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Review
Research progress of radiomics in the treatment of ovarian cancer
LIU Na  WU Hui  LIU Jiarui  GAO Kaihua  YANG Jiao 

Cite this article as: LIU N, WU H, LIU J R, et al. Research progress of radiomics in the treatment of ovarian cancer[J]. Chin J Magn Reson Imaging, 2024, 15(7): 221-226. DOI:10.12015/issn.1674-8034.2024.07.037.


[Abstract] Ovarian cancer, a prevalent malignant gynecological tumor, is often associated with dismal prognosis and high recurrence rates. This is primarily attributed to its late-stage diagnosis, frequent peritoneal metastasis, and the development of resistance to platinum-based chemotherapy, a first-line treatment. In treating ovarian cancer, utmost consideration must be given to the prognosis and quality of life of patients. Timely diagnosis and the selection of appropriate chemotherapy drugs are pivotal in extending progression-free survival (PFS) and enhancing the patient's overall well-being. This review endeavors to encapsulate the advancements in radiomics research pertaining to ovarian cancer's preoperative prediction, assessment of chemotherapy response, platinum chemoresistance, and prognosis prediction. Its objective is to empower clinicians with the knowledge to leverage radiomics technology in more precisely forecasting disease progression and treatment outcomes, ultimately leading to the formulation of personalized treatment plans that optimize patient quality of life. Future research aims to delve deeper into the fusion of multi-omics data, combining radiomics with genomics and proteomics, and this review hopes to serve as a valuable resource and inspiration for researchers in their endeavors.
[Keywords] ovarian cancer;radiomics;platinum-based chemotherapy resistance;prognosis;magnetic resonance imaging

LIU Na   WU Hui*   LIU Jiarui   GAO Kaihua   YANG Jiao  

Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010050, China

Corresponding author: WU H, E-mail: terrywuhui@sina.com

Conflicts of interest   None.

Received  2024-04-04
Accepted  2024-07-09
DOI: 10.12015/issn.1674-8034.2024.07.037
Cite this article as: LIU N, WU H, LIU J R, et al. Research progress of radiomics in the treatment of ovarian cancer[J]. Chin J Magn Reson Imaging, 2024, 15(7): 221-226. DOI:10.12015/issn.1674-8034.2024.07.037.

