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Wan Y, Tong Y, Liu Y, Huang Y, Yao G, Chen DQ, Liu B. Evaluation of the Combination of Artificial Intelligence and Radiologist Assessments to Interpret Malignant Architectural Distortion on Mammography. Front Oncol 2022; 12:880150. [PMID: 35515107 PMCID: PMC9067265 DOI: 10.3389/fonc.2022.880150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 03/29/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose To compare the mammographic malignant architectural distortion (AD) detection performance of radiologists who read mammographic examinations unaided versus those who read these examinations with the support of artificial intelligence (AI) systems. Material and Methods This retrospective case-control study was based on a double-reading of clinical mammograms between January 2011 and December 2016 at a large tertiary academic medical center. The study included 177 malignant and 90 benign architectural distortion (AD) patients. The model was built based on the ResNeXt-50 network. Algorithms used deep learning convolutional neural networks, feature classifiers, image analysis algorithms to depict AD and output a score that translated to malignant. The accuracy for malignant AD detection was evaluated using area under the curve (AUC). Results The overall AUC was 0.733 (95% CI, 0.673-0.792) for Reader First-1, 0.652 (95% CI, 0.586-0.717) for Reader First-2, and 0.655 (95% CI, 0.590-0.719) for Reader First-3. and the overall AUCs for Reader Second-1, 2, 3 were 0.875 (95% CI, 0.830-0.919), 0.882 (95% CI, 0.839-0.926), 0.884 (95% CI, 0.841-0.927),respectively. The AUCs for all the reader-second radiologists were significantly higher than those for all the reader-first radiologists (Reader First-1 vs. Reader Second-1, P= 0.004). The overall AUC was 0.792 (95% CI, 0.660-0.925) for AI algorithms. The combination assessment of AI algorithms and Reader First-1 achieved an AUC of 0.880 (95% CI, 0.793-0.968), increased than the Reader First-1 alone and AI algorithms alone. AI algorithms alone achieved a specificity of 61.1% and a sensitivity of 80.6%. The specificity for Reader First-1 was 55.5%, and the sensitivity was 86.1%. The results of the combined assessment of AI and Reader First-1 showed a specificity of 72.7% and sensitivity of 91.7%. The performance showed significant improvements compared with AI alone (p<0.001) as well as the reader first-1 alone (p=0.006). Conclusion While the single AI algorithm did not outperform radiologists, an ensemble of AI algorithms combined with junior radiologist assessments were found to improve the overall accuracy. This study underscores the potential of using machine learning methods to enhance mammography interpretation, especially in remote areas and primary hospitals.
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Affiliation(s)
- Yun Wan
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yunfei Tong
- AI Research Lab, Boston Meditech Group, Burlington, MA, United States.,AI Research Lab, Shanghai Yanghe Huajian Artificial Intelligence Technology Co., Ltd, Shanghai, China
| | - Yuanyuan Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yan Huang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Guoyan Yao
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Daniel Q Chen
- AI Research Lab, Boston Meditech Group, Burlington, MA, United States
| | - Bo Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
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Rehman KU, Li J, Pei Y, Yasin A, Ali S, Saeed Y. Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network. BIOLOGY 2021; 11:15. [PMID: 35053013 PMCID: PMC8773233 DOI: 10.3390/biology11010015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/15/2021] [Accepted: 12/17/2021] [Indexed: 01/29/2023]
Abstract
Architectural distortion is the third most suspicious appearance on a mammogram representing abnormal regions. Architectural distortion (AD) detection from mammograms is challenging due to its subtle and varying asymmetry on breast mass and small size. Automatic detection of abnormal ADs regions in mammograms using computer algorithms at initial stages could help radiologists and doctors. The architectural distortion star shapes ROIs detection, noise removal, and object location, affecting the classification performance, reducing accuracy. The computer vision-based technique automatically removes the noise and detects the location of objects from varying patterns. The current study investigated the gap to detect architectural distortion ROIs (region of interest) from mammograms using computer vision techniques. Proposed an automated computer-aided diagnostic system based on architectural distortion using computer vision and deep learning to predict breast cancer from digital mammograms. The proposed mammogram classification framework pertains to four steps such as image preprocessing, augmentation and image pixel-wise segmentation. Architectural distortion ROI's detection, training deep learning, and machine learning networks to classify AD's ROIs into malignant and benign classes. The proposed method has been evaluated on three databases, the PINUM, the CBIS-DDSM, and the DDSM mammogram images, using computer vision and depth-wise 2D V-net 64 convolutional neural networks and achieved 0.95, 0.97, and 0.98 accuracies, respectively. Experimental results reveal that our proposed method outperforms as compared with the ShuffelNet, MobileNet, SVM, K-NN, RF, and previous studies.
