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Baidya Kayal E, Ganguly S, Sasi A, Sharma S, DS D, Saini M, Rangarajan K, Kandasamy D, Bakhshi S, Mehndiratta A. A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models. Front Oncol 2023; 13:1212526. [PMID: 37671060 PMCID: PMC10476362 DOI: 10.3389/fonc.2023.1212526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/31/2023] [Indexed: 09/07/2023] Open
Abstract
The presence of lung metastases in patients with primary malignancies is an important criterion for treatment management and prognostication. Computed tomography (CT) of the chest is the preferred method to detect lung metastasis. However, CT has limited efficacy in differentiating metastatic nodules from benign nodules (e.g., granulomas due to tuberculosis) especially at early stages (<5 mm). There is also a significant subjectivity associated in making this distinction, leading to frequent CT follow-ups and additional radiation exposure along with financial and emotional burden to the patients and family. Even 18F-fluoro-deoxyglucose positron emission technology-computed tomography (18F-FDG PET-CT) is not always confirmatory for this clinical problem. While pathological biopsy is the gold standard to demonstrate malignancy, invasive sampling of small lung nodules is often not clinically feasible. Currently, there is no non-invasive imaging technique that can reliably characterize lung metastases. The lung is one of the favored sites of metastasis in sarcomas. Hence, patients with sarcomas, especially from tuberculosis prevalent developing countries, can provide an ideal platform to develop a model to differentiate lung metastases from benign nodules. To overcome the lack of optimal specificity of CT scan in detecting pulmonary metastasis, a novel artificial intelligence (AI)-based protocol is proposed utilizing a combination of radiological and clinical biomarkers to identify lung nodules and characterize it as benign or metastasis. This protocol includes a retrospective cohort of nearly 2,000-2,250 sample nodules (from at least 450 patients) for training and testing and an ambispective cohort of nearly 500 nodules (from 100 patients; 50 patients each from the retrospective and prospective cohort) for validation. Ground-truth annotation of lung nodules will be performed using an in-house-built segmentation tool. Ground-truth labeling of lung nodules (metastatic/benign) will be performed based on histopathological results or baseline and/or follow-up radiological findings along with clinical outcome of the patient. Optimal methods for data handling and statistical analysis are included to develop a robust protocol for early detection and classification of pulmonary metastasis at baseline and at follow-up and identification of associated potential clinical and radiological markers.
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Affiliation(s)
- Esha Baidya Kayal
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Shuvadeep Ganguly
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Archana Sasi
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Swetambri Sharma
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Dheeksha DS
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Manish Saini
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Krithika Rangarajan
- Radiodiagnosis, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | | | - Sameer Bakhshi
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Amit Mehndiratta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, Delhi, India
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Sakshiwala, Singh MP. An ensemble of three-dimensional deep neural network models for multi-attribute scoring and classification of pulmonary nodules. Proc Inst Mech Eng H 2023; 237:946-957. [PMID: 37366554 DOI: 10.1177/09544119231182037] [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] [Indexed: 06/28/2023]
Abstract
Lung cancer is the uncontrolled growth of cells that originates in the lung parenchyma or cells that line the air passages. These cells divide rapidly to form malicious tumors. This paper proposes a multi-task ensemble of three dimensional (3D) deep neural network (DNN) based model, namely: pre-trained EfficientNetB0, BiGRU-based SEResNext101, and the proposed LungNet. The ensemble model performs binary classification and regression tasks to accurately classify the benign and malignant pulmonary nodules. This study also explores the attribute importance and proposes a domain knowledge-based regularization technique. The proposed model is evaluated on the public benchmark LIDC-IDRI dataset. Through a comparative study, it was shown that when coefficients generated by the random forest (RF) are used in the loss function, the proposed ensemble model offers a better prediction capability of the accuracy of 96.4% compared to the state-of-the-art methods. In addition, the receiver operating characteristic curves show that the proposed ensemble model has better performance than the base learners. Thus, the proposed CAD-based model can efficiently detect malignant pulmonary nodules.
