A Systematic Review on Ovarian Cancer
Abstract
Most women suffer late-stage ovarian disease endure a high pace of mortality. It is required to identify and analyze cancer right on time in its initial phase of development. It is generally suggested clinical screening of women through different detection techniques like imaging modalities (ultrasound, CT scan, MRI) and also detection using different biomarkers for cancer symptoms existing in patient’s blood. Biomarker, a distinguished device that are available for patients with ovarian malignant growth, the particular one is cancer anigen CA-125, is usually tried for medical use which is supposed to be more efficient biomarker to detect the infection. Here, we depict elective biomarkers like CA-125 which conquer a significant number of the issues related to malignancy, for example, expanded affectability and specificity, particularly in the beginning phases of ovarian cancer, and which could be utilized effectively in a biosensor design.
References
B. Franier, and M. Thompson, “Early stage detection and screening of ovarian cancer: A research opportunity and significant challenge for biosensor technology,” Biosens Bioelectron, vol. 135, pp. 71-81, Jun 15, 2019.
A. Jemal, F. Bray, M. M. Center, J. Ferlay, E. Ward, and D. J. C. a. c. j. f. c. Forman, “Global cancer statistics,” vol. 61, no. 2, pp. 69-90, 2011.
S. Vaughan, J. I. Coward, R. C. Bast, A. Berchuck, J. S. Berek, J. D. Brenton, G. Coukos, C. C. Crum, R. Drapkin, and D. J. N. R. C. Etemadmoghadam, “Rethinking ovarian cancer: recommendations for improving outcomes,” vol. 11, no. 10, pp. 719-725, 2011.
B. Mughal, M. Sharif, N. Muhammad, T. J. M. r. Saba, and technique, “A novel classification scheme to decline the mortality rate among women due to breast tumor,” vol. 81, no. 2, pp. 171-180, 2018.
R. Siegel, D. Naishadham, and A. J. C. a. c. j. f. c. Jemal, “Cancer statistics, 2012,” vol. 62, no. 1, pp. 10-29, 2012.
A. M. Fayyaz, K. A. Al-Dhlan, S. U. Rehman, M. Raza, W. Mehmood, M. Shafiq, and J.-G. Choi, “Leaf Blights Detection and Classification in Large Scale Applications,” INTELLIGENT AUTOMATION AND SOFT COMPUTING, vol. 31, no. 1, pp. 507-522, 2022.
R. Siegel, E. Ward, O. Brawley, and A. J. C. a. c. j. f. c. Jemal, “Cancer statistics, 2011: the impact of eliminating socioeconomic and racial disparities on premature cancer deaths,” vol. 61, no. 4, pp. 212-236, 2011.
J. M. Torpy, A. E. Burke, and R. M. J. J. Golub, “Ovarian cancer,” vol. 305, no. 23, pp. 2484-2484, 2011.
P. Mohaghegh, and A. G. J. R. Rockall, “Imaging strategy for early ovarian cancer: characterization of adnexal masses with conventional and advanced imaging techniques,” vol. 32, no. 6, pp. 1751-1773, 2012.
A. Sharma, S. Apostolidou, M. Burnell, S. Campbell, M. Habib, A. Gentry‐Maharaj, N. Amso, M. Seif, G. Fletcher, N. J. U. i. o. Singh, and gynecology, “Risk of epithelial ovarian cancer in asymptomatic women with ultrasound‐detected ovarian masses: a prospective cohort study within the UK collaborative trial of ovarian cancer screening (UKCTOCS),” vol. 40, no. 3, pp. 338-344, 2012.
T. Kaku, S. Ogawa, Y. Kawano, Y. Ohishi, H. Kobayashi, T. Hirakawa, and H. J. M. e. m. Nakano, “Histological classification of ovarian cancer,” vol. 36, no. 1, pp. 9-17, 2003.
S. N. Thomas, B. Friedrich, M. Schnaubelt, D. W. Chan, H. Zhang, and R. J. i. Aebersold, “Orthogonal proteomic platforms and their implications for the stable classification of high-grade serous ovarian cancer subtypes,” pp. 101079, 2020.
A. Muiz Fayyaz, M. Kolivand, J. Alyami, S. Roy, and A. Rehman, "Computer Vision-Based Prognostic Modelling of COVID-19 from Medical Imaging," Prognostic Models in Healthcare: AI and Statistical Approaches, pp. 25-45: Springer, 2022.
A. Babaier, and P. J. D. Ghatage, “Mucinous cancer of the ovary: overview and current status,” vol. 10, no. 1, pp. 52, 2020.
S. Liu, Y. Guo, B. Li, H. Zhang, R. Zhang, S. J. C. M. Zheng, and Research, “Analysis of Clinicopathological Features of Cervical Mucinous Adenocarcinoma with a Solitary Ovarian Metastatic Mass as the First Manifestation,” vol. 12, pp. 8965, 2020.
