Quantitative Dynamic Contrast-enhanced MRI Parameters Research Articles

Machine learning models based on quantitative dynamic contrast-enhanced MRI parameters assess the expression levels of CD3+, CD4+, and CD8+ tumor-infiltrating lymphocytes in advanced gastric carcinoma.

To explore the effectiveness of machine learning classifiers based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the expression levels of CD3+, CD4+, and CD8+ tumor-infiltrating lymphocytes (TILs) in patients with advanced gastric cancer (AGC). This study investigated 103 patients with confirmed AGC through DCE-MRI and immunohistochemical staining. Immunohistochemical staining was used to evaluate CD3+, CD4+, and CD8+ T-cell expression. Utilizing Omni Kinetics software, radiomics features (Ktrans, Kep, and Ve) were extracted and underwent selection via variance threshold, SelectKBest, and LASSO methods. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) are the four classifiers used to build four machine learning (ML) models, and their performance was evaluated using 10-fold cross-validation. The model's performance was evaluated and compared using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. In terms of CD3+, CD4+, and CD8+ T lymphocyte prediction models, the random forest model outperformed the other classifier models in terms of CD4+ and CD8+ T cell prediction, with AUCs of 0.913 and 0.970 on the training set and 0.904 and 0.908 on the validation set, respectively. In terms of CD3+ T cell prediction, the logistic regression model fared the best, with AUCs on the training and validation sets of 0.872 and 0.817, respectively. Machine learning classifiers based on DCE-MRI have the potential to accurately predict CD3+, CD4+, and CD8+ tumor-infiltrating lymphocyte expression levels in patients with AGC.

Influence of Different Measurement Methods of Arterial Input Function on Quantitative Dynamic Contrast-Enhanced MRI Parameters in Head and Neck Cancer.

Head and neck cancer (HNC) is the sixth most prevalent cancer worldwide. Dynamic contrast-enhanced MRI (DCE-MRI) helps in diagnosis and prognosis. Quantitative DCE-MRI requires an arterial input function (AIF), which affects the values of pharmacokinetic parameters (PKP). To evaluate influence of four individual AIF measurement methods on quantitative DCE-MRI parameters values (Ktrans , ve , kep , and vp ), for HNC and muscle. Prospective. A total of 34 HNCpatients (23 males, 11 females, age range 24-91) FIELD STRENGTH/SEQUENCE: A 3 T; 3D SPGR gradient echo sequence with partial saturation of inflowing spins. Four AIF methods were applied: automatic AIF (AIFa) with up to 50 voxels selected from the whole FOV, manual AIF (AIFm) with four voxels selected from the internal carotid artery, both conditions without (Mc-) or with (Mc+) motion correction. Comparison endpoints were peak AIF values, PKP values in tumor and muscle, and tumor/muscle PKP ratios. Nonparametric Friedman test for multiple comparisons. Nonparametric Wilcoxon test, without and with Benjamini Hochberg correction, for pairwise comparison of AIF peak values and PKP values for tumor, muscle and tumor/muscle ratio, P value ≤ 0.05 was considered statistically significant. Peak AIF values differed significantly for all AIF methods, with mean AIFmMc+ peaks being up to 66.4% higher than those for AIFaMc+. Almost all PKP values were significantly higher for AIFa in both, tumor and muscle, up to 76% for mean Ktrans values. Motion correction effect was smaller. Considering tumor/muscle parameter ratios, most differences were not significant (0.068 ≤ Wilcoxon P value ≤ 0.8). We observed important differences in PKP values when using either AIFa or AIFm, consequently choice of a standardized AIF method is mandatory for DCE-MRI on HNC. From the study findings, AIFm and inflow compensation are recommended. The use of the tumor/muscle PKP ratio should be of interest for multicenter studies. 2 TECHNICAL EFFICACY: Stage 1.

Free-breathing radial volumetric interpolated breath-hold examination sequence and dynamic contrast-enhanced MRI combined with diffusion-weighted imaging for assessment of solitary pulmonary nodules

ObjectiveTo test the performance of free-breathing Dynamic Contrast-Enhanced MRI (DCE-MRI) using a radial volumetric interpolated breath-hold examination (VIBE) sequence combined with diffusion-weighted imaging (DWI) for quantitative solitary pulmonary nodule (SPN) assessment. MethodsA total of 67 SPN cases receiving routine MRI routine scans, DWI, and dynamic-enhanced MRI in our hospital from May 2017 to November 2018 were collected. These cases were divided into a malignant group and a benign group according to the characteristics of the SPNs. The quantitative DCE-MRI parameters (Ktrans, Kep, Ve) and apparent diffusion coefficient (ADC) values of the nodules were measured. ResultsThe Ktrans and Kep values in the malignant group were higher than those in the benign group, while the ADC values in the malignant group were lower than those in the benign group. Furthermore, the Ktrans value of adenocarcinoma was higher than that of squamous cell carcinoma and small cell carcinoma (P < 0.05). The Ve value was significantly different between non-small cell carcinoma and small cell carcinoma (P < 0.05). With an ADC value of 0.98 × 10−3 mm2/s as the threshold, the specificity and sensitivity to diagnose benign and malignant nodules was 90.6% and 80%, respectively. ConclusionHigh-temporal-resolution DCE-MRI using the r-VIBE technique in combination with DWI could contribute to pulmonary nodule analysis and possibly serve as a potential alternative to distinguish malignant from benign nodules as well as differentiate different types of malignancies.

