A comparative study of mono-exponential and advanced diffusion-weighted imaging in differentiating stage IA endometrial carcinoma from benign endometrial lesions
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ABSTRACT:
SUBMITTER: Li H
PROVIDER: S-EPMC10951008 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundTo assess the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in differentiating stage IA endometrial carcinoma (EC) from benign endometrial lesions (BELs) and characterizing histopathologic features of stage IA EC preoperatively.MethodsOne hundred and six BEL and 126 stage IA EC patients were retrospectively enrolled. Eighteen volumetric histogram parameters were extracted from the ADC map of each lesion. The Mann-Whitney U or Student's t-test was used to compare the differences between the two groups. Models based on clinical parameters and histogram features were established using multivariate logistic regression. Receiver operating characteristic (ROC) analysis and calibration curves were used to assess the models.ResultsStage IA EC showed lower ADC10th, ADC90th, ADCmin, ADCmax, ADCmean, ADCmedian, interquartile range, mean absolute deviation, robust mean absolute deviation (rMAD), root mean squared, energy, total energy, entropy, variance, and higher skewness, kurtosis and uniformity than BELs (all p < 0.05). ADCmedian yielded the highest area under the ROC curve (AUC) of 0.928 (95% confidence interval [CI] 0.895-0.960; cut-off value = 1.161 × 10-3 mm2/s) for differentiating stage IA EC from BELs. Moreover, multivariate analysis demonstrated that ADC-score (ADC10th + skewness + rMAD + total energy) was the only significant independent predictor (OR = 2.641, 95% CI 2.045-3.411; p < 0.001) for stage IA EC when considering clinical parameters. This ADC histogram model (ADC-score) achieved an AUC of 0.941 and a bias-corrected AUC of 0.937 after bootstrap resampling. The model performed well for both premenopausal (accuracy = 0.871) and postmenopausal (accuracy = 0.905) patients. Besides, ADCmin and ADC10th were significantly lower in Grade 3 than in Grade 1/2 stage IA EC (p = 0.022 and 0.047). At the same time, no correlation was found between ADC histogram parameters and the expression of Ki-67 in stage IA EC (all p > 0.05).ConclusionsWhole-lesion ADC histogram analysis could serve as an imaging biomarker for differentiating stage IA EC from BELs and assisting in tumor grading of stage IA EC, thus facilitating personalized clinical management for premenopausal and postmenopausal patients.
| S-EPMC9358891 | biostudies-literature
Project description: Not available
| S-EPMC10696773 | biostudies-literature
Project description:BackgroundAccurate differentiation between benign and malignant endometrial lesions holds substantial clinical importance. This study aimed to evaluate the efficacy of various diffusion models in the preoperative diagnosis of early-stage endometrial carcinoma (EC).MethodsA total of 72 consecutive patients with benign or malignant endometrial lesions from the First People's Hospital of Yunnan Province were prospectively enrolled between April 2021 and July 2023. Fourteen diffusion parameters derived from monoexponential diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), stretched exponential model (SEM), continuous-time random walk (CTRW), and fractional order calculus (FROC) models were calculated and compared. Independent predictors of early-stage EC were identified using logistic regression analysis. The performance of the diffusion parameters, both individually and in combination with effective clinical indicators, for differentiating benign and malignant endometrial lesions was evaluated.ResultsThis study consisted of 17 patients with benign endometrial lesions and 55 patients with EC. Significant differences in age and menopausal status were observed between the benign and malignant endometrial groups (P=0.015 and P=0.011, respectively). With the exception of the pseudodiffusion coefficient (D*) and perfusion fraction (f), all other parameters exhibited significant differences between the benign and malignant groups (P<0.05). Mean kurtosis (MK), true diffusion coefficient (D), and temporal diffusion heterogeneity index (αCTRW) were identified as independent predictors of early-stage EC, achieving an area under the curve (AUC) of 0.903 [95% confidence interval (CI): 0.824-0.982], surpassing that of any individual diffusion parameter. The combination of these independent predictors with menopausal status yielded the highest AUC (0.922, 95% CI: 0.845-0.999), accuracy (93.1%), and sensitivity (100.0%).ConclusionsMK, D, and αCTRW have the potential to serve as independent predictors in predicting early-stage EC, and the performance can be enhanced when combined with menopausal status.
