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Yaqoob A, Verma NK, Aziz RM. Optimizing Gene Selection and Cancer Classification with Hybrid Sine Cosine and Cuckoo Search Algorithm. J Med Syst 2024; 48:10. [PMID: 38193948 DOI: 10.1007/s10916-023-02031-1] [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: 09/15/2023] [Accepted: 12/28/2023] [Indexed: 01/10/2024]
Abstract
Gene expression datasets offer a wide range of information about various biological processes. However, it is difficult to find the important genes among the high-dimensional biological data due to the existence of redundant and unimportant ones. Numerous Feature Selection (FS) techniques have been created to get beyond this obstacle. Improving the efficacy and precision of FS methodologies is crucial in order to identify significant genes amongst complicated complex biological data. In this work, we present a novel approach to gene selection called the Sine Cosine and Cuckoo Search Algorithm (SCACSA). This hybrid method is designed to work with well-known machine learning classifiers Support Vector Machine (SVM). Using a dataset on breast cancer, the hybrid gene selection algorithm's performance is carefully assessed and compared to other feature selection methods. To improve the quality of the feature set, we use minimum Redundancy Maximum Relevance (mRMR) as a filtering strategy in the first step. The hybrid SCACSA method is then used to enhance and optimize the gene selection procedure. Lastly, we classify the dataset according to the chosen genes by using the SVM classifier. Given the pivotal role gene selection plays in unraveling complex biological datasets, SCACSA stands out as an invaluable tool for the classification of cancer datasets. The findings help medical practitioners make well-informed decisions about cancer diagnosis and provide them with a valuable tool for navigating the complex world of gene expression data.
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Affiliation(s)
- Abrar Yaqoob
- School of Advanced Sciences and Languages, VIT Bhopal University, Kothrikalan, Sehore, 466114, India.
| | - Navneet Kumar Verma
- School of Advanced Sciences and Languages, VIT Bhopal University, Kothrikalan, Sehore, 466114, India
| | - Rabia Musheer Aziz
- School of Advanced Sciences and Languages, VIT Bhopal University, Kothrikalan, Sehore, 466114, India
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2
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Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol 2023; 16:114. [PMID: 38012673 PMCID: PMC10680201 DOI: 10.1186/s13045-023-01514-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.
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Affiliation(s)
- Chaoyi Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jianhui Yang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
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Hermoso-Durán S, Domper-Arnal MJ, Roncales P, Vega S, Sanchez-Gracia O, Ojeda JL, Lanas Á, Velazquez-Campoy A, Abian O. Bowel Preparation for Colonoscopy Changes Serum Composition as Detected by Thermal Liquid Biopsy and Fluorescence Spectroscopy. Cancers (Basel) 2023; 15:cancers15071952. [PMID: 37046613 PMCID: PMC10093451 DOI: 10.3390/cancers15071952] [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: 01/20/2023] [Revised: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 04/14/2023] Open
Abstract
(1) Background: About 50% of prescribed colonoscopies report no pathological findings. A secondary screening test after fecal immunochemical test positivity (FIT+) would be required. Considering thermal liquid biopsy (TLB) as a potential secondary test, the aim of this work was to study possible interferences of colonoscopy bowel preparation on TLB outcome on a retrospective study; (2) Methods: Three groups were studied: 1/514 FIT(+) patients enrolled in a colorectal screening program (CN and CP with normal and pathological colonoscopy, respectively), with blood samples obtained just before colonoscopy and after bowel preparation; 2/55 patients from the CN group with blood sample redrawn after only standard 8-10 h fasting and no bowel preparation (CNR); and 3/55 blood donors from the biobank considered as a healthy control group; (3) Results: The results showed that from the 514 patients undergoing colonoscopy, 247 had CN and 267 had CP. TLB parameters in these two groups were similar but different from those of the blood donors. The resampled patients (with normal colonoscopy and no bowel preparation) had similar TLB parameters to those of the blood donors. TLB parameters together with fluorescence spectra and other serum indicators (albumin and C-reactive protein) confirmed the statistically significant differences between normal colonoscopy patients with and without bowel preparation; (4) Conclusions: Bowel preparation seemed to alter serum protein levels and altered TLB parameters (different from a healthy subject). The diagnostic capability of other liquid-biopsy-based methods might also be compromised. Blood extraction after bowel preparation for colonoscopy should be avoided.