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Affiliation(s)
- Khalil ur Rehman
- The School of Software Engineering, Beijing University of Technology, Beijing 100024, China; (K.u.R.); (J.L.); (A.Y.); (S.A.); (Y.S.)
| | - Jianqiang Li
- The School of Software Engineering, Beijing University of Technology, Beijing 100024, China; (K.u.R.); (J.L.); (A.Y.); (S.A.); (Y.S.)
- Beijing Engineering Research Center for IoT Software and Systems, Beijing 100124, China
| | - Yan Pei
- Computer Science Division, University of Aizu, Aizuwakamatsu 965-8580, Fukushima, Japan
| | - Anaa Yasin
- The School of Software Engineering, Beijing University of Technology, Beijing 100024, China; (K.u.R.); (J.L.); (A.Y.); (S.A.); (Y.S.)
| | - Saqib Ali
- The School of Software Engineering, Beijing University of Technology, Beijing 100024, China; (K.u.R.); (J.L.); (A.Y.); (S.A.); (Y.S.)
| | - Yousaf Saeed
- The School of Software Engineering, Beijing University of Technology, Beijing 100024, China; (K.u.R.); (J.L.); (A.Y.); (S.A.); (Y.S.)
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Oyelade ON, Ezugwu AE. A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102366] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Addressing architectural distortion in mammogram using AlexNet and support vector machine. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100551] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
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Junior OP, Oliveira HCR, Ferraz CT, Saito JH, Vieira MADC, Gonzaga A. A Novel Fusion-Based Texture Descriptor to Improve the Detection of Architectural Distortion in Digital Mammography. J Digit Imaging 2020; 34:36-52. [PMID: 33179194 DOI: 10.1007/s10278-020-00391-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 08/12/2020] [Accepted: 09/22/2020] [Indexed: 11/28/2022] Open
Abstract
Architectural distortion (AD) is the earliest sign of breast cancer that can be detected on a mammogram, and it is usually associated with malignant tumors. Breast cancer is one of the major causes of death among women, and the chance of cure can increase significantly when detected early. Computer-aided detection (CAD) systems have been used in clinical practice to assist radiologists with the task of detecting breast lesions. However, due to the complexity and subtlety of AD, its detection is still a challenge, even with the assistance of CAD. Recently, the fusion of descriptors has become a trend for improving the performance of computer vision algorithms. In this work, we evaluated some local texture descriptors and their possible combinations, considering different fusion approaches, for application in CAD systems to improve AD detection. In addition, we present a novel fusion-based texture descriptor, the Completed Mean Local Mapped Pattern (CMLMP), which is based on complementary information between three LMP operators (signal, magnitude and center) and the local differences between pixel values and the mean value of a neighborhood. We compared the performance of the proposed descriptor with two other well-known descriptors: the Completed Local Binary Pattern (CLBP) and the Completed Local Mapped Pattern (CLMP), for the task of detecting AD in 350 digital mammography clinical images. The results showed that the descriptor proposed in this work outperforms the others, for both individual and fused approaches. Moreover, the choice of the fusion operator is crucial because it results in different detection performances.
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Affiliation(s)
- Osmando Pereira Junior
- Federal Institute of Education Science and Technology of Triângulo Mineiro (IFTM), Patrocínio, Minas Gerais, Brazil.
