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Ruiz Sarrias O, Gónzalez Deza C, Rodríguez Rodríguez J, Arrizibita Iriarte O, Vizcay Atienza A, Zumárraga Lizundia T, Sayar Beristain O, Aldaz Pastor A. Predicting Severe Haematological Toxicity in Gastrointestinal Cancer Patients Undergoing 5-FU-Based Chemotherapy: A Bayesian Network Approach. Cancers (Basel) 2023; 15:4206. [PMID: 37686482 PMCID: PMC10486471 DOI: 10.3390/cancers15174206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/28/2023] [Accepted: 08/16/2023] [Indexed: 09/10/2023] Open
Abstract
PURPOSE Severe toxicity is reported in about 30% of gastrointestinal cancer patients receiving 5-Fluorouracil (5-FU)-based chemotherapy. To date, limited tools exist to identify at risk patients in this setting. The objective of this study was to address this need by designing a predictive model using a Bayesian network, a probabilistic graphical model offering robust, explainable predictions. METHODS We utilized a dataset of 267 gastrointestinal cancer patients, conducting preprocessing, and splitting it into TRAIN and TEST sets (80%:20% ratio). The RandomForest algorithm assessed variable importance based on MeanDecreaseGini coefficient. The bnlearn R library helped design a Bayesian network model using a 10-fold cross-validation on the TRAIN set and the aic-cg method for network structure optimization. The model's performance was gauged based on accuracy, sensitivity, and specificity, using cross-validation on the TRAIN set and independent validation on the TEST set. RESULTS The model demonstrated satisfactory performance with an average accuracy of 0.85 (±0.05) and 0.80 on TRAIN and TEST datasets, respectively. The sensitivity and specificity were 0.82 (±0.14) and 0.87 (±0.07) for the TRAIN dataset, and 0.71 and 0.83 for the TEST dataset, respectively. A user-friendly tool was developed for clinical implementation. CONCLUSIONS Despite several limitations, our Bayesian network model demonstrated a high level of accuracy in predicting the risk of developing severe haematological toxicity in gastrointestinal cancer patients receiving 5-FU-based chemotherapy. Future research should aim at model validation in larger cohorts of patients and different clinical settings.
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Affiliation(s)
- Oskitz Ruiz Sarrias
- Department of Mathematics and Statistic, NNBi, 31191 Esquiroz, Navarra, Spain; (O.R.S.)
| | - Cristina Gónzalez Deza
- Department of Medical Oncology, Clínica Universidad De Navarra, 31008 Pamplona, Navarra, Spain; (C.G.D.); (J.R.R.); (T.Z.L.)
| | - Javier Rodríguez Rodríguez
- Department of Medical Oncology, Clínica Universidad De Navarra, 31008 Pamplona, Navarra, Spain; (C.G.D.); (J.R.R.); (T.Z.L.)
| | | | - Angel Vizcay Atienza
- Department of Medical Oncology, Clínica Universidad De Navarra, 31008 Pamplona, Navarra, Spain; (C.G.D.); (J.R.R.); (T.Z.L.)
| | - Teresa Zumárraga Lizundia
- Department of Medical Oncology, Clínica Universidad De Navarra, 31008 Pamplona, Navarra, Spain; (C.G.D.); (J.R.R.); (T.Z.L.)
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Chattopadhyay S, Singh PK, Ijaz MF, Kim S, Sarkar R. SnapEnsemFS: a snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis. Sci Rep 2023; 13:9937. [PMID: 37336964 DOI: 10.1038/s41598-023-36921-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 06/12/2023] [Indexed: 06/21/2023] Open
Abstract
Colorectal cancer is the third most common type of cancer diagnosed annually, and the second leading cause of death due to cancer. Early diagnosis of this ailment is vital for preventing the tumours to spread and plan treatment to possibly eradicate the disease. However, population-wide screening is stunted by the requirement of medical professionals to analyse histological slides manually. Thus, an automated computer-aided detection (CAD) framework based on deep learning is proposed in this research that uses histological slide images for predictions. Ensemble learning is a popular strategy for fusing the salient properties of several models to make the final predictions. However, such frameworks are computationally costly since it requires the training of multiple base learners. Instead, in this study, we adopt a snapshot ensemble method, wherein, instead of the traditional method of fusing decision scores from the snapshots of a Convolutional Neural Network (CNN) model, we extract deep features from the penultimate layer of the CNN model. Since the deep features are extracted from the same CNN model but for different learning environments, there may be redundancy in the feature set. To alleviate this, the features are fed into Particle Swarm Optimization, a popular meta-heuristic, for dimensionality reduction of the feature space and better classification. Upon evaluation on a publicly available colorectal cancer histology dataset using a five-fold cross-validation scheme, the proposed method obtains a highest accuracy of 97.60% and F1-Score of 97.61%, outperforming existing state-of-the-art methods on the same dataset. Further, qualitative investigation of class activation maps provide visual explainability to medical practitioners, as well as justifies the use of the CAD framework in screening of colorectal histology. Our source codes are publicly accessible at: https://github.com/soumitri2001/SnapEnsemFS .
