Tiantian Bian1, Zengjie Wu2, Qing Lin1, Haibo Wang1, Yaqiong Ge3, Shaofeng Duan3, Guangming Fu4, Chunxiao Cui1, Xiaohui Su1. 1. Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China. 2. Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China. 3. GE Healthcare, Pudong, 210000, Shanghai, China. 4. Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China.
Abstract
OBJECTIVES: To investigate the ability of radiomic signatures based on MRI to evaluate the response and efficiency of neoadjuvant chemotherapy (NAC) for treating breast cancers. METHODS: 152 patients were included in this study at our institution between March 2017 and September 2019. All patients with breast cancer underwent a preoperative breast MRI and the Miller-Payne grading system was applied to evaluate response to NAC. Quantitative parameters were compared between patients with sensitive and insensitive responses to NAC and between those with pathological complete responses (pCR) and non-pCR. Four radiomic signatures were built based on T2W imaging, diffusion-weighted imaging, dynamic contrast-enhanced imaging and their combination, and radiomics scores (Rad-score) were calculated. The combination of the clinical factors and Rad-scores created a nomogram model. Multivariate logistic regression was performed to assess the association between MRI features and independent clinical risk factors. RESULTS: 20 features and 18 features were selected to build the radiomic signature for evaluating sensitivity and the possibility of pCR, respectively. The combined radiomic signature and nomogram model showed a similar discrimination in the training (AUC 0.91, 0.92, 95% confidence interval [CI], 0.85-0.96, 0.86-0.98) and validation (AUC 0.93, 0.91, 95% CI, 0.86-1.00, 0.82-1.00) sets. The clinical factor model exhibited reduced performance (AUC 0.74, 0.64, 95% CI, 0.64-0.84, 0.46-0.82) in terms of NAC sensitivity and pCR. CONCLUSIONS: The combined radiomic signature and nomogram model exhibited potential predictive power for predicting effective NAC treatment which can aid in the prognosis and guidance of treatment regimens. ADVANCES IN KNOWLEDGE: Identifying a means of assessing the efficacy of NAC before surgery can guide follow-up treatment and avoid chemotherapy-induced toxicity.
OBJECTIVES: To investigate the ability of radiomic signatures based on MRI to evaluate the response and efficiency of neoadjuvant chemotherapy (NAC) for treating breast cancers. METHODS: 152 patients were included in this study at our institution between March 2017 and September 2019. All patients with breast cancer underwent a preoperative breast MRI and the Miller-Payne grading system was applied to evaluate response to NAC. Quantitative parameters were compared between patients with sensitive and insensitive responses to NAC and between those with pathological complete responses (pCR) and non-pCR. Four radiomic signatures were built based on T2W imaging, diffusion-weighted imaging, dynamic contrast-enhanced imaging and their combination, and radiomics scores (Rad-score) were calculated. The combination of the clinical factors and Rad-scores created a nomogram model. Multivariate logistic regression was performed to assess the association between MRI features and independent clinical risk factors. RESULTS: 20 features and 18 features were selected to build the radiomic signature for evaluating sensitivity and the possibility of pCR, respectively. The combined radiomic signature and nomogram model showed a similar discrimination in the training (AUC 0.91, 0.92, 95% confidence interval [CI], 0.85-0.96, 0.86-0.98) and validation (AUC 0.93, 0.91, 95% CI, 0.86-1.00, 0.82-1.00) sets. The clinical factor model exhibited reduced performance (AUC 0.74, 0.64, 95% CI, 0.64-0.84, 0.46-0.82) in terms of NAC sensitivity and pCR. CONCLUSIONS: The combined radiomic signature and nomogram model exhibited potential predictive power for predicting effective NAC treatment which can aid in the prognosis and guidance of treatment regimens. ADVANCES IN KNOWLEDGE: Identifying a means of assessing the efficacy of NAC before surgery can guide follow-up treatment and avoid chemotherapy-induced toxicity.
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