Application of radiomics in the diagnosis of cervical cancer: systematic review

Objective: To analyze the results of a study on the effectiveness of radiomic analysis in the interpretation of radiation images in clarifying the diagnosis of cervical cancer.

Materials and Methods. A systematic literature search was conducted in the PubMed/MEDLINE, eLibrary, and Scopus databases, as well as in NCCN, ESUR, and ACR resources.

Results. When selecting medical articles, a total of 289 unique publications were identified, 218 of which met the exclusion criteria. The final review included 71 articles. The average accuracy characteristics of the models were estimated based on the area under the ROC curve (AUC), including accuracy, sensitivity, specificity, and C-index.

Conclusion. The main key aspects and advantages of the use of radiomics and textural image analysis in the diagnosis of cervical cancer are considered. The introduction of radiomic analysis has led to a renewed perception of medical image analysis. The results of a number of studies demonstrate that the data extracted using radiomic analysis have significant diagnostic and prognostic value, allowing an objective assessment of tumor characteristics, its stage and prevalence, and differential diagnosis of neoplasms.

1. World Health Organization, 2023, november 17. Cervical cancer. (In Russian)

2. https://www.who.int/ru/news-room/fact-sheets/detail/cervical-cancer?utm_source=chatgpt.com

3. Kaprin A.D., Starinsky V.V., Shakhzadova A.O. (eds). The state of oncologic assistance to the Russian population in 2023. M.: P.A. Herzen Moscow Research Institute of Oncology – a department of the Federal State Budgetary Institution NMRC of Radiology, 2024. 262 р. (In Russian)

4. Statistics at a glance. https://gco.iarc.who.int/media/globocan/factsheets/populations/900-world-fact-sheet.pdf

5. Pecorelli S. Revised FIGO staging for carcinoma of the vulva, cervix, and endometrium. Int. J. Gynaecol. Obstet. 2009; 105 (2): 103–104. http://doi.org/10.1016/j.ijgo.2009.02.012.

6. Clinical guidelines “Cervical Cancer”, 2023. (In Russian)

7. http://disuria.ru/_ld/13/1376_kr20C53MZ.pdf

8. Dappa E., Elger T., Hasenburg A. et al. The value of advanced MRI techniques in the assessment of cervical cancer: a review. Insights. Imaging. 2017; 8 (5): 471–481. http://doi.org/10.1007/s13244-017-0567-0

9. Bizzarri N., Russo L., Dolciami M. et al. Radiomics systematic review in cervical cancer: gynecological oncologists' perspective. Int. J. Gynecol. Cancer. 2023; 33 (10): 1522–1541. http://doi.org/10.1136/ijgc-2023-004589

10. Antonova I.B., Aksenova S.P., Nudnov N.V., Kriger A.V. Possibilities and limitations of magnetic resonance imaging in the diagnostics of endocervical adenocarcinomas. Digital Diagnostics. 2024; 5 (2): 149–166. http://doi.org/10.17816/dd585195 (In Russian)

11. Tarachkova E.V., Streltsova O.N., Panov V.O. et al. Possibilities of multiparametric MRI in the differential diagnosis of histological types of cervical cancer in the preoperative period. Tumors of female reproductive system. 2016; 12 (2): 60–69. http://doi.org/10.17650/1994-4098-2016-12-2-60-69 (In Russian)

12. Aerts H.J. The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review. JAMA Oncol. 2016; 2 (12): 1636–1642. http://doi.org/10.1001/jamaoncol.2016.2631

13. Bizzarri N., Russo L., Dolciami M. et al. Radiomics systematic review in cervical cancer: gynecological oncologists' perspective. Int. J. Gynecol. Cancer. 2023; 33 (10): 1522–1541. http://doi.org/10.1136/ijgc-2023-004589

14. Zhao X., Wang X., Zhang B. et al. Classifying early stages of cervical cancer with MRI-based radiomics. Magn. Reson. Imaging. 2022; 89: 70–76. http://doi.org/10.1016/j.mri.2022.03.002

15. Wang W., Jiao Y., Zhang L. et al. Multiparametric MRI-based radiomics analysis: differentiation of subtypes of cervical cancer in the early stage. Acta Radiol. 2022; 63 (6): 847–856. http://doi.org/10.1177/02841851211014188

16. Aouadi S., Torfeh T., Bouhali O. et al. Prediction of cervix cancer stage and grade from diffusion weighted imaging using EfficientNet. Biomed. Phys. Eng. Express. 2024; 10 (4). http://doi.org/10.1088/2057-1976/ad5207

