1
|
Gisca T, Munteanu IV, Vasilache IA, Melinte-Popescu AS, Volovat S, Scripcariu IS, Balan RA, Pavaleanu I, Socolov R, Carauleanu A, Vaduva C, Melinte-Popescu M, Adam AM, Adam G, Vicoveanu P, Socolov D. A Prospective Study on the Progression, Recurrence, and Regression of Cervical Lesions: Assessing Various Screening Approaches. J Clin Med 2024; 13:1368. [PMID: 38592206 PMCID: PMC10931951 DOI: 10.3390/jcm13051368] [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: 01/25/2024] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 04/10/2024] Open
Abstract
(1) Background: The prediction of cervical lesion evolution is a challenge for clinicians. This prospective study aimed to determine and compare the predictive accuracy of cytology, HPV genotyping, and p16/Ki67 dual staining alone or in combination with personal risk factors in the prediction of progression, regression, or persistence of cervical lesions in human papillomavirus (HPV)-infected patients; (2) Methods: This prospective study included HPV-positive patients with or without cervical lesions who underwent follow-up in a private clinic. We calculated the predictive performance of individual tests (cervical cytology, HPV genotyping, CINtecPlus results, and clinical risk factors) or their combination in the prediction of cervical lesion progression, regression, and persistence; (3) Results: The highest predictive performance for the progression of cervical lesions was achieved by a model comprising a Pap smear suggestive of high-grade squamous intraepithelial lesion (HSIL), the presence of 16/18 HPV strains, a positive p16/Ki67 dual staining result along with the presence of at least three clinical risk factors, which had a sensitivity (Se) of 74.42%, a specificity of 97.92%, an area under the receiver operating curve (AUC) of 0.961, and an accuracy of 90.65%. The prediction of cervical lesion regression or persistence was modest when using individual or combined tests; (4) Conclusions: Multiple testing or new biomarkers should be used to improve HPV-positive patient surveillance, especially for cervical lesion regression or persistence prediction.
Collapse
Affiliation(s)
- Tudor Gisca
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania (I.-S.S.); (I.P.); (R.S.); (P.V.); (D.S.)
| | - Iulian-Valentin Munteanu
- Clinical and Surgical Department, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, Romania;
| | - Ingrid-Andrada Vasilache
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania (I.-S.S.); (I.P.); (R.S.); (P.V.); (D.S.)
| | - Alina-Sinziana Melinte-Popescu
- Department of Mother and Newborn Care, Faculty of Medicine and Biological Sciences, ‘Ștefan cel Mare’ University, 720229 Suceava, Romania;
| | - Simona Volovat
- Department of Medical Oncology, University of Medicine and Pharmacy ‘Grigore T Popa’, 700115 Iasi, Romania
| | - Ioana-Sadyie Scripcariu
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania (I.-S.S.); (I.P.); (R.S.); (P.V.); (D.S.)
| | - Raluca-Anca Balan
- Department of Morphofunctional Sciences I, “Grigore T. Popa” University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania
| | - Ioana Pavaleanu
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania (I.-S.S.); (I.P.); (R.S.); (P.V.); (D.S.)
| | - Razvan Socolov
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania (I.-S.S.); (I.P.); (R.S.); (P.V.); (D.S.)
| | - Alexandru Carauleanu
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania (I.-S.S.); (I.P.); (R.S.); (P.V.); (D.S.)
| | - Constantin Vaduva
- Department of Mother and Child Medicine, Faculty of Medicine, University of Medicine and Pharmacy, 200349 Craiova, Romania;
| | - Marian Melinte-Popescu
- Department of Internal Medicine, Faculty of Medicine and Biological Sciences, ‘Ștefan cel Mare’ University, 720229 Suceava, Romania;
| | - Ana-Maria Adam
- Clinical and Surgical Department, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, Romania;
| | - Gigi Adam
- Department of Pharmaceutical Sciences, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, Romania
| | - Petronela Vicoveanu
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania (I.-S.S.); (I.P.); (R.S.); (P.V.); (D.S.)
