Progesterone Receptor Status Analysis in Breast Cancer Patients using DCE- MR Images and Gabor Derived Anisotropy Index

Priscilla Dinkar Moyya, Mythili Asaithambi, A. K. Ramaniharan
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Abstract

Hormone receptors play a key role in female breast cancers as predictive biomarkers. Breast cancer subtype with Progesterone receptor (PgR) expression is one of the important hormone receptors in predicting prognosis and evaluating the Neoadjuvant Chemotherapy (NAC) treatment response. PgR (-) breast cancers are associated with a higher response to NAC compared to PgR (+) breast cancer patients. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is the widely used imaging modality in assessing the NAC response in patients. However, evaluating the treatment response of PgR breast cancers is complicated and challenging since breast cancer with positive receptor statuses will respond differently to NAC. Therefore, in this work, an attempt has been made to differentiate the PgR (+) and PgR (-) breast cancer patients due to NAC using Gabor derived Anisotropy Index (AI). A total of 50 PgR (+) and 63 PgR (-) DCE-MR images at 4 time points of NAC treatment are considered from the openly available I-SPY1 of the TCIA database. AI is calculated within the PgR status groups from Gabor energies that are acquired after designing the Gabor filter bank with 5 scales and 7 orientations. Results demonstrate that the AI values can significantly differentiate PgR (+) and PgR (-) breast cancer patients $(\mathrm{p}\leq 0.05)$ due to NAC. The mean AI values are observed to be high in PgR (+) patients $(4.14\mathrm{E}+10\pm$ 1.17E+ 11) than PgR (-) patients $(1.95\mathrm{E}+10\pm 8.06\mathrm{E}+10)$. AI could statistically differentiate visit 1 & visit 4 of NAC treatment in both PgR status patients with a p-value of 0.0246 and 0.0387 respectively. Further, the percentage difference in the mean value of AI is observed to be high in PgR (-) between visit 1 V s 4, visit 2 V s 4, visit 1 V s 3, and visit 2 Vs 3 compared to PgR (+) subjects. Hence, AI could be used as a single index value in assessing the treatment response in both PgR (+) and PgR (-) subjects.
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应用DCE- MR图像和Gabor衍生各向异性指数分析乳腺癌患者的孕激素受体状态
激素受体作为预测性生物标志物在女性乳腺癌中发挥着关键作用。孕激素受体(Progesterone receptor, PgR)表达的乳腺癌亚型是预测预后和评价新辅助化疗(NAC)治疗反应的重要激素受体之一。与PgR(+)乳腺癌患者相比,PgR(-)乳腺癌患者对NAC的反应更高。动态对比增强磁共振成像(DCE-MRI)是广泛应用于评估患者NAC反应的成像方式。然而,评估PgR乳腺癌的治疗反应是复杂和具有挑战性的,因为具有阳性受体状态的乳腺癌对NAC的反应不同。因此,本研究尝试利用Gabor衍生的各向异性指数(Anisotropy Index, AI)来区分NAC导致的PgR(+)和PgR(-)乳腺癌患者。从公开的TCIA数据库I-SPY1中选取NAC治疗4个时间点的50张PgR(+)和63张PgR (-) DCE-MR图像。人工智能是根据Gabor能量在PgR状态组内计算的,Gabor能量是在设计具有5个尺度和7个方向的Gabor滤波器组后获得的。结果显示AI值可明显区分NAC所致PgR(+)和PgR(-)乳腺癌患者$(\mathrm{p}\leq 0.05)$。PgR(+)患者的平均AI值$(4.14\mathrm{E}+10\pm$ (1.17E+ 11)高于PgR(-)患者$(1.95\mathrm{E}+10\pm 8.06\mathrm{E}+10)$。AI对两种PgR状态患者NAC治疗的第1次和第4次就诊均有统计学差异,p值分别为0.0246和0.0387。此外,与PgR(+)受试者相比,在访问1 vs4、访问2 vs4、访问1 vs3和访问2 vs3之间,观察到AI平均值的百分比差异在PgR(-)中较高。因此,AI可作为评估PgR(+)和PgR(-)受试者治疗反应的单一指标值。
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