Imaging in Gynecological Cancers

Imaging in Gynecological Cancers Özet Gynecological cancers of the cervix, endometrium, ovary, vulva, and vagina remain a major global health burden, with rising incidence and mortality underscoring the need for accurate diagnosis, staging, and treatment planning. Imaging is central to every step of the patient pathway. Ultrasound, particularly transvaginal ultrasound, is the first-line modality for evaluating uterine and adnexal pathology, enabling early detection, assessment of tumor extent, and guidance of biopsy, with risk stratification enhanced by standardized systems such as O-RADS and structured reporting. Computed tomography (CT) provides rapid whole-abdomen and thoracic assessment for nodal and distant metastases, while magnetic resonance imaging (MRI) offers superior soft-tissue contrast and functional techniques such as diffusion-weighted and dynamic contrast-enhanced imaging, which are now embedded in modern FIGO staging of cervical and endometrial cancers. For ovarian and adnexal masses, MRI and O-RADS MRI refine characterization of indeterminate lesions and optimize surgical planning. Molecular imaging, particularly 18F-FDG PET-CT, contributes to accurate staging, detection of recurrence, assessment of treatment response, and radiotherapy planning, with emerging roles for PET-MRI and novel tracers, including hypoxia- and receptor-targeted agents. Hybrid PET-MRI integrates high-resolution anatomic, functional, and metabolic data with reduced radiation dose, showing promise in complex pelvic disease and recurrent malignancy. Increasingly, artificial intelligence, deep learning, radiomics, and radiogenomics enable extraction of quantitative imaging biomarkers that capture tumor heterogeneity, support prognostication, and may predict treatment response. The integration of multiparametric imaging with AI-driven analysis is poised to advance precision, standardization, and personalization in the management of gynecological cancers. Referanslar Zhu Y, Liu Y, Wang H, et al. Global burden of gynaecological cancers in 2022 and projections to 2050. J Glob Health. 2024;14:04155. doi:10.7189/jogh.14.04155 Singh D, Vignat J, Lorenzoni V, et al. Global estimates of incidence and mortality of cervical cancer in 2020: a baseline analysis of the WHO Global Cervical Cancer Elimination Initiative. Lancet Glob Health. 2023;11(2):e197–e206. doi:10.1016/S2214-109X(22)00501-0 Zander D, Hüske S, Hoffmann B, Cui XW, Dong Y, Lim A, et al. Ultrasound image optimization (“knobology”): B-mode. Ultrasound Int Open. 2020;6(1):E14–E24. doi:10.1055/a-1223-1134 Fischerova D, Cibula D. Ultrasound in gynecological cancer: is it time for re-evaluation of its uses? Curr Oncol Rep. 2015;17(6):28. doi:10.1007/s11912-015-0449-x Chu LC, Coquia SF, Hamper UM. Ultrasonography evaluation of pelvic masses. Radiol Clin North Am. 2014;52(6):1237–1252. doi:10.1016/j.rcl.2014.07.012 Arleo EK, Schwartz PE, Hui P, et al. Review of leiomyoma variants. AJR Am J Roentgenol. 2015;205(4):912–921. doi:10.2214/AJR.14.14252 Miccò M, Sala E, Lakhman Y, et al. Imaging features of uncommon gynecologic cancers. AJR Am J Roentgenol. 