Evaluation of non-monoexponential diffusion models for hepatocellular carcinoma using b values up to 2000 s/mm2 : A short-term repeatability study

Novel MRI-based Hyper-Fused Radiomics for Predicting Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer

RATIONALE AND OBJECTIVES

To propose a novel MRI-based hyper-fused radiomic approach to predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer (BC).

MATERIALS AND METHODS

Pretreatment dynamic contrast-enhanced (DCE) MRI and ultra-multi-b-value (UMB) diffusion-weighted imaging (DWI) data were acquired in BC patients who received NAT followed by surgery at two centers. Hyper-fused radiomic features (RFs) and conventional RFs were extracted from DCE-MRI or UMB-DWI. After feature selection, the following models were built using logistic regression and the retained RFs: hyper-fused model, conventional model, and compound model that integrates the hyper-fused and conventional RFs. The output probability of each model was used to generate a radiomic signature. The model's performance was quantified by the area under the receiver-operating characteristic curve (AUC). Multivariable logistic regression was used to identify variables (clinicopathological variables and the generated radiomic signatures) associated with pCR.

RESULTS

The training/external test set (center 1/2) included 547/295 women. The hyper-fused models (AUCs=0.81-0.85) outperformed (p<0.05) the conventional models (AUCs=0.74-0.80) in predicting pCR. The compound models (AUCs=0.88-0.93) outperformed (p<0.05) the hyper-fused models and conventional models for pCR prediction. The hyper-fused radiomic signatures (odds ratios=5.70-12.98; p<0.05) and compound radiomic signatures (odds ratios=1.57-7.71; p<0.05) were independently associated with pCR. These are true for the training and external test sets.

CONCLUSION

The hyper-fused radiomic approach had significantly better performance for predicting pCR to NAT than the conventional radiomic approach, and the hyper-fused RFs provided incremental discrimination of pCR beyond the conventional RFs. The generated hyper-fused radiomic signatures were independent predictors of pCR.

Combination of DCE-MRI and NME-DWI via Deep Neural Network for Predicting Breast Cancer Molecular Subtypes

BACKGROUND

To explore whether the combination of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and nonmono-exponential (NME) model-based diffusion-weighted imaging (DWI) via deep neural network (DNN) can improve the prediction of breast cancer molecular subtypes compared to either imaging technique used alone.

PATIENTS AND METHODS

This prospective study examined 480 breast cancers in 475 patients undergoing DCE-MRI and NME-DWI at 3.0 T. Breast cancers were classified as follows: human epidermal growth factor receptor 2 enriched (HER2-enriched), luminal A, luminal B (HER2-), luminal B (HER2+), and triple-negative subtypes. A total of 20% cases were withheld as an independent test dataset, and the remaining cases were used to train DNN with an 80% to 20% training-validation split and 5-fold cross-validation. The diagnostic accuracies of DNN in 5-way subtype classification between the DCE-MRI, NME-DWI, and their combined multiparametric-MRI datasets were compared using analysis of variance with least significant difference posthoc test. Areas under the receiver-operating characteristic curves were calculated to assess the performances of DNN in binary subtype classification between the 3 datasets.

RESULTS

The 5-way classification accuracies of DNN on both DCE-MRI (0.71) and NME-DWI (0.64) were significantly lower (P < .05) than on multiparametric-MRI (0.76), while on DCE-MRI was significantly higher (P < .05) than on NME-DWI. The comparative results of binary classification between the 3 datasets were consistent with the 5-way classification.

CONCLUSION

The combination of DCE-MRI and NME-DWI via DNN achieved a significant improvement in breast cancer molecular subtype prediction compared to either imaging technique used alone. Additionally, DCE-MRI outperformed NME-DWI in differentiating subtypes.

Differentiating tumour progression from pseudoprogression in glioblastoma patients: a monoexponential, biexponential, and stretched-exponential model-based DWI study

BACKGROUND

To investigate the diagnostic performance of parameters derived from monoexponential, biexponential, and stretched-exponential diffusion-weighted imaging models in differentiating tumour progression from pseudoprogression in glioblastoma patients.

METHODS

Forty patients with pathologically confirmed glioblastoma exhibiting enhancing lesions after completion of chemoradiation therapy were enrolled in the study, which were then classified as tumour progression and pseudoprogression. All patients underwent conventional and multi-b diffusion-weighted MRI. The apparent diffusion coefficient (ADC) from a monoexponential model, the true diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f) from a biexponential model, and the distributed diffusion coefficient (DDC) and intravoxel heterogeneity index (α) from a stretched-exponential model were compared between tumour progression and pseudoprogression groups. Receiver operating characteristic curves (ROC) analysis was used to investigate the diagnostic performance of different DWI parameters. Interclass correlation coefficient (ICC) was used to evaluate the consistency of measurements.

