Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)
- Authors:
- Yanli Wang
- Weihong Lin
- Xiaoling Zhuang
- Xiali Wang
- Yifang He
- Luhong Li
- Guorong Lyu
-
Affiliations: Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China, Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China, Department of Pathology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China, Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, Fujian 362000, P.R. China - Published online on: January 19, 2024 https://doi.org/10.3892/or.2024.8705
- Article Number: 46
-
Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
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|
Siegel RL, Miller KD, Wagle NS and Jemal A: Cancer statistics, 2023. CA Cancer J Clin. 73:17–48. 2023. View Article : Google Scholar : PubMed/NCBI | |
|
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A and Bray F: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 71:209–249. 2021. View Article : Google Scholar : PubMed/NCBI | |
|
Allemani C, Weir HK, Carreira H, Harewood R, Spika D, Wang XS, Bannon F, Ahn JV, Johnson CJ, Bonaventure A, et al: Global surveillance of cancer survival 1995–2009: Analysis of individual data for 25,676,887 patients from 279 population-based registries in 67 countries (CONCORD-2). Lancet. 385:977–1010. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Millstein J, Budden T, Goode EL, Anglesio MS, Talhouk A, Intermaggio MP, Leong HS, Chen S, Elatre W, Gilks B, et al: Prognostic gene expression signature for high-grade serous ovarian cancer. Ann Oncol. 31:1240–1250. 2020. View Article : Google Scholar : PubMed/NCBI | |
|
Kurman RJ and Shih IM: The origin and pathogenesis of epithelial ovarian cancer: A proposed unifying theory. Am J Surg Pathol. 34:433–443. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Schmeler KM, Tao X, Frumovitz M, Deavers MT, Sun CC, Sood AK, Brown J, Gershenson DM and Ramirez PT: Prevalence of lymph node metastasis in primary mucinous carcinoma of the ovary. Obstet Gynecol. 116:269–273. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Wang KH and Ding DC: The role and applications of exosomes in gynecological cancer: A review. Cell Transplant. 32:96368972311952402023. View Article : Google Scholar : PubMed/NCBI | |
|
Khella CA, Mehta GA, Mehta RN and Gatza ML: Recent advances in integrative multi-omics research in breast and ovarian cancer. J Pers Med. 11:1492021. View Article : Google Scholar : PubMed/NCBI | |
|
Yu H, Wang J, Wu B, Li J and Chen R: Prognostic significance and risk factors for pelvic and para-aortic lymph node metastasis in type I and type II ovarian cancer: A large population-based database analysis. J Ovarian Res. 16:282023. View Article : Google Scholar : PubMed/NCBI | |
|
Chang YH, Wu KC, Harnod T and Ding DC: The organoid: A research model for ovarian cancer. Tzu Chi Med J. 34:255–260. 2022. View Article : Google Scholar : PubMed/NCBI | |
|
Kurman RJ and Shih IM: The dualistic model of ovarian carcinogenesis: Revisited, revised, and expanded. Am J Pathol. 186:733–747. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Zhang T, Liu Q, Zhu Y, Huang Y, Qin J, Wu X and Zhang S: Lymphocyte and macrophage infiltration in omental metastases indicates poor prognosis in advance stage epithelial ovarian cancer. J Int Med Res. 49:30006052110662452021. View Article : Google Scholar : PubMed/NCBI | |
|
Shrestha P, Poudyal B, Yadollahi S, Wrigh DE, Gregor AV, Warne JD, Korfiati P, Gree IC, Rassie SL, Mariani A, et al: A systematic review on the use of artificial intelligence in gynecologic imaging-Background, state of the art, and future directions. Gynecol Oncol. 166:596–605. 2022. View Article : Google Scholar : PubMed/NCBI | |
