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Rustandi T, Yumassik AM, Ilahi FS, Alfian R, Prihandiwati E, Susanto Y, Susilo YH, Ulfah M, Faizatun F. Repurposing therapy of ibrexafungerp vulvovaginal candidiasis drugs as cancer therapeutics. Front Pharmacol 2024; 15:1428755. [PMID: 38994207 PMCID: PMC11236599 DOI: 10.3389/fphar.2024.1428755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 05/31/2024] [Indexed: 07/13/2024] Open
Affiliation(s)
- Tedi Rustandi
- Pharmacy, Sekolah Tinggi Ilmu Kesehatan ISFI Banjarmasin, Banjarmasin, Indonesia
| | | | - Fitrah Shafran Ilahi
- Pharmacy, Sekolah Tinggi Ilmu Kesehatan ISFI Banjarmasin, Banjarmasin, Indonesia
| | - Riza Alfian
- Pharmacy, Sekolah Tinggi Ilmu Kesehatan ISFI Banjarmasin, Banjarmasin, Indonesia
| | - Erna Prihandiwati
- Pharmacy, Sekolah Tinggi Ilmu Kesehatan ISFI Banjarmasin, Banjarmasin, Indonesia
| | - Yugo Susanto
- Pharmacy, Sekolah Tinggi Ilmu Kesehatan ISFI Banjarmasin, Banjarmasin, Indonesia
| | - Yudi Hardi Susilo
- Pharmacy, Sekolah Tinggi Ilmu Kesehatan ISFI Banjarmasin, Banjarmasin, Indonesia
| | - Maria Ulfah
- Pharmacy, Sekolah Tinggi Ilmu Kesehatan ISFI Banjarmasin, Banjarmasin, Indonesia
| | - Faizatun Faizatun
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Pancasila University, Jakarta, Indonesia
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Didier AJ, Nigro A, Noori Z, Omballi MA, Pappada SM, Hamouda DM. Application of machine learning for lung cancer survival prognostication-A systematic review and meta-analysis. Front Artif Intell 2024; 7:1365777. [PMID: 38646415 PMCID: PMC11026647 DOI: 10.3389/frai.2024.1365777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 03/18/2024] [Indexed: 04/23/2024] Open
Abstract
Introduction Machine learning (ML) techniques have gained increasing attention in the field of healthcare, including predicting outcomes in patients with lung cancer. ML has the potential to enhance prognostication in lung cancer patients and improve clinical decision-making. In this systematic review and meta-analysis, we aimed to evaluate the performance of ML models compared to logistic regression (LR) models in predicting overall survival in patients with lung cancer. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A comprehensive search was conducted in Medline, Embase, and Cochrane databases using a predefined search query. Two independent reviewers screened abstracts and conflicts were resolved by a third reviewer. Inclusion and exclusion criteria were applied to select eligible studies. Risk of bias assessment was performed using predefined criteria. Data extraction was conducted using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist. Meta-analytic analysis was performed to compare the discriminative ability of ML and LR models. Results The literature search resulted in 3,635 studies, and 12 studies with a total of 211,068 patients were included in the analysis. Six studies reported confidence intervals and were included in the meta-analysis. The performance of ML models varied across studies, with C-statistics ranging from 0.60 to 0.85. The pooled analysis showed that ML models had higher discriminative ability compared to LR models, with a weighted average C-statistic of 0.78 for ML models compared to 0.70 for LR models. Conclusion Machine learning models show promise in predicting overall survival in patients with lung cancer, with superior discriminative ability compared to logistic regression models. However, further validation and standardization of ML models are needed before their widespread implementation in clinical practice. Future research should focus on addressing the limitations of the current literature, such as potential bias and heterogeneity among studies, to improve the accuracy and generalizability of ML models for predicting outcomes in patients with lung cancer. Further research and development of ML models in this field may lead to improved patient outcomes and personalized treatment strategies.
