直方图
医学
有效扩散系数
乳腺癌
核医学
接收机工作特性
孕酮受体
肿瘤科
内科学
病理
癌症
雌激素受体
数学
放射科
磁共振成像
人工智能
计算机科学
图像(数学)
作者
Yanjin Qin,Caili Tang,Qilan Hu,Jingru Yi,Ting Yin,Tao Ai
摘要
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: 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.
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