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Loizzi V, Comes MC, Arezzo F, Apostol AI, Bove S, Fanizzi A, Fruscio R, Gregorc V, Legge F, Mancari R, Marchetti C, Negri S, Russo G, Vertechy L, Scambia G, Massafra R, Cormio G. Validation of machine learning-based models to predict and explain the risk of ovarian cancer: a multicentric study on BRCA-mutated patients undergoing risk-reducing salpingo-oophorectomy. Front Oncol 2025; 15:1574037. [PMID: 40303993 PMCID: PMC12037974 DOI: 10.3389/fonc.2025.1574037] [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: 02/10/2025] [Accepted: 03/24/2025] [Indexed: 05/02/2025] Open
Abstract
Objective BRCA-mutated women are recommended to undergo bilateral risk-reducing salpingo-oophorectomy (RRSO) after childbearing, due to the lack of effective methods that could be able to early detect the occurrence of ovarian cancer. Thus, predictive machine learning (ML) techniques could be crucial to aid clinicians in identifying high-risk BRCA-mutated patients and determining the appropriate timing for performing RRSO. Methods In this work, we addressed this task by developing explainable ML models using clinical data referred to a multicentric cohort of 694 BRCA-mutated patients from six Italian centers (Policlinico Gemelli, IRCCS San Gerardo, Policlinico Bari, Istituto Tumori Regina Elena, Istituto Tumori Giovanni Paolo II, Ospedale F. Miulli), who performed salpingo-oophorectomy, out of which 39 patients showed tumor (5.6%). Data from Istituto Tumori Regina Elena and Policlinico Bari were used as External Validation Cohort (EVC). The other data were employed as Investigational Cohort (IC). Resampling and ensemble techniques were implemented to handle dataset imbalance. Explainable techniques enabled us to identify some protective and risk factors predicted by the models with respect to the task under study. Results The best ML model achieved an AUC value of 79.3% (95% CI: 75.3% - 83.0%), an accuracy value of 73.8% (95% CI: 69.6% - 78.2%), a sensitivity value of 66.7% (95% CI: 58.1% - 75.3%), a specificity value of 74.3% (95% CI: 68.7% - 80.0%), and a G-mean value of 70.4% (95% CI: 63.0% - 76.0%) on EVC. Although the model demonstrated good overall performance, its limited sensitivity reduces its effectiveness in this high-risk population. The variables CA125, age and MatoRRSO were found to be the most significant risk factors, in agreement with the clinical perspective. Conversely, variables such as Estroprogestinuse and PregnancyNfdt played a protective factor role. Conclusion Our ML proposal explores the intricate relationships between multiple clinical variables, with a particular emphasis on understanding their non-linear associations. However, while our approach provides valuable insights into risk assessment for BRCA-mutated patients, its current predictive capacity does not significantly improve upon existing clinical models.
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Affiliation(s)
- Vera Loizzi
- S.S.D. Ginecologia Oncologica Clinicizzata, IRCCS Istituto Tumori Giovanni Paolo II, Bari, Italy
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), University of Bari Aldo Moro, Bari, Italy
| | - Maria Colomba Comes
- Laboratorio di Biostatistica e Bioinformatica, IRCCS Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Francesca Arezzo
- S.S.D. Ginecologia Oncologica Clinicizzata, IRCCS Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Adriana Ionelia Apostol
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
- Dipartimento Scienze della Vita e Sanità Pubblica, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Samantha Bove
- Laboratorio di Biostatistica e Bioinformatica, IRCCS Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Annarita Fanizzi
- Laboratorio di Biostatistica e Bioinformatica, IRCCS Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Robert Fruscio
- Department of Medicine and Surgery, University of Milan-Bicocca, Milan, Italy
- Division of Gynecologic Surgery, IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
| | | | - Francesco Legge
- Unità di Ginecologia Oncologica, “F. Miulli” Ospedale Generale Regionale, Bari, Italy
| | - Rosanna Mancari
- Gynecologic Oncology Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Claudia Marchetti
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
- Dipartimento Scienze della Vita e Sanità Pubblica, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Serena Negri
- Department of Medicine and Surgery, University of Milan-Bicocca, Milan, Italy
- Division of Gynecologic Surgery, IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
| | - Giorgia Russo
- Dipartimento Scienze della Vita e Sanità Pubblica, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Laura Vertechy
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Giovanni Scambia
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
- Dipartimento Scienze della Vita e Sanità Pubblica, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Raffaella Massafra
- Laboratorio di Biostatistica e Bioinformatica, IRCCS Istituto Tumori Giovanni Paolo II, Bari, Italy
| | - Gennaro Cormio
- S.S.D. Ginecologia Oncologica Clinicizzata, IRCCS Istituto Tumori Giovanni Paolo II, Bari, Italy
- Dipartimento Interdisciplinare di Medicina (DIM), University of Bari Aldo Moro, Bari, Italy
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Yang S, Seo J, Choi J, Kim SH, Kuk Y, Park KC, Kang M, Byun S, Joo JY. Towards understanding cancer dormancy over strategic hitching up mechanisms to technologies. Mol Cancer 2025; 24:47. [PMID: 39953555 PMCID: PMC11829473 DOI: 10.1186/s12943-025-02250-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2024] [Accepted: 01/28/2025] [Indexed: 02/17/2025] Open
Abstract
Delving into cancer dormancy has been an inherent task that may drive the lethal recurrence of cancer after primary tumor relief. Cells in quiescence can survive for a short or long term in silence, may undergo genetic or epigenetic changes, and can initiate relapse through certain contextual cues. The state of dormancy can be induced by multiple conditions including cancer drug treatment, in turn, undergoes a life cycle that generally occurs through dissemination, invasion, intravasation, circulation, immune evasion, extravasation, and colonization. Throughout this cascade, a cellular machinery governs the fate of individual cells, largely affected by gene regulation. Despite its significance, a precise view of cancer dormancy is yet hampered. Revolutionizing advanced single cell and long read sequencing through analysis methodologies and artificial intelligence, the most recent stage in the research tool progress, is expected to provide a holistic view of the diverse aspects of cancer dormancy.
