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Radiomics-based prognostic model for progesterone resistance in endometrial cancer: insights into extracellular matrix and type III collagen.

BACKGROUND: Progesterone resistance in fertility-preserving patients with endometrial cancer (EC) remains a significant challenge, and radiomics has not yet been used to predict progestin therapy in these cases. RESULTS: In this study, we constructed a radiomics model to predict progesterone resistant for fertility preservation patients. Distribution of clinical features have significant differences in the high and low risk of progesterone-resistant subgroups, which we call predicting-sensitive (PS) versus predicting-resistant (PR) subgroups. The radiomics model achieved high predictive accur…

A Study on Predicting the Efficacy of Posterior Lumbar Interbody Fusion Surgery Using a Deep Learning Radiomics Model.

RATIONALE AND OBJECTIVES: This study seeks to develop a combined model integrating clinical data, radiomics, and deep learning (DL) for predicting the efficacy of posterior lumbar interbody fusion (PLIF) surgery. MATERIALS AND METHODS: A retrospective review was conducted on 461 patients who underwent PLIF for degenerative lumbar diseases. These patients were partitioned into a training set (n=368) and a test set (n=93) in an 8:2 ratio. Clinical models, radiomics models, and DL models were constructed based on logistic regression and random forest, respectively. A combined model was establish…

Preoperative MRI-based radiomics analysis of intra- and peritumoral regions for predicting CD3 expression in early cervical cancer.

The study investigates the correlation between CD3 T-cell expression levels and cervical cancer (CC) while developing a magnetic resonance (MR) imaging-based radiomics model for preoperative prediction of CD3 T-cell expression levels. Prognostic correlations between CD3D, CD3E, and CD3G gene expressions and various cancers were analyzed using the Cancer Genome Atlas (TCGA) database. Protein-protein interaction (PPI) analysis via the STRING database identified associations between these genes and T lymphocyte activity. Gene Set Enrichment Analysis (GSEA) revealed immune pathway enrichment by ca…

Radiomics-Based Prediction of Treatment Response to TRuC-T Cell Therapy in Patients with Mesothelioma: A Pilot Study.

BACKGROUND/OBJECTIVES: T cell receptor fusion constructs (TRuCs), a next generation engineered T cell therapy, hold great promise. To accelerate the clinical development of these therapies, improving patient selection is a crucial pathway forward. METHODS: We retrospectively analyzed 23 mesothelioma patients (85 target tumors) treated in a phase 1/2 single arm clinical trial (NCT03907852). Five imaging sites were involved, the settings for the evaluations were Blinded Independent Central Reviews (BICRs) with double reads. The reproducibility of 3416 radiomics and delta-radiomics (Δradiomics) …

Differentiation of multiple adrenal adenoma subtypes based on a radiomics and clinico-radiological model: a dual-center study.

BACKGROUND: The prevalence and detection rates of adrenal incidentalomas have been on the rise globally, with more than 90% of these lesions … retrospective study was performed on 169 patients from two tertiary medical centers. Subsequently, radiomics features were extracted after tumor margins were delineated layer-by-layer using a semi-automatic contouring approach. Feature selection was achieved in two cycles, with the first round utilizing a support vector machine (SVM) and the second round u …

Автоматизированная радиомасса для прогнозирования реакции терапии и минимальных остаточных заболеваний от базовой МРТ в множестве миеломы.

Это многоцентровое исследование снимков было направлено на создание и проверку автоматизированных моделей радиомикалов, прогнозирующих терапию (TR) и минимальную остаточную реакцию (MRD) в недавно диагностированном миеломе (MM) из базового МРТ. Ретроспективно, 118 Пациенты ММ из GMMG-HD7 судебное разбирательство (EudraCT: 2017-004768-37) с данными о TR и/или MRD после индуктивной терапии и базовой МРТ. Данные были разделены по центрам в комплект учебных материалов (центр 1-2; n = 79) и комплект испытаний (центр 3-10; n = 39). TR был классифицирован как очень хороший частичный ответ или лучше п

Совпадение в аннотации оригинала
This multicenter imaging study aimed to establish and validate automated radiomics models predicting therapy response (TR) and minimal residual response (MRD) in newly diagnosed multiple myeloma (MM) from baseline MRI. Retrospectively, 118 MM patients from the GMMG-HD7 trial (EudraCT: 2017-004768-37) with data on TR and/or MRD after induction therapy and bas…

Radiomics-Based Machine Learning for the Detection of Myometrial Invasion in Endometrial Cancer: Systematic Review and Meta-Analysis.

