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Advances in deep learning and radiomics on ovarian cancer
YANG Zeting  WU Hui  GAO Hongyan  LIU Jiarui  LIU Na 

Cite this article as: YANG Z T, WU H, GAO H Y, et al. Advances in deep learning and radiomics on ovarian cancer[J]. Chin J Magn Reson Imaging, 2025, 16(9): 223-228. DOI:10.12015/issn.1674-8034.2025.09.034.


[Abstract] Ovarian cancer (OC), one of the most common malignancies in the female reproductive system, presents a critical clinical challenge as approximately 70% of patients are diagnosed at advanced stages due to its insidious early symptoms and the lack of effective screening methods. This urgent reality highlights the pressing need for breakthroughs in precision diagnostics and therapy. In recent years, the collaborative development of deep learning (DL) and radiomics technologies has provided a novel perspective to address this challenge. By extracting high-throughput features from medical imaging data, these technologies have demonstrated significant advantages throughout the entire disease management of OC. This review systematically sorts out the key technologies and clinical transformation achievements of DL and radiomics in the diagnosis and treatment of OC, clarifies their core values in improving diagnostic accuracy, optimizing treatment decisions and prognosis assessment, and at the same time points out the limitations of current studies in model interpretability, multi-center validation and multi-omics integration. By summarizing the existing progress and future directions, the aim is to provide evidence-based basis for clinical practice, assist in achieving the clinical goals of early screening, individualized treatment and dynamic monitoring of OC, and ultimately improve the quality of life and prognosis of patients.
[Keywords] ovarian cancer；radiomics；deep learning；magnetic resonance imaging；image segmentation；diagnosis；prognostic evaluation

YANG Zeting   WU Hui*   GAO Hongyan   LIU Jiarui   LIU Na  

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  2025-04-18
Accepted  2025-07-31
DOI: 10.12015/issn.1674-8034.2025.09.034
Cite this article as: YANG Z T, WU H, GAO H Y, et al. Advances in deep learning and radiomics on ovarian cancer[J]. Chin J Magn Reson Imaging, 2025, 16(9): 223-228. DOI:10.12015/issn.1674-8034.2025.09.034.

