B-Value Optimization in the Estimation of Intravoxel Incoherent Motion Parameters in Patients with Cervical Cancer

Calculation of intravoxel incoherent motion parameter maps using a kernelized total difference-based method

Quantitative analysis of diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for many clinical applications since its development. In particular, the intravoxel incoherent motion (IVIM) model for DW-MRI has been commonly utilized in various organs. However, because of the presence of excessive noise, the IVIM parameter maps obtained from pixel-wise fitting are often unreliable. In this study, we propose a kernelized total difference-based curve-fitting method to estimate the IVIM parameters. Simulated DW-MRI data at five signal-to-noise ratios (i.e., 10, 20, 30, 50, and 100) and real abdominal DW-MRI data acquired on a 1.5-T MRI scanner with nine b-values (i.e., 0, 10, 25, 50, 100, 200, 300, 400, and 500 s/mm2) and six diffusion-encoding gradient directions were used to evaluate the performance of the proposed method. The results were compared with those obtained by three existing methods: trust-region reflective (TRR) algorithm, Bayesian probability (BP), and deep neural network (DNN). Our simulation results showed that the proposed method outperformed the other three comparing methods in terms of root-mean-square error. Moreover, the proposed method could preserve small details in the estimated IVIM parameter maps. The experimental results showed that, compared with the TRR method, the proposed method as well as the BP (and DNN) method could reduce the overestimation of the pseudodiffusion coefficient and improve the quality of IVIM parameter maps. For all studied abdominal organs except the pancreas, both the proposed method and the BP method could provide IVIM parameter estimates close to the reference values; the former had higher precision. The kernelized total difference-based curve-fitting method has the potential to improve the reliability of IVIM parametric imaging.

Intravoxel incoherent motion magnetic resonance imaging to assess early tumor response to radiation therapy: Review and future directions

Early prediction of radiation response by imaging is a dynamic field of research and it can be obtained using a variety of noninvasive magnetic resonance imaging methods. Recently, intravoxel incoherent motion (IVIM) has gained interest in cancer imaging. IVIM carries both diffusion and perfusion information, making it a promising tool to assess tumor response. Here, we briefly introduced the basics of IVIM, reviewed existing studies of IVIM in various type of tumors during radiotherapy in order to show whether IVIM is a useful technique for an early assessment of radiation response. 31/40 studies reported an increase of IVIM parameters during radiotherapy compared to baseline. In 27 studies, this increase was higher in patients with good response to radiotherapy. Future directions including implementation of IVIM on MR-Linac and its limitation are discussed. Obtaining new radiologic biomarkers of radiotherapy response could open the way for a more personalized, biology-guided radiation therapy.

Image denoising and model-independent parameterization for IVIM MRI

Objective. To improve intravoxel incoherent motion imaging (IVIM) magnetic resonance Imaging quality using a new image denoising technique and model-independent parameterization of the signal versusb-value curve.Approach. IVIM images were acquired for 13 head-and-neck patients prior to radiotherapy. Post-radiotherapy scans were also acquired for five of these patients. Images were denoised prior to parameter fitting using neural blind deconvolution, a method of solving the ill-posed mathematical problem of blind deconvolution using neural networks. The signal decay curve was then quantified in terms of several area under the curve (AUC) parameters. Improvements in image quality were assessed using blind image quality metrics, total variation (TV), and the correlations between parameter changes in parotid glands with radiotherapy dose levels. The validity of blur kernel predictions was assessed by the testing the method's ability to recover artificial 'pseudokernels'. AUC parameters were compared with monoexponential, biexponential, and triexponential model parameters in terms of their correlations with dose, contrast-to-noise (CNR) around parotid glands, and relative importance via principal component analysis.Main results. Image denoising improved blind image quality metrics, smoothed the signal versusb-value curve, and strengthened correlations between IVIM parameters and dose levels. Image TV was reduced and parameter CNRs generally increased following denoising.AUCparameters were more correlated with dose and had higher relative importance than exponential model parameters.Significance. IVIM parameters have high variability in the literature and perfusion-related parameters are difficult to interpret. Describing the signal versusb-value curve with model-independent parameters like theAUCand preprocessing images with denoising techniques could potentially benefit IVIM image parameterization in terms of reproducibility and functional utility.

