Pre-treatment prediction of response to neoadjuvant chemotherapy in breast cancer patients using a nomogram based on findings from cone-beam breast computed tomography
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Published on: September 27, 2024
Yu-Jiao Zhang1, Zhu-Ming Liang1, Xiang-Yang Huang2
1Department of Breast Surgery, Guangxi Medical University Cancer Hospital, Nanning, China.
Background:
Cone-beam breast computed tomography (CBBCT) can provide detailed information about breast tissue, but whether such information can help predict treatment response is unclear.
Purpose:
To develop a nomogram based on findings from CBBCT as well as conventional clinical variables to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with breast cancer.
Materials And Methods:
Medical data were retrospectively analyzed for a consecutive series of women with breast cancer who underwent NAC followed within three months by resection surgery at our hospital between September 2019 and March 2022. Patients were randomized into a development cohort and validation cohort. A nomogram to predict pCR after chemotherapy was formulated based on univariable and multivariable logistic regression of pre-treatment data from the development cohort, and it was tested against data from the validation cohort. The performance of the nomogram was evaluated in terms of the area under receiver operating characteristic curves (AUC), calibration plots and decision curve analysis.
Results:
Of the 215 breast cancer patients in this study, 69 (32.1%) achieved pCR after NAC. Multivariable logistic regression of the development cohort linked such response independently to absence of estrogen receptor (ER) expression, expression of human epidermal growth factor receptor 2 (HER-2), small tumor diameter and non-mass enhancement (NME) on CBBCT. The resulting nomogram predicted response with AUCs of 0.841 (95% CI: 0.78-0.90) in the development cohort (n = 150) and 0.836 (95% CI: 0.74-0.94) in the validation cohort (n = 65), and it was efficient against data from both cohorts based on calibration curves. Decision curve analysis suggested that the nomogram is clinically useful.
Conclusion:
A nomogram incorporating molecular biomarkers and findings from CBBCT may help predict breast cancer patients more likely to respond to NAC.
