Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy
| Autor: | Jingying Jiang, Chi Chen, Fei Ji, Jie Tian, Xiaosheng Zhuang, Min-Yi Cheng, Teng Zhu, Liulu Zhang, Zhenyu Liu, Xuezhi Zhou, Junsheng Zhang, Kun Wang, Chuqian Lei |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
NAC
neoadjuvant chemotherapy 0301 basic medicine Oncology Original article Cancer Research medicine.medical_specialty PD progressive disease ER estrogen receptor Feature selection lcsh:RC254-282 03 medical and health sciences 0302 clinical medicine Breast cancer Internal medicine HER-2 human epidermal growth factor receptor 2 Medicine PC primary cohort VC validation cohort Receiver operating characteristic business.industry SD stable disease pCR pathologic complete response PR progesterone receptor Stepwise regression Nomogram medicine.disease lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens Regression Confidence interval 030104 developmental biology 030220 oncology & carcinogenesis Cohort BCS breast-conserving surgery business |
| Zdroj: | Translational Oncology, Vol 13, Iss 11, Pp 100831-(2020) Translational Oncology |
| ISSN: | 1936-5233 |
| Popis: | Objectives Breast cancers show different regression patterns after neoadjuvant chemotherapy. Certain regression patterns are associated with more reliable margins in breast-conserving surgery. Our study aims to establish a nomogram based on radiomic features and clinicopathological factors to predict regression patterns in breast cancer patients. Methods We retrospectively reviewed 144 breast cancer patients who received neoadjuvant chemotherapy and underwent definitive surgery in our center from January 2016 to December 2019. Tumor regression patterns were categorized as type 1 (concentric regression + pCR) and type 2 (multifocal residues + SD + PD) based on pathological results. We extracted 1158 multidimensional features from 2 sequences of MRI images. After feature selection, machine learning was applied to construct a radiomic signature. Clinical characteristics were selected by backward stepwise selection. The combined prediction model was built based on both the radiomic signature and clinical factors. The predictive performance of the combined prediction model was evaluated. Results Two radiomic features were selected for constructing the radiomic signature. Combined with two significant clinical characteristics, the combined prediction model showed excellent prediction performance, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval 0.8343–0.9701) in the primary cohort and 0.826 (95% confidence interval 0.6774–0.9753) in the validation cohort. Conclusions Our study established a unique model combining a radiomic signature and clinicopathological factors to predict tumor regression patterns prior to the initiation of NAC. The early prediction of type 2 regression offers the opportunity to modify preoperative treatments or aids in determining surgical options. |
| Databáze: | OpenAIRE |
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