P. Krämer, A. Talhouk, M. A. Brett, D. S. Chiu, E. S. Cairns, D. A. Scheunhage, R. F. Hammond, D. Farnell, T. M. Nazeran, and M. J. C. C. R. Grube, “Endometrial cancer molecular risk stratification is equally prognostic for endometrioid ovarian carcinoma,” vol. 26, no. 20, pp. 5400-5410, 2020.
J. E. Steffen, T. Tran, M. Yimam, K. M. Clancy, T. B. Bird, M. Rigler, W. Longo, D. S. J. J. o. O. Egilman, and E. Medicine, “Serous ovarian cancer caused by exposure to asbestos and fibrous talc in cosmetic talc powders—A case series,” vol. 62, no. 2, pp. e65-e77, 2020.
H. Assem, P. F. Rambau, S. Lee, T. Ogilvie, A. Sienko, L. E. Kelemen, and M. J. T. A. j. o. s. p. Köbel, “High-grade Endometrioid Carcinoma of the Ovary,” vol. 42, no. 4, pp. 534-544, 2018.
M. H. Roh, Y. Yassin, A. Miron, K. K. Mehra, M. Mehrad, N. M. Monte, G. L. Mutter, M. R. Nucci, G. Ning, and F. D. J. M. P. Mckeon, “High-grade fimbrial-ovarian carcinomas are unified by altered p53, PTEN and PAX2 expression,” vol. 23, no. 10, pp. 1316-1324, 2010.
C. B. Gilks, B. A. Clarke, G. Han, M. Köbel, T. Longacre, W. G. McCluggage, J. D. Seidman, P. Shaw, and R. A. J. M. P. Soslow, “Letter to the editor regarding ‘Roh MH, Lassin Y, Miron A et al. High-grade fimbrial-ovarian carcinomas are unified by p53, PTEN and PAX2 expression’,” vol. 24, no. 9, pp. 1281-1282, 2011.
M. Köbel, S. E. Kalloger, N. Boyd, S. McKinney, E. Mehl, C. Palmer, S. Leung, N. J. Bowen, D. N. Ionescu, and A. J. P. m. Rajput, “Ovarian carcinoma subtypes are different diseases: implications for biomarker studies,” vol. 5, no. 12, pp. e232, 2008.
C. P. J. M. O. Crum, “Intercepting pelvic cancer in the distal fallopian tube: theories and realities,” vol. 3, no. 2, pp. 165-170, 2009.
C. B. Gilks, J. Irving, M. Köbel, C. Lee, N. Singh, N. Wilkinson, and W. G. J. T. A. j. o. s. p. McCluggage, “Incidental nonuterine high-grade serous carcinomas arise in the fallopian tube in most cases: further evidence for the tubal origin of high-grade serous carcinomas,” vol. 39, no. 3, pp. 357-364, 2015.
M. S. Anglesio, A. Bashashati, Y. K. Wang, J. Senz, G. Ha, W. Yang, M. R. Aniba, L. M. Prentice, H. Farahani, and H. J. T. J. o. p. Li Chang, “Multifocal endometriotic lesions associated with cancer are clonal and carry a high mutation burden,” vol. 236, no. 2, pp. 201-209, 2015.
N. Kato, S.-i. Sasou, and T. J. M. P. Motoyama, “Expression of hepatocyte nuclear factor-1beta (HNF-1beta) in clear cell tumors and endometriosis of the ovary,” vol. 19, no. 1, pp. 83-89, 2006.
P. Vercellini, F. Parazzini, G. Bolis, S. Carinelli, M. Dindelli, N. Vendola, L. Luchini, P. G. J. A. J. o. O. Crosignani, and Gynecology, “Endometriosis and ovarian cancer,” vol. 169, no. 1, pp. 181-182, 1993.
J. A. Rauh-Hain, W. B. Growdon, N. Rodriguez, A. Goodman, D. M. Boruta II, J. O. Schorge, N. S. Horowitz, and M. G. J. G. o. Del Carmen, “Carcinosarcoma of the ovary: a case–control study,” vol. 121, no. 3, pp. 477-481, 2011.
D. Berton-Rigaud, M. Devouassoux-Shisheboran, J. A. Ledermann, M. M. Leitao, M. A. Powell, A. Poveda, P. Beale, R. M. Glasspool, C. L. Creutzberg, and P. J. I. J. o. G. C. Harter, “Gynecologic Cancer InterGroup (GCIG) consensus review for uterine and ovarian carcinosarcoma,” vol. 24, no. Supp 3, 2014.