[1]
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.
[2]
WEBB P M, JORDAN S J. Epidemiology of epithelial ovarian cancer[J]. Best Pract Res Clin Obstet Gynaecol, 2017, 41: 3-14. DOI: 10.1016/j.bpobgyn.2016.08.006.
[3]
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.
[4]
ZENG H M, CHEN W Q, ZHENG R S, et al. Changing cancer survival in China during 2003-15: a pooled analysis of 17 population-based cancer registries[J/OL]. Lancet Glob Health, 2018, 6(5): e555-e567 [2025-03-09]. https://pubmed.ncbi.nlm.nih.gov/29653628/. DOI: 10.1016/S2214-109X(18)30127-X.
[5]
AVESANI G, TRAN H E, CAMMARATA G, et al. CT-based radiomics and deep learning for BRCA mutation and progression-free survival prediction in ovarian cancer using a multicentric dataset[J/OL]. Cancers (Basel), 2022, 14(11): 2739 [2025-04-24]. https://pubmed.ncbi.nlm.nih.gov/35681720/. DOI: 10.3390/cancers14112739.
[6]
WANG Y A, WANG M D, CAO P, et al. CT-based deep learning segmentation of ovarian cancer and the stability of the extracted radiomics features[J]. Quant Imaging Med Surg, 2023, 13(8): 5218-5229. DOI: 10.21037/qims-22-1135.
[7]
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.
[8]
XIAO Y N, LI H, GU T Y, et al. Ti3C2Tx composite aerogels enable pressure sensors for dialect speech recognition assisted by deep learning[J/OL]. Nanomicro Lett, 2024, 17(1): 101 [2025-03-09]. https://pubmed.ncbi.nlm.nih.gov/39738742/. DOI: 10.1007/s40820-024-01605-z.
[9]
ONISHI M, ISE T. Explainable identification and mapping of trees using UAV RGB image and deep learning[J/OL]. Sci Rep, 2021, 11(1): 903 [2025-03-09]. https://pubmed.ncbi.nlm.nih.gov/33441689/. DOI: 10.1038/s41598-020-79653-9.
[10]
BEUQUE M P L, LOBBES M B I, VAN WIJK Y, et al. Combining deep learning and handcrafted radiomics for classification of suspicious lesions on contrast-enhanced mammograms[J/OL]. Radiology, 2023, 307(5): e221843 [2025-03-09]. https://pubmed.ncbi.nlm.nih.gov/37338353/. DOI: 10.1148/radiol.221843.
[11]
LIANG X H, TANG K Q, KE X A, et al. Development of an MRI-based comprehensive model fusing clinical, radiomics and deep learning models for preoperative histological stratification in intracranial solitary fibrous tumor[J]. J Magn Reson Imaging, 2024, 60(2): 523-533. DOI: 10.1002/jmri.29098.
[12]
GHEZZO S, MONGARDI S, BEZZI C, et al. External validation of a convolutional neural network for the automatic segmentation of intraprostatic tumor lesions on 68Ga-PSMA PET images[J/OL]. Front Med (Lausanne), 2023, 10: 1133269 [2025-03-10]. https://pubmed.ncbi.nlm.nih.gov/36910493/. DOI: 10.3389/fmed.2023.1133269.
[13]
KOSTYSZYN D, FECHTER T, BARTL N, et al. Intraprostatic tumor segmentation on PSMA PET images in patients with primary prostate cancer with a convolutional neural network[J]. J Nucl Med, 2021, 62(6): 823-828. DOI: 10.2967/jnumed.120.254623.
[14]
XU X H, WEN Y H, ZHAO L, et al. CARes-UNet: Content-aware residual UNet for lesion segmentation of COVID-19 from chest CT images[J]. Med Phys, 2021, 48(11): 7127-7140. DOI: 10.1002/mp.15231.
[15]
DASTIDAR P, MÄENPÄÄ J, HEINONEN T, et al. Magnetic resonance imaging based volume estimation of ovarian tumours: use of a segmentation and 3D reformation software[J]. Comput Biol Med, 2000, 30(6): 329-340. DOI: 10.1016/s0010-4825(00)00015-9.
[16]
HOU B, LEE S, LEE J M, et al. Deep learning segmentation of ascites on abdominal CT scans for automatic volume quantification[J/OL]. Radiol Artif Intell, 2024, 6(5): e230601 [2025-03-09]. https://pubmed.ncbi.nlm.nih.gov/38900043/. DOI: 10.1148/ryai.230601.
[17]
HU D D, JIAN J M, LI Y A, et al. Deep learning-based segmentation of epithelial ovarian cancer on T2-weighted magnetic resonance images[J]. Quant Imaging Med Surg, 2023, 13(3): 1464-1477. DOI: 10.21037/qims-22-494.
[18]
WANG Y D, ZHANG H, WANG T P, et al. Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging[J/OL]. Sci Rep, 2023, 13(1): 2770 [2025-03-09]. https://pubmed.ncbi.nlm.nih.gov/36797331/. DOI: 10.1038/s41598-023-29814-3.
[19]
LIU P F. Classification of Benign and Malignant Ovarian Tumor Based on Radiomics and Deep Learning[D]. Guangzhou: Southern Medical University, 2022. DOI: 10.27003/d.cnki.gojyu.2022.000417.
[20]
ZHOU G X. Deep Learning-Based Automatic Segmentation of Lung and Ovarian Cancer CT Images[D]. Tianjin: Tianjin Polytechnic University, 2023. DOI: 10.27357/d.cnki.gtgyu.2023.000523.
[21]
SADEGHI M H, SINA S, ALAVI M, et al. PET/CT-based 3D multi-class semantic segmentation of ovarian cancer and the stability of the extracted radiomics features[J]. Phys Eng Sci Med, 2024, 47(4): 1739-1749. DOI: 10.1007/s13246-024-01485-y.
[22]
BUDDENKOTTE T, RUNDO L, WOITEK R, et al. Deep learning-based segmentation of multisite disease in ovarian cancer[J/OL]. Eur Radiol Exp, 2023, 7(1): 77 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/38057616/. DOI: 10.1186/s41747-023-00388-z.
[23]
MYTTON J, EVISON F, CHILTON P J, et al. Removal of all ovarian tissue versus conserving ovarian tissue at time of hysterectomy in premenopausal patients with benign disease: study using routine data and data linkage[J/OL]. BMJ, 2017, 356: j372 [2025-04-24]. https://pubmed.ncbi.nlm.nih.gov/28167486/. DOI: 10.1136/bmj.j372.
