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Identification of Biliary Atresia in Infantile Cholestasis: Integrating Radiomics With MRCP for Unobservable Extrahepatic Biliary Systems.

PURPOSE: Magnetic resonance cholangiopancreatography (MRCP) may assist in the workup of infantile cholestasis as nonvisualization of the biliary tree is seen with biliary atresia (BA). However, this finding can also be seen with other causes of infantile cholestasis. The purpose of this study is to differentiate BA from other causes of infantile cholestasis using a classification tool integrating MRCP-based radiomics and clinical signatures in patients with nonvisualization of the extrahepatic biliary tree on MRCP. METHODS: Data from infants with cholestasis due to BA, cytomegalovirus infecti…

CT delta-radiomics predicts the risks of blood transfusion and massive bleeding during spinal tumor surgery.

BACKGROUND: Intraoperative bleeding is a serious complication of spinal tumor surgery. Preoperative identification of patients at high risk of intraoperative blood transfusion (IBT) and intraoperative massive bleeding (IMB) before spinal tumor resection surgery is difficult but critical for surgical planning and blood management. This study aims to develop and validate delta radiomics prediction models for IBT and IMB in spinal tumor surgery. METHODS: Patients diagnosed with spinal tumors who underwent spinal tumor resection surgery were retrospectively recruited. CT, CTE, delta, and clinical…

A severity classification model of cervical spondylotic radiculopathy symptoms based on MRI radiomics: A retrospective study.

OBJECTIVE: To develop a severity classification model for symptoms of cervical spondylotic radiculopathy (CSR) based on magnetic resonance imaging (MRI) radiomics and to evaluate the predictive value of MRI radiomics features in the classification of symptoms severity, providing an objective basis for personalized therapeutic interventions. METHODS: This retrospective study included 99 patients diagnosed with CSR, admitted between August 2022 and April 2023. Symptom severity was assessed using the neck disability index (NDI) scale, which facilitated the categorization of participants into mil…

Radiomics to discriminate between axial spondyloarthritis and axial psoriatic arthritis and to predict TNFi therapy persistence.

OBJECTIVES: Axial spondyloarthritis (axSpA) and axial psoriatic arthritis (axPsA) represent entities with ongoing debate regarding their cla … ort-tau inversion-recovery sequences, and 120 standardised radiomic features were extracted using PyRadiomics. For diagnostic discrimination, an XGBoost algorithm was employed and evaluated through 5-fold cross-validation. For patients with axSpA who initiated TNFi therapy, an exploratory Cox regression analysis was performed to identify radiomic predictors …

Using interpretable rule-learning artificial intelligence to optimally differentiate adrenal pheochromocytomas from adenomas with CT radiomics.

PURPOSE: To identify interpretable CT-based radiomics features that can differentiate adrenal pheochromocytomas from adenomas. METHODS: An institutional database was used to identify patients with pathologically proven adrenal pheochromocytomas 5/1/05-5/1/23. To be included, patients needed to have a contrast-enhanced abdominal CT with an adrenal mass within 12 months of pathology (n = 95). For comparison, 57 adenomas were identified from a set of consecutive CT examinations. The final dataset included 152 adrenal masses (95 pheochromocytomas; 57 adenomas) with 121 used in the development set…

Automated segmentation by SCA-UNet can be directly used for radiomics diagnosis of thymic epithelial tumors.

BACKGROUND: Automatic segmentation of thymic lesions in preoperative computed tomography (CT) images is crucial for accurate diagnosis but r … learning network called Space Channel Attention UNet (SCA-UNet). The model was further utilized for radiomics-based identification and risk assessment of thymic epithelial tumors (TETs). The code of SCA-UNet is available at: https://github.com/GovernTony/SCA-UNet. RESULT: The SCA-UNet model was developed using 107 selected radiomic features. Based on our C …

Differentiating between adrenocortical carcinoma and pheochromocytoma by a CT-based radiomics model: a multicenter retrospective study.