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Affiliation(s)
- Sonia Hermoso-Durán
- Institute of Biocomputation and Physics of Complex Systems (BIFI), Joint Unit GBsC-CSIC-BIFI, University of Zaragoza, 50018 Zaragoza, Spain
- Aragón Health Research Institute (IIS Aragón), 50009 Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain
| | - María José Domper-Arnal
- Aragón Health Research Institute (IIS Aragón), 50009 Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain
- Department of Gastroenterology, Lozano Blesa Clinic University Hospital, 50009 Zaragoza, Spain
| | - Pilar Roncales
- Aragón Health Research Institute (IIS Aragón), 50009 Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain
- Department of Gastroenterology, Lozano Blesa Clinic University Hospital, 50009 Zaragoza, Spain
| | - Sonia Vega
- Institute of Biocomputation and Physics of Complex Systems (BIFI), Joint Unit GBsC-CSIC-BIFI, University of Zaragoza, 50018 Zaragoza, Spain
| | - Oscar Sanchez-Gracia
- Department of Electronic Engineering and Communications, University of Zaragoza, 50009 Zaragoza, Spain
- SOTER BioAnalytics, Enrique Val, 50011 Zaragoza, Spain
| | - Jorge L Ojeda
- Department of Statistical Methods, University of Zaragoza, 50009 Zaragoza, Spain
| | - Ángel Lanas
- Aragón Health Research Institute (IIS Aragón), 50009 Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain
- Department of Gastroenterology, Lozano Blesa Clinic University Hospital, 50009 Zaragoza, Spain
- Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, 50009 Zaragoza, Spain
| | - Adrian Velazquez-Campoy
- Institute of Biocomputation and Physics of Complex Systems (BIFI), Joint Unit GBsC-CSIC-BIFI, University of Zaragoza, 50018 Zaragoza, Spain
- Aragón Health Research Institute (IIS Aragón), 50009 Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain
- Department of Biochemistry and Molecular and Cell Biology, University of Zaragoza, 50009 Zaragoza, Spain
| | - Olga Abian
- Institute of Biocomputation and Physics of Complex Systems (BIFI), Joint Unit GBsC-CSIC-BIFI, University of Zaragoza, 50018 Zaragoza, Spain
- Aragón Health Research Institute (IIS Aragón), 50009 Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain
- Department of Biochemistry and Molecular and Cell Biology, University of Zaragoza, 50009 Zaragoza, Spain
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Gayer AV, Yakimov BP, Sluchanko NN, Shirshin EA. Multifarious analytical capabilities of the UV/Vis protein fluorescence in blood plasma. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 286:122028. [PMID: 36327910 DOI: 10.1016/j.saa.2022.122028] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/09/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
Autofluorescence of blood plasma has been broadly considered as a prospective disease screening method. However, the assessment of such intrinsic fluorescence is mostly phenomenological, and its origin is still not fully understood, complicating its use in the clinical practice. Here we present the detailed evaluation of analytical capabilities, variability, and formation of blood plasma protein fluorescence based on the open dataset of excitation-emission matrices measured for ∼300 patients with suspected colorectal cancer, and our supporting model experiments. Using high-resolution size-exclusion chromatography coupled with comprehensive spectral analysis, we demonstrate, for the first time, the dominant role of HSA in the formation of blood plasma fluorescence in the visible spectral range (excitation wavelength >350 nm), presumably caused by its oxidative modifications. Furthermore, the diagnostic value of the tryptophan emission, as well as of the tyrosine fluorescence and visible fluorescence of proteins is shown by building a tree-based classification model that uses a small subset of physically interpretable fluorescence features for distinguishing between the control group and cancer patients with >80% accuracy. The obtained results extend current understanding and approaches used for the analysis of blood plasma fluorescence and pave the way for novel autofluorescence-based disease screening methods.