2015;205(6):1346–1359. doi:10.2214/AJR.15.14486 Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin. 2012;62(1):10–29. doi:10.3322/caac.20138 Epstein E, Skoog L, Isberg PE, et al. An algorithm including results of gray-scale and power Doppler ultrasound examination to predict endometrial malignancy in women with postmenopausal bleeding. Ultrasound Obstet Gynecol. 2002;20(4):370–376. doi:10.1046/j.1469-0705.2002.00810.x Opolskiene G, Sladkevicius P, Valentin L. Prediction of endometrial malignancy in women with postmenopausal bleeding and sonographic endometrial thickness 4.5 mm. Ultrasound Obstet Gynecol. 2011;37(2):232–240. doi:10.1002/uog.8817 Nalaboff KM, Pellerito JS, Ben-Levi E. Imaging the endometrium: disease and normal variants. Radiographics. 2001;21(6):1409–1424. doi:10.1148/radiographics.21.6.g01nv111409 Kaveh M, Sadegi K, Salarzaei M, Parooei F. Comparison of diagnostic accuracy of saline infusion sonohysterography, transvaginal sonography, and hysteroscopy in evaluating the endometrial polyps in women with abnormal uterine bleeding: a systematic review and meta-analysis. Videosurgery Miniinv. 2020;15(4):639–648. doi:10.5114/wiitm.2020.93791 Green RW, Epstein E. Dynamic contrast-enhanced ultrasound improves diagnostic performance in endometrial cancer staging. Ultrasound Obstet Gynecol. 2020;56(1):96–105. doi:10.1002/uog.21954 Wildenberg JC, Yam BL, Langer JE, et al. US of the nongravid cervix with multimodality imaging correlation: normal appearance, pathologic conditions, and diagnostic pitfalls. Radiographics. 2016;36(2):596–617. doi:10.1148/rg.2016150112 Testa AC, Ludovisi M, Manfredi R, et al. Transvaginal ultrasonography and magnetic resonance imaging for assessment of presence, size and extent of invasive cervical cancer. Ultrasound Obstet Gynecol. 2009;34(3):335–344. doi:10.1002/uog.6450 Sayasneh A, Ekechi C, Ferrara L, Kaijser J, Stalder C, Sur S. The characteristic ultrasound features of specific types of ovarian pathology. Int J Oncol. 2015;46(2):445–458. doi:10.3892/ijo.2014.2752 Murotsuki J. Contrast-enhanced ultrasound in obstetrics and gynecology. Donald Sch J Ultrasound Obstet Gynecol. 2007;1:16–19. Basha MAA, Metwally MI, Gamil SA, et al. Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses. Eur Radiol. 2021;31(2):674–684. doi:10.1007/s00330-020-07105-3 Timmerman D, Ameye L, Fischerova D, et al. Simple ultrasound rules to distinguish between benign and malignant adnexal masses before surgery: prospective validation by IOTA group. BMJ. 2010;341:c6839. doi:10.1136/bmj.c6839 Andreotti RF, Timmerman D, Strachowski LM, et al. O-RADS US risk stratification and management system: a consensus guideline from the ACR Ovarian-Adnexal Reporting and Data System Committee. Radiology. 2020;294(1):168–185. doi:10.1148/radiol.2019191150 Miccò M, Sala E, Lakhman Y, Hricak H, Vargas HA. Role of imaging in the pretreatment evaluation of common gynecological cancers. Womens Health (Lond). 2014;10(3):299–319. doi:10.2217/whe.14.13 Re GL, Cucinella G, Zaccaria G, et al. Role of MRI in the assessment of cervical cancer. Semin Ultrasound CT MR. 2023;44(3):228–237. doi:10.1053/j.sult.2023.04.003 Zhou L, Zhang Y, Li X, et al. Multi-model quantitative MRI of uterine cancers in precision medicine. Insights Imaging. 2025;16(1):1–12. doi:10.1186/s13244-024-01852-1 Berek JS, Matias-Guiu X, Creutzberg C, et al. FIGO staging of endometrial cancer: 2023. Int J Gynaecol Obstet. 