| S-EPMC11744118 | biostudies-literature
Project description:PurposeTo quantify changes and prognostic value of diffusion MRI measurements obtained using mono-exponential, diffusion kurtosis imaging (DKI) and stretched exponential (SE) models prior and after chemoradiation in newly diagnosed glioblastoma (GBM).MethodsDiffusion-weighted images (DWIs) were acquired in twenty-three patients following surgery, prior chemoradiation and within 7 days following completion of treatment, using b-values ranging from 0 to 5000s/mm2. Mono-exponential diffusion (apparent diffusion coefficient: ADC), isotropic (non-directional) DKI model with apparent diffusivity (Dapp) and kurtosis (Kapp) estimates as well as SE model with distributed-diffusion coefficient (DDC) and mean intra-voxel heterogeneity (α) were computed for all patients prior and after chemoradiation. Median values were calculated for normal appearing white matter (NAWM) and contrast-enhancing tumor (CET). The magnitudes of diffusion change prior and after chemoradiation were used to predict overall survival (OS).ResultsDiffusivity in NAWM was consistent for all diffusion measures during chemoradiation, while diffusivity measurements (ADC, Dapp and DDC) within CET changed significantly. A strong positive correlation existed between ADC, Dapp, and DDC measurements prior to chemoradiation; however, this association was weak following chemoradiation, suggesting a more complex microstructural environment after cytotoxic therapy. When combined with baseline tumor volume and MGMT status, age and ADC changes added significant prognostic values, whereas more complex diffusion models did not show significant value in predicting OS.ConclusionsDespite increased tissue complexity following chemoradiation, advanced diffusion models have longer acquisition times, provide largely comparable measures of diffusivity, and do not appear to provide additional prognostic value compared to mono-exponential ADC maps.
| S-EPMC6126989 | biostudies-literature
Project description:ObjectivesTo compare the gamma distribution (GD), intravoxel incoherent motion (IVIM), and monoexponential (ME) models in terms of their goodness-of-fit, correlations among the parameters, and the effectiveness in the differential diagnosis of various orofacial lesions.MethodsA total of 85 patients underwent turbo spin-echo diffusion-weighted imaging with six b-values. The goodness-of-fit of three models was assessed using Akaike Information Criterion. We analysed the correlations and compared the effectiveness in the differential diagnosis among the parameters of GD model (κ, shape parameter; θ, scale parameter; fractions of diffusion: ƒ1, cellular component; ƒ2, extracellular diffusion; ƒ3, perfusion component), IVIM model (D, true diffusion coefficient; D*, pseudodiffusion coefficient; f, perfusion fraction), and ME model (apparent diffusion coefficient, ADC).ResultsThe GD and IVIM models showed a better goodness-of-fit than the ME model (p < 0.05). ƒ1 had strong negative correlations with D and ADC (ρ = -0.901 and -0.937, respectively), while ƒ3 had a moderate positive correlation with f (ρ = 0.661). Malignant entity presented significantly higher ƒ1 and lower D and ADC than benign entity (p < 0.0001). Malignant lymphoma had significantly higher ƒ1 in comparison to squamous cell carcinoma (p = 0.0007) and granulation (p = 0.0075). The trend in ƒ1 was opposite to the trend in D. Malignant lymphoma had significant lower ƒ3 than squamous cell carcinoma (p = 0.005) or granulation (p = 0.0075).ConclusionsThe strong correlations were found between the GD- and IVIM-derived parameters. Furthermore, the GD model's parameters were useful for characterising the pathological structure in orofacial lesions.
| S-EPMC8693325 | biostudies-literature
Project description: Not available
| S-EPMC11685987 | biostudies-literature
Project description:PurposeDiffusion-weighted imaging (DWI) plays an important role in the preoperative assessment of gliomas; however, the diagnostic performance of histogram-derived parameters from mono-, bi-, and stretched-exponential DWI models in the grading of gliomas has not been fully investigated. Therefore, we compared these models' ability to differentiate between high-grade and low-grade gliomas.MethodsThis retrospective study included 22 patients with diffuse gliomas (age, 23-74 years; 12 males; 11 high-grade and 11 low-grade gliomas) who underwent preoperative 3 T-magnetic resonance imaging from October 2014 to August 2019. The apparent diffusion coefficient was calculated from the mono-exponential model. Using 13 b-values, the true-diffusion coefficient, pseudo-diffusion coefficient, and perfusion fraction were obtained from the bi-exponential model, and the distributed-diffusion coefficient and heterogeneity index were obtained from the stretched-exponential model. Region-of-interests were drawn on each imaging parameter map for subsequent histogram analyses.ResultsThe skewness of the apparent diffusion, true-diffusion, and distributed-diffusion coefficients was significantly higher in high-grade than in low-grade gliomas (0.67 ± 0.67 vs. - 0.18 ± 0.63, 0.68 ± 0.74 vs. - 0.08 ± 0.66, 0.63 ± 0.72 vs. - 0.15 ± 0.73; P = 0.0066, 0.0192, and 0.0128, respectively). The 10th percentile of the heterogeneity index was significantly lower (0.77 ± 0.08 vs. 0.88 ± 0.04; P = 0.0004), and the 90th percentile of the perfusion fraction was significantly higher (12.64 ± 3.44 vs. 7.14 ± 1.70%: P < 0.0001), in high-grade than in low-grade gliomas. The combination of the 10th percentile of the true-diffusion coefficient and 90th percentile of the perfusion fraction showed the best area under the receiver operating characteristic curve (0.96).ConclusionThe bi-exponential model exhibited the best diagnostic performance for differentiating high-grade from low-grade gliomas.