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Affiliation(s)
- Sakshiwala
- Department of Computer Science and Engineering, NIT Patna, Patna, Bihar, India
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Khan MA, Rajinikanth V, Satapathy SC, Taniar D, Mohanty JR, Tariq U, Damaševičius R. VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images. Diagnostics (Basel) 2021; 11:2208. [PMID: 34943443 PMCID: PMC8699868 DOI: 10.3390/diagnostics11122208] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 11/17/2021] [Accepted: 11/24/2021] [Indexed: 12/27/2022] Open
Abstract
Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier.
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Affiliation(s)
| | - Venkatesan Rajinikanth
- Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai, Tamilnadu 600119, India;
| | - Suresh Chandra Satapathy
- School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to Be University), Bhubaneswar, Odisha 751024, India;
| | - David Taniar
- Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia;
| | - Jnyana Ranjan Mohanty
- School of Computer Applications, Kalinga Institute of Industrial Technology (Deemed to Be University), Bhubaneswar, Odisha 751024, India;
| | - Usman Tariq
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Robertas Damaševičius
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
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Yang Y, Zhang Q. Multiview framework using a 3D residual network for pulmonary micronodule malignancy risk classification. Biomed Mater Eng 2021; 31:253-267. [PMID: 32894237 DOI: 10.3233/bme-206005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Pulmonary micronodules account for 80% of all lung nodules. Generally, pulmonary micronodules in the early stages can be detected on thoracic computed tomography (CT) scans. Early diagnosis is crucial for improving the patient's survival rate. OBJECTIVE This paper aims to estimate the malignancy risk of pulmonary micronodules and potentially improve the survival rate. METHODS We extract 3D features of the CT images to obtain richer characteristics. Because superior performance can be achieved by having deep layers, we apply a 3D residual network (3D-ResNet) to classify the pulmonary micronodule. We construct a framework by using three parallel ResNets whose inputs are CT images in different regions of interest, i.e., the multiview of the image. To further evaluate the applicability of the framework, we make a five-category classification and achieve good performance. RESULTS By fusing different characteristics from three views, we achieve the area under the receiver operating characteristic curve (AUC) of 0.9681. Based on the results of the experiments, our 3D-ResNet has a better performance than 3D-VGG and 3D-Inception in terms of precision (the increase rates are 13.7% and 7.4%), AUC (the increase rates are 15.8% and 5.3%), and accuracy (the increase rates are 14.3% and 4.5%). Meanwhile, the recall performance is close to that of the 3D-Inception network. CONCLUSION Overall, the framework we propose has applicability and feasibility in pulmonary micronodule classification.
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Affiliation(s)
- Yujie Yang
- Institute of Computer and Information Engineering, Henan Normal University, Henan Province, Xinxiang, China.,Big Data Engineering Laboratory for Teaching Resources and Assessment of Education Quality, Henan Province, Xinxiang, China
| | - Qianqian Zhang
- Institute of Computer and Information Engineering, Henan Normal University, Henan Province, Xinxiang, China.,Big Data Engineering Laboratory for Teaching Resources and Assessment of Education Quality, Henan Province, Xinxiang, China
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Liu H, Cao H, Song E, Ma G, Xu X, Jin R, Liu C, Hung CC. Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification. J Digit Imaging 2020; 33:1242-1256. [PMID: 32607905 PMCID: PMC7649841 DOI: 10.1007/s10278-020-00372-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Classification of benign and malignant in lung nodules using chest CT images is a key step in the diagnosis of early-stage lung cancer, as well as an effective way to improve the patients' survival rate. However, due to the diversity of lung nodules and the visual similarity of lung nodules to their surrounding tissues, it is difficult to construct a robust classification model with conventional deep learning-based diagnostic methods. To address this problem, we propose a multi-model ensemble learning architecture based on 3D convolutional neural network (MMEL-3DCNN). This approach incorporates three key ideas: (1) Constructed multi-model network architecture can be well adapted to the heterogeneity of lung nodules. (2) The input that concatenated of the intensity image corresponding to the nodule mask, the original image, and the enhanced image corresponding to which can help training model to extract advanced feature with more discriminative capacity. (3) Select the corresponding model to different nodule size dynamically for prediction, which can improve the generalization ability of the model effectively. In addition, ensemble learning is applied in this paper to further improve the robustness of the nodule classification model. The proposed method has been experimentally verified on the public dataset, LIDC-IDRI. The experimental results show that the proposed MMEL-3DCNN architecture can obtain satisfactory classification results.
