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Kaur I, Doja M, Ahmad T. Data Mining and Machine Learning in Cancer Survival Research: An Overview and Future Recommendations. J Biomed Inform 2022; 128:104026. [DOI: 10.1016/j.jbi.2022.104026] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 02/07/2022] [Accepted: 02/09/2022] [Indexed: 12/29/2022]
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An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2021:6342226. [PMID: 34992648 PMCID: PMC8727098 DOI: 10.1155/2021/6342226] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 11/27/2021] [Indexed: 12/31/2022]
Abstract
Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients' survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. Deep Neural Networks for Neuro-oncology: Towards Patient Individualized Design of Chemo-Radiation Therapy for Glioblastoma Patients. J Biomed Inform 2022; 127:104006. [DOI: 10.1016/j.jbi.2022.104006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/04/2022] [Accepted: 01/25/2022] [Indexed: 11/28/2022]
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Williams S, Layard Horsfall H, Funnell JP, Hanrahan JG, Khan DZ, Muirhead W, Stoyanov D, Marcus HJ. Artificial Intelligence in Brain Tumour Surgery-An Emerging Paradigm. Cancers (Basel) 2021; 13:cancers13195010. [PMID: 34638495 PMCID: PMC8508169 DOI: 10.3390/cancers13195010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/02/2021] [Accepted: 10/03/2021] [Indexed: 01/01/2023] Open
Abstract
Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced.
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Affiliation(s)
- Simon Williams
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
- Correspondence:
| | - Hugo Layard Horsfall
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Jonathan P. Funnell
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - John G. Hanrahan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Danyal Z. Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - William Muirhead
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Danail Stoyanov
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Hani J. Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. A neuro evolutionary algorithm for patient calibrated prediction of survival in Glioblastoma patients. J Biomed Inform 2021; 115:103694. [PMID: 33545332 DOI: 10.1016/j.jbi.2021.103694] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 01/28/2021] [Accepted: 01/29/2021] [Indexed: 01/30/2023]
Abstract
BACKGROUND AND OBJECTIVES Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumors. Radiation therapy (RT) plus concomitant and adjuvant Temozolomide (TMZ) constitute standard treatment of GBM. Existing models for GBM growth do not consider the effect of different schedules on tumor growth and patient survival. However, clinical trials show that treatment schedule and drug dosage significantly affect patient survival. The goal is to provide a patient calibrated model for predicting survival according to the treatment schedule. METHODS We propose a top-down method based on artificial neural networks (ANN) and genetic algorithm (GA) to predict survival of GBM patients. A feed forward undercomplete Autoencoder network is integrated with the neuro-evolutionary (NE) algorithm in order to extract a compressed representation of input clinical data. The proposed NE algorithm uses GA to obtain optimal architecture of a multi-layer perceptron (MLP). Taguchi L16 orthogonal design of experiments is used to tune parameters of the proposed NE algorithm. Finally, the optimal MLP is used to predict survival of GBM patients. RESULTS Data from 8 related clinical trials have been collected and integrated to train the model. From 847 evaluable cases, 719 were used for train and validation and the remaining 128 cases were used to test the model. Mean absolute error of the predictions on the test data is 0.087 months which shows excellent performance of the proposed model in predicting survival of the patients. Also, the results show that the proposed NE algorithm is superior to other existing models in both the mean and variability of the prediction error.
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Affiliation(s)
- Amir Ebrahimi Zade
- Faculty of Industrial Engineering and Systems Management, Amirkabir University of Technology, Tehran, Iran
| | | | - M Soltani
- Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran; Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
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Zhang B, Yan J, Chen W, Dong Y, Zhang L, Mo X, Chen Q, Cheng J, Liu X, Wang W, Zhang Z, Zhang S. Machine learning classifiers for predicting 3-year progression-free survival and overall survival in patients with gliomas after surgery. J Cancer 2021; 12:1604-1615. [PMID: 33613747 PMCID: PMC7890310 DOI: 10.7150/jca.52183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 12/15/2020] [Indexed: 01/22/2023] Open
Abstract
Background: To develop machine-learning based models to predict the progression-free survival (PFS) and overall survival (OS) in patients with gliomas and explore the effect of different feature selection methods on the prediction. Methods: We included 505 patients (training cohort, n = 354; validation cohort, n = 151) with gliomas between January 1, 2011 and December 31, 2016. The clinical, neuroimaging, and molecular genetic data of patients were retrospectively collected. The multi-causes discovering with structure learning (McDSL) algorithm, least absolute shrinkage and selection operator regression (LASSO), and Cox proportional hazards regression model were employed to discover the predictors for 3-year PFS and OS, respectively. Eight machine learning classifiers with 5-fold cross-validation were developed to predict 3-year PFS and OS. The area under the curve (AUC) was used to evaluate the prognostic performance of classifiers. Results: McDSL identified four causal factors (tumor location, WHO grade, histologic type, and molecular genetic group) for 3-year PFS and OS, whereas LASSO and Cox identified wide-range number of factors associated with 3-year PFS and OS. The performance of each machine learning classifier based on McDSL, LASSO, and Cox was not significantly different. Logistic regression yielded the optimal performance in predicting 3-year PFS based on the McDSL (AUC, 0.872, 95% confidence interval [CI]: 0.828-0.916) and 3-year OS based on the LASSO (AUC, 0.901, 95% CI: 0.861-0.940). Conclusions: McDSL is more reproducible than LASSO and Cox model in the feature selection process. Logistic regression model may have the highest performance in predicting 3-year PFS and OS of gliomas.
