A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy
Background: To investigate whether the radiomics signature (Rad-score) of DCE-MRI images obtained in triple-negative breast cancer (TNBC) patients before neoadjuvant chemotherapy (NAC) is associated with disease-free survival (DFS). Develop and validate an intuitive nomogram based on radiomics signa...
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oai:doaj.org-article:36bef524e7724877961a611ef037cbaf2021-11-12T05:27:31ZA Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy1664-802110.3389/fgene.2021.783513https://doaj.org/article/36bef524e7724877961a611ef037cbaf2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.783513/fullhttps://doaj.org/toc/1664-8021Background: To investigate whether the radiomics signature (Rad-score) of DCE-MRI images obtained in triple-negative breast cancer (TNBC) patients before neoadjuvant chemotherapy (NAC) is associated with disease-free survival (DFS). Develop and validate an intuitive nomogram based on radiomics signatures, MRI findings, and clinicopathological variables to predict DFS.Methods: Patients (n = 150) from two hospitals who received NAC from August 2011 to May 2017 were diagnosed with TNBC by pathological biopsy, and follow-up through May 2020 was retrospectively analysed. Patients from one hospital (n = 109) were used as the training group, and patients from the other hospital (n = 41) were used as the validation group. ROIs were drawn on 1.5 T MRI T1W enhancement images of the whole volume of the tumour obtained with a 3D slicer. Radiomics signatures predicting DFS were identified, optimal cut-off value for Rad-score was determined, and the associations between DFS and radiomics signatures, MRI findings, and clinicopathological variables were analysed. A nomogram was developed and validated for individualized DFS estimation.Results: The median follow-up time was 53.5 months, and 45 of 150 (30.0%) patients experienced recurrence and metastasis. The optimum cut-off value of the Rad-score was 0.2528, which stratified patients into high- and low-risk groups for DFS in the training group (p<0.001) and was validated in the external validation group. Multivariate analysis identified three independent indicators: multifocal/centric disease status, pCR status, and Rad-score. A nomogram based on these factors showed discriminatory ability, the C-index of the model was 0.834 (95% CI, 0.761–0.907) and 0.868 (95% CI, 0.787–949) in the training and the validation groups, respectively, which is better than clinicoradiological nomogram(training group: C-index = 0.726, 95% CI = 0.709–0.743; validation group: C-index = 0.774,95% CI = 0.743–0.805).Conclusion: The Rad-score derived from preoperative MRI features is an independent biomarker for DFS prediction in patients with TNBC to NAC, and the combined radiomics nomogram improved individualized DFS estimation.Bingqing XiaBingqing XiaHe WangZhe WangZhaoxia QianQin XiaoYin LiuZhimin ShaoShuling ZhouWeimin ChaiChao YouYajia GuFrontiers Media S.A.articleradiomicsneoadjuvant chemotherapynomogramtriple-negative breast cancerdisease-free survivalGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021) |
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radiomics neoadjuvant chemotherapy nomogram triple-negative breast cancer disease-free survival Genetics QH426-470 |
| spellingShingle |
radiomics neoadjuvant chemotherapy nomogram triple-negative breast cancer disease-free survival Genetics QH426-470 Bingqing Xia Bingqing Xia He Wang Zhe Wang Zhaoxia Qian Qin Xiao Yin Liu Zhimin Shao Shuling Zhou Weimin Chai Chao You Yajia Gu A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy |
| description |
Background: To investigate whether the radiomics signature (Rad-score) of DCE-MRI images obtained in triple-negative breast cancer (TNBC) patients before neoadjuvant chemotherapy (NAC) is associated with disease-free survival (DFS). Develop and validate an intuitive nomogram based on radiomics signatures, MRI findings, and clinicopathological variables to predict DFS.Methods: Patients (n = 150) from two hospitals who received NAC from August 2011 to May 2017 were diagnosed with TNBC by pathological biopsy, and follow-up through May 2020 was retrospectively analysed. Patients from one hospital (n = 109) were used as the training group, and patients from the other hospital (n = 41) were used as the validation group. ROIs were drawn on 1.5 T MRI T1W enhancement images of the whole volume of the tumour obtained with a 3D slicer. Radiomics signatures predicting DFS were identified, optimal cut-off value for Rad-score was determined, and the associations between DFS and radiomics signatures, MRI findings, and clinicopathological variables were analysed. A nomogram was developed and validated for individualized DFS estimation.Results: The median follow-up time was 53.5 months, and 45 of 150 (30.0%) patients experienced recurrence and metastasis. The optimum cut-off value of the Rad-score was 0.2528, which stratified patients into high- and low-risk groups for DFS in the training group (p<0.001) and was validated in the external validation group. Multivariate analysis identified three independent indicators: multifocal/centric disease status, pCR status, and Rad-score. A nomogram based on these factors showed discriminatory ability, the C-index of the model was 0.834 (95% CI, 0.761–0.907) and 0.868 (95% CI, 0.787–949) in the training and the validation groups, respectively, which is better than clinicoradiological nomogram(training group: C-index = 0.726, 95% CI = 0.709–0.743; validation group: C-index = 0.774,95% CI = 0.743–0.805).Conclusion: The Rad-score derived from preoperative MRI features is an independent biomarker for DFS prediction in patients with TNBC to NAC, and the combined radiomics nomogram improved individualized DFS estimation. |
| format |
article |
| author |
Bingqing Xia Bingqing Xia He Wang Zhe Wang Zhaoxia Qian Qin Xiao Yin Liu Zhimin Shao Shuling Zhou Weimin Chai Chao You Yajia Gu |
| author_facet |
Bingqing Xia Bingqing Xia He Wang Zhe Wang Zhaoxia Qian Qin Xiao Yin Liu Zhimin Shao Shuling Zhou Weimin Chai Chao You Yajia Gu |
| author_sort |
Bingqing Xia |
| title |
A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy |
| title_short |
A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy |
| title_full |
A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy |
| title_fullStr |
A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy |
| title_full_unstemmed |
A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy |
| title_sort |
combined nomogram model to predict disease-free survival in triple-negative breast cancer patients with neoadjuvant chemotherapy |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/36bef524e7724877961a611ef037cbaf |
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