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Jones MA, Sadeghipour N, Chen X, Islam W, Zheng B. A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms. Med Phys 2023; 50:7670-7683. [PMID: 37083190 PMCID: PMC10589387 DOI: 10.1002/mp.16419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/29/2023] [Accepted: 03/31/2023] [Indexed: 04/22/2023] Open
Abstract
BACKGROUND Developing computer aided diagnosis (CAD) schemes of mammograms to classify between malignant and benign breast lesions has attracted a lot of research attention over the last several decades. However, unlike radiologists who make diagnostic decisions based on the fusion of image features extracted from multi-view mammograms, most CAD schemes are single-view-based schemes, which limit CAD performance and clinical utility. PURPOSE This study aims to develop and test a novel CAD framework that optimally fuses information extracted from ipsilateral views of bilateral mammograms using both deep transfer learning (DTL) and radiomics feature extraction methods. METHODS An image dataset containing 353 benign and 611 malignant cases is assembled. Each case contains four images: the craniocaudal (CC) and mediolateral oblique (MLO) view of the left and right breast. First, we extract four matching regions of interest (ROIs) from images that surround centers of two suspicious lesion regions seen in CC and MLO views, as well as matching ROIs in the contralateral breasts. Next, the handcrafted radiomics (HCRs) features and VGG16 model-generated automated features are extracted from each ROI resulting in eight feature vectors. Then, after reducing feature dimensionality and quantifying the bilateral and ipsilateral asymmetry of four ROIs to yield four new feature vectors, we test four fusion methods to build three support vector machine (SVM) classifiers by an optimal fusion of asymmetrical image features extracted from four view images. RESULTS Using a 10-fold cross-validation method, results show that a SVM classifier trained using an optimal fusion of four view images yields the highest classification performance (AUC = 0.876 ± 0.031), which significantly outperforms SVM classifiers trained using one projection view alone, AUC = 0.817 ± 0.026 and 0.792 ± 0.026 for the CC and MLO view of bilateral mammograms, respectively (p < 0.001). CONCLUSIONS The study demonstrates that the shift from single-view CAD to four-view CAD and the inclusion of both DTL and radiomics features significantly increases CAD performance in distinguishing between malignant and benign breast lesions.
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Affiliation(s)
- Meredith A. Jones
- School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Negar Sadeghipour
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Xuxin Chen
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Warid Islam
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
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Werner de Vargas V, Schneider Aranda JA, dos Santos Costa R, da Silva Pereira PR, Victória Barbosa JL. Imbalanced data preprocessing techniques for machine learning: a systematic mapping study. Knowl Inf Syst 2023; 65:31-57. [PMID: 36405957 PMCID: PMC9645765 DOI: 10.1007/s10115-022-01772-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 09/27/2022] [Accepted: 10/02/2022] [Indexed: 11/10/2022]
Abstract
Machine Learning (ML) algorithms have been increasingly replacing people in several application domains-in which the majority suffer from data imbalance. In order to solve this problem, published studies implement data preprocessing techniques, cost-sensitive and ensemble learning. These solutions reduce the naturally occurring bias towards the majority sample through ML. This study uses a systematic mapping methodology to assess 9927 papers related to sampling techniques for ML in imbalanced data applications from 7 digital libraries. A filtering process selected 35 representative papers from various domains, such as health, finance, and engineering. As a result of a thorough quantitative analysis of these papers, this study proposes two taxonomies-illustrating sampling techniques and ML models. The results indicate that oversampling and classical ML are the most common preprocessing techniques and models, respectively. However, solutions with neural networks and ensemble ML models have the best performance-with potentially better results through hybrid sampling techniques. Finally, none of the 35 works apply simulation-based synthetic oversampling, indicating a path for future preprocessing solutions.
