1
|
Khateri M, Babapour Mofrad F, Geramifar P, Jenabi E. Machine learning-based analysis of 68Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade. Phys Eng Sci Med 2024; 47:741-753. [PMID: 38526647 DOI: 10.1007/s13246-024-01402-3] [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: 09/14/2023] [Accepted: 02/07/2024] [Indexed: 03/27/2024]
Abstract
Early diagnosis of prostate cancer, the most common malignancy in men, can improve patient outcomes. Since the tissue sampling procedures are invasive and sometimes inconclusive, an alternative image-based method can prevent possible complications and facilitate treatment management. We aim to propose a machine-learning model for tumor grade estimation based on 68 Ga-PSMA-11 PET/CT images in prostate cancer patients. This study included 90 eligible participants out of 244 biopsy-proven prostate cancer patients who underwent staging 68Ga-PSMA-11 PET/CT imaging. The patients were divided into high and low-intermediate groups based on their Gleason scores. The PET-only images were manually segmented, both lesion-based and whole prostate, by two experienced nuclear medicine physicians. Four feature selection algorithms and five classifiers were applied to Combat-harmonized and non-harmonized datasets. To evaluate the model's generalizability across different institutions, we performed leave-one-center-out cross-validation (LOOCV). The metrics derived from the receiver operating characteristic curve were used to assess model performance. In the whole prostate segmentation, combining the ANOVA algorithm as the feature selector with Random Forest (RF) and Extra Trees (ET) classifiers resulted in the highest performance among the models, with an AUC of 0.78 and 083, respectively. In the lesion-based segmentation, the highest AUC was achieved by MRMR feature selector + Linear Discriminant Analysis (LDA) and Logistic Regression (LR) classifiers (0.76 and 0.79, respectively). The LOOCV results revealed that both the RF_ANOVA and ET_ANOVA models showed high levels of accuracy and generalizability across different centers, with an average AUC value of 0.87 for the ET_ANOVA combination. Machine learning-based analysis of radiomics features extracted from 68Ga-PSMA-11 PET/CT scans can accurately classify prostate tumors into low-risk and intermediate- to high-risk groups.
Collapse
Affiliation(s)
- Maziar Khateri
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Farshid Babapour Mofrad
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Elnaz Jenabi
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
2
|
Radiya K, Joakimsen HL, Mikalsen KØ, Aahlin EK, Lindsetmo RO, Mortensen KE. Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review. Eur Radiol 2023; 33:6689-6717. [PMID: 37171491 PMCID: PMC10511359 DOI: 10.1007/s00330-023-09609-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 05/13/2023]
Abstract
OBJECTIVES Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging? METHODS A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography. RESULTS One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians' intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy. CONCLUSION Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this. KEY POINTS • ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.
Collapse
Affiliation(s)
- Keyur Radiya
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway.
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway.
| | - Henrik Lykke Joakimsen
- Institute of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Centre for Clinical Artificial Intelligence (SPKI), University Hospital of North Norway, Tromso, Norway
| | - Karl Øyvind Mikalsen
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Centre for Clinical Artificial Intelligence (SPKI), University Hospital of North Norway, Tromso, Norway
- UiT Machine Learning Group, Department of Physics and Technology, UiT the Arctic University of Norway, Tromso, Norway
| | - Eirik Kjus Aahlin
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway
| | - Rolv-Ole Lindsetmo
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Head Clinic of Surgery, Oncology and Women Health, University Hospital of North Norway, Tromso, Norway
| | - Kim Erlend Mortensen
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
| |
Collapse
|
3
|
Valizadeh G, Babapour Mofrad F, Shalbaf A. Parametric-based feature selection via spherical harmonic coefficients for the left ventricle myocardial infarction screening. Med Biol Eng Comput 2021; 59:1261-1283. [PMID: 33983494 DOI: 10.1007/s11517-021-02372-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 04/27/2021] [Indexed: 11/30/2022]
Abstract