|
Mikdadi D, O'Connell KA, Meacham PJ, Dugan MA, Ojiere MO, Carlson TB and Klenk JA: Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery. Cancer Biomark. 33:173–184. 2022. View Article : Google Scholar : PubMed/NCBI | |
|
Breen J, Allen K, Zucker K, Adusumilli P, Scarsbrook A, Hall G, Orsi NM and Ravikumar N: Artificial intelligence in ovarian cancer histopathology: A systematic review. NPJ Precis Oncol. 7:832023. View Article : Google Scholar : PubMed/NCBI | |
|
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A and Aerts HJ: Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer. 48:441–446. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Tagliafico AS, Piana M, Schenone D, Lai R, Massone AM and Houssami N: Overview of radiomics in breast cancer diagnosis and prognostication. Breast. 49:74–80. 2020. View Article : Google Scholar : PubMed/NCBI | |
|
Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, et al: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 5:40062014. View Article : Google Scholar : PubMed/NCBI | |
|
Sun R, Orlhac F, Robert C, Reuzé S, Schernberg A, Buvat I, Deutsch E and Ferté C: In regard to mattonen et al. Int J Radiat Oncol Biol Phys. 95:1544–1545. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Tong Y, Zhang J, Wei Y, Yu J, Zhan W, Xia H, Zhou S, Wang Y and Chang C: Ultrasound-based radiomics analysis for preoperative prediction of central and lateral cervical lymph node metastasis in papillary thyroid carcinoma: A multi-institutional study. BMC Med Imaging. 22:822022. View Article : Google Scholar : PubMed/NCBI | |
|
Du Y, Zha HL, Wang H, Liu XP, Pan JZ, Du LW, Cai MJ, Zong M and Li CY: Ultrasound-based radiomics nomogram for differentiation of triple-negative breast cancer from fibroadenoma. Br J Radiol. 95:202105982022. View Article : Google Scholar : PubMed/NCBI | |
|
Peng Y, Lin P, Wu L, Wan D, Zhao Y, Liang L, Ma X, Qin H, Liu Y, Li X, et al: Ultrasound-Based radiomics analysis for preoperatively predicting different histopathological subtypes of primary liver cancer. Front Oncol. 10:16462020. View Article : Google Scholar : PubMed/NCBI | |
|
Ou W, Lei J, Li M, Zhang X, Liang R, Long L, Wang C, Chen L, Chen J, Zhang J and Wang Z: Ultrasound-based radiomics score for pre-biopsy prediction of prostate cancer to reduce unnecessary biopsies. Prostate. 83:109–118. 2023. View Article : Google Scholar : PubMed/NCBI | |
|
Avesani G, Tran HE, Cammarata G, Botta F, Raimondi S, Russo L, Persiani S, Bonatti M, Tagliaferri T, Dolciami M, et al: CT-based radiomics and deep learning for BRCA mutation and progression-free survival prediction in ovarian cancer using a multicentric dataset. Cancers (Basel). 14:23792022. View Article : Google Scholar | |
|
Levy MA, Freymann JB, Kirby JS, Fedorov A, Fennessy FM, Eschrich SA, Berglund AE, Fenstermacher DA, Tan Y, Guo X, et al: Informatics methods to enable sharing of quantitative imaging research data. Magn Reson Imaging. 30:1249–1256. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Beer L, Martin-Gonzalez P, Delgado-Ortet M, Reinius M, Rundo L, Woitek R, Ursprung S, Escudero L, Sahin H, Funingana IG, et al: Ultrasound-guided targeted biopsies of CT-based radiomic tumour habitats: Technical development and initial experience in metastatic ovarian cancer. Eur Radiol. 31:3765–3772. 2021. View Article : Google Scholar : PubMed/NCBI | |
|