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Affiliation(s)
- Hong Liu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Haichao Cao
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Enmin Song
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Guangzhi Ma
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Xiangyang Xu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Renchao Jin
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Chuhua Liu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Chih-Cheng Hung
- Laboratory for Machine Vision and Security Research, Kennesaw State University, Kennesaw, GA, USA
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Xiao N, Qiang Y, Zia MB, Wang S, Lian J. Ensemble classification for predicting the malignancy level of pulmonary nodules on chest computed tomography images. Oncol Lett 2020; 20:401-408. [PMID: 32537025 DOI: 10.3892/ol.2020.11576] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 03/13/2020] [Indexed: 12/24/2022] Open
Abstract
Early identification and classification of pulmonary nodules are essential for improving the survival rates of individuals with lung cancer and are considered to be key requirements for computer-assisted diagnosis. To address this topic, the present study proposed a method for predicting the malignant phenotype of pulmonary nodules based on weighted voting rules. This method used the pulmonary nodule regions of interest as the input data and extracted the features of the pulmonary nodules using the Denoising Auto Encoder, ResNet-18. Moreover, the software also modifies texture and shape features to assess the malignant phenotype of the pulmonary nodules. Based on their classification accuracy (Acc), the different classifiers were assigned to different weights. Finally, an integrated classifier was obtained to score the malignant phenotype of the pulmonary nodules. The present study included training and testing experiments conducted by extracting the corresponding lung nodule image data from the Lung Image Database Consortium-Image Database Resource Initiative. The results of the present study indicated a final classification Acc of 93.10±2.4%, demonstrating the feasibility and effectiveness of the proposed method. This method includes the powerful feature extraction ability of deep learning combined with the ability to use traditional features in image representation.
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Affiliation(s)
- Ning Xiao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi 030600, P.R. China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi 030600, P.R. China
| | - Muhammad Bilal Zia
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi 030600, P.R. China
| | - Sanhu Wang
- Department of Computer Science and Technology, Lvliang University, Lvliang, Shanxi 033000, P.R. China
| | - Jianhong Lian
- Department of Thoracic Surgery, Shanxi Cancer Hospital, Taiyuan, Shanxi 030000, P.R. China
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Bonavita I, Rafael-Palou X, Ceresa M, Piella G, Ribas V, González Ballester MA. Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 185:105172. [PMID: 31710985 DOI: 10.1016/j.cmpb.2019.105172] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 08/13/2019] [Accepted: 10/31/2019] [Indexed: 05/23/2023]
Abstract
The early identification of malignant pulmonary nodules is critical for a better lung cancer prognosis and less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is extremely useful for planning a preventive intervention but is, unfortunately, a complex, time-consuming and error-prone task. This explains the lack of large datasets containing radiologists malignancy characterization of nodules; METHODS: In this article, we propose to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung cancer detection. For training and testing purposes we used independent subsets of the LIDC dataset; RESULTS: Adding the probabilities of nodules malignity in a baseline lung cancer pipeline improved its F1-weighted score by 14.7%, whereas integrating the malignancy model itself using transfer learning outperformed the baseline prediction by 11.8% of F1-weighted score; CONCLUSIONS: Despite the limited size of the lung cancer datasets, integrating predictive models of nodule malignancy improves prediction of lung cancer.
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Affiliation(s)
- Ilaria Bonavita
- Eurecat, Centre Tecnològic de Catalunya, eHealth Unit, Barcelona, Spain.
| | - Xavier Rafael-Palou
- Eurecat, Centre Tecnològic de Catalunya, eHealth Unit, Barcelona, Spain; BCN Medtech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Mario Ceresa
- BCN Medtech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Gemma Piella
- BCN Medtech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Vicent Ribas
- Eurecat, Centre Tecnològic de Catalunya, eHealth Unit, Barcelona, Spain.
| | - Miguel A González Ballester
- BCN Medtech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; ICREA, Barcelona, Spain.
