Lung Cancer: Short‐Term Reproducibility of Intravoxel Incoherent Motion Parameters and Apparent Diffusion Coefficient at 3T

MRI-Based Clinical-Imaging-Radiomics Nomogram Model for Discriminating Between Benign and Malignant Solid Pulmonary Nodules or Masses

RATIONALE AND OBJECTIVES

Pulmonary nodules or masses are highly prevalent worldwide, and differential diagnosis of benign and malignant lesions remains difficult. Magnetic resonance imaging (MRI) can provide functional and metabolic information of pulmonary lesions. This study aimed to establish a nomogram model based on clinical features, imaging features, and multi-sequence MRI radiomics to identify benign and malignant solid pulmonary nodules or masses.

MATERIALS AND METHODS

A total of 145 eligible patients (76 male; mean age, 58.4 years ± 13.7 [SD]) with solid pulmonary nodules or masses were retrospectively analyzed. The patients were randomized into two groups (training cohort, n = 102; validation cohort, n = 43). The nomogram was used for predicting malignant pulmonary lesions. The diagnostic performance of different models was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

Of these patients, 95 patients were diagnosed with benign lesions and 50 with malignant lesions. Multivariate analysis showed that age, DWI value, LSR value, and ADC value were independent predictors of malignant lesions. Among the radiomics models, the multi-sequence MRI-based model (T1WI+T2WI+ADC) achieved the best diagnosis performance with AUCs of 0.858 (95%CI: 0.775, 0.919) and 0.774 (95%CI: 0.621, 0.887) for the training and validation cohorts, respectively. Combining multi-sequence radiomics, clinical and imaging features, the predictive efficacy of the clinical-imaging-radiomics model was significantly better than the clinical model, imaging model and radiomics model (all P < 0.05).

CONCLUSION

The MRI-based clinical-imaging-radiomics model is helpful to differentiate benign and malignant solid pulmonary nodules or masses, and may be useful for precision medicine of pulmonary diseases.

Intravoxel incoherent motion diffusion-weighted imaging of solitary pulmonary lesions: initial study with gradient- and spin-echo sequences

AIM

To evaluate the feasibility and image quality of intravoxel incoherent motion diffusion-weighted imaging (IVIM) using gradient- and spin-echo (GRASE) in solitary pulmonary lesions (SPLs) compared to echo planar imaging (EPI) and turbo spin-echo (TSE) at 3 T.

MATERIALS AND METHODS

Forty-two patients with SPLs underwent lung magnetic resonance imaging (MRI) using TSE-IVIM, GRASE-IVIM, and EPI-IVIM at 3 T. Signal ratio (SR), contrast ratio (CR), and image distortion ratio (DR) of three sequences were compared. The reproducibility and repeatability of the apparent diffusion coefficient (ADC) and IVIM-derived parameters were assessed using the interclass correlation coefficient (ICC) and coefficient of variation (CV). The repeatability of the ADC and IVIM-derived parameters between all sequences was evaluated using the Bland-Altman method.

RESULTS

EPI-IVIM had a higher SR, lower CR, and higher DR (p<0.05); however, there was no significant difference between TSE-IVIM and GRASE-IVIM (p>0.05). Compared to the D and f values of TSE-IVIM (ICC lower limit >0.90), GRASE-IVIM and EPI-IVIM showed poor reproducibility (ICC lower limit<0.90). The repeatability of the ADC and D values obtained by TSE-IVIM (CV, 1.93-2.96% and 2.44-3.18%, respectively) and GRASE-IVIM (CV, 2.56-3.12% and 3.21-3.51%, respectively) were superior to those of EPI-IVIM (CV, 10.03-10.2% and 11.30-11.57%). The repeatability of D∗ and f values for all sequences was poor. Bland-Altman analysis showed wide limits of agreement between the ADC and IVIM-derived parameters for all sequences.

CONCLUSION

GRASE-IVIM reduced the DR, improved the stability of the ADC and D values on repeated scans, and had the shortest scanning time.