| | - Demetra Socolov
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania (I.-S.S.); (I.P.); (R.S.); (P.V.); (D.S.)
| |
Collapse
|
2
|
Pei J, Wang G, Li Y, Li L, Li C, Wu Y, Liu J, Tian G. Utility of four machine learning approaches for identifying ulcerative colitis and Crohn's disease. Heliyon 2024; 10:e23439. [PMID: 38148824 PMCID: PMC10750181 DOI: 10.1016/j.heliyon.2023.e23439] [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: 08/29/2023] [Revised: 12/04/2023] [Accepted: 12/04/2023] [Indexed: 12/28/2023] Open
Abstract
Objective Peripheral blood routine parameters (PBRPs) are simple and easily acquired markers to identify ulcerative colitis (UC) and Crohn's disease (CD) and reveal the severity, whereas the diagnostic performance of individual PBRP is limited. We, therefore used four machine learning (ML) models to evaluate the diagnostic and predictive values of PBRPs for UC and CD. Methods A retrospective study was conducted by collecting the PBRPs of 414 inflammatory bowel disease (IBD) patients, 423 healthy controls (HCs), and 344 non-IBD intestinal diseases (non-IBD) patients. We used approximately 70 % of the PBRPs data from both patients and HCs for training, 30 % for testing, and another group for external verification. The area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnosis and prediction performance of these four ML models. Results Multi-layer perceptron artificial neural network model (MLP-ANN) yielded the highest diagnostic performance than the other three models in six subgroups in the training set, which is helpful for discriminating IBD and HCs, UC and CD, active CD and remissive CD, active UC and remissive UC, non-IBD and HCs, and IBD and non-IBD with the AUC of 1.00, 0.988, 0.942, 1.00, 0.986, and 0.97 in the testing set, as well as the AUC of 1.00, 1.00, 0.773, 0.904, 1.00 and 0.992 in the external validation set. Conclusion PBRPs-based MLP-ANN model exhibited good performance in discriminating between UC and CD and revealing the disease activity; however, a larger sample size and more models need to be considered for further research.
Collapse
Affiliation(s)
- Jingwen Pei
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan 646000, China
| | - Guobing Wang
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan 646000, China
| | - Yi Li
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan 646000, China
| | - Lan Li
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan 646000, China
| | - Chang Li
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan 646000, China
| | - Yu Wu
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan 646000, China
| | - Jinbo Liu
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan 646000, China
| | - Gang Tian
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan 646000, China
| |
Collapse
|
3
|
Phan TA, Sarower F, Duan J, Tian JP. Stochastic dynamics of human papillomavirus delineates cervical cancer progression. J Math Biol 2023; 87:85. [PMID: 37951849 PMCID: PMC11085997 DOI: 10.1007/s00285-023-02018-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/05/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023]
Abstract
Starting from a deterministic model, we propose and study a stochastic model for human papillomavirus infection and cervical cancer progression. Our analysis shows that the chronic infection state as random variables which have the ergodic invariant probability measure is necessary for progression from infected cell population to cervical cancer cells. It is shown that small progression rate from infected cells to precancerous cells and small microenvironmental noises associated with the progression rate and viral infection help to establish such chronic infection states. It implicates that large environmental noises associated with viral infection and the progression rate in vivo can reduce chronic infection. We further show that there will be a cervical cancer if the noise associated with precancerous cell growth is large enough. In addition, comparable numerical studies for the deterministic model and stochastic model, together with Hopf bifurcations in both deterministic and stochastic systems, highlight our analytical results.
Collapse
Affiliation(s)
- Tuan Anh Phan
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID, 83844, USA
| | - Farhana Sarower
- Department of Mathematical Sciences, New Mexico State University, Las Cruces, NM, 88001, USA
| | - Jinqiao Duan
- Departments of Mathematics, School of Sciences, Great Bay University, Dongguan, 523000, Guangdong, China
| | - Jianjun Paul Tian
- Department of Mathematical Sciences, New Mexico State University, Las Cruces, NM, 88001, USA.
| |
Collapse
|