| | | | - Carolina Toledo Ferraz
- University Center Campo Limpo Paulista (UNIFACCAMP), Campo Limpo Paulista (SP), São Paulo, Brazil
| | - José Hiroki Saito
- University Center Campo Limpo Paulista (UNIFACCAMP), Campo Limpo Paulista (SP), São Paulo, Brazil
| | | | - Adilson Gonzaga
- São Carlos School of Engineering, University of São Paulo (EESC/USP), São Carlos, São Paulo, Brazil
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Kayode AA, Akande NO, Adegun AA, Adebiyi MO. An automated mammogram classification system using modified support vector machine. MEDICAL DEVICES (AUCKLAND, N.Z.) 2019; 12:275-284. [PMID: 31496841 PMCID: PMC6697673 DOI: 10.2147/mder.s206973] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 06/18/2019] [Indexed: 11/23/2022]
Abstract
Purpose Breast cancer remains a serious public health problem that results in the loss of lives among women. However, early detection of its signs increases treatment options and the likelihood of cure. Although mammography has been established to be a proven technique of examining symptoms of cancer in mammograms, the manual observation by radiologists is demanding and often prone to diagnostic errors. Therefore, computer aided diagnosis (CADx) systems could be a viable alternative that could facilitate and ease cancer diagnosis process; hence this study. Methodology The inputs to the proposed model are raw mammograms downloaded from the Mammographic Image Analysis Society database. Prior to the classification, the raw mammograms were preprocessed. Then, gray level co-occurrence matrix was used to extract fifteen textural features from the mammograms at four different angular directions: θ={0°, 45°, 90°, 135°}, and two distances: D={1,2}. Afterwards, a two-stage support vector machine was used to classify the mammograms as normal, benign and malignant. Results All of the 37 normal images used as test data were classified as normal (no false positive) and all 41 abnormal images were correctly classified to be abnormal (no false negative), meaning that the sensitivity and specificity of the model in detecting abnormality is 100%. After the detection of abnormality, the system further classified the abnormality on the mammograms to be either "benign" or "malignant". Out of 23 benign images, 21 were truly classified as benign. Also, out of 18 malignant images, 17 were truly classified to be malignant. From these findings, the sensitivity, specificity, positive predictive value, and negative predictive value of the system are 94.4%, 91.3%, 89.5%, and 95.5%, respectively. Conclusion This article has further affirmed the prowess of automated CADx systems as a viable tool that could facilitate breast cancer diagnosis by radiologists.
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Affiliation(s)
| | | | | | - Marion Olubunmi Adebiyi
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria
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Houssami N, Kirkpatrick-Jones G, Noguchi N, Lee CI. Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI's potential in breast screening practice. Expert Rev Med Devices 2019; 16:351-362. [PMID: 30999781 DOI: 10.1080/17434440.2019.1610387] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Various factors are driving interest in the application of artificial intelligence (AI) for breast cancer (BC) detection, but it is unclear whether the evidence warrants large-scale use in population-based screening. AREAS COVERED We performed a scoping review, a structured evidence synthesis describing a broad research field, to summarize knowledge on AI evaluated for BC detection and to assess AI's readiness for adoption in BC screening. Studies were predominantly small retrospective studies based on highly selected image datasets that contained a high proportion of cancers (median BC proportion in datasets 26.5%), and used heterogeneous techniques to develop AI models; the range of estimated AUC (area under ROC curve) for AI models was 69.2-97.8% (median AUC 88.2%). We identified various methodologic limitations including use of non-representative imaging data for model training, limited validation in external datasets, potential bias in training data, and few comparative data for AI versus radiologists' interpretation of mammography screening. EXPERT OPINION Although contemporary AI models have reported generally good accuracy for BC detection, methodological concerns, and evidence gaps exist that limit translation into clinical BC screening settings. These should be addressed in parallel to advancing AI techniques to render AI transferable to large-scale population-based screening.
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Affiliation(s)
- Nehmat Houssami
- a The University of Sydney, Faculty of Medicine and Health , Sydney School of Public Health (A27) , Sydney , Australia
| | - Georgia Kirkpatrick-Jones
- a The University of Sydney, Faculty of Medicine and Health , Sydney School of Public Health (A27) , Sydney , Australia
| | - Naomi Noguchi
- a The University of Sydney, Faculty of Medicine and Health , Sydney School of Public Health (A27) , Sydney , Australia
| | - Christoph I Lee
- b Department of Radiology , University of Washington School of Medicine , Seattle , WA , USA.,c Department of Health Services , University of Washington School of Public Health , Seattle , WA , USA.,d Hutchinson Institute for Cancer Outcomes Research , Seattle , WA , USA
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de Oliveira HC, Mencattini A, Casti P, Catani JH, de Barros N, Gonzaga A, Martinelli E, da Costa Vieira MA. A cross-cutting approach for tracking architectural distortion locii on digital breast tomosynthesis slices. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.01.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Peng T, Wang Y, Xu TC, Shi L, Jiang J, Zhu S. Detection of Lung Contour with Closed Principal Curve and Machine Learning. J Digit Imaging 2018; 31:520-533. [PMID: 29450843 DOI: 10.1007/s10278-018-0058-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Radiation therapy plays an essential role in the treatment of cancer. In radiation therapy, the ideal radiation doses are delivered to the observed tumor while not affecting neighboring normal tissues. In three-dimensional computed tomography (3D-CT) scans, the contours of tumors and organs-at-risk (OARs) are often manually delineated by radiologists. The task is complicated and time-consuming, and the manually delineated results will be variable from different radiologists. We propose a semi-supervised contour detection algorithm, which firstly uses a few points of region of interest (ROI) as an approximate initialization. Then the data sequences are achieved by the closed polygonal line (CPL) algorithm, where the data sequences consist of the ordered projection indexes and the corresponding initial points. Finally, the smooth lung contour can be obtained, when the data sequences are trained by the backpropagation neural network model (BNNM). We use the private clinical dataset and the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset to measure the accuracy of the presented method, respectively. To the private dataset, experimental results on the initial points which are as low as 15% of the manually delineated points show that the Dice coefficient reaches up to 0.95 and the global error is as low as 1.47 × 10-2. The performance of the proposed algorithm is also better than the cubic spline interpolation (CSI) algorithm. While on the public LIDC-IDRI dataset, our method achieves superior segmentation performance with average Dice of 0.83.