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Affiliation(s)
- Sumin Yang
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Korea
| | - Jieun Seo
- Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Korea
- Department of Functional Genomics, University of Science and Technology, Daejeon, 34113, Korea
| | - Jeonghyeon Choi
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Korea
| | - Sung-Hyun Kim
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Korea
| | - Yunmin Kuk
- Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Korea
- Department of Functional Genomics, University of Science and Technology, Daejeon, 34113, Korea
| | - Kyung Chan Park
- Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Korea
- Department of Functional Genomics, University of Science and Technology, Daejeon, 34113, Korea
| | - Mingon Kang
- Department of Computer Science, University of Nevada, Las Vegas, NV, 89154, USA
| | - Sangwon Byun
- Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Korea.
- Department of Functional Genomics, University of Science and Technology, Daejeon, 34113, Korea.
| | - Jae-Yeol Joo
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Korea.
- Department of Pharmacy, College of Pharmacy, Hanyang University, Rm 407, Bldg.42, 55 Hanyangdaehak-ro, Sangnok-gu Ansan, Gyeonggi-do, 15588, Republic of Korea.
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Lewis F, Beirne J, Henderson B, Norris L, Cadoo K, Kelly T, Martin C, Hurley S, Kanjuga M, O'Driscoll L, Gately K, Oner E, Saini VM, Brooks D, Selemidis S, Kamran W, Haughey N, Maguire P, O'Gorman C, Saadeh FA, Ward MP, O'Leary JJ, O'Toole SA. Unravelling the biological and clinical challenges of circulating tumour cells in epithelial ovarian carcinoma. Cancer Lett 2024; 605:217279. [PMID: 39341451 DOI: 10.1016/j.canlet.2024.217279] [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: 07/04/2024] [Revised: 09/22/2024] [Accepted: 09/24/2024] [Indexed: 10/01/2024]
Abstract
Epithelial ovarian carcinoma (EOC) is the eighth most common cancer in women and the leading cause of gynaecological cancer death, predominantly due to the absence of effective screening tools, advanced stage at diagnosis, and high rates of recurrence. Circulating tumour cells (CTCs), a rare subset of tumour cells that disseminate from a tumour and migrate into the circulation, play a pivotal role in the metastatic cascade, and therefore hold promise as biomarkers for disease monitoring and prognostication. Exploring CTCs from liquid biopsies is an appealing approach for research and clinical practice, given it is minimally invasive, facilitates serial sampling and enables the capture of the entire spectrum of cancer cells circulating in the blood. The prognostic utility of CTC enumeration has been FDA-approved for clinical use in metastatic breast, prostate, and colorectal cancers. However, the unique biology of EOC, discussed herein, compounds the detection and characterisation complexities already inherent in CTC research, consequently hindering progress towards clinical applications. The aim of this review is to provide an overview of both the biological and clinical challenges encountered in harnessing the power of CTCs in EOC.
