Application progress of MRI-based artificial intelligence in endometrial and cervical cancers

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WANG Xianhong BI Qiu BI Guoli

WANG X H, BI Q, BI G L. Application progress of MRI-based artificial intelligence in endometrial and cervical cancers[J]. Chin J Magn Reson Imaging, 2023, 14(8): 182-186, 202. DOI:10.12015/issn.1674-8034.2023.08.032.

Abstract

[Abstract] The evaluation efficiency of traditional imaging observation in the diagnosis, staging, and prognosis of endometrial carcinoma (EC) and cervical cancer (CC) remains to be improved. In recent years, artificial intelligence (AI) has made significant advances in medical imaging fields such as ultrasound, CT, MRI, etc. With the advantage of high throughput extraction of data features, AI can observe the internal heterogeneity of lesions that cannot be recognized by the naked eye. At present, AI analysis is widely used in the diagnosis and treatment of EC and CC, but there is still a lack of systematic review of the application of MRI-based AI analysis in EC and CC. In this article, we review the definition and medical applications of AI, as well as preoperative diagnosis, staging, pathological histological assessment, and prognosis prediction of MRI-based AI analysis in EC and CC, in order to further achieve early diagnosis, individualized treatment, and accurate prognosis for patients with EC and CC. It is expected that MRI-based AI technology can penetrate to the pathological, molecular, and even genetic levels in the future, providing new ideas for promoting personalized precision medicine.

[Keywords] endometrial cancer；cervical cancer；magnetic resonance imaging；radiomics；artificial intelligence；diagnosis；staging；prognosis

Contributor Information

WANG Xianhong^{1,
2} BI Qiu² BI Guoli^2*

¹ College of Medicine, Kunming University of Science and Technology, Kunming 650000, China

² Department of MRI, the Affiliated Hospital of Kunming University of Science and Technology, the First People's Hospital of Yunnan Province, Kunming 650032, China

Corresponding author: Bi GL, E-mail: guolibi76@163.com

Conflicts of interest None.

Publication Information

ACKNOWLEDGMENTS Basic Research on Application of Joint Special Funding of Science and Technology Department of Yunnan Province-Kunming Medical University (No. 202001AY070001-110); Kunming University of Science and Technology & the First People's Hospital of Yunnan Province Joint Special Project on Medical Research (No. KUST-KH2022027Y).

Received 2023-01-06

Accepted 2023-06-25

DOI: 10.12015/issn.1674-8034.2023.08.032

Text

References

[1]

MILLER K D, NOGUEIRA L, DEVASIA T, et al. Cancer treatment and survivorship statistics, 2022[J]. CA Cancer J Clin, 2022, 72(5): 409-436. DOI: 10.3322/caac.21731.

[2]

OAKNIN A, BOSSE T J, CREUTZBERG C L, et al. Endometrial cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up[J]. Ann Oncol, 2022, 33(9): 860-877. DOI: 10.1016/j.annonc.2022.05.009.

[3]

SIEGEL R L, MILLER K D, FUCHS H E, et al. Cancer statistics, 2022[J]. CA A Cancer J Clinicians, 2022, 72(1): 7-33. DOI: 10.3322/caac.21708.

[4]

KASIUS J C, PIJNENBORG J M A, LINDEMANN K, et al. Risk stratification of endometrial cancer patients: FIGO stage, biomarkers and molecular classification[J/OL]. Cancers, 2021, 13(22): 5848 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/34831000/. DOI: 10.3390/cancers13225848.

[5]

WRIGHT J D, MATSUO K, HUANG Y M, et al. Prognostic performance of the 2018 international federation of gynecology and obstetrics cervical cancer staging guidelines[J]. Obstet Gynecol, 2019, 134(1): 49-57. DOI: 10.1097/AOG.0000000000003311.

[6]

MANGANARO L, NICOLINO G M, DOLCIAMI M, et al. Radiomics in cervical and endometrial cancer[J/OL]. Br J Radiol, 2021, 94(1125): 20201314 [2023-05-01]. https://pubmed.ncbi.nlm.nih.gov/34233456/. DOI: 10.1259/bjr.20201314.