M. G. del Carmen, M. Birrer, and J. O. J. G. o. Schorge, “Carcinosarcoma of the ovary: a review of the literature,” vol. 125, no. 1, pp. 271-277, 2012.
J. A. Rauh-Hain, E. J. Diver, J. T. Clemmer, L. S. Bradford, R. M. Clark, W. B. Growdon, A. Goodman, D. M. Boruta II, J. O. Schorge, and M. G. J. G. o. Del Carmen, “Carcinosarcoma of the ovary compared to papillary serous ovarian carcinoma: a SEER analysis,” vol. 131, no. 1, pp. 46-51, 2013.
R. H. Ali, J. D. Seidman, M. Luk, S. Kalloger, and C. B. J. I. j. o. g. p. Gilks, “Transitional cell carcinoma of the ovary is related to high-grade serous carcinoma and is distinct from malignant brenner tumor,” vol. 31, no. 6, pp. 499-506, 2012.
M. Carcangiu, R. J. Kurman, M. L. Carcangiu, and C. S. Herrington, WHO classification of tumours of female reproductive organs: International Agency for Research on Cancer, 2014.
S. K. Sharma, B. Nemieboka, E. Sala, J. S. Lewis, and B. M. J. J. o. N. M. Zeglis, “Molecular imaging of ovarian cancer,” vol. 57, no. 6, pp. 827-833, 2016.
L. Valentin, L. Ameye, A. Testa, F. Lécuru, J.-P. Bernard, D. Paladini, S. Van Huffel, and D. J. G. o. Timmerman, “Ultrasound characteristics of different types of adnexal malignancies,” vol. 102, no. 1, pp. 41-48, 2006.
M. Yasmin, M. Sharif, S. Masood, M. Raza, and S. J. W. A. S. J. Mohsin, “Brain image reconstruction: A short survey,” vol. 19, no. 1, pp. 52-62, 2012.
M. Yasmin, M. Sharif, and S. J. R. J. o. R. S. Mohsin, “Survey paper on diagnosis of breast cancer using image processing techniques,” vol. 2277, pp. 2502, 2013.
M. A. Khan, I. U. Lali, A. Rehman, M. Ishaq, M. Sharif, T. Saba, S. Zahoor, T. J. M. r. Akram, and technique, “Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection,” vol. 82, no. 6, pp. 909-922, 2019.
M. Yasmin, M. Sharif, S. Masood, M. Raza, and S. J. W. A. S. J. Mohsin, “Brain image enhancement-A survey,” vol. 17, no. 9, pp. 1192-1204, 2012.
M. Sharif, U. Tanvir, E. U. Munir, M. A. Khan, M. J. J. o. A. I. Yasmin, and H. Computing, “Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection,” pp. 1-20, 2018.
S. Sohaib, T. Mills, A. Sahdev, J. Webb, P. Vantrappen, I. Jacobs, and R. J. C. r. Reznek, “The role of magnetic resonance imaging and ultrasound in patients with adnexal masses,” vol. 60, no. 3, pp. 340-348, 2005.
J. Amin, M. Sharif, M. A. Anjum, M. Raza, and S. A. C. Bukhari, “Convolutional neural network with batch normalization for glioma and stroke lesion detection using MRI,” Cognitive Systems Research, vol. 59, pp. 304-311, 2020.
B. De La Franier, M. J. B. Thompson, and Bioelectronics, “Early stage detection and screening of ovarian cancer: A research opportunity and significant challenge for biosensor technology,” vol. 135, pp. 71-81, 2019.
B. Aktas, S. Kasimir-Bauer, P. Wimberger, R. Kimmig, and M. J. A. r. Heubner, “Utility of mesothelin, L1CAM and Afamin as biomarkers in primary ovarian cancer,” vol. 33, no. 1, pp. 329-336, 2013.
E. Kobayashi, Y. Ueda, S. Matsuzaki, T. Yokoyama, T. Kimura, K. Yoshino, M. Fujita, T. Kimura, T. J. C. E. Enomoto, and P. Biomarkers, “Biomarkers for screening, diagnosis, and monitoring of ovarian cancer,” vol. 21, no. 11, pp. 1902-1912, 2012.
F. Su, J. Lang, A. Kumar, C. Ng, B. Hsieh, M. A. Suchard, S. T. Reddy, and R. J. B. i. Farias-Eisner, “Validation of candidate serum ovarian cancer biomarkers for early detection,” vol. 2, pp. 117727190700200011, 2007.
D. G. Peters, D. M. Kudla, J. A. DeLoia, T. J. Chu, L. Fairfull, R. P. Edwards, R. E. J. C. E. Ferrell, and P. Biomarkers, “Comparative gene expression analysis of ovarian carcinoma and normal ovarian epithelium by serial analysis of gene expression,” vol. 14, no. 7, pp. 1717-1723, 2005.
