Application of Machine Learning Analyses Using Clinical and [18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer-Reference-Cited by-同舟云学术

Application of Machine Learning Analyses Using Clinical and [18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer

Published:2023-05-16 Issue:5 Volume:25 Page:923-934
ISSN:1536-1632
Container-title:Molecular Imaging and Biology
language:en
Short-container-title:Mol Imaging Biol

Author:

Kawaji Kodai,Nakajo Masatoyo,Shinden Yoshiaki,Jinguji Megumi,Tani Atsushi,Hirahara Daisuke,Kitazono Ikumi,Ohtsuka Takao,Yoshiura Takashi

Publisher

Springer Science and Business Media LLC

Subject

Cancer Research,Radiology, Nuclear Medicine and imaging,Oncology

Link

https://link.springer.com/content/pdf/10.1007/s11307-023-01823-8.pdf

Reference42 articles.

1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A (2015) Global cancer statisics, 2012. CA Cancer J Clin 65:87–108

2. Harbeck N, Gnant M (2017) Breast cancer. Lancet 389(10074):1134–1150

3. Siegel R, DeSantis C, Virgo K et al (2012) Cancer treatment and survivorship statistics, 2012. CA Cancer J Clin 62:220–241

4. Boundouki G, Wong Sik Hee JR, Croghan N et al (2019) Comparing long-term local recurrence rates of surgical and non-surgical management of close anterior margins in breast conserving surgery. Breast Cancer Res Treat 176:311–319

5. von Schulthess GK, Steinert HC, Hany TF (2006) Integrated PET/CT: current applications and future directions. Radiology 238:405–422

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