Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI
Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI
复制标题
预测乳腺癌新辅助化疗的反应：MRI 中的小波变换放射组学
DOI：
10.1186/s12885-020-6523-2
复制
发表时间：
2020-02-05
期刊：
影响因子：
3.8
通讯作者：
Xu, Maosheng
中科院分区：
文献类型：
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作者：
Zhou, Jiali;Lu, Jinghui;Xu, Maosheng
本研究的目的是探讨小波变换放射组学MRI在预测局部晚期乳腺癌（LABC）患者新辅助化疗（NAC）的病理完全缓解（pCR）中的价值。方法回顾性分析55例女性LABC患者在NAC术前行MRI增强扫描（CE-MRI）的临床资料。根据NAC后的病理学评估，将患者对NAC的反应分为pCR和非pCR。在分割的病变中计算三组放射组学纹理，包括（1）体积纹理，（2）外周纹理和（3）小波变换纹理。预测pCR的6个模型为模型I：组（1）、模型II：组（1）+（2）、模型III：组（3）、模型IV：组（1）+（3）、模型V：组（2）+（3）和模型VI：组（1）+（2）+（3）。使用受试者工作特征（ROC）曲线下面积（AUC）比较预测模型的性能。结果6个模型预测pCR的AUC分别为0.816 ± 0.033（模型I）、0.823 ± 0.020（模型I）和0.823 ± 0.020（模型I）。（模型II）、0.888 +/- 0.025（模型III）、0.876 +/- 0.015（模型IV）、0.885 +/- 0.030（模型V）和0.874 +/- 0.019（模型VI）。四个模型与小波变换的纹理（模型III，IV，V和VI）的性能显着优于那些没有小波变换的纹理（模型I和II）。此外，包含体积纹理或外围纹理或两者都没有导致性能的任何改进。结论在LABC患者pCR对NAC的放射组学MRI预测中，小波变换纹理优于体积和/或外周纹理，这可能可以作为预测LABC对NAC反应的替代生物标志物。
Background The purpose of this study was to investigate the value of wavelet-transformed radiomic MRI in predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) for patients with locally advanced breast cancer (LABC). Methods Fifty-five female patients with LABC who underwent contrast-enhanced MRI (CE-MRI) examination prior to NAC were collected for the retrospective study. According to the pathological assessment after NAC, patient responses to NAC were categorized into pCR and non-pCR. Three groups of radiomic textures were calculated in the segmented lesions, including (1) volumetric textures, (2) peripheral textures, and (3) wavelet-transformed textures. Six models for the prediction of pCR were Model I: group (1), Model II: group (1) + (2), Model III: group (3), Model IV: group (1) + (3), Model V: group (2) + (3), and Model VI: group (1) + (2) + (3). The performance of predicting models was compared using the area under the receiver operating characteristic (ROC) curves (AUC). Results The AUCs of the six models for the prediction of pCR were 0.816 +/- 0.033 (Model I), 0.823 +/- 0.020 (Model II), 0.888 +/- 0.025 (Model III), 0.876 +/- 0.015 (Model IV), 0.885 +/- 0.030 (Model V), and 0.874 +/- 0.019 (Model VI). The performance of four models with wavelet-transformed textures (Models III, IV, V, and VI) was significantly better than those without wavelet-transformed textures (Model I and II). In addition, the inclusion of volumetric textures or peripheral textures or both did not result in any improvements in performance. Conclusions Wavelet-transformed textures outperformed volumetric and/or peripheral textures in the radiomic MRI prediction of pCR to NAC for patients with LABC, which can potentially serve as a surrogate biomarker for the prediction of the response of LABC to NAC.