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Mallikarjuna T, Thummadi NB, Vindal V, Manimaran P. Prioritizing cervical cancer candidate genes using chaos game and fractal-based time series approach. Theory Biosci 2024:10.1007/s12064-024-00418-3. [PMID: 38807013 DOI: 10.1007/s12064-024-00418-3] [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: 08/17/2023] [Accepted: 05/14/2024] [Indexed: 05/30/2024]
Abstract
Cervical cancer is one of the most severe threats to women worldwide and holds fourth rank in lethality. It is estimated that 604, 127 cervical cancer cases have been reported in 2020 globally. With advancements in high throughput technologies and bioinformatics, several cervical candidate genes have been proposed for better therapeutic strategies. In this paper, we intend to prioritize the candidate genes that are involved in cervical cancer progression through a fractal time series-based cross-correlations approach. we apply the chaos game representation theory combining a two-dimensional multifractal detrended cross-correlations approach among the known and candidate genes involved in cervical cancer progression to prioritize the candidate genes. We obtained 16 candidate genes that showed cross-correlation with known cancer genes. Functional enrichment analysis of the candidate genes shows that they involve GO terms: biological processes, cell-cell junction assembly, cell-cell junction organization, regulation of cell shape, cortical actin cytoskeleton organization, and actomyosin structure organization. KEGG pathway analysis revealed genes' role in Rap1 signaling pathway, ErbB signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, Acute myeloid leukemia, chronic myeloid leukemia, Breast cancer, Thyroid cancer, Bladder cancer, and Gastric cancer. Further, we performed survival analysis and prioritized six genes CDH2, PAIP1, BRAF, EPB41L3, OSMR, and RUNX1 as potential candidate genes for cervical cancer that has a crucial role in tumor progression. We found that our study through this integrative approach an efficient tool and paved a new way to prioritize the candidate genes and these genes could be evaluated experimentally for potential validation. We suggest this may be useful in analyzing the nucleotide sequences and protein sequences for clustering, classification, class affiliation, etc.
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Affiliation(s)
- T Mallikarjuna
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India
| | - N B Thummadi
- Department of Animal Biology, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India
| | - Vaibhav Vindal
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India
| | - P Manimaran
- School of Physics, University of Hyderabad, Gachibowli, Hyderabad, Telangana, 500046, India.
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Mustafa WA, Ismail S, Mokhtar FS, Alquran H, Al-Issa Y. Cervical Cancer Detection Techniques: A Chronological Review. Diagnostics (Basel) 2023; 13:diagnostics13101763. [PMID: 37238248 DOI: 10.3390/diagnostics13101763] [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: 05/03/2023] [Revised: 05/12/2023] [Accepted: 05/15/2023] [Indexed: 05/28/2023] Open
Abstract
Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included "(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)". Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease's burden on women worldwide.
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Affiliation(s)
- Wan Azani Mustafa
- Faculty of Electrical Engineering Technology, Campus Pauh Putra, Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia
- Advanced Computing (AdvComp), Centre of Excellence (CoE), Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia
| | - Shahrina Ismail
- Faculty of Science and Technology, Universiti Sains Islam Malaysia (USIM), Bandar Baru Nilai 71800, Negeri Sembilan, Malaysia
| | - Fahirah Syaliza Mokhtar
- Faculty of Business, Economy and Social Development, Universiti Malaysia Terengganu, Kuala Nerus 21300, Terengganu, Malaysia
| | - Hiam Alquran
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, 556, Irbid 21163, Jordan
| | - Yazan Al-Issa
- Department of Computer Engineering, Yarmouk University, Irbid 22110, Jordan
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Allahqoli L, Laganà AS, Mazidimoradi A, Salehiniya H, Günther V, Chiantera V, Karimi Goghari S, Ghiasvand MM, Rahmani A, Momenimovahed Z, Alkatout I. Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review. Diagnostics (Basel) 2022; 12:2771. [PMID: 36428831 PMCID: PMC9689914 DOI: 10.3390/diagnostics12112771] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/06/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. MATERIALS AND METHODS Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. RESULTS The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80-100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9-98.22% and 51.8-96.2%, respectively. CONCLUSION The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.
