Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model

Value of fractional-order calculus (FROC) model diffusion-weighted imaging combined with simultaneous multi-slice (SMS) acceleration technology for evaluating benign and malignant breast lesions

BACKGROUND

This study explores the diagnostic value of combining fractional-order calculus (FROC) diffusion-weighted model with simultaneous multi-slice (SMS) acceleration technology in distinguishing benign and malignant breast lesions.

METHODS

178 lesions (73 benign, 105 malignant) underwent magnetic resonance imaging with diffusion-weighted imaging using multiple b-values (14 b-values, highest 3000 s/mm2). Independent samples t-test or Mann-Whitney U test compared image quality scores, FROC model parameters (D,, ), and ADC values between two groups. Multivariate logistic regression analysis identified independent variables and constructed nomograms. Model discrimination ability was assessed with receiver operating characteristic (ROC) curve and calibration chart. Spearman correlation analysis and Bland-Altman plot evaluated parameter correlation and consistency.

RESULTS

Malignant lesions exhibited lower D, and ADC values than benign lesions (P < 0.05), with higher values (P < 0.05). In SSEPI-DWI and SMS-SSEPI-DWI sequences, the AUC and diagnostic accuracy of D value are maximal, with D value demonstrating the highest diagnostic sensitivity, while value exhibits the highest specificity. The D and combined model had the highest AUC and accuracy. D and ADC values showed high correlation between sequences, and moderate. Bland-Altman plot demonstrated unbiased parameter values.

CONCLUSION

SMS-SSEPI-DWI FROC model provides good image quality and lesion characteristic values within an acceptable time. It shows consistent diagnostic performance compared to SSEPI-DWI, particularly in D and values, and significantly reduces scanning time.

Multiple diffusion models for predicting pathologic response of esophageal squamous cell carcinoma to neoadjuvant chemotherapy

BACKGROUND

To assess the feasibility and diagnostic performance of the fractional order calculus (FROC), continuous-time random-walk (CTRW), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), mono-exponential (MEM) and stretched exponential models (SEM) for predicting response to neoadjuvant chemotherapy (NACT) in patients with esophageal squamous cell carcinoma (ESCC).

MATERIALS AND METHODS

This study prospectively included consecutive ESCC patients with baseline and follow up MR imaging and pathologically confirmed cT1-4aN + M0 or T3-4aN0M0 and underwent radical resection after neoadjuvant chemotherapy (NACT) between July 2019 and January 2023. Patients were divided into pCR (TRG 0) and non-pCR (TRG1 + 2 + 3) groups according to tumor regression grading (TRG). The Pre-, Post- and Delta-treatment models were built. 18 predictive models were generated according to different feature categories, based on six models by five-fold cross-validation. Areas under the curve (AUCs) of the models were compared by using DeLong method.

RESULTS

Overall, 90 patients (71 men, 19 women; mean age, 64 years ± 6 [SD]) received NACT and underwent baseline and Post-NACT esophageal MRI, with 29 patients in the pCR group and 61 patients in the non-pCR group. Among 18 predictive models, The Pre-, Post-, and Delta-CTRW model showed good predictive efficacy (AUC = 0.722, 0.833 and 0.790). Additionally, the Post-FROC model (AUC = 0.907) also exhibited good diagnostic performance.

CONCLUSIONS

Our study indicates that the CTRW model, along with the Post-FROC model, holds significant promise for the future of NACT efficacy prediction in ESCC patients.

A multicenter study of cervical cancer using quantitative diffusion-weighted imaging

BACKGROUND

Parameters from diffusion-weighted imaging (DWI) have been increasingly used as imaging biomarkers for the diagnosis and monitoring of treatment responses in cancer. The consistency of DWI measurements across different centers remains uncertain, which limits the widespread use of quantitative DWI in clinical settings.

PURPOSE

To investigate the consistency of quantitative metrics derived from DWI between different scanners in a multicenter clinical setting.

MATERIAL AND METHODS

A total of 193 patients with cervical cancer from four scanners (MRI1, MRI2, MRI3, and MRI4) at three centers were included in this retrospective study. DWI data were processed using the mono-exponential and intravoxel incoherent motion (IVIM) model, yielding the following parameters: apparent diffusion coefficient (ADC); true diffusion coefficient (D); pseudo-diffusion coefficient (D*); perfusion fraction (f); and the product of f and D* (fD*). Various parameters of cervical cancer obtained from different scanners were compared.

RESULTS

The parameters D and ADC derived from MRI1 and MRI2 were significantly different from those derived from MRI3 or MRI4 (P <0.01 for all comparisons). However, there was no significant difference in cervical cancer perfusion parameters (D* and fD*) between the different scanners (P >0.05). The P values of comparisons of all DWI parameters (D, D*, fD*, and ADC) between MRI3 and MRI4 (same vendor in different centers) for cervical cancer were all >0.05, except for f (P = 0.05).

CONCLUSION

Scanners of the same model by the same vendor can yield close measurements of the ADC and IVIM parameters. The perfusion parameters showed higher consistency among the different scanners.

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.

Association between paravertebral muscle radiological parameter alterations and non-alcoholic fatty liver disease

PURPOSE

To assess changes in laboratory indices, paravertebral muscle (PVM) fat infiltration and multi b-value DWI parameters and their potential correlation with NAFLD.

METHODS

This retrospective analysis included 178 patients with histopathologically confirmed NAFLD, incluiding 76 with non-alcoholic steatohepatitis (NASH). Differences in PVM fat infiltration ratio (FIR), DWI parameters, and laboratory indices were compared between two groups. The correlation between FIR and NAFLD activity score (NAS) was also analysed. Binary logistic regression was used to identify the independent risk factors for NASH. The clinical utility of PVM fat infiltration, DWI parameters, and laboratory indices for diagnosing NASH in patients with NAFLD was evaluated using receiver operating characteristic (ROC) curves.

RESULTS

The FIRs at the L2 and L3 levels were significantly higher in the with NASH group than those in the without NASH group. The heterogeneity index (α) and perfusion fraction (f) values at the L3 level of PVM were lower in the with NASH group. Moreover, the FIR at the L3 level was positively correlated with NAS. FIR at the L3 level was an independent risk factor for NASH along with alanine aminotransferase level. The area under the ROC curve (AUC) using L3 level PVM radiological parameters and laboratory indices for diagnosing NASH in patients with NAFLD was significantly higher than that using the degree of PVM fat infiltration, DWI parameters, or laboratory indices alone.

CONCLUSIONS

Radiological parameters of the PVM were correlated with NAFLD. An integrated curve combining PVM radiological parameters may help distinguish NASH from NAFLD, thereby offering novel insights into the diagnosis of NASH.

Amide Proton Transfer-Weighted Imaging and Multiple Models Intravoxel Incoherent Motion-Based 18F-FDG PET/MRI for Predicting Progression-Free Survival in Non-Small Cell Lung Cancer

BACKGROUND

Amide proton transfer-weighted imaging (APTWI) and multiple models intravoxel incoherent motion (IVIM) based 18F-FDG PET/MR could reflect the microscopic information of the tumor from multiple perspectives. However, its value in the prognostic assessment of non-small cell lung cancer (NSCLC) still needs to be further explored.

PURPOSE

To determine whether pretreatment APTWI, mono-, bi-, and stretched-exponential model IVIM, and 18F-FDG PET-derived parameters of the primary lesion may be associated with progression-free survival (PFS) in NSCLC.

STUDY TYPE

Prospective.

POPULATION

Seventy-seven patients (mean age, 62 years, range, 20-81 years) with 37 men and 40 women were included.

FIELD STRENGTH/SEQUENCE

3.0 T 18F-FDG PET/MRI, single shot echo planar imaging sequences for IVIM and fast spin-echo sequences with magnetization transfer pulses for APTWI.

ASSESSMENT

Patient clinical characteristics (age, sex, smoke, subtype, TNM stage, and surgery), PFS (chest CT every 3 months, median follow-up was 18 months, range, 4-27 months), and APTWI (MTRasym(3.5 ppm)), IVIM (ADCstand, D, D*, f, DDC, and α), and 18F-FDG PET (SUVmax, MTV, and TLG) parameters were recorded.

STATISTICAL TESTS

Proportional hazards model, concordance index, calibration curve, decision curve analysis (DCA), and Log-rank test. A P value <0.05 was considered statistically significant.

RESULTS

Histological subtype, TNM stage, MTV, D*, and MTRasym(3.5 ppm) were all independent predictors of PFS. A prediction model based on these predictors was developed with a C-index of 0.895 (95% CI: 0.839-0.951), which was significantly superior to each of the above predictors alone (C-index = 0.629, 0.707, 0.692, 0.678, and 0.558, respectively). The calibration curve and DCA indicated good consistency and clinical utility of the prediction model, respectively. Log-rank test results showed a significant difference in PFS between the high- and low-risk groups.

DATA CONCLUSION

APTWI and multiple models IVIM based 18F-FDG PET/MRI can be used for PFS assessment in NSCLC.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Breast Cancer: Multi-b-Value Diffusion Weighted Habitat Imaging in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy

RATIONALE AND OBJECTIVES

The aim of this study was to ascertain whether the utilization of multiple b-value diffusion-weighted habitat imaging, a technique that depicts tumor heterogeneity, could aid in identifying breast cancer patients who would derive substantial benefit from neoadjuvant chemotherapy (NAC).

