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Ramesh S, Chokkara S, Shen T, Major A, Volchenboum SL, Mayampurath A, Applebaum MA. Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review. JCO Clin Cancer Inform 2021; 5:1208-1219. [PMID: 34910588 DOI: 10.1200/cci.21.00102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
PURPOSE There is a need for an improved understanding of clinical and biologic risk factors in pediatric cancer to improve patient outcomes. Machine learning (ML) represents the application of computational inference from advanced statistical methods that can be applied to increasing amount of data available for study in pediatric oncology. The goal of this systematic review was to systematically characterize the state of ML in pediatric oncology and highlight advances and opportunities in the field. METHODS We conducted a systematic review of the Embase, Scopus, and MEDLINE databases for applications of ML in pediatric oncology. Query results from all three databases were aggregated and duplicate studies were removed. RESULTS A total of 42 unique articles that examined the applications of ML in pediatric oncology met inclusion criteria for review. We identified 20 studies of CNS tumors, 13 of solid tumors, and nine of leukemia. ML tasks included classification, prediction of treatment response, and dose optimization with a variety of methods being used including neural network, k-nearest neighbor, random forest, naive Bayes, and support vector machines. Strengths of the identified studies included matching or outperforming physician comparators via automated analysis and predicting therapeutic response. Common limitations included significant heterogeneity in reporting standards, clinical applicability, small sample sizes, and missing external validation cohorts. CONCLUSION We identified areas where ML can enhance clinical care in ways that may not otherwise be achievable. Although ML promises enormous potential in improving diagnostics, decision making, and monitoring for children with cancer, the field remains in early stages and future work will be aided by standards and guidelines to ensure rigorous methodologic design and maximizing clinical utility.
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Affiliation(s)
- Siddhi Ramesh
- Pritzker School of Medicine, University of Chicago, Chicago, IL
| | - Sukarn Chokkara
- Pritzker School of Medicine, University of Chicago, Chicago, IL
| | - Timothy Shen
- Pritzker School of Medicine, University of Chicago, Chicago, IL
| | - Ajay Major
- Department of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Samuel L Volchenboum
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Anoop Mayampurath
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Mark A Applebaum
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL
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FAB classification of acute leukemia using an ensemble of neural networks. EVOLUTIONARY INTELLIGENCE 2020. [DOI: 10.1007/s12065-020-00491-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Rawat J, Singh A, HS B, Virmani J, Devgun JS. Computer assisted classification framework for prediction of acute lymphoblastic and acute myeloblastic leukemia. Biocybern Biomed Eng 2017. [DOI: 10.1016/j.bbe.2017.07.003] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Agaian S, Madhukar M, Chronopoulos AT. A new acute leukaemia-automated classification system. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2016. [DOI: 10.1080/21681163.2016.1234948] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Sos Agaian
- Department of Electrical Engineering, University of Texas at San Antonio, San Antonio, TX, USA
| | - Monica Madhukar
- Department of Electrical Engineering, University of Texas at San Antonio, San Antonio, TX, USA
| | - Anthony T. Chronopoulos
- Department of Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA
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Viswanathan P. Fuzzy C Means Detection of Leukemia Based on Morphological Contour Segmentation. ACTA ACUST UNITED AC 2015. [DOI: 10.1016/j.procs.2015.08.017] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Bountris P, Haritou M, Pouliakis A, Margari N, Kyrgiou M, Spathis A, Pappas A, Panayiotides I, Paraskevaidis EA, Karakitsos P, Koutsouris DD. An intelligent clinical decision support system for patient-specific predictions to improve cervical intraepithelial neoplasia detection. BIOMED RESEARCH INTERNATIONAL 2014; 2014:341483. [PMID: 24812614 PMCID: PMC4000928 DOI: 10.1155/2014/341483] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2013] [Revised: 02/10/2014] [Accepted: 03/16/2014] [Indexed: 12/24/2022]
Abstract
Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV), including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS), composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%), high specificity (97.1%), high positive predictive value (89.4%), and high negative predictive value (97.1%), for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.