Stability and repeatability of diffusion-weighted imaging (DWI) of normal pancreas on 5.0 Tesla magnetic resonance imaging (MRI)

To explore the stability and repeatability of diffusion-weighted imaging (DWI) of normal pancreas with different field of views (FOV) on 5.0 T magnetic resonance imaging (MRI) system. Twenty healthy subjects underwent two sessions of large FOV (lFOV) and reduced FOV (rFOV) DWI sequence scanning. Two radiologists measured the apparent diffusion coefficient (ADC) values and the signal-to-noise ratio (SNR) of the pancreatic head, body, and tail on DWI images, simultaneously, using a 5-point scale, evaluate the artifacts and image quality. One radiologist re-measured the ADC on DWI images again after a 4-week interval. The test-retest repeatability of two scan sessions were also evaluated. Intra-observer and inter-observer at lFOV and rFOV, the ADC values were not significantly different (P > 0.05), intraclass correlation coefficients (ICCs) and coefficient of variations were excellence (ICCs 0.85-0.99, CVs < 8.0%). The ADC values were lower with rFOV than lFOV DWI for the head, body, tail, and overall pancreas. The consistency of the two scan sessions were high. The high stability and repeatability of pancreas DWI has been confirmed at 5.0 T. Scan durations are reduced while resolution and image quality are improved with rFOV DWI, which is more preferable than lFOV for routine pancreas imaging.

Volumetric analysis of intravoxel incoherent motion diffusion-weighted imaging in preoperative assessment of non-small cell lung cancer

PURPOSE

To evaluate the potential of intravoxel incoherent motion (IVIM) and apparent diffusion coefficient (ADC) in the prediction of tumor grade, lymph node metastasis and pleural invasion of non-small cell lung cancer (NSCLC) before surgery.

MATERIALS AND METHODS

65 patients diagnosed with NSCLC by surgery were enrolled. IVIM-DWI (10 b-values, 0-1000 s/mm²) was performed before surgery. The mean and minimum ADC (ADC_mean, ADC_min) and IVIM parameters D, D* and f were independently measured and calculated by 2 radiologists by drawing regions of interest (ROIs) including the solid component of the whole tumor. Intraclass correlation coefficients (ICCs) were analysed. Spearman analysis was used to determine the correlation between IVIM parameters and tumor differentiation. Independent sample t-tests (normal distribution) or Mann-Whitney U tests (non-normal distribution) were used to compare the differences between the parameters in moderately-well and poorly differentiated groups, with and without lymph node metastasis and pleural invasion groups. Receiver operating characteristic (ROC) curves were generated.

RESULTS

The ADC_mean, ADC_min, D and f values were negatively correlated with the pathological grades of tumor (P < 0.05). The ADC_mean and D values of patients with poor differentiation and lymph node metastasis were significantly lower than that of patients with moderately-well differentiation and without lymph node metastasis (P < 0.001-0.012). The D value was significantly lower and f value was significantly higher among patients with pleural invasion than those without (P =   0.033 and < 0.001). ROC analysis showed that the area under the ROC curve (AUC) was larger for D in predicting the degree of differentiation (0.832) and lymph node metastasis (0.806), and higher for f in predicting pleural invasion (0.832).

CONCLUSIONS

IVIM is useful for predicting the tumor differentiation, lymph node metastasis and pleural invasion in NSCLC patients before surgery.

Volumetric Analysis of Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Predicting the Response to Chemotherapy in Patients With Locally Advanced Non-Small Cell Lung Cancer

OBJECTIVE

We aimed to prospectively investigate intravoxel incoherent motion parameters to predict the response to chemotherapy in locally advanced non-small cell lung cancer (NSCLC) patients.

METHODS

From July 2016 to March 2018, 30 advanced NSCLC patients were enrolled and underwent chest intravoxel incoherent motion-diffusion-weighted imaging at Siemens 3T magnetic resonance imaging before and at the end of the first cycle of chemotherapy. Regions of interest were drawn including the whole tumor volume to derive the apparent diffusion coefficient value, D, D*, and f, respectively. Time-dependent receiver operating characteristic curves were generated to evaluate the cutoff values of continuous variables. A Cox proportional hazards model was used to assess the independent predictors of progression-free survival (PFS) and overall survival (OS). Kaplan-Meier curves and log-rank test were generated.