[1]
HU H, ZHANG T, YANG J, et al. Radiomics predicts the heterogeneity and prognosis of high-grade serous ovarian cancer[J]. Chin J Magn Reson Imag, 2023, 14(6): 176-181. DOI: 10.12015/issn.1674-8034.2023.06.032.
[2]
KUROKI L, GUNTUPALLI S R. Treatment of epithelial ovarian cancer[J/OL]. BMJ, 2020, 371: m3773 [2024-06-15]. https://pubmed.ncbi.nlm.nih.gov/33168565/. DOI: 10.1136/bmj.m3773.
[3]
DI W, HU Y. Big data research in ovary cancer[J]. Chin J Pract Gynecol Obstet, 2018, 34(1): 18-22. DOI: 10.19538/j.fk2018010105.
[4]
FORSTNER R. Early detection of ovarian cancer[J]. Eur Radiol, 2020, 30(10): 5370-5373. DOI: 10.1007/s00330-020-06937-z.
[5]
LHEUREUX S, BRAUNSTEIN M, OZA A M. Epithelial ovarian cancer: evolution of management in the era of precision medicine[J]. CA Cancer J Clin, 2019, 69(4): 280-304. DOI: 10.3322/caac.21559.
[6]
WANG R S,WANG S N,ZHANG W Y, et al. Emerging prospects of radiomics in the management of ovarian cancer: from diagnosis to treatment[J]. Chinese Bulletin of Life Sciences, 2024, 36(4): 580-592. DOI: 10.13376/j.cbls/2024061.
[7]
XU H L, GONG T T, LIU F H, et al. Artificial intelligence performance in image-based ovarian cancer identification: a systematic review and meta-analysis[J/OL]. EClinicalMedicine, 2022, 53: 101662 [2024-06-15]. https://pubmed.ncbi.nlm.nih.gov/36147628/. DOI: 10.1016/j.eclinm.2022.101662.
[8]
LAMBIN P, RIOS-VELAZQUEZ E, LEIJENAAR R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[9]
MAYERHOEFER M E, MATERKA A, LANGS G, et al. Introduction to radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. DOI: 10.2967/jnumed.118.222893.
[10]
LAFATA K J, WANG Y Q, KONKEL B, et al. Radiomics: a primer on high-throughput image phenotyping[J]. Abdom Radiol (NY), 2022, 47(9): 2986-3002. DOI: 10.1007/s00261-021-03254-x.
[11]
CHEN S R, ZHAO S J, LAN Q. Residual block based nested U-type architecture for multi-modal brain tumor image segmentation[J/OL]. Front Neurosci, 2022, 16: 832824 [2024-06-15]. https://pubmed.ncbi.nlm.nih.gov/35356052/. DOI: 10.3389/fnins.2022.832824.
[12]
ELMAHDY M, SEBRO R. Radiomics analysis in medical imaging research[J]. J Med Radiat Sci, 2023, 70(1): 3-7. DOI: 10.1002/jmrs.662.
[13]
FUSCO R, GRANATA V, GRAZZINI G, et al. Radiomics in medical imaging: pitfalls and challenges in clinical management[J]. Jpn J Radiol, 2022, 40(9): 919-929. DOI: 10.1007/s11604-022-01271-4.
[14]
BODALAL Z, TREBESCHI S, NGUYEN-KIM T D L, et al. Radiogenomics: bridging imaging and genomics[J]. Abdom Radiol, 2019, 44(6): 1960-1984. DOI: 10.1007/s00261-019-02028-w.
[15]
国家卫生健康委员会. 卵巢癌诊疗指南(2022年版)[EB/OL]. [2024-06-15]. http://wwwnhc.gov.cn/yzygj/s2911/202204/a0e67177df1f439898683e1333957c74/files/0feefc11d98840898b136ac3d9a4ee20.pdf.
[16]
NEBGEN D R, LU K H, BAST R C. Novel approaches to ovarian cancer screening[J/OL]. Curr Oncol Rep, 2019, 21(8): 75 [2024-06-15]. https://link.springer.com/article/10.1007/s11912-019-0816-0. DOI: 10.1007/s11912-019-0816-0.
[17]
QIAN L D, REN J L, LIU A S, et al. MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes[J]. Eur Radiol, 2020, 30(11): 5815-5825. DOI: 10.1007/s00330-020-06993-5.
[18]
Vergote I, Coens C, Nankivell M, et al. Neoadjuvant chemotherapy versus debulking surgery in advanced tubo-ovarian cancers: pooled analysis of individual patient data from the EORTC 55971 and CHORUS trials [J]. Lancet Oncol, 2018, 19(12): 1680-1687. DOI: 10.1016/S1470-2045(18)30566-7.
[19]