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Affiliation(s)
- Soumitri Chattopadhyay
- Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata, 700106, West Bengal, India
| | - Pawan Kumar Singh
- Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata, 700106, West Bengal, India
| | - Muhammad Fazal Ijaz
- Department of Mechanical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Grattam Street, Parkville, VIC, 3010, Australia.
| | - SeongKi Kim
- National Centre of Excellence in Software, Sangmyung University, Seoul, 03016, Korea.
| | - Ram Sarkar
- Department of Computer Science & Engineering, Jadavpur University, Kolkata, 700032, India
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3
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Alshammari A. Ensemble recurrent neural network with whale optimization algorithm-based DNA sequence classification for medical applications. Soft comput 2023:1-14. [PMID: 37362270 PMCID: PMC10231859 DOI: 10.1007/s00500-023-08435-y] [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] [Accepted: 05/04/2023] [Indexed: 06/28/2023]
Abstract
The modern data-driven era has facilitated the gathering of large quantities of biomedical and clinical data. The deoxyribonucleic acid gene expression datasets have become a vital focus for the research community because of their capability to detect pathogens via 'biomarkers' or particular modifications in the gene sequence which portray a specific pathogen. Metaheuristic-related feature selection (FS) efficiently filters out only the pertinent genes out of large feature sets to lessen the data storage and computation requirements. This paper embraces the whale optimization algorithm for the FS issue in HD microarray data for the effectual propagation of candidate solutions to reach global optima over sufficient iterations. The chosen data are classified by employing an ensemble recurrent network (ERNN) that retains the amalgamation of long short-term memory, bidirectional long short-term memory, and gated recurrent units. Analysis of this proposed ERNN methodology would be performed by correlating with diverse advanced methodologies, and thus, the ERNN attains 99.59% precision and 99.59% accuracy.
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Affiliation(s)
- Abdulaziz Alshammari
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
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4
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Qiu Y, Wu Z, Wang J, Zhang C, Zhang H. Introduction of Materials Genome Technology and Its Applications in the Field of Biomedical Materials. MATERIALS (BASEL, SWITZERLAND) 2023; 16:1906. [PMID: 36903027 PMCID: PMC10004319 DOI: 10.3390/ma16051906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 02/13/2023] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
Traditional research and development (R&D) on biomedical materials depends heavily on the trial and error process, thereby leading to huge economic and time burden. Most recently, materials genome technology (MGT) has been recognized as an effective approach to addressing this problem. In this paper, the basic concepts involved in the MGT are introduced, and the applications of MGT in the R&D of metallic, inorganic non-metallic, polymeric, and composite biomedical materials are summarized; in view of the existing limitations of MGT for R&D of biomedical materials, potential strategies are proposed on the establishment and management of material databases, the upgrading of high-throughput experimental technology, the construction of data mining prediction platforms, and the training of relevant materials talents. In the end, future trend of MGT for R&D of biomedical materials is proposed.
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Affiliation(s)
| | | | | | - Chao Zhang
- Correspondence: (C.Z.); (H.Z.); Tel.: +86-20-39332145 (C.Z. & H.Z.)
| | - Heye Zhang
- Correspondence: (C.Z.); (H.Z.); Tel.: +86-20-39332145 (C.Z. & H.Z.)
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Al-Shaikh A, Mahafzah BA, Alshraideh M. Hybrid harmony search algorithm for social network contact tracing of COVID-19. Soft comput 2023; 27:3343-3365. [PMID: 34220301 PMCID: PMC8237257 DOI: 10.1007/s00500-021-05948-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/04/2021] [Indexed: 02/05/2023]
Abstract
The coronavirus disease 2019 (COVID-19) was first reported in December 2019 in Wuhan, China, and then moved to almost every country showing an unprecedented outbreak. The world health organization declared COVID-19 a pandemic. Since then, millions of people were infected, and millions have lost their lives all around the globe. By the end of 2020, effective vaccines that could prevent the fast spread of the disease started to loom on the horizon. Nevertheless, isolation, social distancing, face masks, and quarantine are the best-known measures, in the time being, to fight the pandemic. On the other hand, contact tracing is an effective procedure in tracking infections and saving others' lives. In this paper, we devise a new approach using a hybrid harmony search (HHS) algorithm that casts the problem of finding strongly connected components (SCCs) to contact tracing. This new approach is named as hybrid harmony search contact tracing (HHS-CT) algorithm. The hybridization is achieved by integrating the stochastic hill climbing into the operators' design of the harmony search algorithm. The HHS-CT algorithm is compared to other existing algorithms of finding SCCs in directed graphs, where it showed its superiority over these algorithms. The devised approach provides a 77.18% enhancement in terms of run time and an exceptional average error rate of 1.7% compared to the other existing algorithms of finding SCCs.
