基于连续时间随机游走磁共振扩散模型的乳腺癌预后因素和分子亚型评估：使用全肿瘤直方图分析。

Assessment of Prognostic Factors and Molecular Subtypes of Breast Cancer With a Continuous-Time Random-Walk MR Diffusion Model: Using Whole Tumor Histogram Analysis.

机构信息

Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

MR Collaborations, Siemens Healthineers Ltd., Chengdu, China.

出版信息

J Magn Reson Imaging. 2023 Jul;58(1):93-105. doi: 10.1002/jmri.28474. Epub 2022 Oct 17.

DOI:10.1002/jmri.28474

PMID:36251468

Abstract

BACKGROUND

The continuous-time random-walk (CTRW) diffusion model to evaluate breast cancer prognosis is rarely reported.

PURPOSE

To investigate the correlations between apparent diffusion coefficient (ADC) and CTRW-specific parameters with prognostic factors and molecular subtypes of breast cancer.

STUDY TYPE

Retrospective.

POPULATION

One hundred fifty-seven women (median age, 50 years; range, 26-81 years) with histopathology-confirmed breast cancer.

FIELD STRENGTH/SEQUENCE: Simultaneous multi-slice readout-segmented echo-planar imaging at 3.0T.

ASSESSMENT

The histogram metrics of ADC, anomalous diffusion coefficient (D), temporal diffusion heterogeneity (α), and spatial diffusion heterogeneity (β) were calculated for whole-tumor volume. Associations between histogram metrics and prognostic factors (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67 proliferation index), axillary lymph node metastasis (ALNM), and tumor grade were assessed. The performance of histogram metrics, both alone and in combination, for differentiating molecular subtypes (HER2-positive, Luminal or triple negative) was also assessed.

STATISTICAL TESTS

Comparisons were made using Mann-Whitney test between different prognostic factor statuses and molecular subtypes. Receiver operating characteristic curve analysis was used to assess the performance of mean and median histogram metrics in differentiating the molecular subtypes. A P value <0.05 was considered statistically significant.

RESULTS

The histogram metrics of ADC, D, and α differed significantly between ER-positive and ER-negative status, and between PR-positive and PR-negative status. The histogram metrics of ADC, D, α, and β were also significantly different between the HER2-positive and HER2-negative subgroups, and between ALNM-positive and ALNM-negative subgroups. The histogram metrics of α and β significantly differed between high and low Ki-67 proliferation subgroups, and between histological grade subgroups. The combination of α and β achieved the highest performance (AUC = 0.702) to discriminate the Luminal and HER2-positive subtypes.

DATA CONCLUSION

Whole-tumor histogram analysis of the CTRW model has potential to provide additional information on the prognosis and intrinsic subtyping classification of breast cancer.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

摘要

背景

连续时间随机游走（CTRW）扩散模型用于评估乳腺癌预后的应用较少。

目的

探讨表观扩散系数（ADC）与 CTRW 特定参数与乳腺癌预后因素和分子亚型的相关性。

研究类型

回顾性研究。

人群

157 名经组织病理学证实的乳腺癌女性患者（中位年龄 50 岁；范围 26-81 岁）。

磁场强度/序列：3.0T 同步多片读出分段回波平面成像。

评估

对全肿瘤体积计算 ADC、反常扩散系数（D）、时间扩散异质性（α）和空间扩散异质性（β）的直方图指标。评估直方图指标与预后因素（雌激素受体[ER]、孕激素受体[PR]、人表皮生长因子受体 2 [HER2]和 Ki-67 增殖指数）、腋窝淋巴结转移（ALNM）和肿瘤分级之间的相关性。还评估了直方图指标（单独或组合）在区分分子亚型（HER2 阳性、Luminal 或三阴性）方面的性能。

统计学检验

采用 Mann-Whitney 检验比较不同预后因素状态和分子亚型之间的差异。采用受试者工作特征曲线分析评估平均和中位数直方图指标在区分分子亚型方面的性能。P 值<0.05 为统计学显著。

结果

ER 阳性和 ER 阴性状态以及 PR 阳性和 PR 阴性状态之间 ADC、D 和 α 的直方图指标差异有统计学意义。HER2 阳性和 HER2 阴性亚组以及 ALNM 阳性和 ALNM 阴性亚组之间 ADC、D、α 和 β 的直方图指标也有显著差异。α 和 β 的直方图指标在 Ki-67 增殖高和低亚组以及组织学分级亚组之间存在显著差异。α 和 β 的组合在区分 Luminal 和 HER2 阳性亚型方面表现出最高的性能（AUC=0.702）。