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Li Z, Zeng CM, Dong YG, Cao Y, Yu LY, Liu HY, Tian X, Tian R, Zhong CY, Zhao TT, Liu JS, Chen Y, Li LF, Huang ZY, Wang YY, Hu Z, Zhang J, Liang JX, Zhou P, Lu YQ. A segmentation model to detect cevical lesions based on machine learning of colposcopic images. Heliyon 2023; 9:e21043. [PMID: 37928028 PMCID: PMC10623278 DOI: 10.1016/j.heliyon.2023.e21043] [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: 01/31/2023] [Revised: 10/12/2023] [Accepted: 10/13/2023] [Indexed: 11/07/2023] Open
Abstract
Background Semantic segmentation is crucial in medical image diagnosis. Traditional deep convolutional neural networks excel in image classification and object detection but fall short in segmentation tasks. Enhancing the accuracy and efficiency of detecting high-level cervical lesions and invasive cancer poses a primary challenge in segmentation model development. Methods Between 2018 and 2022, we retrospectively studied a total of 777 patients, comprising 339 patients with high-level cervical lesions and 313 patients with microinvasive or invasive cervical cancer. Overall, 1554 colposcopic images were put into the DeepLabv3+ model for learning. Accuracy, Precision, Specificity, and mIoU were employed to evaluate the performance of the model in the prediction of cervical high-level lesions and cancer. Results Experiments showed that our segmentation model had better diagnosis efficiency than colposcopic experts and other artificial intelligence models, and reached Accuracy of 93.29 %, Precision of 87.2 %, Specificity of 90.1 %, and mIoU of 80.27 %, respectively. Conclution The DeepLabv3+ model had good performance in the segmentation of cervical lesions in colposcopic post-acetic-acid images and can better assist colposcopists in improving the diagnosis.
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Affiliation(s)
- Zhen Li
- Department of Gynecological Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, China
| | - Chu-Mei Zeng
- Department of Obstetrics and gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510062, China
| | - Yan-Gang Dong
- Institute for Brain Research and Rehabilitation, the South China Normal University, Guangzhou, Guangdong, 510631, China
| | - Ying Cao
- Department of Obstetrics and Gynecology, Academician expert workstation, The Central Hospital of Wuhan, Tongji Medical College Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China
| | - Li-Yao Yu
- Department of Obstetrics and Gynecology, Academician expert workstation, The Central Hospital of Wuhan, Tongji Medical College Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China
| | - Hui-Ying Liu
- Department of Obstetrics and gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510062, China
| | - Xun Tian
- Department of Obstetrics and Gynecology, Academician expert workstation, The Central Hospital of Wuhan, Tongji Medical College Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China
| | - Rui Tian
- the Generulor Company Bio-X Lab, Zhuhai, Guangdong, 519060, China
| | - Chao-Yue Zhong
- the Generulor Company Bio-X Lab, Zhuhai, Guangdong, 519060, China
| | - Ting-Ting Zhao
- the Generulor Company Bio-X Lab, Zhuhai, Guangdong, 519060, China
| | - Jia-Shuo Liu
- Department of Obstetrics and gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510062, China
| | - Ye Chen
- Department of Obstetrics and gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510062, China
| | - Li-Fang Li
- Department of Obstetrics and gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510062, China
| | - Zhe-Ying Huang
- Department of Obstetrics and gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510062, China
| | - Yu-Yan Wang
- Department of Obstetrics and gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510062, China
| | - Zheng Hu
- Department of Gynecological Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, China
| | - Jingjing Zhang
- Department of Gynecological Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, China
| | - Jiu-Xing Liang
- Institute for Brain Research and Rehabilitation, the South China Normal University, Guangzhou, Guangdong, 510631, China
| | - Ping Zhou
- Department of Gynecology, Dongguan Maternal and Child Hospital, Dongguan, Guangdong, 523057, China
| | - Yi-Qin Lu
- Department of Gynecology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 101121, China
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Wu A, Xue P, Abulizi G, Tuerxun D, Rezhake R, Qiao Y. Artificial intelligence in colposcopic examination: A promising tool to assist junior colposcopists. Front Med (Lausanne) 2023; 10:1060451. [PMID: 37056736 PMCID: PMC10088560 DOI: 10.3389/fmed.2023.1060451] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 02/08/2023] [Indexed: 03/17/2023] Open