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Affiliation(s)
- Faye Lewis
- Department of Histopathology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Department of Obstetrics and Gynaecology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland
| | - James Beirne
- Blackrock Health Hermitage Clinic, Old Lucan Road, Dublin, Ireland
| | - Brian Henderson
- Department of Histopathology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Department of Obstetrics and Gynaecology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland
| | - Lucy Norris
- Department of Obstetrics and Gynaecology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland
| | - Karen Cadoo
- Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland; The Haematology, Oncology and Palliative Care (HOPe) Directorate, St James's Hospital, Dublin, Ireland
| | - Tanya Kelly
- Department of Histopathology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Department of Obstetrics and Gynaecology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland
| | - Cara Martin
- Department of Histopathology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland
| | - Sinéad Hurley
- Department of Histopathology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland; Department of Clinical Medicine, School of Medicine, Trinity College Dublin, Ireland; Thoracic Oncology Research Group, Trinity Translational Medicine Institute, St James's Hospital, Dublin, Ireland
| | - Marika Kanjuga
- Department of Histopathology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Department of Obstetrics and Gynaecology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland
| | - Lorraine O'Driscoll
- Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland; School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Ireland; Trinity Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Kathy Gately
- Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland; Department of Clinical Medicine, School of Medicine, Trinity College Dublin, Ireland; Thoracic Oncology Research Group, Trinity Translational Medicine Institute, St James's Hospital, Dublin, Ireland
| | - Ezgi Oner
- Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland; Department of Clinical Medicine, School of Medicine, Trinity College Dublin, Ireland; Thoracic Oncology Research Group, Trinity Translational Medicine Institute, St James's Hospital, Dublin, Ireland
| | - Volga M Saini
- Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland; Department of Clinical Medicine, School of Medicine, Trinity College Dublin, Ireland; Thoracic Oncology Research Group, Trinity Translational Medicine Institute, St James's Hospital, Dublin, Ireland
| | - Doug Brooks
- Cancer Research Institute, University of South Australia, 5001, Adelaide, Australia
| | - Stavros Selemidis
- School of Health and Biomedical Sciences, RMIT University, Victoria, 3083, Bundoora, Australia
| | - Waseem Kamran
- Department of Obstetrics and Gynaecology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland; Division of Gynaecological Oncology, St James's Hospital, Dublin, Ireland
| | - Niamh Haughey
- Department of Obstetrics and Gynaecology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland; Division of Gynaecological Oncology, St James's Hospital, Dublin, Ireland
| | - Patrick Maguire
- Department of Obstetrics and Gynaecology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland; Division of Gynaecological Oncology, St James's Hospital, Dublin, Ireland
| | - Catherine O'Gorman
- Department of Obstetrics and Gynaecology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland; Division of Gynaecological Oncology, St James's Hospital, Dublin, Ireland
| | - Feras Abu Saadeh
- Department of Obstetrics and Gynaecology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland; Division of Gynaecological Oncology, St James's Hospital, Dublin, Ireland
| | - Mark P Ward
- Department of Histopathology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Department of Obstetrics and Gynaecology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland.
| | - John J O'Leary
- Department of Histopathology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland.
| | - Sharon A O'Toole
- Department of Histopathology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Department of Obstetrics and Gynaecology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity St. James's Cancer Institute, Trinity College Dublin, Dublin, Ireland.
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4
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Ayyoubzadeh SM, Ahmadi M, Yazdipour AB, Ghorbani‐Bidkorpeh F, Ahmadi M. Prediction of ovarian cancer using artificial intelligence tools. Health Sci Rep 2024; 7:e2203. [PMID: 38946777 PMCID: PMC11211920 DOI: 10.1002/hsr2.2203] [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] [Received: 01/29/2024] [Revised: 06/05/2024] [Accepted: 06/10/2024] [Indexed: 07/02/2024] Open
Abstract
Purpose Ovarian cancer is a common type of cancer and a leading cause of death in women. Therefore, accurate and fast prediction of ovarian tumors is crucial. One of the appropriate and precise methods for predicting and diagnosing this cancer is to build a model based on artificial intelligence methods. These methods provide a tool for predicting ovarian cancer according to the characteristics and conditions of each person. Method In this study, a data set included records related to 171 cases of benign ovarian tumors, and 178 records related to cases of ovarian cancer were analyzed. The data set contains the records of blood test results and tumor markers of the patients. After data preprocessing, including removing outliers and replacing missing values, the weight of the effective factors was determined using information gain indices and the Gini index. In the next step, predictive models were created using random forest (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN) models. The performance of these models was evaluated using the 10-fold cross-validation method using the indicators of specificity, sensitivity, accuracy, and the area under the receiver operating characteristic curve. Finally, by comparing the performance of the models, the best predictive model of ovarian cancer was selected. Results The most important predictive factors were HE4, CA125, and NEU. The RF model was identified as the best predictive model, with an accuracy of more than 86%. The predictive accuracy of DT, SVM, and ANN models was estimated as 82.91%, 85.25%, and 79.35%, respectively. Various artificial intelligence (AI) tools can be used with high accuracy and sensitivity in predicting ovarian cancer. Conclusion Therefore, the use of these tools can help specialists and patients with early, easier, and less expensive diagnosis of ovarian cancer. Future studies can leverage AI to integrate image data with serum biomarkers, thereby facilitating the creation of novel models and advancing the diagnosis and treatment of ovarian cancer.
