Automated Colorectal Tumour Segmentation in DCE-MRI Using Supervoxel Neighbourhood Contrast Characteristics

Abstract Objective To evaluate the performance of dynamic contrast-enhanced CT (DCE-CT) in detecting and quantitatively assessing perfusion parameters in patients with arthritis of the hand compared with dynamic contrast-enhanced MRI (DCE-MRI) as a standard of reference. Materials and methods In this IRB-approved randomized prospective single-centre study, 36 consecutive patients with suspected rheumatoid arthritis underwent DCE-CT (320-row, tube voltage 80 kVp, tube current 8.25 mAs) and DCE-MRI (1.5 T) of the hand. Perfusion maps were calculated separately for mean transit time (MTT), time to peak (TTP), relative blood volume (rBV), and relative blood flow (rBF) using four different decomposition techniques. Region of interest (ROI) analysis was performed in metacarpophalangeal joints II–V and in the wrist. Pairs of perfusion parameters in DCE-CT and DCE-MRI were compared using a two-tailed t test for paired samples and interpreted for effect size (Cohen’s d). According to the Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS) scoring results, differentiation of synovitis-positive and synovitis-negative joints with both modalities was assessed with the independent t test. Results The two modalities yielded similar perfusion parameters. Identified differences had small effects (d 0.01–0.4). DCE-CT additionally differentiates inflamed and noninflamed joints based on rBF and rBV but tends to underestimate these parameters in severe inflammation. The total dose-length product (DLP) was 48 mGy*cm with an estimated effective dose of 0.038 mSv. Conclusion DCE-CT is a promising imaging technique in arthritis. In patients with a contraindication to MRI or when MRI is not available, DCE-CT is a suitable alternative to detect and assess arthritis.

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Dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) for differential diagnosis in head and neck cancers

European Journal of Radiology ◽

10.1016/j.ejrad.2011.01.089 ◽

2012 ◽

Vol 81 (4) ◽

pp. 784-788 ◽

Cited By ~ 45

Author(s):

Francis Kar-ho Lee ◽

Ann Dorothy King ◽

Brigitte Buig-Yue Ma ◽

David Ka-wai Yeung

Keyword(s):

Magnetic Resonance Imaging ◽

Differential Diagnosis ◽

Magnetic Resonance ◽

Head And Neck ◽

Contrast Enhancement ◽

Head And Neck Cancers ◽

Resonance Imaging ◽

Dynamic Contrast Enhancement ◽

Dce Mri

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Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions

Applied Sciences ◽

10.3390/app11041880 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1880

Author(s):

Roberta Fusco ◽

Adele Piccirillo ◽

Mario Sansone ◽

Vincenza Granata ◽

Paolo Vallone ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Artificial Intelligence ◽

Feature Selection ◽

Resonance Imaging ◽

Dynamic Features ◽

Surgical Specimen ◽

Dce Mri ◽

Dynamic Contrast Enhanced ◽

Contrast Enhanced ◽

Malignant Lesions

Purpose: The aim of the study was to estimate the diagnostic accuracy of textural, morphological and dynamic features, extracted by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. Methods: In total, 85 patients with known breast lesion were enrolled in this retrospective study according to regulations issued by the local Institutional Review Board. All patients underwent DCE-MRI examination. The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy for benign lesions. In total, 91 samples of 85 patients were analyzed. Furthermore, 48 textural metrics, 15 morphological and 81 dynamic parameters were extracted by manually segmenting regions of interest. Statistical analyses including univariate and multivariate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. Results: The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance (accuracy (ACC) = 0.78; AUC = 0.78) was reached with all 48 metrics and an LDA trained with balanced data. The best performance (ACC = 0.75; AUC = 0.80) using morphological features was reached with an SVM trained with 10-fold cross-variation (CV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of five robust morphological features (circularity, rectangularity, sphericity, gleaning and surface). The best performance (ACC = 0.82; AUC = 0.83) using dynamic features was reached with a trained SVM and balanced data (with ADASYN function). Conclusion: Multivariate analyses using pattern recognition approaches, including all morphological, textural and dynamic features, optimized by adaptive synthetic sampling and feature selection operations obtained the best results and showed the best performance in the discrimination of benign and malignant lesions.