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8
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Zhang G, Yang Z, Gong L, Jiang S, Wang L, Zhang H. Classification of lung nodules based on CT images using squeeze-and-excitation network and aggregated residual transformations. Radiol Med 2020; 125:374-383. [PMID: 31916105 DOI: 10.1007/s11547-019-01130-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 12/27/2019] [Indexed: 12/19/2022]
Abstract
Lung cancer is pointed as a leading cause of cancer death worldwide. Early lung nodule diagnosis has great significance for treating lung cancer and increasing patient survival. In this paper, we present a novel method to classify the malignant from benign lung nodules based on CT images using squeeze-and-excitation network and aggregated residual transformations (SE-ResNeXt). The state-of-the-art SE-ResNeXt module, which integrates the advantages of SENet for feature recalibration and ResNeXt for feature reuse, has great ability in boosting feature discriminability on imaging pattern recognition. The method is evaluated on the public available LUng Nodule Analysis 2016 (LUNA16) database with 1004 (450 malignant and 554 benign) nodules, achieving an area under the receiver operating characteristic curve (AUC) of 0. 9563 and accuracy of 91.67%. The promising results demonstrate that our method has strong robustness in the classification of nodules. The method has the potential to help radiologists better interpret diagnostic data and differentiate the benign from malignant lung nodules on CT images in clinical practice. To our best knowledge, the effectiveness of SE-ResNeXt on lung nodule classification has not been extensively explored.
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Affiliation(s)
- Guobin Zhang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Zhiyong Yang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Li Gong
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Shan Jiang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China. .,Centre for Advanced Mechanisms and Robotics, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, 300350, China.
| | - Lu Wang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Hongyun Zhang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
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Shen S, Han SX, Aberle DR, Bui AA, Hsu W. An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification. EXPERT SYSTEMS WITH APPLICATIONS 2019; 128:84-95. [PMID: 31296975 PMCID: PMC6623975 DOI: 10.1016/j.eswa.2019.01.048] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
While deep learning methods have demonstrated performance comparable to human readers in tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by end users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level semantic features; and 2) a high-level prediction of nodule malignancy. The low-level outputs reflect diagnostic features often reported by radiologists and serve to explain how the model interprets the images in an expert-interpretable manner. The information from these low-level outputs, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level output. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to using a 3D CNN alone.
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Affiliation(s)
- Shiwen Shen
- Department of Bioengineering, University of California, Los Angeles, CA, USA
- Medical & Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Simon X Han
- Department of Bioengineering, University of California, Los Angeles, CA, USA
- Medical & Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Denise R Aberle
- Department of Bioengineering, University of California, Los Angeles, CA, USA
- Medical & Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Alex A Bui
- Medical & Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - William Hsu
- Medical & Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA
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Zhang G, Yang Z, Gong L, Jiang S, Wang L, Cao X, Wei L, Zhang H, Liu Z. An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images. J Med Syst 2019; 43:181. [PMID: 31093830 DOI: 10.1007/s10916-019-1327-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Accepted: 05/08/2019] [Indexed: 12/17/2022]
Abstract
As "the second eyes" of radiologists, computer-aided diagnosis systems play a significant role in nodule detection and diagnosis for lung cancer. In this paper, we aim to provide a systematic survey of state-of-the-art techniques (both traditional techniques and deep learning techniques) for nodule diagnosis from computed tomography images. This review first introduces the current progress and the popular structure used for nodule diagnosis. In particular, we provide a detailed overview of the five major stages in the computer-aided diagnosis systems: data acquisition, nodule segmentation, feature extraction, feature selection and nodule classification. Second, we provide a detailed report of the selected works and make a comprehensive comparison between selected works. The selected papers are from the IEEE Xplore, Science Direct, PubMed, and Web of Science databases up to December 2018. Third, we discuss and summarize the better techniques used in nodule diagnosis and indicate the existing future challenges in this field, such as improving the area under the receiver operating characteristic curve and accuracy, developing new deep learning-based diagnosis techniques, building efficient feature sets (fusing traditional features and deep features), developing high-quality labeled databases with malignant and benign nodules and promoting the cooperation between medical organizations and academic institutions.
