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MRI and CT radiomics for the diagnosis of acute pancreatitis.

PURPOSE: To evaluate the single and combined diagnostic performances of CT and MRI radiomics for diagnosis of acute pancreatitis (AP). MATERIALS AND METHODS: We prospectively enrolled 78 patients (mean age 55.7 ± 17 years, 48.7 % male) diagnosed with AP between 2020 and 2022. Patients underwent contrast-enhanced CT (CECT) within 48-72 h of symptoms and MRI ≤ 24 h after CECT. The entire pancreas was manually segmented tridimensionally by two operators on portal venous phase (PVP) CECT images, T2-weighted imaging (WI) MR sequence and non-enhanced and PVP T1-WI MR sequences. A matched control gr…

Automated grading of rectocele with an MRI radiomics model.

To develop an automated grading model for rectocele (RC) based on radiomics and evaluate its efficacy. This study retrospectively analyzed a total of 9,392 magnetic resonance imaging (MRI) images obtained from 222 patients who underwent dynamic magnetic resonance defecography (DMRD) over the period from August 2021 to June 2023. The focus was specifically on the defecation phase images of the DMRD, as this phase provides critical information for assessing RC. To develop and evaluate the model, the MRI images from all patients were randomly divided into two groups. 70% of the data were allocate…

The comparison of deep learning and radiomics in the prediction of polymyositis.

T2 weighted magnetic resonance imaging has become a commonly used noninvasive examination method for the diagnosis of Polymyositis (PM). The data regarding the comparison of deep learning and radiomics in the diagnosis of PM is still lacking. This study investigates the feasibility of 3D convolutional neural network (CNN) in the prediction of PM, with comparison to radiomics. A total of 120 patients (with 60 PM) were from center A, and 30 (with 15 PM) were from B, and 46 (with 23 PM) were from C. The data from center A was used as training data, and data from B as validation data, and data fro…

Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review.

Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and …

Muscle CT Radiomics is Feasible in the Identification of Gout.

OBJECTIVE: The aim of this study was to investigate the feasibility of muscle CT radiomics in identifying gout. MATERIALS AND METHODS: A total of 30 gout patients and 20 non-gout cases with CT examinations of ankles were analyzed by using the methods of CT radiomics. CT radiomics features of the soleus muscle were extracted using the software of a 3D slicer, and then gout cases and non-gout cases were compared. The radiomics features that were significantly different between the two groups were then processed with machine learning methods. Receiver operating characteristic curve analysis was …

Correlation of Radiomics with Treatment Response in Liver Metastases.

RATIONALE AND OBJECTIVES: To assess differences in radiomics derived from semi-automatic segmentation of liver metastases for stable disease (SD), partial response (PR), and progressive disease (PD) based on RECIST1.1 and to assess if radiomics alone at baseline can predict response. MATERIALS AND METHODS: Our IRB-approved study included 203 women (mean age 54 ± 11 years) with metastatic liver disease from breast cancer. All patients underwent contrast abdomen-pelvis CT in the portal venous phase at two points: baseline (pre-treatment) and follow-up (between 3 and 12 months following treatmen…

Radiomics integration into a picture archiving and communication system.

Radiomics is referred to as quantitative imaging of biomarkers used for clinical outcome prognosis or tumor characterization. In order to bridge radiomics and its clinical application, we aimed to build an integrated solution of radiomics extraction with an open-source Picture Archiving and Communication System (PACS). The integrated SQLite4Radiomics software was tested in three different imaging modalities and its performance was benchmarked in lung cancer open datasets RIDER and MMD with median extraction time of 10.7 (percentiles 25-75: 8.9-18.7) seconds per ROI in three different configura…

Development and clinical application of radiomics in lung cancer.

Since the discovery of X-rays at the end of the 19th century, medical imageology has progressed for 100 years, and medical imaging has becom … evolution in the traditional visually identifiable imaging technology and constitutes a new branch, radiomics. Radiomics is aimed at extracting quantitative imaging features automatically and developing models to predict lesion phenotypes in a non-invasive manner. Here, we summarize the advent and development of radiomics, the basic process and challenges i …

Role of radiomics in predicting early disease recurrence in locally advanced breast cancer patients: integration of radiomic features and RECIST criteria.

BACKGROUND: Breast cancer (BC) is a major global health issue with significant heterogeneity among its subtypes. Neoadjuvant treatment (NAT) has been extended to include early BC patients, particularly those with HER2 + and triple-negative subtypes, to achieve pathological complete response and improve long-term outcomes. However, disease recurrence remains a challenge, highlighting the need for predictive biomarkers. This study evaluates the role of radiomics from pre-treatment breast MRI, integrated with clinical and radiological variables, in predicting early disease recurrence (EDR) after …

Diffusion MRI Radiomics Predict Renal Function Decline in ADPKD.

INTRODUCTION: Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disorder and a leading cause of end-stage kidney disease (ESKD). Current risk stratification relies on height-adjusted total kidney volume (ht-TKV), which captures cyst burden but not alterations in the noncystic parenchyma. Radiomics enables extraction of quantitative features reflecting tissue microstructure across both cystic and noncystic kidney compartments. We investigated whether radiomic features from apparent diffusion coefficient (ADC) maps improve prediction of renal function decl…

Radiomics-based Modelling Unveils Cerebellar Involvement in Parkinson's Disease.

