scholarly journals Comparing Low and High-Temporal Resolution DCE-MRI Texture Analysis for Discrimination of Breast Lesions from Background Enhancement

Author(s):  
Yufeng Liu ◽  
Jiaying Li ◽  
Jingjing Qu ◽  
Rui Tang ◽  
Kun Lv ◽  
...  

Abstract Background Breast cancer is the most common cancer in women worldwide, high-resolution dynamic contrast-enhanced MRI (DCE-MRI) can better evaluate the tissue microenvironment and texture characteristics. The purpose of this study was to investigate the value of the texture-based analysis for breast DCE-MRI in the diagnosis of breast lesions and background enhancement. Methods This study prospectively enrolled 128 patients with clinically suspected breast lesions in our hospital from April 2015 to June 2017. Among them, 62 patients underwent preoperative high temporal resolution DCE-MRI (1 + 26 phases) scan with 39 malignant and 23 benign lesions. The control group retrospectively and randomly contained 78 patients who underwent preoperative low temporal resolution DCE-MRI (1 + 5 phases) scans with 46 malignant and 32 benign lesions. Quantitative parameters were obtained using a two-compartment Extended Tofts and volume of interest model for the lesion center, surrounding peripheral area and background enhancement, including pharmacokinetic parameters (Ktrans, Kep, Ve and Vp) and texture features based on the Ktrans map. The Student’s t-test was used to compare the differences of means. LASSO was used for dimension reduction and logistic regression analysis was used for model construction. A receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance. Results Pharmacokinetic parameters were significantly different between high temporal resolution and low temporal resolution DCE-MRI (P < 0.05). In the malignant group, the average Ktrans of the lesion area on high temporal resolution DCE-MRI was significantly correlated to the pathological grading (r = 0.400, P = 0.012). In the differentiation between benign and malignant lesions, the ROC analysis demonstrated that the diagnostic value of high temporal resolution DCE-MRI offered slightly significant advantages in the realms of the lesion, peripheral areas and background enhancement. Conclusions The use of texture analysis based on high temporal resolution DCE-MRI may potentially improve breast cancer diagnostic performance. Specifically, combining the lesion, peripheral, BE area, and Ktrans-mean parameters can contribute to the diagnosis of breast lesions, background enhancement and the pathological grading of malignant tumors.

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Tiebao Meng ◽  
Ni He ◽  
Haoqiang He ◽  
Kuiyuan Liu ◽  
Liangru Ke ◽  
...  

Abstract Background Previous studies have indicated that quantitative MRI (qMR) is beneficial for diagnosis of breast cancer. As a novel qMR technology, synthetic MRI (syMRI) may be advantageous by offering simultaneous generation of T1 and T2 mapping in one scan within a few minutes and without concern to the deposition of the gadolinium contrast agent in cell nucleus. In this study, the potential of quantitative mapping derived from Synthetic MRI (SyMRI) to diagnose breast cancer was investigated. Methods From April 2018 to May 2019, a total of 87 patients with suspicious breast lesions underwent both conventional and SyMRI before treatment. The quantitative metrics derived from SyMRI, including T1 and T2 values, were measured in breast lesions. The diagnostic performance of SyMRI was evaluated with unpaired Student’s t-tests, receiver operating characteristic curve analysis and multivariate logistic regression analysis. The AUCs of quantitative values were compared using Delong test. Results Among 77 patients who met the inclusion criteria, 48 were diagnosed with histopathological confirmed breast cancers, and the rest had benign lesions. The breast cancers showed significantly higher T1 (1611.61 ± 215.88 ms) values and lower T2 (80.93 ± 7.51 ms) values than benign lesions. The area under the ROC curve (AUC) values were 0.931 (95% CI: 0.874–0.989) and 0.883 (95% CI: 0.810–0.956) for T1 and T2 maps, respectively, in diagnostic discrimination between breast cancers and benign lesions. A slightly increased AUC of 0.978 (95% CI: 0.915–0.993) was achieved by combining those two relaxation-based quantitative metrics. Conclusion In conclusion, our preliminary study showed that the quantitative T1 and T2 values obtained by SyMRI could distinguish effectively between benign and malignant breast lesions, and T1 relaxation time showed the highest diagnostic efficiency. Furthermore, combining the two quantitative relaxation metrics further improved their diagnostic performance.