RESULTS

Among the 30 patients, 28 cases (93.3%) died and 2 cases (6.7%) survived till the closeout date. Univariate Cox regression analyses revealed that the significant predictors of PFS and OS were the tumor size reduction rate, the change rates of D and apparent diffusion coefficient values, and the D value before therapy (PFS: P = 0.015, hazard ratio [HR] = 2.841; P < 0.001, HR = 5.840; P = 0.044, HR = 2.457; and P = 0.027, HR = 2.715; OS: P = 0.008, HR = 2.987; P < 0.001, HR = 4.357; P = 0.006, HR = 3.313; and P = 0.013, HR = 2.941, respectively). Multivariate Cox regression analysis suggested that △D% was identified as independent predictors of both PFS and OS (P = 0.003, HR = 9.200 and P = 0.016, HR = 4.617). In addition, the cutoff value of △D% was 21.06% calculated by receiver operating characteristic curve analysis. In the Kaplan-Meier analysis, the PFS and OS were significantly greater in the group of patients with △D% larger than 21.06% (log-rank test, χ2 = 16.453, P < 0.001; χ2 = 13.952, P < 0.001).

CONCLUSIONS

Intravoxel incoherent motion-diffusion-weighted imaging was preferred for predicting the prognosis of advanced NSCLC patients treated with chemotherapy. A D increase more than 21.06% at 1 month was associated with a lower rate of disease progression and death.

Apparent Diffusion Coefficient Value as a Biomarker for Detecting Muscle-Invasive and High-Grade Bladder Cancer: A Systematic Review

Background: Several studies have investigated the potential role of the apparent diffusion coefficient (ADC) value of diffusion-weighted magnetic resonance imaging as a biomarker of high-grade and invasive bladder cancer. Methods: PubMed and the Cochrane Library were systematically searched in September 2021 to extract studies that evaluated the associations between ADC values, pathological T stage, and histological grade bladder cancers. The diagnostic performance of ADC values in detecting muscle-invasive bladder cancer (MIBC) and high-grade disease was systematically reviewed. Results: Six studies were included in this systematic review. MIBC showed significantly lower ADC values than non-muscle-invasive bladder cancer (NMIBC) in all six studies. The median (range) sensitivity, specificity, and area under the curve (AUC) of ADC values to detect MIBC among the four eligible studies were 73.5% (68.8–90.0%), 79.9% (66.7–84.4%), and 0.762 (0.730–0.884), respectively. Similarly, high-grade disease showed significantly lower ADC values than did low-grade disease in all four eligible studies. The median (range) sensitivity, specificity, and AUC of ADC values for detecting high-grade disease among the three eligible studies were 75.0% (73.0–76.5%), 95.8% (76.2–100%), and 0.902 (0.804–0.906), respectively. Conclusions: The ADC value is a non-invasive diagnostic biomarker for discriminating muscle-invasive and high-grade bladder cancer.

Comparison of Conventional DWI, Intravoxel Incoherent Motion Imaging, and Diffusion Kurtosis Imaging in Differentiating Lung Lesions

Purpose

To compare conventional diffusion weighted imaging (DWI), intravoxel incoherent motion imaging (IVIM) and diffusion kurtosis imaging (DKI) in differentiating malignant and benign lung lesions.

Method

Fifty-five consecutive patients with lung lesions underwent multiple b-value DWI. The apparent diffusion coefficient (ADC), IVIM and DKI parameters were calculated using postprocessing software and compared between the malignant and benign groups. Receiver operating characteristic (ROC) analysis was performed for all parameters.

Results

ADC and D were lower in malignant lesions than in benign lesions, while Kapp was higher (P < 0.05). The differences in D*, f, and Dapp between the two groups were not significant (P > 0.05). The areas under the curves (AUCs) of ADC, D, and Kapp were 0.816, 0.864, and 0.822. The combination of all the significant parameters yielded an AUC of 0.880. There were no significant differences in diagnostic efficacy among ADC, D, Kapp and the predictor factor (PRE).

Conclusions

In this study, traditional DWI (ADC), IVIM (D), and DKI (Kapp) all had good diagnostic performance in differentiating malignant lung lesions from benign lesions, but the combination of ADC, D, and Kapp value had better diagnostic efficacy than these parameters alone.

A Comparative Study of Amide Proton Transfer Weighted Imaging and Intravoxel Incoherent Motion MRI Techniques Versus (18) F-FDG PET to Distinguish Solitary Pulmonary Lesions and Their Subtypes

BACKGROUND

Amide proton transfer weighted imaging (APTw), intravoxel incoherent motion (IVIM), and positron emission tomography (PET) imaging all have the potential to characterize solitary pulmonary lesions (SPLs).

PURPOSE

To compare APTw and IVIM with PET imaging for distinguishing between benign and malignant SPLs and their subtypes.

