|
1
|
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
|
|
2
|
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
|
|
3
|
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
|
|
4
|
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
|
|
5
|
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
|
|
6
|
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
|
|
7
|
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
|
|
8
|
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
|
|
9
|
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
|
|
10
|
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
|
|
11
|
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
|
|
12
|
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
|
|
13
|
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
|
|
14
|
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
|
|
15
|
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
|
|
16
|
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
|
|
17
|
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
|
|
18
|
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
|
|
19
|
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
|
|
20
|
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
|
|
21
|
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
|
|
22
|
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
|
|
23
|
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
|
|
24
|
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
|
|
25
|
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
|
|
26
|
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
|
|
27
|
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
|
|
28
|
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
|
|
29
|
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
|
|
30
|
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
|
|
31
|
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
|
|
32
|
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
|
|
33
|
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
|
|
34
|
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
|
|
35
|
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
|
|
36
|
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
|
|
37
|
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
|
|
38
|
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
|
|
39
|
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
|
|
40
|
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
|
|
41
|
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
|
|
42
|
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
|
|
43
|
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
|
|
44
|
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
|
|
45
|
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
|
|
46
|
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
|
|
47
|
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
|
|
48
|
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
|
|
49
|
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
|
|
50
|
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
|
|
51
|
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
|
|
52
|
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
|
|
53
|
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
|
|
54
|
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
|
|
55
|
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
|
|
56
|
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
|
|
57
|
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
|
|
58
|
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
|
|
59
|
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
|
|
60
|
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
|
|
61
|
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
|
|
62
|
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
|
|
63
|
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
|
|
64
|
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
|
|
65
|
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
|
|
66
|
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
|
|
67
|
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
|
|
68
|
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
|
|
69
|
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
|
|
70
|
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
|
|
71
|
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
|
|
72
|
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
|
|
73
|
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
|
|
74
|
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
|
|
75
|
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
|
|
76
|
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
|
|
77
|
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.
|
|
78
|
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
|
|
79
|
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
|
|
80
|
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
|
|
81
|
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
|
|
82
|
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
|
|
83
|
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
|
|
84
|
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
|
|
85
|
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
|
|
86
|
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
|
|
87
|
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
|
|
88
|
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
|
|
89
|
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
|
|
90
|
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
|
|
91
|
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
|
|
92
|
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
|
|
93
|
Fereidouni F and Levenson R: Beyond
brightfield: A possible future of slide scanners. Biotechniques.
70:5–6. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
94
|
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
|
|
95
|
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
|
|
96
|
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
|
|
97
|
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
|
|
98
|
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
|
|
99
|
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
|
|
100
|
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
|
|
101
|
Lim HJ and Ledger W: Targeted therapy in
ovarian cancer. Womens Health (Lond). 12:363–378. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
102
|
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
|
|
103
|
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
|
|
104
|
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
|
|
105
|
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
|
|
106
|
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
|
|
107
|
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
|
|
108
|
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
|
|
109
|
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
|
|
110
|
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
|