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Affiliation(s)
- Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Jing Yan
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weiqi Chen
- Big Data Decision Institute, Jinan University, Guangzhou, Guangdong, China.,School of management, Jinan University. Department of Catheterization Lab, Guangdong Cardiovascular Institute, Guangdong, Provincial Key Laboratory of South China
| | - Yuhao Dong
- Structural Heart Disease, Guangdong Provincial; People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, P.R. China
| | - Lu Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Xiaokai Mo
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Qiuying Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xianzhi Liu
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weiwei Wang
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhenyu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
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Kaur I, Doja MN, Ahmad T. Time-range based sequential mining for survival prediction in prostate cancer. J Biomed Inform 2020; 110:103550. [PMID: 32882394 DOI: 10.1016/j.jbi.2020.103550] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 07/30/2020] [Accepted: 08/27/2020] [Indexed: 11/19/2022]
Abstract
BACKGROUND AND OBJECTIVE Metastatic prostate cancer has a higher mortality rate than localized cancers. There is a need to investigate the survival outcome of metastatic prostate cancers separately. Also, the treatments undertaken by the patients affect their overall survival. The present study tries to analyze the sequence of treatments given to the patients, along with the time intervals between each set of treatments. The time when medication needs to be changed may provide some useful insights into the survival outcome of the patients. MATERIALS AND METHODS A total of 407 metastatic prostate cancer patients' data was collected and analyzed from an Indian tertiary care center. Popular sequence mining algorithms with exact order constraint have been applied to the treatment data. Appropriate time intervals were added in the resulted frequent sequences and fed to machine learning techniques along with other clinical data. RESULTS The study suggests that the proposed methodology of the time range based sequence mining approach gave better results than the existing methods with 84.5% accuracy and 0.89 AUC. The time intervals in the existing sequence mining algorithms can give the clinicians some useful insights into the survival analysis and in determining the best lines of treatments for a particular patient.
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Affiliation(s)
| | - M N Doja
- Indian Institute of Information Technology, Sonepat, India
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Tong D, Tian Y, Zhou T, Ye Q, Li J, Ding K, Li J. Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data. BMC Med Inform Decis Mak 2020; 20:22. [PMID: 32033604 PMCID: PMC7006213 DOI: 10.1186/s12911-020-1043-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 01/31/2020] [Indexed: 12/16/2022] Open
Abstract
Background Colon cancer is common worldwide and is the leading cause of cancer-related death. Multiple levels of omics data are available due to the development of sequencing technologies. In this study, we proposed an integrative prognostic model for colon cancer based on the integration of clinical and multi-omics data. Methods In total, 344 patients were included in this study. Clinical, gene expression, DNA methylation and miRNA expression data were retrieved from The Cancer Genome Atlas (TCGA). To accommodate the high dimensionality of omics data, unsupervised clustering was used as dimension reduction method. The bias-corrected Harrell’s concordance index was used to verify which clustering result provided the best prognostic performance. Finally, we proposed a prognostic prediction model based on the integration of clinical data and multi-omics data. Uno’s concordance index with cross-validation was used to compare the discriminative performance of the prognostic model constructed with different covariates. Results Combinations of clinical and multi-omics data can improve prognostic performance, as shown by the increase of the bias-corrected Harrell’s concordance of the prognostic model from 0.7424 (clinical features only) to 0.7604 (clinical features and three types of omics features). Additionally, 2-year, 3-year and 5-year Uno’s concordance statistics increased from 0.7329, 0.7043, and 0.7002 (clinical features only) to 0.7639, 0.7474 and 0.7597 (clinical features and three types of omics features), respectively. Conclusion In conclusion, this study successfully combined clinical and multi-omics data for better prediction of colon cancer prognosis.