MATERIALS AND METHODS

This prospective study enrolled 143 women (II-III breast cancer), who underwent multi-b-value diffusion-weighted imaging (DWI) in 3-T magnetic resonance (MR) before NAC. The patient cohort was partitioned into a training set (consisting of 100 patients, of which 36 demonstrated a pathologic complete response [pCR]) and a test set (featuring 43 patients, 16 of whom exhibited pCR). Utilizing the training set, predictive models for pCR, were constructed using different parameters: whole-tumor radiomics (ModelWH), diffusion-weighted habitat-imaging (ModelHabitats), conventional MRI features (ModelCF), along with combined models ModelHabitats+CF. The performance of these models was assessed based on the area under the receiver operating characteristic curve (AUC) and calibration slope.

RESULTS

In the prediction of pCR, ModelWH, ModelHabitats, ModelCF, and ModelHabitats+CF achieved AUCs of 0.733, 0.722, 0.705, and 0.756 respectively, within the training set. These scores corresponded to AUCs of 0.625, 0.801, 0.700, and 0.824 respectively in the test set. The DeLong test revealed no significant difference between ModelWH and ModelHabitats (P = 0.182), between ModelHabitats and ModelHabitats+CF (P = 0.113).

CONCLUSION

The habitat model we developed, incorporating first-order features along with conventional MRI features, has demonstrated accurate predication of pCR prior to NAC. This model holds the potential to augment decision-making processes in personalized treatment strategies for breast cancer.

Comparison of continuous-time random walk and fractional order calculus models in characterizing breast lesions using histogram analysis

OBJECTIVE

To compare the diagnostic performance of different mathematical models for DWI and explore whether parameters reflecting spatial and temporal heterogeneity can demonstrate better diagnostic accuracy than the diffusion coefficient parameter in distinguishing benign and malignant breast lesions, using whole-tumor histogram analysis.

METHODS

This retrospective study was approved by the institutional ethics committee and included 104 malignant and 42 benign cases. All patients underwent breast magnetic resonance imaging (MRI) with a 3.0 T MR scanner using the simultaneous multi-slice (SMS) readout-segment ed echo-planar imaging (rs-EPI). Histogram metrics of Mono- apparent diffusion coefficient (ADC), CTRW, and FROC-derived parameters were compared between benign and malignant breast lesions, and the diagnostic performance of each diffusion parameter was evaluated. Statistical analysis was performed using Mann-Whitney U test and receiver operating characteristic (ROC) curve.

RESULTS

The DFROC-median exhibited the highest AUC for distinguishing benign and malignant breast lesions (AUC = 0.965). The temporal heterogeneity parameter αCTRW-median generated a statistically higher AUC compared to the spatial heterogeneity parameter βCTRW-median (AUC = 0.850 and 0.741, respectively; p = 0.047). Finally, the combination of median values of CTRW parameters displayed a slightly higher AUC than that of FROC parameters, with no significant difference however (AUC = 0.971 and 0.965, respectively; p = 0.172).

CONCLUSIONS

The diffusion coefficient parameter exhibited superior diagnostic performance in distinguishing breast lesions when compared to the temporal and spatial heterogeneity parameters.

A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI

BACKGROUND

Dynamic contrast-enhanced (DCE) MRI commonly outperforms diffusion-weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE-MRI, particularly in patients with chronic kidney disease.

PURPOSE

To develop a novel deep learning model to fully exploit the potential of overall b-value DW-MRI without the need for a contrast agent in predicting breast cancer molecular subtypes and to evaluate its performance in comparison with DCE-MRI.

STUDY TYPE

Prospective.

SUBJECTS

486 female breast cancer patients (training/validation/test: 64%/16%/20%).

FIELD STRENGTH/SEQUENCE

3.0 T/DW-MRI (13 b-values) and DCE-MRI (one precontrast and five postcontrast phases).

ASSESSMENT

The breast cancers were divided into four subtypes: luminal A, luminal B, HER2+, and triple negative. A channel-dimensional feature-reconstructed (CDFR) deep neural network (DNN) was proposed to predict these subtypes using pathological diagnosis as the reference standard. Additionally, a non-CDFR DNN (NCDFR-DNN) was built for comparative purposes. A mixture ensemble DNN (ME-DNN) integrating two CDFR-DNNs was constructed to identify subtypes on multiparametric MRI (MP-MRI) combing DW-MRI and DCE-MRI.

STATISTICAL TESTS

Model performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Model comparisons were performed using the one-way analysis of variance with least significant difference post hoc test and the DeLong test. P < 0.05 was considered significant.

RESULTS

The CDFR-DNN (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.94) demonstrated significantly improved predictive performance than the NCDFR-DNN (accuracies, 0.76 ~ 0.78; AUCs, 0.92 ~ 0.93) on DW-MRI. Utilizing the CDFR-DNN, DW-MRI attained the predictive performance equal (P = 0.065 ~ 1.000) to DCE-MRI (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.95). The predictive performance of the ME-DNN on MP-MRI (accuracies, 0.85 ~ 0.87; AUCs, 0.96 ~ 0.97) was superior to those of both the CDFR-DNN and NCDFR-DNN on either DW-MRI or DCE-MRI.

DATA CONCLUSION

The CDFR-DNN enabled overall b-value DW-MRI to achieve the predictive performance comparable to DCE-MRI. MP-MRI outperformed DW-MRI and DCE-MRI in subtype prediction.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 1.

Multi-b-value DWI to evaluate the synergistic antiproliferation and anti-heterogeneity effects of bufalin plus sorafenib in an orthotopic HCC model

BACKGROUND

Multi-b-value diffusion-weighted imaging (DWI) with different postprocessing models allows for evaluating hepatocellular carcinoma (HCC) proliferation, spatial heterogeneity, and feasibility of treatment strategies. We assessed synergistic effects of bufalin+sorafenib in orthotopic HCC-LM3 xenograft nude mice by using intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), a stretched exponential model (SEM), and a fractional-order calculus (FROC) model.

METHODS

Twenty-four orthotopic HCC-LM3 xenograft mice were divided into bufalin+sorafenib, bufalin, sorafenib treatment groups, and a control group. Multi-b-value DWI was performed using a 3-T scanner after 3 weeks' treatment to obtain true diffusion coefficient Dt, pseudo-diffusion coefficient Dp, perfusion fraction f, mean diffusivity (MD), mean kurtosis (MK), distributed diffusion coefficient (DDC), heterogeneity index α, diffusion coefficient D, fractional order parameter β, and microstructural quantity μ. Necrotic fraction (NF), standard deviation (SD) of hematoxylin-eosin staining, and microvessel density (MVD) of anti-CD31 staining were evaluated. Correlations of DWI parameters with histopathological results were analyzed, and measurements were compared among four groups.

RESULTS

In the final 22 mice, f positively correlated with MVD (r = 0.679, p = 0.001). Significantly good correlations of MK (r = 0.677), α (r = -0.696), and β (r= -0.639) with SD were observed (all p < 0.010). f, MK, MVD, and SD were much lower, while MD, α, β, and NF were higher in bufalin plus sorafenib group than control group (all p < 0.050).

CONCLUSION

Evaluated by IVIM, DKI, SEM, and FROC, bufalin+sorafenib was found to inhibit tumor proliferation and angiogenesis and reduce spatial heterogeneity in HCC-LM3 models.

RELEVANCE STATEMENT

Multi-b-value DWI provides potential metrics for evaluating the efficacy of treatment in HCC.

KEY POINTS

• Bufalin plus sorafenib combination may increase the effectiveness of HCC therapy. • Multi-b-value DWI depicted HCC proliferation, angiogenesis, and spatial heterogeneity. • Multi-b-value DWI may be a noninvasive method to assess HCC therapeutic efficacy.

Amide proton transfer-weighted imaging and stretch-exponential model DWI based 18F-FDG PET/MRI for differentiation of benign and malignant solitary pulmonary lesions

OBJECTIVES

To differentiate benign and malignant solitary pulmonary lesions (SPLs) by amide proton transfer-weighted imaging (APTWI), mono-exponential model DWI (MEM-DWI), stretched exponential model DWI (SEM-DWI), and 18F-FDG PET-derived parameters.

METHODS

A total of 120 SPLs patients underwent chest 18F-FDG PET/MRI were enrolled, including 84 in the training set (28 benign and 56 malignant) and 36 in the test set (13 benign and 23 malignant). MTRasym(3.5 ppm), ADC, DDC, α, SUVmax, MTV, and TLG were compared. The area under receiver-operator characteristic curve (AUC) was used to assess diagnostic efficacy. The Logistic regression analysis was used to identify independent predictors and establish prediction model.

RESULTS

SUVmax, MTV, TLG, α, and MTRasym(3.5 ppm) values were significantly lower and ADC, DDC values were significantly higher in benign SPLs than malignant SPLs (all P < 0.01). SUVmax, ADC, and MTRasym(3.5 ppm) were independent predictors. Within the training set, the prediction model based on these independent predictors demonstrated optimal diagnostic efficacy (AUC, 0.976; sensitivity, 94.64%; specificity, 92.86%), surpassing any single parameter with statistical significance. Similarly, within the test set, the prediction model exhibited optimal diagnostic efficacy. The calibration curves and DCA revealed that the prediction model not only had good consistency but was also able to provide a significant benefit to the related patients, both in the training and test sets.

CONCLUSION

The SUVmax, ADC, and MTRasym(3.5 ppm) were independent predictors for differentiation of benign and malignant SPLs, and the prediction model based on them had an optimal diagnostic efficacy.

Diffusion-Weighted MRI of the Liver in Patients With Chronic Liver Disease: A Comparative Study Between Different Fitting Approaches and Diffusion Models

BACKGROUND

Diffusion-weighted imaging (DWI) has been considered for chronic liver disease (CLD) characterization. Grading of liver fibrosis is important for disease management.

