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Mirniaharikandehei S, VanOsdol J, Heidari M, Danala G, Sethuraman SN, Ranjan A, Zheng B. Developing a Quantitative Ultrasound Image Feature Analysis Scheme to Assess Tumor Treatment Efficacy Using a Mouse Model. Sci Rep 2019; 9:7293. [PMID: 31086267 PMCID: PMC6513863 DOI: 10.1038/s41598-019-43847-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 05/02/2019] [Indexed: 12/16/2022] Open
Abstract
The aim of this study is to investigate the feasibility of identifying and applying quantitative imaging features computed from ultrasound images of athymic nude mice to predict tumor response to treatment at an early stage. A computer-aided detection (CAD) scheme with a graphic user interface was developed to conduct tumor segmentation and image feature analysis. A dataset involving ultrasound images of 23 athymic nude mice bearing C26 mouse adenocarcinomas was assembled. These mice were divided into 7 treatment groups utilizing a combination of thermal and nanoparticle-controlled drug delivery. Longitudinal ultrasound images of mice were taken prior and post-treatment in day 3 and day 6. After tumor segmentation, CAD scheme computed image features and created four feature pools including features computed from (1) prior treatment images only and (2) difference between prior and post-treatment images of day 3 and day 6, respectively. To predict tumor treatment efficacy, data analysis was performed to identify top image features and an optimal feature fusion method, which have a higher correlation to tumor size increase ratio (TSIR) determined at Day 10. Using image features computed from day 3, the highest Pearson Correlation coefficients between the top two features selected from two feature pools versus TSIR were 0.373 and 0.552, respectively. Using an equally weighted fusion method of two features computed from prior and post-treatment images, the correlation coefficient increased to 0.679. Meanwhile, using image features computed from day 6, the highest correlation coefficient was 0.680. Study demonstrated the feasibility of extracting quantitative image features from the ultrasound images taken at an early treatment stage to predict tumor response to therapies.
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Affiliation(s)
| | - Joshua VanOsdol
- Center for Veterinary Health Science, Oklahoma State University, Stillwater, 74078, OK, USA
| | - Morteza Heidari
- School of Electrical and Computer Engineering, University of Oklahoma, 73019, Norman, OK, USA
| | - Gopichandh Danala
- School of Electrical and Computer Engineering, University of Oklahoma, 73019, Norman, OK, USA
| | | | - Ashish Ranjan
- Center for Veterinary Health Science, Oklahoma State University, Stillwater, 74078, OK, USA
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, 73019, Norman, OK, USA.
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Tan M, Aghaei F, Wang Y, Zheng B. Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions. Phys Med Biol 2016; 62:358-376. [PMID: 27997380 DOI: 10.1088/1361-6560/aa5081] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a 'scoring fusion' artificial neural network classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC = 0.793 ± 0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions.
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Affiliation(s)
- Maxine Tan
- Electrical and Computer Systems Engineering (ECSE) Discipline, School of Engineering, Monash University Malaysia, 47500 Bandar Sunway, Malaysia. School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
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Qian W, Sun W, Zheng B. Improving the efficacy of mammography screening: the potential and challenge of developing new computer-aided detection approaches. Expert Rev Med Devices 2015; 12:497-9. [DOI: 10.1586/17434440.2015.1068115] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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Tan M, Qian W, Pu J, Liu H, Zheng B. A new approach to develop computer-aided detection schemes of digital mammograms. Phys Med Biol 2015; 60:4413-27. [PMID: 25984710 DOI: 10.1088/0031-9155/60/11/4413] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
The purpose of this study is to develop a new global mammographic image feature analysis based computer-aided detection (CAD) scheme and evaluate its performance in detecting positive screening mammography examinations. A dataset that includes images acquired from 1896 full-field digital mammography (FFDM) screening examinations was used in this study. Among them, 812 cases were positive for cancer and 1084 were negative or benign. After segmenting the breast area, a computerized scheme was applied to compute 92 global mammographic tissue density based features on each of four mammograms of the craniocaudal (CC) and mediolateral oblique (MLO) views. After adding three existing popular risk factors (woman's age, subjectively rated mammographic density, and family breast cancer history) into the initial feature pool, we applied a sequential forward floating selection feature selection algorithm to select relevant features from the bilateral CC and MLO view images separately. The selected CC and MLO view image features were used to train two artificial neural networks (ANNs). The results were then fused by a third ANN to build a two-stage classifier to predict the likelihood of the FFDM screening examination being positive. CAD performance was tested using a ten-fold cross-validation method. The computed area under the receiver operating characteristic curve was AUC = 0.779 ± 0.025 and the odds ratio monotonically increased from 1 to 31.55 as CAD-generated detection scores increased. The study demonstrated that this new global image feature based CAD scheme had a relatively higher discriminatory power to cue the FFDM examinations with high risk of being positive, which may provide a new CAD-cueing method to assist radiologists in reading and interpreting screening mammograms.