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Affiliation(s)
- Vitor Werner de Vargas
- Applied Computing Graduate Program, University of Vale do Rio dos Sinos, São Leopoldo, Rio Grande do Sul 93022-750 Brazil
| | - Jorge Arthur Schneider Aranda
- Applied Computing Graduate Program, University of Vale do Rio dos Sinos, São Leopoldo, Rio Grande do Sul 93022-750 Brazil
| | - Ricardo dos Santos Costa
- Electrical Engineering Graduate Program, University of Vale do Rio dos Sinos, São Leopoldo, Rio Grande do Sul 93022-750 Brazil
| | - Paulo Ricardo da Silva Pereira
- Electrical Engineering Graduate Program, University of Vale do Rio dos Sinos, São Leopoldo, Rio Grande do Sul 93022-750 Brazil
| | - Jorge Luis Victória Barbosa
- Applied Computing Graduate Program, University of Vale do Rio dos Sinos, São Leopoldo, Rio Grande do Sul 93022-750 Brazil ,Electrical Engineering Graduate Program, University of Vale do Rio dos Sinos, São Leopoldo, Rio Grande do Sul 93022-750 Brazil
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Kubo K, Kawahara D, Murakami Y, Takeuchi Y, Katsuta T, Imano N, Nishibuchi I, Saito A, Konishi M, Kakimoto N, Yoshioka Y, Toratani S, Ono S, Ueda T, Takeno S, Nagata Y. Development of a radiomics and machine learning model for predicting occult cervical lymph node metastasis in patients with tongue cancer. Oral Surg Oral Med Oral Pathol Oral Radiol 2022; 134:93-101. [PMID: 35431177 DOI: 10.1016/j.oooo.2021.12.122] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 11/10/2021] [Accepted: 12/10/2021] [Indexed: 10/19/2022]
Abstract
OBJECTIVE We aimed to develop a predictive model for occult cervical lymph node metastasis in patients with tongue cancer using radiomics and machine learning from pretreatment contrast-enhanced computed tomography. STUDY DESIGN This study included 161 patients with tongue cancer who received local treatment. Computed tomography images were transferred to a radiomics platform. The volume of interest was the total neck node level, including levels Ia, Ib, II, III, and IVa at the ipsilateral side, and each neck node level. The dimensionality of the radiomics features was reduced using least absolute shrinkage and selection operator logistic regression analysis. We compared 5 classifiers with or without the synthetic minority oversampling technique (SMOTE). RESULTS For the analysis at the total neck node level, random forest with SMOTE was the best model, with an accuracy of 0.85 and an area under the curve score of 0.92. For the analysis at each neck node level, a support vector machine with SMOTE was the best model, with an accuracy of 0.96 and an area under the curve score of 0.98. CONCLUSIONS Predictive models using radiomics and machine learning have potential as clinical decision support tools in the management of patients with tongue cancer for prediction of occult cervical lymph node metastasis.
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Affiliation(s)
- Katsumaro Kubo
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yuji Murakami
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
| | - Yuki Takeuchi
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Tsuyoshi Katsuta
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Nobuki Imano
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Ikuno Nishibuchi
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Akito Saito
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Masaru Konishi
- Department of Oral and Maxillofacial Radiology, Hiroshima University Hospital, Hiroshima, Japan
| | - Naoya Kakimoto
- Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yukio Yoshioka
- Department of Molecular Oral Medicine and Maxillofacial Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Shigeaki Toratani
- Department of Molecular Oral Medicine and Maxillofacial Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Shigehiro Ono
- Department of Oral and Maxillofacial Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Tsutomu Ueda
- Department of Otolaryngology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Department of Head and Neck Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Sachio Takeno
- Department of Otolaryngology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Department of Head and Neck Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yasushi Nagata
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
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Danala G, Desai M, Ray B, Heidari M, Maryada SKR, Prodan CI, Zheng B. Applying Quantitative Radiographic Image Markers to Predict Clinical Complications After Aneurysmal Subarachnoid Hemorrhage: A Pilot Study. Ann Biomed Eng 2022; 50:413-425. [PMID: 35112157 PMCID: PMC8918043 DOI: 10.1007/s10439-022-02926-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 01/24/2022] [Indexed: 12/14/2022]
Abstract
Accurately predicting clinical outcome of aneurysmal subarachnoid hemorrhage (aSAH) patients is difficult. The purpose of this study was to develop and test a new fully-automated computer-aided detection (CAD) scheme of brain computed tomography (CT) images to predict prognosis of aSAH patients. A retrospective dataset of 59 aSAH patients was assembled. Each patient had 2 sets of CT images acquired at admission and prior-to-discharge. CAD scheme was applied to segment intracranial brain regions into four subregions, namely, cerebrospinal fluid (CSF), white matter (WM), gray matter (GM), and leaked extraparenchymal blood (EPB), respectively. CAD then detects sulci and computes 9 image features related to 5 volumes of the segmented sulci, EPB, CSF, WM, and GM and 4 volumetrical ratios to sulci. Subsequently, applying a leave-one-case-out cross-validation method embedded with a principal component analysis (PCA) algorithm to generate optimal feature vector, 16 support vector machine (SVM) models were built using CT images acquired either at admission or prior-to-discharge to predict each of eight clinically relevant parameters commonly used to assess patients' prognosis. Finally, a receiver operating characteristics (ROC) method was used to evaluate SVM model performance. Areas under ROC curves of 16 SVM models range from 0.62 ± 0.07 to 0.86 ± 0.07. In general, SVM models trained using CT images acquired at admission yielded higher accuracy to predict short-term clinical outcomes, while SVM models trained using CT images acquired prior-to-discharge demonstrated higher accuracy in predicting long-term clinical outcomes. This study demonstrates feasibility to predict prognosis of aSAH patients using new quantitative image markers generated by SVM models.