PURPOSE

To investigate the relationship between DWI's parameters and CLD-related features (particularly regarding fibrosis assessment).

STUDY TYPE

Retrospective.

SUBJECTS

Eighty-five patients with CLD (age: 47.9 ± 15.5, 42.4% females).

FIELD STRENGTH/SEQUENCE

3-T, spin echo-echo planar imaging (SE-EPI) with 12 b-values (0-800 s/mm2 ).

ASSESSMENT

Several models statistical models, stretched exponential model, and intravoxel incoherent motion were simulated. The corresponding parameters (Ds , σ, DDC, α, f, D, D*) were estimated on simulation and in vivo data using the nonlinear least squares (NLS), segmented NLS, and Bayesian methods. The fitting accuracy was analyzed on simulated Rician noised DWI. In vivo, the parameters were averaged from five central slices entire liver to compare correlations with histological features (inflammation, fibrosis, and steatosis). Then, the differences between mild (F0-F2) or severe (F3-F6) groups were compared respecting to statistics and classification. A total of 75.3% of patients used to build various classifiers (stratified split strategy and 10-folders cross-validation) and the remaining for testing.

STATISTICAL TESTS

Mean squared error, mean average percentage error, spearman correlation, Mann-Whitney U-test, receiver operating characteristic (ROC) curve, area under ROC curve (AUC), sensitivity, specificity, accuracy, precision. A P-value <0.05 was considered statistically significant.

RESULTS

In simulation, the Bayesian method provided the most accurate parameters. In vivo, the highest negative significant correlation (Ds , steatosis: r = -0.46, D*, fibrosis: r = -0.24) and significant differences (Ds , σ, D*, f) were observed for Bayesian fitted parameters. Fibrosis classification was performed with an AUC of 0.92 (0.91 sensitivity and 0.70 specificity) with the aforementioned diffusion parameters based on the decision tree method.

DATA CONCLUSION

These results indicate that Bayesian fitted parameters may provide a noninvasive evaluation of fibrosis with decision tree.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

Comparative study of stretched-exponential and kurtosis models of diffusion-weighted imaging in renal assessment to distinguish patients with primary aldosteronism from healthy controls

PURPOSE

To compare the ability of diffusion parameters obtained by stretched-exponential and kurtosis models of diffusion-weighted imaging (DWI) to distinguish between patients with primary aldosteronism (PA) and healthy controls (HCs) in renal assessment.

MATERIALS AND METHODS

A total of 44 participants (22 patients and 22 HCs) underwent renal MRI with an 11 b-value DWI sequence and a 3 b-value diffusion kurtosis imaging (DKI) sequence from June 2021 to April 2022. Binary logistic regression was used to construct regression models combining different diffusion parameters. Receiver-operating characteristic (ROC) curve analysis and comparisons were used to evaluate the ability of single diffusion parameters and combined diffusion models to distinguish between the two groups.

RESULTS

A total of six diffusion parameters (including the cortical anomalous exponent term [α_Cortex], medullary fractional anisotropy [FA_Medulla], cortical FA [FA_Cortex], cortical axial diffusivity [Da_Cortex], medullary mean diffusivity [MD_Medulla] and medullary radial diffusivity [Dr_Medulla]) were included, and 10 regression models were studied. The area under the curve (AUC) of Dr_Medulla was 0.855, comparable to that of FA_Cortex and FA_Medulla and significantly higher than that of α_Cortex, Da_Cortex and MD_Medulla. The AUC of the Model_all parameters was 0.967, comparable to that of Model_FA (0.946) and Model_DKI (0.966) and significantly higher than that of the other models. The sensitivity and specificity of Model_all parameters were 87.2% and 95%, respectively.

CONCLUSION

The Model_all parameters, Model_FA and Model_DKI were valid for differentiating between PA patients and HCs with similar differentiation efficacy and were superior to single diffusion parameters and other models.

Value of multiple models of diffusion-weighted imaging to predict hepatic lymph node metastases in colorectal liver metastases patients

BACKGROUND

About 10%-31% of colorectal liver metastases (CRLM) patients would concomitantly show hepatic lymph node metastases (LNM), which was considered as sign of poor biological behavior and a relative contraindication for liver resection. Up to now, there's still lack of reliable preoperative methods to assess the status of hepatic lymph nodes in patients with CRLM, except for pathology examination of lymph node after resection.

AIM

To compare the ability of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) models in distinguishing between benign and malignant hepatic lymph nodes in patients with CRLM who received neoadjuvant chemotherapy prior to surgery.

METHODS

In this retrospective study, 97 CRLM patients with pathologically confirmed hepatic lymph node status underwent magnetic resonance imaging, including DWI with ten b values before and after chemotherapy. Various parameters, such as the apparent diffusion coefficient from the mono-exponential model, and the true diffusion coefficient, the pseudo-diffusion coefficient, and the perfusion fraction derived from the intravoxel incoherent motion model, along with distributed diffusion coefficient (DDC) and α from the stretched-exponential model (SEM), were measured. The parameters before and after chemotherapy were compared between positive and negative hepatic lymph node groups. A nomogram was constructed to predict the hepatic lymph node status. The reliability and agreement of the measurements were assessed using the coefficient of variation and intraclass correlation coefficient.

RESULTS

Multivariate analysis revealed that the pre-treatment DDC value and the short diameter of the largest lymph node after treatment were independent predictors of metastatic hepatic lymph nodes. A nomogram combining these two factors demonstrated excellent performance in distinguishing between benign and malignant lymph nodes in CRLM patients, with an area under the curve of 0.873. Furthermore, parameters from SEM showed substantial repeatability.

CONCLUSION

The developed nomogram, incorporating the pre-treatment DDC and the short axis of the largest lymph node, can be used to predict the presence of hepatic LNM in CRLM patients undergoing chemotherapy before surgery. This nomogram was proven to be more valuable, exhibiting superior diagnostic performance compared to quantitative parameters derived from multiple b values of DWI. The nomogram can serve as a preoperative assessment tool for determining the status of hepatic lymph nodes and aiding in the decision-making process for surgical treatment in CRLM patients.

Evaluating Mediastinal Lymph Node Metastasis of Non-Small Cell Lung Cancer Using Mono-exponential, Bi-exponential, and Stretched-exponential Models of Diffusion-weighted Imaging

PURPOSE

To explore and compare the diagnostic values of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) parameters of primary lesions and lymph nodes (LNs) to predict mediastinal LN metastasis in patients with non-small cell lung cancer.

PATIENTS AND METHODS

Sixty-one patients with non-small cell lung cancer underwent preoperative magnetic resonance imaging, including multiple b-value DWI. The DWI parameters, including apparent diffusion coefficient (ADC) from a mono-exponential model, true diffusion (D) coefficient, pseudo-diffusion (D*) coefficient, and perfusion fraction (f) from a bi-exponential model, distributed diffusion coefficient (DDC) and intravoxel diffusion heterogeneity index (α) from a stretched-exponential model of primary tumors and LNs and the size characteristics of LNs, were measured and compared. Multivariate logistic regression analysis was used to establish models for predicting mediastinal LN metastasis. Receiver operating characteristic analysis was applied to evaluate diagnostic performances.

RESULTS

The DWI parameters of primary tumors showed no statistical significance between LN metastasis-positive and LN metastasis-negative groups. Nonmetastatic LNs had significantly higher ADC, D, DDC, and α values compared with metastatic LNs (all P < 0.05). The short-dimension, long-dimension, and short-long dimension ratio of metastatic LNs was significantly larger than those of nonmetastatic ones (all P < 0.05). The D value showed the best diagnostic performance among all DWI-derived single parameters, and the short dimension of LNs performed the same among all the size variables. Furthermore, the combination of DWI parameters (ADC and D) and the short dimension of LNs can significantly improve diagnostic efficiency.

CONCLUSIONS

The ADC, D, DDC, and α from the mono-exponential, bi-exponential, and stretched-exponential models were demonstrated efficient in differentiating benign from metastatic LNs, and the combination of ADC, D, and short dimension of LNs may have a better diagnostic performance than DWI or size-derived parameters either in combination or individually.

Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas

BACKGROUND

The consistency of meningiomas is critical to determine surgical planning and has a significant impact on surgical outcomes. Our aim was to compare mono-exponential, bi-exponential and stretched exponential MR diffusion-weighted imaging in predicting the consistency of meningiomas before surgery.

METHODS

Forty-seven consecutive patients with pathologically confirmed meningiomas were prospectively enrolled in this study. Two senior neurosurgeons independently evaluated tumour consistency and classified them into soft and hard groups. A volume of interest was placed on the preoperative MR diffusion images to outline the whole tumour area. Histogram parameters (mean, median, 10th percentile, 90th percentile, kurtosis, skewness) were extracted from 6 different diffusion maps including ADC (DWI), D*, D, f (IVIM), alpha and DDC (SEM). Comparisons between two groups were made using Student's t-Test or Mann-Whitney U test. Parameters with significant differences between the two groups were included for Receiver operating characteristic analysis. The DeLong test was used to compare AUCs.

RESULTS

DDC, D* and ADC 10th percentile were significantly lower in hard tumours than in soft tumours (P ≤ 0.05). The alpha 90th percentile was significantly higher in hard tumours than in soft tumours (P < 0.02). For all histogram parameters, the alpha 90th percentile yielded the highest AUC of 0.88, with an accuracy of 85.10%. The D* 10th percentile had a relatively higher AUC value, followed by the DDC and ADC 10th percentile. The alpha 90th percentile had a significantly greater AUC value than the ADC 10th percentile (P ≤ 0.05). The D* 10th percentile had a significantly greater AUC value than the ADC 10th percentile and DDC 10th percentile (P ≤ 0.03).

