Evaluation of diffusion models in breast cancer

Combination of IVIM with DCE-MRI for diagnostic and prognostic evaluation of breast cancer

PURPOSE

To identify the most effective combination of DCE-MRI (Ktrans,Kep) and IVIM (D,f) and analyze the correlations of these parameters with prognostic indicators (ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size) to improve the diagnostic and prognostic efficiency in breast cancer.

METHODS

This is a prospective study. We performed T1WI, T2WI, IVIM, DCE-MRI at 3 T MRI examinations on benign and malignant breast lesions that met the inclusion criteria. We also collected pathological results of corresponding lesions, including ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size. The diagnostic efficacy of DCE-MRI, IVIM imaging, and their combination for benign and malignant breast lesions was assessed. Correlations between the DCE-MRI and IVIM parameters and prognostic indicators were assessed.

RESULTS

Overall,59 female patients with 62 lesions (22 benign lesions and 40 malignant lesions) were included in this study. The malignant group showed significantly lower D values (p < 0.05) and significantly higher Ktrans, Kep, and f values (p < 0.05). The AUC values of DCE, IVIM, DCE + IVIM were 0.828, 0.882, 0.901. Ktrans, Kep, D and f values were correlated with the pathological grade (p < 0.05); Ktrans was negatively correlated with ER expression (r = -0.519, p < 0.05); Kep was correlated with PR expression and the Ki-67 index (r = -0.489, 0.330, p < 0.05); the DCE and IVIM parameters showed no significant correlations with the HER2 and ALN (p > 0.05). Tumor diameter was correlated with the Kep, D and f values (r = 0.246, -0.278, 0.293; p < 0.05).

CONCLUSION

IVIM and DCE-MRI allowed differential diagnosis of benign and malignant breast lesions, and their combination showed significantly better diagnostic efficiency. DCE- and IVIM-derived parameters showed correlations with some prognostic factors for breast cancer.

Simplified intravoxel incoherent motion DWI for differentiating malignant from benign breast lesions

Background

To evaluate simplified intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for differentiating malignant versus benign breast lesions as (i) stand-alone tool and (ii) add-on to dynamic contrast-enhanced magnetic resonance imaging.

Methods

1.5-T DWI data (b = 0, 50, 250, 800 s/mm²) were retrospectively analysed for 126 patients with malignant or benign breast lesions. Apparent diffusion coefficient (ADC) ADC (0, 800) and IVIM-based parameters D₁′ = ADC (50, 800), D₂′ = ADC (250, 800), f₁′ = f (0, 50, 800), f₂′ = f (0, 250, 800) and D*′ = D* (0, 50, 250, 800) were voxel-wise calculated without fitting procedures. Regions of interest were analysed in vital tumour and perfusion hot spots. Beside the single parameters, the combined use of D₁′ with f₁′ and D₂′ with f₂′ was evaluated. Lesion differentiation was investigated for lesions (i) with hyperintensity on DWI with b = 800 s/mm² (n = 191) and (ii) with suspicious contrast-enhancement (n = 135).

Results

All lesions with suspicious contrast-enhancement appeared also hyperintense on DWI with b = 800 s/mm². For task (i), best discrimination was reached for the combination of D₁′ and f₁′ using perfusion hot spot regions-of-interest (accuracy 93.7%), which was higher than that of ADC (86.9%, p = 0.003) and single IVIM parameters D₁′ (88.0%) and f₁′ (87.4%). For task (ii), best discrimination was reached for single parameter D₁′ using perfusion hot spot regions-of-interest (92.6%), which were slightly but not significantly better than that of ADC (91.1%) and D₂′ (88.1%). Adding f₁′ to D₁′ did not improve discrimination.

Conclusions

IVIM analysis yielded a higher accuracy than ADC. If stand-alone DWI is used, perfusion analysis is of special relevance.

Diffusion MRI of the breast: Current status and future directions

Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.

Dual-Phase ADC Modelling of Breast Masses in Diffusion-Weighted Imaging: Comparison with Histopathologic Findings

Objective

To investigate the diagnostic value of dual-phase apparent diffusion coefficient (ADC) compared to traditional ADC values in quantitative diffusion-weighted imaging (DWI) for differentiating between benign and malignant breast masses.

Materials and Methods

Diffusion-weighted images of pathologically confirmed 88 benign and 85 malignant lesions acquired using a 3.0T MR scanner were analyzed. Small region-of-interests focusing on the highest signal intensity of lesions were used. Lesion ADC estimates were obtained separately from all b-value images (ADC; b=50, 400 and 800s/mm²), lower b-value images (ADC_low; b=50 and 400s/mm²) and higher b-value images (ADC_high; b=400 and 800s/mm²). A set of dual-phase ADC (dpADC) models were constructed using ADC_low, ADC_high and a perfusion influence factor ranging from 0 to 1.