2023;162(3):383–394. doi:10.1002/ijgo.14739 Neves TR, Correia MT, Serrado MA, et al. Staging of endometrial cancer using fusion T2-weighted images with diffusion-weighted images: a way to avoid gadolinium? Cancers (Basel). 2022;14(2):384. doi:10.3390/cancers14020384 Rechichi G, Galimberti S, Signorelli M, et al. Endometrial cancer: correlation of apparent diffusion coefficient with tumor grade, depth of myometrial invasion, and presence of lymph node metastases. AJR Am J Roentgenol. 2011;197(1):256–262. doi:10.2214/AJR.10.5583 Saleh M, Virarkar M, Bhosale P, El Sherif S, Javadi S, Faria SC. Endometrial cancer, the current International Federation of Gynecology and Obstetrics staging system, and the role of imaging. J Comput Assist Tomogr. 2020;44(5):714–729. doi:10.1097/RCT.0000000000001042 Sbarra M, Lupinelli M, Brook OR, Venkatesan AM, Nougaret S. Imaging of endometrial cancer. Radiol Clin North Am. 2023;61(4):609–625. doi:10.1016/j.rcl.2023.02.007 Bourgioti C, Chatoupis K, Antoniou A, et al. T2-weighted MRI findings predictive of parametrial involvement in patients with cervical cancer and histologically confirmed full thickness stromal invasion. Hell J Radiol. 2018;3(1):23–32. doi:10.36162/hjr.v3i1.13 Otero-García MM, Mesa-Álvarez A, Nikolic O, et al. Role of MRI in staging and follow-up of endometrial and cervical cancer: pitfalls and mimickers. Insights Imaging. 2019;10:19. doi:10.1186/s13244-019-0701-0 Khan SR, Arshad M, Wallitt K, Stewart V, Bharwani N, Barwick TD. What’s new in imaging for gynecologic cancer? Curr Oncol Rep. 2017;19(12):85. doi:10.1007/s11912-017-0648-3 Sadowski EA, Rockall AG, Maturen KE, Robbins JB, Thomassin-Naggara I. Adnexal lesions: imaging strategies for ultrasound and MR imaging. Diagn Interv Imaging. 2019;100(10):635–646. doi:10.1016/j.diii.2019.08.001 Iyer VR, Lee SI. MRI, CT, and PET/CT for ovarian cancer detection and adnexal lesion characterization. AJR Am J Roentgenol. 2010;194(2):311–321. doi:10.2214/AJR.09.3522 Sadowski EA, Thomassin-Naggara I, Rockall A, et al. O-RADS MRI risk stratification system: guide for assessing adnexal lesions from the ACR O-RADS Committee. Radiology. 2022;303(1):25–37. doi:10.1148/radiol.210437 Friedman SN, Itani M, Dehdashti F. PET imaging for gynecologic malignancies. Radiol Clin North Am. 2021;59(5):813–833. doi:10.1016/j.rcl.2021.05.006 Dejanovic D, Hansen NL, Loft A. PET/CT variants and pitfalls in gynecological cancers. Semin Nucl Med. 2021;51(6):593–610. doi:10.1053/j.semnuclmed.2021.06.002 Narayanan P, Sahdev A. The role of 18F-FDG PET/CT in common gynaecological malignancies. Br J Radiol. 2017;90(1071):20170283. doi:10.1259/bjr.20170283 Burger IA, Vargas HA, Donati OF, et al. The value of 18F-FDG PET/CT in recurrent gynecologic malignancies prior to pelvic exenteration. Gynecol Oncol. 2013;129(3):586–592. doi:10.1016/j.ygyno.2013.03.014 Fruscio R, Sina F, Dolci C, et al. Preoperative 18F-FDG PET/CT in the management of advanced epithelial ovarian cancer. Gynecol Oncol. 2013;131(3):689–693. doi:10.1016/j.ygyno.2013.09.002 Hynninen J, Kemppainen J, Lavonius M, et al. A prospective comparison of integrated FDG-PET/contrast-enhanced CT and contrast-enhanced CT for pretreatment imaging of advanced epithelial ovarian cancer. Gynecol Oncol. 