Emerging pathological and neurophysiological evidence has highlighted the cerebellum's involvement in Parkinson's disease (PD). This study aimed to explore the potential of cerebellum-derived magnetic resonance imaging (MRI) radiomics in distinguishing PD patients from healthy controls (HC). A retrospective analysis was conducted using three-dimensional-T1 MRI data (n= 374) from the Parkinson's Progression Markers Initiative (PPMI) dataset (n= 204) and an independent in-house cohort (n= 170). Radiomic features (n= 883) were extracted from the cerebellar gray and white matter of each individual…

Radiomics-driven perfusion prediction in clinical photoacoustic foot imaging.

Accurate assessment of tissue perfusion is essential for managing chronic foot ulcers in patients with diabetes and peripheral arterial disease. While photoacoustic (PA) imaging enables high-resolution visualization of vascular structures, current perfusion evaluation methods are limited. We propose a fully automated radiomics-based framework for predicting perfusion conditions using single-wavelength clinical PA foot imaging. Radiomics features were extracted from both raw radiofrequency (RF) signals and reconstructed maximum amplitude projection (MAP) images. After reproducibility testing an…

Ultrasound radiomics-based logistic regression model for fibrotic NASH.

BACKGROUND: Those who have severe fibrosis (F2 ≥ 2 stage) are at the greatest risk for the advancement of the illness among non-alcoholic fatty liver patients. To forecast the non-alcoholic steatohepatitis (NASH) probability accompanied by significant fibrosis, we propose to develop and validate a nomogram liver imaging reporting and data system, providing robust evidence for preventing and treating clinical liver diseases. METHODS: The study used SD rats to create a model of hepatic steatosis and fibrosis by feeding them a high-fat diet and injecting Ccl4 subcutaneously. Radiomics characteri…

Радиомика в различных формах: всеобъемлющий обзор нейродегенеративных заболеваний.

Радиомика позволяет извлекать из медицинских снимков количественные характеристики, которые позволяют выявить ткани, которые, как правило, невидимы для наблюдателей. Несмотря на трудности с визуальной интерпретацией радиочастот и вычислительных ресурсов, необходимых для их создания, они имеют большое значение для автоматизированной обработки данных на выходе из системы. Например, в рамках статистического или машинного обучения радиотехнические элементы повышают чувствительность и специфичность, делая их незаменимыми для решения таких задач, как диагностика, прогнозирование, мониторинг, коррект

Совпадение в аннотации оригинала
Radiomics allows extraction from medical images of quantitative features that are able to reveal tissue patterns that are generally invisible to human observers. Despite the challenges in visually interpreting radiomic features and the computational resources required to generate them, they hold significant value in downstream automated processing. For insta…

Radiomics for Alzheimer's Disease: Fundamental Principles and Clinical Applications.

Alzheimer's disease is a neurodegenerative disease with a huge impact on people's quality of life, life expectancy, and morbidity. The ongoi … e field of medical imaging. Deep learning approaches have been tested for use in this domain, while radiomics, an emerging quantitative method, are already being evaluated to be used in various medical imaging modalities. This chapter aims to provide an insight into the fundamental principles behind radiomics, discuss the most common techniques alongside th …

MRI radiomics-based evaluation of tuberculous and brucella spondylitis.

OBJECTIVES: We analyzed magnetic resonance imaging (MRI) and radiomics labels from tuberculous spondylitis (TBS) and brucella spondylitis (BS) to build machine learning models that differentiate TBS from BS and culture-positive TBS (TBS(+)) from culture-negative TBS (TBS(-). METHODS: This retrospective study included 56 patients with BS, 63 patients with TBS(+) and 71 patients with TBS(-). Radiomics labels were extracted from T2-weighted fat-suppression images. MRI labels were analyzed via logistic regression (LR); radiomics labels were analyzed by t-tests, SelectKBest, and least absolute shr…

Radiomics for intracerebral hemorrhage: are all small hematomas benign?

OBJECTIVES: We hypothesized that not all small hematomas are benign and that radiomics could predict hematoma expansion (HE) and short-term outcomes in small hematomas. METHODS: We analyzed 313 patients with small (<10 ml) intracerebral hemorrhage (ICH) who underwent baseline non-contrast CT within 6 h of symptom onset between September 2013 and February 2019. Poor outcome was defined as a Glasgow Outcome Scale score ≤3. A radiomic model and a clinical model were built using least absolute shrinkageand selection operator algorithm or multivariate analysis. A combined model that incorporated t…

Radiomics in multiple sclerosis and neuromyelitis optica spectrum disorder.

OBJECTIVE: To develop and validate an individual radiomics nomogram for differential diagnosis between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD). METHODS: We retrospectively collected 67 MS and 68 NMOSD with spinal cord lesions as a primary cohort and prospectively recruited 28 MS and 26 NMOSD patients as a validation cohort. Radiomic features were extracted from the spinal cord lesions. A prediction model for differentiating MS and NMOSD was built by combining the radiomic features with several clinical and routine MRI measurements. The performance of the mod…

Preliminary utilization of radiomics in differentiating uterine sarcoma from atypical leiomyoma: Comparison on diagnostic efficacy of MRI features and radiomic features.

OBJECTIVES: To explore whether MRI and radiomic features can differentiate uterine sarcoma from atypical leiomyoma. And to compare diagnostic performance of radiomic model with radiologists. METHODS: 78 patients (29 sarcomas, 49 leiomyomas) imaged with pelvic MRI prior to surgery were included in this retrospective study. Certain clinical and MRI features were evaluated for one lesion per patient. Radiological diagnosis was made based on MRI features. A radiomic model using automated texture analysis based on ADC maps was built to predict pathological results. The association between MRI feat