scholarly journals Radiomics Based on Thyroid Ultrasound Can Predict Distant Metastasis of Follicular Thyroid Carcinoma

2020 ◽  
Vol 9 (7) ◽  
pp. 2156
Author(s):  
Mi-ri Kwon ◽  
Jung Hee Shin ◽  
Hyunjin Park ◽  
Hwanho Cho ◽  
Eunjin Kim ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Distant Metastasis ◽  
Area Under The Curve ◽  
Distant Metastases ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Level ◽  
Thyroid Ultrasound ◽  
Widely Invasive ◽  
Radiomics Signature

We aimed to evaluate whether radiomics analysis based on gray-scale ultrasound (US) can predict distant metastasis of follicular thyroid cancer (FTC). We retrospectively included 35 consecutive FTCs with distant metastases and 134 FTCs without distant metastasis. We extracted a total of 60 radiomics features derived from the first order, shape, gray-level cooccurrence matrix, and gray-level size zone matrix features using US imaging. A radiomics signature was generated using the least absolute shrinkage and selection operator and was used to train a support vector machine (SVM) classifier in five-fold cross-validation. The SVM classifier showed an area under the curve (AUC) of 0.90 on average on the test folds. Age, size, widely invasive histology, extrathyroidal extension, lymph node metastases on pathology, nodule-in-nodule appearance, marked hypoechogenicity, and rim calcification on the US were significantly more frequent among FTCs with distant metastasis compared to those without metastasis (p < 0.05). Radiomics signature and widely invasive histology were significantly associated with distant metastasis on multivariate analysis (p < 0.01 and p = 0.003). The classifier using the results of the multivariate analysis showed an AUC of 0.93. The radiomics signature from thyroid ultrasound is an independent biomarker for noninvasively predicting distant metastasis of FTC.

scholarly journals Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

2019 ◽  
Vol 45 (10) ◽  
pp. 3193-3201 ◽  
Author(s):  
Yajuan Li ◽  
Xialing Huang ◽  
Yuwei Xia ◽  
Liling Long
Keyword(s):  
Machine Learning ◽  
Differential Diagnosis ◽  
Cell Carcinoma ◽  
Area Under The Curve ◽  
Image Features ◽  
Renal Tumors ◽  
Support Vector ◽  
Svm Classifier ◽  
Renal Oncocytoma ◽  
Lasso Regression

Abstract Purpose To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO). Methods Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy. Results In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance. Conclusions Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CT-enhanced quantitative features and machine learning.

scholarly journals Breast cancer detection using deep convolutional neural networks and support vector machines

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6201 ◽  
Author(s):  
Dina A. Ragab ◽  
Maha Sharkas ◽  
Stephen Marshall ◽  
Jinchang Ren
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Input Data ◽  
Data Augmentation ◽  
Area Under The Curve ◽  
Screening Mammography ◽  
Support Vector ◽  
Svm Classifier ◽  
Deep Convolutional Neural Networks ◽  
Cad System

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.

scholarly journals Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

2017 ◽  
Vol 131 (13) ◽  
pp. 1465-1481 ◽  
Author(s):  
Víctor González-Castro ◽  
María del C. Valdés Hernández ◽  
Francesca M. Chappell ◽  
Paul A. Armitage ◽  
Stephen Makin ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Human Performance ◽  
Small Vessel Disease ◽  
Brain Mri ◽  
Area Under The Curve ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Svm Classifier ◽  
Perivascular Spaces ◽  
Scale Invariant