Computer-aided diagnosis (CAD) of heart diseases using machine learning techniques has recently received much attention. In this study, we present a novel parametric-based feature selection method using the three-dimensional spherical harmonic (SHs) shape descriptors of the left ventricle (LV) for intelligent myocardial infarction (MI) classification. The main hypothesis is that the SH coefficients of the parameterized endocardial shapes in MI patients are recognizable and distinguishable from healthy subjects. The SH parameterization, expansion, and registration of the LV endocardial shapes were performed, then parametric-based features were extracted. The proposed method performance was investigated by varying considered phases (i.e., the end-systole (ES) or the end-diastole (ED) frames), the spatial alignment procedures based on three modes (i.e., the center of the apical (CoA), the center of mass (CoM), and the center of the basal (CoB)), and considered orders of SH coefficients. After applying principal component analysis (PCA) on the feature vectors, support vector machine (SVM), K-nearest neighbors (K-NN), and random forest (RF) were trained and tested using the leave-one-out cross-validation (LOOCV). The proposed method validation was performed via a dataset containing healthy and MI subjects selected from the automated cardiac diagnosis challenge (ACDC) database. The promising results show the effectiveness of the proposed classification model. SVM reached the best performance with accuracy, sensitivity, specificity, and F-score of 97.50%, 95.00%, 100.00%, and 97.56%, respectively, using the introduced optimum feature set. This study demonstrates the robustness of combining the SH coefficients and machine learning techniques. We also quantify and notably highlight the contribution of different parameters in the classification and finally introduce an optimal feature set with maximum discriminant strength for the MI classification task. Moreover, the obtained results confirm that the proposed method performs more accurately than conventional point-based methods and also the current start-of-the-art, i.e., clinical measures. We showed our method's generalizability using employing it in dilated cardiomyopathy (DCM) detection and achieving promising results too. Parametric-based feature selection via spherical harmonics coefficients for the left ventricle myocardial infarction screening.
Collapse
Affiliation(s)
- Gelareh Valizadeh
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Farshid Babapour Mofrad
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| |
Collapse
|
4
|
Afzali A, Babapour Mofrad F, Pouladian M. Contour-based lung shape analysis in order to tuberculosis detection: modeling and feature description. Med Biol Eng Comput 2020; 58:1965-1986. [PMID: 32572669 DOI: 10.1007/s11517-020-02192-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 05/18/2020] [Indexed: 11/26/2022]
Abstract
Statistical shape analysis of lung is a reliable alternative method for diagnosing pulmonary diseases such as tuberculosis (TB). The 2D contour-based lung shape analysis is investigated and developed using Fourier descriptors (FDs). The proposed 2D lung shape analysis is carried out in threefold: (1) represent the normal and the abnormal (i.e. pulmonary tuberculosis (PTB)) lung shape models using Fourier descriptors modeling (FDM) framework from chest X-ray (CXR) images, (2) estimate and compare the 2D inter-patient lung shape variations for the normal and abnormal lungs by applying principal component analysis (PCA) techniques, and (3) describe the optimal type of contour-based feature vectors to train a classifier in order to detect TB using one publicly available dataset-namely the Montgomery dataset. Since almost all of the previous works in lung shape analysis are content-based analysis, we proposed contour-based lung shape analysis for statistical modeling and feature description of PTB cases. The results show that the proposed approach is able to explain more than 95% of total variations in both of the normal and PTB cases using only 6 and 7 principal component modes for the right and the left lungs, respectively. In case of PTB detection, using 138 lung cases (80 normal and 58 PTB cases), we achieved the accuracy (ACC) and the area under the curve (AUC) of 82.03% and 88.75%, respectively. In comparison with existing state-of-art studies in the same dataset, the proposed approach is a very promising supplement for diagnosis of PTB disease. The method is robust and valuable for application in 2D automatic segmentation, classification, and atlas registration. Moreover, the approach could be used for any kind of pulmonary diseases. Graphical abstract Contour-based lung shape analysis in order to detect tuberculosis: modeling and feature description.
