Signal Intensity Slope Research Articles

Texture Analysis Using Semiquantitative Kinetic Parameter Maps from DCE-MRI: Preoperative Prediction of HER2 Status in Breast Cancer.

ObjectiveTo evaluate whether texture features derived from semiquantitative kinetic parameter maps based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can determine human epidermal growth factor receptor 2 (HER2) status of patients with breast cancer.Materials and MethodsThis study included 102 patients with histologically confirmed breast cancer, all of whom underwent preoperative breast DCE-MRI and were enrolled retrospectively. This cohort included 48 HER2-positive cases and 54 HER2-negative cases. Seven semiquantitative kinetic parameter maps were calculated on the lesion area. A total of 55 texture features were extracted from each kinetic parameter map. Patients were randomly divided into training (n = 72) and test (n = 30) sets. The least absolute shrinkage and selection operator (LASSO) was used to select features in the training set, and then, multivariate logistic regression analysis was conducted to establish the prediction models. The classification performance was evaluated by receiver operating characteristic (ROC) analysis.ResultsAmong the seven prediction models, the model with features extracted from the early signal enhancement ratio (ESER) map yielded an area under the ROC curve (AUC) of 0.83 in the training set (sensitivity of 70.59%, specificity of 92.11%, and accuracy of 81.94%), and the highest AUC of 0.83 in the test set (sensitivity of 57.14%, specificity of 100.00%, and accuracy of 80.00%). The model with features extracted from the slope of signal intensity (SIslope) map yielded the highest AUC of 0.92 in the training set (sensitivity of 82.35%, specificity of 97.37%, and accuracy of 90.28%), and an AUC of 0.79 in the test set (sensitivity of 92.86%, specificity of 68.75%, and accuracy of 80.00%).ConclusionsTexture features derived from kinetic parameter maps, calculated based on breast DCE-MRI, have the potential to be used as imaging biomarkers to distinguish HER2-positive and HER2-negative breast cancer.

Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE-MRI in classifying benign from malignant lesions.

To investigate the performance of texture analysis based on enhancement kinetic parametric maps derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in discriminating benign from malignant tumors. A total of 192 cases confirmed by histopathology were retrospectively selected from our Picture Archiving and Communication System, including 93 benign and 99 malignant tumors. Lesion areas were delineated semi-automatically, and six kinetic parametric maps reflecting the perfusion information were generated, namely the maximum slope of increase (MSI), slope of signal intensity (SIslope ), initial percentage of peak enhancement (Einitial ), percentage of peak enhancement (Epeak ), early signal enhancement ratio (ESER), and second enhancement percentage (SEP) maps. A total of 286 texture features were extracted from those quantitative parametric maps. The Student t test or Mann-Whitney U test was used to select features that were statistically significantly different between the benign and malignant groups. A support vector machine was employed with a leave-one-out cross-validation method to establish the classification model. Classification performance was evaluated according to the receiver operating characteristic (ROC) theory. The areas under ROC curves (AUCs) indicating the diagnostic performance were 0.925 for MSI, 0.854 for SIslope , 0.756 for Einitial , 0.923 for Epeak , 0.871 for ESER and 0.881 for SEP. Significant differences in AUCs were found between Einitial vs MSI, Einitial vs Epeak and Einitial vs SEP (P < .05). There were no significant differences in other pairwise comparisons. Texture analysis of the kinetic parametric maps derived from breast DCE-MRI can contribute to the discrimination between malignant and benign lesions. It can be considered as a supplementary tool for breast diagnosis.

Classification of breast mass lesions on dynamic contrast-enhanced magnetic resonance imaging by a computer-assisted diagnosis system based on quantitative analysis.

The aim of the current study was to develop a semi-automatic and quantitative method for the analysis of a time-intensity curve (TIC) from breast dynamic contrast-enhanced magnetic resonance imaging. The performance of the proposed method, based on the level set segmentation algorithm, was evaluated by comparison with the traditional method. In the traditional method, the lesion area is delineated manually and the corresponding mean TIC is classified subjectively as one of three washout patterns. In addition, only one quantitative parameter, the maximum slope of increase (MSI), is calculated. In the proposed method, the lesion region was determined semi-automatically and the corresponding mean TIC was categorized quantitatively. In addition to MSI, a number of quantitative parameters were derived from the mean TIC and lesion area, including signal intensity slope (SIslope), initial percentage of enhancement (Einitial), percentage of peak enhancement (Epeak), early signal enhancement ratio (ESER) and second enhancement percentage (SEP). Wilcoxon signed-rank test and receiver operating characteristic analyses were performed for statistical analysis. For TIC categorization the accuracy was 61.54% for the traditional method and 82.05% for the proposed method. Using the proposed method, mean curve accuracies were 84.0% for SIslope, 66.7% for MSI, 66.0% for Einitial, 66.0% for Epeak, 68.0% for ESER and 44.9% for SEP. In the lesion region, the accuracies for the aforementioned parameters were 80.8, 65.4, 66.7, 62.2, 69.2 and 57.1%, respectively. Accuracy of the MSI value derived from the traditional method was 63.4%. Compared with the traditional method, the proposed semi-automatic method in the current study may provide results with a higher accuracy to differentiate benign and malignant lesions. Therefore, the proposed method should be considered as a supplementary tool for the diagnosis of breast lesions.