CONCLUSION

Histogram parameters of Alpha and D* may serve as better imaging biomarkers to aid in predicting the consistency of meningioma.

Advanced Diffusion-Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology

Diffusion-weighted magnetic resonance imaging (DW-MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW-MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state-of-the-art of DW-MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW-MRI method is essential. In this article, we review the current landscape of DW-MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b-tensor diffusion encoding or joint diffusion-relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter-/intra-scanner robustness, and on assessing histological validity in broader contexts. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.

Comparison of Dynamic Contrast-Enhanced MRI and Non-Mono-Exponential Model-Based Diffusion-Weighted Imaging for the Prediction of Prognostic Biomarkers and Molecular Subtypes of Breast Cancer Based on Radiomics

BACKGROUND

Dynamic contrast-enhanced (DCE) MRI and non-mono-exponential model-based diffusion-weighted imaging (NME-DWI) that does not require contrast agent can both characterize breast cancer. However, which technique is superior remains unclear.

PURPOSE

To compare the performances of DCE-MRI, NME-DWI and their combination as multiparametric MRI (MP-MRI) in the prediction of breast cancer prognostic biomarkers and molecular subtypes based on radiomics.

STUDY TYPE

Prospective.

POPULATION

A total of 477 female patients with 483 breast cancers (5-fold cross-validation: training/validation, 80%/20%).

FIELD STRENGTH/SEQUENCE

A 3.0 T/DCE-MRI (6 dynamic frames) and NME-DWI (13 b values).

ASSESSMENT

After data preprocessing, high-throughput features were extracted from each tumor volume of interest, and optimal features were selected using recursive feature elimination method. To identify ER+ vs. ER-, PR+ vs. PR-, HER2+ vs. HER2-, Ki-67+ vs. Ki-67-, luminal A/B vs. nonluminal A/B, and triple negative (TN) vs. non-TN, the following models were implemented: random forest, adaptive boosting, support vector machine, linear discriminant analysis, and logistic regression.

STATISTICAL TESTS

Student's t, chi-square, and Fisher's exact tests were applied on clinical characteristics to confirm whether significant differences exist between different statuses (±) of prognostic biomarkers or molecular subtypes. The model performances were compared between the DCE-MRI, NME-DWI, and MP-MRI datasets using the area under the receiver-operating characteristic curve (AUC) and the DeLong test. P < 0.05 was considered significant.

RESULTS

With few exceptions, no significant differences (P = 0.062-0.984) were observed in the AUCs of models for six classification tasks between the DCE-MRI (AUC = 0.62-0.87) and NME-DWI (AUC = 0.62-0.91) datasets, while the model performances on the two imaging datasets were significantly poorer than on the MP-MRI dataset (AUC = 0.68-0.93). Additionally, the random forest and adaptive boosting models (AUC = 0.62-0.93) outperformed other three models (AUC = 0.62-0.90).

DATA CONCLUSION

NME-DWI was comparable with DCE-MRI in predictive performance and could be used as an alternative technique. Besides, MP-MRI demonstrated significantly higher AUCs than either DCE-MRI or NME-DWI.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 2.

A concise continuous time random-walk diffusion model for characterization of non-exponential signal decay in magnetic resonance imaging

Diffusion-weighted magnetic resonance imaging (dMRI) is a method of capturing the signal of water molecules diffusing in heterogeneous materials. Gaussian diffusion is interrupted when water mobility is hampered by obstructions in complex structures, and the dMRI signal decay does not match the single exponential decay in Brownian motion. In this study, a concise continuous time random-walk diffusion model is derived with less parameters than the continuous time random walk (CTRW) model and used to characterize the attenuation signal of brain tissue. The fitting results are compared with the CTRW model and the mono-exponential model reflecting the sub-diffusion and the long tail phenomenon of signal decay. Three sample experiments on rat brain and human brain are chosen to evaluate the validity in explaining the anomalous diffusion of water molecules in biological tissues, particularly in brain tissues in diverse directions, which also extends the applications of the concise continuous time random-walk diffusion model. Furthermore, we note that the concise continuous time random-walk diffusion model has practical advantages over the classical exponential model from the perspective of computational accuracy especially in the case of large b values, and has less parameters and is comparable to the CTRW model.

Multi-model sequential analysis of MRI data for microstructure prediction in heterogeneous tissue

We propose a general method for combining multiple models to predict tissue microstructure, with an exemplar using in vivo diffusion-relaxation MRI data. The proposed method obviates the need to select a single 'optimum' structure model for data analysis in heterogeneous tissues where the best model varies according to local environment. We break signal interpretation into a three-stage sequence: (1) application of multiple semi-phenomenological models to predict the physical properties of tissue water pools contributing to the observed signal; (2) from each Stage-1 semi-phenomenological model, application of a tissue microstructure model to predict the relative volumes of tissue structure components that make up each water pool; and (3) aggregation of the predictions of tissue structure, with weightings based on model likelihood and fractional volumes of the water pools from Stage-1. The multiple model approach is expected to reduce prediction variance in tissue regions where a complex model is overparameterised, and bias where a model is underparameterised. The separation of signal characterisation (Stage-1) from biological assignment (Stage-2) enables alternative biological interpretations of the observed physical properties of the system, by application of different tissue structure models. The proposed method is exemplified with human prostate diffusion-relaxation MRI data, but has potential application to a wide range of analyses where a single model may not be optimal throughout the sampled domain.

Fractal relaxation model with a nonlinear diffusion coefficient for fitting anomalous diffusion data in magnetic resonance imaging

In this paper a relaxation model of signal attenuation in diffusion-weighted magnetic resonance imaging (dMRI) with varying diffusion coefficient in terms of fractal derivative is proposed, in which the diffusion coefficient is a power law of the effective diffusion time. The relaxation model provides measures of diffusion constant, fractal dimension of diffusive trajectory of water molecule and the time power-law behavior of the diffusion coefficient. The proposed model was used to describe the magnetic resonance attenuation signal of the bullfrog sciatic nerve, and the corresponding spectral entropy was calculated to detect the environmental complexity in bullfrog sciatic nerve for water molecular diffusion. The results showed that the fractal derivative relaxation model (the VDC model) can accurately depict the diffusion pattern of water molecules in complex heterogeneous biological media at large b values. The VDC model provides an alternative theoretical reference for biological tissue detection based on time-dependent diffusion of water molecules.

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.

Comparison of different neurite density metrics with brain asymmetry evaluation

The standard diffusion MRI model with intra- and extra-axonal water pools offers a set of microstructural parameters describing brain white matter architecture. However, non-linearities in the standard model and diffusion data contamination by noise and imaging artefacts make estimation of diffusion metrics challenging. In order to develop reliable diffusion approaches and to avoid computational model degeneracy, additional theoretical assumptions allowing stable numerical implementations are required. Advanced diffusion approaches allow for estimation of intra-axonal water fraction (AWF), describing a key structural characteristic of brain tissue. AWF can be interpreted as an indirect measure or proxy of neurite density and has a potential as useful clinical biomarker. Established diffusion approaches such as white matter tract integrity, neurite orientation dispersion and density imaging (NODDI), and spherical mean technique provide estimates of AWF within their respective theoretical frameworks. In the present study, we estimated AWF metrics using different diffusion approaches and compared measures of brain asymmetry between the different metrics in a sub-sample of 182 subjects from the UK Biobank. Multivariate decomposition by mean of linked independent component analysis revealed that the various AWF proxies derived from the different diffusion approaches reflect partly non-overlapping variance of independent components, with distinct anatomical distributions and sensitivity to age. Further, voxel-wise analysis revealed age-related differences in AWF-based brain asymmetry, indicating less apparent left-right hemisphere difference with higher age. Finally, we demonstrated that NODDI metrics suffer from a quite strong dependence on used numerical algorithms and post-processing pipeline. The analysis based on AWF metrics strongly depends on the used diffusion approach and leads to poorly reproducible results.

A fractional order-based mixture of central Wishart (FMoCW) model for reconstructing white matter fibers from diffusion MRI

This paper introduces an algorithm for reconstructing the brain's white matter fibers (WMFs). In particular, a fractional order mixture of central Wishart (FMoCW) model is proposed to reconstruct the WMFs from diffusion MRI data. The pseudo super diffusive modality of anomalous diffusion is coupled with the mixture of central Wishart (MoCW) model to derive the proposed model. We have shown results on multiple synthetic simulations, including fibers orientations in 2 and 3 directions per voxel and experiments on real datasets of rat optic chiasm and a healthy human brain. In synthetic simulations, a varying Rician distributed noise levels, σ=0.01-0.09 is also considered. The proposed model can efficiently distinguish multiple fibers even when the angle of separation between fibers is very small. This model outperformed, giving the least angular error when compared to fractional mixture of Gaussian (MoG), MoCW and mixture of non-central Wishart (MoNCW) models.

Intravoxel incoherent motion diffusion weighted imaging for preoperative evaluation of liver regeneration after hepatectomy in hepatocellular carcinoma

OBJECTIVES

To explore whether intravoxel incoherent motion (IVIM) parameters could evaluate liver regeneration preoperatively.