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Affiliation(s)
- Panagiotis Bountris
- Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Politechniou 9, 15773 Zografou Campus, Athens, Greece
| | - Maria Haritou
- Institute of Communication and Computer Systems, National Technical University of Athens, Iroon Politechniou 9, 15773 Zografou Campus, Athens, Greece
| | - Abraham Pouliakis
- Department of Cytopathology, School of Medicine, University General Hospital “ATTIKON”, University of Athens, Rimini 1, 12462 Athens, Greece
| | - Niki Margari
- Department of Cytopathology, School of Medicine, University General Hospital “ATTIKON”, University of Athens, Rimini 1, 12462 Athens, Greece
| | - Maria Kyrgiou
- West London Gynaecological Cancer Center, Queen Charlotte's and Chelsea, Hammersmith Hospital, Imperial Healthcare NHS Trust, London W12 0HS, UK
- Division of Surgery and Cancer, Faculty of Medicine, Imperial College, London W12 0NN, UK
| | - Aris Spathis
- Department of Cytopathology, School of Medicine, University General Hospital “ATTIKON”, University of Athens, Rimini 1, 12462 Athens, Greece
| | - Asimakis Pappas
- 3rd Department of Obstetrics and Gynecology, University General Hospital “ATTIKON”, School of Medicine, University of Athens, Rimini 1, 12462 Athens, Greece
| | - Ioannis Panayiotides
- 2nd Department of Pathology, University General Hospital “ATTIKON”, School of Medicine, University of Athens, Rimini 1, 12462 Athens, Greece
| | - Evangelos A. Paraskevaidis
- Department of Obstetrics and Gynecology, University Hospital of Ioannina, St. Niarchou Str, 45500 Ioannina, Greece
| | - Petros Karakitsos
- Department of Cytopathology, School of Medicine, University General Hospital “ATTIKON”, University of Athens, Rimini 1, 12462 Athens, Greece
| | - Dimitrios-Dionyssios Koutsouris
- Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Politechniou 9, 15773 Zografou Campus, Athens, Greece
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Amaral JLM, Lopes AJ, Jansen JM, Faria ACD, Melo PL. An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 112:441-454. [PMID: 24001924 DOI: 10.1016/j.cmpb.2013.08.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Revised: 07/23/2013] [Accepted: 08/07/2013] [Indexed: 06/02/2023]
Abstract
The purpose of this study was to develop an automatic classifier to increase the accuracy of the forced oscillation technique (FOT) for diagnosing early respiratory abnormalities in smoking patients. The data consisted of FOT parameters obtained from 56 volunteers, 28 healthy and 28 smokers with low tobacco consumption. Many supervised learning techniques were investigated, including logistic linear classifiers, k nearest neighbor (KNN), neural networks and support vector machines (SVM). To evaluate performance, the ROC curve of the most accurate parameter was established as baseline. To determine the best input features and classifier parameters, we used genetic algorithms and a 10-fold cross-validation using the average area under the ROC curve (AUC). In the first experiment, the original FOT parameters were used as input. We observed a significant improvement in accuracy (KNN=0.89 and SVM=0.87) compared with the baseline (0.77). The second experiment performed a feature selection on the original FOT parameters. This selection did not cause any significant improvement in accuracy, but it was useful in identifying more adequate FOT parameters. In the third experiment, we performed a feature selection on the cross products of the FOT parameters. This selection resulted in a further increase in AUC (KNN=SVM=0.91), which allows for high diagnostic accuracy. In conclusion, machine learning classifiers can help identify early smoking-induced respiratory alterations. The use of FOT cross products and the search for the best features and classifier parameters can markedly improve the performance of machine learning classifiers.