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Affiliation(s)
- Gopichandh Danala
- School of Electrical and Computer Engineering, University of Oklahoma, 101 David L Boren Blvd, Norman, OK, 73019, USA.
| | - Masoom Desai
- Department of Neurology, University of Oklahoma Medical Center, Oklahoma City, OK, USA
| | - Bappaditya Ray
- Division of Neurocritical Care, Department of Neurology and Neurological Surgery, UT Southwestern Medical Center, Dallas, TX, USA
| | - Morteza Heidari
- School of Electrical and Computer Engineering, University of Oklahoma, 101 David L Boren Blvd, Norman, OK, 73019, USA
| | | | - Calin I Prodan
- Department of Neurology, University of Oklahoma Medical Center, Oklahoma City, OK, USA
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, 101 David L Boren Blvd, Norman, OK, 73019, USA
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Ma JH, Feng Z, Wu JY, Zhang Y, Di W. Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks. BMC Med Inform Decis Mak 2021; 21:127. [PMID: 33845834 PMCID: PMC8042715 DOI: 10.1186/s12911-021-01486-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 03/31/2021] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE To explore an effective algorithm based on artificial neural network to pick correctly the minority of pregnant women with SLE suffering fetal loss outcomes from the majority with live birth and train a well behaved model as a clinical decision assistant. METHODS We integrated the thoughts of comparative and focused study into the artificial neural network and presented an effective algorithm aiming at imbalanced learning in small dataset. RESULTS We collected 469 non-trivial pregnant patients with SLE, where 420 had live-birth outcomes and the other 49 patients ended in fetal loss. A well trained imbalanced-learning model had a high sensitivity of 19/21 ([Formula: see text]) for the identification of patients with fetal loss outcomes. DISCUSSION The misprediction of the two patients was explainable. Algorithm improvements in artificial neural network framework enhanced the identification in imbalanced learning problems and the external validation increased the reliability of algorithm. CONCLUSION The well-trained model was fully qualified to assist healthcare providers to make timely and accurate decisions.
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Affiliation(s)
- Jing-Hang Ma
- Department of Obstetrics and Gynecology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhen Feng
- First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jia-Yue Wu
- Department of Obstetrics and Gynecology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yu Zhang
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wen Di
- Department of Obstetrics and Gynecology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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Mirniaharikandehei S, Heidari M, Danala G, Lakshmivarahan S, Zheng B. Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105937. [PMID: 33486339 PMCID: PMC7920928 DOI: 10.1016/j.cmpb.2021.105937] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 01/06/2021] [Indexed: 05/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Non-invasively predicting the risk of cancer metastasis before surgery can play an essential role in determining which patients can benefit from neoadjuvant chemotherapy. This study aims to investigate and test the advantages of applying a random projection algorithm to develop and optimize a radiomics-based machine learning model to predict peritoneal metastasis in gastric cancer patients using a small and imbalanced computed tomography (CT) image dataset. METHODS A retrospective dataset involving CT images acquired from 159 patients is assembled, including 121 and 38 cases with and without peritoneal metastasis, respectively. A computer-aided detection scheme is first applied to segment primary gastric tumor volumes and initially compute 315 image features. Then, five gradients boosting machine (GBM) models embedded with five feature selection methods (including random projection algorithm, principal component analysis, least absolute shrinkage, and selection operator, maximum relevance and minimum redundancy, and recursive feature elimination) along with a synthetic minority oversampling technique, are built to predict the risk of peritoneal metastasis. All GBM models are trained and tested using a leave-one-case-out cross-validation method. RESULTS Results show that the GBM model embedded with a random projection algorithm yields a significantly higher prediction accuracy (71.2%) than the other four GBM models (p<0.05). The precision, sensitivity, and specificity of this optimal GBM model are 65.78%, 43.10%, and 87.12%, respectively. CONCLUSIONS This study demonstrates that CT images of the primary gastric tumors contain discriminatory information to predict the risk of peritoneal metastasis, and a random projection algorithm is a promising method to generate optimal feature vector, improving the performance of machine learning based prediction models.
