Predicting malignant nodules from screening CTs
Determine if quantitative analyses (“radiomics”) of low dose CT lung cancer screening images at baseline can predict subsequent emergence of cancer.
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Everyone knows 3D Slicer, a powereful medic-images clinical, biomedical and reaserch tool. But we focus on Drawing ROI with Segmentation and Features Extracting with Radiomics module.
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Radiomics can provide powerful tools for cancer diagnosis and prognosis.
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Powerful & popular tools for radiomics feature extraction and analysis.
Learn moreThere are some cases and reaserch about Radiomics, which providing a demonstration of the clinical potential of radiomics as a powerful to for personalized therapy.
Determine if quantitative analyses (“radiomics”) of low dose CT lung cancer screening images at baseline can predict subsequent emergence of cancer.
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Assess the stability and reproducibility of CT radiomic features extracted from the peritumoral regions of lung lesions.
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Review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.
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Evaluates CT radiomic features for their capability to predict distant metastasis for lung adenocarcinoma patients.
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