A study of dynamic contrast-enhanced MR imaging features and influence factors of pelvic bone marrow in adult females.

To investigate the effect of menstrual status and age on the perfusion of pelvic bone marrow in adult females using quantitative dynamic contrast-enhanced MRI (DCE-MRI). In total, 96 adult females who underwent DCE-MRI between September 2017 and December 2017 were included. All the subjects' quantitative DCE-MRI parameters of pelvic bone marrow were measured and retrospectively analyzed, including Ktrans (volume transfer constant), Kep (efflux rate constant), and Ve (interstitial volume). According to their menstrual status, the subjects were divided into a premenopausal group (n = 39) and a postmenopausal group (n = 57), and the two groups were then divided into four subgroups according to age. The intraobserver reliability was assessed by the intraclass correlation coefficient (ICC). The parameters were compared between different menstrual status groups and age subgroups by Mann-Whitney test, and Spearman correlation analysis was used to evaluate the correlation between the age and the quantitative parameters. The ICCs of the Ktrans, Kep, and Ve values were 0.989, 0.974, and 0.920, respectively. Ktrans, Kep, and Ve of the premenopausal group were significantly higher than those of the postmenopausal group (P < 0.05). The overall age was negatively correlated with Ktrans, Kep, and Ve (r = - 0.590, - 0.357, and - 0.381, respectively, P < 0.05). In the premenopausal group, Ktrans and Ve were significantly higher in subgroup 1 (≤ 40years) compared with subgroup 2 (> 40years) (P < 0.05), and age showed a negative correlation with Ktrans and Ve (r = - 0.344 and - 0.334, respectively, P < 0.05). In the postmenopausal group, Ktrans and Kep were significantly higher in subgroup 3 (≤ 60years) compared with subgroup 4 (> 60years) (P < 0.05), and age showed a negative correlation with Ktrans and Kep (r = - 0.460 and - 0.303, respectively, P < 0.05). Menstrual status and age have significant effects on the perfusion of the pelvic bone marrow microenvironment in adult females and that the microenvironment of the pelvic bone marrow displays different changes at different age stages. Quantitative DCE-MRI has contributed to the interpretation of the pelvic bone marrow perfusion status.

Arterial input functions in dynamic contrast-enhanced magnetic resonance imaging: which model performs best when assessing breast cancer response?

To evaluate the performance of six models of population arterial input function (AIF) in the setting of primary breast cancer and neoadjuvant chemotherapy (NAC). The ability to fit patient dynamic contrast-enhanced MRI (DCE-MRI) data, provide physiological plausible data and detect pathological response was assessed. Quantitative DCE-MRI parameters were calculated for 27 patients at baseline and after 2 cycles of NAC for 6 AIFs. Pathological complete response detection was compared with change in these parameters from a reproduction cohort of 12 patients using the Bland-Altman approach and receiver-operating characteristic analysis. There were fewer fit failures pre-NAC for all models, with the modified Fritz-Hansen having the fewest pre-NAC (3.6%) and post-NAC (18.8%), contrasting with the femoral artery AIF (19.4% and 43.3%, respectively). Median transfer constant values were greatest for the Weinmann function and also showed greatest reductions with treatment (-68%). Reproducibility (r) was the lowest for the Weinmann function (r = -49.7%), with other AIFs ranging from r = -27.8 to -39.2%. Using the best performing AIF is essential to maximize the utility of quantitative DCE-MRI parameters in predicting response to NAC treatment. Applying our criteria, the modified Fritz-Hansen and cosine bolus approximated Parker AIF models performed best. The Fritz-Hansen and biexponential approximated Parker AIFs performed less well, and the Weinmann and femoral artery AIFs are not recommended. We demonstrate that using the most appropriate AIF can aid successful prediction of response to NAC in breast cancer.

A role for dynamic contrast-enhanced magnetic resonance imaging in predicting tumour radiation response.