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Affiliation(s)
- Maxine Tan
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
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Ren L, Wu D, Li Y, Wang G, Wu X, Liu H. Three-dimensional x-ray fluorescence mapping of a gold nanoparticle-loaded phantom. Med Phys 2014; 41:031902. [PMID: 24593720 DOI: 10.1118/1.4863510] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE X-ray fluorescence (XRF) is a promising technique with sufficient specificity and sensitivity for identifying and quantifying features in small samples containing high atomic number (Z) materials such as iodine, gadolinium, and gold. In this study, the feasibility of applying XRF to early breast cancer diagnosis and treatment is studied using a novel approach for three-dimensional (3D) x-ray fluorescence mapping (XFM) of gold nanoparticle (GNP)-loaded objects in a physical phantom at the technical level. METHODS All the theoretical analysis and experiments are conducted under the condition of using x-ray pencil beam and a compactly integrated x-ray spectrometer. The penetrability of the fluorescence x-rays from GNPs is first investigated by adopting a combination of BR12 with 70 mm/50 mm in thickness on the excitation/emission path to mimic the possible position of tumor goldin vivo. Then, a physical phantom made of BR12 is designed to translate in 3D space with three precise linear stages and subsequently the step by step XFM scanning is performed. The experimental technique named as background subtraction is applied to isolate the gold fluorescence from each spectrum obtained by the spectrometer. Afterwards, the attenuations of both the incident primary x-ray beam with energies beyond the gold K-edge energy (80.725 keV) and the isolated gold Kα fluorescence x-rays (65.99 -69.80 keV) acquired after background subtraction are well calibrated, and finally the unattenuated Kα fluorescence counts are used to realize mapping reconstruction and to describe the linear relationship between gold fluorescence counts and corresponding concentration of gold solutions. RESULTS The penetration results show that the goldKα fluorescence x-rays have sufficient penetrability for this phantom study, and the reconstructed mapping results indicate that both the spatial distribution and relative concentration of GNPs within the designed BR12 phantom can be well identified and quantified. CONCLUSIONS Although the XFM method in this investigation is still studied at the technical level and is not yet practical for routinein vivo mapping tasks with GNPs, the current penetrability measurements and phantom study strongly suggest the feasibility to establish and develop a 3D XFM system.
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Affiliation(s)
- Liqiang Ren
- Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019
| | - Di Wu
- Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019
| | - Yuhua Li
- Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019
| | - Ge Wang
- Biomedical Imaging Cluster and Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180
| | - Xizeng Wu
- Department of Radiology, University of Alabama, Birmingham, Alabama 35233
| | - Hong Liu
- Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019
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Tan M, Pu J, Zheng B. Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme. Phys Med Biol 2014; 59:4357-73. [PMID: 25029964 DOI: 10.1088/0031-9155/59/15/4357] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The high false-positive recall rate is one of the major dilemmas that significantly reduce the efficacy of screening mammography, which harms a large fraction of women and increases healthcare cost. This study aims to investigate the feasibility of helping reduce false-positive recalls by developing a new computer-aided diagnosis (CAD) scheme based on the analysis of global mammographic texture and density features computed from four-view images. Our database includes full-field digital mammography (FFDM) images acquired from 1052 recalled women (669 positive for cancer and 383 benign). Each case has four images: two craniocaudal (CC) and two mediolateral oblique (MLO) views. Our CAD scheme first computed global texture features related to the mammographic density distribution on the segmented breast regions of four images. Second, the computed features were given to two artificial neural network (ANN) classifiers that were separately trained and tested in a ten-fold cross-validation scheme on CC and MLO view images, respectively. Finally, two ANN classification scores were combined using a new adaptive scoring fusion method that automatically determined the optimal weights to assign to both views. CAD performance was tested using the area under a receiver operating characteristic curve (AUC). The AUC = 0.793 ± 0.026 was obtained for this four-view CAD scheme, which was significantly higher at the 5% significance level than the AUCs achieved when using only CC (p = 0.025) or MLO (p = 0.0004) view images, respectively. This study demonstrates that a quantitative assessment of global mammographic image texture and density features could provide useful and/or supplementary information to classify between malignant and benign cases among the recalled cases, which may eventually help reduce the false-positive recall rate in screening mammography.
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Affiliation(s)
- Maxine Tan
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019
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Yang Q, Li L, Zhang J, Shao G, Zhang C, Zheng B. Computer-aided diagnosis of breast DCE-MRI images using bilateral asymmetry of contrast enhancement between two breasts. J Digit Imaging 2014; 27:152-60. [PMID: 24043592 PMCID: PMC3903971 DOI: 10.1007/s10278-013-9617-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
Abstract
Dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) of breasts is an important imaging modality in breast cancer diagnosis with higher sensitivity but relatively lower specificity. The objective of this study is to investigate a new approach to help improve diagnostic performance of DCE-MRI examinations based on the automated detection and analysis of bilateral asymmetry of characteristic kinetic features between the left and right breast. An image dataset involving 130 DCE-MRI examinations was assembled and used in which 80 were biopsy-proved malignant and 50 were benign. A computer-aided diagnosis (CAD) scheme was developed to segment breast areas depicted on each MR image, register images acquired from the sequential MR image scan series, compute average contrast enhancement of all pixels in one breast, and a set of kinetic features related to the difference of contrast enhancement between the left and right breast, and then use a multi-feature based Bayesian belief network to classify between malignant and benign cases. A leave-one-case-out validation method was applied to test CAD performance. The computed area under a receiver operating characteristic (ROC) curve is 0.78 ± 0.04. The positive and negative predictive values are 0.77 and 0.64, respectively. The study indicates that bilateral asymmetry of kinetic features between the left and right breasts is a potentially useful image biomarker to enhance the detection of angiogenesis associated with malignancy. It also demonstrates the feasibility of applying a simple CAD approach to classify between malignant and benign DCE-MRI examinations based on this new image biomarker.
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Affiliation(s)
- Qian Yang
- />College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, 310018 China
| | - Lihua Li
- />College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, 310018 China
- />Department of Biomedical Engineering, College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, 310018 China
| | - Juan Zhang
- />Zhejiang Cancer Hospital, Hangzhou, China
| | | | - Chengjie Zhang
- />College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, 310018 China
| | - Bin Zheng
- />College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, 310018 China
- />School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019 USA
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