Details

Autor(en) / Beteiligte

• Xiong, Qianqian
• Zhou, Xuezhi
• Liu, Zhenyu
• Lei, Chuqian
• Yang, Ciqiu
• Yang, Mei
• Zhang, Liulu
• Zhu, Teng
• Zhuang, Xiaosheng
• Liang, Changhong
• Liu, Zaiyi
• Tian, Jie
• Wang, Kun

Titel

Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy

Ist Teil von

• Clinical & translational oncology, 2020, Vol.22 (1), p.50-59

Ort / Verlag

Cham: Springer International Publishing

Erscheinungsjahr

2020

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Purpose To evaluate the value of multiparametric magnetic resonance imaging (MRI) in pretreatment prediction of breast cancers insensitive to neoadjuvant chemotherapy (NAC). Methods A total of 125 breast cancer patients (63 in the primary cohort and 62 in the validation cohort) who underwent MRI before receiving NAC were enrolled. All patients received surgical resection, and Miller–Payne grading system was applied to assess the response to NAC. Grade 1–2 cases were classified as insensitive to NAC. We extracted 1941 features in the primary cohort. After feature selection, the optimal feature set was used to construct a radiomic signature using machine learning. We built a combined prediction model incorporating the radiomic signature and independent clinical risk factors selected by multivariable logistic regression. The performance of the combined model was assessed with the results of independent validation. Results Four features were selected for the construction of the radiomic signature based on the primary cohort. Combining with independent clinical factors, the combined prediction model for identifying the Grade 1–2 group reached a better discrimination power than the radiomic signature, with an area under the receiver operating characteristic curve of 0.935 (95% confidence interval 0.848–1) in the validation cohort, and its clinical utility was confirmed by the decision curve analysis. Conclusion The combined model based on radiomics and clinical variables has potential in predicting drug-insensitive breast cancers.