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      Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

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          Abstract

          Artificial intelligence (AI) has emerged as a crucial technique for extracting high-throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI-based radiomics has proven to be a non-invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI-based multi-omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          Hyuna Sung, Jacques Ferlay, Rebecca Siegel … (2021)
          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            Cancer statistics, 2023

            Rebecca L Siegel, Kimberly D Miller, Nikita Sandeep Wagle … (2023)
            Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence and outcomes using incidence data collected by central cancer registries and mortality data collected by the National Center for Health Statistics. In 2023, 1,958,310 new cancer cases and 609,820 cancer deaths are projected to occur in the United States. Cancer incidence increased for prostate cancer by 3% annually from 2014 through 2019 after two decades of decline, translating to an additional 99,000 new cases; otherwise, however, incidence trends were more favorable in men compared to women. For example, lung cancer in women decreased at one half the pace of men (1.1% vs. 2.6% annually) from 2015 through 2019, and breast and uterine corpus cancers continued to increase, as did liver cancer and melanoma, both of which stabilized in men aged 50 years and older and declined in younger men. However, a 65% drop in cervical cancer incidence during 2012 through 2019 among women in their early 20s, the first cohort to receive the human papillomavirus vaccine, foreshadows steep reductions in the burden of human papillomavirus-associated cancers, the majority of which occur in women. Despite the pandemic, and in contrast with other leading causes of death, the cancer death rate continued to decline from 2019 to 2020 (by 1.5%), contributing to a 33% overall reduction since 1991 and an estimated 3.8 million deaths averted. This progress increasingly reflects advances in treatment, which are particularly evident in the rapid declines in mortality (approximately 2% annually during 2016 through 2020) for leukemia, melanoma, and kidney cancer, despite stable/increasing incidence, and accelerated declines for lung cancer. In summary, although cancer mortality rates continue to decline, future progress may be attenuated by rising incidence for breast, prostate, and uterine corpus cancers, which also happen to have the largest racial disparities in mortality.
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              Radiomics: extracting more information from medical images using advanced feature analysis.

              Philippe Lambin, Emmanuel Rios-Velazquez, Ralph Leijenaar … (2012)
              Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory. Copyright © 2011 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Oncol Rep
                Journal ID (iso-abbrev): Oncol Rep
                Title: Oncology Reports
                Publisher: D.A. Spandidos
                ISSN (Print): 1021-335X
                ISSN (Electronic): 1791-2431
                Publication date Collection: March 2024
                Publication date (Electronic): 19 January 2024
                Publication date PMC-release: 19 January 2024
                Volume: 51
                Issue: 3
                Electronic Location Identifier: 46
                Affiliations
                [1 ]Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
                [2 ]Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
                [3 ]Department of Pathology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
                [4 ]Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, Fujian 362000, P.R. China
                Author notes
                Correspondence to: Professor Guorong Lyu, Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, 34 North Zhongshan Road, Quanzhou, Fujian 362000, P.R. China, E-mail: lgr_feus@ 123456sina.com
                Professor Luhong Li, Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, 34 North Zhongshan Road, Quanzhou, Fujian 362000, P.R. China, E-mail: liluhong@ 123456sina.com
                [*]

                Contributed equally

                Article
                Publisher ID: OR-51-3-08705
                DOI: 10.3892/or.2024.8705
                PMC ID: 10828921
                PubMed ID: 38240090
                SO-VID: 7fb68775-79db-4f08-9378-444c356335c5
                Copyright © Copyright: © Wang et al.
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

                History
                Date received : 18 July 2023
                Date accepted : 05 January 2024
                Funding
                Funded by: Special Fund for Doctoral Supervisors of The Second Affiliated Hospital of Fujian Medical University
                Award ID: 2022BD1005
                This study was funded by the Special Fund for Doctoral Supervisors of The Second Affiliated Hospital of Fujian Medical University (grant no. 2022BD1005).
                Categories
                Subject: Review

                Keywords: artificial intelligence,radiomics,ovarian cancer,whole-slide imaging
                Data availability:
                Keywords: artificial intelligence, radiomics, ovarian cancer, whole-slide imaging

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