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Affiliation(s)
- Seyed Mohammad Ayyoubzadeh
- Department of Health Information Management, School of Allied Medical SciencesTehran University of Medical SciencesTehranIran
- Health Information Management Research CenterTehran University of Medical SciencesTehranIran
| | - Marjan Ahmadi
- Department of Obstetrics and GynecologyTehran University of Medical SciencesTehranIran
| | - Alireza Banaye Yazdipour
- Department of Health Information Management, School of Allied Medical SciencesTehran University of Medical SciencesTehranIran
- Students' Scientific Research Center (SSRC)Tehran University of Medical SciencesTehranIran
- Department of Health Information Technology, School of Paramedical and Rehabilitation SciencesMashhad University of Medical SciencesMashhadIran
| | - Fatemeh Ghorbani‐Bidkorpeh
- Department of Pharmaceutics and Pharmaceutical Nanotechnology, School of PharmacyShahid Beheshti University of Medical SciencesTehranIran
| | - Mahnaz Ahmadi
- Medical Nanotechnology and Tissue Engineering Research CenterShahid Beheshti University of Medical SciencesTehranIran
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5
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Lavanya J M S, P S. Innovative approach towards early prediction of ovarian cancer: Machine learning- enabled XAI techniques. Heliyon 2024; 10:e29197. [PMID: 39669371 PMCID: PMC11636890 DOI: 10.1016/j.heliyon.2024.e29197] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 03/28/2024] [Accepted: 04/02/2024] [Indexed: 12/14/2024] Open
Abstract
Globally, ovarian cancer affects women disproportionately, causing significant morbidity and mortality rates. The early diagnosis of ovarian cancer is necessary for enhancing patient health and survival rates. This research article explores the utilization of Machine Learning (ML) techniques alongside eXplainable Artificial Intelligence (XAI) methodologies to aid in the early detection of ovarian cancer. ML techniques have recently gained popularity in developing predictive models to detect early-stage ovarian cancer. These predictions are made using XAI in a transparent and understandable way for healthcare professionals and patients. The primary aim of this study is to evaluate the effectiveness of various ovarian cancer prediction methodologies. This includes assessing K Nearest Neighbors, Support Vector Machines, Decision trees, and ensemble learning techniques such as Max Voting, Boosting, Bagging, and Stacking. A dataset of 349 patients with known ovarian cancer status was collected from Kaggle. The dataset included a comprehensive range of clinical features such as age, family history, tumor markers, and imaging characteristics. Preprocessing techniques were applied to enhance input data, including feature scaling and dimensionality reduction. A Minimum Redundancy Maximum Relevance (MRMR) algorithm was used to select the features in the model. Our experimental results demonstrate that in Support Vector Machines, we found 85 % base model accuracy and 89 % accuracy after stacking several ensemble learning techniques. With the help of XAI, complex ML algorithms can be given more profound insights into their decision-making, improving their applicability. This paper aims to introduce the best practices for integrating ML and artificial intelligence in biomarker evaluation. Building and evaluating Shapley values-based classifiers and visualizing results were the focus of our investigation. The study contributes to the field of oncology and women's health by offering a promising approach to the early diagnosis of ovarian cancer.
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Affiliation(s)
- Sheela Lavanya J M
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
| | - Subbulakshmi P
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
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Díaz del Arco C, Fernández Aceñero MJ, Ortega Medina L. Liquid biopsy for gastric cancer: Techniques, applications, and future directions. World J Gastroenterol 2024; 30:1680-1705. [PMID: 38617733 PMCID: PMC11008373 DOI: 10.3748/wjg.v30.i12.1680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 02/01/2024] [Accepted: 03/08/2024] [Indexed: 03/28/2024] Open
Abstract
After the study of circulating tumor cells in blood through liquid biopsy (LB), this technique has evolved to encompass the analysis of multiple materials originating from the tumor, such as nucleic acids, extracellular vesicles, tumor-educated platelets, and other metabolites. Additionally, research has extended to include the examination of samples other than blood or plasma, such as saliva, gastric juice, urine, or stool. LB techniques are diverse, intricate, and variable. They must be highly sensitive, and pre-analytical, patient, and tumor-related factors significantly influence the detection threshold, diagnostic method selection, and potential results. Consequently, the implementation of LB in clinical practice still faces several challenges. The potential applications of LB range from early cancer detection to guiding targeted therapy or immunotherapy in both early and advanced cancer cases, monitoring treatment response, early identification of relapses, or assessing patient risk. On the other hand, gastric cancer (GC) is a disease often diagnosed at advanced stages. Despite recent advances in molecular understanding, the currently available treatment options have not substantially improved the prognosis for many of these patients. The application of LB in GC could be highly valuable as a non-invasive method for early diagnosis and for enhancing the management and outcomes of these patients. In this comprehensive review, from a pathologist's perspective, we provide an overview of the main options available in LB, delve into the fundamental principles of the most studied techniques, explore the potential utility of LB application in the context of GC, and address the obstacles that need to be overcome in the future to make this innovative technique a game-changer in cancer diagnosis and treatment within clinical practice.