Added-value of 3D amide proton transfer MRI in assessing prognostic factors of cervical cancer: a comparative study with multiple model diffusion-weighted imaging

Background

Amide proton transfer (APT) imaging has been gradually applied to cervical cancer, yet the relationships between APT and multiple model diffusion-weighted imaging (DWI) have yet to be investigated. This study attempted to evaluate the added value of 3-dimensional (3D) APT imaging to multiple model DWI for assessing prognostic factors of cervical cancer.

Methods

This prospective diagnostic study was conducted in The First Affiliated Hospital of Zhengzhou University. A total of 88 consecutive patients with cervical cancer underwent APT imaging and DWI with 11 b-values (0-2,000 s/mm2). The apparent diffusion coefficient (ADC), pure molecular diffusion (D), perfusion fraction (f), pseudo-diffusion (D*), mean kurtosis (MK), and mean diffusivity (MD) were calculated based on mono-exponential, bi-exponential, and kurtosis models. The mean, minimum, and maximum values of APT signal intensity (APT SI) and DWI-derived metrics were compared based on tumor stages, subtypes, grades, and lymphovascular space invasion status by Student's t-test or Mann-Whitney U test. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the parameters.

Results

APT SImax, APT SImin, MKmean, and MKmax showed significant differences between adenocarcinoma (AC) and squamous cell carcinoma (SCC) (all P<0.05). APT SImean, APT SImax, and MKmax were higher and ADCmin, Dmean, Dmin, and MDmin were lower in the high-grade tumor than in low-grade tumor (all P<0.05). For distinguishing lymphovascular space invasion, only MKmean showed significant difference (P=0.010). APT SImax [odds ratio (OR) =2.347, P=0.029], APT SImin (OR =0.352; P=0.024), and MKmean (OR =6.523; P=0.001) were the independent predictors for tumor subtype, and APT SImax (OR =2.885; P=0.044), MDmin (OR =0.155, P=0.012) were the independent predictors for histological grade of cervical cancer. When APT SImin and APT SImax was combined with MKmean and MKmax, the diagnostic performance was significantly improved for differentiating AC and AC [area under the curve (AUC): 0.908, sensitivity: 87.5%; specificity: 83.3%; P<0.001]. The combination of APT SImean, APT SImax, ADCmin, MKmax, and MDmin demonstrated the highest diagnostic performance for predicting tumor grade (AUC: 0.903, sensitivity: 78.6%; specificity: 88.9%; P<0.001).

Conclusions

Addition of APT to DWI may improve the ability to noninvasively predict poor prognostic factors of cervical cancer.

T1 mapping and multimodel diffusion-weighted imaging in the assessment of cervical cancer: a preliminary study

OBJECTIVE

To evaluate the clinical feasibility of T1 mapping and multimodel diffusion-weighted imaging (DWI) for assessing the histological type, grade, and lymphovascular space invasion (LVSI) of cervical cancer.

METHODS

Eighty patients with cervical cancer and 43 patients with a normal cervix underwent T1 mapping and DWI with 11 b-values (0-2000 s/mm²). Monoexponential, biexponential, and kurtosis models were fitted to calculate the apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion (D*), perfusion fraction (f), mean diffusivity (MD), and mean kurtosis (MK). Native T1 and DWI-derived parameters (ADC_mean, ADC_min, D_mean, D_min, D*, f, MD_mean, MD_min, MK_mean, and MK_max) were compared based on histological type, grade, and LVSI status.

RESULTS

Native T1 and DWI-derived parameters differed significantly between cervical cancer and normal cervix (all p < 0.05), except D* (p = 0.637). Native T1 and MK_mean varied significantly between squamous cell carcinoma (SCC) and adenocarcinoma (both p < 0.05). ADC_min, D_min, and MD_min were significantly lower while MK_max was significantly higher in the high-grade SCC group than in the low-grade SCC group (all p < 0.05). LVSI-positive SCC had a significantly higher MK_mean than LVSI-negative SCC (p < 0.05).

