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Spherical Radiomics -- A Novel Approach to Glioblastoma Radiogenomic Analysis of Heterogeneity

• Authors : Feng, Haotian; Sheng, Ke

Subjects: Medical Physics; Quantitative Methods

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Tumor Synthesis conditioned on Radiomics

• Authors : Kim, Jonghun; Na, Inye; Ko, Eun Sook

Subjects: Computer Vision and Pattern Recognition

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Deep-learning-based Radiomics on Mitigating Post-treatment Obesity for Pediatric Craniopharyngioma Patients after Surgery and Proton Therapy

• Authors : Yang, Wenjun; Hua, Chia-Ho; Davis, Tina

Subjects: Medical Physics

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Interpreting Convolutional Neural Network Activation Maps with Hand-crafted Radiomics Features on Progression of Pediatric Craniopharyngioma after Irradiation Therapy

• Authors : Yang, Wenjun; Wang, Chuang; Davis, Tina

Subjects: Medical Physics

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Leveraging Support Vector Regression, Radiomics and Dosiomics for Outcome Prediction in Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy (PULSAR)

• Authors : Yu, Yajun; Jiang, Steve; Timmerman, Robert

Subjects: Machine Learning

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Magnetic Resonance Imaging Virtual Liver Biopsy Using Radiomics Analysis for the Assessment of Chronic Liver Disease

• Authors : Huang, Jiqing; Leporq, Benjamin; Hervieu, Valérie

Subjects: Tissues and Organs

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Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach

• Authors : Tikhonov, Daniil; Scatolin, Matheus; Banerjee, Mohor

Subjects: Computer Vision and Pattern Recognition

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Radiomics and Molecular Classification in Endometrial Cancer (ROME)

• Authors : Valentina Chiappa, M.D.

• Source: Correlation Between RadiOmics and Molecular Classification in Endometrial Cancer (the ROME Study): a Step Forward to a Simplified Precision Medicine

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Semi-Supervised Radiomics for Glioblastoma IDH Mutation: Limited Labels, Data Sensitivity, and SHAP Interpretation

• Authors : Pouria, Amir Hossein; Taeb, Shahram; Mehrnia, Somayeh Sadat

Subjects: Medical Physics; F.2.2; I.2.7

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The added value for MRI radiomics and deep-learning for glioblastoma prognostication compared to clinical and molecular information

• Authors : Abler, D.; Pusterla, O.; Joye-Kühnis, A.

Subjects: Machine Learning; Applications

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