Results

Strong positive correlation is observable between ADC and all dpADCs (ρ=0.80-1.00). Differences in ADC and dpADCs between the benign and the malignant lesions are all significant (p<0.05). In detecting malignancy, traditional lesion ADC provides a good performance (AUC=89.9%) however dpADC_0.5 (dpADC with a factor of 0.5) accomplishes a better performance (AUC=90.8%). At optimal thresholds, ADC achieves 94.1% sensitivity, 72.7% specificity and 83.2% accuracy while dpADC_0.5 leads to 92.9% sensitivity, 79.5% specificity and 86.1% accuracy.

Conclusion

Dual-phase ADC modelling may improve the accuracy in breast cancer diagnosis using DWI. Further prospective studies are needed to justify its benefit in clinical setting.

Model selection for high b-value diffusion-weighted MRI of the prostate

PURPOSE

To assess the abilities of the standard mono-exponential (ME), bi-exponential (BE), diffusion kurtosis (DK) and stretched exponential (SE) models to characterize diffusion signal in malignant and prostatic tissues and determine which of the four models best characterizes these tissues on a per-voxel basis.

MATERIALS AND METHODS

This institutional-review-board-approved, HIPAA-compliant, retrospective study included 55 patients (median age, 61years; range, 42-77years) with untreated, biopsy-proven PCa who underwent endorectal coil MRI at 3-Tesla, diffusion-weighted MRI acquired at eight b-values from 0 to 2000s/mm². Estimated parameters were apparent diffusion coefficent (ME model); diffusion coefficients for the fast (D_fast) and slow (D_slow) components and fraction of fast component, f_fast (BE model); diffusion coefficient D, and kurtosis K (DK model); distributed diffusion coefficient DDC and α for (SE model). For one region-of-interest (ROI) in PZ and another in PCa in each patient, the corrected Akaike information criterion (AICc) and the Akaike weight (w) were calculated for each voxel.

RESULTS

Based on AICc and w, all non-monoexponential models outperformed the ME model in PZ and PCa. The DK model in PZ and SE model in PCa ROIs best fit the greatest average percentages of voxels (39% and 43%, respectively) and had the highest mean w (35±16×10^-2 and 41±22×10^-2, respectively).

CONCLUSION

DK and SE models best fit DWI data in PZ and PCa, and non-ME models consistently outperformed the ME model. Voxel-wise mapping of the preferential model demonstrated that the vast majority of voxels in either tissue type were best fit with one of the non-monoexponential models. At the given SNR levels, the maximum b-value of 2000s/mm² is not sufficiently high to identify the preferred non-monoexponential model.

Intravoxel incoherent motion (IVIM) histogram biomarkers for prediction of neoadjuvant treatment response in breast cancer patients

OBJECTIVE

To examine the prognostic capabilities of intravoxel incoherent motion (IVIM) metrics and their ability to predict response to neoadjuvant treatment (NAT). Additionally, to observe changes in IVIM metrics between pre- and post-treatment MRI.

METHODS

This IRB-approved, HIPAA-compliant retrospective study observed 31 breast cancer patients (32 lesions). Patients underwent standard bilateral breast MRI along with diffusion-weighted imaging before and after NAT. Six patients underwent an additional IVIM-MRI scan 12-14 weeks after initial scan and 2 cycles of treatment. In addition to apparent diffusion coefficients (ADC) from monoexponential decay, IVIM mean values (tissue diffusivity D_t, perfusion fraction f_p, and pseudodiffusivity D_p) and histogram metrics were derived using a biexponential model. An additional filter identified voxels of highly vascular tumor tissue (VTT), excluding necrotic or normal tissue. Clinical data include histology of biopsy and clinical response to treatment through RECIST assessment. Comparisons of treatment response were made using Wilcoxon rank-sum tests.

RESULTS

Average, kurtosis, and skewness of pseudodiffusion D_p significantly differentiated RECIST responders from nonresponders. ADC and D_t values generally increased (∼70%) and VTT% values generally decreased (∼20%) post-treatment.

CONCLUSION

D_p metrics showed prognostic capabilities; slow and heterogeneous pseudodiffusion offer poor prognosis. Baseline ADC/D_t parameters were not significant predictors of response. This work suggests that IVIM mean values and heterogeneity metrics may have prognostic value in the setting of breast cancer NAT.