Karimi D, Dou H, Warfield SK and Gholipour A: Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis. Med Image Anal. 65:1017592020. View Article : Google Scholar : PubMed/NCBI | |
|
Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, et al: Radiomics: The process and the challenges. Magn Reson Imaging. 30:1234–1248. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Peeken JC, Bernhofer M, Wiestler B, Goldberg T, Cremers D, Rost B, Wilkens JJ, Combs SE and Nüsslin F: Radiomics in radiooncology-challenging the medical physicist. Phys Med. 48:27–36. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Koh YW, Lee D and Lee SJ: Intratumoral heterogeneity as measured using the tumor-stroma ratio and PET texture analyses in females with lung adenocarcinomas differs from that of males with lung adenocarcinomas or squamous cell carcinomas. Medicine (Baltimore). 98:e148762019. View Article : Google Scholar : PubMed/NCBI | |
|
Busnatu Ș, Niculescu AG, Bolocan A, Petrescu GED, Păduraru DN, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu AM and Martins H: Clinical applications of artificial intelligence-an updated overview. J Clin Med. 11:22652022. View Article : Google Scholar : PubMed/NCBI | |
|
Oakden-Rayner L, Carneiro G, Bessen T, Nascimento JC, Bradley AP and Palmer LJ: Precision radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework. Sci Rep. 7:16482017. View Article : Google Scholar : PubMed/NCBI | |
|
Li W, Dong S, Wang H, Wu R, Wu H, Tang ZR, Zhang J, Hu Z and Yin C: Risk analysis of pulmonary metastasis of chondrosarcoma by establishing and validating a new clinical prediction model: A clinical study based on SEER database. BMC Musculoskelet Disord. 22:5292021. View Article : Google Scholar : PubMed/NCBI | |
|
Chen L, Zeng H, Xiang Y, Huang Y, Luo Y and Ma X: Histopathological images and multi-omics integration predict molecular characteristics and survival in lung adenocarcinoma. Front Cell Dev Biol. 9:7201102021. View Article : Google Scholar : PubMed/NCBI | |
|
Guo S, Tian M, Fan Y and Zhang X: Recent advances in mass spectrometry-based proteomics and metabolomics in chronic rhinosinusitis with nasal polyps. Front Immunol. 14:12671942023. View Article : Google Scholar : PubMed/NCBI | |
|
Zeng H, Chen L, Zhang M, Luo Y and Ma X: Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer. Gynecol Oncol. 163:171–180. 2021. View Article : Google Scholar : PubMed/NCBI | |
|
Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK and Kumar P: Artificial intelligence to deep learning: Machine intelligence approach for drug discovery. Mol Divers. 25:1315–1360. 2021. View Article : Google Scholar : PubMed/NCBI | |
|
European Society of Radiology (ESR), . What the radiologist should know about artificial intelligence-an ESR white paper. Insights Imaging. 10:442019. View Article : Google Scholar : PubMed/NCBI | |
|
Joda T, Bornstein MM, Jung RE, Ferrari M, Waltimo T and Zitzmann NU: Recent trends and future direction of dental research in the digital era. Int J Environ Res Public Health. 17:19872020. View Article : Google Scholar : PubMed/NCBI | |
|
Covas P, De Guzman E, Barrows I, Bradley AJ, Choi BG, Krepp JM, Lewis JF, Katz R, Tracy CM, Zeman RK, et al: Artificial intelligence advancements in the cardiovascular imaging of coronary atherosclerosis. Front Cardiovasc Med. 9:8394002022. View Article : Google Scholar : PubMed/NCBI | |
|
Li W, Dong Y, Liu W, Tang Z, Sun C, Lowe S, Chen S, Bentley R, Zhou Q, Xu C, et al: A deep belief network-based clinical decision system for patients with osteosarcoma. Front Immunol. 13:10033472022. View Article : Google Scholar : PubMed/NCBI | |