CONCLUSION

Both T1 mapping and multimodel DWI can effectively differentiate cervical cancer from a normal cervix and cervical adenocarcinoma from SCC. Furthermore, multimodel DWI may provide quantitative metrics for non-invasively predicting histological grade and LVSI status in SCC patients.

ADVANCES IN KNOWLEDGE

Combined use of T1 mapping and multimodel DWI may provide more comprehensive information for non-invasive pre-operative evaluation of cervical cancer.

Technical Advancements in Abdominal Diffusion-weighted Imaging

Since its first observation in the 18th century, the diffusion phenomenon has been actively studied by many researchers. Diffusion-weighted imaging (DWI) is a technique to probe the diffusion of water molecules and create a MR image with contrast based on the local diffusion properties. The DWI pixel intensity is modulated by the hindrance the diffusing water molecules experience. This hindrance is caused by structures in the tissue and reflects the state of the tissue. This characteristic makes DWI a unique and effective tool to gain more insight into the tissue's pathophysiological condition. In the past decades, DWI has made dramatic technical progress, leading to greater acceptance in clinical practice. In the abdominal region, however, acquiring DWI with good quality is challenging because of several reasons, such as large imaging volume, respiratory and other types of motion, and difficulty in achieving homogeneous fat suppression. In this review, we discuss technical advancements from the past decades that help mitigate these problems common in abdominal imaging. We describe the use of scan acceleration techniques such as parallel imaging and compressed sensing to reduce image distortion in echo planar imaging. Then we compare techniques developed to mitigate issues due to respiratory motion, such as free-breathing, respiratory-triggering, and navigator-based approaches. Commonly used fat suppression techniques are also introduced, and their effectiveness is discussed. Additionally, the influence of the abovementioned techniques on image quality is demonstrated. Finally, we discuss the current and future clinical applications of abdominal DWI, such as whole-body DWI, simultaneous multiple-slice excitation, intravoxel incoherent motion, and the use of artificial intelligence. Abdominal DWI has the potential to develop further in the future, thanks to scan acceleration and image quality improvement driven by technological advancements. The accumulation of clinical proof will further drive clinical acceptance.

Effects of fitting methods, high b-values and image quality on diffusion and perfusion quantification and reproducibility in the calf

PURPOSES

The study aimed to optimize diffusion-weighted imaging (DWI) image acquisition and analysis protocols in calf muscles by investigating the effects of different model-fitting methods, image quality, and use of high b-value and constraints on parameters of interest (POIs). The optimized modeling methods were used to select the optimal combinations of b-values, which will allow shorter acquisition time while achieving the same reliability as that obtained using 16 b-values.

METHODS

Test-retest baseline and high-quality DWI images of ten healthy volunteers were acquired on a 3T MR scanner, using 16 b-values, including a high b-value of 1200 s/mm², and structural T1-weighted images for calf muscle delineation. Three and six different fitting methods were used to derive ADC from monoexponential (ME) model and D_d, f_p, and D_p from intravoxel incoherent motion (IVIM) model, with or without the high b-value. The optimized ME and IVIM models were then used to determine the optimal combinations of b-values, obtainable with the least number of b-values, using the selection criteria of coefficient of variance (CV) ≤10% for all POIs.

RESULTS

The find minimum multivariate algorithm was more flexible and yielded smaller fitting errors. The 2-steps fitting method, with fixed D_d, performed the best for IVIM model. The inclusion of high b-value reduced outliers, while constraints improved 2-steps fitting only.

CONCLUSIONS

The optimal numbers of b-values for ME and IVIM models were nine and six b-values respectively. Test-retest reliability analyses showed that only ADC and D_d were reliable for calf diffusion evaluation, with CVs of 7.22% and 4.09%.

An optimized b-value sampling for the quantification of interstitial fluid using diffusion-weighted MRI, a genetic algorithm approach

PURPOSE

Multi-b-value diffusion-weighted MRI techniques can simultaneously measure the parenchymal diffusivity, microvascular perfusion, and a third, intermediate diffusion component. This component is related to the interstitial fluid in the brain parenchyma. However, simultaneously estimating three diffusion components from multi-b-value data is difficult and has strong dependence on SNR and chosen b-values. As the number of acquired b-values is limited due to scanning time, it is important to know which b-values are most effective to be included. Therefore, this study evaluates an optimized b-value sampling for interstitial fluid estimation.

