Share:
Share this content in WeChat
X
Review
Advances in radiomics research on platinum resistance in ovarian cance
ZHANG Shipeng  WANG Tao  SUN Bixia  CHAI Yongfeng  ZHANG Zhiqiang  YANG Wengang  ZHU Dalin  XIE Yijing 

Cite this article as: Citation:ZHANG S P, WANG T, SUN B X, et al. Advances in radiomics research on platinum resistance in ovarian cance[J]. Chin J Magn Reson Imaging, Citation:2026, 17(4): 214-218, 234. DOI:10.12015/issn.1674-8034.2026.04.030.


[Abstract] Ovarian cancer, particularly high-grade serous ovarian carcinoma (HGSOC), is frequently diagnosed at an advanced stage due to the lack of effective screening methods. Currently, platinum resistance has emerged as a major contributor to treatment failure and high mortality in ovarian cancer patients. In recent years, radiomic models derived from various imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and positron emission tomography (PET), have shown promising prospects in the preoperative, non-invasive prediction of platinum resistance. This article provides a systematic review of current radiomics research related to platinum resistance in ovarian cancer, systematically analyzes the differences in methodology and predictive efficacy of various modalities in platinum resistance prediction, critically examines the strengths and limitations of existing approaches, aiming to offer theoretical references and practical pathways for the standardized application of radiomics in platinum resistance research for ovarian cancer.
[Keywords] ovarian cancer;epithelial ovarian cancer;platinum resistance;magnetic resonance image;radiomics;multi-omics;prediction model

ZHANG Shipeng1   WANG Tao1   SUN Bixia1   CHAI Yongfeng1   ZHANG Zhiqiang1   YANG Wengang1   ZHU Dalin1   XIE Yijing2*  

1 Medical Imaging Center, Gansu Provincial Maternity and Child-care Hospital (Gansu Provincial Central Hospital), Lanzhou 730050, China

2 Department of Radiology, Lanzhou University the Second Hospital, Lanzhou 730030, China

Corresponding author: XIE Y J, E-mail: 570742057@qq.com

Conflicts of interest   None.

Received  2025-12-05
Accepted  2026-04-08
DOI: 10.12015/issn.1674-8034.2026.04.030
Cite this article as: Citation:ZHANG S P, WANG T, SUN B X, et al. Advances in radiomics research on platinum resistance in ovarian cance[J]. Chin J Magn Reson Imaging, Citation:2026, 17(4): 214-218, 234. DOI:10.12015/issn.1674-8034.2026.04.030.