[7]

SHRESTHA P, POUDYAL B, YADOLLAHI S, et al. A systematic review on the use of artificial intelligence in gynecologic imaging - Background, state of the art, and future directions[J]. Gynecol Oncol, 2022, 166(3): 596-605. DOI: 10.1016/j.ygyno.2022.07.024.

[8]

ZHOU J, ZENG ZHI YING, LI L. Progress of artificial intelligence in gynecological malignant tumors[J]. Cancer Manag Res, 2020, 12: 12823-12840. DOI: 10.2147/cmar.s279990.

[9]

ZHANG C, GU J H, ZHU Y Y, et al. AI in spotting high-risk characteristics of medical imaging and molecular pathology[J]. Prec Clin Med, 2021, 4(4): 271-286. DOI: 10.1093/pcmedi/pbab026.

[10]

LECOINTRE L, DANA J, LODI M, et al. Artificial intelligence-based radiomics models in endometrial cancer: a systematic review[J]. Eur J Surg Oncol, 2021, 47(11): 2734-2741. DOI: 10.1016/j.ejso.2021.06.023.

[11]

EMMERT-STREIB F, YANG Z, FENG H, et al. An introductory review of deep learning for prediction models with big data[J/OL]. Front Artif Intell, 2020, 3: 4 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/33733124/. DOI: 10.3389/frai.2020.00004.

[12]

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.

[13]

DAOUD T, SARDANA S, STANIETZKY N, et al. Recent imaging updates and advances in gynecologic malignancies[J/OL]. Cancers, 2022, 14(22): 5528 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/36428624/. DOI: 10.3390/cancers14225528.

[14]

DI DONATO V, KONTOPANTELIS E, CUCCU I, et al. Magnetic resonance imaging-radiomics in endometrial cancer: a systematic review and meta-analysis[J/OL]. Int J Gynecol Cancer, 2023: ijgc-2023-004313 [2023-05-01]. https://pubmed.ncbi.nlm.nih.gov/37094971/. DOI: 10.1136/ijgc-2023-004313.

[15]

SONG X L, LUO H J, REN J L, et al. Multisequence magnetic resonance imaging-based radiomics models for the prediction of microsatellite instability in endometrial cancer[J]. Radiol Med, 2023, 128(2): 242-251. DOI: 10.1007/s11547-023-01590-0.

[16]

JHA A K, MITHUN S, SHERKHANE U B, et al. Systematic review and meta-analysis of prediction models used in cervical cancer[J/OL]. Artif Intell Med, 2023, 139: 102549 [2023-05-01]. https://pubmed.ncbi.nlm.nih.gov/3710051/. DOI: 10.1016/j.artmed.2023.102549.

[17]

YAQUB M, JINCHAO J C, ARSHID K, et al. Deep learning-based image reconstruction for different medical imaging modalities[J/OL]. Comput Math Methods Med, 2022, 2022: 8750648 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/35756423/. DOI: 10.1155/2022/8750648.

[18]

HODNELAND E, DYBVIK J A, WAGNER-LARSEN K S, et al. Automated segmentation of endometrial cancer on MR images using deep learning[J/OL]. Sci Rep, 2021, 11(1): 179 [2023-05-01]. https://pubmed.ncbi.nlm.nih.gov/33420205/. DOI: 10.1038/s41598-020-80068-9.

[19]

MAO W, CHEN C X, GAO H C, et al. A deep learning-based automatic staging method for early endometrial cancer on MRI images[J/OL]. Front Physiol, 2022, 13: 974245 [2023-05-01]. https://pubmed.ncbi.nlm.nih.gov/36111158/. DOI: 10.3389/fphys.2022.974245.

[20]

KALBHOR M, SHINDE S, POPESCU D E, et al. Hybridization of deep learning pre-trained models with machine learning classifiers and fuzzy Min-max neural network for cervical cancer diagnosis[J/OL]. Diagnostics (Basel), 2023, 13(7): 1363 [2023-05-01]. https://pubmed.ncbi.nlm.nih.gov/37046581/. DOI: 10.3390/diagnostics13071363.

[21]

XU L X, CAI F H, FU Y H, et al. Cervical cell classification with deep-learning algorithms[J]. Med Biol Eng Comput, 2023, 61(3): 821-833. DOI: 10.1007/s11517-022-02745-3.