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Affiliation(s)
- Alexey V Gayer
- Faculty of Physics, M.V. Lomonosov Moscow State University, 1-2 Leninskie Gory, Moscow 119991, Russia; Laboratory of Clinical Biophotonics, Biomedical Science and Technology Park, Sechenov First Moscow State Medical University, Trubetskaya 8, Moscow 119048, Russia
| | - Boris P Yakimov
- Faculty of Physics, M.V. Lomonosov Moscow State University, 1-2 Leninskie Gory, Moscow 119991, Russia; Laboratory of Clinical Biophotonics, Biomedical Science and Technology Park, Sechenov First Moscow State Medical University, Trubetskaya 8, Moscow 119048, Russia
| | - Nikolai N Sluchanko
- A.N. Bach Institute of Biochemistry, Federal Research Center of Biotechnology of the Russian Academy of Sciences, Moscow 119071, Russia
| | - Evgeny A Shirshin
- Faculty of Physics, M.V. Lomonosov Moscow State University, 1-2 Leninskie Gory, Moscow 119991, Russia; Laboratory of Clinical Biophotonics, Biomedical Science and Technology Park, Sechenov First Moscow State Medical University, Trubetskaya 8, Moscow 119048, Russia.
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5
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A novel discrete learning-based intelligent methodology for breast cancer classification purposes. Artif Intell Med 2023; 139:102492. [PMID: 37100500 DOI: 10.1016/j.artmed.2023.102492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 01/20/2023]
Abstract
Classification is one of the most significant subfields of data mining that has been successfully applied to various applications. The literature has expended substantial effort to present more efficient and accurate classification models. Despite the diversity of the proposed models, they were all created using the same methodology, and their learning processes ignored a fundamental issue. In all existing classification model learning processes, a continuous distance-based cost function is optimized to estimate the unknown parameters. The classification problem's objective function is discrete. Consequently, applying a continuous cost function to a classification problem with a discrete objective function is illogical or inefficient. This paper proposes a novel classification methodology utilizing a discrete cost function in the learning process. To this end, one of the most popular intelligent classification models, the multilayer perceptron (MLP), is used to implement the proposed methodology. Theoretically, the classification performance of the proposed discrete learning-based MLP (DIMLP) model is not dissimilar to that of its continuous learning-based counterpart. Nevertheless, in this study, to demonstrate the efficacy of the DIMLP model, it was applied to several breast cancer classification datasets, and its classification rate was compared to that of the conventional continuous learning-based MLP model. The empirical results indicate that the proposed DIMLP model outperforms the MLP model across all datasets. The results demonstrate that the presented DIMLP classification model achieves an average classification rate of 94.70 %, a 6.95 % improvement over the classification rate of the traditional MLP model, which was 88.54 %. Therefore, the classification approach proposed in this study can be utilized as an alternative learning process in intelligent classification methods for medical decision-making and other classification applications, particularly when more accurate results are required.
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6
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Alzheimer's disease diagnosis by blood plasma molecular fluorescence spectroscopy (EEM). Sci Rep 2022; 12:16199. [PMID: 36171258 PMCID: PMC9519548 DOI: 10.1038/s41598-022-20611-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 09/15/2022] [Indexed: 11/08/2022] Open
Abstract
Despite tremendous research advances in detecting Alzheimer's disease (AD), traditional diagnostic tests remain expensive, time-consuming or invasive. The search for a low-cost, rapid, and minimally invasive test has marked a new era of research and technological developments toward establishing blood-based AD biomarkers. The current study has employed excitation-emission matrices (EEM) of fluorescence spectroscopy combined with machine learning to diagnose AD using blood plasma samples from 230 individuals (83 AD patients from 147 healthy controls). To evaluate the performance of the classification algorithms, we calculated the commonly used figures of merit (accuracy, sensitivity and specificity) and figures of merit that take into account the samples unbalance and the discrimination power of the models, as F2-score (F2), Matthews correlation coefficient (MCC) and test effectiveness (\documentclass[12pt]{minimal}
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\begin{document}$$\delta$$\end{document}δ). The classification models achieved satisfactory results: Parallel Factor Analysis with Quadratic Discriminant Analysis (PARAFAC-QDA) with 83.33% sensitivity, 100% specificity, 86.21% F2; and Tucker3-QDA with 91.67% sensitivity, 95.45% specificity and 91.67% F2. In addition, the classifiers show high overall performance with 94.12% accuracy and 0.87 MCC. Regarding the discrimination power between healthy and AD patients, the classification algorithms showed high effectiveness with the mean scores separated by three or more standard deviations. The PARAFAC's spectral profiles and the wavelength values from both models loading profiles can be used in future research to relate this information to plasma AD biomarkers. Our results point to a rapid, low-cost and minimally invasive blood-based method for AD diagnosis.