STUDY TYPE

Prospective.

POPULATION

Ninety-five patients, 78 with malignant SPLs (including 48 with adenocarcinoma [AC] and 17 with squamous cell carcinoma [SCC]), and 17 with benign SPLs.

FIELD STRENGTH/SEQUENCE

Fast spin-echo (FSE) T2WI, FSE APTw and echo-planar imaging IVIM, MR-base attenuation correction (MRAC), and PET imaging on a 3-T whole-body PET/MR system.

ASSESSMENT

The magnetization transfer ratio asymmetry (MTRasym) at 3.5 ppm, diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), and the maximum standardized uptake value (SUVmax) were analyzed.

STATISTICAL TESTS

Individual sample t-test, Delong test, Pearson's correlation analysis, and area under the receiver operating characteristic curve (AUC). P < 0.05 indicated statistical significance.

RESULTS

The MTRasym and SUVmax were significantly higher, and D was significantly lower in the malignant group (3.3 ± 2.6 [%], 7.8 ± 5, and 1.2 ± 0.3 [×10^-3  mm² /second]) compared to the benign group (-0.3 ± 1.6 [%], 3.1 ± 3.8, and 1.6 ± 0.3 [×10^-3  mm² /second]). The MTRasym and D were significantly lower, and SUVmax was significantly higher in the SCC group (0.8 ± 1.0 [%], 1.0 ± 0.2 [×10^-3  mm² /second] than in the AC group (4.1 ± 2.6 [%], 1.3 ± 0.3 [×10^-3  mm² /second], 6.7 ± 4.6). Besides, the combination (AUC = 0.964) of these three methods showed higher diagnostic efficacy than any individual method (AUC = 0.917, 0.851, 0.82, respectively) in identifying malignant and benign SPLs. However, APTw showed better diagnostic efficacy than the combination of three methods or PET imaging alone in distinguishing SCC and AC groups (AUC = 0.934, 0.781, 0.725, respectively).

DATA CONCLUSION

APTw, IVIM, and PET imaging are all effective methods to distinguish benign and malignant SPLs and their subtypes. APTw is potentially more capable than PET imaging of distinguishing lung SCC from AC.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Preliminary study of 3 T-MRI native T1-mapping radiomics in differential diagnosis of non-calcified solid pulmonary nodules/masses

Background

Cumulative CT radiation damage was positively correlated with increased tumor risks. Although it has recently been known that non-radiation MRI is alternative for pulmonary imaging. There is little known about the value of MRI T1-mapping in the diagnosis of pulmonary nodules. This article aimed to investigate the value of native T1-mapping-based radiomics features in differential diagnosis of pulmonary lesions.

Methods

73 patients underwent 3 T-MRI examination in this prospective study. The 99 pulmonary lesions on native T1-mapping images were segmented twice by one radiologist at indicated time points utilizing the in-house semi-automated software, followed by extraction of radiomics features. The inter-class correlation coefficient (ICC) was used for analyzing intra-observer’s agreement. Dimensionality reduction and feature selection were performed via univariate analysis, and least absolute shrinkage and selection operator (LASSO) analysis. Then, the binary logical regression (LR), support vector machine (SVM) and decision tree classifiers with the input of optimal features were selected for differentiating malignant from benign lesions. The receiver operative characteristics (ROC) curve, area under the curve (AUC), sensitivity, specificity and accuracy were calculated. Z-test was used to compare differences among AUCs.

Results

107 features were obtained, of them, 19.5% (n = 21) had relatively good reliability (ICC ≥ 0.6). The remained 5 features (3 GLCM, 1 GLSZM and 1 shape features) by dimensionality reduction were useful. The AUC of LR was 0.82(95%CI: 0.67–0.98), with sensitivity, specificity and accuracy of 70%, 85% and 80%. The AUC of SVM was 0.82(95%CI: 0.67–0.98), with sensitivity, specificity and accuracy of 70, 85 and 80%. The AUC of decision tree was 0.69(95%CI: 0.49–0.87), with sensitivity, specificity and accuracy of 50, 85 and 73.3%.

Conclusions

The LR and SVM models using native T1-mapping-based radiomics features can differentiate pulmonary malignant from benign lesions, especially for uncertain nodules requiring long-term follow-ups.