Non-Mono-Exponential Analysis of Diffusion-Weighted Imaging for Treatment Monitoring in Prostate Cancer Bone Metastases

Diffusion-weighted imaging quantified using the mono-exponential model has shown great promise for monitoring treatment response in prostate cancer bone metastases. The aim of this prospective study is to evaluate whether non-mono-exponential diffusion models better describe the water diffusion properties and may improve treatment response assessment. Diffusion-weighted imaging data of 12 treatment-naïve patients with 34 metastases acquired before and at one, two, and three months after initiation of antiandrogen treatment are analysed using the mono-exponential, the intravoxel incoherent motion, the stretched exponential, and the statistical model. Repeatability of the fitted parameters and changes under therapy are quantified. Model preference is assessed and correlation coefficients across times are calculated to delineate the relationship between the prostate-specific antigen levels and the diffusion parameters as well as between the diffusion parameters within each model. There is a clear preference for non-mono-exponential diffusion models at all time points. Particularly the stretched exponential is favoured in approximately 60% of the lesions. Its parameters increase significantly in response to treatment and are highly repeatable. Thus, the stretched exponential may be utilized as a potential optimal model for monitoring treatment response. Compared with the mono-exponential model, it may provide complementary information on tissue properties and improve response assessment.

Assessment of early treatment response to neoadjuvant chemotherapy in breast cancer using non-mono-exponential diffusion models: a feasibility study comparing the baseline and mid-treatment MRI examinations

OBJECTIVES

To assess the feasibility of the mono-exponential, bi-exponential and stretched-exponential models in evaluating response of breast tumours to neoadjuvant chemotherapy (NACT) at 3 T.

METHODS

Thirty-six female patients (median age 53, range 32-75 years) with invasive breast cancer undergoing NACT were enrolled for diffusion-weighted MRI (DW-MRI) prior to the start of treatment. For assessment of early response, changes in parameters were evaluated on mid-treatment MRI in 22 patients. DW-MRI was performed using eight b values (0, 30, 60, 90, 120, 300, 600, 900 s/mm2). Apparent diffusion coefficient (ADC), tissue diffusion coefficient (D t), vascular fraction (ƒ), distributed diffusion coefficient (DDC) and alpha (α) parameters were derived. Then t tests compared the baseline and changes in parameters between response groups. Repeatability was assessed at inter- and intraobserver levels.

RESULTS

All patients underwent baseline MRI whereas 22 lesions were available at mid-treatment. At pretreatment, mean diffusion coefficients demonstrated significant differences between groups (p < 0.05). At mid-treatment, percentage increase in ADC and DDC showed significant differences between responders (49 % and 43 %) and non-responders (21 % and 32 %) (p = 0.03, p = 0.04). Overall, stretched-exponential parameters showed excellent repeatability.

CONCLUSION

DW-MRI is sensitive to baseline and early treatment changes in breast cancer using non-mono-exponential models, and the stretched-exponential model can potentially monitor such changes.

KEY POINTS

• Baseline diffusion coefficients demonstrated significant differences between complete pathological responders and non-responders. • Increase in ADC and DDC at mid-treatment can discriminate responders and non-responders. • The ƒ fraction at mid-treatment decreased in responders whereas increased in non-responders. • The mono- and stretched-exponential models showed excellent inter- and intrarater repeatability. • Treatment effects can potentially be assessed by non-mono-exponential diffusion models.

Separation of type and grade in cervical tumours using non-mono-exponential models of diffusion-weighted MRI

OBJECTIVES

Assessment of empirical diffusion-weighted MRI (DW-MRI) models in cervical tumours to investigate whether fitted parameters distinguish between types and grades of tumours.

METHODS

Forty-two patients (24 squamous cell carcinomas, 14 well/moderately differentiated, 10 poorly differentiated; 15 adenocarcinomas, 13 well/moderately differentiated, two poorly differentiated; three rare types) were imaged at 3 T using nine b-values (0 to 800 s mm-2). Mono-exponential, stretched exponential, kurtosis, statistical, and bi-exponential models were fitted. Model preference was assessed using Bayesian Information Criterion analysis. Differences in fitted parameters between tumour types/grades and correlation between fitted parameters were assessed using two-way analysis of variance and Pearson's linear correlation coefficient, respectively.

RESULTS

Non-mono-exponential models were preferred by 83 % of tumours with bi-exponential and stretched exponential models preferred by the largest numbers of tumours. Apparent diffusion coefficient (ADC) and diffusion coefficients from non-mono-exponential models were significantly lower in poorly differentiated tumours than well/moderately differentiated tumours. α (stretched exponential), K (kurtosis), f and D* (bi-exponential) were significantly different between tumour types. Strong correlation was observed between ADC and diffusion coefficients from other models.

CONCLUSIONS

Non-mono-exponential models were preferred to the mono-exponential model in DW-MRI data from cervical tumours. Parameters of non-mono-exponential models showed significant differences between types and grades of tumours.

