Kinetic Curve Type Assessment for Classification of Breast Lesions Using Dynamic Contrast-Enhanced MR Imaging

<div><p>Objective</p><p>The aim of this study was to employ a kinetic model with dynamic contrast enhancement-magnetic resonance imaging to develop an approach that can efficiently distinguish malignant from benign lesions.</p><p>Materials and Methods</p><p>A total of 43 patients with 46 lesions who underwent breast dynamic contrast enhancement-magnetic resonance imaging were included in this retrospective study. The distribution of malignant to benign lesions was 31/15 based on histological results. This study integrated a single-compartment kinetic model and dynamic contrast enhancement-magnetic resonance imaging to generate a kinetic modeling curve for improving the accuracy of diagnosis of breast lesions. Kinetic modeling curves of all different lesions were analyzed by three experienced radiologists and classified into one of three given types. Receiver operating characteristic and Kappa statistics were used for the qualitative method. The findings of the three radiologists based on the time-signal intensity curve and the kinetic curve were compared.</p><p>Results</p><p>An average sensitivity of 82%, a specificity of 65%, an area under the receiver operating characteristic curve of 0.76, and a positive predictive value of 82% and negative predictive value of 63% was shown with the kinetic model (p = 0.017, 0.052, 0.068), as compared to an average sensitivity of 80%, a specificity of 55%, an area under the receiver operating characteristic of 0.69, and a positive predictive value of 79% and negative predictive value of 57% with the time-signal intensity curve method (p = 0.003, 0.004, 0.008). The diagnostic consistency of the three radiologists was shown by the κ-value, 0.857 (p<0.001) with the method based on the time-signal intensity curve and 0.826 (p<0.001) with the method of the kinetic model.</p><p>Conclusions</p><p>According to the statistic results based on the 46 lesions, the kinetic modeling curve method showed higher sensitivity, specificity, positive and negative predictive values as compared with the time-signal intensity curve method in lesion classification.</p></div

Percentages of benign and malignant Type 1 (persistent enhancing), Type 2 (plateau) and Type 3 (washout) lesions diagnosed by three radiologists based on the time-signal intensity curve and the kinetic curve.

<p>Percentages of benign and malignant Type 1 (persistent enhancing), Type 2 (plateau) and Type 3 (washout) lesions diagnosed by three radiologists based on the time-signal intensity curve and the kinetic curve.</p

Comparison of the area under the ROC obtained by three radiologists using two different methods.

<p>Comparison of the area under the ROC obtained by three radiologists using two different methods.</p

Application of the time-signal intensity curve and the kinetic curve in breast MRI analysis.

<p>(a) MRI of a 58-year-old female showed an invasive ductal carcinoma on the upper outer quadrant of the left breast. The green circle indicates the region of interest (ROI) that was used for estimating the change in the C1 value, and the red circle represents the ROI for measuring the ΔM. (b) The blue line is the time-signal intensity curve, and the orange line was generated from a plot of the C2 values to form a kinetic modeling curve.</p

The findings of the breast lesions from three radiologists based on the time-signal intensity curve and the kinetic curve.

<p>The sensitivity (SN), specificity (SP), area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), negative predictive value (NPV), and kappa (κ) value are presented.</p

Radiomic features analysis in computed tomography images of lung nodule classification

<div><p>Purpose</p><p>Radiomics, which extract large amount of quantification image features from diagnostic medical images had been widely used for prognostication, treatment response prediction and cancer detection. The treatment options for lung nodules depend on their diagnosis, benign or malignant. Conventionally, lung nodule diagnosis is based on invasive biopsy. Recently, radiomics features, a non-invasive method based on clinical images, have shown high potential in lesion classification, treatment outcome prediction.</p><p>Methods</p><p>Lung nodule classification using radiomics based on Computed Tomography (CT) image data was investigated and a 4-feature signature was introduced for lung nodule classification. Retrospectively, 72 patients with 75 pulmonary nodules were collected. Radiomics feature extraction was performed on non-enhanced CT images with contours which were delineated by an experienced radiation oncologist.</p><p>Result</p><p>Among the 750 image features in each case, 76 features were found to have significant differences between benign and malignant lesions. A radiomics signature was composed of the best 4 features which included Laws_LSL_min, Laws_SLL_energy, Laws_SSL_skewness and Laws_EEL_uniformity. The accuracy using the signature in benign or malignant classification was 84% with the sensitivity of 92.85% and the specificity of 72.73%.</p><p>Conclusion</p><p>The classification signature based on radiomics features demonstrated very good accuracy and high potential in clinical application.</p></div

Selected feature.

<p>Selected feature.</p

Radiomics feature list that had significant difference (p<0.05) between malignant and benign groups.

<p>Radiomics feature list that had significant difference (p<0.05) between malignant and benign groups.</p

Radiomics feature characteristics.

<p>Radiomics feature characteristics.</p

Examples of lung lesion segmentation.

<p>Original CT image (a) and target segmentation (b) of a benign lung lesion (tuberculosis) in patient’s left upper lobe. Another original CT image (c) and target segmentation (d) of a malignant lung tumor (adenocarcinoma) in patient’s left upper lobe.</p