BACKGROUND: Adrenocortical carcinoma (ACC), a rare and highly malignant adrenal gland tumor, exhibits computed tomography (CT) characteristics that resemble those of the less malignant pheochromocytoma (PHEO). While biochemical evaluation is widely accepted for differentiating between ACC and PHEO, non-functioning tumors remain a diagnostic challenge. The similarity in CT imaging and atypical hormone levels can lead to suboptimal accuracy in diagnosis, leading to inappropriate clinical interventions. This study aims to differentiate between large (≥ 4 cm) ACC and PHEO with radiomics features b…

Computed tomography-based absolute delta radiomics nomogram for predicting perineural invasion in hypopharyngeal squamous cell carcinoma.

OBJECTIVE: To assess the efficacy of computed tomography (CT)-based radiomics nomogram in predicting perineural invasion (PNI) in patients with hypopharyngeal squamous cell carcinoma (HPSCC). MATERIALS AND METHODS: Overall, 146 patients were retrospectively recruited and divided into training and test cohorts at a 7:3 ratio. Radiomics features were extracted and delta and absolute delta radiomics features were calculated. Feature selection was performed using maximum relevance minimum redundancy and least absolute shrinkage and selection operator methods. Preliminary models were built using l…

Prediction of Seronegative Hashimoto's thyroiditis using machine learning models based on ultrasound radiomics: a multicenter study.

BACKGROUND: Seronegative Hashimoto's thyroiditis is often underdiagnosed due to the lack of antibody markers. Combining ultrasound radiomics with machine learning offers potential for early detection in patients with normal thyroid function. METHODS: Data from 164 patients with single thyroid lesions and normal thyroid function, treated surgically between 2016 and 2024, were retrospectively collected from four hospitals. Radiomics features were extracted from ultrasound images of non-tumorous hypoechoic areas. Pathological lymphocytic infiltration and hypoechoic ratios were evaluated by senio…

Early treatment monitoring of multidrug-resistant tuberculosis based on CT radiomics of cavity and cavity periphery.

BACKGROUND: Early identification of treatment failure can effectively improve the success rate of antituberculosis treatment. This study aimed to construct a predictive model using radiomics based on cavity and cavity periphery to monitor the early treatment efficacy in multidrug-resistant tuberculosis (MDR-TB). METHODS: We retrospectively collected data on 350 MDR-TB patients who underwent pretreatment chest computed tomography (CT) and received longer regimens from two hospitals. They were subdivided into training (252 patients from hospital 1) and testing (98 patients from hospital 2) coho…

Quantitative CT Perfusion and Radiomics Reveal Complementary Markers of Treatment Response in HCC Patients Undergoing TACE.

Background: Hepatocellular carcinoma (HCC), the most prevalent primary malignancy of the liver, is commonly treated with transarterial chemo … supporting the potential of these features as predictive biomarkers. Conclusions: CT perfusion and radiomics offer complementary insights into the treatment response of patients with HCC. While perfusion parameters reflect macroscopic vascular changes and are correlated with tumor burden, radiomic features can indicate microstructural changes after TACE. T …

Радиомика IVIM-DWI для Phenotyping и клиническое прогнозирование состояния в релапсирующих и излучающих множественных склерозах.

Фон/цели: Множественный склероз (MS) — аутоиммунное расстройство, затрагивающее центральную нервную систему, характеризующееся деградацией миелина, что приводит к различным неврологическим симптомам. Цель этого исследования заключается в использовании радиосигналов для оценки прогнозируемого значения параметров диффузии IVIМ, а именно: реального коэффициента диффузии (D), псевдодиффузного коэффициента (D*) и перфузионной фракции (f) в зависимости от степени инвалидности, оцениваемой с использованием расширенной шкалы взносов для инвалидов (EDSS), и мобильности пациентов с повторными выбросами

Совпадение в аннотации оригинала
Background/Objectives: Multiple sclerosis (MS) is an autoimmune disorder affecting the central nervous system, characterised by the degradation of myelin, which results in various neurological symptoms. This study aims to utilise radiomics features to evaluate the predictive value of IVIM diffusion parameters, namely, the true diffusion coefficient (D), pseu…

Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases.