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Soares F, Yamashita G. Communication regarding the article "An efficient primary screening COVID-19 by serum Raman spectroscopy". JOURNAL OF RAMAN SPECTROSCOPY : JRS 2022; 53:1342-1346. [PMID: 35572166 PMCID: PMC9088369 DOI: 10.1002/jrs.6330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 01/27/2022] [Accepted: 03/03/2022] [Indexed: 06/15/2023]
Abstract
When performing computational modeling and machine learning experiments, it is imperative to follow a protocol that minimizes bias. In this communication, we share our concerns regarding the article "An efficient primary screening COVID-19 by serum Raman spectroscopy" published in this journal. We consider that the authors may have inadvertently biased their results by not guaranteeing complete independence of test samples from the training data. We corroborate our point by reproducing the experiment with the available data, showing that if full independence of the test set was ensured, the reported results should be lower. We ask the authors to provide more information regarding their article, as well as making available all code used to generate their results. Our experiments are available at https://doi.org/10.6084/m9.figshare.14124356.
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Affiliation(s)
- Felipe Soares
- Industrial EngineeringUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
| | - Gabrielli Yamashita
- Industrial EngineeringUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
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Battista A, Battista RA, Battista F, Iovane G, Landi RE. BH-index: A predictive system based on serum biomarkers and ensemble learning for early colorectal cancer diagnosis in mass screening. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 212:106494. [PMID: 34740064 DOI: 10.1016/j.cmpb.2021.106494] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 10/19/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Colorectal cancer is one of the most common malignancies among the general population. Artificial Intelligence methodologies based on serum parameters are in continuous development to obtain less expensive tools for highly sensitive diagnoses. This study proposes a predictive system based on serum biomarkers and ensemble learning to predict colorectal cancer presence and the related TNM stage in patients. METHODS We have selected 17 significant plasmatic proteins, i.e., Carcinoembryonic Antigen, CA 19-9, CA 125, CA 50, CA 72-4, Tissue Polypeptide Antigen, C-Reactive Protein, Ceruloplasmin, Haptoglobin, Transferrin, Ferritin, α-1-Antitrypsin, α-2-Macroglobulin, α-1 Acid Glycoprotein, Complement C4, Complement C3, and Retinol Binding Protein, regarding 345 patients (248 affected by the neoplastic disease). The proposed system consists of two predictors, i.e., binary and staging; the former predicts the presence/absence of cancer, while the latter identifies the related TNM stage (I, II, III, or IV). The experiments were conducted by deploying and comparing Random Forest, XGBoost, Support Vector Machine, and Multilayer Perceptron with feature selection based on Gini Importance and with dimensionality reduction via PCA. RESULTS The results show that the system composed of XGBoost as binary and staging predictor reaches 91.30% accuracy, 90% sensitivity, and 93.33% specificity for the absence/presence outcome, while 66.66% accuracy for the staging response. With the expansion of the training set in favor of positive patients and majority voting, the system composed of the combination of Support Vector Machine, XGBoost, and Multilayer Perceptron as the binary predictor reaches 98.03% accuracy, 100% sensitivity, and 92.30% specificity, while the combination of Random Forest, XGBoost, and Multilayer Perceptron as staging predictor achieves 60% accuracy. The final system reaches, in terms of accuracy, 98.03%, and 66.66% for the binary and staging predictors, respectively. It was also found that the biomarkers which contribute most to the binary decision are Ceruloplasmin and α-2-Macroglobulin, while the least significant dimensions are CA 50 and α-1-Antitrypsin; instead, Carcinoembryonic Antigen and α-1 Acid Glycoprotein are the most significant to the staging decision. CONCLUSIONS The present study proves the effectiveness of deploying serum biomarkers as feature dimensions for early colorectal cancer diagnosis and of using majority voting for noise reduction in the prediction.
