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International BR. Retracted: Healthcare Biclustering-Based Prediction on Gene Expression Dataset. BIOMED RESEARCH INTERNATIONAL 2023; 2023:9803245. [PMID: 38188788 PMCID: PMC10769594 DOI: 10.1155/2023/9803245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 12/26/2023] [Indexed: 01/09/2024]
Abstract
[This retracts the article DOI: 10.1155/2022/2263194.].
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Bailleux C, Chardin D, Guigonis JM, Ferrero JM, Chateau Y, Humbert O, Pourcher T, Gal J. Survival analysis of patient groups defined by unsupervised machine learning clustering methods based on patient metabolomic data. Comput Struct Biotechnol J 2023; 21:5136-5143. [PMID: 37920813 PMCID: PMC10618114 DOI: 10.1016/j.csbj.2023.10.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 10/16/2023] [Accepted: 10/16/2023] [Indexed: 11/04/2023] Open
Abstract
Purpose Meta-analyses failed to accurately identify patients with non-metastatic breast cancer who are likely to benefit from chemotherapy, and metabolomics could provide new answers. In our previous published work, patients were clustered using five different unsupervised machine learning (ML) methods resulting in the identification of three clusters with distinct clinical and simulated survival data. The objective of this study was to evaluate the survival outcomes, with extended follow-up, using the same 5 different methods of unsupervised machine learning. Experimental design Forty-nine patients, diagnosed between 2013 and 2016, with non-metastatic BC were included retrospectively. Median follow-up was extended to 85.8 months. 449 metabolites were extracted from tumor resection samples by combined Liquid chromatography-mass spectrometry (LC-MS). Survival analyses were reported grouping together Cluster 1 and 2 versus cluster 3. Bootstrap optimization was applied. Results PCA k-means, K-sparse and Spectral clustering were the most effective methods to predict 2-year progression-free survival with bootstrap optimization (PFSb); as bootstrap example, with PCA k-means method, PFSb were 94% for cluster 1&2 versus 82% for cluster 3 (p = 0.01). PCA k-means method performed best, with higher reproducibility (mean HR=2 (95%CI [1.4-2.7]); probability of p ≤ 0.05 85%). Cancer-specific survival (CSS) and overall survival (OS) analyses highlighted a discrepancy between the 5 ML unsupervised methods. Conclusion Our study is a proof-of-principle that it is possible to use unsupervised ML methods on metabolomic data to predict PFS survival outcomes, with the best performance for PCA k-means. A larger population study is needed to draw conclusions from CSS and OS analyses.
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Affiliation(s)
- Caroline Bailleux
- University Côte d′Azur, Centre Antoine Lacassagne, Medical Oncology Department, Nice F-06189, France
- University Côte d′Azur, Commissariat à l′Energie Atomique et aux énergies alternatives, Institut Frédéric Joliot, Service Hospitalier Frédéric Joliot, laboratory Transporters in Oncology and Radiotherapy in Oncology (TIRO), School of medicine, Nice F-06100, France
| | - David Chardin
- University Côte d′Azur, Commissariat à l′Energie Atomique et aux énergies alternatives, Institut Frédéric Joliot, Service Hospitalier Frédéric Joliot, laboratory Transporters in Oncology and Radiotherapy in Oncology (TIRO), School of medicine, Nice F-06100, France
- University Côte d′Azur, Centre Antoine Lacassagne, Nuclear medicine Department, Nice F-06189, France
| | - Jean-Marie Guigonis
- University Côte d′Azur, Commissariat à l′Energie Atomique et aux énergies alternatives, Institut Frédéric Joliot, Service Hospitalier Frédéric Joliot, laboratory Transporters in Oncology and Radiotherapy in Oncology (TIRO), School of medicine, Nice F-06100, France
| | - Jean-Marc Ferrero
- University Côte d′Azur, Centre Antoine Lacassagne, Medical Oncology Department, Nice F-06189, France
| | - Yann Chateau
- University Côte d′Azur, Centre Antoine Lacassagne, Epidemiology and Biostatistics Department, Nice F-06189, France
| | - Olivier Humbert
- University Côte d′Azur, Commissariat à l′Energie Atomique et aux énergies alternatives, Institut Frédéric Joliot, Service Hospitalier Frédéric Joliot, laboratory Transporters in Oncology and Radiotherapy in Oncology (TIRO), School of medicine, Nice F-06100, France
- University Côte d′Azur, Centre Antoine Lacassagne, Nuclear medicine Department, Nice F-06189, France
| | - Thierry Pourcher
- University Côte d′Azur, Commissariat à l′Energie Atomique et aux énergies alternatives, Institut Frédéric Joliot, Service Hospitalier Frédéric Joliot, laboratory Transporters in Oncology and Radiotherapy in Oncology (TIRO), School of medicine, Nice F-06100, France
| | - Jocelyn Gal
- University Côte d′Azur, Centre Antoine Lacassagne, Epidemiology and Biostatistics Department, Nice F-06189, France
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Region Convolutional Neural Network for Brain Tumor Segmentation. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8335255. [PMID: 36124122 PMCID: PMC9482475 DOI: 10.1155/2022/8335255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/20/2022] [Accepted: 07/22/2022] [Indexed: 11/17/2022]
Abstract
Gliomas are often difficult to find and distinguish using typical manual segmentation approaches because of their vast range of changes in size, shape, and appearance. Furthermore, the manual annotation of cancer tissue segmentation under the close supervision of a human professional is both time-consuming and exhausting to perform. It will be easier and faster in the future to get accurate and quick diagnoses and treatments thanks to automated segmentation and survival rate prediction models that can be used now. In this article, a segmentation model is designed using RCNN that enables automatic prognosis on brain tumors using MRI. The study adopts a U-Net encoder for capturing the features during the training of the model. The feature extraction extracts geometric features for the estimation of tumor size. It is seen that the shape, location, and size of a tumor are significant factors in the estimation of prognosis. The experimental methods are conducted to test the efficacy of the model, and the results of the simulation show that the proposed method achieves a reduced error rate with increased accuracy than other methods.
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Sreenivasu SVN, Gomathi S, Kumar MJ, Prathap L, Madduri A, Almutairi KMA, Alonazi WB, Kali D, Jayadhas SA. Dense Convolutional Neural Network for Detection of Cancer from CT Images. BIOMED RESEARCH INTERNATIONAL 2022; 2022:1293548. [PMID: 35769667 PMCID: PMC9236787 DOI: 10.1155/2022/1293548] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/17/2022] [Accepted: 04/23/2022] [Indexed: 11/17/2022]
Abstract
In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods.
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Affiliation(s)
- S. V. N. Sreenivasu
- Department of Computer Science and Engineering, Narasaraopeta Engineering College, Narasaraopeta, Andhra Pradesh 522601, India
| | - S. Gomathi
- Department of Information Technology, Sri Sairam Engineering College, Chennai, Tamil Nadu 602109, India
| | - M. Jogendra Kumar
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh 522502, India
| | - Lavanya Prathap
- Department of Anatomy, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu 600077, India
| | - Abhishek Madduri
- Department of Engineering Management, Duke University, North Carolina 27708, USA
| | - Khalid M. A. Almutairi
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, P. O. Box: 10219, Riyadh-11433, Saudi Arabia
| | - Wadi B. Alonazi
- Health Administration Department, College of Business Administration, King Saud University, PO Box: 71115, Riyadh-11587, Saudi Arabia
| | - D. Kali
- Department of Mechanical Engineering, Ryerson University, Canada
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