In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS in the basal ganglia (BG) region as low or high. We assess the performance of three different types of descriptors extracted from the BG region in T2-weighted MRI images: (i) statistics obtained from Wavelet transform’s coefficients, (ii) local binary patterns and (iii) bag of visual words (BoW) based descriptors characterizing local keypoints obtained from a dense grid with the scale-invariant feature transform (SIFT) characteristics. When the latter were used, the SVM classifier achieved the best accuracy (81.16%). The output from the classifier using the BoW descriptors was compared with visual ratings done by an experienced neuroradiologist (Observer 1) and by a trained image analyst (Observer 2). The agreement and cross-correlation between the classifier and Observer 2 (κ = 0.67 (0.58–0.76)) were slightly higher than between the classifier and Observer 1 (κ = 0.62 (0.53–0.72)) and comparable between both the observers (κ = 0.68 (0.61–0.75)). Finally, three logistic regression models using clinical variables as independent variable and each of the PVS ratings as dependent variable were built to assess how clinically meaningful were the predictions of the classifier. The goodness-of-fit of the model for the classifier was good (area under the curve (AUC) values: 0.93 (model 1), 0.90 (model 2) and 0.92 (model 3)) and slightly better (i.e. AUC values: 0.02 units higher) than that of the model for Observer 2. These results suggest that, although it can be improved, an automatic classifier to assess PVS burden from brain MRI can provide clinically meaningful results close to those from a trained observer.

scholarly journals Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1443
Author(s):  
Mai Ramadan Ibraheem ◽  
Shaker El-Sappagh ◽  
Tamer Abuhmed ◽  
Mohammed Elmogy
Keyword(s):  
Area Under The Curve ◽  
Malignant Neoplasm ◽  
Texture Features ◽  
Feature Space ◽  
Skin Neoplasms ◽  
Support Vector ◽  
Svm Classifier ◽  
Abnormal Growth ◽  
Malignant Skin ◽  
High Level

The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.

scholarly journals Classification for Liver Diseases Based on Ultrasound Image Texture Features

2018 ◽  
Author(s):  
Sendren Sheng-Dong Xu ◽  
Chien-Tien Su ◽  
Chun-Chao Chang ◽  
Pham Quoc Phu
Keyword(s):  
Liver Abscess ◽  
Liver Diseases ◽  
Ultrasound Image ◽  
Texture Features ◽  
Image Texture ◽  
Support Vector ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Gray Level ◽  
Forward Selection

This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.

scholarly journals Stereotactic Body Radiotherapy for Hepatocellular Carcinoma: Current Evidence and the Feasibility of Radiomics-based Predictive Models

2020 ◽  
Author(s):  
Yang-Hong Dai ◽  
Po-Chien Shen ◽  
Wei-Chou Chang ◽  
Chen-Hsiang Lo ◽  
Jen-Fu Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Local Control ◽  
Predictive Models ◽  
Stereotactic Body Radiotherapy ◽  
Current Knowledge ◽  
Area Under The Curve ◽  
Current Evidence ◽  
Support Vector ◽  
Svm Classifier

Abstract Background : Stereotactic body radiotherapy (SBRT) is an effective but less focused alternative for treatment of hepatocellular carcinoma (HCC). To date, a personalized model for predicting therapeutic response is lacking. This study aimed to review current knowledge and to propose a radiomics-based machine-learning (ML) strategy for local response (LR) prediction. Methods : We searched the literature for studies conducted between January 1993 and August 2019 that used > 100 patients. Additionally, 172 HCC patients in our hospital were retrospectively analyzed between January 2007 and December 2016. In the radiomic analysis, 41 treated tumors were contoured and 46 radiomic features were extracted. Results : The 1-year local control was 85.4% in our patient cohort, comparable with current results (87-99%). The Support Vector Machine (SVM) classifier, based on computed tomography (CT) scans in the A phase processed by equal probability (Ep) quantization with 8 gray levels, showed the highest mean F1 score (0.7995) for favorable LR within 1 year (W1R), at the end of follow-up (EndR), and condition of in-field failure-free (IFFF). The area under the curve (AUC) for this model was 92.1%, 96.3%, and 99.2% for W1R, EndR, and IFFF, respectively. Conclusions : SBRT has high 1-year local control and our study sets the basis for constructing predictive models for HCC patients receiving SBRT.