[24]
JIANG X D. Diagnostic value of MRI combined with tumor markers in ovarian cancer[J]. Chin J CT MRI, 2023, 21(8): 129-131, 145. DOI: 10.3969/j.issn.1672-5131.2023.08.041.
[25]
LI Y A, JIAN J M, PICKHARDT P J, et al. MRI-based machine learning for differentiating borderline from malignant epithelial ovarian tumors: a multicenter study[J]. J Magn Reson Imag, 2020, 52(3): 897-904. DOI: 10.1002/jmri.27084.
[26]
JIAN J M, LI Y A, XIA W, et al. MRI-based multiple instance convolutional neural network for increased accuracy in the differentiation of borderline and malignant epithelial ovarian tumors[J]. J Magn Reson Imaging, 2022, 56(1): 173-181. DOI: 10.1002/jmri.28008.
[27]
WANG R, CAI Y Y, LEE I K, et al. Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging[J]. Eur Radiol, 2021, 31(7): 4960-4971. DOI: 10.1007/s00330-020-07266-x.
[28]
BHUVANESHWARI K V, LAHZA H, SREENIVASA B R, et al. Optimising ovarian tumor classification using a novel CT sequence selection algorithm[J/OL]. Sci Rep, 2024, 14(1): 25010 [2025-03-27]. https://pubmed.ncbi.nlm.nih.gov/39443517/. DOI: 10.1038/s41598-024-75555-2.
[29]
GAO Y, ZENG S Q, XU X Y, et al. Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study[J/OL]. Lancet Digit Health, 2022, 4(3): e179-e187 [2025-03-10]. https://pubmed.ncbi.nlm.nih.gov/35216752/. DOI: 10.1016/S2589-7500(21)00278-8.
[30]
WU Y M, FAN L F, SHAO H X, et al. Evaluation of a novel ensemble model for preoperative ovarian cancer diagnosis: Clinical factors, O-RADS, and deep learning radiomics[J/OL]. Transl Oncol, 2025, 54: 102335 [2025-03-27]. https://pubmed.ncbi.nlm.nih.gov/40048985/. DOI: 10.1016/j.tranon.2025.102335.
[31]
KURMAN R J, SHIH I M. The dualistic model of ovarian carcinogenesis: revisited, revised, and expanded[J]. Am J Pathol, 2016, 186(4): 733-747. DOI: 10.1016/j.ajpath.2015.11.011.
[32]
JIAN J M, LI Y A, PICKHARDT P J, et al. MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers[J]. Eur Radiol, 2021, 31(1): 403-410. DOI: 10.1007/s00330-020-07091-2.
[33]
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.
[34]
WEI M X, FENG G N, WANG X Y, et al. Deep learning radiomics nomogram based on magnetic resonance imaging for differentiating type I/Ⅱ epithelial ovarian cancer[J]. Acad Radiol, 2024, 31(6): 2391-2401. DOI: 10.1016/j.acra.2023.08.002.
[35]
BEREK J S, RENZ M, KEHOE S, et al. Cancer of the ovary, fallopian tube, and peritoneum: 2021 update[J]. Int J Gynaecol Obstet, 2021, 155(Suppl 1): 61-85. DOI: 10.1002/ijgo.13878.
[36]
WANG J Y. Study on the value of CT-based plain scan imaging model in predicting peritoneal implantation metastasis of ovarian cancer[D]. Nanchang: Nanchang University, 2023. DOI: 10.27232/d.cnki.gnchu.2023.000951.
[37]
YU 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.
[38]
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.
[39]
YU X Y, REN J L, JIA Y S, et al. Multiparameter MRI radiomics model predicts preoperative peritoneal carcinomatosis in ovarian cancer[J]. Front Oncol, 2021, 11: 765652. DOI: 10.3389/fonc.2021.765652.
[40]
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.
[41]
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.
[42]
FLYNN M J, LEDERMANN J A. Ovarian cancer recurrence: is the definition of platinum resistance modified by PARPi and other intervening treatments The evolving landscape in the management of platinum-resistant ovarian cancer[J]. Cancer Drug Resist, 2022, 5(2): 424-435. DOI: 10.20517/cdr.2022.13.
[43]
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.
[44]
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.
[45]
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 [2025-03-10]. https://pubmed.ncbi.nlm.nih.gov/36505880/. DOI: 10.3389/fonc.2022.895177.
[46]
AHN B, MOON D, KIM H S, et al. Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer[J/OL]. Nat Commun, 2024, 15(1): 4253 [2025-03-10]. https://pubmed.ncbi.nlm.nih.gov/38762636/. DOI: 10.1038/s41467-024-48667-6.
[47]
WANG Y G, YIN R, GAO Z P, et al. A deep learning model for predicting the efficacy of neoadjuvant chemotherapy for ovarian cancer based on CT images[J]. Chin J Med Imag, 2024, 32(5): 480-485. DOI: 10.3969/j.issn.1005-5185.2024.05.012.
[48]
YIN R, GUO Y J, WANG Y Y, et al. Predicting neoadjuvant chemotherapy response and high-grade serous ovarian cancer from CT images in ovarian cancer with multitask deep learning: a multicenter study[J]. Acad Radiol, 2023, 30(Suppl 2): S192-S201. DOI: 10.1016/j.acra.2023.04.036.
[49]
HUANG X Y, HUANG Y, LIU K X, et al. Intratumoral and peritumoral radiomics for predicting the prognosis of high-grade serous ovarian cancer patients receiving platinum-based chemotherapy[J]. Acad Radiol, 2025, 32(2): 877-887. DOI: 10.1016/j.acra.2024.09.001.
[50]
WANG S, LIU Z Y, RONG Y, et al. Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer[J]. Radiother Oncol, 2019, 132: 171-177. DOI: 10.1016/j.radonc.2018.10.019.

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