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Blood Oxygenation Level Dependent Magnetic Resonance Imaging (MRI), Dynamic Contrast Enhanced MRI and Diffusion Weighted MRI for Benign and Malignant Breast Cancer Discrimination: A Preliminary Experience

Cancers ◽

10.3390/cancers13102421 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2421

Author(s):

Roberta Fusco ◽

Vincenza Granata ◽

Mauro Mattace Raso ◽

Paolo Vallone ◽

Alessandro Pasquale De Rosa ◽

...

Keyword(s):

Standard Deviation ◽

Univariate Analysis ◽

Mean Value ◽

Blood Oxygenation ◽

Breast Lesions ◽

Dce Mri ◽

Malignant Lesions ◽

The Mean ◽

And Diffusion ◽

Skewness And Kurtosis

Purpose. To combine blood oxygenation level dependent magnetic resonance imaging (BOLD-MRI), dynamic contrast enhanced MRI (DCE-MRI), and diffusion weighted MRI (DW-MRI) in differentiation of benign and malignant breast lesions. Methods. Thirty-seven breast lesions (11 benign and 21 malignant lesions) pathologically proven were included in this retrospective preliminary study. Pharmaco-kinetic parameters including Ktrans, kep, ve, and vp were extracted by DCE-MRI; BOLD parameters were estimated by basal signal S0 and the relaxation rate R2*; and diffusion and perfusion parameters were derived by DW-MRI (pseudo-diffusion coefficient (Dp), perfusion fraction (fp), and tissue diffusivity (Dt)). The correlation coefficient, Wilcoxon-Mann-Whitney U-test, and receiver operating characteristic (ROC) analysis were calculated and area under the ROC curve (AUC) was obtained. Moreover, pattern recognition approaches (linear discrimination analysis and decision tree) with balancing technique and leave one out cross validation approach were considered. Results. R2* and D had a significant negative correlation (−0.57). The mean value, standard deviation, Skewness and Kurtosis values of R2* did not show a statistical significance between benign and malignant lesions (p > 0.05) confirmed by the ‘poor’ diagnostic value of ROC analysis. For DW-MRI derived parameters, the univariate analysis, standard deviation of D, Skewness and Kurtosis values of D* had a significant result to discriminate benign and malignant lesions and the best result at the univariate analysis in the discrimination of benign and malignant lesions was obtained by the Skewness of D* with an AUC of 82.9% (p-value = 0.02). Significant results for the mean value of Ktrans, mean value, standard deviation value and Skewness of kep, mean value, Skewness and Kurtosis of ve were obtained and the best AUC among DCE-MRI extracted parameters was reached by the mean value of kep and was equal to 80.0%. The best diagnostic performance in the discrimination of benign and malignant lesions was obtained at the multivariate analysis considering the DCE-MRI parameters alone with an AUC = 0.91 when the balancing technique was considered. Conclusions. Our results suggest that the combined use of DCE-MRI, DW-MRI and/or BOLD-MRI does not provide a dramatic improvement compared to the use of DCE-MRI features alone, in the classification of breast lesions. However, an interesting result was the negative correlation between R2* and D.

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Diffusion tensor imaging on 3-T MRI breast: diagnostic performance in comparison to diffusion-weighted imaging

Egyptian Journal of Radiology and Nuclear Medicine ◽

10.1186/s43055-021-00473-6 ◽

2021 ◽

Vol 52 (1) ◽

Author(s):

Dalia Abdelhady ◽

Amany Abdelbary ◽

Ahmed H. Afifi ◽

Alaa-eldin Abdelhamid ◽

Hebatallah H. M. Hassan

Keyword(s):

Breast Cancer ◽

Diffusion Tensor Imaging ◽

Sensitivity And Specificity ◽

Diffusion Weighted Imaging ◽

Diffusion Tensor ◽

Breast Mri ◽

Breast Lesions ◽

Dce Mri ◽

Additional Tool ◽

Diffusion Weighted

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.

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Can DWI-MRI be an alternative to DCE-MRI in the diagnosis of troublesome breast lesions?