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Affiliation(s)
- Ala’a Al-Shaikh
- Learning and Teaching Technology Center, Al-Balqa Applied University, Al-Salt, 19117 Jordan
| | - Basel A. Mahafzah
- Department of Computer Science, King Abdulla II School of Information Technology, The University of Jordan, Amman, 11942 Jordan
| | - Mohammad Alshraideh
- Department of Computer Science, King Abdulla II School of Information Technology, The University of Jordan, Amman, 11942 Jordan
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Abdulkhaleq MT, Rashid TA, Alsadoon A, Hassan BA, Mohammadi M, Abdullah JM, Chhabra A, Ali SL, Othman RN, Hasan HA, Azad S, Mahmood NA, Abdalrahman SS, Rasul HO, Bacanin N, Vimal S. Harmony search: Current studies and uses on healthcare systems. Artif Intell Med 2022; 131:102348. [DOI: 10.1016/j.artmed.2022.102348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 05/08/2022] [Accepted: 06/30/2022] [Indexed: 11/29/2022]
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Ginghina O, Hudita A, Zamfir M, Spanu A, Mardare M, Bondoc I, Buburuzan L, Georgescu SE, Costache M, Negrei C, Nitipir C, Galateanu B. Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification. Front Oncol 2022; 12:856575. [PMID: 35356214 PMCID: PMC8959149 DOI: 10.3389/fonc.2022.856575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/16/2022] [Indexed: 01/19/2023] Open
Abstract
Colorectal cancer (CRC) is the second most frequently diagnosed type of cancer and a major worldwide public health concern. Despite the global efforts in the development of modern therapeutic strategies, CRC prognosis is strongly correlated with the stage of the disease at diagnosis. Early detection of CRC has a huge impact in decreasing mortality while pre-lesion detection significantly reduces the incidence of the pathology. Even though the management of CRC patients is based on robust diagnostic methods such as serum tumor markers analysis, colonoscopy, histopathological analysis of tumor tissue, and imaging methods (computer tomography or magnetic resonance), these strategies still have many limitations and do not fully satisfy clinical needs due to their lack of sensitivity and/or specificity. Therefore, improvements of the current practice would substantially impact the management of CRC patients. In this view, liquid biopsy is a promising approach that could help clinicians screen for disease, stratify patients to the best treatment, and monitor treatment response and resistance mechanisms in the tumor in a regular and minimally invasive manner. Liquid biopsies allow the detection and analysis of different tumor-derived circulating markers such as cell-free nucleic acids (cfNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) in the bloodstream. The major advantage of this approach is its ability to trace and monitor the molecular profile of the patient's tumor and to predict personalized treatment in real-time. On the other hand, the prospective use of artificial intelligence (AI) in medicine holds great promise in oncology, for the diagnosis, treatment, and prognosis prediction of disease. AI has two main branches in the medical field: (i) a virtual branch that includes medical imaging, clinical assisted diagnosis, and treatment, as well as drug research, and (ii) a physical branch that includes surgical robots. This review summarizes findings relevant to liquid biopsy and AI in CRC for better management and stratification of CRC patients.