Discrimination between malignant and benign mass-like lesions from breast dynamic contrast enhanced MRI: semi-automatic vs. manual analysis of the signal time-intensity curves.

Purpose: To investigate the performance of a new semi-automatic method for analyzing the signal time-intensity curve (TIC) obtained by breast dynamic contrast enhancement (DCE)-MRI.Methods: In the conventional method, a circular region of interest was drawn manually onto the map reflecting the maximum slope of increase (MSI) to delineate the suspicious lesions. The mean TIC was determined subjectively as one of three different wash-out patterns. In the new method, the lesion area was identified semi-automatically. The mean TIC was categorized quantitatively. In addition to the MSI, other quantitative parameters were calculated, including the signal intensity slope (SIslope), initial percentage of enhancement (Einitial), percentage of peak enhancement (Epeak), early signal enhancement ratio (ESER), and second enhancement percentage (SEP). The performances were compared with receiver operating characteristic (ROC) analysis and Wilcoxon's test.Results: For TIC categorization results, the diagnostic accuracy rates were 61.54% with the traditional manual method and 76.92% with the new method. For the mean MSI values from the manual method, the accuracy was 63.41%. For the mean TIC derived using the semi-automatic method, the diagnostic accuracy were 82.05% for SIslope, 67.31% for MSI, 61.53% for Einitial, 64.75% for Epeak, 64.74% for ESER, and 52.56% for SEP, respectively. For the lesion regions identified by the semi-automatic method, the diagnostic accuracy for above mentioned parameters were 80.13%, 69.87%, 61.54%, 63.47%, 64.74% and 55.13%, respectively.Conclusion: With respect to the analysis of TIC from breast DCE-MRI, the results demonstrated that the new method increased the diagnostic accuracy, and should be considered as a supplementary tool for distinguishing benign and malignant lesions.

Differentiation between malignant and benign breast masses: combination of semi-quantitative analysis on DCE-MRI and histogram analysis of ADC maps

To investigate the performance of combined semi-quantitative analysis on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) and histogram analysis of diffusion-weighted imaging (DWI) for distinguishing malignant from benign breast masses. This study included 178 patients with breast masses (benign:malignant=88:9) who underwent both DCE-MRI and DWI. The semi-quantitative parameters, derived from DCE-MRI, included maximum slope of increase (MSI), signal intensity slope (SIslope), initial percentage of enhancement (Einitial), percentage of peak enhancement (Epeak), early signal enhancement ratio (ESER), and second enhancement percentage (SEP). Histogram parameters derived from apparent diffusion coefficient (ADC) maps included ADCmin, ADCmax, ADCmean, ADC10, ADC25, ADC50, ADC75, ADC90, skewness, and kurtosis. All parameters were compared between malignant and benign groups, and their differences were tested using independent-samples t-test or Mann-Whitney test. Receiver operating characteristic (ROC) curves were used to determine the diagnostic value of each significant parameter. Among semi-quantitative parameters, SIslope exhibited the best diagnostic performance in predicting malignancy (cut-off value, 0.096; ROC, 0.756; sensitivity, 86.7%; specificity, 61.4%). Among histogram parameters, ADC10 exhibited the best diagnostic performance in predicting malignancy (cut-off value, 1.051; ROC, 0.885; sensitivity, 86.7%; specificity, 84.1%). The optimal diagnostic performance of combined ADC10 and SIslope (area under curve [AUC], 0.888; sensitivity, 82.2%; specificity, 95.5%) was significantly better than SIslope alone (p<0.001). Moreover, the combination showed higher AUC (0.888 versus 0.885) than ADC10 alone, but the difference was not statistically significant (p=0.914). SIslope and ADC10 are significant predictors for breast malignancy. The combination of DCE-MRI and DWI improves differentiating performance.