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Affiliation(s)
- Guobin Zhang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Zhiyong Yang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Li Gong
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Shan Jiang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China. .,Centre for advanced Mechanisms and Robotics, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, 300350, China.
| | - Lu Wang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Xi Cao
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Lin Wei
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Hongyun Zhang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Ziqi Liu
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
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Dias RD, Gupta A, Yule SJ. Using Machine Learning to Assess Physician Competence: A Systematic Review. ACADEMIC MEDICINE : JOURNAL OF THE ASSOCIATION OF AMERICAN MEDICAL COLLEGES 2019; 94:427-439. [PMID: 30113364 DOI: 10.1097/acm.0000000000002414] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
PURPOSE To identify the different machine learning (ML) techniques that have been applied to automate physician competence assessment and evaluate how these techniques can be used to assess different competence domains in several medical specialties. METHOD In May 2017, MEDLINE, EMBASE, PsycINFO, Web of Science, ACM Digital Library, IEEE Xplore Digital Library, PROSPERO, and Cochrane Database of Systematic Reviews were searched for articles published from inception to April 30, 2017. Studies were included if they applied at least one ML technique to assess medical students', residents', fellows', or attending physicians' competence. Information on sample size, participants, study setting and design, medical specialty, ML techniques, competence domains, outcomes, and methodological quality was extracted. MERSQI was used to evaluate quality, and a qualitative narrative synthesis of the medical specialties, ML techniques, and competence domains was conducted. RESULTS Of 4,953 initial articles, 69 met inclusion criteria. General surgery (24; 34.8%) and radiology (15; 21.7%) were the most studied specialties; natural language processing (24; 34.8%), support vector machine (15; 21.7%), and hidden Markov models (14; 20.3%) were the ML techniques most often applied; and patient care (63; 91.3%) and medical knowledge (45; 65.2%) were the most assessed competence domains. CONCLUSIONS A growing number of studies have attempted to apply ML techniques to physician competence assessment. Although many studies have investigated the feasibility of certain techniques, more validation research is needed. The use of ML techniques may have the potential to integrate and analyze pragmatic information that could be used in real-time assessments and interventions.
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Affiliation(s)
- Roger D Dias
- R.D. Dias is instructor in emergency medicine, Department of Emergency Medicine and STRATUS Center for Medical Simulation, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; ORCID: http://orcid.org/0000-0003-4959-5052. A. Gupta is research scientist, Center for Surgery and Public Health, Brigham and Women's Hospital, Boston, Massachusetts. S.J. Yule is associate professor of surgery, Harvard Medical School, and faculty, Department of Surgery and STRATUS Center for Medical Simulation, Brigham and Women's Hospital, Boston, Massachusetts
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12
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Kaya A. Cascaded classifiers and stacking methods for classification of pulmonary nodule characteristics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 166:77-89. [PMID: 30415720 DOI: 10.1016/j.cmpb.2018.10.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 08/27/2018] [Accepted: 10/01/2018] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVES Detection and classification of pulmonary nodules are critical tasks in medical image analysis. The Lung Image Database Consortium (LIDC) database is a widely used resource for small pulmonary nodule classification research. This dataset is comprised of nodule characteristic evaluations and CT scans of patients. Although these characteristics are utilized in several studies, they can be used to improve classification performance. METHODS Numerous methods have been proposed to classify malignancy, but there are not many studies that facilitate nodule characteristics in classification steps. In this study, we use information on nodule characteristics and propose cascaded classification schemes. A group of hand-crafted features and deep features are used to define the nodules. In the first step of the classifier, the nodule characteristics are classified based on individual base classifiers. In the second step, the results of the first level classifier are combined for use in malignancy classification. In addition, stacking methods are applied to improve the performance of the cascaded classifiers. RESULTS The results confirmed that combining deep and hand-crafted features contribute to classification performance with an 8% improvement in average classification accuracy, 9% improvement in sensitivity, and 3% in specificity. Deep features from a nodule bounding area are more descriptive than the exact nodule region. The best performing cascaded classifier featured a classification accuracy of 84.70%, sensitivity of 67.37%, and specificity of 95.46%. First level stacking demonstrated similar results on classification accuracy and specificity but sensitivity was measured at 75.59%. Stacking on both levels provided the best classification accuracy and specificity with scores of 86.98% and 96.06%, respectively. When the malignancy ratings were grouped, stacking on both levels demonstrated better performance than other methods with a classification accuracy of 88.80%, sensitivity of 88.41%, and specificity of 94.12%. CONCLUSIONS Information on cascading characteristics with image features is beneficial for the classification of the malignancy ratings. Stacking approaches on both levels demonstrate better classification accuracy, but in the context of sensitivity, first level stacking performs better. Grouping the malignancy ratings results in better classification outcomes as in the case of similar studies in the literature.
