AIMC Topic: Radiomics

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Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors.

Frontiers in endocrinology Jun 17, 2024
OBJECTIVES: To develop and validate radiomics models utilizing endoscopic ultrasonography (EUS) images to distinguish insulinomas from non-functional pancreatic neuroendocrine tumors (NF-PNETs).
Nomograms Adult Diagnosis, Differential Humans Pancreatic Neoplasms Machine Learning Neuroendocrine Tumors Radiomics Endosonography Female Male Insulinoma Aged Middle Aged
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Diagnostic accuracy of CT-based radiomics and deep learning for predicting lymph node metastasis in esophageal cancer.

Clinical imaging Jun 16, 2024
BACKGROUND: Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prognosis, yet traditional diagnostics fall short. Integrating radiomics and deep learning (DL) with CT ...
Lymphatic Metastasis Humans Predictive Value of Tests Tomography, X-Ray Computed Esophageal Neoplasms Sensitivity and Specificity Radiomics Deep Learning Lymph Nodes
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CT-based radiomics of machine-learning to screen high-risk individuals with kidney stones.

Urolithiasis Jun 15, 2024
Screening high-risk populations is crucial for the prevention and treatment of kidney stones. Here, we employed radiomics to screen high-risk patients for kidney stones. A total of 513 independent kidneys from our hospital between 2020 and 2022 were ...
Machine Learning Retrospective Studies Tomography, X-Ray Computed Female Risk Factors Aged Adult Radiomics Humans Male Middle Aged Risk Assessment Kidney Calculi
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A machine learning radiomics based on enhanced computed tomography to predict neoadjuvant immunotherapy for resectable esophageal squamous cell carcinoma.

Frontiers in immunology Jun 14, 2024
BACKGROUND: Patients with resectable esophageal squamous cell carcinoma (ESCC) receiving neoadjuvant immunotherapy (NIT) display variable treatment responses. The purpose of this study is to establish and validate a radiomics based on enhanced comput...
Adult Esophageal Neoplasms Humans Radiomics Esophageal Squamous Cell Carcinoma Machine Learning Male Retrospective Studies Immunotherapy Middle Aged Tomography, X-Ray Computed Nomograms Female Treatment Outcome Aged Neoadjuvant Therapy
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Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks.

Journal of imaging informatics in medicine Jun 11, 2024
Non-small cell lung carcinoma (NSCLC) is the most common type of pulmonary cancer, one of the deadliest malignant tumors worldwide. Given the increased emphasis on the precise management of lung cancer, identifying various subtypes of NSCLC has becom...
Imaging, Three-Dimensional Lung Neoplasms Carcinoma, Non-Small-Cell Lung Neural Networks, Computer Tomography, X-Ray Computed Radiomics Deep Learning Humans
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Predicting disease recurrence in breast cancer patients using machine learning models with clinical and radiomic characteristics: a retrospective study.

Journal of the Egyptian National Cancer Institute Jun 10, 2024
BACKGROUND: The goal is to use three different machine learning models to predict the recurrence of breast cancer across a very heterogeneous sample of patients with varying disease kinds and stages.
Prognosis Retrospective Studies Female Aged Humans Machine Learning Algorithms Neoplasm Staging Neoplasm Recurrence, Local ROC Curve Adult Triple Negative Breast Neoplasms Breast Neoplasms Support Vector Machine Radiomics Magnetic Resonance Imaging Middle Aged
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Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method.

Breast cancer research and treatment Jun 9, 2024
PURPOSE: This study aims to assess the diagnostic value of ultrasound habitat sub-region radiomics feature parameters using a fully connected neural networks (FCNN) combination method L2,1-norm in relation to breast cancer Ki-67 status.
Deep Learning Ultrasonography ROC Curve Ultrasonography, Mammary Breast Neoplasms Radiomics Biomarkers, Tumor Middle Aged Ki-67 Antigen Humans Neural Networks, Computer Female Aged Adult
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Integrating intratumoral and peritumoral radiomics with deep transfer learning for DCE-MRI breast lesion differentiation: A multicenter study comparing performance with radiologists.

European journal of radiology Jun 9, 2024
PURPOSE: To conduct the fusion of radiomics and deep transfer learning features from the intratumoral and peritumoral areas in breast DCE-MRI images to differentiate between benign and malignant breast tumors, and to compare the diagnostic accuracy o...
Diagnosis, Differential Radiologists Image Interpretation, Computer-Assisted Aged Adult Middle Aged Humans Breast Neoplasms Deep Learning Retrospective Studies Radiomics Contrast Media Female Magnetic Resonance Imaging Reproducibility of Results
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Identification of Prolactinoma in Pituitary Neuroendocrine Tumors Using Radiomics Analysis Based on Multiparameter MRI.

Journal of imaging informatics in medicine Jun 6, 2024
This study aims to investigate the feasibility of preoperatively predicting histological subtypes of pituitary neuroendocrine tumors (PitNETs) using machine learning and radiomics based on multiparameter MRI. Patients with PitNETs from January 2016 t...
Machine Learning Retrospective Studies Female Adult Humans Nomograms Magnetic Resonance Imaging Pituitary Neoplasms Prolactinoma Neuroendocrine Tumors Radiomics Male Middle Aged ROC Curve
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The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers.

BMC medical imaging Jun 6, 2024
BACKGROUND: This study investigated whether the Combat compensation method can remove the variability of radiomic features extracted from different scanners, while also examining its impact on the subsequent predictive performance of machine learning...
Principal Component Analysis Phantoms, Imaging Tomography Scanners, X-Ray Computed Humans Machine Learning Neural Networks, Computer Algorithms Tomography, X-Ray Computed Radiomics
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