BACKGROUND: Preoperative endometrial cancer (EC) diagnosis often depends on radiologists' expertise, which introduces subjectivity. Recent studies have explored radiomics-based machine learning (ML) models for detecting myometrial invasion (MI), but a comprehensive evaluation of their diagnostic performance is lacking. Therefore, our study systematically assessed the diagnostic performance of radiomics-based ML approaches for identifying MI in EC, thereby providing evidence to guide the development or improvement of noninvasive diagnostic tools. OBJECTIVE: This study aims to systematically as…

Contrast-enhanced CT-based habitat radiomics for distinguishing low-risk thymomas from high-risk thymomas: a multicenter study.

OBJECTIVE: The purpose of this research was to evaluate the effectiveness of contrast-enhanced computed tomography (CECT)-based habitat radiomics in differentiating low-risk thymomas from high-risk thymomas prior to surgery. MATERIALS AND METHODS: A retrospective study was conducted involving patients with thymomas who had undergone CECT at three medical centers. The patients were divided into two cohorts: a training cohort comprising 134 patients from Centers A and B, and a validation cohort consisting of 41 patients from Center C. The k-means clustering algorithm was employed to segment the…

Enhancing the Prediction of Axillary Lymph Node Metastasis in Breast Cancer through Habitat-Based Radiomics and Voting Algorithms.

OBJECTIVE: To develop a machine learning model integrating habitat-based radiomics and voting algorithms for predicting axillary lymph node metastasis (ALNM) in breast cancer using B-mode and contrast-enhanced ultrasound images. METHODS: This retrospective study included 246 T1/T2 stage breast cancer patients (246 lesions) from Fujian Cancer Hospital (2016.04-2022.12). Lesions were randomly divided into training (n = 197) and testing (n = 49) datasets. A Gaussian Mixture Model partitioned B-mode ultrasound images into three subregions. Radiomics features, including shape features, first-order…

Cross-regional radiomics: a novel framework for relationship-based feature extraction with validation in Parkinson's disease motor subtyping.

Traditional radiomics approaches focus on single-region feature extraction, limiting their ability to capture complex inter-regional relationships crucial for understanding pathophysiological mechanisms in complex diseases. This study introduces a novel cross-regional radiomics framework that systematically extracts relationship-based features between anatomically and functionally connected brain regions. We analyzed T1-weighted magnetic resonance imaging (MRI) data from 140 early-stage Parkinson's disease patients (70 tremor-dominant, 70 postural instability gait difficulty) from the Parkinso…

Ultrasound Habitat Radiomics Plus Clinical Features for Pre-Operative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer.

OBJECTIVES: This study aimed to develop a multimodal model that integrates habitat radiomics and clinical features for non-invasive prediction of axillary sentinel lymph node (SLN) metastasis in breast cancer (BC). METHODS: We retrospectively analyzed ultrasound images and clinical data from 191 female patients with BC treated at Beijing Luhe Hospital, Capital Medical University from May 2023 to January 2025. Patients were randomly assigned to training and test sets at a 7:3 ratio. Four models were constructed: a traditional radiomics model, a clinical model, a habitat model characterizing th…

A deep learning radiomics model for predicting non-sentinel lymph node metastases in early-stage breast cancer patients.

AIMS: To develop and validate a deep learning radiomics model to predict non-sentinel lymph node (NSLN) metastases in early-stage breast cancer patients with 1-2 positive sentinel lymph node (SLN) metastases. METHODS: This retrospective and prospective study encompassed 1,647 patients. Clinical, pathological information, and axillary ultrasound (AUS) findings, collected. Radiomic features of breast cancer lesions were extracted from the ultrasound images. We developed predictive models based on clinical factors alone (C model), clinical factors coupled with AUS (CA model), and clinical factor…

Coronary Computed Tomographic Angiography-Derived Radiomics Combing CT-Fractional Flow Reserve for Detecting Hemodynamically Significant Coronary Artery Disease.

