Development and validation of survival nomograms in elder triple-negative invasive ductal breast carcinoma patients

ABSTRACT

Background

We aimed to develop a nomogram to predict the overall survival of elderly patients with Triple-negative invasive ductal breast carcinoma (TNIDC).

Research design and methods

12165 elderly patients with nonmetastatic TNIDC were retrieved from the SEER database from 2010 to 2019 and were randomly assigned to training and validation cohorts. Stepwise Cox regression analysis was used to select variables for the nomogram based on the training cohort. Univariate and multivariate Cox analyses were used to calculate the correlation between variables and prognosis of the patients. Survival analysis was performed for high- and low-risk subgroups based on risk score.

Results

Eleven predictive factors were identified to construct our nomograms. Compared with the TNM stage, the discrimination of the nomogram revealed good prognostic accuracy and clinical applicability as indicated by C-index values of 0.741 (95% CI 0.728–0.754) against 0.708 (95% CI 0.694–0.721) and 0.765 (95% CI 0.747–0.783) against 0.725 (95% CI 0.705–0.744) for the training and validation cohorts, respectively. Differences in OS were also observed between the high- and low-risk groups (p < 0.001).

Conclusion

The proposed nomogram provides a convenient and reliable tool for individual evaluations for elderly patients with M0_stage TNIDC. However, the model may only for Americans.

KEYWORDS:

• Triple-negative invasive ductal breast carcinoma
• nomogram
• SEER database
• prognostic model
• predictive factors

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Author contributions

Conception and design: Tao Jiang, Sha Yang, Ying Tan, and Shu Liu

Administrative support: Ying Tan and Shu Liu.

Collection and assembly of data: Tao Jiang, Sha Yang and Guanghui Wang.

Data analysis and interpretation: Tao Jiang and Sha Yang.

Data visualization: Tao Jiang.

Drafting the work or reviewing it critically for important intellectual content: Tao Jiang, Ying Tan, Shu Liu, Sha Yang.

Final approval of the version to be published: All authors.

Reviewed and agreed on all versions of the article before submission, during revision, the final version accepted for publication, and any significant changes introduced at the proofing stage: All authors.

Agree to take responsibility and be accountable for the contents of the article and to share responsibility to resolve any questions raised about the accuracy or integrity of the published work: All authors.

Acknowledgments

The authors would like to thank the Surveillance, Epidemiology, and End Results (SEER) database for the support.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14737140.2024.2320815

Additional information

Funding

This manuscript was funded by the National Natural Science Foundation of China [82060480]; Doctoral Research Start-up Fund for 2021 [Gyfybsky-2021-42]; Science and Technology Fund of Guizhou Provincial Health Commission [GZWKJ2021-166].