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Affiliation(s)
- Cristina Díaz del Arco
- Department of Surgical Pathology, Health Research Institute of the Hospital Clínico San Carlos, Hospital Clínico San Carlos, Madrid 28040, Spain
- Department of Legal Medicine, Psychiatry and Pathology, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - M Jesús Fernández Aceñero
- Department of Surgical Pathology, Health Research Institute of the Hospital Clínico San Carlos, Hospital Clínico San Carlos, Madrid 28040, Spain
- Department of Legal Medicine, Psychiatry and Pathology, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - Luis Ortega Medina
- Department of Surgical Pathology, Health Research Institute of the Hospital Clínico San Carlos, Hospital Clínico San Carlos, Madrid 28040, Spain
- Department of Legal Medicine, Psychiatry and Pathology, Universidad Complutense de Madrid, Madrid 28040, Spain
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Nobel SMN, Swapno SMMR, Hossain MA, Safran M, Alfarhood S, Kabir MM, Mridha MF. RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis. Tomography 2024; 10:105-132. [PMID: 38250956 PMCID: PMC11154515 DOI: 10.3390/tomography10010010] [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: 10/30/2023] [Revised: 12/10/2023] [Accepted: 12/11/2023] [Indexed: 01/23/2024] Open
Abstract
Ovarian cancer, a deadly female reproductive system disease, is a significant challenge in medical research due to its notorious lethality. Addressing ovarian cancer in the current medical landscape has become more complex than ever. This research explores the complex field of Ovarian Cancer Subtype Classification and the crucial task of Outlier Detection, driven by a progressive automated system, as the need to fight this unforgiving illness becomes critical. This study primarily uses a unique dataset painstakingly selected from 20 esteemed medical institutes. The dataset includes a wide range of images, such as tissue microarray (TMA) images at 40× magnification and whole-slide images (WSI) at 20× magnification. The research is fully committed to identifying abnormalities within this complex environment, going beyond the classification of subtypes of ovarian cancer. We proposed a new Attention Embedder, a state-of-the-art model with effective results in ovarian cancer subtype classification and outlier detection. Using images magnified WSI, the model demonstrated an astonishing 96.42% training accuracy and 95.10% validation accuracy. Similarly, with images magnified via a TMA, the model performed well, obtaining a validation accuracy of 94.90% and a training accuracy of 93.45%. Our fine-tuned hyperparameter testing resulted in exceptional performance on independent images. At 20× magnification, we achieved an accuracy of 93.56%. Even at 40× magnification, our testing accuracy remained high, at 91.37%. This study highlights how machine learning can revolutionize the medical field's ability to classify ovarian cancer subtypes and identify outliers, giving doctors a valuable tool to lessen the severe effects of the disease. Adopting this novel method is likely to improve the practice of medicine and give people living with ovarian cancer worldwide hope.
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Affiliation(s)
- S. M. Nuruzzaman Nobel
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (S.M.N.N.); (S.M.M.R.S.); (M.A.H.)
| | - S M Masfequier Rahman Swapno
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (S.M.N.N.); (S.M.M.R.S.); (M.A.H.)
| | - Md. Ashraful Hossain
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (S.M.N.N.); (S.M.M.R.S.); (M.A.H.)
| | - Mejdl Safran
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia;
| | - Sultan Alfarhood
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia;
| | - Md. Mohsin Kabir
- Superior Polytechnic School, University of Girona, 17071 Girona, Spain;
| | - M. F. Mridha
- Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh;
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Choi SW, Sun AK, Cheung JPY, Ho JCY. Circulating Tumour Cells in the Prediction of Bone Metastasis. Cancers (Basel) 2024; 16:252. [PMID: 38254743 PMCID: PMC10813668 DOI: 10.3390/cancers16020252] [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: 12/04/2023] [Revised: 12/28/2023] [Accepted: 01/02/2024] [Indexed: 01/24/2024] Open
Abstract
Bone is the most common organ for the development of metastases in many primary tumours, including those of the breast, prostate and lung. In most cases, bone metastasis is incurable, and treatment is predominantly palliative. Much research has focused on the role of Circulating Tumour Cells (CTCs) in the mechanism of metastasis to the bone, and methods have been developed to isolate and count CTCs from peripheral blood. Several methods are currently being used in the study of CTCs, but only one, the CellSearchTM system has been approved by the United States Food and Drug Administration for clinical use. This review summarises the advantages and disadvantages, and outlines which clinical studies have used these methods. Studies have found that CTC numbers are predictive of bone metastasis in breast, prostate and lung cancer. Further work is required to incorporate information on CTCs into current staging systems to guide treatment in the prevention of tumour progression into bone.
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Affiliation(s)
- Siu-Wai Choi
- Department of Orthopaedics and Tramatology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Aria Kaiyuan Sun
- Department of Anaesthesiology, School of Clinical Medicine, Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; (A.K.S.); (J.C.-Y.H.)
| | - Jason Pui-Yin Cheung
- Department of Orthopaedics and Tramatology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jemmi Ching-Ying Ho
- Department of Anaesthesiology, School of Clinical Medicine, Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; (A.K.S.); (J.C.-Y.H.)