XIE F F, VAN BOCXLAER J, COLIN P, et al. PKPD modeling and dosing considerations in advanced ovarian cancer patients treated with cisplatin-based intraoperative intraperitoneal chemotherapy[J/OL]. AAPS J, 2020, 22(5): 96 [2024-06-15] .https://link.springer.com/article/10.1208/s12248-020-00489-2. DOI: 10.1208/s12248-020-00489-2.
[20]
COLOMBO N, SESSA C, BOIS A D, et al. ESMO-ESGO consensus conference recommendations on ovarian cancer: pathology and molecular biology, early and advanced stages, borderline tumours and recurrent disease[J]. Ann Oncol, 2019, 30(5): 672-705. DOI: 10.1093/annonc/mdz062.
[21]
YANG H J, GAO D M, WANG C, et al. Effect of different metastasis patterns on the prognosis of patients with stage Ⅲ high-grade serous ovarian cancer[J]. Am J Cancer Res, 2023, 13(8): 3599-3606.
[22]
ENGBERSEN M P, VAN DRIEL W, LAMBREGTS D, et al. The role of CT, PET-CT, and MRI in ovarian cancer[J/OL]. Br J Radiol, 2021, 94(1125): 20210117 [2024-06-15]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9327775/. DOI: 10.1259/bjr.20210117.
[23]
YÜ, X Y, WU H, NIU G M, et al. Multiparameter MRI radiomics predicts preoperative peritoneal metastasis in patients with epithelial ovarian cancer[J]. Chin J Magn Reson Imag, 2021, 12(8): 44-48. DOI: 10.12015/issn.1674-8034.2021.08.009.
[24]
SONG X L, REN J L, YAO T Y, et al. Radiomics based on multisequence magnetic resonance imaging for the preoperative prediction of peritoneal metastasis in ovarian cancer[J]. Eur Radiol, 2021, 31(11): 8438-8446. DOI: 10.1007/s00330-021-08004-7.
[25]
WEI M X, ZHANG Y, DING C, et al. Associating peritoneal metastasis with T2-weighted MRI images in epithelial ovarian cancer using deep learning and radiomics: a multicenter study[J]. J Magn Reson Imaging, 2024, 59(1): 122-131. DOI: 10.1002/jmri.28761.
[26]
LI J J, ZHANG J N, WANG F, et al. CT-based radiomics for the preoperative prediction of occult peritoneal metastasis in epithelial ovarian cancers[J]. Acad Radiol, 2024, 31(5): 1918-1930. DOI: 10.1016/j.acra.2023.11.032.
[27]
CHEN H Z, WANG X R, ZHAO F M, et al. The development and validation of a CT-based radiomics nomogram to preoperatively predict lymph node metastasis in high-grade serous ovarian cancer[J/OL]. Front Oncol, 2021, 11: 711648 [2024-06-15]. https://pubmed.ncbi.nlm.nih.gov/34532289/. DOI: 10.3389/fonc.2021.711648.
[28]
QI Y, LIU J C, WANG X Y, et al. Development and validation of an ultrasound-based radiomics nomogram to predict lymph node status in patients with high-grade serous ovarian cancer: a retrospective analysis [J/OL]. J Ovarian Res, 2024, 17(1): 48 [2024-06-15]. https://pubmed.ncbi.nlm.nih.gov/38389075/. DOI: 10.1186/s13048-024-01375-7.
[29]
LENG Y P, KAN A, WANG X W, et al. Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study[J/OL]. BMC Cancer, 2024, 24(1): 307 [2024-06-15]. https://pubmed.ncbi.nlm.nih.gov/38448945/. DOI: 10.1186/s12885-024-12037-8.
[30]
LU J J, CAI S Q, WANG F, et al. Development of a prediction model for gross residual in high-grade serous ovarian cancer by combining preoperative assessments of abdominal and pelvic metastases and multiparametric MRI[J]. Acad Radiol, 2023, 30(9): 1823-1831. DOI: 10.1016/j.acra.2022.12.019.
[31]
AI Y, ZHANG J D, JIN J B, et al. Preoperative prediction of metastasis for ovarian cancer based on computed tomography radiomics features and clinical factors[J/OL]. Front Oncol, 2021, 11: 610742 [2024-06-15]. https://pubmed.ncbi.nlm.nih.gov/34178617/. DOI: 10.3389/fonc.2021.610742.
[32]
