image extraction

Radiomics, the science of extracting quantifiable data from routine medical images, is a powerful tool that has many potential applications in oncology. The Response Evaluation Criteria in Solid Tumors Working Group (RWG) held a workshop in May 2022, which brought together various stakeholders to discuss the potential role of radiomics in oncology drug development and clinical trials, particularly with respect to response assessment. This article summarizes the results of that workshop, reviewing radiomics for the practicing oncologist and highlighting the work that needs to be done to move forward the incorporation of radiomics into clinical trials...

PURPOSE: Small left atrial (LA) volume was recently reported to be one of the best predictors of acute pulmonary embolism (PE)-related adverse events (AE). There is currently no data available regarding the impact that body surface area (BSA)-indexing of atrial measurements has on the association with PE-related adverse events. Our aim is to assess the impact of indexing atrial measurements to BSA on the association between computed tomography (CT) atrial measurements and AE. MATERIALS AND METHODS: Retrospective study (IRB: 2015P000425)...

Aneurysmal subarachnoid hemorrhage is a serious medical emergency of brain that has high mortality and poor prognosis. Treatment effect estimation is of high clinical significance to support the treatment decision-making for aneurysmal subarachnoid hemorrhage. However, most existing studies on treatment decision support of this disease are unable to simultaneously compare the potential outcomes of different treatments for a patient. Furthermore, these studies fail to harmoniously integrate the imaging data with non-imaging clinical data, both of which are significant in clinical scenarios...

PURPOSE: Prior data have demonstrated relationships between patient characteristics, the use of surgery to treat lung cancer, and the timeliness of treatment. Our study examines whether these relationships were observable in 2019 in patients with Medicare Advantage health plans being treated for lung cancer. METHODS: Claims data pertaining to patients with Medicare Advantage health plans who had received radiation therapy (RT) or surgery to treat lung cancer within 90 days of diagnostic imaging were extracted...

OBJECTIVES: To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT). MATERIAL AND METHODS: Patients who underwent surgical treatment for renal tumors at two tertiary centers from 2012 to 2022 were included retrospectively. Preoperative arterial (corticomedullary) and venous (nephrogenic) phase CT scans from these centers, as well as from external imaging facilities, were manually segmented, and standardized radiomic features were extracted...

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari...

PURPOSE: To demonstrate the impact of implementing hypofractionated prescription regimens and advanced treatment techniques on institutional operational hours and radiotherapy personnel resources in a multi-institutional setting. The study may be used to describe the impact of advancing the standard of care with modern radiotherapy techniques on patient and staff resources. METHODS: This study uses radiation therapy data extracted from the radiotherapy information system from two tertiary care, university-affiliated cancer centers from 2012 to 2021...

The sphingosine-1-phosphate receptor 1 (S1PR1) radiotracer [11 C] CS1P1 has shown promise in proof-of-concept PET imaging of neuroinflammation in multiple sclerosis (MS). Our HPLC radiometabolite analysis of human plasma samples collected during PET scans with [11 C] CS1P1 detected a radiometabolite peak that is more lipophilic than [11 C] CS1P1 . Radiolabeled metabolites that cross the blood-brain barrier complicate quantitative modeling of neuroimaging tracers; thus, characterizing such radiometabolites is important...

Purpose To perform a meta-analysis of the diagnostic performance of MRI for the detection of pulmonary nodules, with use of CT as the reference standard. Materials and Methods PubMed, Embase, Scopus, and other databases were systematically searched for studies published from January 2000 to March 2023 evaluating the performance of MRI for diagnosis of lung nodules measuring 4 mm or larger, with CT as reference. Studies including micronodules, nodules without size stratification, or those from which data for contingency tables could not be extracted were excluded...

Calcified cartilage digested by chondroclasts provides an excellent scaffold to initiate bone formation. We analyzed bioactive proteins and microarchitecture of calcified cartilage either separately or in combination and evaluated biomimetic osteogenic culture conditions of surface-coated micropatterning. To do so, we prepared a crude extract from porcine femoral growth plates, which enhanced in vitro mineralization when coated on flat-bottom culture dishes, and identified four candidate proteins by fractionation and mass spectrometry...

OBJECTIVES: To apply machine learning to extract radiomics features from thyroid two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) images to classify and predict benign and malignant thyroid nodules, classified according to the Chinese version of the thyroid imaging reporting and data system (C-TIRADS) as category 4. MATERIALS AND METHODS: This retrospective study included 313 pathologically diagnosed thyroid nodules (203 malignant and 110 benign)...