METHODS

A total of 175 HCC patients were initially recruited. The apparent diffusion coefficient, true diffusion coefficient (D), pseudodiffusion coefficient (D*), pseudodiffusion fraction (f), diffusion distribution coefficient, and diffusion heterogeneity index (Alpha) were measured by two independent radiologists. Spearman's correlation test was used to assess correlations between IVIM parameters and the regeneration index (RI), calculated as 100% × (the volume of the postoperative remnant liver - the volume of the preoperative remnant liver) / the volume of the preoperative remnant liver. Multivariate linear regression analyses were used to identify the factors for RI.

RESULTS

Finally, 54 HCC patients (45 men and 9 women, mean age 51.26 ± 10.41 years) were retrospectively analyzed. The intraclass correlation coefficient ranged from 0.842 to 0.918. In all patients, fibrosis stage was reclassified as F0-1 (n = 10), F2-3 (n = 26), and F4 (n = 18) using the METAVIR system. Spearman correlation test showed D* (r = 0.303, p = 0.026) was associated with RI; however, multivariate analysis showed that only D value was a significant predictor (p < 0.05) of RI. D and D*showed moderate correlations with fibrosis stage (r = -0.361, p = 0.007; r = -0.457, p = 0.001). Fibrosis stage showed a negative correlation with RI (r = -0.263, p = 0.015). In the 29 patients who underwent minor hepatectomy, only the D value showed a positive association (p < 0.05) with RI, and a negative correlation with fibrosis stage (r = -0.360, p = 0.018). However, in the 25 patients who underwent major hepatectomy, no IVIM parameters were associated with RI (p > 0.05).

CONCLUSIONS

The D and D* values, especially the D value, may be reliable preoperative predictors of liver regeneration.

KEY POINTS

• The D and D* values, especially the D value, derived from IVIM diffusion-weighted imaging may be useful markers for the preoperative prediction of liver regeneration in patients with HCC. • The D and D* values derived from IVIM diffusion-weighted imaging show significant negative correlations with fibrosis, an important predictor of liver regeneration. • No IVIM parameters were associated with liver regeneration in patients who underwent major hepatectomy, but the D value was a significant predictor of liver regeneration in patients who underwent minor hepatectomy.

Accelerating High b-Value Diffusion-Weighted MRI Using a Convolutional Recurrent Neural Network (CRNN-DWI)

PURPOSE

To develop a novel convolutional recurrent neural network (CRNN-DWI) and apply it to reconstruct a highly undersampled (up to six-fold) multi-b-value, multi-direction diffusion-weighted imaging (DWI) dataset.

METHODS

A deep neural network that combines a convolutional neural network (CNN) and recurrent neural network (RNN) was first developed by using a set of diffusion images as input. The network was then used to reconstruct a DWI dataset consisting of 14 b-values, each with three diffusion directions. For comparison, the dataset was also reconstructed with zero-padding and 3D-CNN. The experiments were performed with undersampling rates (R) of 4 and 6. Standard image quality metrics (SSIM and PSNR) were employed to provide quantitative assessments of the reconstructed image quality. Additionally, an advanced non-Gaussian diffusion model was employed to fit the reconstructed images from the different approaches, thereby generating a set of diffusion parameter maps. These diffusion parameter maps from the different approaches were then compared using SSIM as a metric.

RESULTS

Both the reconstructed diffusion images and diffusion parameter maps from CRNN-DWI were better than those from zero-padding or 3D-CNN. Specifically, the average SSIM and PSNR of CRNN-DWI were 0.750 ± 0.016 and 28.32 ± 0.69 (R = 4), and 0.675 ± 0.023 and 24.16 ± 0.77 (R = 6), respectively, both of which were substantially higher than those of zero-padding or 3D-CNN reconstructions. The diffusion parameter maps from CRNN-DWI also yielded higher SSIM values for R = 4 (>0.8) and for R = 6 (>0.7) than the other two approaches (for R = 4, <0.7, and for R = 6, <0.65).

CONCLUSIONS

CRNN-DWI is a viable approach for reconstructing highly undersampled DWI data, providing opportunities to reduce the data acquisition burden.

Predicting TERT promoter mutation status using 1H-MR spectroscopy and stretched-exponential model of diffusion-weighted imaging in IDH-wildtype diffuse astrocytic glioma without intense enhancement

PURPOSE

Isocitrate dehydrogenase (IDH)-wildtype diffuse astrocytic glioma with telomerase reverse transcriptase (TERT) promoter mutation is defined as glioblastoma by the WHO 2021 criteria, revealing that TERT promotor mutation is highly associated with tumor aggressiveness. The aim of this study was to identify features from MR spectroscopy (MRS) and multi-exponential models of DWI distinguishing wild-type TERT (TERTw) from TERT promoter mutation (TERTm) in IDH-wildtype diffuse astrocytic glioma.

METHODS

Participants comprised 25 adult patients with IDH-wildtype diffuse astrocytic glioma. Participants were classified into TERTw and TERTm groups. Point-resolved spectroscopy sequences were used for MRS data acquisition. DWI was performed with 13 different b-factors. Peak height ratios of NAA/Cr and Cho/Cr were calculated from MRS data. Mean apparent diffusion coefficient (ADC), perfusion fraction (f), diffusion coefficient (D), pseudo-diffusion coefficient (D*), distributed diffusion coefficient (DDC), and heterogeneity index (α) were obtained using multi-exponential models from DWI data. Each parameter was compared between TERTw and TERTm using the Mann-Whitney U test. Correlations between parameters derived from MRS and DWI were also evaluated.

RESULTS

NAA/Cr and Cho/Cr were both higher for TERTw than for TERTm. The α of TERTw was smaller than that of TERTm, while the f of TERTw was higher than that of TERTm. NAA/Cr correlated negatively with α, but not with other DWI parameters. Cho/Cr did not show significant correlations with any DWI parameters.

CONCLUSION

The combination of NAA/Cr and α may have merit in clinical situation to predict the TERT mutation status of IDH-wildtype diffuse astrocytic glioma without intense enhancement.

Conventional and artificial intelligence-based computed tomography and magnetic resonance imaging quantitative techniques for non-invasive liver fibrosis staging

Chronic liver disease (CLD) ultimately develops into liver fibrosis and cirrhosis and is a major public health problem globally. The assessment of liver fibrosis is important for patients with CLD for prognostication, treatment decisions, and surveillance. Liver biopsies are traditionally performed to determine the stage of liver fibrosis. However, the risks of complications and technical limitations restrict their application to screening and sequential monitoring in clinical practice. CT and MRI are essential for evaluating cirrhosis-associated complications in patients with CLD, and several non-invasive methods based on them have been proposed. Artificial intelligence (AI) techniques have also been applied to stage liver fibrosis. This review aimed to explore the values of conventional and AI-based CT and MRI quantitative techniques for non-invasive liver fibrosis staging and summarized their diagnostic performance, advantages, and limitations.

Preoperative prediction of VETC in hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging at high b values: a pilot study

Background

Vessels encapsulating tumor clusters (VETC) have been considered an important cause of hepatocellular carcinoma (HCC) metastasis.

Purpose

To compare the potential of various diffusion parameters derived from the monoexponential model and four non-Gaussian models (DKI, SEM, FROC, and CTRW) in preoperatively predicting the VETC of HCC.

Methods

86 HCC patients (40 VETC-positive and 46 VETC-negative) were prospectively enrolled. Diffusion-weighted images were acquired using six b-values (range from 0 to 3000 s/mm2). Various diffusion parameters derived from diffusion kurtosis (DK), stretched-exponential (SE), fractional-order calculus (FROC), and continuous-time random walk (CTRW) models, together with the conventional apparent diffusion coefficient (ADC) derived from the monoexponential model were calculated. All parameters were compared between VETC-positive and VETC-negative groups using an independent sample t-test or Mann-Whitney U test, and then the parameters with significant differences between the two groups were combined to establish a predictive model by binary logistic regression. Receiver operating characteristic (ROC) analyses were used to assess diagnostic performance.

Results

Among all studied diffusion parameters, only DKI_K and CTRW_α significantly differed between groups (P=0.002 and 0.004, respectively). For predicting the presence of VETC in HCC patients, the combination of DKI_K and CTRW_α had the larger area under the ROC curve (AUC) than the two parameters individually (AUC=0.747 vs. 0.678 and 0.672, respectively).

Conclusion

DKI_K and CTRW_α outperformed traditional ADC for predicting the VETC of HCC.

Evaluating Biliary Malignancy with Measured and Calculated Ultra-high b-value Diffusion-weighted MR Imaging at 3T

PURPOSE

Although diffusion-weighted imaging (DWI) with ultra-high b-values is reported to be advantageous in the detection of some tumors, its applicability is not yet known in biliary malignancy. Therefore, this study aimed to evaluate the impact of measured b = 1400 s/mm² (M1400) and calculated b = 1400 s/mm² (C1400) DWI on image quality and quality of lesion discernibility using a modern 3T MR system compared to conventional b = 800 s/mm² DWI (M800).

METHODS

We evaluated 56 patients who had pathologically proven biliary malignancy. All the patients underwent preoperative or baseline 3T MRI using DWI (b = 50, 400, 800, and 1400 s/mm²). The calculated DWI was obtained using a conventional DWI set (b = 50, 400, and 800). The tumor-to-bile contrast ratio (CR) and tumor SNR were compared between the different DWI images. Likert scores were given on a 5-point scale to assess the overall image quality, overall artifacts, ghost artifacts, misregistration artifacts, margin sharpness, and lesion discernibility. Repeated-measures analysis of variance with post hoc analyses was used for statistical evaluations.

RESULTS

The CR of the tumor-to-bile was significantly higher in both M1400 and C1400 than in M800 (P^a < 0.01). SNRs were significantly higher in M800, followed by C1400 and M1400 (P^a < 0.01). Lesion discernibility was significantly improved for M1400, followed by C1400 and M800 for both readers (P^a < 0.01).

