Manual versus Automated Carotid Artery Plaque Component Segmentation in High and Lower Quality 3.0 Tesla MRI Scans

Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer

Ovarian cancer (OC) remains one of the most lethal gynecological malignancies globally. Despite the implementation of various medical imaging approaches for OC screening, achieving accurate differential diagnosis of ovarian tumors continues to pose significant challenges due to variability in image performance, resulting in a lack of objectivity that relies heavily on the expertise of medical professionals. This challenge can be addressed through the emergence and advancement of radiomics, which enables high-throughput extraction of valuable information from conventional medical images. Furthermore, radiomics can integrate with genomics, a novel approach termed radiogenomics, which allows for a more comprehensive, precise, and personalized assessment of tumor biological features. In this review, we present an extensive overview of the application of radiomics and radiogenomics in diagnosing and predicting ovarian tumors. The findings indicate that artificial intelligence methods based on imaging can accurately differentiate between benign and malignant ovarian tumors, as well as classify their subtypes. Moreover, these methods are effective in forecasting survival rates, treatment outcomes, metastasis risk, and recurrence for patients with OC. It is anticipated that these advancements will function as decision-support tools for managing OC while contributing to the advancement of precision medicine.

3D Ultrasound Reconstructions of the Carotid Artery and Thyroid Gland Using Artificial-Intelligence-Based Automatic Segmentation-Qualitative and Quantitative Evaluation of the Segmentation Results via Comparison with CT Angiography

The aim of this study was to evaluate the feasibility of a noninvasive and low-operator-dependent imaging method for carotid-artery-stenosis diagnosis. A previously developed prototype for 3D ultrasound scans based on a standard ultrasound machine and a pose reading sensor was used for this study. Working in a 3D space and processing data using automatic segmentation lowers operator dependency. Additionally, ultrasound imaging is a noninvasive diagnosis method. Artificial intelligence (AI)-based automatic segmentation of the acquired data was performed for the reconstruction and visualization of the scanned area: the carotid artery wall, the carotid artery circulated lumen, soft plaque, and calcified plaque. A qualitative evaluation was conducted via comparing the US reconstruction results with the CT angiographies of healthy and carotid-artery-disease patients. The overall scores for the automated segmentation using the MultiResUNet model for all segmented classes in our study were 0.80 for the IoU and 0.94 for the Dice. The present study demonstrated the potential of the MultiResUNet-based model for 2D-ultrasound-image automated segmentation for atherosclerosis diagnosis purposes. Using 3D ultrasound reconstructions may help operators achieve better spatial orientation and evaluation of segmentation results.

Radiomics and radiogenomics in ovarian cancer: a literature review

Ovarian cancer remains one of the most lethal gynecological cancers in the world despite extensive progress in the areas of chemotherapy and surgery. Many studies have postulated that this is because of the profound heterogeneity that underpins response to therapy and prognosis. Standard imaging evaluation using CT or MRI does not take into account this tumoral heterogeneity especially in advanced stages with peritoneal carcinomatosis. As such, newly emergent fields in the assessment of tumor heterogeneity have been proposed using radiomics to evaluate the whole tumor burden heterogeneity as opposed to single biopsy sampling. This review provides an overview of radiomics, radiogenomics, and proteomics and examines the use of these newly emergent fields in assessing tumor heterogeneity and its implications in ovarian cancer.

An investigation into the critical role of fibre orientation in the ultimate tensile strength and stiffness of human carotid plaque caps

The development and subsequent rupture of atherosclerotic plaques in human carotid arteries is a major cause of ischaemic stroke. Mechanical characterization of atherosclerotic plaques can aid our understanding of this rupture risk. Despite this however, experimental studies on human atherosclerotic carotid plaques, and fibrous plaque caps in particular, are very limited. This study aims to provide further insights into atherosclerotic plaque rupture by mechanically testing human fibrous plaque caps, the region of the atherosclerotic lesion most often attributed the highest risk of rupture. The results obtained highlight the variability in the ultimate tensile stress, strain and stiffness experienced in atherosclerotic plaque caps. By pre-screening all samples using small angle light scattering (SALS) to determine the dominant fibre direction in the tissue, along with supporting histological analysis, this work suggests that the collagen fibre alignment in the circumferential direction plays the most dominant role for determining plaque structural stability. The work presented in this study could provide the basis for new diagnostic approaches to be developed, which non-invasively identify carotid plaques at greatest risk of rupture. STATEMENT OF SIGNIFICANCE: Mechanical characterisation of the atherosclerotic plaque cap is of utmost importance for understanding the mechanisms that govern the rupture strength of this tissue in-vivo. Studies has shown that plaque tissue is heterogenous and comprises of many structural components, each of which exhibits a varying mechanical response. However, rupture generally is located to the plaque cap, whereby the stress exerted on this location exceeds its mechanical strength causing failure. This work shows, for the first time, that the underlying collagen fibre architecture of carotid plaque caps governs their strength and stiffness. This study shows that plaque caps with collagen fibres aligned in the predominately circumferential direction experience higher stresses and lower strains before failure while those with predominately axial fibres display the opposite trend. Furthermore, total collagen content was found not to play a dominant role in determining the mechanical response of the tissue. The present study provides critical insights into human atherosclerotic plaque tissue mechanics and offers clinically relevant insights for mechanically sensitive imaging techniques, such as strain-based ultrasound or MRI.