[1]
BRAY F, LAVERSANNE M, SUNG H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263. DOI: 10.3322/caac.21834.
[2]
CABASAG C J, FAGAN P J, FERLAY J, et al. Ovarian cancer today and tomorrow: a global assessment by world region and Human Development Index using GLOBOCAN 2020[J]. Int J Cancer, 2022, 151(9): 1535-1541. DOI: 10.1002/ijc.34002.
[3]
HAN B F, ZHENG R S, ZENG H M, et al. Cancer incidence and mortality in China, 2022[J]. J Natl Cancer Cent, 2024, 4(1): 47-53. DOI: 10.1016/j.jncc.2024.01.006.
[4]
REED N S, SYMONDS R P. Ovarian cancer[J]. Clin Oncol (R Coll Radiol), 2018, 30(8): 461-462. DOI: 10.1016/j.clon.2018.06.002.
[5]
BARNARD M E, FARLAND L V, YAN B, et al. Endometriosis typology and ovarian cancer risk[J]. JAMA, 2024, 332(6): 482-489. DOI: 10.1001/jama.2024.9210.
[6]
TANHA K, MOTTAGHI A, NOJOMI M, et al. Investigation on factors associated with ovarian cancer: an umbrella review of systematic review and meta-analyses[J/OL]. J Ovarian Res, 2021, 14(1): 153 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/34758846/. DOI: 10.1186/s13048-021-00911-z.
[7]
KONSTANTINOPOULOS P A, NORQUIST B, LACCHETTI C, et al. Germline and somatic tumor testing in epithelial ovarian cancer: ASCO guideline[J]. J Clin Oncol, 2020, 38(11): 1222-1245. DOI: 10.1200/JCO.19.02960.
[8]
QUESADA S, PENAULT-LLORCA F, MATIAS-GUIU X, et al. Homologous recombination deficiency in ovarian cancer: Global expert consensus on testing and a comparison of companion diagnostics[J/OL]. Eur J Cancer, 2025, 215: 115169 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/39693891/. DOI: 10.1016/j.ejca.2024.115169.
[9]
GONZÁLEZ-MARTÍN A, HARTER P, LEARY A, et al. Newly diagnosed and relapsed epithelial ovarian cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up[J]. Ann Oncol, 2023, 34(10): 833-848. DOI: 10.1016/j.annonc.2023.07.011.
[10]
HUEPENBECKER S P, SUN C C, FU S S, et al. Factors impacting the time to ovarian cancer diagnosis based on classic symptom presentation in the United States[J]. Cancer, 2021, 127(22): 4151-4160. DOI: 10.1002/cncr.33829.
[11]
MENON U, GENTRY-MAHARAJ A, BURNELL M, et al. Ovarian cancer population screening and mortality after long-term follow-up in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial[J]. Lancet, 2021, 397(10290): 2182-2193. DOI: 10.1016/S0140-6736(21)00731-5.
[12]
LEDERMANN J A, MATIAS-GUIU X, AMANT F, et al. ESGO-ESMO-ESP consensus conference recommendations on ovarian cancer: pathology and molecular biology and early, advanced and recurrent disease[J]. Ann Oncol, 2024, 35(3): 248-266. DOI: 10.1016/j.annonc.2023.11.015.
[13]
LIU J, BERCHUCK A, BACKES F J, et al. NCCN guidelines® insights: ovarian cancer/fallopian tube cancer/primary peritoneal cancer, version 3.2024[J]. J Natl Compr Canc Netw, 2024, 22(8): 512-519. DOI: 10.6004/jnccn.2024.0052.
[14]
DUAN Y Q, ZHANG P X, ZHANG T Y, et al. Characterization of global research trends and prospects on platinum-resistant ovarian cancer: a bibliometric analysis[J/OL]. Front Oncol, 2023, 13: 1151871 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/37342181/. DOI: 10.3389/fonc.2023.1151871.
[15]
VERGOTE I, GONZALEZ-MARTIN A, LORUSSO D, et al. Clinical research in ovarian cancer: consensus recommendations from the Gynecologic Cancer InterGroup[J/OL]. Lancet Oncol, 2022, 23(8): e374-e384 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/35901833/. DOI: 10.1016/S1470-2045(22)00139-5.
[16]
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.
[17]
LI C P, WANG H F, CHEN Y L, et al. A nomogram combining MRI multisequence radiomics and clinical factors for predicting recurrence of high-grade serous ovarian carcinoma[J/OL]. J Oncol, 2022, 2022: 1716268 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/35571486/. DOI: 10.1155/2022/1716268.
[18]
NOUGARET S, MCCAGUE C, TIBERMACINE H, et al. Radiomics and radiogenomics in ovarian cancer: a literature review[J]. Abdom Radiol (NY), 2021, 46(6): 2308-2322. DOI: 10.1007/s00261-020-02820-z.
[19]
GAN L, HUA L, CHEN S J. Research progress of radiomics in common gynecologic malignancies[J]. Chin J Magn Reson Imaging, 2024, 15(5): 227-234. DOI: 10.12015/issn.1674-8034.2024.05.037.
[20]
