AIMC Topic: Radiomics

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Establishment of an interpretable MRI radiomics-based machine learning model capable of predicting axillary lymph node metastasis in invasive breast cancer.

Scientific reports
This study sought to develop a radiomics model capable of predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer (IBC) based on dual-sequence magnetic resonance imaging(MRI) of diffusion-weighted imaging (DWI) and dy...

Comparison of conventional and radiomics-based analysis of myocardial infarction using multimodal non-linear optical microscopy.

Scientific reports
Myocardial infarction, a leading cause of mortality worldwide, leaves survivors at significant risk of recurrence caused by scar-related re-entrant ventricular tachyarrhythmias. Effective treatment with ablation therapy requires a precise guidance sy...

The application of super-resolution ultrasound radiomics models in predicting the failure of conservative treatment for ectopic pregnancy.

Reproductive biology and endocrinology : RB&E
BACKGROUND: Conservative treatment remains a viable option for selected patients with ectopic pregnancy (EP), but failure may lead to rupture and serious complications. Currently, serum β-hCG is the main predictor for treatment outcomes, yet its accu...

Integrative radiomics of intra- and peri-tumoral features for enhanced risk prediction in thymic tumors: a multimodal analysis of tumor microenvironment contributions.

BMC medical imaging
OBJECTIVES: This study aims to explore the role of intra- and peri-tumoral radiomics features in tumor risk prediction, with a particular focus on the impact of peri-tumoral characteristics on the tumor microenvironment.

A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study.

World journal of emergency surgery : WJES
BACKGROUND: Accurately identifying difficult laparoscopic cholecystectomy (DLC) preoperatively remains a clinical challenge. Previous studies utilizing clinical variables or morphological imaging markers have demonstrated suboptimal predictive perfor...

Integrated clinicopathological-radiomic-blood model for glioma survival prediction via machine learning: a multicenter cohort study.

Neurosurgical review
BACKGROUND: Glioma is characterized by a poor prognosis and limited possibilities for treatment. Previous studies have developed prediction models for glioma using genetic, clinical, pathological, imaging and other aspects; however, few studies have ...

Integrative multimodal ultrasound and radiomics for early prediction of neoadjuvant therapy response in breast cancer: a clinical study.

BMC cancer
PURPOSE: This study aimed to develop an early predictive model for neoadjuvant therapy (NAT) response in breast cancer by integrating multimodal ultrasound (conventional B-mode, shear-wave elastography, and contrast-enhanced ultrasound) and radiomics...

Integrating radiomic texture analysis and deep learning for automated myocardial infarction detection in cine-MRI.

Scientific reports
Robust differentiation between infarcted and normal myocardial tissue is essential for improving diagnostic accuracy and personalizing treatment in myocardial infarction (MI). This study proposes a hybrid framework combining radiomic texture analysis...

Multilayer perceptron deep learning radiomics model based on Gd-BOPTA MRI to identify vessels encapsulating tumor clusters in hepatocellular carcinoma: a multi-center study.

Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVES: The purpose of this study is to mainly develop a predictive model based on clinicoradiological and radiomics features from preoperative gadobenate-enhanced (Gd-BOPTA) magnetic resonance imaging (MRI) using multilayer perceptron (MLP) deep...