J.-H. Kim, S. J. Skates, T. Uede, K.-k. Wong, J. O. Schorge, C. M. Feltmate, R. S. Berkowitz, D. W. Cramer, and S. C. J. J. Mok, “Osteopontin as a potential diagnostic biomarker for ovarian cancer,” vol. 287, no. 13, pp. 1671-1679, 2002.
Y. Zhang, B. Guo, R. J. A. b. Bi, and biotechnology, “Ovarian cancer: biomarker proteomic diagnosis in progress,” vol. 168, no. 4, pp. 910-916, 2012.
S. P. Langdon, G. J. Rabiasz, G. L. Hirst, R. King, R. A. Hawkins, J. F. Smyth, and W. R. J. C. C. R. Miller, “Expression of the heat shock protein HSP27 in human ovarian cancer,” vol. 1, no. 12, pp. 1603-1609, 1995.
K. L. Abbott, J. M. Lim, L. Wells, B. B. Benigno, J. F. McDonald, and M. J. P. Pierce, “Identification of candidate biomarkers with cancer‐specific glycosylation in the tissue and serum of endometrioid ovarian cancer patients by glycoproteomic analysis,” vol. 10, no. 3, pp. 470-481, 2010.
Y. Xu, Z. Shen, D. W. Wiper, M. Wu, R. E. Morton, P. Elson, A. W. Kennedy, J. Belinson, M. Markman, and G. J. J. Casey, “Lysophosphatidic acid as a potential biomarker for ovarian and other gynecologic cancers,” vol. 280, no. 8, pp. 719-723, 1998.
I. Sedláková, J. Vávrová, J. Tošner, and L. J. T. B. Hanousek, “Lysophosphatidic acid (LPA)—a perspective marker in ovarian cancer,” vol. 32, no. 2, pp. 311-316, 2011.
O. Onen, A. Sisman, N. D. Gallant, P. Kruk, and R. J. S. Guldiken, “A urinary Bcl-2 surface acoustic wave biosensor for early ovarian cancer detection,” vol. 12, no. 6, pp. 7423-7437, 2012.
R. Guldiken, P. Kruk, N. Gallant, A. Sisman, and O. Onen, “A urinary Bcl-2 surface acoustic wave biosensor for early ovarian cancer detection,” 2012.
D. J. Ho, M. H. Chui, C. M. Vanderbilt, J. Jung, M. E. Robson, C.-S. Park, J. Roh, and T. J. Fuchs, “Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation,” arXiv preprint arXiv:2203.15015, 2022.
K. Srilatha, F. Jayasudha, M. Sumathi, and P. Chitra, "Automated Ultrasound Ovarian Tumour Segmentation and Classification Based on Deep Learning Techniques," Advances in Electrical and Computer Technologies, pp. 59-70: Springer, 2022.
C.-W. Wang, C.-C. Chang, Y.-C. Lee, Y.-J. Lin, S.-C. Lo, P.-C. Hsu, Y.-A. Liou, C.-H. Wang, and T.-K. Chao, “Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images,” Computerized Medical Imaging and Graphics, vol. 99, pp. 102093, 2022.
T. Buddenkotte, “Fully Automated Segmentation of High Grade Serous Ovarian Cancer on Computed Tomography Images using Deep Learning,” University of Cambridge, 2022.
M. Jeya Sundari, and N. Brintha, “An Intelligent Black Widow Optimization on Image Enhancement with Deep Learning Based Ovarian Tumor Diagnosis model,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp. 1-8, 2022.
D. Mikdadi, K. A. O’Connell, P. J. Meacham, M. A. Dugan, M. O. Ojiere, T. B. Carlson, and J. A. Klenk, “Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery,” Cancer Biomarkers, vol. 33, no. 2, pp. 173-184, 2022.
S. Sheela, and M. Sumathi, “An Evaluation of Effectiveness of a Texture Feature Based Computerized Diagnostic Model in Classifying the Ovarian Cyst as Benign and Malignant from Static 2D B-Mode Ultrasound Images,” Current Medical Imaging, 2022.
Q. Zhao, S. Lyu, W. Bai, L. Cai, B. Liu, M. Wu, X. Sang, M. Yang, and L. Chen, “A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation,” arXiv preprint arXiv:2207.06799, 2022.
G. Avesani, H. E. Tran, G. Cammarata, F. Botta, S. Raimondi, L. Russo, S. Persiani, M. Bonatti, T. Tagliaferri, and M. Dolciami, “CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric Dataset,” Cancers, vol. 14, no. 11, pp. 2739, 2022.
H. Zeng, L. Chen, M. Zhang, Y. Luo, and X. Ma, “Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer,” Gynecologic Oncology, vol. 163, no. 1, pp. 171-180, 2021.