RATIONALE AND OBJECTIVES: To develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases. MATERIALS AND METHODS: In total, 657 liver metastatic lesions, including breast cancer (BC), lung cancer (LC), colorectal cancer (CRC), gastric cancer (GC), and pancreatic cancer (PC), from 428 patients were collected at three clinical centers from January 2018 to October 2023 series. The lesions were randomly assigned to the training and validation sets in a 7:3 ratio. An additional 112 lesions from 61 …

[Differentiation of temporal lobe epilepsy and temporal plus epilepsy using radiomics nomogram based on MPRAGE images].

A total of 82 patients with temporal lobe epilepsy (TLE) and temporal plus epilepsy (TPE)admitted in Xuanwu Hospital from January 1, 2019, to January 1, 2021 were restrospectively analyzed, including 41 males and 41 females, aged 2 to 52 (24±10) years. The patients were randomly divided into the training set (58 cases) and test set (24 cases) by Python. FreeSurfer software was used to segment the cortex of the affected hemisphere, defining 33 regions of interest (ROIs), and radiomics features were extracted by Python. After selecting features using the filter-based feature selection method, a …

Ultrasound-based radiomics to predict the volume reduction rate of benign thyroid nodules after microwave ablation.

OBJECTIVE: To evaluate the predictive power of ultrasound-based radiomics models for benign thyroid nodules with a volume reduction rate (VRR) of < or ≥75% at 12 months after microwave ablation. METHODS: A retrospective study was conducted on 194 individuals with benign thyroid nodules who received ultrasound-guided microwave ablation between November 2019 and June 2023. The clinical and ultrasound features, including age, gender, volume, echogenicity, duration of ablation, and so on were analysed by t-test or chi-square test. Radiomics features were extracted from longitudinal and transverse…

Is PET Radiomics Useful to Predict Pathologic Tumor Response and Prognosis in Locally Advanced Cervical Cancer?

This study investigated whether radiomic features extracted from pretreatment [18F]FDG PET could improve the prediction of both histopatholo … er study with standardized protocols for all phases of the process of radiomic analysis to validate radiomics before its use in clinical routine.

Predicting treatment responses using magnetic resonance imaging-based radiomics in hepatocellular carcinoma patients undergoing transarterial radioembolization.

OBJECTIVE: This study evaluates the efficacy of magnetic resonance imaging-based radiomics in predicting treatment responses in hepatocellular carcinoma patients undergoing transarterial radioembolization. METHODS: Pre-treatment magnetic resonance imaging scans from 65 hepatocellular carcinoma patients were analyzed. Radiomic features were extracted from axial T1-weighted and T2-weighted sequences using a standardized workflow involving image preprocessing, segmentation, and feature extraction. Multivariate logistic regression models combining radiomic and clinical features were developed to …

Multimodal MRI-based radiomics models for the preoperative prediction of lymphovascular space invasion of endometrial carcinoma.

PURPOSE: To evaluate the predictive capabilities of MRI-based radiomics for detecting lymphovascular space invasion (LVSI) in patients diagnosed with endometrial carcinoma (EC). MATERIALS AND METHODS: A retrospective analysis was conducted on 160 female patients diagnosed with EC. The radiomics model including T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) images was established. Additionally, a conventional MRI model, which incorporated MRI-reported FIGO stage, deep myometrial infiltration (DMI), adnexal involvement, and vaginal/parametrial involvement, was established. Finally, a c…

MRI-based radiomics for predicting histology in malignant salivary gland tumors: methodology and "proof of principle".

Defining the exact histological features of salivary gland malignancies before treatment remains an unsolved problem that compromises the ability to tailor further therapeutic steps individually. Radiomics, a new methodology to extract quantitative information from medical images, could contribute to characterizing the individual cancer phenotype already before treatment in a fast and non-invasive way. Consequently, the standardization and implementation of radiomic analysis in the clinical routine work to predict histology of salivary gland cancer (SGC) could also provide improvements in clin…