METHOD

The optimized b-value sampling scheme is determined using a genetic algorithm. Subsequently, the performance of this optimized sampling is assessed by comparing it with a linear, logarithmic, and previously proposed sampling scheme, in terms of the RMS error (RMSE) for the intermediate component estimation. The in vivo performance of the optimized sampling is assessed using 7T data with 101 equally spaced b-values ranging from 0 to 1000 s/mm² . In this case, the RMSE was determined by comparing the fit that includes all b-values.

RESULTS

The optimized b-value sampling for estimating the intermediate component was reported to be [0, 30, 90, 210, 280, 350, 580, 620, 660, 680, 720, 760, 980, 990, 1000] s/mm² . For computer simulations, the optimized sampling had a lower RMSE, compared with the other samplings for varying levels of SNR. For the in vivo data, the voxel-wise RMSE of the optimized sampling was lower compared with other sampling schemes.

CONCLUSION

The genetic algorithm-optimized b-value scheme improves the quantification of the diffusion component related to interstitial fluid in terms of a lower RMSE.

An unsupervised convolutional neural network method for estimation of intravoxel incoherent motion parameters

Objective. Intravoxel incoherent motion (IVIM) imaging obtained by fitting a biexponential model to multiple b-value diffusion-weighted magnetic resonance imaging (DW-MRI) has been shown to be a promising tool for different clinical applications. Recently, several deep neural network (DNN) methods were proposed to generate IVIM imaging. Approach. In this study, we proposed an unsupervised convolutional neural network (CNN) method for estimation of IVIM parameters. We used both simulated and real abdominal DW-MRI data to evaluate the performance of the proposed CNN-based method, and compared the results with those obtained from a non-linear least-squares fit (TRR, trust-region reflective algorithm) and a feed-forward backward-propagation DNN-based method. Main results. The simulation results showed that both the DNN- and CNN-based methods had lower coefficients of variation than the TRR method, but the CNN-based method provided more accurate parameter estimates. The results obtained from real DW-MRI data showed that the TRR method produced many biased IVIM parameter estimates that hit the upper and lower parameter bounds. In contrast, both the DNN- and CNN-based methods yielded less biased IVIM parameter estimates. Overall, the perfusion fraction and diffusion coefficient obtained from the DNN- and CNN-based methods were close to literature values. However, compared with the CNN-based method, both the TRR and DNN-based methods tended to yield increased pseudodiffusion coefficients (55%–180%). Significance. Our preliminary results suggest that it is feasible to estimate IVIM parameters using CNN.

b value and first-order motion moment optimized data acquisition for repeatable quantitative intravoxel incoherent motion DWI

PURPOSE

To design a b value and first-order motion moment (M₁ ) optimized data acquisition for repeatable intravoxel incoherent motion (IVIM) quantification in the liver.

METHODS

Cramer-Rao lower bound optimization was performed to determine optimal monopolar and optimal 2D samplings of the b-M₁ space based on noise performance. Monte Carlo simulations were used to evaluate the bias and variability in estimates obtained using the proposed optimal samplings and conventional monopolar sampling. Diffusion MRI of the liver was performed in 10 volunteers using 3 IVIM acquisitions: conventional monopolar, optimized monopolar, and b-M₁ -optimized gradient waveforms (designed based on the optimal 2D sampling). IVIM parameter maps of diffusion coefficient, perfusion fraction, and blood velocity SD were obtained using nonlinear least squares fitting. Noise performance (SDs), stability (outlier percentage), and test-retest or scan-rescan repeatability (intraclass correlation coefficients) were evaluated and compared across acquisitions.

