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

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 αmean and βmean 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

Histogram Analysis of Computed Tomography Images to Differentiate Lacrimal Lymphoma and Lacrimal Inflammatory Pseudotumor

“…By calculating the characteristic parameters of ROI in the images, it evaluates the distribution of ROI grayscale intensity, provides more and more comprehensive quantitative informations, and has the advantages of simple operation, quantitative objective, and strong repeatability. To date, multiple studies available in the literature have reported that histogram analysis performs well in distinguishing between benign and malignant tumors, evaluating response to treatment, as well as predicting prognosis [22][23][24]. In this study, the histogram parameters of LL and LIP were analyzed to explore the feasibility and the value of CT images gray-scale histogram in identifying between LL and LIP.…”

Diffusion-Weighted MRI for the Assessment of Molecular Prognostic Biomarkers in Breast Cancer

“…The standardized mean difference in ADC with a 95% confidence interval was used as a summary statistic for ER, PgR, and HER2 categories. A total of 52 articles [ 3 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 ] were finally reviewed and quantitatively summarized ( Fig. 1 ).…”

“…A forest plot of the mean differences in ADCs between ER-positive and -negative breast cancers from 44 studies [ 3 13 14 16 17 18 19 20 21 22 24 26 27 28 29 30 31 32 33 34 35 36 37 38 41 42 43 44 45 46 47 48 49 50 51 52 53 54 56 57 58 59 60 61 ] is given in Figure 2A . There was large heterogeneity among the studies (I 2 = 80%), but overall, ER-positive cancers exhibited significantly lower ADCs than ER-negative cancers ( P < 0.01).…”

“…A forest plot of the mean difference in ADCs between HER2-negative and positive cancers from 40 studies [ 3 15 16 17 18 20 21 24 25 26 27 28 29 31 32 33 34 36 37 38 41 42 43 44 45 46 47 48 49 50 51 52 53 54 56 57 58 59 60 61 ] is given in Figure 3A . Again, this showed a large heterogeneity across the studies (I 2 = 92%).…”

Editorial for “Assessment of Prognostic Factors and Molecular Subtypes of Breast Cancer With a Continuous‐Time Random‐Walk MR Diffusion Model: Using Whole Tumor Histogram Analysis”

“…4 The CTRW model has shown promising results in tumor gradings of pediatric brain tumors, 5 gastrointestinal stromal tumors, 6 and recently breast cancer tumors. 7 In the study by Ai et al, 7 the authors applied a wholetumor histogram analysis of diffusion parameters (D, α, and β) derived from a CTRW model to evaluate the relations of the diffusion parameters with some prognostic factors and molecular subtypes of breast cancer. From a total of 157 patients, histogram metrics of the CTRW parameters showed differences in expression of the molecular biomarkers (eg, ER, PR, and Ki-67) as well as the luminal and HER2-positive subtypes of breast cancers.…”