Share：
Share this content in WeChat
X
[Chinese][PDF]2892
• Clinical Article •
Preoperative prediction of FIGO stage of epithelial ovarian cancer based on T2-weighted MRI peritumoral and intratumoral radiomics models
WANG Xinyi  WEI Mingxiang  CHEN Shuangqing 

Cite this article as: WANG X Y, WEI M X, CHEN S Q. Preoperative prediction of FIGO stage of epithelial ovarian cancer based on T2-weighted MRI peritumoral and intratumoral radiomics models[J]. Chin J Magn Reson Imaging, 2024, 15(6): 100-106. DOI:10.12015/issn.1674-8034.2024.06.015.


[Abstract] Objective To investigate the accuracy and value of peritumoral and intratumoral radiomics models based on T2-weighted MRI in predicting the International Federation of Gynecology and Obstetrics (FIGO) stage of epithelial ovarian cancer (EOC).Materials and Methods A total of 189 EOC patients from Suzhou Municipal Hospital (Center I) and the First Affiliated Hospital of Soochow University (Center Ⅱ) were retrospectively collected, including 87 patients with FIGO stage I/Ⅱ and 102 patients with FIGO stage Ⅲ/Ⅳ. The data from Center I were used for model training, while the data from Center Ⅱ were used as an external validation set. The region of interest (ROI) was drawn based on the tumor boundary and extended outwardly by 2 mm, 4 mm, 6 mm, 8 mm, and 10 mm to obtain multiple peritumoral information. A total of 1223 radiomics features were extracted from both intra- and peritumoral regions. Univariate analysis, correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator (LASSO) algorithm were employed for feature selection. The performance of peritumoral radiomics models with different peritumoral extension ranges was compared in the training set to determine the optimal extension for constructing the peritumoral model. Subsequently, both intratumoral and clinical models were developed. The combined model was constructed based on intratumoral, peritumoral, and clinical features using a nomogram. Each individual model was subsequently applied to the external validation set, and their diagnostic performance was assessed through receiver operating characteristic (ROC) analysis. The DeLong test was employed to compare the differences in diagnostic efficacy between these models.Results The peritumoral radiomics model demonstrated superior performance within an extended range of 2 mm, exhibiting an area under the ROC curve (AUC) of 0.840 in the training set. In the external validation set, the combined model exhibited optimal diagnostic performance, showcasing exceptional accuracy (74.2%), specificity (80.8%), and AUC (0.837). According to the DeLong test, the combined model significantly outperformed the peritumoral model (P=0.047).Conclusions The T2-weighted MRI-based peritumoral and intratumoral radiomics method demonstrated promising potential in effectively predicting the FIGO stage of EOC. Notably, the model combining peritumoral, intratumoral radiomics, and clinical information exhibits superior performance. This advanced model is anticipated to assist clinicians in accurately assessing patient conditions and devising personalized treatment plans.
[Keywords] ovarian cancer；International Federation of Gynecology and Obstetrics stage；radiomics；peri-tumor；magnetic resonance imaging

WANG Xinyi   WEI Mingxiang   CHEN Shuangqing*  

Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School of Nanjing Medical University, Suzhou 215000, China

Corresponding author: CHEN S Q, E-mail: sznaonao@163.com

Conflicts of interest   None.

Received  2023-11-27
Accepted  2024-05-12
DOI: 10.12015/issn.1674-8034.2024.06.015
Cite this article as: WANG X Y, WEI M X, CHEN S Q. Preoperative prediction of FIGO stage of epithelial ovarian cancer based on T2-weighted MRI peritumoral and intratumoral radiomics models[J]. Chin J Magn Reson Imaging, 2024, 15(6): 100-106. DOI:10.12015/issn.1674-8034.2024.06.015.