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Affiliation(s)
- Antonio Battista
- A.O.U. S. Giovanni di Dio e Ruggi d'Aragona, UOC Chir Urg, UOC Laboratorio Analisi, Salerno, Italy
| | | | - Federica Battista
- IRCCS Foundation Policlinico San Matteo, University of Pavia, Pavia, Italy
| | - Gerardo Iovane
- Department of Computer Science, University of Salerno, Salerno, Italy
| | - Riccardo Emanuele Landi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
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Morcuende-Ventura V, Hermoso-Durán S, Abian-Franco N, Pazo-Cid R, Ojeda JL, Vega S, Sanchez-Gracia O, Velazquez-Campoy A, Sierra T, Abian O. Fluorescence Liquid Biopsy for Cancer Detection Is Improved by Using Cationic Dendronized Hyperbranched Polymer. Int J Mol Sci 2021; 22:6501. [PMID: 34204408 PMCID: PMC8234380 DOI: 10.3390/ijms22126501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/14/2021] [Accepted: 06/15/2021] [Indexed: 12/19/2022] Open
Abstract
(1) Background: Biophysical techniques applied to serum samples characterization could promote the development of new diagnostic tools. Fluorescence spectroscopy has been previously applied to biological samples from cancer patients and differences from healthy individuals were observed. Dendronized hyperbranched polymers (DHP) based on bis(hydroxymethyl)propionic acid (bis-MPA) were developed in our group and their potential biomedical applications explored. (2) Methods: A total of 94 serum samples from diagnosed cancer patients and healthy individuals were studied (20 pancreatic ductal adenocarcinoma, 25 blood donor, 24 ovarian cancer, and 25 benign ovarian cyst samples). (3) Results: Fluorescence spectra of serum samples (fluorescence liquid biopsy, FLB) in the presence and the absence of DHP-bMPA were recorded and two parameters from the signal curves obtained. A secondary parameter, the fluorescence spectrum score (FSscore), was calculated, and the diagnostic model assessed. For pancreatic ductal adenocarcinoma (PDAC) and ovarian cancer, the classification performance was improved when including DHP-bMPA, achieving high values of statistical sensitivity and specificity (over 85% for both pathologies). (4) Conclusions: We have applied FLB as a quick, simple, and minimally invasive promising technique in cancer diagnosis. The classification performance of the diagnostic method was further improved by using DHP-bMPA, which interacted differentially with serum samples from healthy and diseased subjects. These preliminary results set the basis for a larger study and move FLB closer to its clinical application, providing useful information for the oncologist during patient diagnosis.
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Affiliation(s)
- Violeta Morcuende-Ventura
- Instituto de Nanociencia y Materiales de Aragón (INMA), Química Orgánica, Facultad de Ciencias, CSIC-Universidad de Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Spain;
- Joint Units IQFR-CSIC-BIFI and GBsC-CSIC-BIFI, Institute of Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain; (S.H.-D.), (S.V.), (A.V.-C.)
| | - Sonia Hermoso-Durán
- Joint Units IQFR-CSIC-BIFI and GBsC-CSIC-BIFI, Institute of Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain; (S.H.-D.), (S.V.), (A.V.-C.)
