Development and validation of a machine learning-based predictive model to improve the prediction of inguinal status of anal cancer patients: A preliminary report.

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Development and validation of a machine learning-based predictive model to improve the prediction of inguinal status of anal cancer patients: A preliminary report.

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2016 nî lūn-bûn

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2016年の論文

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2016年学术文章

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2016年学术文章

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2016年学术文章

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2016年学术文章

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2016年学术文章

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2016年学术文章

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2016年學術文章

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2016年學術文章

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Development and validation of ...... atients: A preliminary report.

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Development and validation of ...... atients: A preliminary report.

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Development and validation of ...... atients: A preliminary report.

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Development and validation of ...... atients: A preliminary report.

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Development and validation of ...... atients: A preliminary report.

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Development and validation of ...... atients: A preliminary report.

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P1476

Development and validation of ...... patients: A preliminary report

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P2093

Laëtitia Lestrade

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Roberto Gatta

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P2860

Surgical management of epidermoid carcinoma of the anus.

Predicting breast cancer survivability: a comparison of three data mining methods.

Impact of overall treatment time on survival and local control in patients with anal cancer: a pooled data analysis of Radiation Therapy Oncology Group trials 87-04 and 98-11.

Nomograms for predicting local recurrence, distant metastases, and overall survival for patients with locally advanced rectal cancer on the basis of European randomized clinical trials.

Size does matter: can we reduce the radiotherapy field size for selected cases of anal canal cancer undergoing chemoradiation?

Carcinoma of the anal canal. A clinical and pathologic study of 188 cases.

Prognostic factors for recurrence and survival in anal cancer: generating hypotheses from the mature outcomes of the first United Kingdom Coordinating Committee on Cancer Research Anal Cancer Trial (ACT I).

Role of mitomycin in combination with fluorouracil and radiotherapy, and of salvage chemoradiation in the definitive nonsurgical treatment of epidermoid carcinoma of the anal canal: results of a phase III randomized intergroup study.

Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study.

Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers.

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108509-108521

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scholarly article

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10.18632/ONCOTARGET.10749

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English

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2016-07-21T00:00:00Z

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29312547

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machine learning

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5752460

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