|
Chen L, Han Z, Wang J and Yang C: The emerging roles of machine learning in cardiovascular diseases: A narrative review. Ann Transl Med. 10:6112022. View Article : Google Scholar : PubMed/NCBI | |
|
Zhao J, Luo Y, Xiao R, Wu R and Fan T: Tri-training algorithm for adaptive nearest neighbor density editing and cross entropy evaluation. Entropy (Basel). 25:4802023. View Article : Google Scholar : PubMed/NCBI | |
|
Awassa L, Jdey I, Dhahri H, Hcini G, Mahmood A, Othman E and Haneef M: Study of different deep learning methods for coronavirus (COVID-19) pandemic: Taxonomy, survey and insights. Sensors (Basel). 22:18902022. View Article : Google Scholar : PubMed/NCBI | |
|
Zhang Z, Zhu Y, Liu M, Zhang Z, Zhao Y, Yang X, Xie M and Zhang L: Artificial intelligence-enhanced echocardiography for systolic function assessment. J Clin Med. 11:28932022. View Article : Google Scholar : PubMed/NCBI | |
|
Chen S, Zhao S and Lan Q: Residual block based nested U-type architecture for multi-modal brain tumor image segmentation. Front Neurosci. 16:8328242022. View Article : Google Scholar : PubMed/NCBI | |
|
Park CW, Oh SJ, Kim KS, Jang MC, Kim IS, Lee YK, Chung MJ, Cho BH and Seo SW: Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation. PLoS One. 17:e02641402022. View Article : Google Scholar : PubMed/NCBI | |
|
Wu W, Huang Y and Wu X: A new deep learning method with self-supervised learning for delineation of the electrocardiogram. Entropy (Basel). 24:18282022. View Article : Google Scholar : PubMed/NCBI | |
|
Kaka H, Zhang E and Khan N: Artificial intelligence and deep learning in neuroradiology: Exploring the new frontier. Can Assoc Radiol J. 72:35–44. 2021. View Article : Google Scholar : PubMed/NCBI | |
|
Liu P, Liang X, Liao S and Lu Z: Pattern classification for ovarian tumors by integration of radiomics and deep learning features. Curr Med Imaging. 18:1486–1502. 2022. View Article : Google Scholar : PubMed/NCBI | |
|
Qin X, Hu X, Xiao W, Zhu C, Ma Q and Zhang C: Preoperative evaluation of hepatocellular carcinoma differentiation using contrast-enhanced ultrasound-based deep-learning radiomics model. J Hepatocell Carcinoma. 10:157–168. 2023. View Article : Google Scholar : PubMed/NCBI | |
|
Wang M, Perucho JAU, Hu Y, Choi MH, Han L, Wong EMF, Ho G, Zhang X, Ip P and Lee EYP: Computed tomographic radiomics in differentiating histologic subtypes of epithelial ovarian carcinoma. JAMA Netw Open. 5:e22451412022. View Article : Google Scholar : PubMed/NCBI | |
|
Li S, Liu J, Xiong Y, Pang P, Lei P, Zou H, Zhang M, Fan B and Luo P: A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography. Sci Rep. 11:87302021. View Article : Google Scholar : PubMed/NCBI | |
|
Saida T, Mori K, Hoshiai S, Sakai M, Urushibara A, Ishiguro T, Minami M, Satoh T and Nakajima T: Diagnosing ovarian cancer on MRI: A preliminary study comparing deep learning and radiologist assessments. Cancers (Basel). 14:9872022. View Article : Google Scholar : PubMed/NCBI | |
|
Gao Y, Zeng S, Xu X, Li H, Yao S, Song K, Li X, Chen L, Tang J, Xing H, et al: Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: A retrospective, multicentre, diagnostic study. Lancet Digit Health. 4:e179–e187. 2022. View Article : Google Scholar : PubMed/NCBI | |
|
Wang Y, Zhang H, Wang T, Yao L, Zhang G, Liu X, Yang G and Yuan L: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging. Sci Rep. 13:27702023. View Article : Google Scholar : PubMed/NCBI | |
|