- Instituto de Investigación Sanitaria Aragón (IIS Aragón), 50009 Zaragoza, Spain
| | | | - Roberto Pazo-Cid
- Hospital Universitario Miguel Servet (HUMS), Paseo Isabel la Católica, 1-3, 50009 Zaragoza, Spain;
| | - Jorge L. Ojeda
- Department of Statistical Methods, Universidad de Zaragoza, 50009 Zaragoza, Spain;
| | - Sonia Vega
- Joint Units IQFR-CSIC-BIFI and GBsC-CSIC-BIFI, Institute of Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain; (S.H.-D.), (S.V.), (A.V.-C.)
| | | | - Adrian Velazquez-Campoy
- Joint Units IQFR-CSIC-BIFI and GBsC-CSIC-BIFI, Institute of Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain; (S.H.-D.), (S.V.), (A.V.-C.)
- Instituto de Investigación Sanitaria Aragón (IIS Aragón), 50009 Zaragoza, Spain
- Departamento de Bioquímica y Biología Molecular y Celular, Universidad de Zaragoza, 50009 Zaragoza, Spain
- Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain
- Fundación ARAID, Gobierno de Aragón, 50018 Zaragoza, Spain
| | - Teresa Sierra
- Instituto de Nanociencia y Materiales de Aragón (INMA), Química Orgánica, Facultad de Ciencias, CSIC-Universidad de Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Spain;
| | - Olga Abian
- Joint Units IQFR-CSIC-BIFI and GBsC-CSIC-BIFI, Institute of Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain; (S.H.-D.), (S.V.), (A.V.-C.)
- Instituto de Investigación Sanitaria Aragón (IIS Aragón), 50009 Zaragoza, Spain
- Departamento de Bioquímica y Biología Molecular y Celular, Universidad de Zaragoza, 50009 Zaragoza, Spain
- Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain
- Instituto Aragonés de Ciencias de la Salud (IACS), 50009 Zaragoza, Spain
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Nikookar E, Naderi E, Rahnavard A. Cervical Cancer Prediction by Merging Features of Different Colposcopic Images and Using Ensemble Classifier. JOURNAL OF MEDICAL SIGNALS & SENSORS 2021; 11:67-78. [PMID: 34268095 PMCID: PMC8253312 DOI: 10.4103/jmss.jmss_16_20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 03/15/2020] [Accepted: 05/02/2020] [Indexed: 11/04/2022]
Abstract
Background Cervical cancer is a significant cause of cancer mortality in women, particularly in low-income countries. In regular cervical screening methods, such as colposcopy, an image is taken from the cervix of a patient. The particular image can be used by computer-aided diagnosis (CAD) systems that are trained using artificial intelligence algorithms to predict the possibility of cervical cancer. Artificial intelligence models had been highlighted in a number of cervical cancer studies. However, there are a limited number of studies that investigate the simultaneous use of three colposcopic screening modalities including Greenlight, Hinselmann, and Schiller. Methods We propose a cervical cancer predictor model which incorporates the result of different classification algorithms and ensemble classifiers. Our approach merges features of different colposcopic images of a patient. The feature vector of each image includes semantic medical features, subjective judgments, and a consensus. The class label of each sample is calculated using an aggregation function on expert judgments and consensuses. Results We investigated different aggregation strategies to find the best formula for aggregation function and then we evaluated our method using the quality assessment of digital colposcopies dataset, and our approach performance with 96% of sensitivity and 94% of specificity values yields a significant improvement in the field. Conclusion Our model can be used as a supportive clinical decision-making strategy by giving more reliable information to the clinical decision makers. Our proposed model also is more applicable in cervical cancer CAD systems compared to the available methods.
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Affiliation(s)
- Elham Nikookar
- Department of Computer Engineering, Faculty of Engineering, Shiahd Chamran University of Ahvaz, Ahvaz, Iran
| | - Ebrahim Naderi
- Department of Computer Engineering, University of Applied Science and Technology, Ahvaz, Iran
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington D.C., United States
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Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK. Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era. ACTA ACUST UNITED AC 2021; 28:1581-1607. [PMID: 33922402 PMCID: PMC8161764 DOI: 10.3390/curroncol28030149] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/09/2021] [Accepted: 04/20/2021] [Indexed: 12/24/2022]
Abstract
The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.