Author(s):  
Dalia Abdelhady ◽  
Amany Abdelbary ◽  
Ahmed H. Afifi ◽  
Alaa-eldin Abdelhamid ◽  
Hebatallah H. M. Hassan

Abstract Background Breast cancer is the most prevalent cancer among females. Dynamic contrast-enhanced MRI (DCE-MRI) breast is highly sensitive (90%) in the detection of breast cancer. Despite its high sensitivity in detecting breast cancer, its specificity (72%) is moderate. Owing to 3-T breast MRI which has the advantage of a higher signal to noise ratio and shorter scanning time rather than the 1.5-T MRI, the adding of new techniques as diffusion tensor imaging (DTI) to breast MRI became more feasible. Diffusion-weighted imaging (DWI) which tracks the diffusion of the tissue water molecule as well as providing data about the integrity of the cell membrane has been used as a valuable additional tool of DCE-MRI to increase its specificity. Based on DWI, more details about the microstructure could be detected using diffusion tensor imaging. The DTI applies diffusion in many directions so apparent diffusion coefficient (ADC) will vary according to the measured direction raising its sensitivity to microstructure elements and cellular density. This study aimed to investigate the diagnostic accuracy of DTI in the assessment of breast lesions in comparison to DWI. Results By analyzing the data of the 50 cases (31 malignant cases and 19 benign cases), the sensitivity and specificity of DWI in differentiation between benign and malignant lesions were about 90% and 63% respectively with PPV 90% and NPV 62%, while the DTI showed lower sensitivity and specificity about 81% and 51.7%, respectively, with PPV 78.9% and NPV 54.8% (P-value ≤ 0.05). Conclusion While the DWI is still the most established diffusion parameter, DTI may be helpful in the further characterization of tumor microstructure and differentiation between benign and malignant breast lesions.


2020 ◽  
Author(s):  
Na Guo ◽  
Weike Zeng ◽  
Hong Deng ◽  
Huijun Hu ◽  
Ziliang Cheng ◽  
...  

Abstract Background: To investigate whether quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) pharmacokinetic parameters can be used to predict the pathologic stages of oral tongue squamous cell carcinoma (OTSCC). Methods: For this prospective study, DCE-MRI was performed in participants with OTSCC from May 2016 to June 2017. The pharmacokinetic parameters, including K trans , K ep , V e , and V p , were derived from DCE-MRI by utilizing a two-compartment extended Tofts model and a three-dimensional volume of interest. The postoperative pathologic stage was determined in each patient based on the 8th AJCC cancer staging manual. The quantitative DCE-MRI parameters were compared between stage I-II and stage III-IV lesions. Logistic regression analysis was used to determine independent predictors of tumor stages, followed by receiver operating characteristic (ROC) analysis to evaluate the predictive performance. Results: The mean K trans , K ep and V p values were significantly lower in stage III-IV lesions compared with stage I-II lesions ( p = 0.013, 0.005 and 0.011, respectively). K ep was an independent predictor for the advanced stages as determined by univariate and multivariate logistic analysis. ROC analysis showed that K ep had the highest predictive capability, with a sensitivity of 64.3%, a specificity of 82.6%, a positive predictive value of 81.8%, a negative predictive value of 65.5%, and an accuracy of 72.5%. Conclusion: The quantitative DCE-MRI parameter K ep can be used as a biomarker for predicting pathologic stages of OTSCC.


2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Changmin Liu ◽  
Roger Sun ◽  
Jing Wang ◽  
Fangling Ning ◽  
Zhenbo Wang ◽  
...  