CONCLUSION

Using a 3T MRI, both measured and calculated DWI with an ultra-high b-value offer superior lesion discernibility for biliary malignancy compared to the conventional DWI.

Diffusion-weighted MRI for predicting and assessing treatment response of liver metastases from CRC - A systematic review and meta-analysis

The evaluation of response to chemotherapy and targeted therapies in colorectal liver metastases has traditionally been based on size changes, as per the RECIST criteria. However, therapy may alter tissue composition and not only tumor size, therefore, functional imaging techniques such as diffusion-weighted magnetic resonance imaging (DWI) may offer a more comprehensive assessment of treatment response. The aim of this systematic review and meta-analysis was to evaluate the use of DWI in the prediction and assessment of response to treatment in colorectal liver metastases and to determine if there is a baseline apparent diffusion coefficient (ADC) cut-off value that can predict a favorable response. A literature search was conducted using the MEDLINE/PubMed database, and risk of bias was evaluated using the QUADAS-2 tool. The mean differences between responders and non-responders were pooled. A total of 16 studies met the inclusion criteria, and various diffusion-derived techniques and coefficients were found to have potential for predicting and assessing treatment response. However, discrepancies were noted between studies. The most consistent predictor of response was a lower baseline ADC value calculated using traditional mono-exponential methods. Non-mono-exponential techniques for calculating DWI-derived parameters were also reported. A meta-analysis of a subset of studies failed to establish a cut-off value of ADC due to heterogeneity, but revealed a pooled mean difference of -0.12 × 10^-3 mm²/s between responders and non-responders. The results of this systematic review suggest that diffusion-derived techniques and coefficients may contribute to the evaluation and prediction of treatment response in colorectal liver metastases. Further controlled prospective studies are needed to confirm these findings and to guide clinical and radiological decision-making in the management of patients with CRC liver metastases.

Histogram array and convolutional neural network of DWI for differentiating pancreatic ductal adenocarcinomas from solid pseudopapillary neoplasms and neuroendocrine neoplasms

PURPOSE

This study aimed to investigate the diagnostic performance of the histogram array and convolutional neural network (CNN) based on diffusion-weighted imaging (DWI) with multiple b-values under magnetic resonance imaging (MRI) to distinguish pancreatic ductal adenocarcinomas (PDACs) from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine neoplasms (PNENs).

METHODS

This retrospective study consisted of patients diagnosed with PDACs (n = 132), PNENs (n = 45) and SPNs (n = 54). All patients underwent 3.0-T MRI including DWI with 10 b values. The regions of interest (ROIs) of pancreatic tumor were manually drawn using ITK-SNAP software, which included entire tumor at DWI (b = 1500 s/m²). The histogram array was obtained through the ROIs from multiple b-value data. PyTorch (version 1.11) was used to construct a CNN classifier to categorize the histogram array into PDACs, PNENs or SPNs.

RESULTS

The area under the curves (AUCs) of the histogram array and the CNN model for differentiating PDACs from PNENs and SPNs were 0.896, 0.846, and 0.839 in the training, validation and testing cohorts, respectively. The accuracy, sensitivity and specificity were 90.22%, 96.23%, and 82.05% in the training cohort, 84.78%, 96.15%, and 70.0% in the validation cohort, and 81.72%, 90.57%, and 70.0% in the testing cohort. The performance of CNN with AUC of 0.865 for this differentiation was significantly higher than that of f with AUC = 0.755 (P = 0.0057) and α with AUC = 0.776 (P = 0.0278) in all patients.

CONCLUSION

The histogram array and CNN based on DWI data with multiple b-values using MRI provided an accurate diagnostic performance to differentiate PDACs from PNENs and SPNs.

A review of quantitative diffusion-weighted MR imaging for breast cancer: Towards noninvasive biomarker

Quantitative diffusion-weighted imaging (DWI) is an important adjunct to conventional breast MRI and shows promise as a noninvasive biomarker of breast cancer in multiple clinical scenarios, from the discrimination of benign and malignant lesions, prediction, and evaluation of treatment response to a prognostic assessment of breast cancer. Various quantitative parameters are derived from different DWI models based on special prior knowledge and assumptions, have different meanings, and are easy to confuse. In this review, we describe the quantitative parameters derived from conventional and advanced DWI models commonly used in breast cancer and summarize the promising clinical applications of these quantitative parameters. Although promising, it is still challenging for these quantitative parameters to become clinically useful noninvasive biomarkers in breast cancer, as multiple factors may result in variations in quantitative parameter measurements. Finally, we briefly describe some considerations regarding the factors that cause variations.

Prognostic value of multi b-value DWI in patients with locally advanced rectal cancer

OBJECTIVES

To evaluate the potential of multi b-value DWI in predicting the prognosis of patients with locally advanced rectal cancer (LARC).

METHODS

From 2015 to 2019, a total of 161 patients with LARC were enrolled and randomly sampled into a training set (n = 113) and validation set (n = 48). Multi b-value DWI (b = 0~1500 s/mm²) scans were postprocessed to generate functional parameters, including apparent diffusion coefficient (ADC), Dt, Dp, f, distributed diffusion coefficient (DDC), and α. Histogram features of each functional parameter were submitted into Least absolute shrinkage and selection operator (LASSO) and stepwise multivariate COX analysis to generate DWI_score based on the training set. The prognostic model was constructed with functional parameter, DWI_score, and clinicopathologic factors by using univariate and multivariate COX analysis on the training set and verified on the validation set.

RESULTS

Multivariate COX analysis revealed that DWI_score was an independent indicator for 5-year progression-free survival (PFS, HR = 5.573, p < 0.001), but not for overall survival (OS, HR = 2.177, p = 0.051). No mean value of functional parameters was correlated with PFS or OS. Prognostic model for 5-year PFS based on DWI_score, TNM-stage, mesorectal fascia (MRF), and extramural venous invasion (EMVI) showed good performance both in the training set (AUC = 0.819) and validation set (AUC = 0.815).

CONCLUSIONS

The DWI_score based on histogram features of multi b-value DWI functional parameters was an independent factor for PFS of LARC and the prognostic model with a combination of DWI_score and clinicopathologic factors could indicate the progression risk before treatment.

KEY POINTS

• Mean value of functional parameters obtained from multi b-value DWI might not be useful to assess the prognosis of LARC. • The DWI_score based on histogram features of multi b-value DWI functional parameters was an independent prognosis factor for PFS of LARC. • Prognostic model based on DWI_score and clinicopathologic factors could indicate the progression risk of LARC before treatment.

Diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast lesions

OBJECTIVE

The conventional breast Diffusion-weighted imaging (DWI) was subtly influenced by microcirculation owing to the insufficient selection of the b values. However, the multiparameter derived from multiple b-value exhibits more reliable image quality and maximize the diagnostic accuracy. We aim to evaluate the diagnostic performance of stand-alone parameter or in combination with multiparameter derived from multiple b-value DWI in differentiating malignant from benign breast lesions.

METHODS

A total of forty-one patients diagnosed with benign breast tumor and thirty-eight patients with malignant breast tumor underwent DWI using thirteen b values and other MRI functional sequence at 3.0 T magnetic resonance. Data were accepted mono-exponential, bi-exponential, stretched-exponential, aquaporins (AQP) model analysis. A receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance of quantitative parameter or multiparametric combination. The Youden index, sensitivity and specificity were used to assess the optimal diagnostic model. T-test, logistic regression analysis, and Z-test were used. P value < 0.05 was considered statistically significant.

RESULT

The ADC_avg, ADC_max, f, and α value of the malignant group were lower than the benign group, while the ADC_fast value was higher instead. The ADC_min, ADC_slow, DDC and ADC_AQP showed no statistical significance. The combination (ADC_avg-ADC_fast) yielded the largest area under curve (AUC = 0.807) with sensitivity (68.42%), specificity (87.8%) and highest Youden index, indicating that multiparametric combination (ADC_avg-ADC_fast) was validated to be a useful model in differentiating the benign from breast malignant lesion.

CONCLUSION

The current study based on the multiple b-value diffusion model demonstrated quantitatively multiparametric combination (ADC_avg-ADC_fast) exhibited the optimal diagnostic efficacy to differentiate malignant from benign breast lesions, suggesting that multiparameter would be a promising non-invasiveness to diagnose breast lesions.

Multi-b-value diffusion stretched-exponential model parameters correlate with MIB-1 and CD34 expression in Glioma patients, an intraoperative MR-navigated, biopsy-based histopathologic study

Background

To understand the pathological correlations of multi-b-value diffusion-weighted imaging (MDWI) stretched-exponential model (SEM) parameters of α and diffusion distribution index (DDC) in patients with glioma. SEM parameters, as promising biomarkers, played an important role in histologically grading gliomas.

Methods

Biopsy specimens were grouped as high-grade glioma (HGG) or low-grade glioma (LGG). MDWI-SEM parametric mapping of DDC₁₅₀₀, α₁₅₀₀ fitted by 15 b-values (0-1,500 sec/mm²)and DDC₅₀₀₀ and α₅₀₀₀ fitted by 22 b-values (0-5,000 sec/mm²) were matched with pathological samples (stained by MIB-1 and CD34) by coregistered localized biopsies, and all SEM parameters were correlated with these pathological indices pMIB-1(percentage of MIB-1 expression positive rate) and CD34-MVD (CD34 expression positive microvascular density for each specimen). The two-tailed Spearman's correlation was calculated for pathological indexes and SEM parameters, as well as WHO grades and SEM parameters.