RESULTS

Cramer-Rao lower bound and Monte Carlo simulations demonstrated improved noise performance of the optimal 2D sampling in comparison to monopolar samplings. Evaluating the designed b-M₁ -optimized waveforms in healthy volunteers, significant decreases (p < 0.05) in the SDs and outlier percentages were observed for measurements of diffusion coefficient, perfusion fraction, and blood velocity SD in comparison to measurements obtained using monopolar samplings. Good-to-excellent repeatability (intraclass correlation coefficients ≥ 0.77) was observed for all 3 parameters in both the right and left liver lobes using the b-M₁ -optimized waveforms.

CONCLUSIONS

2D b-M₁ -optimized data acquisition enables repeatable IVIM quantification with improved noise performance. 2D acquisitions may advance the establishment of IVIM quantitative biomarkers for liver diseases.

Improved Performance of Compartments in Detecting the Activity of Axial Spondyloarthritis Based on IVIM DWI with Optimized Threshold b Value

Purpose

To explore the diagnostic performance of the optimized threshold b values on IVIM to detect the activity in axial spondyloarthritis (axSpA) patients.

Method

40 axSpA patients in the active group, 144 axSpA patients in the inactive group, and 20 healthy volunteers were used to evaluate the tissue diffusion coefficient (D_slow), perfusion fraction (f), and pseudodiffusion coefficient (D_fast) with b thresholds of 10, 20, and 30 s/mm². The Kruskal-Wallis test and one way ANOVA test was used to compare the different activity among the three groups in axSpA patients, and receiver operating characteristic (ROC) curve analysis was applied to evaluate the performance for D_slow, f, and D_fast to detect the activity in axSpA patients, respectively.

Results

D_slow demonstrated a statistical difference between two groups (P < 0.05) with all threshold b values. With the threshold b value of 30 s/mm², f could discriminate the active from control groups (P < 0.05). D_slow had similar performance between the active and the inactive groups with threshold b values of 10, 20, and 30 s/mm² (AUC: 0.877, 0.882, and 0.881, respectively, all P < 0.017). Using the optimized threshold b value of 30 s/mm², f showed the best performance to separate the active from the inactive and the control groups with AUC of 0.613 and 0.738 (both P < 0.017) among all threshold b values.

Conclusion

D_slow and f exhibited increased diagnostic performance using the optimized threshold b value of 30 s/mm² compared with 10 and 20 s/mm², whereas D_fast did not.

Diagnostic Value of Combined Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging with Diffusion Tensor Imaging in Predicting Parametrial Infiltration in Cervical Cancer

Objective

This study sought to determine the diagnostic value of combined intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI) in predicting parametrial infiltration (PMI) in patients with cervical cancer.

Materials and Methods

We enrolled 65 patients with cervical cancer confirmed by radical hysterectomy (25 PMI-negative and 40 PMI-positive) who underwent IVIM and DTI pretreatment. The parameters of IVIM (ADC, D, D  ^∗ , and f) and DTI (average diffusion coefficient (DCavg) and fractional anisotropy (FA)) were recorded by two observers. All parameter differences were tested, and the receiver operating characteristic (ROC) curves were generated to estimate the diagnostic performance of significant metrics and their combinations.

Results

Compared to the PMI-negative group, the PMI-positive group had significantly lower D (0.632 ± 0.017 vs. 0.773 ± 0.024, p < 0.001) and lower FA (0.073 ± 0.002 vs. 0.085 ± 0.003, p=0.003). The area under the ROC curve (AUC) of D and FA was 0.801 and 0.726, respectively, and the combination of D and FA improved the AUC to 0.931, with a sensitivity and specificity of 80.0% and 97.5%, respectively.

Conclusion

D and FA values could be used to help diagnose PMI in patients with cervical cancer. The combination of IVIM and DTI was more valuable than either option alone.