Background. Concurrent chemoradiotherapy (CCRT) is the main treatment for esophageal cancer, but the response to treatment varies from individual to individual. MR imaging methods, such as diffusion-weighted (DW) MRI and the use of dynamic contrast-enhanced (DCE) MRI, have the potential to provide additional biomarkers that could evaluate the effect of CCRT in patients with esophageal carcinoma. Materials and Methods. Fifty-six patients with esophageal carcinoma, verified by histopathology, underwent MRI examination before and at midtreatment (4th week, radiotherapy 30–40 Gy) using the Siemens 3.0 T MR System. Parameter maps of apparent diffusion coefficient (ADC), and DCE maps of volume transfer constant (Krans), rate contrast (kep), and extracellular fluid space (ve), were computed using a Siemens Company Multimodality Workplace (MMWP) model. Comparison of histogram parameters and their diagnostic performance was determined using the Mann–Whitney U test and receiver operating characteristic (ROC) analysis. Results. 56 patient MRI scans were available for analysis at baseline and at the third week, respectively. Pretreatment Krans, pretreatment kep, pretreatment ADC (P<0.05), and during-treatment Krans (P<0.05) and ΔKrans and ΔADC (P<0.05) were significantly different after CCRT. Based on the binary logistic model, the ROC analysis demonstrated that the combined predictors demonstrated a high diagnostic performance with an AUC of 0.939. The sensitivity and specificity were 98.6% and 73.8%, respectively. Conclusion. The combination of DCE and DWI can be used as an early biomarker in the prediction of the effect of CCRT three weeks after treatment in esophageal carcinoma.


2020 ◽  
Vol 14 ◽  
pp. 117822342093806
Author(s):  
Siddhant Khare ◽  
Tulika Singh ◽  
Irrinki Santosh ◽  
Ishita Laroiya ◽  
Gurpreet Singh

Background: Excision of nonpalpable breast lesions requires intraoperative guidance. Wire-guided localization and intraoperative ultrasounds have been used successfully but suffer from some disadvantages. We describe a new modification of the standard technique using a combination of preoperative ultrasound in conjunction with standard wire-guided localization. Methods: Wire and ultrasound-guided localization (WUGL) technique was used for the excision of nonpalpable breast lesions. Results: Sixty-nine patients with nonpalpable breast lesions were subjected to excision using WUGL, out of whom 63 patients had a preoperative diagnosis of invasive/noninvasive breast cancer. Six patients had a preoperative diagnosis of benign lesions, out of which 3 patients were converted to invasive breast cancer on final pathology. Only 1 patient had positive margin. Conclusions: WUGL is a technique that uses a combination of well-accepted and easily available techniques. It has given good results and has the potential for widespread acceptance in resource-constrained situations.


2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Meijie Liu ◽  
Ning Mao ◽  
Heng Ma ◽  
Jianjun Dong ◽  
Kun Zhang ◽  
...  

Abstract Background To establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer. Methods A total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models. Results Seven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer. Conclusions The model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer.


Author(s):  
A. Niukkanen ◽  
H. Okuma ◽  
M. Sudah ◽  
P. Auvinen ◽  
A. Mannermaa ◽  
...  

AbstractWe aimed to assess the feasibility of three-dimensional (3D) segmentation and to investigate whether semi-quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters are associated with traditional prognostic factors for breast cancer. In addition, we evaluated whether both intra-tumoural and peri-tumoural DCE parameters can differentiate the breast cancers that are more aggressive from those that are less aggressive. Consecutive patients with newly diagnosed invasive breast cancer and structural breast MRI (3.0 T) were included after informed consent. Fifty-six patients (mean age, 57 years) with mass lesions of > 7 mm in diameter were included. A semi-automatic image post-processing algorithm was developed to measure 3D pharmacokinetic information from the DCE-MRI images. The kinetic parameters were extracted from time-signal curves, and the absolute tissue contrast agent concentrations were calculated with a reference tissue model. Markedly, higher intra-tumoural and peri-tumoural tissue concentrations of contrast agent were found in high-grade tumours (n = 44) compared to low-grade tumours (n = 12) at every time point (P = 0.006–0.040), providing positive predictive values of 90.6–92.6% in the classification of high-grade tumours. The intra-tumoural and peri-tumoural signal enhancement ratios correlated with tumour grade, size, and Ki67 activity. The intra-observer reproducibility was excellent. We developed a model to measure the 3D intensity data of breast cancers. Low- and high-grade tumours differed in their intra-tumoural and peri-tumoural enhancement characteristics. We anticipate that pharmacokinetic parameters will be increasingly used as imaging biomarkers to model and predict tumour behavior, prognoses, and responses to treatment.


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