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Affiliation(s)
- Athanasia Mitsala
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
- Correspondence: ; Tel.: +30-6986423707
| | - Christos Tsalikidis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Michail Pitiakoudis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Constantinos Simopoulos
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Alexandra K. Tsaroucha
- Laboratory of Experimental Surgery & Surgical Research, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece;
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Yin B, Mi JY, Zhai HL, Zhao BQ, Bi KX. An effective approach to the early diagnosis of colorectal cancer based on three-dimensional fluorescence spectra of human blood plasma. J Pharm Biomed Anal 2020; 193:113757. [PMID: 33197831 DOI: 10.1016/j.jpba.2020.113757] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 11/03/2020] [Accepted: 11/04/2020] [Indexed: 11/16/2022]
Abstract
Colorectal cancer (CRC) is a common malignancy in the gastrointestinal tract, and its screening rates remain relatively low in the general population due to the lack of specific symptoms and effective methods. It is still in urgent need to develop rapid and reliable approach to the early diagnosis of CRC. Herein, based on the three-dimensional (3D) fluorescence spectra of human blood plasma, a combination strategy of Tchebichef image moments coupled with partial least squares-discriminate analysis (TM-PLS-DA) was proposed for the detection of CRC from three classes (CRC samples, adenomas samples and non-malignant findings). The established TM-PLS-DA classification model provided an 84 % correct classification for CRC prediction. Venetian blinds 10-fold cross validation was carried out. The error rates both in cross validation and test sets were less than 0.16. Sensitivity and specificity for CRC prediction were 0.95 and 0.88, respectively. At the same time, the diagnostic capacity of the proposed method was tested by receiver operating characteristics (ROC) analysis with area under the curve (AUC) of 0.94 for CRC diagnosis. These results demonstrate that the proposed TM-PLS-DA method based on the 3D fluorescence spectra of blood plasma has great advantage for the accurate CRC detection, which will provide a potential alternative approach for cancer diagnostics.
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Affiliation(s)
- Bo Yin
- College of Chemistry & Chemical Engineering, Lanzhou University, Lanzhou, 730000, PR China; College of Chemistry & Chemical Engineering, Qinghai Normal University, Xining, 810000, PR China
| | - Jia Ying Mi
- College of Chemistry & Chemical Engineering, Lanzhou University, Lanzhou, 730000, PR China
| | - Hong Lin Zhai
- College of Chemistry & Chemical Engineering, Lanzhou University, Lanzhou, 730000, PR China.
| | - Bing Qiang Zhao
- College of Chemistry & Chemical Engineering, Lanzhou University, Lanzhou, 730000, PR China
| | - Ke Xin Bi
- College of Chemistry & Chemical Engineering, Lanzhou University, Lanzhou, 730000, PR China
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Wang Y, Nie H, He X, Liao Z, Zhou Y, Zhou J, Ou C. The emerging role of super enhancer-derived noncoding RNAs in human cancer. Theranostics 2020; 10:11049-11062. [PMID: 33042269 PMCID: PMC7532672 DOI: 10.7150/thno.49168] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 08/23/2020] [Indexed: 02/06/2023] Open
Abstract
Super enhancers (SEs) are large clusters of adjacent enhancers that drive the expression of genes which regulate cellular identity; SE regions can be enriched with a high density of transcription factors, co-factors, and enhancer-associated epigenetic modifications. Through enhanced activation of their target genes, SEs play an important role in various diseases and conditions, including cancer. Recent studies have shown that SEs not only activate the transcriptional expression of coding genes to directly regulate biological functions, but also drive the transcriptional expression of non-coding RNAs (ncRNAs) to indirectly regulate biological functions. SE-derived ncRNAs play critical roles in tumorigenesis, including malignant proliferation, metastasis, drug resistance, and inflammatory response. Moreover, the abnormal expression of SE-derived ncRNAs is closely related to the clinical and pathological characterization of tumors. In this review, we summarize the functions and roles of SE-derived ncRNAs in tumorigenesis and discuss their prospective applications in tumor therapy. A deeper understanding of the potential mechanism underlying the action of SE-derived ncRNAs in tumorigenesis may provide new strategies for the early diagnosis of tumors and targeted therapy.
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