Whole-lesion histogram analysis of mono-exponential and bi-exponential diffusion-weighted imaging in differentiating lung cancer from benign pulmonary lesions using 3 T MRI

AIM

To investigate whether whole-lesion histogram analysis of apparent diffusion coefficient (ADC) values derived from mono-exponential and bi-exponential diffusion-weighted imaging (DWI) can differentiate lung cancer from benign pulmonary lesions.

MATERIALS AND METHODS

Thirty-two patients with lung cancer and 17 patients with benign pulmonary lesions were included retrospectively. All patients underwent DWI before surgery or biopsy. ADC histogram parameters, including mean, percentile values (10^th and 90^th), kurtosis, and skewness, were calculated independently by two radiologists. The histogram parameters were compared between patients with lung cancer and benign lesions. Receiver operating characteristic curves were constructed to evaluate the diagnostic performance.

RESULTS

The ADC_Mean, ADC_10th, D_Mean, D_10th were significantly lower in lung cancer (1.187 ± 0.144 × 10^-3; 0.440 ± 0.062 × 10^-3; 1.068 ± 0.108 × 10^-3; and 0.422 ± 0.049 × 10^-3 mm/s) compared to benign lesions (1.418 ± 0.274 × 10^-3; 0.555 ± 0.113 × 10^-3; 1.216 ± 0.149 × 10^-3; and 0.490 ± 0.044 × 10^-3 mm/s; p<0.05). The ADC_Skewness and D_Skewness were significantly different between lung cancer (2.35 ± 0.72; 2.58 ± 1.14) and benign lesions (1.85 ± 0.54; 1.59 ± 1.47; p<0.05). D_10th was robust in differentiating lung cancer from benign lesions. Using 0.453 × 10^-3 mm/s as the optimal threshold, the sensitivity, specificity, and accuracy of D_10th were 78.12%, 82.35%, and 79.6%, respectively.

CONCLUSION

Whole-lesion histogram analysis of ADC values derived by mono-exponential and bi-exponential DWI using 3 T magnetic resonance imaging helps distinguish lung cancer from benign pulmonary lesions.

B1 -Corrected T1 Mapping in Lung Cancer: Repeatability, Reproducibility, and Identification of Histological Types

BACKGROUND

T1 mapping can potentially quantitatively assess the intrinsic properties of tumors. B₁ correction can reduce the magnetic field inhomogeneity.

PURPOSE

To assess the repeatability and reproducibility of B₁ -corrected T1 mapping for lung cancer and the ability to identify pathological types.

STUDY TYPE

Prospective reproducibility study.

POPULATION

Sixty lung cancer patients (22 with emphysema) with a total of 60 lesions (adenocarcinoma [n = 23], squamous cell carcinoma [n = 19], and small-cell lung cancer [SCLC] [n = 18]).

FIELD STRENGTH/SEQUENCE

A 3 T/B₁ -corrected 3D variable flip angle T1 mapping and free-breathing diffusion-weighted imaging.

ASSESSMENT

Intraobserver, interobserver, and test-retest reproducibility of minimum, maximum, mean, and SD of lung tumor T1 values were assessed. The correlation between mean T1 and apparent diffusion coefficient (ADC) and differences between different histological types of lung cancer were evaluated.

STATISTICAL TESTS

Intraclass correlation coefficients (ICCs), within-subject coefficients of variation (WCVs), Bland-Altman plots, Pearson's correlation coefficient (r), and analysis of variance (ANOVA). A P value <0.05 was considered to be statistically significant.

RESULTS

No significant differences were found in minimum, maximum, mean, and SD T1 values for repeated measurements (intraobserver and interobserver) and repeated examinations (P = 0.103-0.979). All parameters showed good intraobserver, interobserver and test-retest reproducibility (ICC, 0.780-0.978), except the maximum T1 value (ICC, 0.645-0.922). The mean T1 exhibited the best reproducibility and repeatability, with an average difference <6% for repeated measurements, <8% for repeated scans in lung cancer patients, and<10% for repeated scans in those with emphysema. The mean T1 correlated moderately with ADC (r = -0.580, -0.516, and -0.511 for observers A, B, and C). Both mean T1 and mean ADC were significantly different in SCLC patients compared with those in adenocarcinoma and squamous cell carcinoma patients.