17. Chong G.O., Park S.H., Park N.J. et al. Predicting Tumor Budding Status in Cervical Cancer Using MRI Radiomics: Linking Imaging Biomarkers to Histologic Characteristics. Cancers (Basel). 2021; 13(20): 5140. http://doi.org/10.3390/cancers13205140

18. Deng X., Liu M., Sun J. et al. Feasibility of MRI-based radiomics features for predicting lymph node metastases and VEGF expression in cervical cancer. Eur. J. Radiol. 2021; 134: 109429. http://doi.org/10.1016/j.ejrad.2020.109429

19. Li X.R., Jin J.J., Yu Y. et al. PET-CT radiomics by integrating primary tumor and peritumoral areas predicts E-cadherin expression and correlates with pelvic lymph node metastasis in early-stage cervical cancer. Eur. Radiol. 2021; 31 (8): 5967–5979. http://doi.org/10.1007/s00330-021-07690-7

20. Huang K., Huang X., Zeng C. et al. Radiomics signature for dynamic changes of tumor-infiltrating CD8+ T cells and macrophages in cervical cancer during chemoradiotherapy. Cancer Imaging. 2024; 24 (1): 54. http://doi.org/10.1186/s40644-024-00680-0

21. Yu Z., Zhihui Q., Linrui L. et al. Machine Learning-Based Models for Assessing Postoperative Risk Factors in Patients with Cervical Cancer. Acad. Radiol. 2024; 31 (4): 1410–1418. http://doi.org/10.1016/j.acra.2023.09.031

22. Liu Y., Zhang Y., Cheng R. et al. Radiomics analysis of apparent diffusion coefficient in cervical cancer: A preliminary study on histological grade evaluation. J. Magn. Reson. Imaging. 2019; 49 (1): 280–290. http://doi.org/10.1002/jmri.26192

23. Wang W., Jiao Y., Zhang L. et al. Multiparametric MRI-based radiomics analysis: differentiation of subtypes of cervical cancer in the early stage. Acta Radiol. 2022; 63 (6): 847–856. http://doi.org/10.1177/02841851211014188

24. Chong G.O., Park S.H., Park N.J. et al. Predicting Tumor Budding Status in Cervical Cancer Using MRI Radiomics: Linking Imaging Biomarkers to Histologic Characteristics. Cancers (Basel). 2021; 13 (20): 5140. http://doi.org/10.3390/cancers13205140

25. Li X.R., Jin J.J., Yu Y. et al. PET-CT radiomics by integrating primary tumor and peritumoral areas predicts E-cadherin expression and correlates with pelvic lymph node metastasis in early-stage cervical cancer. Eur. Radiol. 2021; 31 (8): 5967–5979. http://doi.org/10.1007/s00330-021-07690-7

26. Liu Y., Song T., Dong T.F. et al. MRI-based radiomics analysis to evaluate the clinicopathological characteristics of cervical carcinoma: a multicenter study. Acta Radiol. 2023; 64 (1): 395–403. http://doi.org/10.1177/02841851211065142

27. Umutlu L., Nensa F., Demircioglu A. et al. Radiomics Analysis of Multiparametric PET/MRI for N- and M-Staging in Patients with Primary Cervical Cancer. Rofo. 2020; 192 (8): 754–763. http://doi.org/10.1055/a-1100-0127

28. Wu Q., Shi D., Dou S. et al. Radiomics Analysis of Multiparametric MRI Evaluates the Pathological Features of Cervical Squamous Cell Carcinoma. J. Magn. Reson. Imaging. 2019; 49 (4): 1141–1148. http://doi.org/10.1002/jmri.26301

29. Chong G.O., Park S.H., Jeong S.Y. et al. Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer. Diagnostics (Basel). 2021; 11 (8): 1517. http://doi.org/10.3390/diagnostics11081517

30. Wang M., Perucho J.A.U., Vardhanabhuti V. et al. Radiomic Features of T2-weighted Imaging and Diffusion Kurtosis Imaging in Differentiating Clinicopathological Characteristics of Cervical Carcinoma. Acad. Radiol. 2022; 29 (8): 1133–1140. http://doi.org/10.1016/j.acra.2021.08.018

31. Zhao X., Wang X., Zhang B. et al. Classifying early stages of cervical cancer with MRI-based radiomics. Magn. Reson. Imaging. 2022; 89: 70–76. http://doi.org/10.1016/j.mri.2022.03.002

32. Huang Q., Deng B., Wang Y. et al. Reduced field-of-view DWI derived clinical-radiomics model for the prediction of stage in cervical cancer. Insights. Imaging. 2023; 14 (1): 18. http://doi.org/10.1186/s13244-022-01346-w