2013;131(2):389–394. doi:10.1016/j.ygyno.2013.07.100 Li Z, Chu T. Recent advances on radionuclide labeled hypoxia-imaging agents. Curr Pharm Des. 2012;18(8):1084–1097. doi:10.2174/138161212799504774 Nguyen NC, Beriwal S, Moon CH, et al. Diagnostic value of FDG PET/MRI in females with pelvic malignancy: a systematic review of the literature. Front Oncol. 2020;10:519440. doi:10.3389/fonc.2020.519440 Virarkar M, Viswanathan C, Iyer R, et al. The role of positron emission tomography/magnetic resonance imaging in gynecological malignancies. J Comput Assist Tomogr. 2019;43(6):825–834. doi:10.1097/RCT.0000000000000919 Zhou J, Zeng ZY, Li L. Progress of artificial intelligence in gynecological malignant tumors. Cancer Manag Res. 2020;12:12823–12840. doi:10.2147/CMAR.S284906 Sone K, Toyohara Y, Taguchi A, et al. Application of artificial intelligence in gynecologic malignancies: a review. J Obstet Gynaecol Res. 2021;47(7):2577–2585. doi:10.1111/jog.14838 Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin. 2019;69(2):127–157. doi:10.3322/caac.21552 Emmert-Streib F, Yang Z, Feng H, Tripathi S, Dehmer M. An introductory review of deep learning for prediction models with big data. Front Artif Intell. 2020;3:4. doi:10.3389/frai.2020.00004 Lo Gullo R, Daimiel I, Morris EA, Pinker K. Combining molecular and imaging metrics in cancer: radiogenomics. Insights Imaging. 2020;11(1):1. doi:10.1186/s13244-019-0836-0 El Naqa I, Napel S, Zaidi H. Radiogenomics is the future of treatment response assessment in clinical oncology. Med Phys. 2018;45(11):4325–4328. doi:10.1002/mp.13136 Bhatla N, Berek JS, Cuello Fredes M, et al. Revised FIGO staging for carcinoma of the cervix uteri, 2018. Int J Gynaecol Obstet. 2019;145(1):129–135. doi:10.1002/ijgo.12749 Berek JS, Matias-Guiu X, Creutzberg C, et al. Revised FIGO staging for carcinoma of the corpus uteri, 2023. Int J Gynaecol Obstet. 2023;162(2):383–394. doi:10.1002/ijgo.14739 Thomassin-Naggara I, Poncelet E, Jalaguier-Coudray A, et al. O-RADS MRI risk stratification system: a consensus guideline from the ACR Ovarian-Adnexal Reporting and Data System Committee. Radiology. 2020;294(1):199–209. doi:10.1148/radiol.2019191151 Chronas D, Jörg I, Bolten K, et al. First Case Report of Uterine Leiomyosarcoma Diagnosed After Transcervical Fibroid Ablation. J Clin Med. 2025;14(1):88. doi:10.3390/jcm14010088 Referanslar Zhu Y, Liu Y, Wang H, et al. Global burden of gynaecological cancers in 2022 and projections to 2050. J Glob Health. 2024;14:04155. doi:10.7189/jogh.14.04155 Singh D, Vignat J, Lorenzoni V, et al. Global estimates of incidence and mortality of cervical cancer in 2020: a baseline analysis of the WHO Global Cervical Cancer Elimination Initiative. Lancet Glob Health. 2023;11(2):e197–e206. doi:10.1016/S2214-109X(22)00501-0 Zander D, Hüske S, Hoffmann B, Cui XW, Dong Y, Lim A, et al. Ultrasound image optimization (“knobology”): B-mode. Ultrasound Int Open. 2020;6(1):E14–E24. doi:10.1055/a-1223-1134 Fischerova D, Cibula D. Ultrasound in gynecological cancer: is it time for re-evaluation of its uses? Curr Oncol Rep. 2015;17(6):28. doi:10.1007/s11912-015-0449-x Chu LC, Coquia SF, Hamper UM. Ultrasonography evaluation of pelvic masses. Radiol Clin North Am. 2014;52(6):1237–1252. doi:10.1016/j.rcl.2014.07.012 Arleo EK, Schwartz PE, Hui P, et al. Review of leiomyoma variants. AJR Am J Roentgenol. 