Andreotti RF, Timmerman D, Strachowski LM, Froyman W, Benacerraf BR, Bennett GL, Bourne T, Brown DL, Coleman BG, Frates MC, et al: O-RADS US risk stratification and management system: A consensus guideline from the ACR ovarian-adnexal reporting and data system committee. Radiology. 294:168–185. 2020. View Article : Google Scholar : PubMed/NCBI | |
|
Chen H, Yang BW, Qian L, Meng YS, Bai XH, Hong XW, He X, Jiang MJ, Yuan F, Du QW and Feng WW: Deep learning prediction of ovarian malignancy at US compared with O-RADS and expert assessment. Radiology. 304:106–113. 2022. View Article : Google Scholar : PubMed/NCBI | |
|
Jung Y, Kim T, Han MR, Kim S, Kim G, Lee S and Choi YJ: Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder. Sci Rep. 12:170242022. View Article : Google Scholar : PubMed/NCBI | |
|
Christiansen F, Epstein EL, Smedberg E, Åkerlund M, Smith K and Epstein E: Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: Comparison with expert subjective assessment. Ultrasound Obstet Gynecol. 57:155–163. 2021. View Article : Google Scholar : PubMed/NCBI | |
|
Harris HR, Guertin KA, Camacho TF, Johnson CE, Wu AH, Moorman PG, Myers E, Bethea TN, Bandera EV, Joslin CE, et al: Racial disparities in epithelial ovarian cancer survival: An examination of contributing factors in the ovarian cancer in women of African Ancestry consortium. Int J Cancer. 151:1228–1239. 2022. View Article : Google Scholar : PubMed/NCBI | |
|
Tang ZP, Ma Z, He Y, Liu RC, Jin BB, Wen DY, Wen R, Yin HH, Qiu CC, Gao RZ, et al: Ultrasound-based radiomics for predicting different pathological subtypes of epithelial ovarian cancer before surgery. BMC Med Imaging. 22:1472022. View Article : Google Scholar : PubMed/NCBI | |
|
Xu Y, Luo HJ, Ren J, Guo LM, Niu J and Song X: Diffusion-weighted imaging-based radiomics in epithelial ovarian tumors: Assessment of histologic subtype. Front Oncol. 12:9781232022. View Article : Google Scholar : PubMed/NCBI | |
|
Jian J, Li Y, Pickhardt PJ, Xia W, He Z, Zhang R, Zhao S, Zhao X, Cai S, Zhang J, et al: MR image-based radiomics to differentiate type I and type II epithelial ovarian cancers. Eur Radiol. 31:403–410. 2021. View Article : Google Scholar : PubMed/NCBI | |
|
Konstantinopoulos PA, Ceccaldi R, Shapiro GI and D'Andrea AD: Homologous recombination deficiency: Exploiting the fundamental vulnerability of ovarian cancer. Cancer Discov. 5:1137–1154. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Tutt A, Tovey H, Cheang MCU, Kernaghan S, Kilburn L, Gazinska P, Owen J, Abraham J, Barrett S, Barrett-Lee P, et al: Carboplatin in BRCA1/2-mutated and triple-negative breast cancer BRCAness subgroups: The TNT trial. Nat Med. 24:628–637. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Golan T, Sella T, O'Reilly EM, Katz MH, Epelbaum R, Kelsen DP, Borgida A, Maynard H, Kindler H, Friedmen E, et al: Overall survival and clinical characteristics of BRCA mutation carriers with stage I/II pancreatic cancer. Br J Cancer. 116:697–702. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Li MR, Liu MZ, Ge YQ, Zhou Y and Wei W: Assistance by routine CT features combined with 3D texture analysis in the diagnosis of BRCA gene mutation status in advanced epithelial ovarian cancer. Front Oncol. 11:6967802021. View Article : Google Scholar : PubMed/NCBI | |
|