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Affiliation(s)
- Danyang Tong
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang Province, China
| | - Yu Tian
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang Province, China
| | - Tianshu Zhou
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang Province, China
| | - Qiancheng Ye
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang Province, China
| | - Jun Li
- Department of Surgical Oncology, Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88 Jiefang Road, Hangzhou, 31009, Zhejiang Province, China
| | - Kefeng Ding
- Department of Surgical Oncology, Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88 Jiefang Road, Hangzhou, 31009, Zhejiang Province, China
| | - Jingsong Li
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang Province, China. .,Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
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Sarkiss CA, Germano IM. Machine Learning in Neuro-Oncology: Can Data Analysis From 5346 Patients Change Decision-Making Paradigms? World Neurosurg 2019; 124:287-294. [PMID: 30684706 DOI: 10.1016/j.wneu.2019.01.046] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 01/13/2019] [Accepted: 01/14/2019] [Indexed: 12/30/2022]
Abstract
BACKGROUND Machine learning (ML) is an application of artificial intelligence (AI) that gives computer systems the ability to learn data, without being explicitly programmed. Currently, ML has been successfully used for optical character recognition, spam filtering, and face recognition. The aim of the present study was to review the current applications of ML in the field of neuro-oncology. METHODS We conducted a systematic literature review using the PubMed and Cochrane databases using a keyword search for January 30, 2000 to March 31, 2018. The data were clustered for neuro-oncology scope of ML into 3 categories: patient outcome predictors, imaging analysis, and gene expression. RESULTS Data from 5346 patients in 29 studies were used to develop ML-based algorithms (MLBAs) in neuro-oncology. MLBAs were used to predict the outcomes for 2483 patients, with a sensitivity range of 78%-98% and specificity range of 76%-95%. In all studies, the MLBAs had greater accuracy than the conventional ones. MLBAs for image analysis showed accuracy in diagnosing low-grade versus high-grade gliomas, ranging from 80% to 93% and 90% for diagnosing high-grade glioma versus lymphoma. Seven studies used MLBAs to analyze gene expression in neuro-oncology. CONCLUSIONS MLBAs in neuro-oncology have been shown to predict patients' outcomes more accurately than conventional parameters in a retrospective analysis. If their high diagnostic accuracy in imaging analysis and detection of somatic mutations are corroborated in prospective studies, the use of tissue diagnosis or liquid biopsy might be curtailed. Finally, MLBAs are promising to help guide targeted therapy, can lead to personalized medicine, and open areas of study in the cancer cellular signaling system, not otherwise known.
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Affiliation(s)
- Christopher A Sarkiss
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, New York, USA
| | - Isabelle M Germano
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, New York, USA; Department of Economics, New York University Leonard N. Stern School of Business, New York University, New York, New York, USA.
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Panesar SS, D'Souza RN, Yeh FC, Fernandez-Miranda JC. Machine Learning Versus Logistic Regression Methods for 2-Year Mortality Prognostication in a Small, Heterogeneous Glioma Database. World Neurosurg X 2019; 2:100012. [PMID: 31218287 PMCID: PMC6581022 DOI: 10.1016/j.wnsx.2019.100012] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 01/08/2019] [Indexed: 12/15/2022] Open
Abstract
Background Machine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization, or prediction. ML techniques have been traditionally applied to large, highly dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathologic features. Recently, the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly dimensional database of patients with glioma. Methods We applied 3 ML techniques (artificial neural networks [ANNs], decision trees [DTs], and support vector machines [SVMs]) and classical logistic regression (LR) to a dataset consisting of 76 patients with glioma of all grades. We compared the effect of applying the algorithms to the raw database versus a database where only statistically significant features were included into the algorithmic inputs (feature selection). Results Raw input consisted of 21 variables and achieved performance of accuracy/area (C.I.) under the curve of 70.7%/0.70 (49.9-88.5) for ANN, 68%/0.72 (53.4-90.4) for SVM, 66.7%/0.64 (43.6-85.0) for LR, and 65%/0.70 (51.6-89.5) for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 (62.9-87.9) for ANN, 73.3%/0.74 (62.1-87.4) for SVM, 69.3%/0.73 (60.0-85.8) for LR, and 65.2%/0.63 (49.1-76.9) for DT. Conclusions We demonstrate that these techniques can also be applied to small, highly dimensional datasets. Our ML techniques achieved reasonable performance compared with similar studies in the literature. Although local databases may be small versus larger cancer repositories, we demonstrate that ML techniques can still be applied to their analysis; however, traditional statistical methods are of similar benefit.