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Affiliation(s)
- Alexander J. Didier
- Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
| | - Anthony Nigro
- Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
| | - Zaid Noori
- Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
| | - Mohamed A. Omballi
- Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
| | - Scott M. Pappada
- Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
- Department of Anesthesiology, The University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
| | - Danae M. Hamouda
- Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
- Division of Hematology and Oncology, Department of Medicine, The University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
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Kaynar G, Cakmakci D, Bund C, Todeschi J, Namer IJ, Cicek AE. PiDeeL: metabolic pathway-informed deep learning model for survival analysis and pathological classification of gliomas. Bioinformatics 2023; 39:btad684. [PMID: 37952175 PMCID: PMC10663986 DOI: 10.1093/bioinformatics/btad684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/19/2023] [Accepted: 11/10/2023] [Indexed: 11/14/2023] Open
Abstract
MOTIVATION Online assessment of tumor characteristics during surgery is important and has the potential to establish an intra-operative surgeon feedback mechanism. With the availability of such feedback, surgeons could decide to be more liberal or conservative regarding the resection of the tumor. While there are methods to perform metabolomics-based tumor pathology prediction, their model complexity predictive performance is limited by the small dataset sizes. Furthermore, the information conveyed by the feedback provided on the tumor tissue could be improved both in terms of content and accuracy. RESULTS In this study, we propose a metabolic pathway-informed deep learning model (PiDeeL) to perform survival analysis and pathology assessment based on metabolite concentrations. We show that incorporating pathway information into the model architecture substantially reduces parameter complexity and achieves better survival analysis and pathological classification performance. With these design decisions, we show that PiDeeL improves tumor pathology prediction performance of the state-of-the-art in terms of the Area Under the ROC Curve by 3.38% and the Area Under the Precision-Recall Curve by 4.06%. Similarly, with respect to the time-dependent concordance index (c-index), PiDeeL achieves better survival analysis performance (improvement of 4.3%) when compared to the state-of-the-art. Moreover, we show that importance analyses performed on input metabolite features as well as pathway-specific neurons of PiDeeL provide insights into tumor metabolism. We foresee that the use of this model in the surgery room will help surgeons adjust the surgery plan on the fly and will result in better prognosis estimates tailored to surgical procedures. AVAILABILITY AND IMPLEMENTATION The code is released at https://github.com/ciceklab/PiDeeL. The data used in this study are released at https://zenodo.org/record/7228791.
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Affiliation(s)
- Gun Kaynar
- Computer Engineering Department, Bilkent University, 06800 Ankara, Turkey
| | - Doruk Cakmakci
- School of Computer Science, McGill University, Montreal, QC, H3A 0E9, Canada
| | - Caroline Bund
- MNMS Platform, University Hospitals of Strasbourg, Strasbourg 67098, France
- ICube, University of Strasbourg, CNRS UMR, 7357, Strasbourg 67000, France
- Department of Nuclear Medicine and Molecular Imaging, ICANS, Strasbourg 67000, France
| | - Julien Todeschi
- Department of Neurosurgery, University Hospitals of Strasbourg, Strasbourg, 67091, France
| | - Izzie Jacques Namer
- MNMS Platform, University Hospitals of Strasbourg, Strasbourg 67098, France
- ICube, University of Strasbourg, CNRS UMR, 7357, Strasbourg 67000, France
- Department of Nuclear Medicine and Molecular Imaging, ICANS, Strasbourg 67000, France
| | - A Ercument Cicek
- Computer Engineering Department, Bilkent University, 06800 Ankara, Turkey
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
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Ellen JG, Jacob E, Nikolaou N, Markuzon N. Autoencoder-based multimodal prediction of non-small cell lung cancer survival. Sci Rep 2023; 13:15761. [PMID: 37737469 PMCID: PMC10517020 DOI: 10.1038/s41598-023-42365-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 09/09/2023] [Indexed: 09/23/2023] Open
Abstract
The ability to accurately predict non-small cell lung cancer (NSCLC) patient survival is crucial for informing physician decision-making, and the increasing availability of multi-omics data offers the promise of enhancing prognosis predictions. We present a multimodal integration approach that leverages microRNA, mRNA, DNA methylation, long non-coding RNA (lncRNA) and clinical data to predict NSCLC survival and identify patient subtypes, utilizing denoising autoencoders for data compression and integration. Survival performance for patients with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) was compared across modality combinations and data integration methods. Using The Cancer Genome Atlas data, our results demonstrate that survival prediction models combining multiple modalities outperform single modality models. The highest performance was achieved with a combination of only two modalities, lncRNA and clinical, at concordance indices (C-indices) of 0.69 ± 0.03 for LUAD and 0.62 ± 0.03 for LUSC. Models utilizing all five modalities achieved mean C-indices of 0.67 ± 0.04 and 0.63 ± 0.02 for LUAD and LUSC, respectively, while the best individual modality performance reached C-indices of 0.64 ± 0.03 for LUAD and 0.59 ± 0.03 for LUSC. Analysis of biological differences revealed two distinct survival subtypes with over 900 differentially expressed transcripts.