Moore K, Colombo N, Scambia G, Kim BG, Oaknin A, Friedlander M, Lisyanskaya A, Floquet A, Leary A, Sonke GS, et al: Maintenance olaparib in patients with newly diagnosed advanced ovarian cancer. N Engl J Med. 379:2495–2505. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Soslow RA, Han G, Park KJ, Garg K, Olvera N, Spriggs DR, Kauff ND and Levine DA: Morphologic patterns associated with BRCA1 and BRCA2 genotype in ovarian carcinoma. Mod Pathol. 25:625–636. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Alsop K, Fereday S, Meldrum C, deFazio A, Emmanuel C, George J, Dobrovic A, Birrer MJ, Webb PM, Stewart C, et al: BRCA mutation frequency and patterns of treatment response in BRCA mutation-positive women with ovarian cancer: A report from the Australian ovarian cancer study group. J Clin Oncol. 30:2654–2663. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Sánchez-Lorenzo L, Salas-Benito D, Villamayor J, Patiño-García A and González-Martín A: The BRCA gene in epithelial ovarian cancer. Cancers (Basel). 14:12352022. View Article : Google Scholar : PubMed/NCBI | |
|
Meier A, Veeraraghavan H, Nougaret S, Lakhman Y, Sosa R, Soslow RA, Sutton EJ, Hricak H, Sala E and Vargas HA: Association between CT-texture-derived tumor heterogeneity, outcomes, and BRCA mutation status in patients with high-grade serous ovarian cancer. Abdom Radiol (NY). 44:2040–2047. 2019. View Article : Google Scholar : PubMed/NCBI | |
|
Verhaak RG, Tamayo P, Yang JY, Hubbard D, Zhang H, Creighton CJ, Fereday S, Lawrence M, Carter SL, Mermel CH, et al: Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. J Clin Invest. 123:517–525. 2013.PubMed/NCBI | |
|
Vargas HA, Huang EP, Lakhman Y, Ippolito JE, Bhosale P, Mellnick V, Shinagare AB, Anello M, Kirby J, Fevrier-Sullivan B, et al: Radiogenomics of high-grade serous ovarian cancer: Multireader multi-institutional study from the cancer genome atlas ovarian cancer imaging research group. Radiology. 285:482–492. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Wang X, Xu C, Grzegorzek M and Sun H: Habitat radiomics analysis of pet/ct imaging in high-grade serous ovarian cancer: Application to Ki-67 status and progression-free survival. Front Physiol. 13:9487672022. View Article : Google Scholar : PubMed/NCBI | |
|
Heintz AP, Odicino F, Maisonneuve P, Quinn MA, Benedet JL, Creasman WT, Ngan HY, Pecorelli S and Beller U: Carcinoma of the ovary. FIGO 26th annual report on the results of treatment in gynecological cancer. Int J Gynaecol Obstet. 95 (Suppl 1):S161–S192. 2006. | |
|
Suidan RS, Ramirez PT, Sarasohn DM, Teitcher JB, Mironov S, Iyer RB, Zhou Q, Iasonos A, Paul H, Hosaka M, et al: A multicenter prospective trial evaluating the ability of preoperative computed tomography scan and serum CA-125 to predict suboptimal cytoreduction at primary debulking surgery for advanced ovarian, fallopian tube, and peritoneal cancer. Gynecol Oncol. 134:455–461. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Peng Z, Lin Z, He A, Yi L, Jin M, Chen Z, Tao Y, Yang Y, Cui C, Liu Y and Zuo M: Development and validation of a comprehensive model for predicting distant metastasis of solid lung adenocarcinoma: 3D radiomics, 2D radiomics and clinical features. Cancer Manag Res. 14:3437–3448. 2022. View Article : Google Scholar : PubMed/NCBI | |
|
Ai Y, Zhang J, Jin J, Zhang J, Zhu H and Jin X: Preoperative prediction of metastasis for ovarian cancer based on computed tomography radiomics features and clinical factors. Front Oncol. 11:6107422021. View Article : Google Scholar : PubMed/NCBI | |
|
Yu XY, Ren J, Jia Y, Wu H, Niu G, Liu A, Gao Y, Hao F and Xie L: Multiparameter MRI radiomics model predicts preoperative peritoneal carcinomatosis in ovarian cancer. Front Oncol. 11:7656522021. View Article : Google Scholar : PubMed/NCBI | |
|