Egyptian Journal of Radiology and Nuclear Medicine ◽

10.1186/s43055-021-00514-0 ◽

2021 ◽

Vol 52 (1) ◽

Author(s):

Lamiaa Mohamed Bassam Hashem ◽

Sherihan W. Y. Gareer ◽

Aya Mohamed Bassam Hashem ◽

Sherihan Fakhry ◽

Yasmin Mounir Tohamey

Keyword(s):

Problem Solver ◽

Breast Lesions ◽

Diffusion Weighted Mri ◽

Resonance Imaging ◽

Contrast Administration ◽

Dce Mri ◽

Water Diffusivity ◽

Contrast Enhanced ◽

Mri Study ◽

Established Technique

Abstract Background Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has always been a problem solver in troublesome breast lesions. Despite its many advantages, the encountered low specificity results in unnecessary biopsies. Diffusion-weighted MRI (DW-MRI) is a well-established technique that helps in characterizing breast lesions according to their water diffusivity. So this work aimed to assess the diagnostic performance of DW-MRI in troublesome breast lesions and see if it can replace DCE-MRI study. Results In our prospective study, we included 86 patients with mammography and/or ultrasound-detected 90 probably benign or probably malignant (BIRADS 3 or 4) breast lesions. Among the studied cases, 49/90 lesions were benign, and 41/90 were malignant. Combined analysis of morphological and kinetic findings in DCE-MRI had achieved the highest sensitivity of 95.1%. DW-MRI alone was less sensitive (73.2%) yet more specific (83.7%) than DCE-MRI (77.6%). Diagnostic accuracy of DCE-MRI was higher (85.6%) as compared to DW-MRI which was (78.9%). Conclusion DCE-MRI is the cornerstone in the workup of troublesome breast lesions. DW-MRI should not be used as supplementary tool unless contrast administration is contraindicated. Combining both DCE-MRI and DW-MRI is the ultimate technique for better lesion evaluation.

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Diagnosing breast cancer tumors using stacked ensemble model

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219176 ◽

2021 ◽

pp. 1-9

Author(s):

Ahmet Haşim Yurttakal ◽

Hasan Erbay ◽

Türkan İkizceli ◽

Seyhan Karaçavuş ◽

Cenker Biçer

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Medical Imaging ◽

Early Stage ◽

False Negative ◽

Gradient Boosting ◽

Physical Sign ◽

Ensemble Model ◽

Learning Methods ◽

Dce Mri

Breast cancer is the most common cancer that progresses from cells in the breast tissue among women. Early-stage detection could reduce death rates significantly, and the detection-stage determines the treatment process. Mammography is utilized to discover breast cancer at an early stage prior to any physical sign. However, mammography might return false-negative, in which case, if it is suspected that lesions might have cancer of chance greater than two percent, a biopsy is recommended. About 30 percent of biopsies result in malignancy that means the rate of unnecessary biopsies is high. So to reduce unnecessary biopsies, recently, due to its excellent capability in soft tissue imaging, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been utilized to detect breast cancer. Nowadays, DCE-MRI is a highly recommended method not only to identify breast cancer but also to monitor its development, and to interpret tumorous regions. However, in addition to being a time-consuming process, the accuracy depends on radiologists’ experience. Radiomic data, on the other hand, are used in medical imaging and have the potential to extract disease characteristics that can not be seen by the naked eye. Radiomics are hard-coded features and provide crucial information about the disease where it is imaged. Conversely, deep learning methods like convolutional neural networks(CNNs) learn features automatically from the dataset. Especially in medical imaging, CNNs’ performance is better than compared to hard-coded features-based methods. However, combining the power of these two types of features increases accuracy significantly, which is especially critical in medicine. Herein, a stacked ensemble of gradient boosting and deep learning models were developed to classify breast tumors using DCE-MRI images. The model makes use of radiomics acquired from pixel information in breast DCE-MRI images. Prior to train the model, radiomics had been applied to the factor analysis to refine the feature set and eliminate unuseful features. The performance metrics, as well as the comparisons to some well-known machine learning methods, state the ensemble model outperforms its counterparts. The ensembled model’s accuracy is 94.87% and its AUC value is 0.9728. The recall and precision are 1.0 and 0.9130, respectively, whereas F1-score is 0.9545.

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