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Affiliation(s)
- Octav Ginghina
- Department II, University of Medicine and Pharmacy “Carol Davila” Bucharest, Bucharest, Romania
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Ariana Hudita
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
| | - Marius Zamfir
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Andrada Spanu
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Mara Mardare
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Irina Bondoc
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | | | - Sergiu Emil Georgescu
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
| | - Marieta Costache
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
| | - Carolina Negrei
- Department of Toxicology, University of Medicine and Pharmacy “Carol Davila” Bucharest, Bucharest, Romania
| | - Cornelia Nitipir
- Department II, University of Medicine and Pharmacy “Carol Davila” Bucharest, Bucharest, Romania
- Department of Oncology, Elias University Emergency Hospital, Bucharest, Romania
| | - Bianca Galateanu
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
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Shadowed Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search and Differential Evolution for Optimal Design of Fuzzy Controllers. MATHEMATICS 2021. [DOI: 10.3390/math9192439] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
This article mainly focuses on the utilization of shadowed type-2 fuzzy systems used to achieve the goal of dynamically adapting the parameters of two already known algorithms in the literature: the harmony search and the differential evolution algorithms. It has already been established that type-2 fuzzy logic enhances the performance of metaheuristics by enabling parameter adaptation; however, the utilization of fuzzy logic results in an increased execution time. For this reason, in this article, the shadowed type-2 fuzzy approach is put forward as a way of reducing execution time, while maintaining the good results that the complete type-2 fuzzy model produces. The harmony search and differential evolution algorithms with shadowed type-2 parameter adaptations were applied to the problem of optimally designing fuzzy controllers. The simulations were performed with the controllers working in an ideal situation, and then with a real situation under different noise levels in order to reach a conclusion regarding the performance of each of the algorithms that were applied.
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9
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Predicting the Reputation of Pharmaceutical Firms with Financing and Geographical Location Data. MATHEMATICS 2021. [DOI: 10.3390/math9161893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Reputation is a strategic asset for firms, but has been poorly studied in the pharmaceutical industry, particularly in relation to their financial and stock-market performance. This work aimed to predict the probability of a firm being included in a pharmaceutical reputation index (Merco and PatientView), and the position it occupies, according to its economic–financial and stock-market outcomes and its geographical location. Fifty firms with excellent sales in 2019 and their rankings in 2017–2019 were employed. The methodology followed was logistic regression. Their research and development (R&D) expenditures and dividends strongly influenced them being included in both rankings. Non-Asian pharmaceutical companies were more likely to belong to the two reputation indices than Asian ones, and to occupy the best positions in the Merco ranking. Although no large differences appeared in the firms in both indices, differences were found in the position that pharmaceutical companies occupied in rankings and in the variables that contribute to them occupying these positions. Being in PatientView influenced dividends, sales, and income, while appearing in Merco showed accounting aspects like value in books and debt ratio.
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Al-Shaikh A, Mahafzah BA, Alshraideh M. Hybrid harmony search algorithm for social network contact tracing of COVID-19. Soft comput 2021. [DOI: https://doi.org/10.1007/s00500-021-05948-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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11
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Al-Shaikh A, Mahafzah BA, Alshraideh M. Hybrid harmony search algorithm for social network contact tracing of COVID-19. Soft comput 2021. [DOI: https:/doi.org/10.1007/s00500-021-05948-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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12
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Karbhari Y, Basu A, Geem ZW, Han GT, Sarkar R. Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach. Diagnostics (Basel) 2021; 11:895. [PMID: 34069841 PMCID: PMC8157360 DOI: 10.3390/diagnostics11050895] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/14/2021] [Accepted: 05/16/2021] [Indexed: 12/13/2022] Open
Abstract
COVID-19 is a disease caused by the SARS-CoV-2 virus. The COVID-19 virus spreads when a person comes into contact with an affected individual. This is mainly through drops of saliva or nasal discharge. Most of the affected people have mild symptoms while some people develop acute respiratory distress syndrome (ARDS), which damages organs like the lungs and heart. Chest X-rays (CXRs) have been widely used to identify abnormalities that help in detecting the COVID-19 virus. They have also been used as an initial screening procedure for individuals highly suspected of being infected. However, the availability of radiographic CXRs is still scarce. This can limit the performance of deep learning (DL) based approaches for COVID-19 detection. To overcome these limitations, in this work, we developed an Auxiliary Classifier Generative Adversarial Network (ACGAN), to generate CXRs. Each generated X-ray belongs to one of the two classes COVID-19 positive or normal. To ensure the goodness of the synthetic images, we performed some experimentation on the obtained images using the latest Convolutional Neural Networks (CNNs) to detect COVID-19 in the CXRs. We fine-tuned the models and achieved more than 98% accuracy. After that, we also performed feature selection using the Harmony Search (HS) algorithm, which reduces the number of features while retaining classification accuracy. We further release a GAN-generated dataset consisting of 500 COVID-19 radiographic images.
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Affiliation(s)
- Yash Karbhari
- Department of Information Technology, Pune Vidyarthi Griha’s College of Engineering and Technology, Pune 411009, India;
| | - Arpan Basu
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India; (A.B.); (R.S.)
| | - Zong Woo Geem
- College of IT Convergence, Gachon University, 1342 Seongnam Daero, Seongnam 13120, Korea;
| | - Gi-Tae Han
- College of IT Convergence, Gachon University, 1342 Seongnam Daero, Seongnam 13120, Korea;
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India; (A.B.); (R.S.)
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