Chien J and Poole EM: Ovarian cancer prevention, screening, and early detection: Report from the 11th biennial ovarian cancer research symposium. Int J Gynecol Cancer. 27:S20–S22. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Yang R, Niepel M, Mitchison TK and Sorger PK: Dissecting variability in responses to cancer chemotherapy through systems pharmacology. Clin Pharmacol Ther. 88:34–38. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Luvero D, Milani A and Ledermann JA: Treatment options in recurrent ovarian cancer: Latest evidence and clinical potential. Ther Adv Med Oncol. 6:229–239. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Lu H, Arshad M, Thornton A, Avesani G, Cunnea P, Curry E, Kanavati F, Liang J, Nixon K, Williams ST, et al: A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer. Nat Commun. 10:7642019. View Article : Google Scholar : PubMed/NCBI | |
|
Hong Y, Liu Z, Lin D, Peng J, Yuan Q, Zeng Y, Wang X and Luo C: Development of a radiomic-clinical nomogram for prediction of survival in patients with serous ovarian cancer. Clin Radiol. 77:352–359. 2022. View Article : Google Scholar : PubMed/NCBI | |
|
Wei W, Liu Z, Rong Y, Zhou B, Bai Y, Wei W, Wang S, Wang M, Guo Y and Tian J: A computed tomography-based radiomic prognostic marker of advanced high-grade serous ovarian cancer recurrence: A multicenter study. Front Oncol. 9:2552019. View Article : Google Scholar : PubMed/NCBI | |
|
Wang S, Liu Z, Rong Y, Zhou B, Bai Y, Wei W, Wei W, Wang M, Guo Y and Tian J: Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer. Radiother Oncol. 132:171–177. 2019. View Article : Google Scholar : PubMed/NCBI | |
|
Liu L, Wan H, Liu L, Wang J, Tang Y, Cui S and Li Y: Deep learning provides a new magnetic resonance imaging-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer. Diagnostics (Basel). 13:7482023. View Article : Google Scholar : PubMed/NCBI | |
|
Yao F, Ding J, Hu Z, Cai M, Liu J, Huang X, Zheng R, Lin F and Lan L: Ultrasound-based radiomics score: A potential biomarker for the prediction of progression-free survival in ovarian epithelial cancer. Abdom Radiol (NY). 46:4936–4945. 2021. View Article : Google Scholar : PubMed/NCBI | |
|
Laios A, De Freitas DLD, Saalmink G, Tan YS, Johnson R, Zubayraeva A, Munot S, Hutson R, Thangavelu A, Broadhead T, et al: Stratification of length of stay prediction following surgical cytoreduction in advanced high-grade serous ovarian cancer patients using artificial intelligence; the leeds L-AI-OS score. Curr Oncol. 29:9088–9104. 2022. View Article : Google Scholar : PubMed/NCBI | |
|
Lei R, Yu Y, Li Q, Ya Q, Wan J, Ga M, Zhuo W, Ren W, Ta Y, Zhan B, et al: Deep learning magnetic resonance imaging predicts platinum sensitivity in patients with epithelial ovarian cancer. Front Oncol. 12:8951772022. View Article : Google Scholar : PubMed/NCBI | |
|
Fereidouni F and Levenson R: Beyond brightfield: A possible future of slide scanners. Biotechniques. 70:5–6. 2021. View Article : Google Scholar : PubMed/NCBI | |
|
Boehm KM, Aherne EA, Ellenson L, Nikolovski I, Alghamdi M, Vázquez-García I, Zamarin D, Roche KL, Liu Y, Patel D, et al: Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. Nat Cancer. 3:723–733. 2022. View Article : Google Scholar : PubMed/NCBI | |
|
Jiang Z, Song L, Lu H and Yin J: The potential use of DCE-MRI texture analysis to predict HER2 2+ status. Front Oncol. 9:2422019. View Article : Google Scholar : PubMed/NCBI | |
|
Farahani H, Boschman J, Farnell D, Darbandsari A, Zhang A, Ahmadvand P, Jones SJM, Huntsman D, Köbel M, Gilks CB, et al: Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images. Mod Pathol. 35:1983–1990. 2022. View Article : Google Scholar : PubMed/NCBI | |