KEY POINTS

• Non-mono-exponential DW-MRI models are preferred in the majority of cervical tumours. • Poorly differentiated cervical tumours exhibit lower diffusion coefficients than well/moderately differentiated tumours. • Non-mono-exponential model parameters α, K, f, and D* differ between tumour types. • Micro-structural features are likely to affect parameters in non-mono-exponential models differently.

A comparative assessment of preclinical chemotherapeutic response of tumors using quantitative non-Gaussian diffusion MRI

BACKGROUND

Diffusion-weighted MRI (DWI) signal attenuation is often not mono-exponential (i.e. non-Gaussian diffusion) with stronger diffusion weighting. Several non-Gaussian diffusion models have been developed and may provide new information or higher sensitivity compared with the conventional apparent diffusion coefficient (ADC) method. However the relative merits of these models to detect tumor therapeutic response is not fully clear.

METHODS

Conventional ADC, and three widely-used non-Gaussian models, (bi-exponential, stretched exponential, and statistical model), were implemented and compared for assessing SW620 human colon cancer xenografts responding to barasertib, an agent known to induce apoptosis via polyploidy. Bayesian Information Criterion (BIC) was used for model selection among all three non-Gaussian models.

RESULTS

All of tumor volume, histology, conventional ADC, and three non-Gaussian DWI models could show significant differences between control and treatment groups after four days of treatment. However, only the non-Gaussian models detected significant changes after two days of treatment. For any treatment or control group, over 65.7% of tumor voxels indicate the bi-exponential model is strongly or very strongly preferred.

CONCLUSION

Non-Gaussian DWI model-derived biomarkers are capable of detecting tumor earlier chemotherapeutic response of tumors compared with conventional ADC and tumor volume. The bi-exponential model provides better fitting compared with statistical and stretched exponential models for the tumor and treatment models used in the current work.

Diffusion-weighted breast MRI: Clinical applications and emerging techniques

Diffusion-weighted MRI (DWI) holds potential to improve the detection and biological characterization of breast cancer. DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. Potential benefits include improved differentiation of benign and malignant breast lesions, assessment and prediction of therapeutic efficacy, and noncontrast detection of breast cancer. The breast presents a unique imaging environment with significant physiologic and inter-subject variations, as well as specific challenges to achieving reliable high quality diffusion-weighted MR images. Technical innovations are helping to overcome many of the image quality issues that have limited widespread use of DWI for breast imaging. Advanced modeling approaches to further characterize tissue perfusion, complexity, and glandular organization may expand knowledge and yield improved diagnostic tools.

LEVEL OF EVIDENCE

5 J. Magn. Reson. Imaging 2016 J. Magn. Reson. Imaging 2017;45:337-355.

Semi-automated quantitative intravoxel incoherent motion analysis and its implementation in breast diffusion-weighted imaging

BACKGROUND

To optimize and validate intravoxel incoherent motion (IVIM) modeled diffusion-weighted imaging (DWI) compared with the apparent diffusion coefficient (ADC) for semi-automated analysis of breast lesions using a multi-reader setup.

MATERIALS AND METHODS

Patients (n = 176) with breast lesions (≥1 cm) and known pathology were prospectively examined (1.5 Tesla) with DWI (b = 0, 50, 200, 500, 800, 1000 s/mm(2) ) between November 2008 and July 2014 and grouped into a training and test set. Three independent readers applied a semi-automated procedure for setting regions-of-interest for each lesion and recorded ADC and IVIM parameters: molecular diffusion (Dslow ), microperfusion (Dfast ), and the fraction of Dfast (ffast ). In the training set (24 lesions, 12 benign), a semi-automated method was optimized to yield maximum true negatives (TN) with minimal false negatives (FN): only the optimal fraction (Fo) of voxels in the lesions was used and optimal thresholds were determined. The optimal Fo and thresholds were then applied to a consecutive test set (139 lesions, 23 benign) to obtain specificity and sensitivity.

RESULTS

In the training set, optimal thresholds were 1.44 × 10(-3) mm(2) /s (Dslow ), 18.55 × 10(-3) mm(2) /s (Dfast ), 0.247 (ffast ) and 2.00 × 10(-3) mm(2) /s (ADC) with Fo set to 0.61, 0.85, 1.0, and 1.0, respectively, this resulted in TN = 5 (IVIM) and TN = 1 (ADC), with FN = 0. In the test set, sensitivity and specificity among the readers were 90.5-93.1% and 43.5-52.2%, respectively, for IVIM, and 94.8-95.7% and 13.0-21.7% for ADC (P ≤ 0.0034) without inter-reader differences (P = 1.000).

CONCLUSION

The presented semi-automated method for breast lesion evaluation is reader independent and yields significantly higher specificity for IVIM compared with the ADC.