Collapse
Affiliation(s)
- Ali Afzali
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Farshid Babapour Mofrad
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
| | - Majid Pouladian
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| |
Collapse
|
5
|
Silva VKS, Vieira WA, Bernardino ÍM, Travençolo BAN, Bittencourt MAV, Blumenberg C, Paranhos LR, Galvão HC. Accuracy of computer-assisted image analysis in the diagnosis of maxillofacial radiolucent lesions: A systematic review and meta-analysis. Dentomaxillofac Radiol 2019; 49:20190204. [PMID: 31709811 DOI: 10.1259/dmfr.20190204] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES This study aimed to search for scientific evidence concerning the accuracy of computer-assisted analysis for diagnosing maxillofacial radiolucent lesions. METHODS A systematic review was conducted according to the statements of Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols and considering 10 databases, including the gray literature. Protocol was registered at the International Prospective Register of Systematic Reviews (CRD42018089945). The population, intervention, comparison and outcome strategy was used to define the eligibility criteria and only diagnostic test studies were included. Their risk of bias was assessed by the Joanna Briggs Institute Critical Appraisal tool. Random-effects model meta-analysis was performed and heterogeneity among the included studies was estimated using the I2 statistic. The grade of recommendation, assessment, development, and evaluation (GRADE) tool assessed the quality of evidence and strength of recommendation across included studies. RESULTS Out of 715 identified citations, four papers, published between 2009 and 2017, fulfilled the criteria and were included in this systematic review. A total of 191 lesions, classified as periapical granuloma and cyst, dentigerous cyst or keratocystic odontogenic tumor, were analyzed. All selected articles scored low risk of bias. The pooled accuracy estimation, regardless of the classification method used, was 88.75% (95% CI = 85.19-92.30). Heterogeneity test reached moderate values (I2 = 57.89%). According to the GRADE tool, the analyzed outcome was classified as having low level of certainty. CONCLUSIONS The overall evaluation showed all studies presented high accuracy rates of computer-aided diagnosis systems in classifying radiolucent maxillofacial lesions compared to histopathological biopsy. However, due to the moderate heterogeneity found among the studies included in this meta-analysis, a pragmatic recommendation about the use of computer-assisted analysis is not possible.
Collapse
Affiliation(s)
- Virginia K S Silva
- Department of Dentistry, Postgraduate Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Walbert A Vieira
- Postgraduate Program in Dentistry, Endodontics Division, Piracicaba Dental School, State University of Campinas, Piracicaba, São Paulo, Brazil
| | - Ítalo M Bernardino
- Department of Dentistry, Postgraduate Program in Dentistry, State University of Paraíba, Campina Grande, Paraíba, Brazil
| | - Bruno A N Travençolo
- Center for Exact Sciences and Technology, School of Computing, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil
| | - Marcos A V Bittencourt
- Department of Pediatric and Community Dentistry, School of Dentistry, Federal University of Bahia, Salvador, Bahia, Brazil
| | - Cauane Blumenberg
- Department of Social Medicine, Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Rio Grande do Sul, Brazil
| | - Luiz R Paranhos
- Department of Preventive and Community Dentistry, School of Dentistry, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil
| | - Hebel C Galvão
- Department of Dentistry, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| |
Collapse
|
6
|
Abdolali F, Zoroofi RA, Otake Y, Sato Y. Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 139:197-207. [PMID: 28187891 DOI: 10.1016/j.cmpb.2016.10.024] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Revised: 09/16/2016] [Accepted: 10/24/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate detection of maxillofacial cysts is an essential step for diagnosis, monitoring and planning therapeutic intervention. Cysts can be of various sizes and shapes and existing detection methods lead to poor results. Customizing automatic detection systems to gain sufficient accuracy in clinical practice is highly challenging. For this purpose, integrating the engineering knowledge in efficient feature extraction is essential. METHODS This paper presents a novel framework for maxillofacial cysts detection. A hybrid methodology based on surface and texture information is introduced. The proposed approach consists of three main steps as follows: At first, each cystic lesion is segmented with high accuracy. Then, in the second and third steps, feature extraction and classification are performed. Contourlet and SPHARM coefficients are utilized as texture and shape features which are fed into the classifier. Two different classifiers are used in this study, i.e. support vector machine and sparse discriminant analysis. Generally SPHARM coefficients are estimated by the iterative residual fitting (IRF) algorithm which is based on stepwise regression method. In order to improve the accuracy of IRF estimation, a method based on extra orthogonalization is employed to reduce linear dependency. We have utilized a ground-truth dataset consisting of cone beam CT images of 96 patients, belonging to three maxillofacial cyst categories: radicular cyst, dentigerous cyst and keratocystic odontogenic tumor. RESULTS Using orthogonalized SPHARM, residual sum of squares is decreased which leads to a more accurate estimation. Analysis of the results based on statistical measures such as specificity, sensitivity, positive predictive value and negative predictive value is reported. The classification rate of 96.48% is achieved using sparse discriminant analysis and orthogonalized SPHARM features. Classification accuracy at least improved by 8.94% with respect to conventional features. CONCLUSIONS This study demonstrated that our proposed methodology can improve the computer assisted diagnosis (CAD) performance by incorporating more discriminative features. Using orthogonalized SPHARM is promising in computerized cyst detection and may have a significant impact in future CAD systems.