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Affiliation(s)
- Leila Allahqoli
- Midwifery Department, Ministry of Health and Medical Education, Tehran 1467664961, Iran
| | - Antonio Simone Laganà
- Unit of Gynecologic Oncology, ARNAS “Civico-Di Cristina-Benfratelli”, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, 90127 Palermo, Italy
| | - Afrooz Mazidimoradi
- Neyriz Public Health Clinic, Shiraz University of Medical Sciences, Shiraz 7134814336, Iran
| | - Hamid Salehiniya
- Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand 9717853577, Iran
| | - Veronika Günther
- University Hospitals Schleswig-Holstein, Campus Kiel, Kiel School of Gynaecological Endoscopy, Arnold-Heller-Str. 3, Haus 24, 24105 Kiel, Germany
| | - Vito Chiantera
- Unit of Gynecologic Oncology, ARNAS “Civico-Di Cristina-Benfratelli”, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, 90127 Palermo, Italy
| | - Shirin Karimi Goghari
- School of Industrial and Systems Engineering, Tarbiat Modares University (TMU), Tehran 1411713114, Iran
| | - Mohammad Matin Ghiasvand
- Department of Computer Engineering, Amirkabir University of Technology (AUT), Tehran 1591634311, Iran
| | - Azam Rahmani
- Nursing and Midwifery Care Research Centre, School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran 141973317, Iran
| | - Zohre Momenimovahed
- Reproductive Health Department, Qom University of Medical Sciences, Qom 3716993456, Iran
| | - Ibrahim Alkatout
- University Hospitals Schleswig-Holstein, Campus Kiel, Kiel School of Gynaecological Endoscopy, Arnold-Heller-Str. 3, Haus 24, 24105 Kiel, Germany
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Das N, Alexandrov S, Gilligan KE, Dwyer RM, Saager RB, Ghosh N, Leahy M. Characterization of nanosensitive multifractality in submicron scale tissue morphology and its alteration in tumor progression. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-200223R. [PMID: 33432788 PMCID: PMC7797786 DOI: 10.1117/1.jbo.26.1.016003] [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: 07/10/2020] [Accepted: 12/09/2020] [Indexed: 05/02/2023]
Abstract
SIGNIFICANCE Assessment of disease using optical coherence tomography is an actively investigated problem, owing to many unresolved challenges in early disease detection, diagnosis, and treatment response monitoring. The early manifestation of disease or precancer is typically associated with subtle alterations in the tissue dielectric and ultrastructural morphology. In addition, biological tissue is known to have ultrastructural multifractality. AIM Detection and characterization of nanosensitive structural morphology and multifractality in the tissue submicron structure. Quantification of nanosensitive multifractality and its alteration in progression of tumor. APPROACH We have developed a label free nanosensitive multifractal detrended fluctuation analysis(nsMFDFA) technique in combination with multifractal analysis and nanosensitive optical coherence tomography (nsOCT). The proposed method deployed for extraction and quantification of nanosensitive multifractal parameters in mammary fat pad (MFP). RESULTS Initially, the nsOCT approach is numerically validated on synthetic submicron axial structures. The nsOCT technique was applied to pathologically characterized MFP of murine breast tissue to extract depth-resolved nanosensitive submicron structures. Subsequently, two-dimensional MFDFA were deployed on submicron structural en face images to extract nanosensitive tissue multifractality. We found that nanosensitive multifractality increases in transition from healthy to tumor. CONCLUSIONS This method for extraction of nanosensitive tissue multifractality promises to provide a noninvasive diagnostic tool for early disease detection and monitoring treatment response. The novel ability to delineate the dominant submicron scale nanosensitive multifractal properties may also prove useful for characterizing a wide variety of complex scattering media of non-biological origin.
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Affiliation(s)
- Nandan Das
- National University of Ireland, Tissue Optics and Microcirculation Imaging, Galway, Ireland
- Linköping University, Biomedical Imaging and Spectroscopy, Clinical Instrument Translation, Linköping, Sweden
- Address all correspondence to Nandan Das,
| | - Sergey Alexandrov
- National University of Ireland, Tissue Optics and Microcirculation Imaging, Galway, Ireland
| | - Katie E. Gilligan
- National University of Ireland Galway, Discipline of Surgery, Lambe Institute for Translational Research, Galway, Ireland
| | - Róisín M. Dwyer
- National University of Ireland Galway, Discipline of Surgery, Lambe Institute for Translational Research, Galway, Ireland
| | - Rolf B. Saager
- Linköping University, Biomedical Imaging and Spectroscopy, Clinical Instrument Translation, Linköping, Sweden
| | - Nirmalya Ghosh
- Indian Institute of Science Education and Research Kolkata, Bio-Optics and Nano-Photonics, Kolkata, India
| | - Martin Leahy
- National University of Ireland, Tissue Optics and Microcirculation Imaging, Galway, Ireland
- Institute of Photonic Sciences, Barcelona, Spain
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