RESULTS

The values of ADC, D, DDC, and α values were lower in tumour progression patients than that in pseudoprogression patients (p < 0.05). The values of D* and f were higher in tumour progression patients than that in pseudoprogression patients (p < 0.05). Diagnostic accuracy for differentiating tumour progression from pseudoprogression was highest for α(AUC = 0.94) than that for ADC (AUC = 0.91), D (AUC = 0.92), D* (AUC = 0.81), f (AUC = 0.75), and DDC (AUC = 0.88).

CONCLUSIONS

Multi-b DWI is a promising method for differentiating tumour progression from pseudoprogression with high diagnostic accuracy. In addition, the α derived from stretched-exponential model is the most promising DWI parameter for the prediction of tumour progression in glioblastoma patients.

Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when

This article provides an overview of diffusion kurtosis (DKI) imaging in abdominal oncology. DKI allows for more data on tissue structures than the conventional diffusion model (DWI). However, DKI requires high quality images at b-values greater than 1000 s/mm² and high signal-to-noise ratio (SNR) that traditionally MRI systems are not able to acquire and therefore there are generally amplified anatomical distortions on the images due to less homogeneity of the field. Advances in both hardware and software on modern MRI scanners have currently enabled ultra-high b-value imaging and offered the ability to apply DKI to multiple extracranial sites. Previous studies have evaluated the ability of DKI to characterize and discriminate tumor grade compared to conventional DWI. Additionally, in several studies the DKI sequences used were based on planar echo (EPI) acquisition, which is susceptible to motion, metal and air artefacts and prone to low SNRs and distortions, leading to low quality images for some small lesions, which may affect the accuracy of the results. Another problem is the optimal b-value of DKI, which remains to be explored and not yet standardized, as well as the manual selection of the ROI, which could affect the accuracy of some parameters.

Evaluation of microvascular invasion of hepatocellular carcinoma using whole-lesion histogram analysis with the stretched-exponential diffusion model

OBJECTIVES

To evaluate the potential role of histogram analysis of stretched exponential model (SEM) through whole-tumor volume for preoperative prediction of microvascular invasion (MVI) in single hepatocellular carcinoma (HCC).

METHODS

This study included 43 patients with pathologically proven HCCs by surgery who underwent multiple b-values diffusion-weighted imaging (DWI) and contrast-enhanced MRI.The histogram metrics of distributed diffusion coefficient (DDC) and heterogeneity index (α) from SEM were compared between HCCs with and without MVI, by using the independent t-test. Morphologic features of conventional MRI and clinical data were evaluated with chi-squared or Fisher's exact tests. Receiver operating characteristic (ROC) and multivariable logistic regression analyses were performed to evaluate the diagnostic performance of different parameters for predicting MVI.

RESULTS

The tumor size and non-smooth tumor margin were significantly associated with MVI (all p < 0.05). The mean, fifth, 25th, 50th percentiles of DDC, and the fifth percentile of ADC between HCCs with and without MVI were statistically significant differences (all p < 0.05). The histogram parameters of α showed no statistically significant differences (all p > 0.05). At multivariate analysis,the fifth percentile of DDC was independent risk factor for MVI of HCC(p = 0.006).

CONCLUSIONS

Histogram parameters DDC and ADC, but not the α value, are useful predictors of MVI. The fifth percentile of DDC was the most useful value to predict MVI of HCC.

ADVANCES IN KNOWLEDGE

There is limited literature addressing the role of SEM for evaluating MVI of HCC. Our findings suggest that histogram analysis of SEM based on whole-tumor volume can be useful for MVI prediction.

Feasibility study of simultaneous multislice diffusion kurtosis imaging with different acceleration factors in the liver

Background

Simultaneous multislice diffusion-weighted imaging (SMS-DWI) has been used to reduce image acquisition time. The purpose of this study was to investigate the feasibility of diffusion kurtosis imaging (DKI) based on the SMS technique in the liver and the influence of this method compared with that of conventional DWI sequences on image quality and DKI-derived quantitative parameters.