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Affiliation(s)
| | - Morteza Heidari
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Gopichandh Danala
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
| | | | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
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Yin RH, Yang YC, Tang XQ, Shi HF, Duan SF, Pan CJ. Enhanced computed tomography radiomics-based machine-learning methods for predicting the Fuhrman grades of renal clear cell carcinoma. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2021; 29:1149-1160. [PMID: 34657848 DOI: 10.3233/xst-210997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
OBJECTIVE To develop and test an optimal machine learning model based on the enhanced computed tomography (CT) to preoperatively predict pathological grade of clear cell renal cell carcinoma (ccRCC). METHODS A retrospective analysis of 53 pathologically confirmed cases of ccRCC was performed and 25 consecutive ccRCC cases were selected as a prospective testing set. All patients underwent routine preoperative abdominal CT plain and enhanced scans. Renal tumor lesions were segmented on arterial phase images and 396 radiomics features were extracted. In the training set, seven discrimination classifiers for high- and low-grade ccRCCs were constructed based on seven different machine learning models, respectively, and their performance and stability for predicting ccRCC grades were evaluated through receiver operating characteristic (ROC) analysis and cross-validation. Prediction accuracy and area under ROC curve were used as evaluation indices. Finally, the diagnostic efficacy of the optimal model was verified in the testing set. RESULTS The accuracies and AUC values achieved by support vector machine with radial basis function kernel (svmRadial), random forest and naïve Bayesian models were 0.860±0.158 and 0.919±0.118, 0.840±0.160 and 0.915±0.138, 0.839±0.147 and 0.921±0.133, respectively, which showed high predictive performance, whereas K-nearest neighborhood model yielded lower accuracy of 0.720±0.188 and lower AUC value of 0.810±0.150. Additionally, svmRadial had smallest relative standard deviation (RSD, 0.13 for AUC, 0.17 for accuracy), which indicates higher stability. CONCLUSION svmRadial performs best in predicting pathological grades of ccRCC using radiomics features computed from the preoperative CT images, and thus may have high clinical potential in guiding preoperative decision.
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Affiliation(s)
- Ruo-Han Yin
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - You-Chang Yang
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Xiao-Qiang Tang
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Hai-Feng Shi
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Shao-Feng Duan
- Precision Health Institution, GE Healthcare (China), Shanghai, China
| | - Chang-Jie Pan
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China
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Wang X, Duan H, Li X, Ye X, Huang G, Nie S. A prognostic analysis method for non-small cell lung cancer based on the computed tomography radiomics. Phys Med Biol 2020; 65:045006. [PMID: 31962301 DOI: 10.1088/1361-6560/ab6e51] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In order to assist doctors in arranging the postoperative treatments and re-examinations for non-small cell lung cancer (NSCLC) patients, this study was initiated to explore a prognostic analysis method for NSCLC based on computed tomography (CT) radiomics. The data of 173 NSCLC patients were collected retrospectively and the clinically meaningful 3-year survival was used as the predictive limit to predict the patient's prognosis survival time range. Firstly, lung tumors were segmented and the radiomics features were extracted. Secondly, the feature weighting algorithm was used to screen and optimize the extracted original feature data. Then, the selected feature data combining with the prognosis survival of patients were used to train machine learning classification models. Finally, a prognostic survival prediction model and radiomics prognostic factors were obtained to predict the prognosis survival time range of NSCLC patients. The classification accuracy rate under cross-validation was up to 88.7% in the prognosis survival analysis model. When verifying on an independent data set, the model also yielded a high prediction accuracy which is up to 79.6%. Inverse different moment, lobulation sign and angular second moment were NSCLC prognostic factors based on radiomics. This study proved that CT radiomics features could effectively assist doctors to make more accurate prognosis survival prediction for NSCLC patients, so as to help doctors to optimize treatment and re-examination for NSCLC patients to extend their survival time.