RUNDO L, BEER L, ESCUDERO SANCHEZ L, et al. Clinically interpretable radiomics-based prediction of histopathologic response to neoadjuvant chemotherapy in high-grade serous ovarian carcinoma[J/OL]. Front Oncol, 2022, 12: 868265 [2024-06-15]. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.868265/full. DOI: 10.3389/fonc.2022.868265.
[33]
CRISPIN-ORTUZAR M, WOITEK R, REINIUS M A V, et al. Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer[J/OL]. Nat Commun, 2023, 14(1): 6756 [2024-06-15]. https://www.nature.com/articles/s41467-023-41820-7. DOI: 10.1038/s41467-023-41820-7.
[34]
ZHANG K, ABDOLI N, GILLEY P, et al. Developing a novel image marker to predict the clinical outcome of neoadjuvant chemotherapy (NACT) for ovarian cancer patients[J/OL]. Comput Biol Med, 2024, 172: 108240 [2024-06-15]. https://www.sciencedirect.com/science/article/abs/pii/S001048252400324X?via%3Dihub. DOI: 10.1016/j.compbiomed.2024.108240.
[35]
LI Y A, JIAN J M, GE H J, et al. Peritumoral MRI radiomics features increase the evaluation efficiency for response to chemotherapy in patients with epithelial ovarian cancer[J/OL]. J Magn Reson Imaging, 2024 [2024-06-15]. https://onlinelibrary.wiley.com/doi/10.1002/jmri.29359. DOI: 10.1002/jmri.29359.
[36]
VEERARAGHAVAN H, VARGAS H A, JIMENEZ-SANCHEZ A, et al. Integrated multi-tumor radio-genomic marker of outcomes in patients with high serous ovarian carcinoma[J/OL]. Cancers, 2020, 12(11): 3403 [2024-06-15]. https://www.mdpi.com/2072-6694/12/11/3403. DOI: 10.3390/cancers12113403.
[37]
MAO M M, LI H M, SHI J, et al. Prediction of platinum-based chemotherapy sensitivity for epithelial ovarian cancer by multi-sequence MRI-based radiomic nomogram[J]. Zhonghua Yi Xue Za Zhi, 2022, 102(3): 201-208. DOI: 10.3760/cma.j.cn112137-20210816-01844.
[38]
LI H M, CAI S Q, DENG L, et al. Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram[J]. Eur Radiol, 2023, 33(8): 5298-5308. DOI: 10.1007/s00330-023-09552-w.
[39]
BI Q, MIAO K, XU N, et al. Habitat radiomics based on MRI for predicting platinum resistance in patients with high-grade serous ovarian carcinoma: a MulticenterStudy[J]. Acad Radiol, 2024, 31(6): 2367-2380. DOI: 10.1016/j.acra.2023.11.038.
[40]
YI X P, LIU Y Z, ZHOU B L, et al. Incorporating SULF1 polymorphisms in a pretreatment CT-based radiomic model for predicting platinum resistance in ovarian cancer treatment[J/OL]. Biomedecine Pharmacother, 2021, 133: 111013 [2024-06-15]. https://linkinghub.elsevier.com/retrieve/pii/S0753-3322(20)31205-1. DOI: 10.1016/j.biopha.2020.111013.
[41]
LEI R L, YU Y F, LI Q J, et al. Deep learning magnetic resonance imaging predicts platinum sensitivity in patients with epithelial ovarian cancer[J/OL]. Front Oncol, 2022, 12: 895177 [2024-06-15]. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.895177/full. DOI: 10.3389/fonc.2022.895177.
[42]
CALVO E, SESSA C, HARADA G, et al. Phase I study of lurbinectedin in combination with weekly paclitaxel with or without bevacizumab in patients with advanced solid tumors[J]. Invest New Drugs, 2022, 40(6): 1263-1273. DOI: 10.1007/s10637-022-01281-z.
[43]
MAI J, WU L M, YANG L, et al. Therapeutic strategies targeting folate receptor α for ovarian cancer[J/OL]. Front Immunol, 2023, 14: 1254532 [2024-06-15]. https://www.frontiersin.org/articles/10.3389/fimmu.2023.1254532/full. DOI: 10.3389/fimmu.2023.1254532.
[44]
QIU D M, CAI W Q, ZHANG Z Q, et al. High Ki-67 expression is significantly associated with poor prognosis of ovarian cancer patients: evidence from a meta-analysis[J]. Arch Gynecol Obstet, 2019, 299(5): 1415-1427. DOI: 10.1007/s00404-019-05082-3.