33. Huang K., Huang X., Zeng C. et al. Radiomics signature for dynamic changes of tumor-infiltrating CD8+ T cells and macrophages in cervical cancer during chemoradiotherapy. Cancer Imaging. 2024; 24 (1): 54. http://doi.org/10.1186/s40644-024-00680-0

34. Liu H., Lao M., Zhang Y. et al. Radiomics-based machine learning models for differentiating pathological subtypes in cervical cancer: a multicenter study. Front. Oncol. 2024; 14: 1346336. http://doi.org/10.3389/fonc.2024.1346336

35. Wu F., Zhang R., Li F. et al. Radiomics analysis based on multiparametric magnetic resonance imaging for differentiating early stage of cervical cancer. Front. Med. (Lausanne). 2024; 11: 1336640. http://doi.org/10.3389/fmed.2024.1336640

36. Yu Z., Zhihui Q., Linrui L. et al. Machine Learning-Based Models for Assessing Postoperative Risk Factors in Patients with Cervical Cancer. Acad. Radiol. 2024; 31 (4): 1410–1418. http://doi.org/10.1016/j.acra.2023.09.031

37. Wang S., Jiang T., Hu X. et al. Can the combination of DWI and T2WI radiomics improve the diagnostic efficiency of cervical squamous cell carcinoma? Magn. Reson. Imaging. 2022; 92: 197–202. http://doi.org/10.1016/j.mri.2022.07.005

38. Liu Y., Dong T.F., Li P.J. et al. MRI-based radiomics features for the non-invasive prediction of FIGO stage in cervical carcinoma: A multi-center study. Magn. Reson. Imaging. 2024; 110: 170–175. http://doi.org/10.1016/j.mri.2023.11.012

39. Zhang Y., Hu Y., Zhao S., Xu S. Validation of the 2018 FIGO staging system for stage IIIC cervical cancer by determining the metabolic and radiomic heterogeneity of primary tumors based on 18F-FDG PET/CT. Abdom Radiol (NY). 2024; 49 (6): 2027–2039. http://doi.org/10.1007/s00261-024-04226-7

40. Li Z., Li H., Wang S. et al. MR-Based Radiomics Nomogram of Cervical Cancer in Prediction of the Lymph-Vascular Space Invasion preoperatively. J. Magn. Reson. Imaging. 2019; 49 (5): 1420–1426. http://doi.org/10.1002/jmri.26531

41. Du W., Wang Y., Li D. et al. Preoperative Prediction of Lymphovascular Space Invasion in Cervical Cancer With Radiomics – Based Nomogram. Front. Oncol. 2021; 11: 637794. http://doi.org/10.3389/fonc.2021.637794

42. Wang T., Gao T., Guo H. et al. Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram. Eur. Radiol. 2020; 30 (6): 3585–3593. http://doi.org/10.1007/S00330-019-06655-1

43. Huang G., Cui Y., Wang P. et al. Multi-Parametric Magnetic Resonance Imaging-Based Radiomics Analysis of Cervical Cancer for Preoperative Prediction of Lymphovascular Space Invasion. Front. Oncol. 2022; 11: 663370. http://doi.org/10.3389/fonc.2021.663370

44. Li X., Xu C., Yu Y. et al. Prediction of lymphovascular space invasion using a combination of tenascin-C, cox-2, and PET/CT radiomics in patients with early-stage cervical squamous cell carcinoma. BMC Cancer. 2021; 21 (1): 866. http://doi.org/10.1186/S12885-021-08596-9

45. Wu Y, Wang S., Liu X.. et al. Habitat-based radiomics enhances the ability to predict lymphovascular space invasion in cervical cancer: a multi-center study. Front. Oncol. 2023; 13: 1252074. http://doi.org/10.3389/fonc.2023.1252074

46. Xiao M., Li Y., Ma F. et al. Multiparametric MRI radiomics nomogram for predicting lymph-vascular space invasion in early-stage cervical cancer. Br. J. Radiol. 2022; 95 (1134): 20211076. http://doi.org/10.1259/bjr.20211076

47. Cui L., Yu T., Kan Y. et al. Multi-parametric MRI-based peritumoral radiomics on prediction of lymph-vascular space invasion in early-stage cervical cancer. Diagn. Interv. Radiol. 2022; 28 (4): 312–321. http://doi.org/10.5152/dir.2022.20657

48. Liu F.H., Zhao X.R., Zhang X.L. et al. Multiparametric mri-based radiomics nomogram for predicting lymph-vascular space invasion in cervical cancer. BMC Med. Imaging. 2024; 24 (1): 167. http://doi.org/10.1186/s12880-024-01344-y