[22]

LI Y X, CHEN F, SHI J J, et al. Convolutional neural networks for classifying cervical cancer types using histological images[J]. J Digit Imaging, 2023, 36(2): 441-449. DOI: 10.1007/s10278-022-00722-8.

[23]

KOH W J, ABU-RUSTUM N R, BEAN S, et al. Uterine neoplasms, version 1.2018, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2018, 16(2): 170-199. DOI: 10.6004/jnccn.2018.0006.

[24]

FERRARI F, FORTE S, ARRIGONI G, et al. Impact of endometrial sampling technique and biopsy volume on the diagnostic accuracy of endometrial cancer[J]. Transl Cancer Res, 2020, 9(12): 7697-7705. DOI: 10.21037/tcr-20-2074.

[25]

SERTIC M, KILCOYNE A, CATALANO O A, et al. Quantitative imaging of uterine cancers with diffusion-weighted MRI and 18-fluorodeoxyglucose PET/CT[J]. Abdom Radiol (NY), 2022, 47(9): 3174-3188. DOI: 10.1007/s00261-021-03218-1.

[26]

JIANG X T, SONG J C, ZHANG A N, et al. Preoperative assessment of MRI-invisible early-stage endometrial cancer with MRI-based radiomics analysis[J]. J Magn Reson Imaging, 2023, 58(1): 247-255. DOI: 10.1002/jmri.28492.

[27]

ZHANG J Y, ZHANG Q, WANG T T, et al. Multimodal MRI-based radiomics-clinical model for preoperatively differentiating concurrent endometrial carcinoma from atypical endometrial hyperplasia[J/OL]. Front Oncol, 2022, 12: 887546 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/35692806/. DOI: 10.3389/fonc.2022.887546.

[28]

BI Q, WANG Y X, DENG Y C, et al. Different multiparametric MRI-based radiomics models for differentiating stage IA endometrial cancer from benign endometrial lesions: a multicenter study[J/OL]. Front Oncol, 2022, 12: 939930 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/35992858/. DOI: 10.3389/fonc.2022.939930.

[29]

WANG T T, GONG J, LI Q, et al. A combined radiomics and clinical variables model for prediction of malignancy in T2 hyperintense uterine mesenchymal tumors on MRI[J]. Eur Radiol, 2021, 31(8): 6125-6135. DOI: 10.1007/s00330-020-07678-9.

[30]

DAI M Y, LIU Y, HU Y, et al. Combining multiparametric MRI features-based transfer learning and clinical parameters: application of machine learning for the differentiation of uterine sarcomas from atypical leiomyomas[J]. Eur Radiol, 2022, 32(11): 7988-7997. DOI: 10.1007/s00330-022-08783-7.

[31]

STANZIONE A, CUOCOLO R, DEL GROSSO R, et al. Deep myometrial infiltration of endometrial cancer on MRI: a radiomics-powered machine learning pilot study[J]. Acad Radiol, 2021, 28(5): 737-744. DOI: 10.1016/j.acra.2020.02.028.

[32]

GHOSH A, SINGH T, SINGLA V, et al. DTI histogram parameters correlate with the extent of myoinvasion and tumor type in endometrial carcinoma: a preliminary analysis[J]. Acta Radiol, 2020, 61(5): 675-684. DOI: 10.1177/0284185119875019.

[33]

YAN B C, LI Y, MA F H, et al. Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study[J]. Eur Radiol, 2021, 31(1): 411-422. DOI: 10.1007/s00330-020-07099-8.

[34]

FANG R Q, CHEN X P, ZHANG G W, et al. Radiomics nomogram for prediction of cervical stromal invasion in endometrial cancer[J]. J Clin Radiol, 2022, 41(6): 1105-1109. DOI: 10.19401/j.cnki.1007-3639.2021.06.08.

[35]

BONATTI M, PEDRINOLLA B, CYBULSKI A J, et al. Prediction of histological grade of endometrial cancer by means of MRI[J]. Eur J Radiol, 2018, 103: 44-50. DOI: 10.1016/j.ejrad.2018.04.008.