DATA CONCLUSION

The mean T1 from B₁ -corrected T1 mapping is a repeatable parameter with the potential to identify histological types of lung cancer and thus may be a promising imaging biomarker for characterizing lung cancer.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

Repeatability of Apparent Diffusion Coefficient at 3.0 Tesla in Normal Pancreas

Purpose: To evaluate the apparent diffusion coefficient (ADC) test-retest repeatability of the normal pancreas based on diffusion-weighted imaging (DWI).

Methods: Twenty-six healthy volunteers (mean 47.6 years; 13 men) were included and scanned twice with reposition for a DWI sequence at 3.0-T. Two readers measured the ADCs of pancreatic head, body and tail for two DWIs, independently. The mean ADCs of the pancreatic head, body and tail were calculated as the global pancreatic ADC. Test-retest repeatability and agreement of ADC measurement were evaluated by the Bland-Altman analysis, intra-class correlation coefficient (ICC) and coefficient of variation (CV).

Results: The global pancreatic ADC showed the best test-retest repeatability (mean difference ± limits of agreement were 0.05 ± 0.25×10^-3 mm²/s; ICC, 0.79; CV, 6%). Test-retest repeatabilities for ADC of pancreatic head, body or tail were scattered, with mean difference ± limits of agreement between two tests were 0.03 ± 0.47, 0.05 ± 0.42 and 0.06 ± 0.31 (×10^-3 mm²/s) (ICCs, 0.81, 0.52 and 0.68; CVs, 9%, 8% and 8%), respectively. Both intra-observer repeatability and inter-observer reproducibility were acceptable for global pancreatic ADC between measurements of the two DWIs.

Conclusions:The best test-retest repeatability of ADC in the normal pancreas was only for the whole pancreas with a CV of 6%. Cautions should be taken in interpreting longitudinal clinical changes in ADC values of the normal pancreas for the measurements do have an inherent variability by locations.

Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis

Background and objectives

The diagnostic performance of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the differential diagnosis of pulmonary tumors remained debatable among published studies. This study aimed to pool and summary the relevant results to provide more robust evidence in this issue using a meta-analysis method.

Materials and methods

The researches regarding the differential diagnosis of lung lesions using IVIM-DWI were systemically searched in Pubmed, Embase, Web of science and Wangfang database without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan’s nomogram was used to predict the post-test probabilities.

Results

Eleven studies with 481 malignant and 258 benign lung lesions were included. Most include studies showed a low to unclear risk of bias and low concerns regarding applicability. Lung cancer demonstrated a significant lower ADC (SMD = -1.17, P < 0.001), D (SMD = -1.02, P < 0.001) and f values (SMD = -0.43, P = 0.005) than benign lesions, except D* value (SMD = 0.01, P = 0.96). D value demonstrated the best diagnostic performance (sensitivity = 89%, specificity = 71%, AUC = 0.90) and highest post-test probability (57, 57, 43 and 43% for D, ADC, f and D* values) in the differential diagnosis of lung tumors, followed by ADC (sensitivity = 85%, specificity = 72%, AUC = 0.86), f (sensitivity = 71%, specificity = 61%, AUC = 0.71) and D* values (sensitivity = 70%, specificity = 60%, AUC = 0.66).

Conclusion

IVIM-DWI parameters show potentially strong diagnostic capabilities in the differential diagnosis of lung tumors based on the tumor cellularity and perfusion characteristics, and D value demonstrated better diagnostic performance compared to mono-exponential ADC.

Perfusion-driven Intravoxel Incoherent Motion (IVIM) MRI in Oncology: Applications, Challenges, and Future Trends

Recent developments in MR hardware and software have allowed a surge of interest in intravoxel incoherent motion (IVIM) MRI in oncology. Beyond diffusion-weighted imaging (and the standard apparent diffusion coefficient mapping most commonly used clinically), IVIM provides information on tissue microcirculation without the need for contrast agents. In oncology, perfusion-driven IVIM MRI has already shown its potential for the differential diagnosis of malignant and benign tumors, as well as for detecting prognostic biomarkers and treatment monitoring. Current developments in IVIM data processing, and its use as a method of scanning patients who cannot receive contrast agents, are expected to increase further utilization. This paper reviews the current applications, challenges, and future trends of perfusion-driven IVIM in oncology.

T2 mapping of the extraocular muscles in healthy volunteers: preliminary research on scan-rescan and observer-observer reproducibility

BACKGROUND

T2-mapping technique and derived T2 relaxation time allows quantitative assessment of extraocular muscles; however, the reproducibility of T2 mapping-derived parameters was seldom studied till now.