Methods

Forty volunteers underwent SMS-DWI sequences with acceleration factors of 2 and 3 (SMS2-DWI, SMS3-DWI) and conventional DWI (C-DWI) of the liver with three b-values (50, 800, 2000 s/mm²) in a 3T system. Qualitative image quality parameters and quantitative measurements of the signal-to-noise ratio (SNR), mean kurtosis (MK), mean apparent diffusivity (MD) and apparent diffusion coefficient (ADC) for the liver were compared between the three sequences.

Results

The scan times of C-DWI, SMS2-DWI, and SMS3-DWI were 4 min 11 s, 2 min 2 s, and 1 min 34 s, respectively. For all image quality parameters, there were no significant differences observed between C-DWI and SMS2-DWI (all p > 0.05) in the images with b-values of 800 and 2000 s/mm². C-DWI and SMS2-DWI exhibited better scores than SMS3-DWI (all p < 0.01) in the images with b-values of 2000 s/mm². In the images with b-values of 800 s/mm², C-DWI and SMS2-DWI exhibited better scores than SMS3-DWI for artefacts and overall image quality (all p < 0.01), and C-DWI exhibited better scores than SMS3-DWI for the visibility of intrahepatic vessels (p < 0.001). There were no significant differences in the sharpness of the right lobe edge (p = 0.144), conspicuity of the left lobe (p = 0.370) or visibility of intrahepatic vessels (p = 0.109) between SMS2-DWI and SMS3-DWI. There were no significant differences in the sharpness of the right lobe edge (p = 0.066) or conspicuity of the left lobe (p = 0.131) between C-DWI and SMS3-DWI. For the b-value of 800 s/mm², there were no statistically significant differences between SMS2-DWI and C-DWI (p = 1.000) or between SMS2-DWI and SMS3-DWI (p = 0.059), whereas SMS3-DWI had a significantly lower SNR than C-DWI (p = 0.024). For the DKI-derived parameters (MK and MD) and ADC values, there were no significant differences between the three sequences (MK, p = 0.606; MD, p = 0.831; ADC, p = 0.264).

Conclusions

SMS-DWI with an acceleration factor of 2 is feasible for the liver, resulting in considerable reductions in scan time while maintaining similar image quality, comparable DKI parameters and ADC values compared with those of C-DWI.

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

This article provides an overview of diagnostic evaluation and ablation treatment assessment in Hepatocellular Carcinoma (HCC). Only studies, in the English language from January 2010 to January 202, evaluating the diagnostic tools and assessment of ablative therapies in HCC patients were included. We found 173 clinical studies that satisfied the inclusion criteria.HCC may be noninvasively diagnosed by imaging findings. Multiphase contrast-enhanced imaging is necessary to assess HCC. Intravenous extracellular contrast agents are used for CT, while the agents used for MRI may be extracellular or hepatobiliary. Both gadoxetate disodium and gadobenate dimeglumine may be used in hepatobiliary phase imaging. For treatment-naive patients undergoing CT, unenhanced imaging is optional; however, it is required in the post treatment setting for CT and all MRI studies. Late arterial phase is strongly preferred over early arterial phase. The choice of modality (CT, US/CEUS or MRI) and MRI contrast agent (extracelllar or hepatobiliary) depends on patient, institutional, and regional factors. MRI allows to link morfological and functional data in the HCC evaluation. Also, Radiomics is an emerging field in the assessment of HCC patients.Postablation imaging is necessary to assess the treatment results, to monitor evolution of the ablated tissue over time, and to evaluate for complications. Post- thermal treatments, imaging should be performed at regularly scheduled intervals to assess treatment response and to evaluate for new lesions and potential complications.

Scan Time Reduction in Intravoxel Incoherent Motion Diffusion-Weighted Imaging and Diffusion Kurtosis Imaging of the Abdominal Organs: Using a Simultaneous Multislice Technique With Different Acceleration Factors

OBJECTIVE

To investigate the feasibility of quantitative intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) analyses in the upper abdominal organs by simultaneous multislice diffusion-weighted imaging (SMS-DWI).

SUBJECTS AND METHODS

In this prospective study, a total of 32 participants underwent conventional DWI (C-DWI) and SMS-DWI sequences with acceleration factors of 2 and 3 (SMS2-DWI and SMS3-DWI, respectively) in the upper abdomen with multiple b-values (0, 10, 20, 50, 80, 100, 150, 200, 500, 800, 1000, 1500, and 2000 seconds/mm2) on a 3 T system (MAGNETOM Prisma; Siemens Healthcare, Erlangen, Germany). Image quality and quantitatively measurements of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), mean kurtosis (MK), and mean apparent diffusivity (MD) for the liver, pancreas, kidney cortex and medulla, spleen, and erector spine muscle were compared between the 3 sequences.