[36]

REYES-PÉREZ J A, VILLASEÑOR-NAVARRO Y, JIMÉNEZ DE LOS SANTOS M E, et al. The apparent diffusion coefficient (ADC) on 3-T MRI differentiates myometrial invasion depth and histological grade in patients with endometrial cancer[J]. Acta Radiol, 2020, 61(9): 1277-1286. DOI: 10.1177/0284185119898658.

[37]

ZHANG K Y, ZHANG Y, FANG X, et al. Nomograms of combining apparent diffusion coefficient value and radiomics for preoperative risk evaluation in endometrial carcinoma[J/OL]. Front Oncol, 2021, 11: 705456 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/34386425/. DOI: 10.3389/fonc.2021.705456.

[38]

ZHENG T, YANG L S, DU J, et al. Combination analysis of a radiomics-based predictive model with clinical indicators for the preoperative assessment of histological grade in endometrial carcinoma[J/OL]. Front Oncol, 2021, 11: 582495 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/34235069/. DOI: 10.3389/fonc.2021.582495.

[39]

MICCÒ M, GUI B, RUSSO L, et al. Preoperative tumor texture analysis on MRI for high-risk disease prediction in endometrial cancer: a hypothesis-generating study[J/OL]. J Pers Med, 2022, 12(11): 1854 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/36579601/. DOI: 10.3390/jpm12111854.

[40]

LONG L, SUN J Q, JIANG L L, et al. MRI-based traditional radiomics and computer-vision nomogram for predicting lymphovascular space invasion in endometrial carcinoma[J]. Diagn Interv Imaging, 2021, 102(7/8): 455-462. DOI: 10.1016/j.diii.2021.02.008.

[41]

LIU X F, YAN B C, LI Y, et al. Radiomics feature as a preoperative predictive of lymphovascular invasion in early-stage endometrial cancer: a multicenter study[J/OL]. Front Oncol, 2022, 12: 966529 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/36059674/. DOI: 10.3389/fonc.2022.966529.

[42]

CELLI V, GUERRERI M, PERNAZZA A, et al. MRI- and histologic-molecular-based radio-genomics nomogram for preoperative assessment of risk classes in endometrial cancer[J/OL]. Cancers, 2022, 14(23): 5881 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/36497362/. DOI: 10.3390/cancers14235881.

[43]

LEFEBVRE T L, UENO Y, DOHAN A, et al. Development and validation of multiparametric MRI-based radiomics models for preoperative risk stratification of endometrial cancer[J]. Radiology, 2022, 305(2: 375-386. DOI: 10.1148/radiol.212873.

[44]

JACOB H, DYBVIK J A, YTRE-HAUGE S, et al. An MRI-based radiomic prognostic index predicts poor outcome and specific genetic alterations in endometrial cancer[J/OL]. J Clin Med, 2021, 10(3: 538 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/33540589/. DOI: 10.3390/jcm10030538.

[45]

LIU D F, YANG L S, DU D, et al. Multi-parameter MR radiomics based model to predict 5-year progression-free survival in endometrial cancer[J/OL]. Front Oncol, 2022, 12: 813069 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/35433486/. DOI: 10.3389/fonc.2022.813069.

[46]

BHATLA N, AOKI D, SHARMA D N, et al. Cancer of the cervix uteri: 2021 update[J]. Int J Gynaecol Obstet, 2021, 155(Suppl 1): 28-44. DOI: 10.1002/ijgo.13865.

[47]

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[J/OL]. Insights Imaging, 2022, 13(1: 17 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/35092505/. DOI: 10.1186/s13244-022-01156-0.

[48]

ZHAO X, WANG X Y, ZHANG B H, et al. Classifying early stages of cervical cancer with MRI-based radiomics[J]. Magn Reson Imaging, 2022, 89: 70-76. DOI: 10.1016/j.mri.2022.03.002.

[49]

WANG M D, 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[J]. Acad Radiol, 2022, 29(8: 1133-1140. DOI: 10.1016/j.acra.2021.08.018.

[50]

XIA X M, LI D D, DU W, et al. Radiomics based on nomogram predict pelvic lymphnode metastasis in early-stage cervical cancer[J/OL]. Diagnostics (Basel), 2022, 12(10: 2446 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/36292135/. DOI: 10.3390/diagnostics12102446.