In recent years, eye diseases in diabetic patients are one of the most common has been diabetic retinopathy (DR). which leads to complete blindness in advanced stages. Diabetes affects the blood vessels in the retina and causes vision loss. One of the ways to decrease the risk of this issue is to detect diabetic retinopathy in its early stages. This study describes a computer-aided screening system (DREAM) that uses a neural network classification model in machine learning to assess fundus images with different illumination and fields of vision and provide a severity grade for diabetic retinopathy...

Fully supervised learning methods necessitate a substantial volume of labelled training instances, a process that is typically both labour-intensive and costly. In the realm of medical image analysis, this issue is further amplified, as annotated medical images are considerably more scarce than their unlabelled counterparts. Consequently, leveraging unlabelled images to extract meaningful underlying knowledge presents a formidable challenge in medical image analysis. This paper introduces a simple triple-view unsupervised representation learning model (SimTrip) combined with a triple-view architecture and loss function, aiming to learn meaningful inherent knowledge efficiently from unlabelled data with small batch size...

Many promising biomedical applications have been proposed for terahertz (THz) spectroscopy and diagnostic imaging techniques. Polarimetric imaging systems are generally useful for enhancing imaging contrasts, yet the interplay between THz polarization changes and the random discrete structures in biological samples is not well understood. In this work, we performed Monte Carlo simulations of the propagation of polarized THz waves in skin and adipose tissues based on the Mie scattering from intrinsic structures, such as hair follicles or sweat glands...

Automatic and precise polyp segmentation in colonoscopy images is highly valuable for diagnosis at an early stage and surgery of colorectal cancer. Nevertheless, it still posed a major challenge due to variations in the size and intricate morphological characteristics of polyps coupled with the indistinct demarcation between polyps and mucosas. To alleviate these challenges, we proposed an improved dual-aggregation polyp segmentation network, dubbed Dua-PSNet, for automatic and accurate full-size polyp prediction by combining both the transformer branch and a fully convolutional network (FCN) branch in a parallel style...

Hypertension is typically manifested as a latent symptom that requires detection through specialized equipment. This poses an inconvenience for individuals who need to undergo long-term blood pressure monitoring in their daily lives. Therefore, there is a need for a portable, non-contact method for estimating blood pressure. However, current non-contact blood pressure estimation methods often rely on relatively narrow datasets, lacking a broad range of blood pressure distributions. Additionally, their applicability is confined to controlled experimental environments...

In recent years, imaging photoplethysmograph (iPPG) pulse signals have been widely used in the research of non-contact blood pressure (BP) estimation, in which BP estimation based on pulse features is the main research direction. Pulse features are directly related to the shape of pulse signals while iPPG pulse signals are easily disturbed during the extraction process. To mitigate the impact of pulse feature distortion on BP estimation, it is necessary to eliminate interference while retaining valuable shape details in the iPPG pulse signal...

Comprehensive visualization and accurate extraction of tumor vasculature are essential to study the nature of glioma. Nowadays, tissue clearing technology enables 3D visualization of human glioma vasculature at micron resolution, but current vessel extraction schemes cannot well cope with the extraction of complex tumor vessels with high disruption and irregularity under realistic conditions. Here, we developed a framework, FineVess, based on deep learning to automatically extract glioma vessels in confocal microscope images of cleared human tumor tissues...

Breast cancer has a poor prognosis due to the toxic side effects associated with high doses of chemotherapy. Liposomal drug encapsulation has resulted in clinical success in enhancing chemotherapy tolerability. However, the formulation faces severe limitations with a lack of colloidal stability, reduced drug efficiency, and difficulties in storage conditions. Nanoarchaeosomes (NA) are a new generation of highly stable nanovesicles composed of the natural ether lipids extracted from archaea. In our study, we synthesized and characterized the NA, evaluated their colloidal stability, drug release potential, and anticancer efficacy...

OBJECTIVE: FDG PET imaging plays a crucial role in the evaluation of demented patients by assessing regional cerebral glucose metabolism. In recent years, both radiomics and deep learning techniques have emerged as powerful tools for extracting valuable information from medical images. This article aims to provide a comparative analysis of radiomics features, 3D-deep learning convolutional neural network (CNN) and the fusion of them, in the evaluation of 18F-FDG PET whole brain images in patients with dementia and normal controls...