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Research Paper|Volume 12, Issue 18|pp 18151—18162

Distant metastasis prediction via a multi-feature fusion model in breast cancer

Wenjuan Ma1, Xin Wang2, Guijun Xu3, Zheng Liu3, Zhuming Yin4, Yao Xu3, Haixiao Wu3, Vladimir P. Baklaushev5, Karl Peltzer6, Henian Sun7, Natalia V. Kharchenko8, Lisha Qi9, Min Mao10, Yanbo Li1, Peifang Liu1, Vladimir P. Chekhonin11, Chao Zhang3
  • 1Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China
  • 2Department of Epidemiology and Biostatistics, First Affiliated Hospital, Army Medical University, Chongqing 400038, China
  • 3Department of Bone and Soft Tissue Tumors, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China
  • 4Department of Breast Oncoplastic Surgery, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Sino-Russian Joint Research Center for Oncoplastic Breast Surgery, Tianjin 300060, China
  • 5Federal Research and Clinical Center of Specialized Medical Care and Medical Technologies, Federal Biomedical Agency of the Russian Federation, Moscow 115682, Russian Federation
  • 6Department of Research and Innovation, University of Limpopo, Turfloop 0527, South Africa
  • 7Department of Oncology, N.N. Blokhin National Medical Research Center of Oncology, Moscow 115478, Russian Federation
  • 8Department of Oncology, Radiology and Nuclear Medicine, Medical Institute of Peoples’ Friendship University of Russia, Moscow 117198, Russian Federation
  • 9Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China
  • 10Department of Pathology and Southwest Cancer Center, First Affiliated Hospital, Army Medical University, Chongqing 400038, China
  • 11Department of Basic and Applied Neurobiology, Federal Medical Research Center for Psychiatry and Narcology, Moscow 117997, Russian Federation
* Equal contribution
Received: April 21, 2020Accepted: June 22, 2020Published: September 28, 2020
https://doi.org/10.18632/aging.103630

Copyright: © 2020 Ma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

This study aimed to develop a model that fused multiple features (multi-feature fusion model) for predicting metachronous distant metastasis (DM) in breast cancer (BC) based on clinicopathological characteristics and magnetic resonance imaging (MRI). A nomogram based on clinicopathological features (clinicopathological-feature model) and a nomogram based on the multi-feature fusion model were constructed based on BC patients with DM (n=67) and matched patients (n=134) without DM. DM was diagnosed on average (17.31±13.12) months after diagnosis. The clinicopathological-feature model included seven features: reproductive history, lymph node metastasis, estrogen receptor status, progesterone receptor status, CA153, CEA, and endocrine therapy. The multi-feature fusion model included the same features and an additional three MRI features (multiple masses, fat-saturated T2WI signal, and mass size). The multi-feature fusion model was relatively better at predicting DM. The sensitivity, specificity, diagnostic accuracy and AUC of the multi-feature fusion model were 0.746 (95% CI: 0.623-0.841), 0.806 (0.727-0.867), 0.786 (0.723-0.841), and 0.854 (0.798-0.911), respectively. Both internal and external validations suggested good generalizability of the multi-feature fusion model to the clinic. The incorporation of MRI factors significantly improved the specificity and sensitivity of the nomogram. The constructed multi-feature fusion nomogram may guide DM screening and the implementation of prophylactic treatment for BC.