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9
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Mishra PK, Kaur P. Future-ready technologies for sensing the stemness of circulating tumor cells. Nanomedicine (Lond) 2023; 18:1327-1330. [PMID: 37585672 DOI: 10.2217/nnm-2023-0066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 07/12/2023] [Indexed: 08/18/2023] Open
Affiliation(s)
- Pradyumna Kumar Mishra
- Division of Environmental Biotechnology, Genetics and Molecular Biology, ICMR-National Institute for Research in Environmental Health, Bhopal, 462030, India
| | - Prasan Kaur
- Division of Environmental Biotechnology, Genetics and Molecular Biology, ICMR-National Institute for Research in Environmental Health, Bhopal, 462030, India
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10
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Comes MC, Arezzo F, Cormio G, Bove S, Calabrese A, Fanizzi A, Kardhashi A, La Forgia D, Legge F, Romagno I, Loizzi V, Massafra R. An explainable machine learning ensemble model to predict the risk of ovarian cancer in BRCA-mutated patients undergoing risk-reducing salpingo-oophorectomy. Front Oncol 2023; 13:1181792. [PMID: 37519818 PMCID: PMC10374844 DOI: 10.3389/fonc.2023.1181792] [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: 03/07/2023] [Accepted: 06/23/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction It has been estimated that 19,880 new cases of ovarian cancer had been diagnosed in 2022. Most epithelial ovarian cancer are sporadic, while in 15%-25% of cases, there is evidence of a familial or inherited component. Approximately 20%-25% of high-grade serous carcinoma cases are caused by germline mutations in the BRCA1 and BRCA2 genes. However, owing to a lack of effective early detection methods, women with BRCA mutations are recommended to undergo bilateral risk-reducing salpingo-oophorectomy (RRSO) after childbearing. Determining the right timing for this procedure is a difficult decision. It is crucial to find a clinical signature to identify high-risk BRCA-mutated patients and determine the appropriate timing for performing RRSO. Methods In this work, clinical data referred to a cohort of 184 patients, of whom 7.6% were affected by adnexal tumors including invasive carcinomas and intraepithelial lesions after RSSO has been analyzed. Thus, we proposed an explainable machine learning (ML) ensemble approach using clinical data commonly collected in clinical practice to early identify BRCA-mutated patients at high risk of ovarian cancer and consequentially establish the correct timing for RRSO. Results The ensemble model was able to handle imbalanced data achieving an accuracy value of 83.2%, a specificity value of 85.3%, a sensitivity value of 57.1%, a G-mean value of 69.8%, and an AUC value of 71.1%. Discussion In agreement with the promising results achieved, the application of suitable ML techniques could play a key role in the definition of a BRCA-mutated patient-centric clinical signature for ovarian cancer risk and consequently personalize the management of these patients. As far as we know, this is the first work addressing this task from an ML perspective.
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Affiliation(s)
- Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Francesca Arezzo
- Dipartimento di Medicina di Precisione e Rigenerativa e Area Jonica - (DiMePRe-J), Università di Bari “Aldo Moro”, Bari, Italy
- Ginecologia Oncologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Gennaro Cormio
- Ginecologia Oncologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
- Dipartimento Interdisciplinare di Medicina (DIM), Università di Bari “Aldo Moro”, Bari, Italy
| | - Samantha Bove
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Angela Calabrese
- Unità Operativa Semplice di Radiodiagnostica Avanzata, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Anila Kardhashi
- Ginecologia Oncologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | - Francesco Legge
- Unità di Ginecologia Oncologica, “F. Miulli” Ospedale Generale Regionale, Acquaviva delle Fonti, Bari, Italy
| | | | - Vera Loizzi
- Ginecologia Oncologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
- Dipartimento Interdisciplinare di Medicina (DIM), Università di Bari “Aldo Moro”, Bari, Italy
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy
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11
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Lawrence R, Watters M, Davies CR, Pantel K, Lu YJ. Circulating tumour cells for early detection of clinically relevant cancer. Nat Rev Clin Oncol 2023:10.1038/s41571-023-00781-y. [PMID: 37268719 DOI: 10.1038/s41571-023-00781-y] [Citation(s) in RCA: 136] [Impact Index Per Article: 68.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/02/2023] [Indexed: 06/04/2023]
Abstract
Given that cancer mortality is usually a result of late diagnosis, efforts in the field of early detection are paramount to reducing cancer-related deaths and improving patient outcomes. Increasing evidence indicates that metastasis is an early event in patients with aggressive cancers, often occurring even before primary lesions are clinically detectable. Metastases are usually formed from cancer cells that spread to distant non-malignant tissues via the blood circulation, termed circulating tumour cells (CTCs). CTCs have been detected in patients with early stage cancers and, owing to their association with metastasis, might indicate the presence of aggressive disease, thus providing a possible means to expedite diagnosis and treatment initiation for such patients while avoiding overdiagnosis and overtreatment of those with slow-growing, indolent tumours. The utility of CTCs as an early diagnostic tool has been investigated, although further improvements in the efficiency of CTC detection are required. In this Perspective, we discuss the clinical significance of early haematogenous dissemination of cancer cells, the potential of CTCs to facilitate early detection of clinically relevant cancers, and the technological advances that might improve CTC capture and, thus, diagnostic performance in this setting.
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Affiliation(s)
- Rachel Lawrence
- Centre for Biomarkers and Therapeutics, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Melissa Watters
- Barts and London School of Medicine and Dentistry, Queen Mary University London, London, UK
| | - Caitlin R Davies
- Centre for Biomarkers and Therapeutics, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Klaus Pantel
- Department of Tumour Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Yong-Jie Lu
- Centre for Biomarkers and Therapeutics, Barts Cancer Institute, Queen Mary University of London, London, UK.
