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10.21203/rs.3.rs-875843/v1

Comparison of radiomic pre-processing steps in the reproducible prediction of disease free survival across multi-scanners/centers


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10.1007/s00259-020-05119-9

Radiomics analysis of [18F]FDG PET/CT for microvascular invasion and prognosis prediction in very-early- and early-stage hepatocellular carcinoma


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10.3389/fonc.2021.702055

Quantitative Prediction of Microsatellite Instability in Colorectal Cancer With Preoperative PET/CT-Based Radiomics


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10.1186/s13550-021-00760-3

Preoperative prediction of pathological grade in pancreatic ductal adenocarcinoma based on 18F-FDG PET/CT radiomics


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10.3389/fonc.2021.759897

Habitat Imaging-Based 18F-FDG PET/CT Radiomics for the Preoperative Discrimination of Non-small Cell Lung Cancer and Benign Inflammatory Diseases


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10.1097/MNM.0000000000001437

Improved 18-FDG PET/CT diagnosis of multiple myeloma diffuse disease by radiomics analysis


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10.1158/1538-7445.AM2021-SY05-03

Abstract SY05-03: Radiomics of immune therapy in NSCLC


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10.1007/s00259-021-05220-7

Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach


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10.1007/s11307-021-01635-8

Letter to the Editor: “Integration of PET/CT Radiomics and Semantic Features for Differentiation Between Active Pulmonary Tuberculosis and Lung Cancer”


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10.1038/s41416-021-01375-0

Radiomics predicts risk of cachexia in advanced NSCLC patients treated with immune checkpoint inhibitors.


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10.1016/j.ijrobp.2021.02.030

Multi-block Discriminant Analysis of Integrative 18F-FDG-PET/CT Radiomics for Predicting Circulating Tumor Cells in Early Stage Non-small Cell Lung Cancer Treated with Stereotactic Body Radiation Therapy.


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10.3389/fonc.2021.723345

18F-FDG PET/CT Radiomics for Preoperative Prediction of Lymph Node Metastases and Nodal Staging in Gastric Cancer


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10.1007/s00330-020-07676-x

A clinically practical radiomics-clinical combined model based on PET/CT data and nomogram predicts EGFR mutation in lung adenocarcinoma


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10.3389/fonc.2021.603882

A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma



Decision-support for treatment with 177Lu-PSMA: machine learning predicts response with high accuracy based on PSMA-PET/CT and clinical parameters.


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