[51]

ZHANG Z H, WAN X J, LEI X Y, et al. Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer[J/OL]. Insights Imaging, 2023, 14(1: 65 [2023-05-01]. https://pubmed.ncbi.nlm.nih.gov/37060378/. DOI: 10.1186/s13244-023-01405-w.

[52]

RAO Q X, HAN X, WEI Y, et al. Novel prognostic nomograms in cervical cancer based on analysis of 1075 patients[J]. Cancer Med, 2023, 12(5: 6092-6104. DOI: 10.1002/cam4.5335.

[53]

WANG W, JIAO Y N, ZHANG L C, et al. Multiparametric MRI-based radiomics analysis: differentiation of subtypes of cervical cancer in the early stage[J]. Acta Radiol, 2022, 63(6: 847-856. DOI: 10.1177/02841851211014188.

[54]

LIU Y, SONG T, DONG T F, et al. MRI-based radiomics analysis to evaluate the clinicopathological characteristics of cervical carcinoma: a multicenter study[J]. Acta Radiol, 2023, 64(1: 395-403. DOI: 10.1177/02841851211065142.

[55]

ZHANG H, YU R, ZHANG L, et al. Chemotherapy versus chemoradiotherapy for FIGO stages IB1 and IIA1 cervical squamous cancer patients with lymphovascular space invasion: a retrospective study[J/OL]. BMC Cancer, 2022, 22(1: 202 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/35193525/. DOI: 10.1186/s12885-022-09309-6.

[56]

CUI L P, YU T, KAN Y Y, et al. Multi-parametric MRI-based peritumoral radiomics on prediction of lymph-vascular space invasion in early-stage cervical cancer[J]. Diagn Interv Radiol, 2022, 28(4: 312-321. DOI: 10.5152/dir.2022.20657.

[57]

JIANG X R, LI J X, KAN Y Y, et al. MRI based radiomics approach with deep learning for prediction of vessel invasion in early-stage cervical cancer[J]. IEEE/ACM Trans Comput Biol Bioinform, 2021, 18(3: 995-1002. DOI: 10.1109/TCBB.2019.2963867.

[58]

XIAO M L, LI Y, MA F H, et al. Multiparametric MRI radiomics nomogram for predicting lymph-vascular space invasion in early-stage cervical cancer[J/OL]. Br J Radiol, 2022, 95(1134: 20211076 [2022-12-27]. https://pubmed.ncbi.nlm.nih.gov/35312379/. DOI: 10.1259/bjr.20211076.

[59]

WEI M Z, ZHAO Z H, HU H J, et al. MRI radiomics parameters in predicting p53 of cervical squamous cell carcinoma[J]. Chin J Med Imaging, 2019, 27(12: 934-937, 947. DOI: 10.3969/j.issn.1005-5185.2019.12.013.

[60]

LIU D F, ZHANG X H, ZHENG T, et al. Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images[J]. Arch Gynecol Obstet, 2021, 303(3: 811-820. DOI: 10.1007/s00404-020-05908-5.

[61]

LALISCIA C, GADDUCCI A, MATTIONI R, et al. MRI-based radiomics: promise for locally advanced cervical cancer treated with a tailored integrated therapeutic approach[J]. Tumori, 2022, 108(4: 376-385. DOI: 10.1177/03008916211014274.

[62]

ZHANG Y, LIU L, ZHANG K Y, et al. Nomograms combining clinical and imaging parameters to predict recurrence and disease-free survival after concurrent chemoradiotherapy in patients with locally advanced cervical cancer[J]. Acad Radiol, 2023, 30(3: 499-508. DOI: 10.1016/j.acra.2022.08.002.

[63]

ZHOU Y, GU H L, ZHANG X L, et al. Multiparametric magnetic resonance imaging-derived radiomics for the prediction of disease-free survival in early-stage squamous cervical cancer[J]. Eur Radiol, 2022, 32(4: 2540-2551. DOI: 10.1007/s00330-021-08326-6.

[64]

ZWANENBURG A, VALLIÈRES M, ABDALAH M A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping[J]. Radiology, 2020, 295(2: 328-338. DOI: 10.1148/radiol.2020191145.

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