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Survival outcome of local excision versus radical resection of colon or rectal carcinoma: a Surveillance, Epidemiology, and End Results SEER population-based study. Local recurrence after complete clinical response and watch and wait in rectal cancer after neoadjuvant chemoradiation: impact of salvage therapy on local disease control.

Appelt, A. High-dose chemoradiotherapy and watchful waiting for distal rectal cancer: a prospective observational study. Renehan, A. Watch-and-wait approach versus surgical resection after chemoradiotherapy for patients with rectal cancer the OnCoRe project : a propensity-score matched cohort analysis. Martens, M. Cancer Inst. Aerts, H. JAMA Oncol. Nie, K. Cancer Res. Kourou, K. Machine learning applications in cancer prognosis and prediction. Kang, J.

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Bibault, J. Big Data and machine learning in radiation oncology: State of the art and future prospects. Cancer Lett. Esteva, A. Dermatologist-level classification of skin cancer with deep neural networks. Nature , — Gulshan, V. JAMA , — Miotto, R. Oakden-Rayner, L.

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Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework. AMIA Annu. AMIA Symp. Gillies, R.

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Radiology , — Lambin, P. Radiomics: the bridge between medical imaging and personalized medicine.

Gan, J. Zhang, L. IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Deep Radiomics Rectal Cancer: Code , parameters and scripts used for our study predicting pathologic complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer Liu, Z. CCR Lovinfosse, P.

Kumar, V.

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Radiomics: the process and the challenges. Imaging 30 , — Abernethy, A. Rapid-learning system for cancer care. Obermeyer, Z. Beam, A. Cabitza, F. Unintended Consequences of Machine Learning in Medicine. Rajpurkar, P. Zapletal, E. Methodology of integration of a clinical data warehouse with a clinical information system: the HEGP case. Health Technol. Rance, B. Varian Incorporated. Varian Developers Forum.

Accessed: 15th April PloS One 13 , e Integrating Multimodal Radiation Therapy Data into i2b2. Applied Clinical Informatics 09 02 , — Grove, O. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PloS One 10 , e Parmar, C. Robust Radiomics feature quantification using semiautomatic volumetric segmentation. PloS One 9 , e Shrout, P. Intraclass correlations: uses in assessing rater reliability. Wolak, M. Machine Learning methods for Quantitative Radiomic Biomarkers. R Core Team.

R: A language and environment for statistical computing. Wickham, H. Therneau, T. Kassambara, A.


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Nair, V. Proceedings of the 27th International Conference on Machine Learning Duchi, J. Friedman, J. Robin, X. Download references. The study design was performed by J. D performed the segmentation task. The paper was written by J. Correspondence to Jean-Emmanuel Bibault. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Your site is very cool. It reveals how nicely you perceive this subject. Bookmarked this web page, will come back for extra articles. You, my friend, ROCK!

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