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Affiliation(s)
- Xu Wang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
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Gong J, Liu J, Hao W, Nie S, Wang S, Peng W. Computer-aided diagnosis of ground-glass opacity pulmonary nodules using radiomic features analysis. Phys Med Biol 2019; 64:135015. [PMID: 31167172 DOI: 10.1088/1361-6560/ab2757] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
This study aims to develop a CT-based radiomic features analysis approach for diagnosis of ground-glass opacity (GGO) pulmonary nodules, and also assess whether computer-aided diagnosis (CADx) performance changes in classifying between benign and malignant nodules associated with histopathological subtypes namely, adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC), respectively. The study involves 182 histopathology-confirmed GGO nodules collected from two cancer centers. Among them, 59 are benign, 50 are AIS, 32 are MIA, and 41 are IAC nodules. Four training/testing data sets-(1) all nodules, (2) benign and AIS nodules, (3) benign and MIA nodules, (4) benign and IAC nodules-are assembled based on their histopathological subtypes. We first segment pulmonary nodules depicted in CT images by using a 3D region growing and geodesic active contour level set algorithm. Then, we computed and extracted 1117 quantitative imaging features based on the 3D segmented nodules. After conducting radiomic features normalization process, we apply a leave-one-out cross-validation (LOOCV) method to build models by embedding with a Relief feature selection, synthetic minority oversampling technique (SMOTE) and three machine-learning classifiers namely, support vector machine classifier, logistic regression classifier and Gaussian Naïve Bayes classifier. When separately using four data sets to train and test three classifiers, the average areas under receiver operating characteristic curves (AUC) are 0.75, 0.55, 0.77 and 0.93, respectively. When testing on an independent data set, our scheme yields higher accuracy than two radiologists (61.3% versus radiologist 1: 53.1% and radiologist 2: 56.3%). This study demonstrates that: (1) the feasibility of using CT-based radiomic features analysis approach to distinguish between benign and malignant GGO nodules, (2) higher performance of CADx scheme in diagnosing GGO nodules comparing with radiologist, and (3) a consistently positive trend between classification performance and invasive grade of GGO nodules. Thus, to improve the CADx performance in diagnosing of GGO nodules, one should assemble an optimal training data set dominated with more nodules associated with non-invasive lung adenocarcinoma (i.e. AIS and MIA).
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Affiliation(s)
- Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai 200032, People's Republic of China. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China. Jing Gong and Jiyu Liu contributed equally to this work
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Developing global image feature analysis models to predict cancer risk and prognosis. Vis Comput Ind Biomed Art 2019; 2:17. [PMID: 32190407 PMCID: PMC7055572 DOI: 10.1186/s42492-019-0026-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 10/09/2019] [Indexed: 12/18/2022] Open
Abstract
In order to develop precision or personalized medicine, identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently. Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images, which include steps in detecting and segmenting suspicious regions or tumors, followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors. However, due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors, segmenting subtle regions is often difficult and unreliable. Additionally, ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches. In our recent studies, we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis. We trained and tested several models using images obtained from full-field digital mammography, magnetic resonance imaging, and computed tomography of breast, lung, and ovarian cancers. Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice. Furthermore, the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis. Therefore, the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.