scholarly journals A New GLLD Operator for Mass Detection in Digital Mammograms

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
N. Gargouri ◽  
A. Dammak Masmoudi ◽  
D. Sellami Masmoudi ◽  
R. Abid
Keyword(s):  
Area Under The Curve ◽  
Screening Mammography ◽  
Support Vector ◽  
Gray Level ◽  
Mass Detection ◽  
K Nearest Neighbors ◽  
Digital Database ◽  
Local Difference ◽  
Ann Classifier ◽  
Local Feature Extraction

During the last decade, several works have dealt with computer automatic diagnosis (CAD) of masses in digital mammograms. Generally, the main difficulty remains the detection of masses. This work proposes an efficient methodology for mass detection based on a new local feature extraction. Local binary pattern (LBP) operator and its variants proposed by Ojala are a powerful tool for textures classification. However, it has been proved that such operators are not able to model at their own texture masses. We propose in this paper a new local pattern model named gray level and local difference (GLLD) where we take into consideration absolute gray level values as well as local difference as local binary features. Artificial neural networks (ANNs), support vector machine (SVM), and k-nearest neighbors (kNNs) are, then, used for classifying masses from nonmasses, illustrating better performance of ANN classifier. We have used 1000 regions of interest (ROIs) obtained from the Digital Database for Screening Mammography (DDSM). The area under the curve of the corresponding approach has been found to beAz=0.95for the mass detection step. A comparative study with previous approaches proves that our approach offers the best performances.

scholarly journals Non-Invasive Radiomics Approach Predict Invasiveness of Adamantinomatous Craniopharyngioma Before Surgery

2021 ◽  
Vol 10 ◽  
Author(s):  
Guofo Ma ◽  
Jie Kang ◽  
Ning Qiao ◽  
Bochao Zhang ◽  
Xuzhu Chen ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Effective Means ◽  
Tumor Resection ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Support Vector ◽  
Benign Tumors ◽  
Incomplete Resection ◽  
Test Sets ◽  
Radiomics Signature

PurposeCraniopharyngiomas (CPs) are benign tumors, complete tumor resection is considered to be the optimal treatment. However, although histologically benign, the local invasiveness of CPs commonly contributes to incomplete resection and a poor prognosis. At present, some advocate less aggressive surgery combined with radiotherapy as a more reasonable and effective means of protecting hypothalamus function and preventing recurrence in patients with tight tumor adhesion to the hypothalamus. Hence, if a method can be developed to predict the invasiveness of CP preoperatively, it will help in the development of a more personalized surgical strategy. The aim of the study was to report a radiomics-clinical nomogram for the individualized preoperative prediction of the invasiveness of adamantinomatous CP (ACPs) before surgery.MethodsIn total, 1,874 radiomics features were extracted from whole tumors on contrast-enhanced T1-weighted images. A support vector machine trained a predictive model that was validated using receiver operating characteristic (ROC) analysis on an independent test set. Moreover, a nomogram was constructed incorporating clinical characteristics and the radiomics signature for individual prediction.ResultsEleven features associated with the invasiveness of ACPs were selected by using the least absolute shrinkage and selection operator (LASSO) method. These features yielded area under the curve (AUC) values of 79.09 and 73.5% for the training and test sets, respectively. The nomogram incorporating peritumoral edema and the radiomics signature yielded good calibration in the training and test sets with the AUCs of 84.79 and 76.48%, respectively.ConclusionThe developed model yields good performance, indicating that the invasiveness of APCs can be predicted using noninvasive radiological data. This reliable, noninvasive tool can help clinical decision making and improve patient prognosis.

