Laura Ichikawa
0000-0002-0882-6662
14 papers found
Refreshing results…
Predicting five-year interval second breast cancer risk in women with prior breast cancer
SARS-CoV-2 Infection and Related Hospitalization Among Cancer Survivors
Data from Performance of Statistical and Machine Learning Risk Prediction Models for Surveillance Benefits and Failures in Breast Cancer Survivors
Supplementary Figure S3 from Performance of Statistical and Machine Learning Risk Prediction Models for Surveillance Benefits and Failures in Breast Cancer Survivors
Supplementary Figure S2 from Performance of Statistical and Machine Learning Risk Prediction Models for Surveillance Benefits and Failures in Breast Cancer Survivors
Supplementary Table S2 from Performance of Statistical and Machine Learning Risk Prediction Models for Surveillance Benefits and Failures in Breast Cancer Survivors
Supplementary Table S1 from Performance of Statistical and Machine Learning Risk Prediction Models for Surveillance Benefits and Failures in Breast Cancer Survivors
Supplementary Methods from Performance of Statistical and Machine Learning Risk Prediction Models for Surveillance Benefits and Failures in Breast Cancer Survivors
Supplementary Figure S1 from Performance of Statistical and Machine Learning Risk Prediction Models for Surveillance Benefits and Failures in Breast Cancer Survivors
Performance of Statistical and Machine Learning Risk Prediction Models for Surveillance Benefits and Failures in Breast Cancer Survivors
Impact of Surveillance Mammography Intervals Less Than One Year on Performance Measures in Women With a Personal History of Breast Cancer
Association between intrauterine device type and risk of perforation and device expulsion: results from the Association of Perforation and Expulsion of Intrauterine Device study
Digital Mammography and Breast Tomosynthesis Performance in Women with a Personal History of Breast Cancer, 2007–2016
Surveillance Breast MRI and Mammography: Comparison in Women with a Personal History of Breast Cancer
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