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Gong J, Liu J, Jiang Y, Sun X, Zheng B, Nie S. Fusion of quantitative imaging features and serum biomarkers to improve performance of computer‐aided diagnosis scheme for lung cancer: A preliminary study. Med Phys 2018; 45:5472-5481. [PMID: 30317652 DOI: 10.1002/mp.13237] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 10/03/2018] [Accepted: 10/03/2018] [Indexed: 12/19/2022] Open
Affiliation(s)
- Jing Gong
- School of Medical Instrument and Food Engineering University of Shanghai for Science and Technology 516 Jun Gong Road Shanghai 200093 China
- Department of Radiology Fudan University Shanghai Cancer Center 270 Dongan Road Shanghai 200032 China
| | - Ji‐yu Liu
- Radiology Department Shanghai Pulmonary Hospital 507 Zheng Min Road Shanghai 200433 China
| | - Yao‐jun Jiang
- Department of Radiology The First Affiliated Hospital of Zhengzhou University Zhengzhou 450052 China
| | - Xi‐wen Sun
- Radiology Department Shanghai Pulmonary Hospital 507 Zheng Min Road Shanghai 200433 China
| | - Bin Zheng
- School of Electrical and Computer Engineering University of Oklahoma Norman OK 73019 USA
| | - Sheng‐dong Nie
- School of Medical Instrument and Food Engineering University of Shanghai for Science and Technology 516 Jun Gong Road Shanghai 200093 China
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Gong J, Liu JY, Sun XW, Zheng B, Nie SD. Computer-aided diagnosis of lung cancer: the effect of training data sets on classification accuracy of lung nodules. ACTA ACUST UNITED AC 2018; 63:035036. [DOI: 10.1088/1361-6560/aaa610] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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Heidari M, Khuzani AZ, Hollingsworth AB, Danala G, Mirniaharikandehei S, Qiu Y, Liu H, Zheng B. Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm. Phys Med Biol 2018; 63:035020. [PMID: 29239858 DOI: 10.1088/1361-6560/aaa1ca] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.
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Affiliation(s)
- Morteza Heidari
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, United States of America. Author to whom any correspondence should be addressed
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Danala G, Thai T, Gunderson CC, Moxley KM, Moore K, Mannel RS, Liu H, Zheng B, Qiu Y. Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy. Acad Radiol 2017; 24:1233-1239. [PMID: 28554551 PMCID: PMC5875685 DOI: 10.1016/j.acra.2017.04.014] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Revised: 04/03/2017] [Accepted: 04/03/2017] [Indexed: 12/19/2022]
Abstract
RATIONALE AND OBJECTIVES The study aimed to investigate the role of applying quantitative image features computed from computed tomography (CT) images for early prediction of tumor response to chemotherapy in the clinical trials for treating ovarian cancer patients. MATERIALS AND METHODS A dataset involving 91 patients was retrospectively assembled. Each patient had two sets of pre- and post-therapy CT images. A computer-aided detection scheme was applied to segment metastatic tumors previously tracked by radiologists on CT images and computed image features. Two initial feature pools were built using image features computed from pre-therapy CT images only and image feature difference computed from both pre- and post-therapy images. A feature selection method was applied to select optimal features, and an equal-weighted fusion method was used to generate a new quantitative imaging marker from each pool to predict 6-month progression-free survival. The prediction accuracy between quantitative imaging markers and the Response Evaluation Criteria in Solid Tumors (RECIST) criteria was also compared. RESULTS The highest areas under the receiver operating characteristic curve are 0.684 ± 0.056 and 0.771 ± 0.050 when using a single image feature computed from pre-therapy CT images and feature difference computed from pre- and post-therapy CT images, respectively. Using two corresponding fusion-based image markers, the areas under the receiver operating characteristic curve significantly increased to 0.810 ± 0.045 and 0.829 ± 0.043 (P < 0.05), respectively. Overall prediction accuracy levels are 71.4%, 80.2%, and 74.7% when using two imaging markers and RECIST, respectively. CONCLUSIONS This study demonstrated the feasibility of predicting patients' response to chemotherapy using quantitative imaging markers computed from pre-therapy CT images. However, using image feature difference computed between pre- and post-therapy CT images yielded higher prediction accuracy.
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Affiliation(s)
- Gopichandh Danala
- School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK 73019
| | - Theresa Thai
- Health Science Center of University of Oklahoma, Oklahoma City, Oklahoma
| | | | - Katherine M Moxley
- Health Science Center of University of Oklahoma, Oklahoma City, Oklahoma
| | - Kathleen Moore
- Health Science Center of University of Oklahoma, Oklahoma City, Oklahoma
| | - Robert S Mannel
- Health Science Center of University of Oklahoma, Oklahoma City, Oklahoma
| | - Hong Liu
- School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK 73019
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK 73019
| | - Yuchen Qiu
- School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK 73019.
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