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Affiliation(s)
- Tao Peng
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China.
| | - Yihuai Wang
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China.
| | - Thomas Canhao Xu
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China
| | - Lianmin Shi
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China
| | - Jianwu Jiang
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China
| | - Shilang Zhu
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China
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Narváez F, Alvarez J, Garcia-Arteaga JD, Tarquino J, Romero E. Characterizing Architectural Distortion in Mammograms by Linear Saliency. J Med Syst 2016; 41:26. [PMID: 28005248 DOI: 10.1007/s10916-016-0672-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2016] [Accepted: 12/07/2016] [Indexed: 12/01/2022]
Abstract
Architectural distortion (AD) is a common cause of false-negatives in mammograms. This lesion usually consists of a central retraction of the connective tissue and a spiculated pattern radiating from it. This pattern is difficult to detect due the complex superposition of breast tissue. This paper presents a novel AD characterization by representing the linear saliency in mammography Regions of Interest (ROI) as a graph composed of nodes corresponding to locations along the ROI boundary and edges with a weight proportional to the line intensity integrals along the path connecting any pair of nodes. A set of eigenvectors from the adjacency matrix is then used to extract discriminant coefficients that represent those nodes with higher salient lines. A dimensionality reduction is further accomplished by selecting the pair of nodes with major contribution for each of the computed eigenvectors. The set of main salient lines is then assembled as a feature vector that inputs a conventional Support Vector Machine (SVM). Experimental results with two benchmark databases, the mini-MIAS and DDSM databases, demonstrate that the proposed linear saliency domain method (LSD) performs well in terms of accuracy. The approach was evaluated with a set of 246 RoI extracted from the DDSM (123 normal tissues and 123 AD) and a set of 38 ROI from the mini-MIAS collections (19 normal tissues and 19 AD) respectively. The classification results showed respectively for both databases an accuracy rate of 89 % and 87 %, a sensitivity rate of 85 % and 95 %, and a specificity rate of 93 % and 84 %. Likewise, the area under curve (A z ) of the Receiver Operating Characteristic (ROC) curve was 0.93 for both databases.
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Affiliation(s)
- Fabián Narváez
- Computer Imaging and Medical Applications Laboratory - Cim@lab, Faculty of Medicine - Universidad Nacional de Colombia, Carrera 30 No 45-03, Bogotá, DC, Colombia
| | - Jorge Alvarez
- Computer Imaging and Medical Applications Laboratory - Cim@lab, Faculty of Medicine - Universidad Nacional de Colombia, Carrera 30 No 45-03, Bogotá, DC, Colombia
| | - Juan D Garcia-Arteaga
- Computer Imaging and Medical Applications Laboratory - Cim@lab, Faculty of Medicine - Universidad Nacional de Colombia, Carrera 30 No 45-03, Bogotá, DC, Colombia
| | - Jonathan Tarquino
- Computer Imaging and Medical Applications Laboratory - Cim@lab, Faculty of Medicine - Universidad Nacional de Colombia, Carrera 30 No 45-03, Bogotá, DC, Colombia
| | - Eduardo Romero
- Computer Imaging and Medical Applications Laboratory - Cim@lab, Faculty of Medicine - Universidad Nacional de Colombia, Carrera 30 No 45-03, Bogotá, DC, Colombia.
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