Non-Invasive Evaluation of Cerebral Microvasculature Using Pre-Clinical MRI: Principles, Advantages and Limitations

Alterations to the cerebral microcirculation have been recognized to play a crucial role in the development of neurodegenerative disorders. However, the exact role of the microvascular alterations in the pathophysiological mechanisms often remains poorly understood. The early detection of changes in microcirculation and cerebral blood flow (CBF) can be used to get a better understanding of underlying disease mechanisms. This could be an important step towards the development of new treatment approaches. Animal models allow for the study of the disease mechanism at several stages of development, before the onset of clinical symptoms, and the verification with invasive imaging techniques. Specifically, pre-clinical magnetic resonance imaging (MRI) is an important tool for the development and validation of MRI sequences under clinically relevant conditions. This article reviews MRI strategies providing indirect non-invasive measurements of microvascular changes in the rodent brain that can be used for early detection and characterization of neurodegenerative disorders. The perfusion MRI techniques: Dynamic Contrast Enhanced (DCE), Dynamic Susceptibility Contrast Enhanced (DSC) and Arterial Spin Labeling (ASL), will be discussed, followed by less established imaging strategies used to analyze the cerebral microcirculation: Intravoxel Incoherent Motion (IVIM), Vascular Space Occupancy (VASO), Steady-State Susceptibility Contrast (SSC), Vessel size imaging, SAGE-based DSC, Phase Contrast Flow (PC) Quantitative Susceptibility Mapping (QSM) and quantitative Blood-Oxygenation-Level-Dependent (qBOLD). We will emphasize the advantages and limitations of each strategy, in particular on applications for high-field MRI in the rodent's brain.

Association between IVIM parameters and treatment response in locally advanced squamous cell cervical cancer treated by chemoradiotherapy

OBJECTIVE

To examine the associations of intravoxel incoherent motion (IVIM) parameters with treatment response in cervical cancer following concurrent chemoradiotherapy (CCRT).

MATERIALS AND METHODS

Forty-five patients, median age of 58 years (range: 28-82), with pre-CCRT and post-CCRT MRI, were retrospectively analysed. The IVIM parameters pure diffusion coefficient (D) and perfusion fraction (f) were estimated using the full b-value distribution (BVD) as well as an optimised subsample BVD. Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC) were used to measure observer repeatability in tumour delineation at both time points. Treatment response was determined by the response evaluation criteria in solid tumour (RECIST) 1.1 between MRI examinations. Mann-Whitney U tests were used to test for significant differences in IVIM parameters between treatment response groups.

RESULTS

Pre-CCRT tumour delineation repeatability was good (DSC = 0.81) while post-CCRT delineation repeatability was moderate (DSC = 0.67). Values of D and f had good repeatability at both time points (ICC > 0.80). Pre-CCRT f estimated using the full BVD and optimised subsample BVD were found to be significantly higher in patients with partial response compared to those with stable disease or disease progression (p = 0.01 and 95% CI = -0.02-0.00 for both cases).

CONCLUSION

Pre-CCRT f was associated with treatment response in cervical cancer with good observer repeatability. Similar discriminative ability was also observed in estimated pre-CCRT f from an optimised subsample BVD.

KEY POINTS

• Pre-treatment tumour delineation and IVIM parameters had good observer repeatability. • Post-treatment tumour delineation was worse than at pre-treatment, but IVIM parameters retained good ICC. • Pre-treatment perfusion fraction estimated from all b-values and an optimised subsample of b-values were associated with treatment response.

Repeatability of IVIM biomarkers from diffusion-weighted MRI in head and neck: Bayesian probability versus neural network

Purpose

The intravoxel incoherent motion (IVIM) model for DWI might provide useful biomarkers for disease management in head and neck cancer. This study compared the repeatability of three IVIM fitting methods to the conventional nonlinear least‐squares regression: Bayesian probability estimation, a recently introduced neural network approach, IVIM‐NET, and a version of the neural network modified to increase consistency, IVIM‐NET_mod.

Methods

Ten healthy volunteers underwent two imaging sessions of the neck, two weeks apart, with two DWI acquisitions per session. Model parameters (ADC, diffusion coefficient Dt, perfusion fraction fp, and pseudo‐diffusion coefficient Dp) from each fit method were determined in the tonsils and in the pterygoid muscles. Within‐subject coefficients of variation (wCV) were calculated to assess repeatability. Training of the neural network was repeated 100 times with random initialization to investigate consistency, quantified by the coefficient of variance.