Objective To develop a diagnostic model combining the CT angiography(CCTA)-derived myocardial radiomics signatures with the CT-derived fractional flow reserve(CT-FFR)based on coronary CCTA and investigate the diagnostic accuracy of the hybrid model for hemodynamically significant coronary artery disease(CAD).Methods The patients presenting stable angina pectoris,diagnosed with CAD,and clinically referred for CCTA examination and invasive coronary angiography were prospectively recruited.Radiomics features of the left ventricular myocardium were extracted from the three main perfusion territori…

Multiparametric MRI-Based Intratumoral and Peritumoral Radiomics for Distinguishing Solitary Intrahepatic Mass-Forming Cholangiocarcinoma From Colorectal Liver Metastases.

OBJECTIVE: To establish a model based on intratumoral and peritumoral radiomics for preoperatively differentiating solitary intrahepatic mass-forming cholangiocarcinoma (IMCC) lesions from colorectal cancer liver metastases (CRLM). METHODS: Preoperative MRI scans from IMCC patients were retrospectively obtained from three academic medical centers. Radiomics features were extracted from the intratumoral and multiple peritumoral regions. After feature selection, the optimal peritumoral range was determined. The radiomics model was developed by integrating both single-sequence and multisequence …

Construction of a predictive model for radiation proctitis after radical radiotherapy for cervical cancer based on MRI radiomics.

PURPOSE: Radiation proctitis (RP) is a frequent and clinically relevant complication of radiotherapy (RT) in cervical cancer (CC). However, effective tools for individualized RP risk prediction remain limited. This study aimed to develop and validate an MRI-based radiomic risk score for early RP prediction in CC patients. METHODS: This retrospective study included 375 CC patients who underwent radical RT. Patients were randomly assigned to training (80%) and validation (20%) cohorts. Radiomics features were extracted from pre-treatment MRI and screened using Mann-Whitney U test, intraclass co…

The predictive value of 18F-FDG PET/CT radiomics for pleural invasion in non-small cell lung cancer.

OBJECTIVE: This study aims to develop and validate a PET/CT radiomics fusion model for preoperative predicting pleural invasion (PI) in non-small cell lung cancer (NSCLC) patients. METHODS: Data from Center A were divided into a training set (n = 260) and an internal validation set (n = 111), while data from Center B (n = 99) served as the external validation set. Radiomic features were extracted using PyRadiomics. Six feature screening methods and 12 machine learning methods were used to build clinical, PET/CT imaging, and radiomics fusion models. The best-performing model was selected based…

Deep Learning with Transfer Learning on Digital Breast Tomosynthesis: A Radiomics-Based Model for Predicting Breast Cancer Risk.

Background: Digital breast tomosynthesis (DBT) is a valuable imaging modality for breast cancer detection; however, its interpretation remains time-consuming and subject to inter-reader variability. This study aimed to develop and evaluate two deep learning (DL) models based on transfer learning for the binary classification of breast lesions (benign vs. malignant) using DBT images to support clinical decision-making and risk stratification. Methods: In this retrospective monocentric study, 184 patients with histologically or clinically confirmed benign (107 cases, 41.8%) or malignant (77 case

Accuracy of MRI-Based Radiomics in Diagnosis of Placenta Accreta Spectrum: A PRISMA Systematic Review and Meta-Analysis.

BACKGROUND Placenta accreta syndrome (PAS) can lead to severe obstetric bleeding, and can be life-threatening. This study aimed to assess the precision of radiomics features derived from magnetic resonance imaging (MRI) for diagnosing PAS. MATERIAL AND METHODS A comprehensive search was conducted in the databases PubMed, Embase, Web of Science, and the Cochrane library from inception to October 2023. We included diagnostic accuracy studies utilizing radiomics-MRI in PAS patients, with histopathology serving as the reference standard. The overall diagnostic odds ratio (DOR), sensitivity, specif…

Preoperative Prediction of Lymph Node Metastases in Nonfunctional Pancreatic Neuroendocrine Tumors Using a Combined CT Radiomics-Clinical Model.

BACKGROUND: PanNETs are a rare group of pancreatic tumors that display heterogeneous histopathological and clinical behavior. Nodal disease … cs predictive of nodal disease included tumor grade and size. Upon internal validation the combined radiomics and clinical feature model demonstrated adequate performance (AUC 0.80) in identifying nodal disease. The model accurately identified nodal disease in 85% of patients with small tumors (< 2 cm). CONCLUSIONS: Non-invasive preoperative assessment of …