Diagnosis of Distant Metastasis of Lung Cancer Using Anarchic Society Optimization with Support Vector Machine

2020 ◽  
Vol 10 (8) ◽  
pp. 1841-1850
Author(s):  
A. Kodieswari ◽  
D. Deepa
Keyword(s):  
Lung Cancer ◽  
Support Vector Machine ◽  
Distant Metastasis ◽  
Research Work ◽  
Significant Risk ◽  
Large Data ◽  
Efficient Solutions ◽  
Support Vector ◽  
Svm Classifier ◽  
Manual Segmentation

The most widely recognized threatening tumours is the lung cancer one amongst the most preeminent disease and mortality which prompts significant risk to individuals’ wellbeing and life. Propelled lung malignant growth is probably prompt to produce comparing side effects in patients with extraordinary torment and life-threatening. Computed Tomography (CT) is one of the efficient solutions to investigate the distant metastasis plausibility for which system with computer aided diagnosis is desirable. So to diagnose distant metastasis of cancer in lung, the past framework is accomplished by Support Vector Machine (SVM) based classification for manual segmentation and the precision achieved is 89.09% for the classification. To enumerate large data sets, manual segmentation is time consuming, tedious and labour-intensive and hence achieving feasibility is not that easy. The desired prediction accuracy is attained by the techniques namely efficient segmentation method and feature selection methods. The proposed system utilizes Improved Markov Random Field (MRF) with SVM based classification, thereby improving the system performance. In this research work, the input is nothing but the CT lung images and adaptive median filtering is utilized for preprocessing technique. Improved MRF approach is one of the solutions to segment the pre-processed image after which CT images are used to extricate the clinical features and radiomic features. Anarchic Society Optimization (ASO) algorithm provides assistance for choosing the optimal features which in turn progresses the classification accuracy. In view of the selected feature, SVM classifier is utilized for cancer classification. The test outcome proves that the projected framework achieves improved performance compared to the existing frameworks parameters like correctness, accuracy and review.

scholarly journals Brain tumor segmentation from multimodal magnetic resonance imaging data based on gray-level co-occurrence matrix (GLCM) and an ensemble Support Vector Machine (SVM) classifier

2020 ◽  
Author(s):  
Na Li ◽  
Zheng Yang
Keyword(s):  
Brain Tumors ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Svm Classifier ◽  
Gray Level ◽  
Resonance Imaging ◽  
Dice Coefficient ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Occurrence Matrix

Abstract Background Brain tumors, abnormal cells growing in the human brain,are common neurological diseases that are extremely harmful to human health. Malignant brain tumors can lead to high mortality. Magnetic resonance imaging (MRI)༌a typical noninvasive imaging technology, can produce high-quality brain images without damage and skull artifacts, as well as provide comprehensive information to facilitate the diagnosis and treatment of brain tumors. Additionally༌the segmentation of MRI brain tumors utilizes computer technology to segment and label tumors and normal tissues automatically on multimodal brain images, which plays an important role in disease diagnosis, treatment planning, and surgical navigation. Methods We propose a solution using gray-level co-occurrence matrix (GLCM) texture and an ensemble Support Vector Machine (SVM) structure. We focus on the effects of GLCM texture on brain tumor segmentation. First, 112 GLCM features for each voxel were extracted. Next, these features were ranked using the SVM-recursive feature elimination (SVM-RFE) method. Based on the sorting results, we found that when the number of features was 60, the value of the Dice similarity coefficient (DSC) tended to be flat. The GLCM texture features maximal correlation coefficient, information measure of correlation, Angular Second Moment, sum of squares, difference variance, contrast, and inverse difference moment were important for segmentation. Finally, we selected the top 60 grayscale features and constructed an ensemble SVM classifier to separate the abnormal mass of tissue from normal brain tissues. Results The experimental material was a dataset called BraTs2015. The proposed model was verified with the Dice coefficient. For low-grade tumors, we obtained a 91.2% average Dice coefficient for segmenting the complete tumor region. For high-grade tumors, the average was slightly higher at 92.4%. Conclusion Our results demonstrated that this method has a better capacity and higher segmentation accuracy with a low computation cost.

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