Results

MDWI-derived α₁₅₀₀ negatively correlated with CD34-MVD in both LGG (6 specimens) and HGG (26 specimens) (r=-0.437, P =0.012). MDWI-derived DDC₁₅₀₀ and DDC₅₀₀₀ negatively correlated with MIB-1 expression in all glioma patients (P<0.05). WHO grades negatively correlated with α₁₅₀₀(r=-0.485; P=0.005) and α₅₀₀₀(r=-0.395; P=0.025).

Conclusions

SEM-derived DDC and α are significant in histologically grading gliomas, DDC may indicate the proliferative ability, and CD34 stained microvascular perfusion may be an important determinant of water diffusion inhomogeneity α in glioma.

Spatiotemporal encoding MRI using subspace-constrained sampling and locally-low-rank regularization: Applications to diffusion weighted and diffusion kurtosis imaging of human brain and prostate

The benefits of performing locally low-rank (LLR) reconstructions on subsampled diffusion weighted and diffusion kurtosis imaging data employing spatiotemporal encoding (SPEN) methods, is investigated. SPEN allows for self-referenced correction of motion-induced phase errors in case of interleaved diffusion-oriented acquisitions, and allows one to overcome distortions otherwise observed along EPI's phase-encoded dimension. In combination with LLR-based reconstructions of the pooled imaging data and with a joint subsampling of b-weighted and interleaved images, additional improvements in terms of sensitivity as well as shortened acquisition times are demonstrated, without noticeable penalties. Details on how the LLR-regularized, subspace-constrained image reconstructions were adapted to SPEN are given; the improvements introduced by adopting these reconstruction frameworks for the accelerated acquisition of diffusivity and of kurtosis imaging data in both relatively homogeneous regions like the human brain and in more challenging regions like the human prostate, are presented and discussed within the context of similar efforts in the field.

Preliminary study of monoexponential, biexponential, and stretched-exponential models of diffusion-weighted MRI and diffusion kurtosis imaging on differential diagnosis of spinal metastases and chordoma

PURPOSE

Quantitative comparison of diffusion parameters from various models of diffusion-weighted (DWI) and diffusion kurtosis (DKI) imaging for distinguishing spinal metastases and chordomas.

METHODS

DWI and DKI examinations were performed in 31 and 13 cases of spinal metastases and chordomas, respectively. DWI derived apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), water molecular distributed diffusion coefficient (DDC), and intravoxel water diffusion heterogeneity (α). DKI derived mean diffusivity (MD) and mean kurtosis (MK). Independent sample t-testing compared statistical differences among parameters. Sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were determined. Pearson correlation analysis evaluated the parameters' correlations.

RESULTS

ADC, D, f, DDC, α, and MD were significantly lower in spinal metastases than chordomas (all P < 0.05). MK was significantly higher in spinal metastases than chordomas (P < 0.05). D had the highest area under the ROC curve (AUC) of 0.886, greater than MD (AUC = 0.706) or DDC (AUC = 0.742) in differentiating the two tumors (both P < 0.05). Combining D with f and α statistically significantly increased the AUC for diagnosis (to 0.995) relative to D alone (P < 0.05). There was a certain correlation among DDC, ADC, and D (all P < 0.05).

CONCLUSIONS

Monoexponential, biexponential, and stretched-exponential models of DWI and DKI can potentially differentiate spinal metastases and chordomas. D combined with f and α performed best.

Incorporating multiple magnetic resonance diffusion models to differentiate low- and high-grade adult gliomas: a machine learning approach

BACKGROUND

Accurate grading of gliomas is a challenge in imaging diagnosis. This study aimed to evaluate the performance of a machine learning (ML) approach based on multiparametric diffusion-weighted imaging (DWI) in differentiating low- and high-grade adult gliomas.

METHODS

A model was developed from an initial cohort containing 74 patients with pathology-confirmed gliomas, who underwent 3 tesla (3T) diffusion magnetic resonance imaging (MRI) with 21 b values. In all, 112 histogram features were extracted from 16 parameters derived from seven diffusion models [monoexponential, intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), fractional order calculus (FROC), continuous-time random walk (CTRW), stretched-exponential, and statistical]. Feature selection and model training were performed using five randomly permuted five-fold cross-validations. An internal test set (15 cases of the primary dataset) and an external cohort (n=55) imaged on a different scanner were used to validate the model. The diagnostic performance of the model was compared with that of a single DWI model and DWI radiomics using accuracy, sensitivity, specificity, and the area under the curve (AUC).

RESULTS

Seven significant multiparametric DWI features (two from the stretched-exponential and FROC models, and three from the CTRW model) were selected to construct the model. The multiparametric DWI model achieved the highest AUC (0.84, versus 0.71 for the single DWI model, P<0.05), an accuracy of 0.80 in the internal test, and both AUC and accuracy of 0.76 in the external test.

CONCLUSIONS

Our multiparametric DWI model differentiated low- (LGG) from high-grade glioma (HGG) with better generalization performance than the established single DWI model. This result suggests that the application of an ML approach with multiple DWI models is feasible for the preoperative grading of gliomas.

Evaluation of Monoexponential, Stretched-Exponential and Intravoxel Incoherent Motion MRI Diffusion Models in Early Response Monitoring to Neoadjuvant Chemotherapy in Patients With Breast Cancer-A Preliminary Study

BACKGROUND

There has been a growing interest in exploring the applications of stretched-exponential (SEM) and intravoxel incoherent motion (IVIM) models of diffusion-weighted imaging (DWI) in breast imaging, with the focus on differentiation of breast lesions. However, the use of SEM and IVIM models to predict early response to neoadjuvant chemotherapy (NACT) has received less attention.

PURPOSE

To investigate the value of monoexponential, SEM, and IVIM models to predict early response to NACT in patients with primary breast cancer.

STUDY TYPE

Prospective.

POPULATION

Thirty-seven patients with primary breast cancer (aged 46 ± 11 years) due to undergo NACT.

FIELD STRENGTH/SEQUENCES

A 1.5-T MR scanner, T1 -weighted three-dimensional spoiled gradient-echo, two-dimensional single-shot spin-echo echo-planar imaging sequence (DWI) at six b-values (0-800 s mm-2 ).

ASSESSMENT

Tumor volume, apparent diffusion coefficient, tissue diffusion (Dt ), pseudo-diffusion coefficient (Dp ), perfusion fraction (f), distributed diffusion coefficient, and alpha (α) were extracted, following volumetric sampling of the tumors, at three time-points: pretreatment, post one and three cycles of NACT.

STATISTICAL TESTS

Mann-Whitney test, receiver operating characteristic (ROC) curve. Statistical significance level was P < 0.05.

RESULTS

Following NACT, 17 patients were determined to be pathological responders and 20 nonresponders. Tumor volume was significantly larger in nonresponders at each MRI time-point and demonstrated reasonable performance in predicting response (area under the ROC curve [AUC] = 0.83-0.87). No significant differences between groups were found in the diffusion coefficients at each time-point (P = 0.09-1). The parameters α (SEM), f, and f × Dp (IVIM) were able to differentiate between response groups after one cycle of NACT (AUC = 0.73, 0.72, and 0.74, respectively).

CONCLUSION

Diffusion coefficients derived from the monoexponential, SEM, and IVIM models did not predict pathological response. However, the IVIM-derived parameters f and f × Dp and the SEM-derived parameter α were able to predict response to NACT in breast cancer patients following one cycle of NACT.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 2.

Multiparametric MRI-based nomograms in predicting positive surgical margins of prostate cancer after laparoscopic radical prostatectomy

Background

Positive surgical margins (PSMs) are an independent risk factor of biochemical recurrence in patients with prostate cancer (PCa) after laparoscopic radical prostatectomy; however, limited MRI-based predictive tools are available. This study aimed to develop a novel nomogram combining clinical and multiparametric MRI (mpMRI) parameters to reduce PSMs by improving surgical planning.

Methods

One hundred and three patients with PCa (55 patients with negative surgical margins [NSMs] and 48 patients with PSMs) were included in this retrospective study. The following parameters were obtained using GE Functool post-processing software: diffusion-weighted imaging (DWI); intravoxel incoherent motion model (IVIM); and diffusion kurtosis imaging (DKI). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to analyze the data set to select the optimal MRI predictors. Preoperatively clinical parameters used to build a clinical nomogram (C-nomogram). Multivariable logistic regression analysis was used to build an MRI nomogram (M-nomogram) by introducing the MRI parameters. Based on the MRI and clinical parameters, build an MRI combined with clinical parameters nomogram (MC-nomogram). Comparisons with the M-nomogram and MC-nomogram were based on discrimination, calibration, and decision curve analysis (DCA). A 3-fold cross-validation method was used to assess the stability of the nomogram.

Results

There was no statistical difference in AUC between the C-nomogram (sensitivity=64%, specificity=65% and AUC=0.683), the M-nomogram (sensitivity=57%, specificity=88% and AUC=0.735) and the MC-nomogram (sensitivity= 64%, specificity=82% and AUC=0.756). The calibration curves of the three nomograms used to predict the risk of PSMs in patients with PCa showed good agreement. The net benefit of the MC-nomogram was higher than the others (range, 0.2-0.7).

Conclusions

The mpMRI-based nomogram can predict PSMs in PCa patients. Although its AUC (0.735) is not statistically different from that of the clinical-based nomogram AUC (0.683). However, mpMRI-based nomogram has higher specificity (88% VS. 63%), model stability, and clinical benefit than clinical-based nomogram. And the predictive ability of mpMRI plus clinical parameters for PSMs is further improved.