[1]
LHEUREUX S, GOURLEY C, VERGOTE I, et al. Epithelial ovarian cancer[J]. Lancet, 2019, 393(10177): 1240-1253. DOI: 10.1016/S0140-6736(18)32552-2.
[2]
CAO W, CHEN H D, YU Y W, et al. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020[J]. Chin Med J, 2021, 134(7): 783-791. DOI: 10.1097/CM9.0000000000001474.
[3]
SIEGEL R L, MILLER K D, WAGLE N S, et al. Cancer statistics, 2023. CA Cancer J Clin. 2023, 73(1): 17-48. DOI: 10.3322/caac.21763.
[4]
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.
[5]
MELAMED A, KEATING N L, CLEMMER J T, et al. Laparoscopic staging for apparent stage I epithelial ovariancancer[J/OL]. Am J Obstet Gynecol, 2017, 216(1): 50.e1-50.e12 [2023-09-11]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5618712. DOI: 10.1016/j.ajog.2016.08.030.
[6]
ARMSTRONG D K, ALVAREZ R D, BAKKUM-GAMEZ J N, et al. Ovarian cancer, version 2.2020, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2021, 19(2): 191-226. DOI: 10.6004/jnccn.2021.0007.
[7]
REID B M, PERMUTH J B, SELLERS T A. Epidemiology of ovarian cancer: a review[J]. Cancer Biol Med, 2017, 14(1): 9-32. DOI: 10.20892/j.issn.2095-3941.2016.0084.
[8]
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.
[9]
POWER J W, DEMPSEY P J, YATES A, et al. Peritoneal malignancy: anatomy, pathophysiology and an update on modern day imaging[J/OL]. Br J Radiol, 2022, 95(1132): 20210217 [2023-09-11]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9153709. DOI: 10.1259/bjr.20210217.
[10]
REN J, MAO L, ZHAO J, et al. Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer[J]. Radiol Med, 2023, 128(8): 900-911. DOI: 10.1007/s11547-023-01666-x.
[11]
HU J Q, WANG Z W, ZUO R C, et al. Development of survival predictors for high-grade serous ovarian cancer based on stable radiomic features from computed tomography images[J/OL]. iScience, 2022, 25(7): 104628 [2023-10-27]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9254345. DOI: 10.1016/j.isci.2022.104628.
[12]
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/OL]. Acad Radiol, 2023: S1076-S6332(23)00401-4 [2023-11-13]. https://pubmed.ncbi.nlm.nih.gov/37643927/. DOI: 10.1016/j.acra.2023.08.002.
[13]
LI M R. The clinical value of enhanced CT radiomics and 3D texture analysis in FIGO staging and BRCA gene mutation of epithelial ovarian cancer[D].Hefei: Anhui Medical University, 2022.
[14]
WANG F, WANG Y X, ZHOU Y, et al. Apparent diffusion coefficient histogram analysis for assessing tumor staging and detection of lymph node metastasis in epithelial ovarian cancer: correlation with p53 and ki-67 expression[J]. Mol Imaging Biol, 2019, 21(4): 731-739. DOI: 10.1007/s11307-018-1295-7.
[15]
WU Y J, JIANG W Y, FU L Y, et al. Intra- and peritumoral radiomics for predicting early recurrence in patients with high-grade serous ovarian cancer[J]. Abdom Radiol, 2023, 48(2): 733-743. DOI: 10.1007/s00261-022-03717-9.
[16]
ALLDREDGE J, SERNA-GALLEGOS T, GALLEGOS N, et al. Evaluation of clear cell subtypes of ovarian and uterine malignancies with anti-PD-L1 and anti-PD1 immunohistochemical expression and their association with stage and survival[J]. 2019, 155(3): 483-488. DOI: 10.1016/j.ygyno.2019.10.008.
[17]
TORKZAD M R, CASTA N, BERGMAN A, et al. Comparison between MRI and CT in prediction of peritoneal carcinomatosis index (PCI) in patients undergoing cytoreductive surgery in relation to the experience of the radiologist[J]. J Surg Oncol, 2015, 111(6): 746-751. DOI: 10.1002/jso.23878.
[18]
GAGLIARDI T, ADEJOLU M, DESOUZA N M. Diffusion-weighted magnetic resonance imaging in ovarian cancer: exploiting strengths and understanding limitations[J/OL]. J Clin Med, 2022, 11(6): 1524 [2023-09-11]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949455. DOI: 10.3390/jcm11061524.
[19]
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.
[20]