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Affiliation(s)
- Jacob G Ellen
- Institute of Health Informatics, University College London, London, UK.
| | - Etai Jacob
- AstraZeneca, Oncology Data Science, Waltham, MA, USA
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Sun B, Chen L. Interpretable deep learning for improving cancer patient survival based on personal transcriptomes. Sci Rep 2023; 13:11344. [PMID: 37443344 PMCID: PMC10344908 DOI: 10.1038/s41598-023-38429-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 07/07/2023] [Indexed: 07/15/2023] Open
Abstract
Precision medicine chooses the optimal drug for a patient by considering individual differences. With the tremendous amount of data accumulated for cancers, we develop an interpretable neural network to predict cancer patient survival based on drug prescriptions and personal transcriptomes (CancerIDP). The deep learning model achieves 96% classification accuracy in distinguishing short-lived from long-lived patients. The Pearson correlation between predicted and actual months-to-death values is as high as 0.937. About 27.4% of patients may survive longer with an alternative medicine chosen by our deep learning model. The median survival time of all patients can increase by 3.9 months. Our interpretable neural network model reveals the most discriminating pathways in the decision-making process, which will further facilitate mechanistic studies of drug development for cancers.
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Affiliation(s)
- Bo Sun
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA, 90089, USA
| | - Liang Chen
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA, 90089, USA.
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Chen M, Kong C, Qiao E, Chen Y, Chen W, Jiang X, Fang S, Zhang D, Chen M, Chen W, Ji J. Multi-algorithms analysis for pre-treatment prediction of response to transarterial chemoembolization in hepatocellular carcinoma on multiphase MRI. Insights Imaging 2023; 14:38. [PMID: 36854872 PMCID: PMC9975141 DOI: 10.1186/s13244-023-01380-2] [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: 11/07/2022] [Accepted: 01/29/2023] [Indexed: 03/02/2023] Open
Abstract
OBJECTIVES This study compared the accuracy of predicting transarterial chemoembolization (TACE) outcomes for hepatocellular carcinoma (HCC) patients in the four different classifiers, and comprehensive models were constructed to improve predictive performance. METHODS The subjects recruited for this study were HCC patients who had received TACE treatment from April 2016 to June 2021. All participants underwent enhanced MRI scans before and after intervention, and pertinent clinical information was collected. Registry data for the 144 patients were randomly assigned to training and test datasets. The robustness of the trained models was verified by another independent external validation set of 28 HCC patients. The following classifiers were employed in the radiomics experiment: machine learning classifiers k-nearest neighbor (KNN), support vector machine (SVM), the least absolute shrinkage and selection operator (Lasso), and deep learning classifier deep neural network (DNN). RESULTS DNN and Lasso models were comparable in the training set, while DNN performed better in the test set and the external validation set. The CD model (Clinical & DNN merged model) achieved an AUC of 0.974 (95% CI: 0.951-0.998) in the training set, superior to other models whose AUCs varied from 0.637 to 0.943 (p < 0.05). The CD model generalized well on the test set (AUC = 0.831) and external validation set (AUC = 0.735). CONCLUSIONS DNN model performs better than other classifiers in predicting TACE response. Integrating with clinically significant factors, the CD model may be valuable in pre-treatment counseling of HCC patients who may benefit the most from TACE intervention.
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Affiliation(s)
- Mingzhen Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China
| | - Chunli Kong
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Enqi Qiao
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China
| | - Yaning Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China
| | - Weiyue Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Xiaole Jiang
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Shiji Fang
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Dengke Zhang
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Minjiang Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Weiqian Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China. .,Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China. .,Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China.
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China. .,Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China. .,Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China.