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Affiliation(s)
- Jorge L M Amaral
- Department of Electronics and Telecommunications Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil
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Amaral JLM, Lopes AJ, Jansen JM, Faria ACD, Melo PL. Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012; 105:183-93. [PMID: 22018532 DOI: 10.1016/j.cmpb.2011.09.009] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2011] [Revised: 08/15/2011] [Accepted: 09/22/2011] [Indexed: 05/02/2023]
Abstract
The purpose of this study is to develop a clinical decision support system based on machine learning (ML) algorithms to help the diagnostic of chronic obstructive pulmonary disease (COPD) using forced oscillation (FO) measurements. To this end, the performances of classification algorithms based on Linear Bayes Normal Classifier, K nearest neighbor (KNN), decision trees, artificial neural networks (ANN) and support vector machines (SVM) were compared in order to the search for the best classifier. Four feature selection methods were also used in order to identify a reduced set of the most relevant parameters. The available dataset consists of 7 possible input features (FO parameters) of 150 measurements made in 50 volunteers (COPD, n = 25; healthy, n = 25). The performance of the classifiers and reduced data sets were evaluated by the determination of sensitivity (Se), specificity (Sp) and area under the ROC curve (AUC). Among the studied classifiers, KNN, SVM and ANN classifiers were the most adequate, reaching values that allow a very accurate clinical diagnosis (Se > 87%, Sp > 94%, and AUC > 0.95). The use of the analysis of correlation as a ranking index of the FOT parameters, allowed us to simplify the analysis of the FOT parameters, while still maintaining a high degree of accuracy. In conclusion, the results of this study indicate that the proposed classifiers may contribute to easy the diagnostic of COPD by using forced oscillation measurements.
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Affiliation(s)
- Jorge L M Amaral
- Department of Electronics and Telecommunications Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil
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Gelelete CB, Pereira SH, Azevedo AMB, Thiago LS, Mundim M, Land MGP, Costa ES. Overweight as a prognostic factor in children with acute lymphoblastic leukemia. Obesity (Silver Spring) 2011; 19:1908-11. [PMID: 21720424 DOI: 10.1038/oby.2011.195] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Our purpose was to investigate the prognostic impact of overweight/obesity in 5-year event-free survival (EFS) in a cohort of children with acute lymphoblastic leukemia (ALL). We retrospectively analyzed 181 newly diagnosed ALL children enrolled between 1990 and 2009 and treated with Berlin-Frankfurt-Munich (BFM) protocols. The majority of children in our cohort were <10 years-old. Our data clearly indicated that overweight/obesity is an independent predictor of relapse risk, mainly in the intermediate- and high-risk groups (HR) of children. These results could be explained by changes in the chemotherapy pharmacokinetics in overweight/obese patients and by the antiapoptotic effects in leukemic cells caused by adipocytes.
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Affiliation(s)
- Cristina B Gelelete
- Pediatrics Institute IPPMG, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
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Amaral JLM, Faria ACD, Lopes AJ, Jansen JM, Melo PL. Automatic identification of Chronic Obstructive Pulmonary Disease Based on forced oscillation measurements and artificial neural networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2010; 2010:1394-1397. [PMID: 21096340 DOI: 10.1109/iembs.2010.5626727] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
The purpose of this study is to develop an automatic classifier based on Artificial Neural Networks (ANNs) to help the diagnostic of Chronic Obstructive Pulmonary Disease (COPD) using forced oscillation measurements (FOT). The classifier inputs are the parameters provided by the FOT and the output is the indication if the parameters indicate COPD or not. The available dataset consists of 7 possible input features (FOT parameters) of 90 measurements made in 30 volunteers. Two feature selection methods (the analysis of the linear correlation and forward search) were used in order to identify a reduced set of the most relevant parameters. Two different training strategies for the ANNs were used and the performance of resulting networks were evaluated by the determination of accuracy, sensitivity (Se), specificity (Sp) and AUC. The ANN classifiers presented high accuracy (Se > 0.9, Se > 0.9 and AUC > 0.9) both in the complete and the reduce sets of FOT parameters. This indicates that ANNs classifiers may contribute to easy the diagnostic of COPD using forced oscillation measurements.
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Affiliation(s)
- Jorge L M Amaral
- Dept. of Electronics and Telecommunications Engineering, Rio de Janeiro State University, 20550-013, RJ, Brazil.
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