Efficacy prediction and evaluation of dynamic contrast-enhanced magnetic resonance imaging texture analysis in the neoadjuvant chemotherapy for breast cancer
10.3760/cma.j.cn115355-20191207-00559
- VernacularTitle:动态增强磁共振成像纹理分析对乳腺癌新辅助化疗效果的预测与评估
- Author:
Huiling SONG
1
;
Yanfen CUI
;
Xiaotang YANG
Author Information
1. 山西医科大学医学影像学系，太原 030001
- From:
Cancer Research and Clinic
2020;32(8):562-568
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the efficacy prediction and evaluation value of neoadjuvant chemotherapy for breast cancer by using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) texture analysis.Methods:The clinical data of 63 patients with pathologically confirmed breast cancer in the Shanxi Provincial Cancer Hospital from September 2014 to October 2018 were retrospectively analyzed. All the patients underwent DCE-MRI before and after neoadjuvant chemotherapy and they were divided into the treatment-effective group (40 cases) and the treatment-ineffective group (23 cases) according to the postoperative pathological results. Texture parameters from volume transfer (Ktrans) maps of DCE-MRI before neoadjuvant chemotherapy and after 4-8 cycles of neoadjuvant chemotherapy were measured by using Omni-Kinetics software. The comparison of texture parameters between the two groups was performed by using independent sample t test or Mann-Whitney U test. The receiver operating characteristic curve was drawn and the prediction efficiency of these texture parameters in the therapeutic efficacy of neoadjuvant chemotherapy for breast cancer according to the corresponding area under the curve (AUC) was evaluated.Results:A total of 33 texture parameters were enrolled, and finally 29 texture parameters were retained. Before and after neoadjuvant chemotherapy 22 texture parameters had statistically significant difference in 63 patients (all P < 0.05). There was a statistically significant difference in 9 texture parameters between the two groups before neoadjuvant chemotherapy (all P < 0.05), including uniformity [0.17 (-0.06, 0.34), 0.39 (0.22, 0.48), Z = -2.955, P < 0.01], histogram energy [169.88 (129.36, 288.77), 116.22 (93.77, 151.95), Z = 3.241, P < 0.01] and histogram entropy [6.33 (5.71, 6.69), 6.68 (6.52, 6.97), Z = -2.991, P < 0.01]. After neoadjuvant chemotherapy, 8 of the 29 texture parameters between the two groups had statistically significant differences (all P < 0.05), including histogram entropy (6.00±0.71, 6.46±0.49, t = -2.720, P < 0.01), entropy (6.81±1.40, 8.02±1.48, t = -3.238, P < 0.01), Haralick entropy [0.49±0.10, 0.55±0.10, Z = -2.613, P < 0.01], grey level non-uniformity (GLN) [1.68 (1.42, 3.37), 4.92 (3.58, 8.50), Z = -3.897, P < 0.01], run length non-uniformity (RLN) [100.38 (65.31, 305.75), 359.75 (176.75, 655.00), Z = -4.033, P < 0.01]. There were statistical differences in 8 parameters change rate before and after neoadjuvant chemotherapy between the two groups (all P < 0.05), mainly including ΔGLN [-0.72 (-0.78, -0.60), -0.23 (-0.55, 0.36), Z = -4.554, P < 0.01], ΔRLN [-0.71 (-0.85, -0.52), -0.33 (-0.48, -0.10), Z = -4.454, P < 0.01], Δhigh grey level run emphasis (HGLRE) [1.28 (0.39, 3.46), 0.11 (-0.24, 0.86), Z = 3.184, P < 0.01]. According to the ROC curve, AUC of GLN, RLN, ΔGLN and ΔRLN after neoadjuvant chemotherapy was 0.80, 0.81, 0.85 and 0.84, respectively. Conclusion:Some texture parameters obtained from DCE-MRI Ktrans map can predict and evaluate the efficacy of neoadjuvant chemotherapy in breast cancer.