Comparison between Linear and Reverse Linear K-Space Order with Partial Fourier Fractions for Modulation Transfer Function in Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Simulation Study

Objective: To assess how the modulation transfer function (MTF) was influenced by the k-space trajectory with partial Fourier fractions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), calculating MTF using computer simulations. Methods: Reference data for signal intensity were acquired using breast model at different concentrations and used to create a digital phantom. Frequency images were created by fast Fourier transform, divided into parts, and a new image formed by taking one part from each. The MTFs were then calculated. Three linear signal intensity slope models (low, medium, and high) and three exponential curve models (slow, medium, and rapid) were created using the reference data. Results: The smaller the partial Fourier fraction used, the faster the decline in MTF. The MTF of the three linear slope models showed that the higher gradient of the slope used, the more rapid was the decline in MTF. The MTF of all three exponential curve models were more gradually decreasing than all three linear slope models. Conclusion: The MTF was influenced by the k-space trajectory with partial Fourier fractions in DCE-MRI using computer simulations. The reverse linear order was found to be less influenced than the linear order by the partial Fourier fraction.

Evaluation of static and dynamic MRI for assessing response of bone sarcomas to preoperative chemotherapy: Correlation with histological necrosis.

Objectives:Preoperative chemotherapy plays a key role in management of bone sarcomas. Postoperative evaluation of histological necrosis has been the gold standard method of assessing response to preoperative chemotherapy. This study was done to evaluate the efficacy of static and dynamic magnetic resonance imaging (MRI) for assessing response preoperatively.Materials and Methods:Our study included 14 patients (12 osteosarcomas and 2 malignant fibrous histiocytomas) with mean age of 21.8 years, treated with preoperative chemotherapy followed by surgery. They were evaluated with static and dynamic MRI twice, before starting chemotherapy and again prior to surgery. Change in tumor volume and slope of signal intensity - time curve were calculated and correlated with percentage of histological necrosis using Pearson correlation test.Results:The change in dynamic MRI slope was significant (P = 0.001). Also, ≥60% reduction in slope of the curve proved to be an indicator of good histological response [positive predictive value (PPV) =80%]. Change in tumor volume failed to show significant correlation (P = 0.071). Although it showed high negative predictive value (NPV = 85.7%), PPV was too low (PPV = 57.14%).Conclusions:Dynamic MRI correctly predicts histological necrosis after administration of preoperative chemotherapy to bone sarcomas. Hence, it can be used as a preoperative indicator of response to neoadjuvant chemotherapy. On the other hand, volumetric assessment by static MRI is not an effective predictor of histological necrosis. This study proves the superiority of dynamic contrast-enhanced study over volumetric study by MRI.

Quantitative discrimination between invasive ductal carcinomas and benign lesions based on semi-automatic analysis of time intensity curves from breast dynamic contrast enhanced MRI.

BackgroundTraditional subjective method for the analysis of time-intensity curves (TICs) from breast dynamic contrast enhanced MRI (DCE-MRI) presented a low specificity. Hence, a semi-automatic quantitative method was proposed and evaluated for distinguishing invasive ductal carcinomas from benign lesions.Materials and methodsIn the traditional method, the lesion was extracted by placing a region of interest (ROI) manually. The mean curve of the TICs from the ROI was subjectively classified as one of three patterns. Only one quantitative parameter, the mean value of maximum slope of increase (MSI), was provided. In the new method, the lesion was identified semi-automatically, and the mean curve was classified quantitatively. Some additional parameters, the signal intensity slope (SIslope), initial percentage of enhancement (Einitial), percentage of peak enhancement (Epeak), early signal enhancement ratio (ESER), and second enhancement percentage (SEP) were derived from the mean curves as well as the lesion areas. Wilcoxon’s test and receiver operating characteristic (ROC) analyses were performed, and P < 0.05 was considered significant.ResultsAccording to the TIC classification results, the accuracies were 59.16% for the traditional manual method and 76.05% for the new method (P < 0.05). For the mean MSI values from the manual method, the accuracy was 63.35%. For the mean TICs derived from the semi-automatic method, the accuracies were 77.47% for SIslope, 65.24% for MSI, 58.45% for Einitial, 66.20% for Epeak, 71.83% for ESER, and 54.93% for SEP, respectively. For the lesion regions identified by the semi-automatic method, the accuracies were 73.24%, 72.54%, 58.45%, 62.68%, 64.09%, and 55.64%, respectively.ConclusionCompared with traditional subjective method, the semi-automatic quantitative method proposed in this study showed a higher performance, and should be used as a supplementary tool to aid radiologist's subjective interpretation.