LI H M, GUO Q H, LU J, et al. Recent advances in preoperative imaging assessment for advanced ovarian cancer[J]. Oncoradiology, 2025, 34(4): 301-311. DOI: 10.19732/j.cnki.2096-6210.2025.04.001.
[21]
SINGH N, KUMAR A. Insights into ovarian cancer: chemo-diversity, dose depended toxicities and survival responses[J/OL]. Med Oncol, 2023, 40(4): 111 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/36871128/. DOI: 10.1007/s12032-023-01976-0.
[22]
PONSIGLIONE A, STANZIONE A, SPADARELLA G, et al. Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative[J]. Eur Radiol, 2023, 33(3): 2239-2247. DOI: 10.1007/s00330-022-09180-w.
[23]
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 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/34773830/. DOI: 10.1016/j.ejrad.2021.110018.
[24]
HE M G, SINGH R, WANG M D, et al. CT-based radiomics model to predict platinum sensitivity in epithelial ovarian carcinoma: a multicentre study[J/OL]. Cancer Imaging, 2025, 25(1): 85 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/40611334/. DOI: 10.1186/s40644-025-00906-9.
[25]
JIAO G L, SHI Z X, CHEN R, et al. Radiomics of enhanced CT in predicting the response to platinum-based chemotherapy for ovarian cancer[J]. J Shandong Univ Health Sci, 2023, 61(12): 62-69. DOI: 10.6040/j.issn.1671-7554.0.2023.0738.
[26]
TANG M X. Preliminary study on the value of radiomic model based on CT plain scan in predicting platinum resistance of advanced serous ovarian cancer[D]. Nanchang: Nanchang University, 2023.
[27]
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 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/36505880/. DOI: 10.3389/fonc.2022.895177.
[28]
SINGLA V, DAWADI K, SINGH T, et al. Multiparametric MRI evaluation of complex ovarian masses[J]. Curr Probl Diagn Radiol, 2021, 50(1): 34-40. DOI: 10.1067/j.cpradiol.2019.07.008.
[29]
DE PERROT T, SADJO ZOUA C, GLESSGEN C G, et al. Diffusion-weighted MRI in the genitourinary system[J/OL]. J Clin Med, 2022, 11(7): 1921 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/36505880/. DOI: 10.3390/jcm11071921.
[30]
SONG X L, REN J L, ZHAO D, et al. Radiomics derived from dynamic contrast-enhanced MRI pharmacokinetic protocol features: the value of precision diagnosis ovarian neoplasms[J]. Eur Radiol, 2021, 31(1): 368-378. DOI: 10.1007/s00330-020-07112-0.
[31]
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 Imaging, 2023, 14(6): 176-181. DOI: 10.12015/issn.1674-8034.2023.06.032.
[32]
ZHOU H Y, BAO H H, WEN S B, et al. The progress of magnetic resonance imaging in predicting biomarkers of ovarian cancer[J]. Chin J Magn Reson Imaging, 2023, 14(9): 171-175, 191. DOI: 10.12015/issn.1674-8034.2023.09.031.
[33]
LU J, LI H M, CAI S Q, et al. Prediction of platinum-based chemotherapy response in advanced high-grade serous ovarian cancer: ADC histogram analysis of primary tumors[J/OL]. Acad Radiol, 2021, 28(3): e77-e85 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/32061467/. DOI: 10.1016/j.acra.2020.01.024.
[34]
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]. Natl Med J China, 2022, 102(3): 201-208. DOI: 10.3760/cma.j.cn112137-20210816-01844.
[35]
LI H M, GONG J, LI R M, et al. Development of MRI-based radiomics model to predict the risk of recurrence in patients with advanced high-grade serous ovarian carcinoma[J]. AJR Am J Roentgenol, 2021, 217(3): 664-675. DOI: 10.2214/AJR.20.23195.
[36]
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]. J Magn Reson Imaging, 2024, 60(6): 2718-2727. DOI: 10.1002/jmri.29359.
[37]
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 multicenter study[J]. Acad Radiol, 2024, 31(6): 2367-2380. DOI: 10.1016/j.acra.2023.11.038.
[38]
HE L Y, CHANG Y H, MA Q, et al. Clinical value of a combined model based on ultrasound radiomics and clinical features in the diagnosis of early ovarian cancer[J]. J Clin Ultrasound Med, 2025, 27(1): 39-47. DOI: 10.3969/j.issn.1008-6978.2025.01.008.
[39]
YANG Y, JI X Y, LI S, et al. Ultrasound-based radiomics for predicting the five major histological subtypes of epithelial ovarian cancer[J/OL]. BMC Med Imaging, 2025, 25(1): 122 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/40234786/. DOI: 10.1186/s12880-025-01624-1.
[40]
