scholarly journals A computer-aided mass diagnosis system based on perceptive features learned from quantitative mammography radiology report: An observer-based study

2021 ◽  
Author(s):  
Zilong He ◽  
Yue Li ◽  
Weimin Xu ◽  
Chanjuan Wen ◽  
Xiangyuan Ma ◽  
...  
Keyword(s):  
Cad Model ◽  
Average Sensitivity ◽  
Radiology Report ◽  
Breast Masses ◽  
Cad System ◽  
Computer Aided ◽  
Feature Extractor ◽  
Mass Classification ◽  
Aided Diagnosis ◽  
Malignant Diagnosis

Abstract Background: Computer-aided diagnosis (CAD) system can provide reference to radiologists in breast mass classification. This study was to verify if a CAD model, based on perceptive features learned from quantitative BI-RADS descriptions, can help radiologists improve diagnosis performance for breast masses in mammography.Methods: A retrospective multi-reader multi-case (MRMC) study was conducted to evaluate a CAD model established on perceptive features. Digital mammograms of 416 patients with breast masses were collected from 2014 to 2017, including 231 benign and 185 malignant masses. Altogether, 214 of them (109 benign, 105 malignant) were selected randomly to train the CAD model which consisted of perceptive feature extractor and classifier. The other 202 patients were used as the test set for evaluation from which 51 patients (29 benign and 22 malignant) were selected. Six radiologists were divided into three groups (junior, middle-senior, and senior).They evaluated 51 patients without and with support from the CAD model. BI-RADS category, benign or malignant diagnosis, probability of malignancy, and diagnosis time were recorded during two evaluation sessions. Results: In the MRMC evaluation, the average AUC of six radiologists with CAD support was significantly higher than that without support (0.896 vs. 0.850, p=0.02). Both of average sensitivity and average specificity increased (p = 0.0253). More cases were assessed as BI-RADS 4 than BI-RADS 2 or 3. Five radiologists showed comparable diagnosis time per case with and without CAD support, and one radiologist showed a significant decrease when the CAD model was involved. Conclusion: The CAD model could improve radiologists’ diagnostic performance for breast masses without improving the diagnosis time.

COMPUTER AIDED DIAGNOSIS SYSTEM FOR AUTOMATIC TWO STAGES CLASSIFICATION OF BREAST MASS IN DIGITAL MAMMOGRAM IMAGES

2019 ◽  
Vol 31 (01) ◽  
pp. 1950007 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Huda M. S. Algharib ◽  
Amal M. S. Algharib ◽  
Hanan M. S. Algharib
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Breast Mass ◽  
Breast Masses ◽  
Cad System ◽  
Computer Aided ◽  
Two Stages ◽  
Aided Diagnosis ◽  
Mammogram Images

Breast cancer is the most frequent cancer type that is diagnosed in women. The exact causes of such cancer are still unknown. Early and precise detection of breast cancer using mammogram images or biopsy to provide the required medications can increase the healing percentage. There are much current research efforts to developed a computer aided diagnosis (CAD) system based on mammogram images for detecting and classification of breast masses. In this research, a CAD system is developed for automated segmentation and two-stages classification of breast masses. The first stage includes the classification of the masses into seven classes (normal, calcification, circumscribed, spiculated, ill-defined, architectural distortion, asymmetry), which is done using probabilistic neural network (PNN). The second classification stage is to define the severity of abnormality into two classes (Benign and Malignant) which were done using support vector machine (SVM). The results of applying the proposed method on two mammogram image show that the accuracy of detection and segmentation of the breast mass was 99.8% for mammographic image analysis society database (MIAS-DB) with 322 images and 97.5% for breast cancer digital repository (BCDR), BCDR-F03 and BCDR-DN01 with 936 images, while for the first classification stage has accuracy of 97.08%, sensitivity of 98.30% and specificity of 89.8%, and the second classification stage has an accuracy of 99.18%, sensitivity of 98.42% and specificity of 94.90%.

scholarly journals Breast elastography: diagnostic performance of computer-aided diagnosis software and interobserver agreement

2020 ◽  
Vol 53 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Eduardo F. C. Fleury ◽  
Karem Marcomini
Keyword(s):  
Breast Imaging ◽  
Interobserver Agreement ◽  
Visual Assessment ◽  
Computer Aided Diagnosis ◽  
Ultrasound Elastography ◽  
Breast Masses ◽  
Cutoff Value ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

Abstract Objective: To determine the best cutoff value for classifying breast masses by ultrasound elastography, using dedicated software for strain elastography, and to determine the level of interobserver agreement. Materials and Methods: We enrolled 83 patients with 83 breast masses identified on ultrasound and referred for biopsy. After B-mode ultrasound examination, the lesions were manually segmented by three radiologists with varying degrees of experience in breast imaging, designated reader 1 (R1, with 15 years), reader 2 (R2, with 2 years), and reader 3 (R3, with 8 years). Elastography was performed automatically on the best image with computer-aided diagnosis (CAD) software. Cutoff values of 70%, 75%, 80%, and 90% of hard areas were applied for determining the performance of the CAD software. The best cutoff value for the most experienced radiologists was then compared with the visual assessment. Interobserver agreement for the best cutoff value was determined, as were the interclass correlation coefficient and concordance among the radiologists for the areas segmented. Results: The best cutoff value of the proportion of hard area within a breast mass, for experienced radiologists, was found to be 75%. At a cutoff value of 75%, the interobserver agreement was excellent between R1 and R2, as well as between R1 and R3, and good between R2 and R3. The interclass concordance coefficient among the three radiologists was 0.950. When assessing the segmented areas by size, we found that the level of agreement was higher among the more experienced radiologists. Conclusion: The best cutoff value for a quantitative CAD system to classify breast masses was 75%.

scholarly journals False-Positive Malignant Diagnosis of Nodule Mimicking Lesions by Computer-Aided Thyroid Nodule Analysis in Clinical Ultrasonography Practice

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 378
Author(s):  
Krisztián Molnár ◽  
Endre Kálmán ◽  
Zsófia Hári ◽  
Omar Giyab ◽  
Tamás Gáspár ◽  
...  
Keyword(s):  
False Positive ◽  
Thyroid Nodules ◽  
Needle Aspiration ◽  
Cad System ◽  
Experienced Radiologist ◽  
Significant Difference ◽  
Computer Aided ◽  
Thyroid Malignancies ◽  
Aided Diagnosis ◽  
Malignant Diagnosis

This study aims to test computer-aided diagnosis (CAD) for thyroid nodules in clinical ultrasonography (US) practice with a focus towards identifying thyroid entities associated with CAD system misdiagnoses. Two-hundred patients referred to thyroid US were prospectively enrolled. An experienced radiologist evaluated the thyroid nodules and saved axial images for further offline blinded analysis using a commercially available CAD system. To represent clinical practice, not only true nodules, but mimicking lesions were also included. Fine needle aspiration biopsy (FNAB) was performed according to present guidelines. US features and thyroid entities significantly associated with CAD system misdiagnosis were identified along with the diagnostic accuracy of the radiologist and the CAD system. Diagnostic specificity regarding the radiologist was significantly (p < 0.05) higher than when compared with the CAD system (88.1% vs. 40.5%) while no significant difference was found in the sensitivity (88.6% vs. 80%). Focal inhomogeneities and true nodules in thyroiditis, nodules with coarse calcification and inspissated colloid cystic nodules were significantly (p < 0.05) associated with CAD system misdiagnosis as false-positives. The commercially available CAD system is promising when used to exclude thyroid malignancies, however, it currently may not be able to reduce unnecessary FNABs, mainly due to the false-positive diagnoses of nodule mimicking lesions.

Clinical Application of Computer-Aided Diagnosis According to S-Detect in Category 4 Breast Lesions: Prospective Single-Center Study (Preprint)

2020 ◽  
Author(s):  
Pengfei Sun ◽  
Chen Chen ◽  
Weiqi Wang ◽  
Lei Liang ◽  
Dan Luo ◽  
...  
Keyword(s):  
Clinical Management ◽  
Diagnostic Performance ◽  
Breast Biopsy ◽  
Computer Aided Diagnosis ◽  
Breast Lesions ◽  
Breast Masses ◽  
Computer Aided ◽  
Conventional Ultrasound ◽  
Us Findings ◽  
Aided Diagnosis

BACKGROUND Computer-aided diagnosis (CAD) is a useful tool that can provide a reference for the differential diagnosis of benign and malignant breast lesion. Previous studies have demonstrated that CAD can improve the diagnostic performance. However, conventional ultrasound (US) combined with CAD were used to adjust the classification of category 4 lesions has been few assessed. OBJECTIVE The objective of our study was to evaluate the diagnosis performance of conventional ultrasound combined with a CAD system S-Detect in the category of BI-RADS 4 breast lesions. METHODS Between December 2018 and May 2020, we enrolled patients in this study who received conventional ultrasound and S-Detect before US-guided biopsy or surgical excision. The diagnostic performance was compared between US findings only and the combined use of US findings with S-Detect, which were correlated with pathology results. RESULTS A total of 98 patients (mean age 51.06 ±16.25 years, range 22-81) with 110 breast masses (mean size1.97±1.38cm, range0.6-8.5) were included in this study. Of the 110 breast masses, 64/110 (58.18%) were benign, 46/110 (41.82%) were malignant. Compared with conventional ultrasound, a significant increase in specificity (0% to 53.12%, P<.001), accuracy (41.81% to70.19%, P<.001) were noted, with no statistically significant decrease on sensitivity(100% to 95.65% ,P=.48). According to S-Detect-guided US BI-RADS re-classification, 30 out of 110 (27.27%) breast lesions underwent a correct change in clinical management, 74of 110 (67.27%) breast lesions underwent no change and 6 of 110 (5.45%) breast lesions underwent an incorrect change in clinical management. The biopsy rate decreased from 100% to 67.27 % (P<.001).Benign masses among subcategory 4a had higher rates of possibly benign assessment on S-Detect for the US only (60% to 0%, P<.001). CONCLUSIONS S-Detect can be used as an additional diagnostic tool to improve the specificity and accuracy in clinical practice. S-Detect have the potential to be used in downgrading benign masses misclassified as BI-RADS category 4 on US by radiologist, and may reduce unnecessary breast biopsy. CLINICALTRIAL none