49. Ren J., Li Y., Yang J.J. et al. MRI-based radiomics analysis improves preoperative diagnostic performance for the depth of stromal invasion in patients with early stage cervical cancer. Insights. Imaging. 2022; 13 (1): 17. http://doi.org/10.1186/s13244-022-01156-0

50. Yan H., Huang G., Yang Z. et al. Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics Model for Preoperative Predicting the Deep Stromal Invasion in Patients with Early Cervical Cancer. J. Imaging Inform. Med. 2024; 37 (1): 230–246. http://doi.org/10.1007/s10278-023-00906-w

51. Yu Z., Zhihui Q., Linrui L. et al. Machine Learning-Based Models for Assessing Postoperative Risk Factors in Patients with Cervical Cancer. Acad. Radiol. 2024; 31 (4): 1410–1418. http://doi.org/10.1016/j.acra.2023.09.031

52. Li J., Cui N., Wang Y. et al. Prediction of preoperative lymph-vascular space invasion and survival outcomes of cervical squamous cell carcinoma by utilizing 18F-FDG PET/CT imaging at early stage. Nucl. Med. Commun. 2024; 45 (12): 1069–1081. http://doi.org/10.1097/MNM.0000000000001909

53. Shang F., Tan Z., Gong T. et al. Evaluation of parametrial infiltration in patients with IB-IIB cervical cancer by a radiomics model integrating features from tumoral and peritumoral regions in 18F-fluorodeoxy glucose positron emission tomography/MR images. NMR Biomed. 2023: e4945. http://doi.org/10.1002/nbm.4945

54. Song J, Hu Q, Ma Z, et al. Feasibility of T2WI-MRI-based radiomics nomogram for predicting normal-sized pelvic lymph node metastasis in cervical cancer patients. Eur. Radiol. 2021; 31 (9): 6938–6948. http://doi.org/10.1007/s00330-021-07735-x

55. Xiao M., Ma F., Li Y. et al. Multiparametric MRI-Based Radiomics Nomogram for Predicting Lymph Node Metastasis in Early-Stage Cervical Cancer. J. Magn. Reson. Imaging. 2020; 52 (3): 885–896. http://doi.org/10.1002/jmri.27101

56. Chen X., Liu W., Thai T.C. et al. Developing a new radiomics-based CT image marker to detect lymph node metastasis among cervical cancer patients. Comput. Methods Programs. Biomed. 2020; 197: 105759. http://doi.org/10.1016/j.cmpb.2020.105759

57. Hou L., Zhou W., Ren J. et al. Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer. Front. Oncol. 2020; 10: 1393. http://doi.org/10.3389/fonc.2020.01393

58. Dong T., Yang C., Cui B. et al. Development and Validation of a Deep Learning Radiomics Model Predicting Lymph Node Status in Operable Cervical Cancer. Front. Oncol. 2020; 10: 464. http://doi.org/10.3389/fonc.2020.00464

59. Shi J., Dong Y., Jiang W. et al. MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: A multi-center study. Magn. Reson. Imaging. 2022; 88: 1–8. http://doi.org/10.1016/j.mri.2021.12.008

60. Deng X., Liu M., Sun J. et al. Feasibility of MRI-based radiomics features for predicting lymph node metastases and VEGF expression in cervical cancer. Eur. J. Radiol. 2021; 134: 109429. http://doi.org/10.1016/j.ejrad.2020.109429

61. Wu Q., Wang S., Chen X. et al. Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer. Radiother. Oncol. 2019; 138: 141–148. http://doi.org/10.1016/j.radonc.2019.04.035

62. Wang T., Gao T., Yang J. et al. Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging. Eur. J. Radiol. 2019; 114: 128–135. http://doi.org/10.1016/j.ejrad.2019.01.003

63. Liu Y., Song T., Dong T.F. et al. MRI-based radiomics analysis to evaluate the clinicopathological characteristics of cervical carcinoma: a multicenter study. Acta Radiol. 2023; 64 (1): 395–403. http://doi.org/10.1177/02841851211065142

64. Liu Y., Fan H., Dong D. et al. Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer. Transl. Oncol. 2021; 14 (8): 101113. http://doi.org/10.1016/j.tranon.2021.101113

65. Kan Y., Dong D., Zhang Y. et al. Radiomic signature as a predictive factor for lymph node metastasis in early-stage cervical cancer. J. Magn. Reson. Imaging. 2019; 49 (1): 304–310. http://doi.org/10.1002/jmri.26209