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Key Words
- ANN, Artificial neural network
- AUC, Area under the curve
- CI, Confidence interval
- DT, Decision tree
- Diagnosis
- Gliomas
- LR, Logistic regression
- Logistic regression
- ML, Machine learning
- Machine learning
- NLR, Negative likelihood ratio
- NPV, Negative predictive value
- Neuro-oncology
- PLR, Positive likelihood ratio
- PPV, Positive predictive value
- Prognostication
- SVM, Support vector machine
- WHO, World Health Organization
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Affiliation(s)
- Sandip S Panesar
- Department of Neurosurgery, Stanford University, Stanford, California, USA
| | - Rhett N D'Souza
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Smedley NF, Ellingson BM, Cloughesy TF, Hsu W. Longitudinal Patterns in Clinical and Imaging Measurements Predict Residual Survival in Glioblastoma Patients. Sci Rep 2018; 8:14429. [PMID: 30258190 PMCID: PMC6158293 DOI: 10.1038/s41598-018-32397-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 07/09/2018] [Indexed: 11/13/2022] Open
Abstract
The growing amount of longitudinal data for a large population of patients has necessitated the application of algorithms that can discover patterns to inform patient management. This study demonstrates how temporal patterns generated from a combination of clinical and imaging measurements improve residual survival prediction in glioblastoma patients. Temporal patterns were identified with sequential pattern mining using data from 304 patients. Along with patient covariates, the patterns were incorporated as features in logistic regression models to predict 2-, 6-, or 9-month residual survival at each visit. The modeling approach that included temporal patterns achieved test performances of 0.820, 0.785, and 0.783 area under the receiver operating characteristic curve for predicting 2-, 6-, and 9-month residual survival, respectively. This approach significantly outperformed models that used tumor volume alone (p < 0.001) or tumor volume combined with patient covariates (p < 0.001) in training. Temporal patterns involving an increase in tumor volume above 122 mm3/day, a decrease in KPS across multiple visits, moderate neurologic symptoms, and worsening overall neurologic function suggested lower residual survival. These patterns are readily interpretable and found to be consistent with known prognostic indicators, suggesting they can provide early indicators to clinicians of changes in patient state and inform management decisions.
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Affiliation(s)
- Nova F Smedley
- Department of Bioengineering, Henry Samueli School of Engineering, University of California Los Angeles, Los Angeles, CA, USA.,Medical Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- Department of Bioengineering, Henry Samueli School of Engineering, University of California Los Angeles, Los Angeles, CA, USA.,UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - William Hsu
- Department of Bioengineering, Henry Samueli School of Engineering, University of California Los Angeles, Los Angeles, CA, USA. .,Medical Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
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Chen J, Sun L, Guo C, Wei W, Xie Y. A data-driven framework of typical treatment process extraction and evaluation. J Biomed Inform 2018; 83:178-195. [PMID: 29902575 DOI: 10.1016/j.jbi.2018.06.004] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2017] [Revised: 05/08/2018] [Accepted: 06/08/2018] [Indexed: 12/28/2022]
Abstract
BACKGROUND A clinical pathway (CP) defines a standardized care process for a well-defined patient group that aims to improve patient outcomes and promote patient safety. However, the construction of a new pathway from scratch is a time-consuming task for medical staff because it involves many factors, including objects, multidisciplinary collaboration, sequential design, and outcome measurements. Recently, the rapid development of hospital information systems has allowed the storage of large volumes of electronic medical records (EMRs), and this information constitutes an abundant data resource for building CPs using data-mining methods. METHODS We provide an automatic method for extracting typical treatment processes from EMRs that consists of four key steps. First, a novel similarity method is proposed to measure the similarity of two treatment records. Then, we perform an affinity propagation (AP) clustering algorithm to cluster doctor order set sequences (DOSSs). Next, a framework is proposed to extract a high-level description of each treatment cluster. Finally, we evaluate the extracted typical treatment processes by matching the treatment cluster with external information, such as the treatment efficacy, length of stay, and treatment cost. RESULTS By experiments on EMRs of 8287 cerebral infarction patients, it is concluded that our proposed method can effectively extract typical treatment processes from treatment records, and also has great potential to improve treatment outcome by personalizing the treatment process for patients with different conditions. CONCLUSION The extracted typical treatment processes are intuitive and can provide managerial guidance for CP redesign and optimization. In addition, our work can assist clinicians in clearly understanding their routine treatment processes and recommend optimal treatment pathways for patients.
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Affiliation(s)
- Jingfeng Chen
- Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, PR China
| | - Leilei Sun
- Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, PR China; School of Economics and Management, Tsinghua University, Beijing 100084, PR China
| | - Chonghui Guo
- Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, PR China.
| | - Wei Wei
- Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, PR China
| | - Yanming Xie
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Scieces, Beijing 100700, PR China
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Wu A, Lim M. Issues to Consider in Designing Immunotherapy Clinical Trials for Glioblastoma Management. ACTA ACUST UNITED AC 2016. [DOI: 10.4236/jct.2016.78060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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