PURPOSE

To evaluate the scan-rescan and observer-observer reproducibility of T2 relaxation time measurements of extraocular muscles in young healthy volunteers.

MATERIAL AND METHODS

Fourteen volunteers underwent T2-mapping examinations of the extraocular muscles three times within one month on a 3.0-T MR system. Scan-rescan and observer-observer reproducibility of T2 relaxation time measurements of the extraocular muscles were assessed using intraclass correlation coefficient and coefficient of variation.

RESULTS

Both scan-rescan (short-term and long-term) and observer-observer could achieve good to excellent reproducibility, while better short-term than long-term scan-rescan reproducibility was obtained. The coefficient of variation of the T2 relaxation time of each extraocular muscles during both scan-rescan and observer-observer reproducibility assessment were <6%.

CONCLUSION

T2 relaxation time measurement of the extraocular muscles is proven to be highly reproducible at 3.0 T. T2 mapping may be a potential imaging technique in the diagnosis and follow-up of orbital diseases involved extraocular muscles in further studies.

The value of diffusion-weighted imaging based on monoexponential and biexponential models for the diagnosis of benign and malignant lung nodules and masses

OBJECTIVES

The objective is to compare the efficacy of diffusion-weighted imaging (DWI) parameters of mean and minimum apparent diffusion coefficient (ADCmean and ADCmin) and intravoxel incoherent motion (IVIM) in the differentiation of benign and malignant lung nodules and masses.

METHODS

Lung lesions measured larger than 1.5 cm on CT were included between August 2015 and September 2018. DWI (10 b-values, 0-1000 s/mm2) scans were performed, and the data were post-processed to derive the ADCmean, ADCmin and IVIM parameters of true diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f). An independent sample t-test or Mann-Whitney U test was used to compare benign and malignant parameters. Receiver operating characteristic curves were generated and a Z test was used.

RESULTS

121 patients were finally enrolled, each with one lesion. Examined 121 lesions were malignant in 88 (72.7%) and benign in 33 (27.3%). The ADCmean of malignant pulmonary nodules was significantly lower than that of benign pulmonary nodules (t = 3.156, p = 0.006), whereas the other parameters revealed no significant differences (p = 0.162-0.690). Receiver operating characteristic curve analysis revealed that an ADCmean threshold value of 1.43 × 10-3 mm2/s yielded 88.57% sensitivity and 64.29% specificity. While for lung masses, the ADCmean, ADCmin, D and D* values in malignant pulmonary masses were significantly lower (P﹤0.001-0.011). Among them, the D value exhibited the best diagnostic performance when the threshold of D was 1.23 × 10-3mm2/s, which yielded a sensitivity of 90.57% and a specificity of 89.47% (Z = 2.230, 3.958, 2.877 and p = 0.026, ﹤0.001 and 0.004, respectively).

CONCLUSION

ADC is the most robust parameter to differentiate benign and malignant lung nodules, whereas D is the most robust parameter to differentiate benign and malignant lung masses.

ADVANCES IN KNOWLEDGE

This is the first study to compare all the quantitative parameters of DWI and IVIM mentioned in the literatures for assessing lung lesions; Second, we divided the lesions into lung nodules and lung masses with the size of 3 cm as the boundary.

Intravoxel Incoherent Motion Diffusion-Weighted Imaging of Lung Cancer: Comparison Between Turbo Spin-Echo and Echo-Planar Imaging

OBJECTIVE

The aim of the study was to compare intravoxel incoherent motion diffusion-weighted imaging (DWI) for evaluating lung cancer using single-shot turbo spin-echo (TSE) and single-shot echo-planar imaging (EPI) in a 3T MR system.

METHODS

Both single-shot TSE-DWI and single-shot EPI-DWI were scanned twice respectively for 15 patients with lung cancer. Distortion ratio, signal-to-noise ratio, and contrast-to-noise ratio were compared between the 2 techniques. The Bland-Altman analysis was performed to analyze reproducibility between the parameters of TSE-DWI and EPI-DWI. Short-term test-retest repeatability, as well as interobserver agreement, was evaluated using the coefficient of variation (CV) and the intraclass correlation coefficient (ICC).