|
Wang CW, Chang CC, Lee YC, Lin YJ, Lo SC, Hsu PC, Liou YA, Wang CH and Chao TK: Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images. Comput Med Imaging Graph. 99:1020932022. View Article : Google Scholar : PubMed/NCBI | |
|
Ho DJ, Chui MH, Vanderbilt CM, Jung J, Robson ME, Park CS, Roh J and Fuchs TJ: Deep interactive learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation. J Pathol Inform. 14:1001602023. View Article : Google Scholar : PubMed/NCBI | |
|
Nero C, Boldrini L, Lenkowicz J, Giudice MT, Piermattei A, Inzani F, Pasciuto T, Minucci A, Fagotti A, Zannoni G, et al: Deep-learning to predict BRCA mutation and survival from digital H&E slides of epithelial ovarian cancer. Int J Mol Sci. 23:113262022. View Article : Google Scholar : PubMed/NCBI | |
|
Laury AR, Blom S, Ropponen T, Virtanen A and Carpén OM: Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone. Sci Rep. 11:191652021. View Article : Google Scholar : PubMed/NCBI | |
|
Lim HJ and Ledger W: Targeted therapy in ovarian cancer. Womens Health (Lond). 12:363–378. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Wang CW, Lee YC, Chang CC, Lin YJ, Liou YA, Hsu PC, Chang CC, Sai AK, Wang CH and Chao TK: A weakly supervised deep learning method for guiding ovarian cancer treatment and identifying an effective biomarker. Cancers (Basel). 14:16512022. View Article : Google Scholar : PubMed/NCBI | |
|
Wu M, Zhu C, Yang J, Cheng S, Yang X, Gu S, Xu S, Wu Y, Shen W, Huang S and Wang Y: Exploring prognostic indicators in the pathological images of ovarian cancer based on a deep survival network. Front Genet. 13:10696732022. View Article : Google Scholar : PubMed/NCBI | |
|
Guo LY, Wu AH, Wang YX, Zhang LP, Chai H and Liang XF: Deep learning-based ovarian cancer subtypes identification using multi-omics data. BioData Min. 13:102020. View Article : Google Scholar : PubMed/NCBI | |
|
Ye L, Zhang Y, Yang X, Shen F and Xu B: An ovarian cancer susceptible gene prediction method based on deep learning methods. Front Cell Dev Biol. 9:7304752021. View Article : Google Scholar : PubMed/NCBI | |
|
Bahado-Singh RO, Ibrahim A, Al-Wahab Z, Aydas B, Radhakrishna U, Yilmaz A and Vishweswaraiah S: Precision gynecologic oncology: Circulating cell free DNA epigenomic analysis, artificial intelligence and the accurate detection of ovarian cancer. Sci Rep. 12:186252022. View Article : Google Scholar : PubMed/NCBI | |
|
Aghayousefi R, Khatibi SM, Vahed SZ, Bastami M, Pirmoradi S and Teshnehlab M: A diagnostic miRNA panel to detect recurrence of ovarian cancer through artificial intelligence approaches. J Cancer Res Clin Oncol. 149:325–341. 2023. View Article : Google Scholar : PubMed/NCBI | |
|
Hamidi F, Gilani N, Belaghi RA, Sarbakhsh P, Edgünlü T and Santaguida P: Exploration of potential miRNA biomarkers and prediction for ovarian cancer using artificial intelligence. Front Genet. 12:7247852021. View Article : Google Scholar : PubMed/NCBI | |
|
Yokoi A, Matsuzaki J, Yamamoto Y, Yoneoka Y, Takahashi K, Shimizu H, Uehara T, Ishikawa M, Ikeda SI, Sonoda T, et al: Integrated extracellular microRNA profiling for ovarian cancer screening. Nat Commun. 9:43192018. View Article : Google Scholar : PubMed/NCBI | |
|
Irajizad E, Han CY, Celestino J, Wu R, Murage E, Spencer R, Dennison JB, Vykoukal J, Long JP, Do KA, et al: A blood-based metabolite panel for distinguishing ovarian cancer from benign pelvic masses. Clin Cancer Res. 28:4669–4676. 2022. View Article : Google Scholar : PubMed/NCBI |