Results

The Bayesian and neural network approaches outperformed nonlinear regression in terms of wCV. Intersession wCV of Dt in the tonsils was 23.4% for nonlinear regression, 9.7% for Bayesian estimation, 9.4% for IVIM‐NET, and 11.2% for IVIM‐NET_mod. However, results from repeated training of the neural network on the same data set showed differences in parameter estimates: The coefficient of variances over the 100 repetitions for IVIM‐NET were 15% for both Dt and fp, and 94% for Dp; for IVIM‐NET_mod, these values improved to 5%, 9%, and 62%, respectively.

Conclusion

Repeatabilities from the Bayesian and neural network approaches are superior to that of nonlinear regression for estimating IVIM parameters in the head and neck.

Associations between IVIM histogram parameters and histopathology in rectal cancer

PURPOSE

Histogram analysis can better reflect tumor heterogeneity than conventional imaging analysis. The present study analyzed possible correlations between histogram analysis parameters derived from Intravoxel-incoherent imaging (IVIM) and histopathological features in rectal cancer (RC).

METHODS

Seventeen patients with histopathologically proven rectal adenocarcinomas were retrospectively acquired. In all cases, pelvic MRI was performed. Diffusion weighted imaging was obtained using a multi-slice single-shot echo-planar imaging sequence with b values of 0, 50, 200, 500 and 1000 s/mm². Simplified IVIM analysis was performed using the IntelliSpace portal, version 10 and the following images were generated: f (perfusion fraction) map, D (true diffusion coefficient) map, and ADC map utilizing all b-values. Histogram based analysis of signal intensities was performed for every IVIM map using an in-house matlab tool. Histopathology was investigated using Ki 67 specimens with calculation of Ki 67-index and cellularity. CD31 stained specimens were used for calculation of microvessel density (MVD).

RESULTS

There were statistically significant correlations between Ki 67 index and mode derived from ADC as well as entropy from f, r=-0.50, p=.04 and r=-0.55, p=.02, respectively. MVD correlated well with parameters derived from f.

CONCLUSION

IVIM histogram analysis parameters can reflect histopathology in RC. ADC and D values are associated with proliferation potential. Perfusion fraction f is associated with MVD.

The value of intravoxel incoherent motion diffusion-weighted imaging in predicting the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma

OBJECTIVE

To explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the prediction of pathologic response to neoadjuvant chemotherapy (NAC) in locally advanced esophageal squamous cell carcinoma (ESCC).

MATERIAL AND METHODS

Forty patients with locally advanced ESCC who were treated with NAC followed by radical resection were prospectively enrolled from September 2015 to May 2018. MRI and IVIM were performed within 1 week before and 2-3 weeks after NAC, prior to surgery. Parameters including apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D^*), and pseudodiffusion fraction (f) before and after NAC were measured. Pathologic response was evaluated according to the AJCC tumor regression grade (TRG) system. The changes in IVIM values before and after therapy in different TRG groups were assessed. Receiver operating characteristic (ROC) curves analysis was used to determine the best cutoff value for predicting the pathologic response to NAC.

RESULTS

Twenty-two patients were identified as TRG 2 (responders), and eighteen as TRG 3 (non-responders) in pathologic evaluation. The ADC, D, and f values increased significantly after NAC. The post-NAC D and ΔD values of responders were significantly higher than those of non-responders. The area under the curve (AUC) was 0.722 for post-NAC D and 0.859 for ΔD in predicting pathologic response. The cutoff values of post-NAC D and ΔD were 1.685 × 10^-3 mm²/s and 0.350 × 10^-3 mm²/s, respectively.

CONCLUSION

IVIM-DWI may be used as an effective functional imaging technique to predict pathologic response to NAC in locally advanced ESCC.

KEY POINTS

• The optimal cutoff values of post-NAC D and ΔD for predicting pathologic response to NAC in locally advanced ESCC were 1.685 × 10^-3 mm²/s and 0.350 × 10^-3 mm²/s, respectively. • Pathologic response to NAC in locally advanced ESCC was favorable in patients with post-NAC D and ΔD values that were higher than the optimal cutoff values. • IVIM-DWI can potentially be used to preoperatively predict pathologic response to NAC in esophageal carcinoma. Accurate quantification of the D value derived from IVIM-DWI may eventually translate into an effective and non-invasive marker to predict therapeutic efficacy.