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Affiliation(s)
- Aydin Kaya
- Hacettepe University, Computer Engineering Department, 06800 Ankara, Turkey.
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13
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Gu Y, Lu X, Yang L, Zhang B, Yu D, Zhao Y, Gao L, Wu L, Zhou T. Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs. Comput Biol Med 2018; 103:220-231. [PMID: 30390571 DOI: 10.1016/j.compbiomed.2018.10.011] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 10/11/2018] [Accepted: 10/11/2018] [Indexed: 12/17/2022]
Abstract
OBJECTIVE A novel computer-aided detection (CAD) scheme for lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy is proposed to assist radiologists by providing a second opinion on accurate lung nodule detection, which is a crucial step in early diagnosis of lung cancer. METHOD A 3D deep convolutional neural network (CNN) with multi-scale prediction was used to detect lung nodules after the lungs were segmented from chest CT scans, with a comprehensive method utilized. Compared with a 2D CNN, a 3D CNN can utilize richer spatial 3D contextual information and generate more discriminative features after being trained with 3D samples to fully represent lung nodules. Furthermore, a multi-scale lung nodule prediction strategy, including multi-scale cube prediction and cube clustering, is also proposed to detect extremely small nodules. RESULT The proposed method was evaluated on 888 thin-slice scans with 1186 nodules in the LUNA16 database. All results were obtained via 10-fold cross-validation. Three options of the proposed scheme are provided for selection according to the actual needs. The sensitivity of the proposed scheme with the primary option reached 87.94% and 92.93% at one and four false positives per scan, respectively. Meanwhile, the competition performance metric (CPM) score is very satisfying (0.7967). CONCLUSION The experimental results demonstrate the outstanding detection performance of the proposed nodule detection scheme. In addition, the proposed scheme can be extended to other medical image recognition fields.
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Affiliation(s)
- Yu Gu
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China; Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Xiaoqi Lu
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China; Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
| | - Lidong Yang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Baohua Zhang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Ying Zhao
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
| | - Lixin Gao
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China; School of Foreign Languages, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Liang Wu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Tao Zhou
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
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14
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Ferreira JR, Oliveira MC, de Azevedo-Marques PM. Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture. J Digit Imaging 2018; 31:451-463. [PMID: 29047033 PMCID: PMC6113151 DOI: 10.1007/s10278-017-0029-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths in the world, and one of its manifestations occurs with the appearance of pulmonary nodules. The classification of pulmonary nodules may be a complex task to specialists due to temporal, subjective, and qualitative aspects. Therefore, it is important to integrate computational tools to the early pulmonary nodule classification process, since they have the potential to characterize objectively and quantitatively the lesions. In this context, the goal of this work is to perform the classification of pulmonary nodules based on image features of texture and margin sharpness. Computed tomography scans were obtained from a publicly available image database. Texture attributes were extracted from a co-occurrence matrix obtained from the nodule volume. Margin sharpness attributes were extracted from perpendicular lines drawn over the borders on all nodule slices. Feature selection was performed by different algorithms. Classification was performed by several machine learning classifiers and assessed by the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Highest classification performance was obtained by a random forest algorithm with all 48 extracted features. However, a decision tree using only two selected features obtained statistically equivalent performance on sensitivity and specificity.
