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Zhao SS, Feng XL, Hu YC, Han Y, Tian Q, Sun YZ, Zhang J, Ge XW, Cheng SC, Li XL, Mao L, Shen SN, Yan LF, Cui GB, Wang W. Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images. BMC Neurol 2020; 20:48. [PMID: 32033580 PMCID: PMC7007642 DOI: 10.1186/s12883-020-1613-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 01/13/2020] [Indexed: 12/13/2022] Open
Abstract
Background The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy. Methods Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1 CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist’s assessment were also compared. Results Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P < 0.05). The AUC, ACC, sensitivity, and specificity of radiomics were 0.798, 0.735, 0.672, 0.789 for T1 CE, 0.774, 0.689, 0.700, 0.683 for FLAIR, as well as 0.861, 0.781, 0.778, 0.783 for the combination, respectively. The AUCs of radiologists 1, 2 and 3 were 0.700, 0.687, and 0.714, respectively. The efficacy of machine learning based on radiomics was superior to the radiologists’ assessment. Conclusions Machine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.
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Affiliation(s)
- Sha-Sha Zhao
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, People's Republic of China
| | - Xiu-Long Feng
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, People's Republic of China
| | - Yu-Chuan Hu
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, People's Republic of China
| | - Yu Han
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, People's Republic of China
| | - Qiang Tian
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, People's Republic of China
| | - Ying-Zhi Sun
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, People's Republic of China
| | - Jie Zhang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, People's Republic of China
| | - Xiang-Wei Ge
- Student Brigade, Air Force Medical University, Xi'an, 710032, Shaanxi, China
| | - Si-Chao Cheng
- Student Brigade, Air Force Medical University, Xi'an, 710032, Shaanxi, China
| | - Xiu-Li Li
- Deepwise AI Lab, Deepwise Inc, No.8 Haidian avenue, Sinosteel International Plaza, Beijing, 100080, China
| | - Li Mao
- Deepwise AI Lab, Deepwise Inc, No.8 Haidian avenue, Sinosteel International Plaza, Beijing, 100080, China
| | - Shu-Ning Shen
- Department of Stomatology, PLA 984 Hospital, Beijing, China
| | - Lin-Feng Yan
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, People's Republic of China
| | - Guang-Bin Cui
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, People's Republic of China
| | - Wen Wang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, People's Republic of China.
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Tewary S, Arun I, Ahmed R, Chatterjee S, Chakraborty C. AutoIHC-scoring: a machine learning framework for automated Allred scoring of molecular expression in ER- and PR-stained breast cancer tissue. J Microsc 2017; 268:172-185. [PMID: 28613390 DOI: 10.1111/jmi.12596] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 05/18/2017] [Accepted: 05/29/2017] [Indexed: 12/11/2022]
Abstract
In prognostic evaluation of breast cancer Immunohistochemical (IHC) markers namely, oestrogen receptor (ER) and progesterone receptor (PR) are widely used. The expert pathologist investigates qualitatively the stained tissue slide under microscope to provide the Allred score; which is clinically used for therapeutic decision making. Such qualitative judgment is time-consuming, tedious and more often suffers from interobserver variability. As a result, it leads to imprecise IHC score for ER and PR. To overcome this, there is an urgent need of developing a reliable and efficient IHC quantifier for high throughput decision making. In view of this, our study aims at developing an automated IHC profiler for quantitative assessment of ER and PR molecular expression from stained tissue images. We propose here to use CMYK colour space for positively and negatively stained cell extraction for proportion score. Also colour features are used for quantitative assessment of intensity scoring among the positively stained cells. Five different machine learning models namely artificial neural network, Naïve Bayes, K-nearest neighbours, decision tree and random forest are considered for learning the colour features using average red, green and blue pixel values of positively stained cell patches. Fifty cases of ER- and PR-stained tissues have been evaluated for validation with the expert pathologist's score. All five models perform adequately where random forest shows the best correlation with the expert's score (Pearson's correlation coefficient = 0.9192). In the proposed approach the average variation of diaminobenzidine (DAB) to nuclear area from the expert's score is found to be 7.58%, as compared to 27.83% for state-of-the-art ImmunoRatio software.