Collapse
Affiliation(s)
- Fatemeh Abdolali
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Reza Aghaeizadeh Zoroofi
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Yoshito Otake
- Graduate school of Information Science, Nara Institute of Science and Technology (NAIST), Nara, Japan
| | - Yoshinobu Sato
- Graduate school of Information Science, Nara Institute of Science and Technology (NAIST), Nara, Japan
| |
Collapse
|
7
|
Functional Region Annotation of Liver CT Image Based on Vascular Tree. BIOMED RESEARCH INTERNATIONAL 2016; 2016:5428737. [PMID: 27891516 PMCID: PMC5116550 DOI: 10.1155/2016/5428737] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Revised: 07/24/2016] [Accepted: 08/04/2016] [Indexed: 12/29/2022]
Abstract
Anatomical analysis of liver region is critical in diagnosis and treatment of liver diseases. The reports of liver region annotation are helpful for doctors to precisely evaluate liver system. One of the challenging issues is to annotate the functional regions of liver through analyzing Computed Tomography (CT) images. In this paper, we propose a vessel-tree-based liver annotation method for CT images. The first step of the proposed annotation method is to extract the liver region including vessels and tumors from the CT scans. And then a 3-dimensional thinning algorithm is applied to obtain the spatial skeleton and geometric structure of liver vessels. With the vessel skeleton, the topology of portal veins is further formulated by a directed acyclic graph with geometrical attributes. Finally, based on the topological graph, a hierarchical vascular tree is constructed to divide the liver into eight segments according to Couinaud classification theory and thereby annotate the functional regions. Abundant experimental results demonstrate that the proposed method is effective for precise liver annotation and helpful to support liver disease diagnosis.
Collapse
|
8
|
Three-dimensional SVM with latent variable: application for detection of lung lesions in CT images. J Med Syst 2014; 39:171. [PMID: 25472729 DOI: 10.1007/s10916-014-0171-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Accepted: 11/25/2014] [Indexed: 10/24/2022]
Abstract
The study aims to improve the performance of current computer-aided schemes for the detection of lung lesions, especially the low-contrast in gray density or irregular in shape. The relative position between suspected lesion and whole lung is, for the first time, added as a latent feature to enrich current Three-dimensional (3D) features such as shape, texture. Subsequently, 3D matrix patterns-based Support Vector Machine (SVM) with the latent variable, referred to as L-SVM3Dmatrix, was constructed accordingly. A CT image database containing 750 abnormal cases with 1050 lesions was used to train and evaluate several similar computer-aided detection (CAD) schemes: traditional features-based SVM (SVMfeature), 3D matrix patterns-based SVM (SVM3Dmatrix) and L-SVM3Dmatrix. The classifier performances were evaluated by computing the area under the ROC curve (AUC), using a 5-fold cross-validation. The L-SVM3Dmatrix sensitivity was 93.0 with 1.23% percentage of False Positive (FP), the SVM3Dmatrix sensitivity was 88.4 with 1.49% percentage of FP, and the SVMfeature sensitivity was 87.2 with 1.78% percentage of FP. The L-SVM3Dmatrix outperformed other current lung CAD schemes, especially regarding the difficult lesions.
Collapse
|