Utilization of ultrasound combined with Ki-67 and CESM quantitative parameters to predict pathological complete response following neoadjuvant chemotherapy in triple-negative breast cancer
Year: 2025
Type: article
Study design: Observational Study
Abstract: • Perform quantitative analysis on the enhancement degree of CESM. • Quantitative calculation was performed using the relative enhancement gray value (CNR), where CNR = (LGVa - LGVb) / LGVb × 100 % (LGVa: mean gray value in the ROI of the breast lesion; LGVb: mean gray value in the ROI of the chest wall). This method more accurately reflects the enhancement degree of the lesion. • This study integrated ultrasound imaging, pathological markers, and CESM parameters to predict pCR in TNBC following NAC, The combined use of these variables yielded superior predictive performance, offering valuable guidance for optimizing subsequent therapeutic strategies in TNBC management. To evaluate the predictive value of ultrasound parameters combined with Ki-67 and contrast-enhanced spectral mammography (CESM) for assessing pathological complete response (pCR) following neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC). This retrospective study included 78 TNBC patients who underwent surgery after NAC and were stratified into pCR and non-pCR (nPCR) groups based on postoperative pathology. Baseline ultrasound imaging was conducted upon admission. Primary tumor ultrasound features and change rates in tumor longest diameter (△D2, △D4) after 2 and 4 NAC cycles were analyzed. CESM was performed pre-treatment and after two cycles to calculate △CNR (percentage reduction in relative enhancement grayscale value). Univariate and multivariate logistic regression analyses were employed to identify independent predictors of pCR. Univariate analysis revealed significant differences in △D2, △D4, retromammary space disappearance, Ki-67 expression, and △CNR between the pCR and nPCR groups following 2 and 4 cycles ( P < 0.05). Variables with P < 0.05 were included in multivariate binary logistic regression. △D4, △CNR, and Ki-67 were identified as independent predictors of pCR in TNBC (OR=1.064, P = 0.041; OR=1.081, P = 0.016; OR=0.140, P = 0.005, respectively). The combined predictive model yielded an AUC of 0.869 (95 % CI: 0.774–0.963), with a sensitivity of 84.0 % and specificity of 84.9 %. The integration of △D4, △CNR, and Ki-67 offers clinical utility in predicting NAC response in TNBC.
Source: Translational Oncology
Authors: YanYan Zhang, Mei Qin Yuan, Tian Li Jia, Jing Guo
Institutions: Yuhuangding Hospital, Dongyang People's Hospital
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,390
OpenAlex ID: W4415992013