RESULTS

The acquisition times for C-DWI, SMS2-DWI, and SMS3-DWI were 10 minutes 57 seconds, 5 minutes 9 seconds, and 3 minutes 54 seconds. For image quality parameters, C-DWI and SMS2-DWI yielded better results than SMS3-DWI (P < 0.05). SMS2-DWI had equivalent IVIM and DKI parameters compared with that of C-DWI (P > 0.05). No statistically significant differences in the ADC, D, f, and MD values between the 3 sequences (P > 0.05) were observed. The D* and MK values of the liver (P = 0.005 and P = 0.012) and pancreas (P = 0.019) between SMS3-DWI and C-DWI were significantly different.

CONCLUSIONS

SMS2-DWI can substantially reduce the scan time while maintaining equivalent IVIM and DKI parameters in the abdominal organs compared with C-DWI.

Liver MRI with amide proton transfer imaging: feasibility and accuracy for the characterization of focal liver lesions

OBJECTIVE

To investigate the feasibility of using amide proton transfer (APT) magnetic resonance imaging (MRI) in the liver and to evaluate its ability to characterize focal liver lesions (FLLs).

METHODS

A total of 203 patients with suspected FLLs who underwent APT imaging at 3T were included. APT imaging was obtained using a single-slice turbo spin-echo sequence to include FLLs through five breath-holds, and its acquisition time was approximately 1 min. APT signals in the background liver and FLL were measured with magnetization transfer ratio asymmetry (MTR_asym) at 3.5 ppm. The technical success rate of APT imaging and the reasons for failure to obtain meaningful MTR_asym values were assessed. The Mann Whitney U test was used to compare MTR_asym values between different FLLs.

RESULTS

The technical success rate of APT imaging in the liver was 62.1% (126/203). The reasons for failure were a too large B₀ inhomogeneity (n = 43), significant respiratory motion (n = 12), and these two factors together (n = 22), respectively. Among 59 FLLs with analyzable APT images, MTR_asym values were compared between 27 patients with liver metastases and 23 patients with hepatocellular carcinomas (HCCs). The MTR_asym values of metastases were significantly higher than those of HCC (0.13 ± 2.15% vs. - 1.41 ± 3.68%, p = 0.027).

CONCLUSIONS

APT imaging could be an imaging biomarker for the differentiation of FLLs. However, further technical improvement is required before APT imaging can be clinically applied to liver MRI.

KEY POINTS

• Liver APT imaging was technically feasible, but with a relatively low success rate (62.1%). • Liver metastases showed higher APT values than hepatocellular carcinomas.

Non-mono-exponential diffusion models for assessing early response of liver metastases to chemotherapy in colorectal Cancer

Background

Preoperative chemotherapy is becoming standard therapy for liver metastasis from colorectal cancer, so early assessment of treatment response is crucial to make a reasonable therapeutic regimen and avoid overtreatment, especially for patients with severe side effects. The role of three non-mono-exponential diffusion models, such as the kurtosis model, the stretched exponential model and the statistical model, were explored in this study to early assess the response to chemotherapy in patients with liver metastasis from colorectal cancer.

Methods

Thirty-three patients diagnosed as colorectal liver metastasis were evaluated in this study. Diffusion-weighted images with b values (0, 200, 500, 1000, 1500, 2000 s/mm²) were acquired at 3.0 T. The parameters (ADC_k, K, DDC,α, D_sand σ) were derived from three non-mono-exponential models (the kurtosis, stretched exponential and statistical models) as well as their corresponding percentage changes before and after chemotherapy. The difference in above parameters between the response and non-response groups were analyzed with independent-samples T-test (normality) and Mann–Whitney U-test (non-normality). Meanwhile, receiver operating characteristic curve (ROC) analyses were performed to assess the response to chemotherapy.

Results

Significantly lower values of K (the kurtosis coefficient derived from the kurtosis model) and σ (the width of diffusion coefficient distribution in the statistical model) (P < 0.05) were observed in the respond group before treatment, as well as higher ΔK and Δσ values (P < 0.05) after the first cycle of chemotherapy were also found compared with the non-respond group. ROC analyses showed the K value acquired before treatment had the highest diagnostic performance (0.746) in distinguishing responders from non-responders. Furthermore, the high sensitivity (100%) and accuracy (76.3%) from the K value before treatment was found in assessing the response of colorectal liver metastasis to chemotherapy.

Conclusions

The non-mono-exponential diffusion models may be able to predict early response to chemotherapy in patients with colorectal liver metastasis.