Abstract
Introduction Well-trained colposcopists are in huge shortage worldwide, especially in low-resource areas. Here, we aimed to evaluate the Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) to detect abnormalities based on digital colposcopy images, especially focusing on its role in assisting junior colposcopist to correctly identify the lesion areas where biopsy should be performed. Materials and methods This is a hospital-based retrospective study, which recruited the women who visited colposcopy clinics between September 2021 to January 2022. A total of 366 of 1,146 women with complete medical information recorded by a senior colposcopist and valid histology results were included. Anonymized colposcopy images were reviewed by CAIADS and a junior colposcopist separately, and the junior colposcopist reviewed the colposcopy images with CAIADS results (named CAIADS-Junior). The diagnostic accuracy and biopsy efficiency of CAIADS and CAIADS-Junior were assessed in detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+), CIN3+, and cancer in comparison with the senior and junior colposcipists. The factors influencing the accuracy of CAIADS were explored. Results For CIN2 + and CIN3 + detection, CAIADS showed a sensitivity at ~80%, which was not significantly lower than the sensitivity achieved by the senior colposcopist (for CIN2 +: 80.6 vs. 91.3%, p = 0.061 and for CIN3 +: 80.0 vs. 90.0%, p = 0.189). The sensitivity of the junior colposcopist was increased significantly with the assistance of CAIADS (for CIN2 +: 95.1 vs. 79.6%, p = 0.002 and for CIN3 +: 97.1 vs. 85.7%, p = 0.039) and was comparable to those of the senior colposcopists (for CIN2 +: 95.1 vs. 91.3%, p = 0.388 and for CIN3 +: 97.1 vs. 90.0%, p = 0.125). In detecting cervical cancer, CAIADS achieved the highest sensitivity at 100%. For all endpoints, CAIADS showed the highest specificity (55-64%) and positive predictive values compared to both senior and junior colposcopists. When CIN grades became higher, the average biopsy numbers decreased for the subspecialists and CAIADS required a minimum number of biopsies to detect per case (2.2-2.6 cut-points). Meanwhile, the biopsy sensitivity of the junior colposcopist was the lowest, but the CAIADS-assisted junior colposcopist achieved a higher biopsy sensitivity. Conclusion Colposcopic Artificial Intelligence Auxiliary Diagnostic System could assist junior colposcopists to improve diagnostic accuracy and biopsy efficiency, which might be a promising solution to improve the quality of cervical cancer screening in low-resource settings.
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Affiliation(s)
- Aiyuan Wu
- The Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, China
| | - Peng Xue
- School of Population Medicine and Public Health, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Guzhalinuer Abulizi
- The Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, China
| | - Dilinuer Tuerxun
- The Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, China
| | - Remila Rezhake
- The Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, China
| | - Youlin Qiao
- The Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, China
- School of Population Medicine and Public Health, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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Pulumati A, Pulumati A, Dwarakanath BS, Verma A, Papineni RVL. Technological advancements in cancer diagnostics: Improvements and limitations. Cancer Rep (Hoboken) 2023; 6:e1764. [PMID: 36607830 PMCID: PMC9940009 DOI: 10.1002/cnr2.1764] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 10/20/2022] [Accepted: 11/27/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Cancer is characterized by the rampant proliferation, growth, and infiltration of malignantly transformed cancer cells past their normal boundaries into adjacent tissues. It is the leading cause of death worldwide, responsible for approximately 19.3 million new diagnoses and 10 million deaths globally in 2020. In the United States alone, the estimated number of new diagnoses and deaths is 1.9 million and 609 360, respectively. Implementation of currently existing cancer diagnostic techniques such as positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance spectroscopy (MRS), and molecular diagnostic techniques, have enabled early detection rates and are instrumental not only for the therapeutic management of cancer patients, but also for early detection of the cancer itself. The effectiveness of these cancer screening programs are heavily dependent on the rate of accurate precursor lesion identification; an increased rate of identification allows for earlier onset treatment, thus decreasing the incidence of invasive cancer in the long-term, and