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Affiliation(s)
- José Raniery Ferreira
- Center of Imaging Sciences and Medical Physics, Ribeirão Preto Medical School, University of São Paulo, Av. dos Bandeirantes, 3900, Monte Alegre, Ribeirão Preto, São Paulo, 14049-900, Brazil.
| | - Marcelo Costa Oliveira
- Institute of Computing, Federal University of Alagoas, Av. Lourival Melo Mota, Cidade Universitária, Maceió, Alagoas, 57072-900, Brazil
| | - Paulo Mazzoncini de Azevedo-Marques
- Center of Imaging Sciences and Medical Physics, Ribeirão Preto Medical School, University of São Paulo, Av. dos Bandeirantes, 3900, Monte Alegre, Ribeirão Preto, São Paulo, 14049-900, Brazil
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15
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Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research. J Digit Imaging 2018; 29:716-729. [PMID: 27440183 DOI: 10.1007/s10278-016-9894-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths in the world, and its main manifestation is pulmonary nodules. Detection and classification of pulmonary nodules are challenging tasks that must be done by qualified specialists, but image interpretation errors make those tasks difficult. In order to aid radiologists on those hard tasks, it is important to integrate the computer-based tools with the lesion detection, pathology diagnosis, and image interpretation processes. However, computer-aided diagnosis research faces the problem of not having enough shared medical reference data for the development, testing, and evaluation of computational methods for diagnosis. In order to minimize this problem, this paper presents a public nonrelational document-oriented cloud-based database of pulmonary nodules characterized by 3D texture attributes, identified by experienced radiologists and classified in nine different subjective characteristics by the same specialists. Our goal with the development of this database is to improve computer-aided lung cancer diagnosis and pulmonary nodule detection and classification research through the deployment of this database in a cloud Database as a Service framework. Pulmonary nodule data was provided by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), image descriptors were acquired by a volumetric texture analysis, and database schema was developed using a document-oriented Not only Structured Query Language (NoSQL) approach. The proposed database is now with 379 exams, 838 nodules, and 8237 images, 4029 of them are CT scans and 4208 manually segmented nodules, and it is allocated in a MongoDB instance on a cloud infrastructure.
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16
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Yang J, Wang H, Geng C, Dai Y, Ji J. Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules. Biomed Eng Online 2018; 17:20. [PMID: 29415726 PMCID: PMC5803858 DOI: 10.1186/s12938-018-0435-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 01/10/2018] [Indexed: 02/06/2023] Open
Abstract
Pulmonary nodule is one of the important lesions of lung cancer, mainly divided into two categories of solid nodules and ground glass nodules. The improvement of diagnosis of lung cancer has significant clinical significance, which could be realized by machine learning techniques. At present, there have been a lot of researches focusing on solid nodules. But the research on ground glass nodules started late, and lacked research results. This paper summarizes the research progress of the method of intelligent diagnosis for pulmonary nodules since 2014. It is described in details from four aspects: nodular signs, data analysis methods, prediction models and system evaluation. This paper aims to provide the research material for researchers of the clinical diagnosis and intelligent analysis of lung cancer, and further improve the precision of pulmonary ground glass nodule diagnosis.