Establishment of a multi-parameters MRI model for predicting small lymph nodes metastases (<10 mm) in patients with resected pancreatic ductal adenocarcinoma

PURPOSE

To evaluate the potential role of MR findings and DWI parameters in predicting small regional lymph nodes metastases (with short-axis diameter < 10 mm) in pancreatic ductal adenocarcinomas (PDACs).

METHODS

A total of 127 patients, 82 in training group and 45 in testing group, with histopathologically diagnosed PDACs who underwent pancreatectomy were retrospectively analyzed. PDACs were divided into two groups of positive and negative lymph node metastases (LNM) based on the pathological results. Pancreatic cancer characteristics, short axis of largest lymph node, and DWI parameters of PDACs were evaluated.

RESULTS

Univariate and multivariate analyses showed that extrapancreatic distance of tumor invasion, short-axis diameter of the largest lymph node, and mean diffusivity of tumor were independently associated with small LNM in patients with PDACs. The combining MRI diagnostic model yielded AUCs of 0.836 and 0.873, and accuracies of 81.7% and 80% in the training and testing groups. The AUC of the MRI model for predicting LNM was higher than that of subjective MRI diagnosis in the training group (rater 1, P = 0.01; rater 2, 0.008) and in a testing group (rater 1, P = 0.036; rater 2, 0.024). Comparing the subjective diagnosis, the error rate of the MRI model was decreased. The defined LNM-positive group by the MRI model showed significantly inferior overall survival compared to the negative group (P = 0.006).

CONCLUSIONS

The MRI model showed excellent performance for individualized and noninvasive prediction of small regional LNM in PDACs. It may be used to identify PDACs with small LNM and contribute to determining an appropriate treatment strategy for PDACs.

Development of a standard phantom for diffusion-weighted magnetic resonance imaging quality control studies: A review

Various materials and compounds have been used in the design of diffusion-weighted magnetic resonance imaging (DWMRI) phantoms to mimic biological tissue properties, including diffusion. This review thus provides an overview of the preparations of the various DW-MRI phantoms available in relation to the limitations and strengths of materials/solutions used to fill them. The narrative review conducted from relevant databases shows that synthesizing all relevant compounds from individual liquids, gels, and solutions based on their identified strengths could contribute to the development of a novel multifunctional DW-MRI phantom. The proposed multifunctional material at varied concentrations, when filled into a multi-compartment Perspex container of cylindrical or spherical geometry, could serve as a standard DW-MRI phantom. The standard multifunctional phantom could potentially provide DW-MRI quality control test parameters in one study session.

Quantitative differentiation of malignant and benign thyroid nodules with multi-parameter diffusion-weighted imaging

BACKGROUND

The value of conventional magnetic resonance imaging in the differential diagnosis of thyroid nodules is limited; however, the value of multi-parameter diffusion-weighted imaging (DWI) in the quantitative evaluation of thyroid nodules has not been well determined.

AIM

To determine the utility of multi-parametric DWI including mono-exponential, bi-exponential, stretched exponential, and kurtosis models for the differentiation of thyroid lesions.

METHODS

Seventy-nine patients (62 with benign and 17 with malignant nodules) underwent multi-b value diffusion-weighted imaging of the thyroid. Multiple DWI parameters were obtained for statistical analysis.

RESULTS

Good agreement was found for diffusion parameters of thyroid nodules. Malignant lesions displayed lower diffusion parameters including apparent diffusion coefficient (ADC), the true diffusion coefficient (D), the perfusion fraction (f), the distributed diffusion coefficient (DDC), the intravoxel water diffusion heterogeneity (α) and kurtosis model-derived ADC (Dapp), and higher apparent diffusional kurtosis (Kapp) than benign entities (all P < 0.01), except for the pseudodiffusion coefficient (D*) (P > 0.05). The area under the ROC curve (AUC) of the ADC_{(0 and 1000)} was not significantly different from that of the ADC_{(0 and 2000)}, ADC_{(0 to 2000)}, ADC_{(0 to 1000)}, D, DDC, Dapp and Kapp (all P > 0.05), but was significantly higher than the AUC of D*, f and α (all P < 0.05) for differentiating benign from malignant lesions.

CONCLUSION

Multiple DWI parameters including ADC, D, f, DDC, α, Dapp and Kapp could discriminate benign and malignant thyroid nodules. The metrics including D, DDC, Dapp and Kapp provide additional information with similar diagnostic performance of ADC, combination of these metrics may contribute to differentiate benign and malignant thyroid nodules. The ADC calculated with higher b values may not lead to improved diagnostic performance.

[Feasibility of Cerebrovascular Reserve Assessment Using Stretched Exponential Model in Major Cerebral Artery Steno-occlusive Disease: Comparison with SPECT]

PURPOSE

To clarify whether diffusion-weighted imaging using stretched exponential model can assess cerebrovascular reserve (CVR) in patients with major cerebral artery steno-occlusive disease, we compared stretched exponential parameters and single-photon emission computed tomography (SPECT).

METHODS

Twenty-nine patients with unilateral major cerebral artery steno-occlusive disease (25 men and 4 women; age, 69±11 years) were analyzed in this study. The patients were divided into three groups: normal CVR (CVR≥30%), moderate CVR (10%≤CVR<30%), and severe CVR (CVR<10%). The distributed diffusion coefficient (DDC) and heterogeneity index (α) from the stretched exponential model, apparent diffusion coefficient (ADC) from the monoexponential model, and CVR and resting cerebral blood flow (CBF) from SPECT were measured in the bilateral middle cerebral artery territories, and ipsilateral-to-contralateral ratios (rDDC, rα, rADC, and rCBF) were obtained.

RESULTS

The rDDC values in severe CVR were significantly higher than those in normal CVR (P=0.003). The rDDC values were significantly negatively correlated with ipsilateral CVR (rho=-0.31, P=0.009). The rDDC values were not significantly correlated with rCBF (P=0.34).

CONCLUSION

We have shown that elevated rDDC values are associated with impaired CVR. Our results suggest that diffusion-weighted imaging using stretched exponential model has a potential to evaluate hemodynamic impairment.

Predicting Grade of Esophageal Squamous Carcinoma: Can Stretched Exponential Model-Based DWI Perform Better Than Bi-Exponential and Mono-Exponential Model?

Background

To evaluate and compare the potential performance of various diffusion parameters obtained from mono-exponential model (MEM)-, bi-exponential model (BEM)-, and stretched exponential model (SEM)-based diffusion-weighted imaging (DWI) in grading of esophageal squamous carcinoma (ESC).

Methods

Eighty-two patients with pathologically confirmed ESC without treatment underwent multi-b-value DWI scan with 13 b values (0~12,00 s/mm²). The apparent diffusion coefficient (ADC) deriving from the MEM; the pure molecular diffusion (ADC_slow), pseudo-diffusion coefficient (ADC_fast), perfusion, and fraction (f) deriving from the BEM; and the distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) deriving from the SEM were calculated and compared between poorly differentiated and well/moderately differentiated ESC, respectively. The prediction parameters and diagnostic efficiency were compared by drawing receiver operating characteristic (ROC) curves.

Results

The ADC, ADC_slow, ADC_fast, and DDC in poorly ESC were significantly lower than those in well/moderately differentiated ones. By using only one parameter, ADC_slow, DDC had the moderate diagnostic efficiency and the areas under the curve (AUC) were 0.758 and 0.813 in differentiating ESC. The DDC had the maximum AUC with sensitivity (88.00%) and specificity (68.42%). Combining ADC with ADC_fast, ADC_slow, and DDC and combining ADC_slow with ADC_fast can provide a higher diagnostic accuracy with AUC ranging from 0.756, 0.771, 0.816, and 0.793, respectively.

Conclusion

Various parameters derived from different DWI models including MEM, BEM, and SEM were potentially helpful in grading ESC. DDC obtained from SEM was the most promising diffusion parameter for predicting the grade of ESC.

Radiomics analysis based on multiple parameters MR imaging in the spine: Predicting treatment response of osteolytic bone metastases to chemotherapy in breast cancer patients

PURPOSE

To assess the value of radiomics, apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and stretched-exponential (SE) MR imaging in prediction of therapeutic response in patients with spinal metastases before chemotherapy.

METHODS

Thirty-six patients with 190 osteolytic metastatic lesions from breast cancer were prospectively enrolled and underwent MR imaging before and after 6 months' treatment on a 1.5 T MRI. According to MDA criteria, 68 lesions were categorized as progressive disease (PD) and 122 lesions were categorized as stable or improvement (non-PD). The regions of interest (ROIs) were manually drawn on DWI, T1WI, T2WI and FS-T2WI by two radiologists with ITK-SNAP. The ADC_all (multiple b-values method), IVIM parameters (D, D* and f) and SE parameters (DDC and α) were generated. The radiomics features were selected from the ROIs.

RESULTS

The mean values of ADC, DDC, and D before treatment were significantly higher in non-PD group than those in PD group (P = 0.001). The radiomics based on ADC_all had the highest AUC value (0.852), followed by that of the T2WI (0.829) and FS-T2WI (0.798). The radiomics model using ADC_all and FS-T2WI showed excellent efficiency in predicting treatment response with AUCs of 0.905 and 0.873 in training and validation cohorts. The radiomics model had better performance than that of ADC_all, D, and DDC for predicting treatment response of bone metastases.

CONCLUSION

Radiomics model based on ADC_all and FS-T2WI could predict the treatment response and contribute to assisting clinicians in accurately choosing appropriated management.