| S-EPMC7311374 | biostudies-literature
Project description:Previous researchers obtained various apparent diffusion coefficient (ADC) cutoff values to differentiate endometrial carcinoma from benign mimickers with 1.5T magnetic resonance imaging (MRI). Few studies have used 3T MRI or validated the effectiveness of these cutoff ADC values prospectively. This study was designed in two stages to obtain a cutoff ADC value at 3T MRI and to validate prospectively the role of the ADC value. First, we conducted a retrospective study of 60 patients to evaluate the diagnostic value of ADC by obtain a theoretical cutoff ADC value for differentiating between benign and malignant endometrial lesions. Student's t test revealed that ADC values for stage I endometrial carcinomas were significantly lower than those for benign lesions. The area under the curve value of the receiver operating characteristic curve was 0.993, and the cutoff ADC value was 0.98 × 10-3 mm2/s. The sensitivity, specificity, and overall accuracy of diagnosing stage I endometrial carcinoma were 100%, 97.1%, and 98.3%, respectively. Second, we conducted a prospective study of 26 patients to validate the use of the cutoff ADC value obtained in the study's first stage. The sensitivity, specificity, and overall accuracy for differentiating malignant from benign endometrial lesions based on the cutoff ADC value obtained earlier were as follows: radiologist 1 attained 86.67%, 100.0%, and 92.31%, respectively; radiologist 2 attained 86.67%, 91.0%, and 88.5%, respectively. Our results suggest that ADC values could be a potential biomarker for use as a quantitative and qualitative tool for differentiating between early-stage endometrial carcinomas and benign mimickers.
| S-EPMC5542275 | biostudies-other
Project description:BackgroundThe aim of this study was to determine whether the indicators obtained from intravoxel incoherent motion (IVIM) imaging can improve the characterization of benign and malignant breast masses compared with conventional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI).Patients and methodsThis study included 23 benign and 31 malignant breast masses of 48 patients. Main indicators were initial enhancement ratio (IER), time-signal intensity curve (TIC), apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f). The discriminative abilities of the different models were compared by means of receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) analysis.ResultsD had the highest AUC (0.980), sensitivity (93.55%), specificity (100%), and diagnostic accuracy (96.36%). Both D and TIC could provide the independent predicted features for malignant breast masses. The combination of D and TIC had an AUC of up to 0.990.ConclusionD of IVIM can effectively complement existing conventional DCE-MRI and DW-MRI in differentiating malignant from benign breast masses. IVIM combined with DCE-MRI is a robust means of evaluating breast masses.
| S-EPMC5040879 | biostudies-literature
Project description:Artificial intelligence as a screening tool for eyelid lesions will be helpful for early diagnosis of eyelid malignancies and proper decision-making. This study aimed to evaluate the performance of a deep learning model in differentiating eyelid lesions using clinical eyelid photographs in comparison with human ophthalmologists. We included 4954 photographs from 928 patients in this retrospective cross-sectional study. Images were classified into three categories: malignant lesion, benign lesion, and no lesion. Two pre-trained convolutional neural network (CNN) models, DenseNet-161 and EfficientNetV2-M architectures, were fine-tuned to classify images into three or two (malignant versus benign) categories. For a ternary classification, the mean diagnostic accuracies of the CNNs were 82.1% and 83.0% using DenseNet-161 and EfficientNetV2-M, respectively, which were inferior to those of the nine clinicians (87.0-89.5%). For the binary classification, the mean accuracies were 87.5% and 92.5% using DenseNet-161 and EfficientNetV2-M models, which was similar to that of the clinicians (85.8-90.0%). The mean AUC of the two CNN models was 0.908 and 0.950, respectively. Gradient-weighted class activation map successfully highlighted the eyelid tumors on clinical photographs. Deep learning models showed a promising performance in discriminating malignant versus benign eyelid lesions on clinical photographs, reaching the level of human observers.
| S-EPMC10011394 | biostudies-literature