Exploring the interplay between colorectal cancer subtypes genomic variants and cellular morphology: A deep-learning approach.

Molecular subtypes of colorectal cancer (CRC) significantly influence treatment decisions. While convolutional neural networks (CNNs) have recently been introduced for automated CRC subtype identification using H&E stained histopathological images, the correlation between CRC subtype genomic variants and their corresponding cellular morphology expressed by their imaging phenotypes is yet to be fully explored. The goal of this study was to determine such correlations by incorporating genomic variants in CNN models for CRC subtype classification from H&E images. We utilized the publicly available TCGA-CRC-DX dataset, which comprises whole slide images from 360 CRC-diagnosed patients (260 for training and 100 for testing). This dataset also provides information on CRC subtype classifications and genomic variations. We trained CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology patterns. We assessed the interplay between CRC subtypes' genomic variations and cellular morphology patterns by evaluating the CRC subtype classification accuracy of the different models in a stratified 5-fold cross-validation experimental setup using the area under the ROC curve (AUROC) and average precision (AP) as the performance metrics. The CNN models that account for potential correlation between genomic variations within CRC subtypes and their cellular morphology pattern achieved superior accuracy compared to the baseline CNN classification model that does not account for genomic variations when using either single-nucleotide-polymorphism (SNP) molecular features (AUROC: 0.824±0.02 vs. 0.761±0.04, p<0.05, AP: 0.652±0.06 vs. 0.58±0.08) or CpG-Island methylation phenotype (CIMP) molecular features (AUROC: 0.834±0.01 vs. 0.787±0.03, p<0.05, AP: 0.687±0.02 vs. 0.64±0.05). Combining the CNN models account for variations in CIMP and SNP further improved classification accuracy (AUROC: 0.847±0.01 vs. 0.787±0.03, p = 0.01, AP: 0.68±0.02 vs. 0.64±0.05). The improved accuracy of CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology as expressed by H&E imaging phenotypes may elucidate the biological cues impacting cancer histopathological imaging phenotypes. Moreover, considering CRC subtypes genomic variations has the potential to improve the accuracy of deep-learning models in discerning cancer subtype from histopathological imaging data.

Association of Speckle-Based Blood Flow Measurements and Fluorescein Angiography in Infants with Retinopathy of Prematurity.

Purpose: To determine the correlation between blood flow metrics measured by intravenous fluorescein angiography (IVFA) and the blood flow velocity index (BFVi) obtained by laser speckle contrast imaging (LSCI) in infants with retinopathy of prematurity (ROP). Design: Prospective comparative pilot study. Subjects: Seven eyes from 7 subjects with ROP. Methods: Unilateral LSCI and IVFA data were obtained from each subject in the neonatal intensive care unit. Five LSCI-based metrics and 5 IVFA-based metrics were extracted from images to quantify blood flow patterns in the same region of interest. Correlation between LSCI-based and IVFA-based blood flow metrics was compared between 2 subgroups of ROP severity: moderate ROP (defined as stage ≤ 2 without Plus disease) and severe ROP (defined as stage ≥3 or Plus disease). Main Outcome Measures: Pearson and Kendall rank correlation coefficients between IVFA and LSCI metrics; Student t test P values comparing LSCI metrics between "severe" and "moderate" ROP groups. Results: Pearson correlations between IVFA and LSCI included arterial-venous transit time (AVTT) and peak BFVi (pBFVi; r = -0.917; P = 0.004), AVTT and dip BFVi (dBFVi; r = -0.920; P = 0.003), AVTT and mean BFVi (r = -0.927- P = 0.003), and AVTT and volumetric rise index (r = -0.779; P = 0.039). Kendall rank correlation between AVTT and dBFVi was r = -0.619 (P = 0.051). pBFVi was higher in severe ROP than in moderate ROP (8.4 ± 0.6 and 4.4 ± 1.8, respectively; P = 0.0045 using the 2-sample t test with pooled variance and P = 0.0952 using the Wilcoxon rank-sum test). Conclusions: Correlation was found between blood flow metrics obtained by IVFA and noninvasive LSCI techniques. We demonstrate the feasibility of obtaining quantitative metrics using LSCI in infants with ROP in this pilot study; however, further investigation is needed to evaluate its potential use in clinical assessment of ROP severity. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Retinal blood flow association with age and weight in infants at risk for retinopathy of prematurity.

