Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer.

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Machine Learning and Radiogenomics: Lessons Learned and Future Directions.

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Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer.

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Radiomics analysis of pulmonar ...... arly detection of lung cancer.

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Radiomics analysis of pulmonar ...... arly detection of lung cancer.

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Radiomics analysis of pulmonar ...... arly detection of lung cancer.

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Radiomics analysis of pulmonar ...... arly detection of lung cancer.

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Radiomics analysis of pulmonar ...... arly detection of lung cancer.

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Radiomics analysis of pulmonar ...... arly detection of lung cancer.

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Radiomics analysis of pulmonar ...... arly detection of lung cancer.

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Andreas Rimner

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Charles White

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Chia-Ju Liu

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Feng Jiang

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James G Mechalakos

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Jung Hun Oh

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Sadegh Riyahi

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Wei Lu

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Wengen Chen

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P2860

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

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Solitary pulmonary nodules: Part I. Morphologic evaluation for differentiation of benign and malignant lesions.

Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation.

Modeling pathologic response of esophageal cancer to chemoradiation therapy using spatial-temporal 18F-FDG PET features, clinical parameters, and demographics

Radiomics: Images Are More than Pictures, They Are Data.

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

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scholarly article

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10.1002/MP.12820

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English

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Joseph Deasy

Wookjin Choi

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2018-02-19T00:00:00Z

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29457229

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5903960

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Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer.

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P2860

Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer.

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