Quartile histogram assessment of glioma malignancy using high <i>b</i>‐value diffusion MRI with a continuous‐time random‐walk model

Karaman, M. Muge; Zhang, Jiaxuan; Zhu, Wenzhen; Zhou, Xiaohong Joe

doi:10.1002/nbm.4485

Cited by 18 publications

(15 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…M.M. [ 26 ] found that the CTRW diffusion model offers potential for exploring tumor structural heterogeneity at a subvoxel level and assessing glioma malignancy. The SM was used to monitor the treatment response (e.g., to explore the possibility of assessing early response to chemotherapy in patients with colorectal liver metastasis) [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

Application of advanced diffusion models from diffusion weighted imaging in a large cohort study of breast lesions

Wang

et al. 2023

BMC Med Imaging

View full text Add to dashboard Cite

Background To evaluate multiple parameters in multiple b-value diffusion-weighted imaging (DWI) in characterizing breast lesions and predicting prognostic factors and molecular subtypes. Methods In total, 504 patients who underwent 3-T magnetic resonance imaging (MRI) with T1-weighted dynamic contrast-enhanced (DCE) sequences, T2-weighted sequences and multiple b-value (7 values, from 0 to 3000 s/mm2) DWI were recruited. The average values of 13 parameters in 6 models were calculated and recorded. The pathological diagnosis of breast lesions was based on the latest World Health Organization (WHO) classification. Results Twelve parameters exhibited statistical significance in differentiating benign and malignant lesions. alpha demonstrated the highest sensitivity (89.5%), while sigma demonstrated the highest specificity (77.7%). The stretched-exponential model (SEM) demonstrated the highest sensitivity (90.8%), while the biexponential model demonstrated the highest specificity (80.8%). The highest AUC (0.882, 95% CI, 0.852–0.912) was achieved when all 13 parameters were combined. Prognostic factors were correlated with different parameters, but the correlation was relatively weak. Among the 6 parameters with significant differences among molecular subtypes of breast cancer, the Luminal A group and Luminal B (HER2 negative) group had relatively low values, and the HER2-enriched group and TNBC group had relatively high values. Conclusions All 13 parameters, independent or combined, provide valuable information in distinguishing malignant from benign breast lesions. These new parameters have limited meaning for predicting prognostic factors and molecular subtypes of malignant breast tumors.

show abstract

Section: Discussionmentioning

confidence: 99%

Application of advanced diffusion models from diffusion weighted imaging in a large cohort study of breast lesions

Wang

et al. 2023

BMC Med Imaging

View full text Add to dashboard Cite

show abstract

“…The CTRW model naturally takes tissue cellularity and heterogeneity into its parameters (D, α, β). 17,18,20,27 The complementary nature of these three tissue properties provides the basis for combining the CTRW parameters to characterize molecular subtypes, as shown in this study.…”

Section: Discussionmentioning

confidence: 94%

“…where S 0 and S are the signal intensity in the voxel with b values of 0 and 800 mm 2 /sec, respectively. The CTRW model was calculated through the following fitting formula 17,20 :…”

Section: Quantitative Measurements Of Ctrw-dwimentioning

confidence: 99%

“…19 Parameters α and β are related to temporal and spatial diffusion heterogeneities, respectively. 20 In a recent study, the fractional-order calculus (FROC) model with new quantitative diffusion parameters (βFROC, DFROC, and μFROC) based on the CTRW theory (its predecessor) was applied to differentiate between malignant and benign breast lesions in breast cancer screening work-up. 21 However, the application of CTRW-based DWI in assessing breast cancer prognosis is still rare.…”

mentioning

confidence: 99%

“…Mathematically and conceptually, parameter D is similar to ADC and can measure abnormal diffusion 19 . Parameters α and β are related to temporal and spatial diffusion heterogeneities, respectively 20 . In a recent study, the fractional‐order calculus (FROC) model with new quantitative diffusion parameters (βFROC, DFROC, and μFROC) based on the CTRW theory (its predecessor) was applied to differentiate between malignant and benign breast lesions in breast cancer screening work‐up 21 .…”

mentioning

confidence: 99%

See 2 more Smart Citations

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

Qin

Tang

et al. 2022

Magnetic Resonance Imaging

View full text Add to dashboard Cite

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

show abstract

Evaluation of a continuous‐time random‐walk diffusion model for the differentiation of malignant and benign breast lesions and its association with Ki‐67 expression

Zhang

Gao

et al. 2023

NMR in Biomedicine

View full text Add to dashboard Cite

The purpose of the current study was to evaluate the performance of a continuous‐time random‐walk (CTRW) diffusion model for differentiating malignant and benign breast lesions and to consider the potential association between CTRW parameters and the Ki‐67 expression. Sixty‐four patients (46.2 ± 11.4 years) with breast lesions (29 malignant and 35 benign) were evaluated with the CTRW model, intravoxel incoherent motion model, and diffusion‐weighted imaging. Echo planar diffusion‐weighted imaging was conducted using 13 b‐values (0‐3000 s/mm2). Three CTRW model parameters, including an anomalous diffusion coefficient Dm, and two parameters related to temporal and spatial diffusion heterogeneity, α and β, respectively, were obtained, and had MRI b‐values of 0–3000 s/mm2. Receiver operating characteristic (ROC) analysis was conducted to determine the sensitivity, specificity, and diagnostic accuracy of CTRW parameters for differentiating malignant from benign breast lesions. In malignant breast lesions, the CTRW parameters Dm, α, and β were significantly lower than the corresponding parameters of benign breast lesions. In the malignant breast lesion group, the CTRW parameter Dm was significantly lower in high Ki‐67 expression than in low Ki‐67 expression. In ROC analysis, the combination of CTRW parameters (Dm, α, β) demonstrated the highest area under the curve value (0.985) and diagnostic accuracy (94.23%) in differentiating malignant and benign breast lesions. The CTRW model effectively differentiated malignant from benign breast lesions. The CTRW diffusion model offers a new way for noninvasive assessment of breast malignancy and better understanding of the proliferation of malignant lesions.

show abstract

Quartile histogram assessment of glioma malignancy using high b‐value diffusion MRI with a continuous‐time random‐walk model

Cited by 18 publications

References 57 publications

Application of advanced diffusion models from diffusion weighted imaging in a large cohort study of breast lesions

Application of advanced diffusion models from diffusion weighted imaging in a large cohort study of breast lesions

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

Evaluation of a continuous‐time random‐walk diffusion model for the differentiation of malignant and benign breast lesions and its association with Ki‐67 expression

Contact Info

Product

Resources

About