Quantitative sonographic image analysis for hepatic nodules: a pilot study

Artificial Intelligence and Image Analysis-Assisted Diagnosis for Fibrosis Stage of Metabolic Dysfunction-Associated Steatotic Liver Disease Using Ultrasonography: A Pilot Study

BACKGROUND/OBJECTIVES

Elastography increased the diagnostic accuracy of liver fibrosis. However, several challenges persist, including the widespread utilization of equipment, difficulties in measuring certain cases, and the influence of viscosity factors. A rough surface and a blunted hepatic margin have long been acknowledged as valuable characteristics indicative of hepatic fibrosis. The objective of this study was to conduct an image analysis and quantitative assessment of the contour of the sagittal section of the left lobe of the liver.

METHODS

Between February and October 2020, 486 consecutive outpatients underwent ultrasound examinations at our hospital. A total of 214 images were manually annotated by delineating the liver contour to create annotation images. U-Net was employed for liver segmentation, with the dataset divided into training (n = 128), testing (n = 42), and validation (n = 44) subsets. Additionally, 43 Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) cases with pathology data from between 2015 and 2020 were included. Segmentation was performed using the program developed in the first step. Subsequently, shape analysis was conducted using ImageJ.

RESULTS

Liver segmentation exhibited high accuracy, as indicated by Dice loss of 0.044, Intersection over Union of 0.935, and an F score of 0.966. The accuracy of the classification of the liver surface as smooth or rough via ResNet 50 was 84.6%. Image analysis showed MinFeret and Minor correlated with liver fibrosis stage (p = 0.046, 0.036, respectively). Sensitivity, specificity, and AUROC of Minor for ≥F3 were 0.571, 0.862, and 0.722, respectively, and F4 were 1, 0.600, and 0.825, respectively.

CONCLUSION

Deep learning segmentation of the sagittal cross-sectional contour of the left lobe of the liver demonstrated commendable accuracy. The roughness of the liver surface was correctly judged by artificial intelligence. Image analysis showed the thickness of the left lobe inversely correlated with liver fibrosis stage.

Microfluidic Platform for Generating and Releasing Patient-Derived Cancer Organoids with Diverse Shapes: Insight into Shape-Dependent Tumor Growth

Multicellular spheroids and patient-derived organoids find many applications in fundamental research, drug discovery, and regenerative medicine. Advances in the understanding and recapitulation of organ functionality and disease development require the generation of complex organoid models, including organoids with diverse morphologies. Microfluidics-based cell culture platforms enable time-efficient confined organoid generation. However, the ability to form organoids with different shapes with a subsequent transfer from microfluidic devices to unconstrained environments for studies of morphology-dependent organoid growth is yet to be demonstrated. Here, a microfluidic platform is introduced that enables high-fidelity formation and addressable release of breast cancer organoids with diverse shapes. Using this platform, the impact of organoid morphology on their growth in unconstrained biomimetic hydrogel is explored. It is shown that proliferative cancer cells tend to localize in high positive curvature organoid regions, causing their faster growth, while the overall growth pattern of organoids with diverse shapes tends to reduce interfacial tension at the organoid-hydrogel interface. In addition to the formation of organoids with diverse morphologies, this platform can be integrated into multi-tissue micro-physiological systems.

Quantitative Ultrasound Image Analysis Helps in the Differentiation of Hepatocellular Carcinoma (HCC) From Borderline Lesions and Predicting the Histologic Grade of HCC and Microvascular Invasion

OBJECTIVES

Quantitative image analysis is one of the methods to overcome the lack of objectivity of ultrasound (US). The aim of this study was to clarify the correlation between the features from a US image analysis and the histologic grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC) and differentiation of HCC smaller than 2 cm from borderline lesions.

METHODS

We retrospectively analyzed grayscale US images with histopathologic evidence of HCC or a precancerous lesion using ImageJ version 1.47 software (National Institutes of Health, Bethesda, MD).

RESULTS

A total of 148 nodules were included (borderline lesion, n = 31; early HCC [eHCC], n = 3; well-differentiated HCC [wHCC], n = 16; moderately differentiated HCC [mHCC], n = 79; and poorly differentiated HCC [pHCC], n = 19). A multivariate analysis selected lower minimum gray values (odds ratio [OR], 0.431; P = .003) and a higher standard deviation (OR, 1.880; P = .019) as predictors of HCC smaller than 2 cm. Median (range) minimum gray values of borderline lesions, eHCC, wHCC, mHCC, and pHCC were 29 (0-103), 7 (0-47), 6 (0-60), 10 (0-53), and 2 (0-38), respectively, and gradually decreased from borderline lesions to pHCC (P < 0.001). The multivariate analysis showed a higher aspect ratio (OR, 2.170; P = .001) and lower minimum gray value (OR, 0.475; P = .043) as predictors of MVI. An anechoic area diagnosed by a subjective evaluation was correlated with the minimum gray value (P < .0001). The proportion of the anechoic area gradually increased from eHCC to pHCC (P = .031).

CONCLUSIONS

In a US image analysis, HCC smaller than 2 cm had features of greater heterogeneity and a lower minimum gray value than borderline lesions. Moderately differentiated HCC was smoother than borderline lesions, and the anechoic area correlated with histologic grading. Microvascular invasion was correlated with a slender shape and a lower minimum gray value.

Pediatric Corneal Structural Development During Childhood Characterized by Ultrasound Biomicroscopy

PURPOSE

To quantitatively describe the structural corneal changes from infancy to early adulthood using ultrasound biomicroscopy.

METHODS

In this prospective study, 168 ultrasound biomicroscopy images were obtained from 24 healthy eyes of 24 patients who consented and enrolled in the Pediatric Anterior Segment Imaging Innovation Study. Their ages ranged from birth to 26 years. An established ultrasound biomicroscopy imaging protocol including seven views of one eye per patient were obtained and measured using ImageJ software (National Institutes of Health). Twelve corneal structural parameters were measured. Means were compared between younger and older groups.

RESULTS

Among the 12 measured structures, 5 demonstrated statistically significant differences (P < .05) between patients younger than 1 year and patients older than 1 year. The mean values for corneal cross-sectional width and length, central corneal thickness, and radii of curvature (anterior and posterior) were significantly different in patients younger than 1 year. Curvature and limbus-to-limbus dimensions changed more dramatically than thickness and tissue density. When comparing the youngest to oldest subgroups, anterior curvature flattened (6.14 to 7.55 radius), posterior curvature flattened (5.53 to 6.72 radius), angle-to-angle distance increased (8.93 to 11.40 mm), and endothelial cross-sectional distance increased (10.63 to 13.61 mm).

CONCLUSIONS

Pediatric corneal structures change with age. The most significant changes occur in the first months of life, with additional changes later in childhood. This study further demonstrates the importance of age in pediatric corneal imaging analysis. [J Pediatr Ophthalmol Strabismus. 2020;57(4):238-245.].