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Zeng J, Li K, Cao F, Zheng Y. Development and validation of survival prediction model for gastric adenocarcinoma patients using deep learning: A SEER-based study. Front Oncol 2023; 13:1131859. [PMID: 36959782 PMCID: PMC10029996 DOI: 10.3389/fonc.2023.1131859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 02/21/2023] [Indexed: 03/09/2023] Open
Abstract
Background The currently available prediction models, such as the Cox model, were too simplistic to correctly predict the outcome of gastric adenocarcinoma patients. This study aimed to develop and validate survival prediction models for gastric adenocarcinoma patients using the deep learning survival neural network. Methods A total of 14,177 patients with gastric adenocarcinoma from the Surveillance, Epidemiology, and End Results (SEER) database were included in the study and randomly divided into the training and testing group with a 7:3 ratio. Two algorithms were chosen to build the prediction models, and both algorithms include random survival forest (RSF) and a deep learning based-survival prediction algorithm (DeepSurv). Also, a traditional Cox proportional hazard (CoxPH) model was constructed for comparison. The consistency index (C-index), Brier score, and integrated Brier score (IBS) were used to evaluate the model's predictive performance. The accuracy of predicting survival at 1, 3, 5, and 10 years was also assessed using receiver operating characteristic curves (ROC), calibration curves, and area under the ROC curve (AUC). Results Gastric adenocarcinoma patients were randomized into a training group (n = 9923) and a testing group (n = 4254). DeepSurv showed the best performance among the three models (c-index: 0.772, IBS: 0.1421), which was superior to that of the traditional CoxPH model (c-index: 0.755, IBS: 0.1506) and the RSF with 3-year survival prediction model (c-index: 0.766, IBS: 0.1502). The DeepSurv model produced superior accuracy and calibrated survival estimates predicting 1-, 3- 5- and 10-year survival (AUC: 0.825-0.871). Conclusions A deep learning algorithm was developed to predict more accurate prognostic information for gastric cancer patients. The DeepSurv model has advantages over the CoxPH and RSF models and performs well in discriminative performance and calibration.
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Han X, Goldstein M, Ranganath R. Survival Mixture Density Networks. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2022; 182:224-248. [PMID: 37706207 PMCID: PMC10498417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
Survival analysis, the art of time-to-event modeling, plays an important role in clinical treatment decisions. Recently, continuous time models built from neural ODEs have been proposed for survival analysis. However, the training of neural ODEs is slow due to the high computational complexity of neural ODE solvers. Here, we propose an efficient alternative for flexible continuous time models, called Survival Mixture Density Networks (Survival MDNs). Survival MDN applies an invertible positive function to the output of Mixture Density Networks (MDNs). While MDNs produce flexible real-valued distributions, the invertible positive function maps the model into the time-domain while preserving a tractable density. Using four datasets, we show that Survival MDN performs better than, or similarly to continuous and discrete time baselines on concordance, integrated Brier score and integrated binomial log-likelihood. Meanwhile, Survival MDNs are also faster than ODE-based models and circumvent binning issues in discrete models.
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Qin X, Yin D, Dong X, Chen D, Zhang S. Survival prediction model for right-censored data based on improved composite quantile regression neural network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:7521-7542. [PMID: 35801434 DOI: 10.3934/mbe.2022354] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
With the development of the field of survival analysis, statistical inference of right-censored data is of great importance for the study of medical diagnosis. In this study, a right-censored data survival prediction model based on an improved composite quantile regression neural network framework, called rcICQRNN, is proposed. It incorporates composite quantile regression with the loss function of a multi-hidden layer feedforward neural network, combined with an inverse probability weighting method for survival prediction. Meanwhile, the hyperparameters involved in the neural network are adjusted using the WOA algorithm, integer encoding and One-Hot encoding are implemented to encode the classification features, and the BWOA variable selection method for high-dimensional data is proposed. The rcICQRNN algorithm was tested on a simulated dataset and two real breast cancer datasets, and the performance of the model was evaluated by three evaluation metrics. The results show that the rcICQRNN-5 model is more suitable for analyzing simulated datasets. The One-Hot encoding of the WOA-rcICQRNN-30 model is more applicable to the NKI70 data. The model results are optimal for k=15 after feature selection for the METABRIC dataset. Finally, we implemented the method for cross-dataset validation. On the whole, the Cindex results using One-Hot encoding data are more stable, making the proposed rcICQRNN prediction model flexible enough to assist in medical decision making. It has practical applications in areas such as biomedicine, insurance actuarial and financial economics.