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12
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Sheehy J, Rutledge H, Acharya UR, Loh HW, Gururajan R, Tao X, Zhou X, Li Y, Gurney T, Kondalsamy-Chennakesavan S. Gynecological cancer prognosis using machine learning techniques: A systematic review of last three decades (1990–2022). Artif Intell Med 2023; 139:102536. [PMID: 37100507 DOI: 10.1016/j.artmed.2023.102536] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 03/19/2023] [Accepted: 03/23/2023] [Indexed: 03/30/2023]
Abstract
OBJECTIVE Many Computer Aided Prognostic (CAP) systems based on machine learning techniques have been proposed in the field of oncology. The objective of this systematic review was to assess and critically appraise the methodologies and approaches used in predicting the prognosis of gynecological cancers using CAPs. METHODS Electronic databases were used to systematically search for studies utilizing machine learning methods in gynecological cancers. Study risk of bias (ROB) and applicability were assessed using the PROBAST tool. 139 studies met the inclusion criteria, of which 71 predicted outcomes for ovarian cancer patients, 41 predicted outcomes for cervical cancer patients, 28 predicted outcomes for uterine cancer patients, and 2 predicted outcomes for gynecological malignancies broadly. RESULTS Random forest (22.30 %) and support vector machine (21.58 %) classifiers were used most commonly. Use of clinicopathological, genomic and radiomic data as predictors was observed in 48.20 %, 51.08 % and 17.27 % of studies, respectively, with some studies using multiple modalities. 21.58 % of studies were externally validated. Twenty-three individual studies compared ML and non-ML methods. Study quality was highly variable and methodologies, statistical reporting and outcome measures were inconsistent, preventing generalized commentary or meta-analysis of performance outcomes. CONCLUSION There is significant variability in model development when prognosticating gynecological malignancies with respect to variable selection, machine learning (ML) methods and endpoint selection. This heterogeneity prevents meta-analysis and conclusions regarding the superiority of ML methods. Furthermore, PROBAST-mediated ROB and applicability analysis demonstrates concern for the translatability of existing models. This review identifies ways that this can be improved upon in future works to develop robust, clinically translatable models within this promising field.
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13
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Luvhengo T, Molefi T, Demetriou D, Hull R, Dlamini Z. Use of Artificial Intelligence in Implementing Mainstream Precision Medicine to Improve Traditional Symptom-driven Practice of Medicine: Allowing Early Interventions and Tailoring better-personalised Cancer Treatments. ARTIFICIAL INTELLIGENCE AND PRECISION ONCOLOGY 2023:49-72. [DOI: 10.1007/978-3-031-21506-3_3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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14
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Szczerba A, Śliwa A, Pieta PP, Jankowska A. The Role of Circulating Tumor Cells in Ovarian Cancer Dissemination. Cancers (Basel) 2022; 14:cancers14246030. [PMID: 36551515 PMCID: PMC9775737 DOI: 10.3390/cancers14246030] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/30/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022] Open
Abstract
Metastatic ovarian cancer is the main reason for treatment failures and consequent deaths. Ovarian cancer is predisposed to intraperitoneal dissemination. In comparison to the transcoelomic route, distant metastasis via lymph vessels and blood is less common. The mechanisms related to these two modes of cancer spread are poorly understood. Nevertheless, the presence of tumor cells circulating in the blood of OC patients is a well-established phenomenon confirming the significant role of lymphatic and hematogenous metastasis. Thus, the detection of CTCs may provide a minimally invasive tool for the identification of ovarian cancer, monitoring disease progression, and treatment effectiveness. This review focuses on the biology of ovarian CTCs and the role they may play in cancer diagnosis and therapy.
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Affiliation(s)
- Anna Szczerba
- Chair and Department of Cell Biology, Poznan University of Medical Sciences, Rokietnicka 5D, 60-806 Poznan, Poland
| | - Aleksandra Śliwa
- Chair and Department of Cell Biology, Poznan University of Medical Sciences, Rokietnicka 5D, 60-806 Poznan, Poland
| | - Pawel P. Pieta
- Department of Bionic and Experimental Medical Biology, Poznan University of Medical Sciences, 60-806 Poznan, Poland
| | - Anna Jankowska
- Chair and Department of Cell Biology, Poznan University of Medical Sciences, Rokietnicka 5D, 60-806 Poznan, Poland
- Correspondence: ; Tel.: +48-618-547-190
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15
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Punzón-Jiménez P, Lago V, Domingo S, Simón C, Mas A. Molecular Management of High-Grade Serous Ovarian Carcinoma. Int J Mol Sci 2022; 23:13777. [PMID: 36430255 PMCID: PMC9692799 DOI: 10.3390/ijms232213777] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/04/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022] Open
Abstract
High-grade serous ovarian carcinoma (HGSOC) represents the most common form of epithelial ovarian carcinoma. The absence of specific symptoms leads to late-stage diagnosis, making HGSOC one of the gynecological cancers with the worst prognosis. The cellular origin of HGSOC and the role of reproductive hormones, genetic traits (such as alterations in P53 and DNA-repair mechanisms), chromosomal instability, or dysregulation of crucial signaling pathways have been considered when evaluating prognosis and response to therapy in HGSOC patients. However, the detection of HGSOC is still based on traditional methods such as carbohydrate antigen 125 (CA125) detection and ultrasound, and the combined use of these methods has yet to support significant reductions in overall mortality rates. The current paradigm for HGSOC management has moved towards early diagnosis via the non-invasive detection of molecular markers through liquid biopsies. This review presents an integrated view of the relevant cellular and molecular aspects involved in the etiopathogenesis of HGSOC and brings together studies that consider new horizons for the possible early detection of this gynecological cancer.
