ANALYSIS OF NEOADJUVANT CHEMOTHERAPY TREATMENT RESPONSE IN BREAST DCE MRI PATIENTS BASED ON ESTROGEN RECEPTOR STATUS AND GABOR FILTER DERIVED ANISOTROPY INDEX

Priscilla Dinkar Moyya, Mythili Asaithambi, Anandh Kilpattu Ramaniharan
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Abstract

Estrogen Receptor (ER) is a molecular biomarker that plays an important role in evaluating the Neoadjuvant Chemotherapy (NAC) treatment response of breast cancer patients. ER (-) breast cancer patients have better tumor response rates than ER (+) patients due to NAC and the result of ER status could change after NAC. However, there are limited studies on the analysis of NAC treatment response using ER status. Further, manual quantification of treatment response is challenging and inconsistent across raters. In this work, an attempt has been made to objectively quantify the radiological differences of Dynamic Contrast Enhanced (DCE) MR images in ER (-) and ER (+) patients due to NAC using Gabor filter derived Anisotropy Index (AI). The images (113 subjects at 4 visits of NAC treatment) used in this study are obtained from the publicly available I-SPY1 dataset. Gabor filter bank is designed with 5 scales and 7 orientations, and AI is calculated from each Gabor energy within the patient group. Results show that AI values can statistically (p < 0.05) differentiate the radiological differences in ER (-) and ER (+) patients due to NAC. The percentage difference in the mean AI values of Visit 1 Vs Visit 4, Visit 1 Vs Visit 3, and Visit 2 Vs Visit 4 is high in ER (-) compared to ER (+) patients. Thus, Gabor filter derived AI could be used as an objective measure in evaluating NAC treatment response in ER (-) and ER (+) patients.
基于雌激素受体状态和GABOR滤波器衍生各向异性指数的乳腺DCE-MRI患者新辅助化疗疗效分析
雌激素受体(Estrogen Receptor, ER)是一种分子生物标志物,在评价乳腺癌患者新辅助化疗(NAC)治疗反应中起着重要作用。NAC对ER(-)型乳腺癌患者的肿瘤缓解率优于ER(+)型乳腺癌患者,且NAC后ER状态的结果可能发生改变。然而,利用ER状态分析NAC治疗反应的研究有限。此外,人工量化治疗反应是具有挑战性的,并且在评分者之间不一致。在这项工作中,我们尝试使用Gabor滤波器衍生的各向异性指数(AI)客观量化NAC导致的ER(-)和ER(+)患者动态对比增强(DCE) MR图像的放射学差异。本研究中使用的图像(113名受试者接受4次NAC治疗)来自公开可用的I-SPY1数据集。Gabor滤波器组设计了5个尺度和7个方向,并根据患者组内的每个Gabor能量计算AI。结果显示,AI值可以统计学(p < 0.05)区分NAC所致ER(-)和ER(+)患者的影像学差异。与ER(+)患者相比,就诊1 Vs就诊4、就诊1 Vs就诊3、就诊2 Vs就诊4的平均AI值的百分比差异在ER(-)患者中较高。因此,Gabor滤波器衍生的AI可作为评价ER(-)和ER(+)患者NAC治疗反应的客观指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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