Quantification of Carotid Intraplaque Hemorrhage: Comparison between Manual Segmentation and Semi-Automatic Segmentation on Magnetization-Prepared Rapid Acquisition with Gradient-Echo Sequences

Purpose: Carotid intraplaque hemorrhage (IPH) increases risk of territorial cerebral ischemic events, but different sequences or criteria have been used to diagnose or quantify carotid IPH. The purpose of this study was to compare manual segmentation and semi-automatic segmentation for quantification of carotid IPH on magnetization-prepared rapid acquisition with gradient-echo (MPRAGE) sequences. Methods: Forty patients with 16–79% carotid stenosis and IPH on MPRAGE sequences were reviewed by two trained radiologists with more than five years of specialized experience in carotid plaque characterization with carotid plaque MRI. Initially, the radiologists manually viewed the IPH based on the MPRAGE sequence. IPH volume was then measured by three different semi-automatic methods, with high signal intensity 150%, 175%, and 200%, respectively, above that of adjacent muscle on the MPRAGE sequence. Agreement on measurements between manual segmentation and semi-automatic segmentation was assessed using the intraclass correlation coefficient (ICC). Results: There was near-perfect agreement between manual segmentation and the 150% and 175% criteria for semi-automatic segmentation in quantification of IPH volume. The ICC of each semi-automatic segmentation were as follows: 150% criteria: 0.861, 175% criteria: 0.809, 200% criteria: 0.491. The ICC value of manual vs. 150% criteria and manual vs. 175% criteria were significantly better than the manual vs. 200% criteria (p < 0.001). Conclusions: The ICC of 150% and 175% criteria for semi-automatic segmentation are more reliable for quantification of IPH volume. Semi-automatic classification tools may be beneficial in large-scale multicenter studies by reducing image analysis time and avoiding bias between human reviewers.

Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks

OBJECTIVE

To develop an automated vessel wall segmentation method using convolutional neural networks to facilitate the quantification on magnetic resonance (MR) vessel wall images of patients with intracranial atherosclerotic disease (ICAD).

METHODS

Vessel wall images of 56 subjects were acquired with our recently developed whole-brain three-dimensional (3-D) MR vessel wall imaging (VWI) technique. An intracranial vessel analysis (IVA) framework was presented to extract, straighten, and resample the interested vessel segment into 2-D slices. A U-net-like fully convolutional networks (FCN) method was proposed for automated vessel wall segmentation by hierarchical extraction of low- and high-order convolutional features.

RESULTS

The network was trained and validated on 1160 slices and tested on 545 slices. The proposed segmentation method demonstrated satisfactory agreement with manual segmentations with Dice coefficient of 0.89 for the lumen and 0.77 for the vessel wall. The method was further applied to a clinical study of additional 12 symptomatic and 12 asymptomatic patients with >50% ICAD stenosis at the middle cerebral artery (MCA). Normalized wall index at the focal MCA ICAD lesions was found significantly larger in symptomatic patients compared to asymptomatic patients.

CONCLUSION

We have presented an automated vessel wall segmentation method based on FCN as well as the IVA framework for 3-D intracranial MR VWI.

SIGNIFICANCE

This approach would make large-scale quantitative plaque analysis more realistic and promote the adoption of MR VWI in ICAD management.

Non-invasive imaging techniques and assessment of carotid vasa vasorum neovascularization: Promises and pitfalls

Carotid adventitia vasa vasorum neovascularization (VVn) is associated with the initial stages of arteriosclerosis and with the formation of unstable plaque. However, techniques to accurately quantify that neovascularization in a standard, fast, non-invasive, and efficient way are still lacking. The development of such techniques holds the promise of enabling wide, inexpensive, and safe screening programs that could stratify patients and help in personalized preventive cardiovascular medicine. In this paper, we review the recent scientific literature pertaining to imaging techniques that could set the stage for the development of standard methods for quantitative assessment of atherosclerotic plaque and carotid VVn. We present and discuss the alternative imaging techniques being used in clinical practice and we review the computational developments that are contributing to speed up image analysis and interpretation. We conclude that one of the greatest upcoming challenges will be the use of machine learning techniques to develop automated methods that assist in the interpretation of images to stratify patients according to their risk.