This prospective study evaluated the relationship between laser speckle contrast imaging (LSCI) ocular blood flow velocity (BFV) and five birth parameters: gestational age (GA), postmenstrual age (PMA) and chronological age (CA) at the time of measurement, birth weight (BW), and current weight (CW) in preterm neonates at risk for retinopathy of prematurity (ROP). 38 Neonates with BW < 2 kg, GA < 32 weeks, and PMA between 27 and 47 weeks underwent 91 LSCI sessions. Correlation tests and regression analysis were performed to quantify relationships between birth parameters and ocular BFV. Mean ocular BFV index in this cohort was 8.8 +/- 4.0 IU. BFV positively correlated with PMA (r = 0.3, p = 0.01), CA (r = 0.3, p = 0.005), and CW (r = 0.3, p = 0.02). BFV did not correlate with GA nor BW (r = - 0.2 and r = - 0.05, p > 0.05). Regression analysis with mixed models demonstrated that BFV increased by 1.2 for every kilogram of CW, by 0.34 for every week of CA, and by 0.36 for every week of PMA (p = 0.03, 0.004, 0.007, respectively). Our findings indicate that increased age and weight are associated with increased ocular BFV measured using LSCI in premature infants. Future studies investigating the associations between ocular BFV and ROP clinical severity must control for age and/or weight of the infant.

Retinal blood flow association with age and weight in infants at risk for retinopathy of prematurity.

This prospective study evaluated the relationship between laser speckle contrast imaging (LSCI) ocular blood flow velocity (BFV) and five birth parameters: gestational age (GA), postmenstrual age (PMA), and chronological age (CA) at the time of measurement, birth weight (BW), and current weight (CW) in preterm neonates at risk for retinopathy of prematurity (ROP).38 Neonates with BW < 2 kg, GA < 32 weeks, and PMA between 27-47 weeks underwent 91 LSCI sessions. Correlation tests and regression analysis were performed to quantify relationships between birth parameters and ocular BFV. Mean ocular BFV index in this cohort was 8.8 +/- 4.0 IU. BFV positively correlated with PMA (r = 0.3, p = 0.01), CA (r = 0.3, p = 0.005), and CW (r = 0.3, p = 0.02). BFV did not correlate with GA nor BW (r=-0.2 and r=-0.05, p > 0.05). Regression analysis with mixed models demonstrated that BFV increased by 1.2 for every kilogram of CW, by 0.34 for every week of CA, and by 0.36 for every week of PMA (p = 0.03, 0.004, 0.007, respectively). Our findings indicate that increased age and weight are associated with increased ocular BFV measured using LSCI in premature infants. Future studies investigating the associations between ocular BFV and ROP clinical severity must control for age and/or weight of the infant.

Surgical Management of Iris Bombe in Muscle-Eye-Brain Disease.

A 2-year-old girl with severe muscular dystrophy presented with unilateral eye pain and corneal clouding. She was found to have absent red reflex, hypotonia, cerebral hypoplasia, and iris bombe on ultrasound biomicroscopy, a feature not previously reported in this syndrome. She responded favorably to surgical management. Iris bombe can be a cause of glaucoma in muscle-eye-brain disease. This highlights the importance of incorporating ultrasound biomicroscopy into the diagnostic algorithm of muscle-eye-brain disease and other types of congenital syndromic glaucoma. [J Pediatr Ophthalmol Strabismus. 2023;60(4):e35-e37.].

AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19.

The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease, governments worldwide have implemented non-pharmaceutical interventions (NPIs) to slow the spread of the virus. Examples of such interventions include community actions, such as school closures or restrictions on mass gatherings, individual actions including mask wearing and self-quarantine, and environmental actions such as cleaning public facilities. We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6,000 NPIs implemented worldwide since the start of the pandemic. WNTRAC covers NPIs implemented across 261 countries and territories, and classifies NPIs into a taxonomy of 16 NPI types. NPIs are automatically extracted daily from Wikipedia articles using natural language processing techniques and then manually validated to ensure accuracy and veracity. We hope that the dataset will prove valuable for policymakers, public health leaders, and researchers in modeling and analysis efforts to control the spread of COVID-19.