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   Articles with "multimodal radiomics" as a keyword



Multimodal radiomics of cerebellar subregions for machine learning-driven Alzheimer’s disease diagnosis

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recommendations! Published in 2025 at "Frontiers in Aging Neuroscience"

DOI: 10.3389/fnagi.2025.1679788

Abstract: Objective This study aimed to develop a machine learning model based on multimodal radiomics features from cerebellar subregions, utilizing the complementarity of cerebellar structural and metabolic imaging data for accurate diagnosis of Alzheimer’s disease (AD).… read more here.

Keywords: fdg pet; multimodal radiomics; 18f fdg; model ... See more keywords

A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer

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recommendations! Published in 2020 at "Frontiers in Oncology"

DOI: 10.3389/fonc.2020.00457

Abstract: Objective: To explore a new predictive model of lymphatic vascular infiltration (LVI) in rectal cancer based on magnetic resonance (MR) and computed tomography (CT). Methods: A retrospective study was conducted on 94 patients with histologically… read more here.

Keywords: multimodal radiomics; preoperative prediction; rectal cancer; radiomics model ... See more keywords

A Novel Multimodal Radiomics Model for Predicting Prognosis of Resected Hepatocellular Carcinoma

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recommendations! Published in 2022 at "Frontiers in Oncology"

DOI: 10.3389/fonc.2022.745258

Abstract: Objective To explore a new model to predict the prognosis of liver cancer based on MRI and CT imaging data. Methods A retrospective study of 103 patients with histologically proven hepatocellular carcinoma (HCC) was conducted.… read more here.

Keywords: hepatocellular carcinoma; prognosis; multimodal radiomics; radiomics model ... See more keywords

Longitudinal and Multimodal Radiomics Models for Head and Neck Cancer Outcome Prediction

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recommendations! Published in 2023 at "Cancers"

DOI: 10.3390/cancers15030673

Abstract: Simple Summary Machine learning based radiomics models for prediction of loco-regional recurrence today mostly rely on features extracted from pre-treatment imaging data. In this work, we investigate the predictive ability of such models when imaging… read more here.

Keywords: head neck; fdg pet; radiomics models; multimodal radiomics ... See more keywords