RESULT

Turbo spin-echo DWI has lower signal-to-noise ratio and similar contrast-to-noise ratio compared with EPI-DWI. Distortion ratio of TSE-DWI was significantly smaller than that of EPI-DWI. The apparent diffusion coefficient (ADC) and true diffusivity (D) of TSE-DWI showed higher values than those of EPI-DWI. The Bland-Altman analysis showed unacceptable limits of agreement between these 2 sequences. Test-retest repeatability was good for ADC and D of EPI-DWI (CV, 14.11%-16.60% and 17.08%-19.53%) and excellent for ADC and D of TSE-DWI (CV, 4.8%-6.19% and 6.05%-8.71%), but relatively poor for perfusion fraction (f) and pseudo-diffusion coefficient (D*) (CV, 25.95%-27.70% and 56.92%-71.84% for EPI, 23.67%-28.67% and 60.85%-70.17% for TSE). For interobserver agreement, both techniques were good to excellent in ADC and D (The lower limit of 95% confidence interval for ICC was almost all greater than 0.75), whereas D* and f had higher interobserver variabilities with D* of TSE-DWI showing poorest reproducibility (ICC, -0.27 to 0.12).

CONCLUSIONS

Lung DWI or IVIM using TSE could provide distortion-free images and improve the test-retest robustness of ADC and D as compared with EPI-DWI; however, it might exert a negative effect on perfusion parameter D*.

Repeatability analysis of ADC histogram metrics of the uterus

BACKGROUND

Recently, histogram analysis based on voxel-wise apparent diffusion coefficient (ADC) value distribution has been increasingly performed. However, few studies have been reported regarding its repeatability.

PURPOSE

To evaluate the repeatability of ADC histogram metrics of the uterus in clinical magnetic resonance imaging (MRI).

MATERIAL AND METHODS

Thirty-three female patients who underwent pelvic MRI including diffusion-weighted imaging (DWI) were prospectively included after providing informed consent. Two sequential DWI acquisitions with identical parameters and position were obtained. Regions of interest (ROIs) for histologically confirmed uterine lesions (five cervical and three endometrial cancers, and one endometrial hyperplasia) and normal appearing tissues (21 endometrium and 33 myometrium) were assigned on the first DWI dataset and then pasted onto the second DWI dataset. ADC histogram metrics within the ROIs were calculated and repeatability was evaluated by calculating within-subject coefficient of variance (%) (wCV (%)) and Bland-Altman plot (%).

RESULTS

ADC 10%, 25%, median, 75%, 90%, maximum, mean, and entropy showed high repeatability (wCV (%) < 7, 95% limit of agreement in Bland-Altman plot (%) < ±20), followed by ADC minimum (wCV (%) = 8.12, 95% limit of agreement in Bland-Altman plot (%) < ±30). However, ADC skewness and kurtosis showed very low repeatability in all evaluations.

CONCLUSION

ADC histogram metrics like ADC 10%, 25%, median, 75%, 90%, maximum, mean, and entropy are robust biomarkers and could be applicable to clinical use. However, ADC skewness and kurtosis lack robustness. Radiologists should keep these characteristics and limitations in mind when interpreting quantitative DWI.

Diffusion MRI of cancer: From low to high b-values

Following its success in early detection of cerebral ischemia, diffusion-weighted imaging (DWI) has been increasingly used in cancer diagnosis and treatment evaluation. These applications are propelled by the rapid development of novel diffusion models to extract biologically valuable information from diffusion-weighted MR signals, and significant advances in MR hardware that has enabled image acquisition with high b-values. This article reviews recent technical developments and clinical applications in cancer imaging using DWI, with a special emphasis on high b-value diffusion models. The article is organized in four sections. First, we provide an overview of diffusion models that are relevant to cancer imaging. The model parameters are discussed in relation to three tissue properties-cellularity, vascularity, and microstructures. An emphasis is placed on characterization of microstructural heterogeneity, given its novelty and close relevance to cancer. Second, we illustrate diffusion MR clinical applications in each of the following three categories: 1) cancer detection and diagnosis; 2) cancer grading, staging, and classification; and 3) cancer treatment response prediction and evaluation. Third, we discuss several practical issues, including selection of image acquisition parameters, reproducibility and reliability, motion management, image distortion, etc., that are commonly encountered when applying DWI to cancer in clinical settings. Lastly, we highlight a few ongoing challenges and provide some possible future directions, particularly in the area of establishing standards via well-organized multicenter clinical trials to accelerate clinical translation of advanced DWI techniques to improving cancer care on a large scale. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:23-40.