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Affiliation(s)
- Jing Yang
- School of Biomedical Engineering, University of Science and Technology of China, Hefei, 230026 People’s Republic of China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163 People’s Republic of China
| | - Hailin Wang
- Radiology Department and Interventional Radiology Center, The Fifth Affiliated Hospital of Wenzhou Medical University, Affiliated Lishui Hospital of Zhejiang University, The Central Hospital of Zhejiang Lishui, Lishui, 323000 People’s Republic of China
| | - Chen Geng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163 People’s Republic of China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163 People’s Republic of China
| | - Jiansong Ji
- Radiology Department and Interventional Radiology Center, The Fifth Affiliated Hospital of Wenzhou Medical University, Affiliated Lishui Hospital of Zhejiang University, The Central Hospital of Zhejiang Lishui, Lishui, 323000 People’s Republic of China
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17
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Learning Lung Nodule Malignancy Likelihood from Radiologist Annotations or Diagnosis Data. J Med Biol Eng 2017. [DOI: 10.1007/s40846-017-0317-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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18
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Cao P, Liu X, Zhang J, Li W, Zhao D, Huang M, Zaiane O. A ℓ 2, 1 norm regularized multi-kernel learning for false positive reduction in Lung nodule CAD. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 140:211-231. [PMID: 28254078 DOI: 10.1016/j.cmpb.2016.12.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Revised: 11/25/2016] [Accepted: 12/12/2016] [Indexed: 06/06/2023]
Abstract
OBJECTIVE The aim of this paper is to describe a novel algorithm for False Positive Reduction in lung nodule Computer Aided Detection(CAD). METHODS In this paper, we describes a new CT lung CAD method which aims to detect solid nodules. Specially, we proposed a multi-kernel classifier with a ℓ2, 1 norm regularizer for heterogeneous feature fusion and selection from the feature subset level, and designed two efficient strategies to optimize the parameters of kernel weights in non-smooth ℓ2, 1 regularized multiple kernel learning algorithm. The first optimization algorithm adapts a proximal gradient method for solving the ℓ2, 1 norm of kernel weights, and use an accelerated method based on FISTA; the second one employs an iterative scheme based on an approximate gradient descent method. RESULTS The results demonstrates that the FISTA-style accelerated proximal descent method is efficient for the ℓ2, 1 norm formulation of multiple kernel learning with the theoretical guarantee of the convergence rate. Moreover, the experimental results demonstrate the effectiveness of the proposed methods in terms of Geometric mean (G-mean) and Area under the ROC curve (AUC), and significantly outperforms the competing methods. CONCLUSIONS The proposed approach exhibits some remarkable advantages both in heterogeneous feature subsets fusion and classification phases. Compared with the fusion strategies of feature-level and decision level, the proposed ℓ2, 1 norm multi-kernel learning algorithm is able to accurately fuse the complementary and heterogeneous feature sets, and automatically prune the irrelevant and redundant feature subsets to form a more discriminative feature set, leading a promising classification performance. Moreover, the proposed algorithm consistently outperforms the comparable classification approaches in the literature.
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Affiliation(s)
- Peng Cao
- Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China.
| | - Xiaoli Liu
- Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China
| | - Jian Zhang
- School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, China
| | - Wei Li
- Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China
| | - Dazhe Zhao
- Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China
| | - Min Huang
- Information Science and Engineering, Northeastern University, Shenyang, China
| | - Osmar Zaiane
- Computing Science, University of Alberta, Edmonton, Alberta, Canada
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19
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Hancock MC, Magnan JF. Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods. J Med Imaging (Bellingham) 2016; 3:044504. [PMID: 27990453 DOI: 10.1117/1.jmi.3.4.044504] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Accepted: 11/14/2016] [Indexed: 01/12/2023] Open
Abstract
In the assessment of nodules in CT scans of the lungs, a number of image-derived features are diagnostically relevant. Currently, many of these features are defined only qualitatively, so they are difficult to quantify from first principles. Nevertheless, these features (through their qualitative definitions and interpretations thereof) are often quantified via a variety of mathematical methods for the purpose of computer-aided diagnosis (CAD). To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capability of statistical learning methods for classifying nodule malignancy. We utilize the Lung Image Database Consortium dataset and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists' annotations. We calculate theoretical upper bounds on the classification accuracy that are achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 [Formula: see text], which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 ([Formula: see text]), which increases to 0.949 ([Formula: see text]) when diameter and volume features are included and has an accuracy of 88.08 [Formula: see text]. Our results are comparable to those in the literature that use algorithmically derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification.
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Affiliation(s)
- Matthew C Hancock
- Florida State University , Department of Mathematics, 208 Love Building, 1017 Academic Way, Tallahassee, Florida 32306-4510, United States
| | - Jerry F Magnan
- Florida State University , Department of Mathematics, 208 Love Building, 1017 Academic Way, Tallahassee, Florida 32306-4510, United States
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