) diagnosis is established by pathological examination via biopsies, which are associated with significant complications and false negatives. Using MRIs to identify locations with high probability of containing cancer could instead be used to guide the biopsy procedure. The present investigation aims to identify target regions within different prostatic zones on MRI with high probability of being cancerous for assisting in the decision of where and how to perform biopsy.

Autism spectrum disorder characterization in children by capturing local-regional brain changes in MRI.

Purpose: To design a multiscale descriptor capable of capturing complex local-regional unfolding patterns to support quantitation and diagnosis of autism spectrum disorders (ASD) using T1-weighted structural magnetic resonance images (MRI) with voxel size of 1 × 1 × 1 mm.

Methods: The proposed image descriptor uses an adapted multiscale representation, the Curvelet transform, interpretable in terms of texture (local) and shape (regional) to characterize brain regions, and a Generalized Gaussian Distribution (GGD) to reduce feature dimensionality. In this approach, each MRI is first parcelled into 3D anatomical regions.