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Affiliation(s)
- Paula Punzón-Jiménez
- Carlos Simon Foundation, INCLIVA Health Research Institute, 46010 Valencia, Spain
| | - Victor Lago
- Department of Gynecologic Oncology, La Fe University and Polytechnic Hospital, 46026 Valencia, Spain
- Department of Obstetrics and Gynecology, CEU Cardenal Herrera University, 46115 Valencia, Spain
| | - Santiago Domingo
- Department of Gynecologic Oncology, La Fe University and Polytechnic Hospital, 46026 Valencia, Spain
- Department of Pediatrics, Obstetrics and Gynecology, Universidad de Valencia, 46010 Valencia, Spain
| | - Carlos Simón
- Carlos Simon Foundation, INCLIVA Health Research Institute, 46010 Valencia, Spain
- Department of Pediatrics, Obstetrics and Gynecology, Universidad de Valencia, 46010 Valencia, Spain
- Department of Pediatrics, Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Harvard University, Boston, MA 02215, USA
- Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Aymara Mas
- Carlos Simon Foundation, INCLIVA Health Research Institute, 46010 Valencia, Spain
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Wu M, Zhao Y, Dong X, Jin Y, Cheng S, Zhang N, Xu S, Gu S, Wu Y, Yang J, Yao L, Wang Y. Artificial intelligence-based preoperative prediction system for diagnosis and prognosis in epithelial ovarian cancer: A multicenter study. Front Oncol 2022; 12:975703. [PMID: 36212430 PMCID: PMC9532858 DOI: 10.3389/fonc.2022.975703] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Abstract
Background Ovarian cancer (OC) is the most lethal gynecological malignancy, with limited early screening methods and poor prognosis. Artificial intelligence technology has made a great breakthrough in cancer diagnosis. Purpose We aim to develop a specific interpretable machine learning (ML) prediction model for the diagnosis and prognosis of epithelial ovarian cancer (EOC) based on a variety of biomarkers. Methods A total of 521 patients with EOC and 144 patients with benign gynecological diseases were enrolled including derivation datasets and an external validation cohort. The predicted information was acquired by 9 supervised ML methods, through 34 parameters. Behind predicted reasons for the best ML were improved by using the SHapley Additive exPlanations (SHAP) algorithm. In addition, the prognosis of EOC was analyzed by unsupervised clustering and Kaplan–Meier (KM) survival analysis. Results ML technology was superior to conventional logistic regression in predicting EOC diagnosis and XGBoost performed best in the external validation datasets. The AUC values of distinguishing EOC and benign disease patients, determining pathological type, grade and clinical stage were 0.958 (0.926-0.989), 0.792 (0.701-0.8834), 0.819 (0.687-0.950) and 0.68 (0.573-0.788) respectively. For negative CA-125 EOC patients, the AUC performance of XGBoost model was 0.835(0.763-0.907). We used unsupervised cluster analysis to identify EOC subgroups with significantly poor overall survival (p-value <0.0001) and recurrence-free survival (p-value <0.0001). Conclusions Based on the preoperative characteristics, we proved that ML algorithm can provide an acceptable diagnosis and prognosis prediction model for EOC patients. Meanwhile, SHAP analysis can improve the interpretability of ML models and contribute to precision medicine.
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Affiliation(s)
- Meixuan Wu
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yaqian Zhao
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Xuhui Dong
- Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Yue Jin
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Shanshan Cheng
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Nan Zhang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Shilin Xu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Sijia Gu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yongsong Wu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Jiani Yang
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- *Correspondence: Yu Wang, ; Liangqing Yao, ; Jiani Yang,
| | - Liangqing Yao
- Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- *Correspondence: Yu Wang, ; Liangqing Yao, ; Jiani Yang,
| | - Yu Wang
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- *Correspondence: Yu Wang, ; Liangqing Yao, ; Jiani Yang,
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Yang J, Cheng S, Zhang N, Jin Y, Wang Y. Liquid biopsy for ovarian cancer using circulating tumor cells: Recent advances on the path to precision medicine. Biochim Biophys Acta Rev Cancer 2021; 1877:188660. [PMID: 34800546 DOI: 10.1016/j.bbcan.2021.188660] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 11/03/2021] [Accepted: 11/13/2021] [Indexed: 12/30/2022]
Abstract
Ovarian cancer (OC) is the most lethal gynecologic malignance worldwide. Considering its metastasis nature, oncologists shift focus towards circulating tumor cells (CTCs), a progenitor that originates from primary tumor and undergoes morphologic/genetic alterations to enter bloodstream and invade nearby tissues. Mountains of evidence suggested that CTCs could provide deep insights into genomic, transcriptomic, and proteomic profiling of OC metastatic cascades. To pave the way for precision medicine, researchers exert great efforts to develop isolation/detection methodologies and construct CTCs-derived propagation platforms, including traditional cell cultures, patient-derived xenografts (PDXs), and organoids. From bench to bedside, CTCs provide minimally-invasive means to inform early diagnosis, predict prognosis, and guide treatment decisions. This review shined a spotlight on biology, detection technologies, and propagation platforms for CTCs. Of note, we also reviewed clinical applications of CTCs in liquid biopsy-based personalized cancer treatment and critically appraised limitations in routine clinical practice on the path to precision medicine.
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Affiliation(s)
- Jiani Yang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Shanshan Cheng
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Nan Zhang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yue Jin
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yu Wang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
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