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Affiliation(s)
- S Tewary
- School of Medical Science & Technology, IIT Kharagpur, West Bengal, India
| | - I Arun
- Tata Medical Center, New Town, Rajarhat, Kolkata, West Bengal, India
| | - R Ahmed
- Tata Medical Center, New Town, Rajarhat, Kolkata, West Bengal, India
| | - S Chatterjee
- Tata Medical Center, New Town, Rajarhat, Kolkata, West Bengal, India
| | - C Chakraborty
- School of Medical Science & Technology, IIT Kharagpur, West Bengal, India
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Jalali A, Buckley EM, Lynch JM, Schwab PJ, Licht DJ, Nataraj C. Prediction of periventricular leukomalacia occurrence in neonates after heart surgery. IEEE J Biomed Health Inform 2014; 18:1453-60. [PMID: 24122606 PMCID: PMC4122287 DOI: 10.1109/jbhi.2013.2285011] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
This paper is concerned with predicting the occurrence of periventricular leukomalacia (PVL) using vital and blood gas data which are collected over a period of 12 h after the neonatal cardiac surgery. A data mining approach has been employed to generate a set of rules for classification of subjects as healthy or PVL affected. In view of the fact that blood gas and vital data have different sampling rates, in this study we have divided the data into two categories: 1) high resolution (vital), and 2) low resolution (blood gas), and designed a separate classifier based on each data category. The developed algorithm is composed of several stages; first, a feature pool has been extracted from each data category and the extracted features have been ranked based on the data reliability and their mutual information content with the output. An optimal feature subset with the highest discriminative capability has been formed using simultaneous maximization of the class separability measure and mutual information of a set. Two separate decision trees (DTs) have been developed for the classification purpose and more importantly to discover hidden relationships that exist among the data to help us better understand PVL pathophysiology. The DT result shows that high amplitude 20 min variations and low sample entropy in the vital data and the defined out of range index as well as maximum rate of change in blood gas data are important factors for PVL prediction. Low sample entropy represents lack of variability in hemodynamic measurement, and constant blood pressure with small fluctuations is an important indicator of PVL occurrence. Finally, using the different time frames of data collection, we show that the first 6 h of data contain sufficient information for PVL occurrence prediction.
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Affiliation(s)
- Ali Jalali
- PhD candidate at the Department of Mechanical Engineering, Villanova University, Villanova, PA, 19085 USA
| | - Erin M. Buckley
- Post-Doctoral researcher at the Neurovascular Imaging Lab, Division of Child Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, 19140 USA
| | - Jennifer M. Lynch
- PhD candidate at the Neurovascular Imaging Lab, Division of Child Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, 19140 USA
| | - Peter J. Schwab
- Neurovascular Imaging Lab, Division of Child Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, 19140 USA
| | - Daniel J. Licht
- Director of the Neurovascular Imaging Lab, Division of Child Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, 19140 USA
| | - C Nataraj
- Mrs. and Mr. Mortiz, Sr. Endowed Professor in Engineered Systems and Chair of the Department of Mechanical Engineering, Villanova University, Villanova, PA, 19085 USA
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Majid A, Ali S, Iqbal M, Kausar N. Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 113:792-808. [PMID: 24472367 DOI: 10.1016/j.cmpb.2014.01.001] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2013] [Revised: 12/29/2013] [Accepted: 01/03/2014] [Indexed: 06/03/2023]
Abstract
This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.
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Affiliation(s)
- Abdul Majid
- Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, 45650 Islamabad, Pakistan.
| | - Safdar Ali
- Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, 45650 Islamabad, Pakistan.
| | - Mubashar Iqbal
- Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, 45650 Islamabad, Pakistan.
| | - Nabeela Kausar
- Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, 45650 Islamabad, Pakistan.
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