improving the overall prognosis. Although these diagnostic techniques are advantageous due to lack of invasiveness and easier accessibility within the clinical setting, several limitations such as optimal target definition, high signal to background ratio and associated artifacts hinder the accurate diagnosis of specific types of deep-seated tumors, besides associated high cost. In this review we discuss various imaging, molecular, and low-cost diagnostic tools and related technological advancements, to provide a better understanding of cancer diagnostics, unraveling new opportunities for effective management of cancer, particularly in low- and middle-income countries (LMICs). RECENT FINDINGS Herein we discuss various technological advancements that are being utilized to construct an assortment of new diagnostic techniques that incorporate hardware, image reconstruction software, imaging devices, biomarkers, and even artificial intelligence algorithms, thereby providing a reliable diagnosis and analysis of the tumor. Also, we provide a brief account of alternative low cost-effective cancer therapy devices (CryoPop®, LumaGEM®, MarginProbe®) and picture archiving and communication systems (PACS), emphasizing the need for multi-disciplinary collaboration among radiologists, pathologists, and other involved specialties for improving cancer diagnostics. CONCLUSION Revolutionary technological advancements in cancer imaging and molecular biology techniques are indispensable for the accurate diagnosis and prognosis of cancer.
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Affiliation(s)
- Akhil Pulumati
- University of Missouri‐Kansas CityKansas CityMissouriUSA
| | - Anika Pulumati
- University of Missouri‐Kansas CityKansas CityMissouriUSA
| | - Bilikere S. Dwarakanath
- Central Research FacilitySri Ramachandra Institute of Higher Education and Research PorurChennaiIndia
- Department of BiotechnologyIndian Academy Degree CollegeBangaloreIndia
| | | | - Rao V. L. Papineni
- PACT & Health LLCBranfordConnecticutUSA
- Department of SurgeryUniversity of Kansas Medical CenterKansas CityKansasUSA
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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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Zheng S, Liang Y, Tan Y, Li L, Liu Q, Liu T, Lu X. Small Tweaks, Major Changes: Post-Translational Modifications That Occur within M2 Macrophages in the Tumor Microenvironment. Cancers (Basel) 2022; 14:5532. [PMID: 36428622 PMCID: PMC9688270 DOI: 10.3390/cancers14225532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/21/2022] [Accepted: 11/07/2022] [Indexed: 11/12/2022] Open
Abstract
The majority of proteins are subjected to post-translational modifications (PTMs), regardless of whether they occur in or after biosynthesis of the protein. Capable of altering the physical and chemical properties and functions of proteins, PTMs are thus crucial. By fostering the proliferation, migration, and invasion of cancer cells with which they communicate in the tumor microenvironment (TME), M2 macrophages have emerged as key cellular players in the TME. Furthermore, growing evidence illustrates that PTMs can occur in M2 macrophages as well, possibly participating in molding the multifaceted characteristics and physiological behaviors in the TME. Hence, there is a need to review the PTMs that have been reported to occur within M2 macrophages. Although there are several reviews available regarding the roles of M2 macrophages, the majority of these reviews overlooked PTMs occurring within M2 macrophages. Considering this, in this review, we provide a review focusing on the advancement of PTMs that have been reported to take place within M2 macrophages, mainly in the TME, to better understand the performance of M2 macrophages in the tumor microenvironment. Incidentally, we also briefly cover the advances in developing inhibitors that target PTMs and the application of artificial intelligence (AI) in the prediction and analysis of PTMs at the end of the review.
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Affiliation(s)
- Shutao Zheng
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Yan Liang
- Department of Pathology, Basic Medicine College, Xinjiang Medical University, Urumqi 830017, China
| | - Yiyi Tan
- Department of Pathology, Basic Medicine College, Xinjiang Medical University, Urumqi 830017, China
| | - Lu Li
- Department of Clinical Laboratory, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Qing Liu
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Tao Liu
- Department of Clinical Laboratory, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Xiaomei Lu
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
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