Perfusion‐weighted MRI in evaluating the intranodular hemodynamic characteristics of dysplastic nodules and hepatocellular carcinomas in an experimental rat model

To investigate the value of perfusion-weighted MRI in the evaluation of the intranodular hemodynamic characteristics of dysplastic nodules (DNs) and hepatocellular carcinomas (HCCs) in an experimental rat model. A total of 40 rats with chemically-induced DNs and HCCs were investigated. Single-slice gadolinium-enhanced perfusion-weighted MRI was performed to evaluate the nodules. Time to peak (Tp), maximal relative signal enhancement (REmax), and the initial slope of signal intensity (SI) vs. time curves of the nodules and cirrhotic liver were evaluated. Nodules precisely corresponding to MRI were examined histologically. Paired Student's t-tests were used to compare the difference between nodules and cirrhotic liver. A total of 20 HCCs and 14 DNs were evaluated. HCCs showed a significantly higher REmax, shorter Tp, and higher slope than adjacent cirrhotic liver. The REmax and slope of DNs were significantly lower than adjacent cirrhotic liver parenchyma. Although the Tp of DNs was delayed two to three seconds compared to adjacent cirrhotic liver, there was no significant difference between them. Perfusion-weighted MRI detected the intranodular hemodynamic characteristics of DNs and HCCs in an experimental rat model. DNs were hypovascular compared to cirrhotic liver, while HCCs were markedly hypervascular.

Dynamic MRI for Breast Cancer: Correlation with the Prognostic factors and the Time-Signal Intensity Curve

Purpose: We wanted to evaluate the relation of the kinetic MRI features of dynamic contrast-enhanced MRI with the histopathological prognostic factors of breast cancer. Materials and Methods: Fifty-four lesions of breast cancer patients were evaluated with using a 1.5 T MR scanner. The dynamic studies were performed in the axial plane with using T1 High Resolution Isotropic Volume Examination (THRIVE) so we obtained the time signal intensity curves. By considering the early peak signal intensity, the maximal signal intensity, the pre-enhanced signal intensity and the last signal intensity, we calculated the absolute value, percentage, slope of enhancement and the washout. The time of the early peak signal intensity and the time of the maximal signal intensity were obtained. We classified the early and delayed enhancement patterns. The kinetic MR features were correlated with the histopathological findings (the histologic and nuclear grades, estrogen receptor, progesteron receptor, Ki-67, p53 and c-erb B2). Results: The early peak signal intensity slope was significantly correlated with the histologic grade (I versus II, III), and the Ki-67 (p=0.045, p=0.009). Ki-67 was also significantly correlated with the time of the peak signal intensity and the time of the maximal signal intensity (p=0.009, p=0.045). Conclusion: Some of the parameters of the time-signal intensity curve of dynamic MRI were associated with the prognostic factors, so these MRI signs may be useful to noninvasively identify prognostic factors in the future.

T1-weighted dynamic MRI with new superparamagnetic iron oxide particles (Resovist): results of a phantom study as well as 25 patients

Evaluation of the diagnostic usefulness of the T1-effect of Resovist (SPIO) for dynamic MRI of the liver. In-vitro measurements of a dilution series with T1-weighted FLASH and SE sequences and investigation of 25 patients with known focal liver lesions with a T2-weighted TSE sequence and a dynamic T1-FLASH sequence. T1-weighted MRI with Resovist in vitro showed a positive enhancement at low concentrations and a negative enhancement at higher concentrations. In-vivo T1-weighted dynamic MRI liver parenchyma demonstrated a positive enhancement 30 s post contrast, followed by a continuous slope of signal intensity and a negative enhancement (> or = 60 s). Spleen, portal venous vessels and haemangiomas showed an early increase in signal intensity followed by a decreasing positive enhancement, but without negative enhancement. During the perfusion phase metastases showed a small but not significant increase in signal intensity. In 80% a positive ring enhancement could be observed around metastases. Resovist exhibits a diagnostically useful T1-effect. An evaluation of the perfusion of focal liver lesions during the distribution phase is possible with dynamic T1-weighted MRI. This approach may further improve characterisation of focal liver lesions.

Contrast enhanced high resolution MRI of cutaneous melanomas using Gd-DTPA and Gd-DTPA-polylysine: experimental results.

An experimental model for the preclinical evaluation of contrast enhancement patterns in melanoma was developed. Melanotic and amelanotic tumor cells B16F1 and B16a implanted into murine skin were depicted with magnetic resonance imaging (MRI) at a pixel size of 78×78 urn and 1 mm slice thickness. Tumor signal intensities were evaluated in user defined ROIs (regions of interest). Signal intensity changes were determined from signal intensity-time curves. Percent enhancement, slope of the signal intensity curve and percent signal intensity increase were also determined. Percent enhancement was calculated: (SIenhanced -SIpiain )/SIplain × 100 [SI=signal intensity]. The slope of the signal intensity curve was calculated: (SImax -SIplain )/(SIplain × SImax ) × 100 (%/min). Percent signal intensity increase was calculated: (SImax -SIplai )/SIplain × 100. Following i.v. administration of Gd-DTPA and G-DTPA-polylysine, all tumors showed pronounced contrast enhancement. Contrast enhancement and the slope of signal intensity to time curves did not distinguish melanotic from amelanotic melanomas (P>0.05). The animal model presented is well suited for evaluation of contrast enhancement in melanoma by MRI. Contrast enhancement of melanoma in MRI is comparable with the enhancement seen in other tumors.