XIE W T, WANG Y Q, DU Z S, et al. A nomogram combining clinical features, O-RADS US, and radiomics based on ultrasound imaging for diagnosing ovarian cancer[J/OL]. Sci Rep, 2025, 15(1): 19279 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/40456815/. DOI: 10.1038/s41598-025-02776-4.
[41]
ZUO R C, LI X R, HU J Q, et al. Prediction of ovarian cancer prognosis using statistical radiomic features of ultrasound images[J/OL]. Phys Med Biol, 2024, 69(12): 125009 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/38729170/. DOI: 10.1088/1361-6560/ad4a02.
[42]
SU C, MIAO K, ZHANG L W, et al. Deep learning based on ultrasound images to predict platinum resistance in patients with epithelial ovarian cancer[J/OL]. Biomed Eng Online, 2025, 24(1): 58 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/40361149/. DOI: 10.1186/s12938-025-01391-8.
[43]
ZHANG P H, YAO W T, LI Z H, et al. Radiomics for predicting sensitivity to neoadjuvant chemotherapy in osteosarcoma: current status and advances[J/OL]. Oncol Rev, 2025, 19: 1633211 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/41180134/. DOI: 10.3389/or.2025.1633211.
[44]
JIANG Z R, LOW J, HUANG C, et al. 18F-FDG PET/CT-based deep radiomic models for enhancing chemotherapy response prediction in breast cancer[J/OL]. Med Oncol, 2025, 42(9): 425 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/40790010/. DOI: 10.1007/s12032-025-02982-0.
[45]
ZHANG X, LIN Y H, HE D N, et al. 18F-fluoro-2-deoxyglucose positron emission tomography/computed tomography measures of spatial heterogeneity for predicting platinum resistance of high-grade serous ovarian cancer[J/OL]. Cancer Med, 2024, 13(20): e70287 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/39435561/. DOI: 10.1002/cam4.70287.
[46]
WANG X H, LU Z M. Radiomics analysis of PET and CT components of 18F-FDG PET/CT imaging for prediction of progression-free survival in advanced high-grade serous ovarian cancer[J/OL]. Front Oncol, 2021, 11: 638124 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/33928029/. DOI: 10.3389/fonc.2021.638124.
[47]
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 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/36091379/. DOI: 10.3389/fphys.2022.948767.
[48]
LI X, LV X H, QUAN Z Y, et al. Surgical evidence-based comparison of [68Ga] Ga-FAPI-04 PET and MRI-DWI for assisting debulking surgery in ovarian cancer patients[J]. Eur J Nucl Med Mol Imaging, 2024, 51(6): 1773-1785. DOI: 10.1007/s00259-023-06582-w.
[49]
XI Y, SUN L L, CHE X X, et al. A comparative study of [68Ga] Ga-FAPI-04 PET/MR and [18F] FDG PET/CT in the diagnostic accuracy and resectability prediction of ovarian cancer[J]. Eur J Nucl Med Mol Imaging, 2023, 50(9): 2885-2898. DOI: 10.1007/s00259-023-06235-y.
[50]
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 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/37875466/. DOI: 10.1038/s41467-023-41820-7.
[51]
LI X, MENG L M, YAO H M. Research progress of multiomics methods for high-grade serous ovarian cancer[J]. Oncol Prog, 2025, 23(5): 515-519. DOI: 10.1177/1872044225123456.
[52]
GERBER E, SINGH R, HWANG C N, et al. Circulating plasma gelsolin and MRI-based radiomics as biomarkers of platinum resistance in epithelial ovarian cancer: building a multiparameteric prediction algorithm[J/OL]. J Ovarian Res, 2025, 19(1): 1 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/41286957/. DOI: 10.1186/s13048-025-01906-w.
[53]
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 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/33212885/. DOI: 10.3390/cancers12113403.
[54]
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]. Biomed Pharmacother, 2021, 133: 111013 [2026-03-15]. https://pubmed.ncbi.nlm.nih.gov/33227705/. DOI: 10.1016/j.biopha.2020.111013.
[55]
XUE C, YUAN J, LO G G, et al. Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review[J]. Quant Imaging Med Surg, 2021, 11(10): 4431-4460. DOI: 10.21037/qims-21-86.
[56]
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.

PREV Research progress of MRI quantification in placenta accreta spectrum
NEXT Zero echo time magnetic resonance imaging in the skeletal system: Advances in clinical application
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn