| Autor: |
Eun NL; From the Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Republic of Korea (N.L.E., E.J.S., J.H.Y., J.A.K., H.M.G.); Department of Radiology, Hanyang University, College of Medicine, Seoul, Republic of Korea (N.L.E., J.S.P.); and Department of Healthcare Information Technology, Inje University, Gimhae, Republic of Korea (D.K.)., Kang D; From the Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Republic of Korea (N.L.E., E.J.S., J.H.Y., J.A.K., H.M.G.); Department of Radiology, Hanyang University, College of Medicine, Seoul, Republic of Korea (N.L.E., J.S.P.); and Department of Healthcare Information Technology, Inje University, Gimhae, Republic of Korea (D.K.)., Son EJ; From the Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Republic of Korea (N.L.E., E.J.S., J.H.Y., J.A.K., H.M.G.); Department of Radiology, Hanyang University, College of Medicine, Seoul, Republic of Korea (N.L.E., J.S.P.); and Department of Healthcare Information Technology, Inje University, Gimhae, Republic of Korea (D.K.)., Park JS; From the Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Republic of Korea (N.L.E., E.J.S., J.H.Y., J.A.K., H.M.G.); Department of Radiology, Hanyang University, College of Medicine, Seoul, Republic of Korea (N.L.E., J.S.P.); and Department of Healthcare Information Technology, Inje University, Gimhae, Republic of Korea (D.K.)., Youk JH; From the Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Republic of Korea (N.L.E., E.J.S., J.H.Y., J.A.K., H.M.G.); Department of Radiology, Hanyang University, College of Medicine, Seoul, Republic of Korea (N.L.E., J.S.P.); and Department of Healthcare Information Technology, Inje University, Gimhae, Republic of Korea (D.K.)., Kim JA; From the Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Republic of Korea (N.L.E., E.J.S., J.H.Y., J.A.K., H.M.G.); Department of Radiology, Hanyang University, College of Medicine, Seoul, Republic of Korea (N.L.E., J.S.P.); and Department of Healthcare Information Technology, Inje University, Gimhae, Republic of Korea (D.K.)., Gweon HM; From the Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Republic of Korea (N.L.E., E.J.S., J.H.Y., J.A.K., H.M.G.); Department of Radiology, Hanyang University, College of Medicine, Seoul, Republic of Korea (N.L.E., J.S.P.); and Department of Healthcare Information Technology, Inje University, Gimhae, Republic of Korea (D.K.). |
| Abstrakt: |
Background Previous studies have suggested that texture analysis is a promising tool in the diagnosis, characterization, and assessment of treatment response in various cancer types. Therefore, application of texture analysis may be helpful for early prediction of pathologic response in breast cancer. Purpose To investigate whether texture analysis of features from MRI is associated with pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. Materials and Methods This retrospective study included 136 women (mean age, 47.9 years; range, 31-70 years) who underwent NAC and subsequent surgery for breast cancer between January 2012 and August 2017. Patients were monitored with 3.0-T MRI before (pretreatment) and after (midtreatment) three or four cycles of NAC. Texture analysis was performed at pre- and midtreatment T2-weighted MRI, contrast material-enhanced T1-weighted MRI, diffusion-weighted MRI, and apparent diffusion coefficient (ADC) mapping by using commercial software. A random forest method was applied to build a predictive model for classifying those with pCR with use of texture parameters. Diagnostic performance for predicting pCR was assessed and compared with that of six other machine learning classifiers (adaptive boosting, decision tree, k-nearest neighbor, linear support vector machine, naive Bayes, and linear discriminant analysis) by using the Wald test and DeLong method. Results Forty of the 136 patients (29%) achieved pCR after NAC. In the prediction of pCR, the random forest classifier showed the lowest diagnostic performance with pretreatment ADC (area under the receiver operating characteristic curve [AUC], 0.53; 95% confidence interval: 0.44, 0.61) and the highest diagnostic performance with midtreatment contrast-enhanced T1-weighted MRI (AUC, 0.82; 95% confidence interval: 0.74, 0.88) among pre- and midtreatment T2-weighted MRI, contrast-enhanced T1-weighted MRI, diffusion-weighted MRI, and ADC mapping. Conclusion Texture parameters using a random forest method of contrast-enhanced T1-weighted MRI at midtreatment of neoadjuvant chemotherapy were valuable and associated with pathologic complete response in breast cancer. © RSNA, 2019 Online supplemental material is available for this article. |