Background:Dynamic contrast-enhanced (DCE) MRI may provide prognostic insights into tumour radiation response. This study examined quantitative DCE MRI parameters in rat tumours, as potential biomarkers of tumour growth delay following single high-dose irradiation.Methods:Dunning R3327-AT1 prostate tumours were evaluated by DCE MRI following intravenous injection of Gd-DTPA. The next day tumours were irradiated (single dose of 30 Gy), while animals breathed air (n=4) or oxygen (n=4); two animals were non-irradiated controls. Growth was followed and tumour volume-quadrupling time (T4) was compared with pre-irradiation DCE assessments.Results:Irradiation caused significant tumour growth delay (T4 ranged from 28 to 48 days for air-breathing rats, and 40 to 75 days for oxygen-breathing rats) compared with the controls (T4=7 to 9 days). A strong correlation was observed between T4 and extravascular-extracellular volume fraction (ve) irrespective of the gas inhaled during irradiation. There was also a correlation between T4 and volume transfer constant (Ktrans) for the air-breathing group alone.Conclusions:The data provide rationale for expanded studies of other tumour sites, types and progressively patients, and are potentially significant, as many patients undergo contrast-enhanced MRI as part of treatment planning.

Quantitative Dynamic Contrast-Enhanced MRI of Pelvic and Lumbar Bone Marrow: Effect of Age and Marrow Fat Content on Pharmacokinetic Parameter Values

The purpose of this study was to determine the effects of age and fat content on quantitative dynamic contrast-enhanced MRI (DCE-MRI) parameters in the bone marrow of the lumbar spine and pelvis. The interreader reproducibility of this technique will also be assessed. Forty-three DCE-MRI studies of the female pelvis defined the study group. Quantitative pharmacokinetic perfusion parameters of lumbar and pelvic marrow were analyzed by three readers on a DCE-MRI postprocessing platform. Linear regression analysis was performed to determine the effect of age and marrow fat fraction on the parameters of transfer constant (K(trans)), efflux rate constant (K(ep)), extravascular extracellular space (V(e)), and initial area under the gadolinium curve at 60 seconds (iAUGC(60)). Interreader agreement was assessed by means of intraclass correlation coefficient calculation. A weak but statistically significant correlation was established between both age and fat fraction and the parameters K(trans) (R(2) = 0.14) and K(ep) (R(2) = 0.09). There was also a weak but statistically significant correlation between fat fraction and V(e) (R(2) = 0.116) and iAUGC(60) (R(2) = 0.108), but no correlation between age and these parameters. Intraclass correlation coefficients of parameter measurements by different readers were all greater than 0.7 at the p < 0.05 level. Age and fat fraction have small measurable effects on quantitative DCE-MRI parameters in bone marrow. However, given the wide interindividual variation of these parameters, these effects are unlikely to confound changes related to malignancy or treatment. Also of note, there was strong interreader reproducibility of parameter measurements among a range of experience levels, suggesting that the reader-reader experience level may not represent a significant source of variability in bone marrow DCE-MRI.

Diffusion-Weighted and Dynamic Contrast-Enhanced MRI of Prostate Cancer: Correlation of Quantitative MR Parameters With Gleason Score and Tumor Angiogenesis

The objective of our study was to investigate whether quantitative parameters derived from diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) correlate with Gleason score and angiogenesis of prostate cancer. Seventy-three patients who underwent preoperative MRI and radical prostatectomy were included in our study. A radiologist and pathologist located the dominant tumor on the MR images based on histopathologic correlation. For each dominant tumor, the apparent diffusion coefficient (ADC) value and quantitative DCE-MRI parameters (i.e., contrast agent transfer rate between blood and tissue [K(trans)], extravascular extracellular fractional volume [v(e)], contrast agent backflux rate constant [k(ep)], and blood plasma fractional volume on a voxel-by-voxel basis [v(p)]) were calculated and the Gleason score was recorded. The mean blood vessel count, mean vessel area fraction, and vascular endothelial growth factor (VEGF) expression of the dominant tumor were determined using CD31, CD34, and VEGF antibody stains. Spearman correlation analysis between MR and histopathologic parameters was conducted. The mean tumor diameter was 15.2 mm (range, 5-28 mm). Of the 73 prostate cancer tumors, five (6.8%) had a Gleason score of 6, 46 (63%) had a Gleason score of 7, and 22 (30.1%) had a Gleason score of greater than 7. ADC values showed a moderate negative correlation with Gleason score (r = -0.376, p = 0.001) but did not correlate with tumor angiogenesis parameters. Quantitative DCE-MRI parameters did not show a significant correlation with Gleason score or VEGF expression (p > 0.05). Mean blood vessel count and mean vessel area fraction parameters estimated from prostate cancer positively correlated with k(ep) (r = 0.440 and 0.453, respectively; p = 0.001 for both). There is a moderate correlation between ADC values and Gleason score and between k(ep) and microvessel density of prostate cancer. Although the strength of the correlations is insufficient for immediate diagnostic utility, these results warrant further investigation on the potential of multiparametric MRI to facilitate noninvasive assessment of prostate cancer aggressiveness and angiogenesis.