GENERALI M, ANNUNZIATA G, PIRILLO D, et al. The role of minimally invasive surgery in epithelial ovarian cancer treatment: a narrative review[J/OL]. Front Med, 2023, 10: 1196496 [2023-11-17]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301737. DOI: 10.3389/fmed.2023.1196496.
[21]
RAN X T, HE X L, LI Z Y. Comparison of laparoscopic and open surgery for women with early-stage epithelial ovarian cancer[J/OL]. Front Oncol, 2022, 12: 879889 [2023-09-25]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9098929. DOI: 10.3389/fonc.2022.879889.
[22]
GREGGI S, FALCONE F, SCAFFA C, et al. Evaluation of surgical resection in advanced ovarian, fallopian tube, and primary peritoneal cancer: laparoscopic assessment. A European Network of Gynaecological Oncology Trial (ENGOT) group survey[J]. Int J Gynecol Cancer, 2020, 30(6): 819-824. DOI: 10.1136/ijgc-2019-001172.
[23]
SONG Y J. Prediction of optimal debulking surgery in ovarian cancer[J]. Gland Surg, 2021, 10(3): 1173-1181. DOI: 10.21037/gs-2019-ursoc-08.
[24]
MACHIDA H, TOKUNAGA H, MATSUO K, et al. Survival outcome and perioperative complication related to neoadjuvant chemotherapy with carboplatin and paclitaxel for advanced ovarian cancer: a systematic review and meta-analysis[J]. Eur J Surg Oncol, 2020, 46(5): 868-875. DOI: 10.1016/j.ejso.2019.11.520.
[25]
GHASEMI D, AMELI F, NILI F, et al. Immunohistochemical expression of PD-L1 and its correlation with microsatellite status in endometrial and ovarian clear cell carcinomas: a cross-sectional study[J/OL]. BMC Cancer, 2022, 22(1): 1362 [2023-09-25]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9801577. DOI: 10.1186/s12885-022-10478-7.
[26]
LIMA C A, JAMMAL M P, ETCHEBEHERE R M, et al. Lymphocytes in peritumoral stroma: evaluation in epithelial ovarian neoplasms[J]. Immunol Invest, 2020, 49(4): 397-405. DOI: 10.1080/08820139.2019.1637435.
[27]
WANG X, CHEN K L, WANG W, et al. Can peritumoral regions increase the efficiency of machine-learning prediction of pathological invasiveness in lung adenocarcinoma manifesting as ground-glass nodules?[J]. J Thorac Dis, 2021, 13(3): 1327-1337. DOI: 10.21037/jtd-20-2981.
[28]
TAKADA A, YOKOTA H, WATANABE NEMOTO M, et al. A multi-scanner study of MRI radiomics in uterine cervical cancer: prediction of in-field tumor control after definitive radiotherapy based on a machine learning method including peritumoral regions[J]. Jpn J Radiol, 2020, 38(3): 265-273. DOI: 10.1007/s11604-019-00917-0.
[29]
CHEN Q F, XIAO H, GU Y Q, et al. Deep learning for evaluation of microvascular invasion in hepatocellular carcinoma from tumor areas of histology images[J]. Hepatol Int, 2022, 16(3): 590-602. DOI: 10.1007/s12072-022-10323-w.
[30]
ZHAO N N, ZHU Y, TANG X M, et al. Prediction of axillary lymph node metastasis in breast cancer based on intra-tumoral and peri-tumoral MRI radiomics nomogram[J]. Chin J Magn Reson Imag, 2023, 14(3): 81-87, 94. DOI: 10.12015/issn.1674-8034.2023.03.014.
[31]
ZHANG C M, DING Z M, CHEN P, et al. Prediction of HER-2 expression in breast cancer patients based on DCE-MRI intratumor and peritumoral imaging combined with TIC typing and Ki-67[J]. Chin J Magn Reson Imag, 2023, 14(4): 68-75. DOI: 10.12015/issn.1674-8034.2023.04.012.
[32]
CHENG Y, XU S, WANG H T, et al. Intra- and peri-tumoral radiomics for predicting the sentinel lymph node metastasis in breast cancer based on preoperative mammography and MRI[J/OL]. Front Oncol, 2022, 12: 1047572 [2023-09-25]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9792138. DOI: 10.3389/fonc.2022.1047572.

PREV Clinical study on MRI features of different pathological subtypes of uterine sarcoma and their relationship with menopausal status
NEXT Magnetic resonance diffusion tensor imaging evaluation of electroacupuncture and stem cell therapy for acute peripheral nerve injury in rats
  


LINK


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