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Affiliation(s)
- Xiwen Qin
- School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
| | - Dongmei Yin
- School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
| | - Xiaogang Dong
- School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
| | - Dongxue Chen
- School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
| | - Shuang Zhang
- School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
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Lou SJ, Hou MF, Chang HT, Lee HH, Chiu CC, Yeh SCJ, Shi HY. Breast Cancer Surgery 10-Year Survival Prediction by Machine Learning: A Large Prospective Cohort Study. BIOLOGY 2021; 11:biology11010047. [PMID: 35053045 PMCID: PMC8773427 DOI: 10.3390/biology11010047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 02/07/2023]
Abstract
Machine learning algorithms have proven to be effective for predicting survival after surgery, but their use for predicting 10-year survival after breast cancer surgery has not yet been discussed. This study compares the accuracy of predicting 10-year survival after breast cancer surgery in the following five models: a deep neural network (DNN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classifier (NBC) and Cox regression (COX), and to optimize the weighting of significant predictors. The subjects recruited for this study were breast cancer patients who had received breast cancer surgery (ICD-9 cm 174-174.9) at one of three southern Taiwan medical centers during the 3-year period from June 2007, to June 2010. The registry data for the patients were randomly allocated to three datasets, one for training (n = 824), one for testing (n = 177), and one for validation (n = 177). Prediction performance comparisons revealed that all performance indices for the DNN model were significantly (p < 0.001) higher than in the other forecasting models. Notably, the best predictor of 10-year survival after breast cancer surgery was the preoperative Physical Component Summary score on the SF-36. The next best predictors were the preoperative Mental Component Summary score on the SF-36, postoperative recurrence, and tumor stage. The deep-learning DNN model is the most clinically useful method to predict and to identify risk factors for 10-year survival after breast cancer surgery. Future research should explore designs for two-level or multi-level models that provide information on the contextual effects of the risk factors on breast cancer survival.
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Affiliation(s)
- Shi-Jer Lou
- Graduate Institute of Technological and Vocational Education, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan;
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
| | - Ming-Feng Hou
- Department of Biomedical Science and Environmental Biology, College of Life Science, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
- Department of Surgery, Division of Breast Oncology and Surgery, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
- Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Hong-Tai Chang
- Department of Surgery, Kaohsiung Municipal United Hospital, Kaohsiung 80457, Taiwan;
| | - Hao-Hsien Lee
- Department of General Surgery, Chi Mei Medical Center, Liouying 73658, Taiwan;
| | - Chong-Chi Chiu
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan;
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
| | - Shu-Chuan Jennifer Yeh
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
- Department of Business Management, National Sun Yat-sen University, Kaohsiung 80420, Taiwan
| | - Hon-Yi Shi
- Graduate Institute of Technological and Vocational Education, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan;
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
- Department of Business Management, National Sun Yat-sen University, Kaohsiung 80420, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
- Correspondence: ; Tel.: +886-7-3211101 (ext. 2648); Fax: +886-7-3137487
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Zhan X, Long H, Gou F, Duan X, Kong G, Wu J. A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer. SENSORS 2021; 21:s21237996. [PMID: 34884000 PMCID: PMC8659811 DOI: 10.3390/s21237996] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/26/2021] [Accepted: 11/28/2021] [Indexed: 12/15/2022]
Abstract
In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients' medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.
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Affiliation(s)
- Xiangbing Zhan
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (X.Z.); (X.D.); (G.K.)
| | - Huiyun Long
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (X.Z.); (X.D.); (G.K.)
- Correspondence: (H.L.); (J.W.)
| | - Fangfang Gou
- School of Computer Science and Engineering, Central South University, Changsha 410083, China;
| | - Xun Duan
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (X.Z.); (X.D.); (G.K.)
| | - Guangqian Kong
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (X.Z.); (X.D.); (G.K.)
| | - Jia Wu
- School of Computer Science and Engineering, Central South University, Changsha 410083, China;
- Research Center for Artificial Intelligence, Monash University, Clayton, VIC 3800, Australia
- Correspondence: (H.L.); (J.W.)
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