Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer
ObjectiveBy identifying the clinicopathological characteristics and prognostic influences of patients with triple-positive breast cancer (TPBC) at Xijing Hospital in China compared with those in the United States, this study aims to construct a nomogram model to forecast the overall survival rate (O...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1071076/full |
| _version_ | 1797937898331308032 |
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| author | Anqi Geng Jingjing Xiao Bingyao Dong Shifang Yuan |
| author_facet | Anqi Geng Jingjing Xiao Bingyao Dong Shifang Yuan |
| author_sort | Anqi Geng |
| collection | DOAJ |
| description | ObjectiveBy identifying the clinicopathological characteristics and prognostic influences of patients with triple-positive breast cancer (TPBC) at Xijing Hospital in China compared with those in the United States, this study aims to construct a nomogram model to forecast the overall survival rate (OS) of TPBC patients.MethodThe Surveillance, Epidemiology, and End Results (SEER) database was used to screen 5769 patients as the training cohort, and 191 patients from Xijing Hospital were used as the validation cohort. Cox risk-proportional model was applied to select variables and the nomogram model was constructed based on the training cohort. The performance of the model was evaluated by calculating the C-index and generating calibration plots in the training and validation cohorts.ResultsCox multifactorial analysis showed that age, chemotherapy, radiotherapy, M-stage, T-stage, N-stage, and the mode of surgery were all independent risk factors for the prognosis of TPBC patients (all P<0.05). With this premise, the nomogram model was constructed and evaluated. The C-index value of the nomogram model was 0.830 in the training group and 0.914 in the validation group. Moreover, both the calibration and ROC curves for the proposed model exhibited reliable performance, and the clinical decision curve analysis showed that the proposed model can bring clinical benefits.ConclusionsThe constructed nomogram can accurately predict individual survival probabilities and may serve as a clinical decision support tool for clinicians to optimize treatment in individuals. |
| first_indexed | 2024-04-10T18:51:24Z |
| format | Article |
| id | doaj.art-5eb8f31b9cf2460795ee7bc4ec0f2e3e |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-10T18:51:24Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-5eb8f31b9cf2460795ee7bc4ec0f2e3e2023-02-01T06:41:25ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-02-011310.3389/fonc.2023.10710761071076Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancerAnqi GengJingjing XiaoBingyao DongShifang YuanObjectiveBy identifying the clinicopathological characteristics and prognostic influences of patients with triple-positive breast cancer (TPBC) at Xijing Hospital in China compared with those in the United States, this study aims to construct a nomogram model to forecast the overall survival rate (OS) of TPBC patients.MethodThe Surveillance, Epidemiology, and End Results (SEER) database was used to screen 5769 patients as the training cohort, and 191 patients from Xijing Hospital were used as the validation cohort. Cox risk-proportional model was applied to select variables and the nomogram model was constructed based on the training cohort. The performance of the model was evaluated by calculating the C-index and generating calibration plots in the training and validation cohorts.ResultsCox multifactorial analysis showed that age, chemotherapy, radiotherapy, M-stage, T-stage, N-stage, and the mode of surgery were all independent risk factors for the prognosis of TPBC patients (all P<0.05). With this premise, the nomogram model was constructed and evaluated. The C-index value of the nomogram model was 0.830 in the training group and 0.914 in the validation group. Moreover, both the calibration and ROC curves for the proposed model exhibited reliable performance, and the clinical decision curve analysis showed that the proposed model can bring clinical benefits.ConclusionsThe constructed nomogram can accurately predict individual survival probabilities and may serve as a clinical decision support tool for clinicians to optimize treatment in individuals.https://www.frontiersin.org/articles/10.3389/fonc.2023.1071076/fulltriple positive breast cancerprognostic modelnomogramoverall survivalSEER |
| spellingShingle | Anqi Geng Jingjing Xiao Bingyao Dong Shifang Yuan Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer Frontiers in Oncology triple positive breast cancer prognostic model nomogram overall survival SEER |
| title | Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer |
| title_full | Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer |
| title_fullStr | Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer |
| title_full_unstemmed | Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer |
| title_short | Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer |
| title_sort | analysis of prognostic factors and construction of prognostic models for triple positive breast cancer |
| topic | triple positive breast cancer prognostic model nomogram overall survival SEER |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1071076/full |
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