[45]
WANG X H, XU C, GRZEGORZEK M, et al. Habitat radiomics analysis of pet/ct imaging in high-grade serous ovarian cancer: application to Ki-67 status and progression-free survival[J/OL]. Front Physiol, 2022, 13: 948767 [2024-06-15]. https://www.frontiersin.org/articles/10.3389/fphys.2022.948767/full. DOI: 10.3389/fphys.2022.948767.
[46]
GU R, TAN S Y, XU Y P, et al. CT radiomics prediction of CXCL9 expression and survival in ovarian cancer[J/OL]. J Ovarian Res, 2023, 16(1): 180 [2024-06-15]. https://ovarianresearch.biomedcentral.com/articles/10.1186/s13048-023-01248-5. DOI: 10.1186/s13048-023-01248-5.
[47]
XU W T, ZHU C Y, JI D, et al. CT-based radiomics prediction of CXCL13 expression in ovarian cancer[J]. Med Phys, 2023, 50(11): 6801-6814. DOI: 10.1002/mp.16730.
[48]
WAN S, ZHOU T F, CHE R H, et al. CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer[J/OL]. J Ovarian Res, 2023, 16(1): 1 [2024-06-15]. https://ovarianresearch.biomedcentral.com/articles/10.1186/s13048-022-01089-8. DOI: 10.1186/s13048-022-01089-8.
[49]
LU H N, ARSHAD M, THORNTON A, et al. A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer[J/OL]. Nat Commun, 2019, 10(1): 764 [2024-06-15].https://www.nature.com/articles/s41467-019-08718-9. DOI: 10.1038/s41467-019-08718-9.
[50]
FOTOPOULOU C, ROCKALL A, LU H N, et al. Validation analysis of the novel imaging-based prognostic radiomic signature in patients undergoing primary surgery for advanced high-grade serous ovarian cancer (HGSOC)[J]. Br J Cancer, 2022, 126(7): 1047-1054. DOI: 10.1038/s41416-021-01662-w.
[51]
LU H N, LOU H T, WENGERT G, et al. Tumor and local lymphoid tissue interaction determines prognosis in high-grade serous ovarian cancer[J/OL]. Cell Rep Med, 2023, 4(7): 101092 [2024-06-15]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10394173/. DOI: 10.1016/j.xcrm.2023.101092.
[52]
RIZZO S, BOTTA F, RAIMONDI S, et al. Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months[J]. Eur Radiol, 2018, 28(11): 4849-4859. DOI: 10.1007/s00330-018-5389-z.
[53]
CHEN H Z, WANG X R, ZHAO F M, et al. A CT-based radiomics nomogram for predicting early recurrence in patients with high-grade serous ovarian cancer[J/OL]. Eur J Radiol, 2021, 145: 110018 [2024-06-15]. https://www.sciencedirect.com/science/article/abs/pii/S0720048X2100499X?via%3Dihub. DOI: 10.1016/j.ejrad.2021.110018.
[54]
ZHANG H, MAO Y F, CHEN X J, et al. Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study[J]. Eur Radiol, 2019, 29(7): 3358-3371. DOI: 10.1007/s00330-019-06124-9.
[55]
WANG T P, WANG H J, WANG Y D, et al. MR-based radiomics-clinical nomogram in epithelial ovarian tumor prognosis prediction: tumor body texture analysis across various acquisition protocols[J/OL]. J Ovarian Res, 2022, 15(1): 6 [2024-06-15]. https://ovarianresearch.biomedcentral.com/articles/10.1186/s13048-021-00941-7. DOI: 10.1186/s13048-021-00941-7.
[56]
YE L, ZHANG Y, YANG X Y, et al. An ovarian cancer susceptible gene prediction method based on deep learning methods[J/OL]. Front Cell Dev Biol, 2021, 9: 730475 [2024-06-15]. https://www.frontiersin.org/articles/10.3389/fcell.2021.730475/full. DOI: 10.3389/fcell.2021.730475.
[57]
AGHAYOUSEFI R, HOSSEINIYAN KHATIBI S M, ZUNUNI VAHED S, et al. A diagnostic miRNA panel to detect recurrence of ovarian cancer through artificial intelligence approaches[J]. J Cancer Res Clin Oncol, 2023, 149(1): 325-341. DOI: 10.1007/s00432-022-04468-2.

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