scholarly journals Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 973
Author(s):  
Valentina Giannini ◽  
Simone Mazzetti ◽  
Giovanni Cappello ◽  
Valeria Maria Doronzio ◽  
Lorenzo Vassallo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Computer Aided Diagnosis ◽  
Multiparametric Mri ◽  
Performance Study ◽  
Predictive Values ◽  
Cad System ◽  
Computer Aided ◽  
Clinically Significant ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time.

scholarly journals The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 694
Author(s):  
Xuejiao Pang ◽  
Zijian Zhao ◽  
Ying Weng
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Clinical Practice ◽  
Gastrointestinal Endoscopy ◽  
Computer Aided Diagnosis ◽  
Learning Methods ◽  
Learning Abilities ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

At present, the application of artificial intelligence (AI) based on deep learning in the medical field has become more extensive and suitable for clinical practice compared with traditional machine learning. The application of traditional machine learning approaches to clinical practice is very challenging because medical data are usually uncharacteristic. However, deep learning methods with self-learning abilities can effectively make use of excellent computing abilities to learn intricate and abstract features. Thus, they are promising for the classification and detection of lesions through gastrointestinal endoscopy using a computer-aided diagnosis (CAD) system based on deep learning. This study aimed to address the research development of a CAD system based on deep learning in order to assist doctors in classifying and detecting lesions in the stomach, intestines, and esophagus. It also summarized the limitations of the current methods and finally presented a prospect for future research.

scholarly journals Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms

2018 ◽  
Vol 46 (9) ◽  
pp. 1419-1431 ◽  
Author(s):  
Gopichandh Danala ◽  
Bhavika Patel ◽  
Faranak Aghaei ◽  
Morteza Heidari ◽  
Jing Li ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Contrast Enhanced ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Guangyuan Zheng ◽  
Guanghui Han ◽  
Nouman Q. Soomro ◽  
Linjuan Ma ◽  
Fuquan Zhang ◽  
...  
Keyword(s):  
Medical Image ◽  
Medical Image Analysis ◽  
Ground Glass Opacity ◽  
Semisupervised Learning ◽  
Small Sample ◽  
Negative Image ◽  
Cad System ◽  
Computer Aided ◽  
Sample Set ◽  
Aided Diagnosis

Purpose. Computer-aided diagnosis (CAD) can aid in improving diagnostic level; however, the main problem currently faced by CAD is that it cannot obtain sufficient labeled samples. To solve this problem, in this study, we adopt a generative adversarial network (GAN) approach and design a semisupervised learning algorithm, named G2C-CAD. Methods. From the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset, we extracted four types of pulmonary nodule sign images closely related to lung cancer: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, obtaining a total of 3,196 samples. In addition, we randomly selected 2,000 non-lesion image blocks as negative samples. We split the data 90% for training and 10% for testing. We designed a DCGAN generative adversarial framework and trained it on the small sample set. We also trained our designed CNN-based fuzzy Co-forest on the labeled small sample set and obtained a preliminary classifier. Then, coupled with the simulated unlabeled samples generated by the trained DCGAN, we conducted iterative semisupervised learning, which continually improved the classification performance of the fuzzy Co-forest until the termination condition was reached. Finally, we tested the fuzzy Co-forest and compared its performance with that of a C4.5 random decision forest and the G2C-CAD system without the fuzzy scheme, using ROC and confusion matrix for evaluation. Results. Four different types of lung cancer-related signs were used in the classification experiment: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, along with negative image samples. For these five classes, the G2C-CAD system obtained AUCs of 0.946, 0.912, 0.908, 0.887, and 0.939, respectively. The average accuracy of G2C-CAD exceeded that of the C4.5 random decision tree by 14%. G2C-CAD also obtained promising test results on the LISS signs dataset; its AUCs for GGO, lobulation, spiculation, pleural indentation, and negative image samples were 0.972, 0.964, 0.941, 0.967, and 0.953, respectively. Conclusion. The experimental results show that G2C-CAD is an appropriate method for addressing the problem of insufficient labeled samples in the medical image analysis field. Moreover, our system can be used to establish a training sample library for CAD classification diagnosis, which is important for future medical image analysis.

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