66. Yan L., Yao H., Long R. et al. A preoperative radiomics model for the identification of lymph node metastasis in patients with early-stage cervical squamous cell carcinoma. Br. J. Radiol. 2020; 93 (1116): 20200358. http://doi.org/10.1259/bjr.20200358

67. Yu Y.Y., Zhang R., Dong R.T. et al. Feasibility of an ADC-based radiomics model for predicting pelvic lymph node metastases in patients with stage IB-IIA cervical squamous cell carcinoma. Br. J. Radiol. 2019; 92 (1097): 20180986. http://doi.org/10.1259/bjr.20180986

68. Chen J., He B., Dong D. et al. Noninvasive CT radiomic model for preoperative prediction of lymph node metastasis in early cervical carcinoma. Br. J. Radiol. 2020; 93 (1108): 20190558. http://doi.org/10.1259/bjr.20190558

69. Xia X., Li D., Du W. et al. Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical Cancer. Diagnostics (Basel). 2022; 12 (10): 2446. http://doi.org/10.3390/diagnostics12102446

70. Wang T., Li Y.Y., Ma N.N. et al. A MRI radiomics-based model for prediction of pelvic lymph node metastasis in cervical cancer. Wld J. Surg. Oncol. 2024; 22 (1): 55. http://doi.org/10.1186/s12957-024-03333-5

71. Zhang Z., Wan X., Lei X. et al. Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer. Insights. Imaging. 2023; 14 (1): 65. http://doi.org/10.1186/s13244-023-01405-w

72. Lucia F., Bourbonne V., Pleyers C. et al. Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer. Eur. J. Nucl. Med. Mol. Imaging. 2023; 50 (8): 2514–2528. http://doi.org/10.1007/s00259-023-06180-w

73. Zhu Y., Fu C., Du J. et al. Prediction of Cervical Cancer Lymph Node Metastasis via a Multimodal Transfer Learning Approach. Br. J. Hosp. Med. (Lond). 2024; 85 (10): 1–14. http://doi.org/10.12968/hmed.2024.0428

74. Liu J., Dong L., Zhang X. et al. Radiomics analysis for prediction of lymph node metastasis after neoadjuvant chemotherapy based on pretreatment MRI in patients with locally advanced cervical cancer. Front. Oncol. 2024; 14: 1376640. http://doi.org/10.3389/fonc.2024.1376640

75. Liu Y., Song T., Dong T.F. et al. MRI-based radiomics analysis to evaluate the clinicopathological characteristics of cervical carcinoma: a multicenter study. Acta Radiol. 2023; 64 (1): 395–403. http://doi.org/10.1177/02841851211065142

76. Yu Z., Zhihui Q., Linrui L. et al. Machine Learning-Based Models for Assessing Postoperative Risk Factors in Patients with Cervical Cancer. Acad. Radiol. 2024; 31 (4): 1410–1418. http://doi.org/10.1016/j.acra.2023.09.031

77. Ai C., Zhang L., Ding W. et al. A nomogram-based optimized Radscore for preoperative prediction of lymph node metastasis in patients with cervical cancer after neoadjuvant chemotherapy. Front. Oncol. 2023; 13: 1117339. http://doi.org/10.3389/fonc.2023.1117339

78. Chan K.C., Perucho J.A.U., Subramaniam R.M., Lee E.Y.P. Utility of pre-treatment 18F-fluorodeoxyglucose PET radiomic analysis in assessing nodal involvement in cervical cancer. Nucl. Med. Commun. 2023; 44 (5): 375–380. http://doi.org/10.1097/MNM.0000000000001672

79. Zhang B., Liu L., Meng D., Kue C.S. Development of a radiomic model for cervical cancer staging based on pathologically verified, retrospective metastatic lymph node data. Acta Radiol. 2024; 65 (12): 1548–1559. http://doi.org/10.1177/02841851241291931.

80. Liu S., Zhou Y., Wang C. et al. Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images. BMC Med. Imaging. 23, 101 (2023). http://doi.org/10.1186/s12880-023-01059-6

81. Zhang Z., Li X., Sun H. Development of machine learning models integrating PET/CT radiomic and immunohistochemical pathomic features for treatment strategy choice of cervical cancer with negative pelvic lymph node by mediating COX-2 expression. Front. Physiol. 2022; 13: 994304. http://doi.org/10.3389/fphys.2022.994304

82. Ștefan P.A., Coțe A., Csutak C. et al. Texture Analysis in Uterine Cervix Carcinoma: Primary Tumour and Lymph Node Assessment. Diagnostics (Basel). 2023; 13 (3): 442. http://doi.org/10.3390/diagnostics13030442