Cancer research and treatment · 2025
Nomograms Integrating Body Composition Metrics Predict Total Pathologic Complete Response after Neoadjuvant Systemic Therapy for Breast Cancer.
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Ding Jingjing, Gong Yichun, Wang Jue, Wang Yuanyuan, Yao Hao, Sheng Xingye, Wang Mingyu, Shen Danni, Li Junhan, Zha Xiaoming, Xu Lu
Аннотация
PURPOSE: Neoadjuvant systemic therapy (NST) is a systemic treatment for locally advanced or initially unresectable breast cancer before surgery. Patients who achieved total pathologic complete response (tpCR) after NST exhibited significantly better overall prognosis than patients with non-pathological complete response.
MATERIALS AND METHODS: This study collected baseline indicators, body composition indicators, and tpCR results of breast cancer patients at the First Affiliated Hospital of Nanjing Medical University. Patients were divided into training set and validation set in a ratio of 7:3. Univariable and multivariable logistic regression analyses were performed, and the probability of tpCR was predicted by constructing nomograms based on the results of the multivariable logistic regression analysis.
RESULTS: The study included 500 patients between 2014 and 2022 with breast cancer who underwent NST. The training set and validation set consist of 350 and 150 patients, respectively. Patients with progesterone receptor-negative status (p < 0.001), human epidermal growth factor receptor 2-positive status (p < 0.001), large body surface area (p=0.091), low skeletal muscle index (p=0.008), and high skeletal muscle density (p=0.004) were more likely to achieve tpCR. Patients with American Joint Committee on Cancer (AJCC) T-stage 4 (p=0.126), AJCC N-stage 1 (p=0.026) were less likely to achieve tpCR.
CONCLUSION: Existing tpCR prediction models mostly focus on tumor biological characteristics and ignore the effect of body compositions. This study constructed a nomogram to predict tpCR in patients with breast cancer undergoing NST based on baseline and body composition indicators. This nomogram can help assess efficacy and optimize treatment strategies, thus improving the overall prognosis of patients.
MATERIALS AND METHODS: This study collected baseline indicators, body composition indicators, and tpCR results of breast cancer patients at the First Affiliated Hospital of Nanjing Medical University. Patients were divided into training set and validation set in a ratio of 7:3. Univariable and multivariable logistic regression analyses were performed, and the probability of tpCR was predicted by constructing nomograms based on the results of the multivariable logistic regression analysis.
RESULTS: The study included 500 patients between 2014 and 2022 with breast cancer who underwent NST. The training set and validation set consist of 350 and 150 patients, respectively. Patients with progesterone receptor-negative status (p < 0.001), human epidermal growth factor receptor 2-positive status (p < 0.001), large body surface area (p=0.091), low skeletal muscle index (p=0.008), and high skeletal muscle density (p=0.004) were more likely to achieve tpCR. Patients with American Joint Committee on Cancer (AJCC) T-stage 4 (p=0.126), AJCC N-stage 1 (p=0.026) were less likely to achieve tpCR.
CONCLUSION: Existing tpCR prediction models mostly focus on tumor biological characteristics and ignore the effect of body compositions. This study constructed a nomogram to predict tpCR in patients with breast cancer undergoing NST based on baseline and body composition indicators. This nomogram can help assess efficacy and optimize treatment strategies, thus improving the overall prognosis of patients.