CrossRef Text and Data Mining
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The application of machine learning for predicting recurrence in patients with early-stage endometrial cancer: a pilot study
Munetoshi Akazawa, Kazunori Hashimoto, Katsuhiko Noda, Kaname Yoshida

DOI: https://doi.org/10.5468/ogs.20248

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Study of factors to predict recurrence in early stage endometrial cancer
16th Annual International Conference RGCON. 2016;   Crossref logo
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Racial disparities in recurrence among patients with early-stage endometrial cancer
Cancer. 2008;113(6):1431-1437   Crossref logo
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Machine learning radiomics for predicting recurrence risk in patients with early-stage invasive breast cancer
. 2020;   Crossref logo
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Recurrence patterns and surveillance for patients with early stage endometrial cancer
Gynecologic Oncology. 2011;123(2):205-207   Crossref logo
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The impact of lymph vascular space invasion on recurrence and survival in patients with early stage endometrial cancer
European Journal of Cancer Care. 2013;23(3):380-384   Crossref logo
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Analysis of clinicopathologic factors predicting disease recurrence in lymph node-negative early-stage endometrial cancer patients who underwent comprehensive surgical staging
Gynecologic Oncology. 2017;145:183   Crossref logo
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A Nomogram Model Involving Immunohistochemical Markers for Predicting the Recurrence of Stage I-II Endometrial Cancer
Frontiers in Oncology. 2021;10:   Crossref logo
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A pilot study about efficacy of immunostimulating therapy in patients radically operated for non small cell lung cancer (stage I). Early results
Lung Cancer. 1994;10(5-6):429   Crossref logo
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Mo1083 MACHINE LEARNING-BASED PREDICTIVE MODELS ARE MORE ACCURATE THAN TNM STAGE IN PREDICTING SURVIVAL IN PATIENTS WITH GASTRIC CANCER
Gastroenterology. 2020;158(6):S-782-S-783   Crossref logo
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Surgical Management of Early-Stage Endometrial Cancer
Current Concepts in Endometrial Cancer. 2017;55-66   Crossref logo
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