2015;205(4):912–921. doi:10.2214/AJR.14.14252 Miccò M, Sala E, Lakhman Y, et al. Imaging features of uncommon gynecologic cancers. AJR Am J Roentgenol. 2015;205(6):1346–1359. doi:10.2214/AJR.15.14486 Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin. 2012;62(1):10–29. doi:10.3322/caac.20138 Epstein E, Skoog L, Isberg PE, et al. An algorithm including results of gray-scale and power Doppler ultrasound examination to predict endometrial malignancy in women with postmenopausal bleeding. Ultrasound Obstet Gynecol. 2002;20(4):370–376. doi:10.1046/j.1469-0705.2002.00810.x Opolskiene G, Sladkevicius P, Valentin L. Prediction of endometrial malignancy in women with postmenopausal bleeding and sonographic endometrial thickness 4.5 mm. Ultrasound Obstet Gynecol. 2011;37(2):232–240. doi:10.1002/uog.8817 Nalaboff KM, Pellerito JS, Ben-Levi E. Imaging the endometrium: disease and normal variants. Radiographics. 2001;21(6):1409–1424. doi:10.1148/radiographics.21.6.g01nv111409 Kaveh M, Sadegi K, Salarzaei M, Parooei F. Comparison of diagnostic accuracy of saline infusion sonohysterography, transvaginal sonography, and hysteroscopy in evaluating the endometrial polyps in women with abnormal uterine bleeding: a systematic review and meta-analysis. Videosurgery Miniinv. 2020;15(4):639–648. doi:10.5114/wiitm.2020.93791 Green RW, Epstein E. Dynamic contrast-enhanced ultrasound improves diagnostic performance in endometrial cancer staging. Ultrasound Obstet Gynecol. 2020;56(1):96–105. doi:10.1002/uog.21954 Wildenberg JC, Yam BL, Langer JE, et al. US of the nongravid cervix with multimodality imaging correlation: normal appearance, pathologic conditions, and diagnostic pitfalls. Radiographics. 2016;36(2):596–617. doi:10.1148/rg.2016150112 Testa AC, Ludovisi M, Manfredi R, et al. Transvaginal ultrasonography and magnetic resonance imaging for assessment of presence, size and extent of invasive cervical cancer. Ultrasound Obstet Gynecol. 2009;34(3):335–344. doi:10.1002/uog.6450 Sayasneh A, Ekechi C, Ferrara L, Kaijser J, Stalder C, Sur S. The characteristic ultrasound features of specific types of ovarian pathology. Int J Oncol. 2015;46(2):445–458. doi:10.3892/ijo.2014.2752 Murotsuki J. Contrast-enhanced ultrasound in obstetrics and gynecology. Donald Sch J Ultrasound Obstet Gynecol. 2007;1:16–19. Basha MAA, Metwally MI, Gamil SA, et al. Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses. Eur Radiol. 2021;31(2):674–684. doi:10.1007/s00330-020-07105-3 Timmerman D, Ameye L, Fischerova D, et al. Simple ultrasound rules to distinguish between benign and malignant adnexal masses before surgery: prospective validation by IOTA group. BMJ. 2010;341:c6839. doi:10.1136/bmj.c6839 Andreotti RF, Timmerman D, Strachowski LM, et al. O-RADS US risk stratification and management system: a consensus guideline from the ACR Ovarian-Adnexal Reporting and Data System Committee. Radiology. 2020;294(1):168–185. doi:10.1148/radiol.2019191150 Miccò M, Sala E, Lakhman Y, Hricak H, Vargas HA. Role of imaging in the pretreatment evaluation of common gynecological cancers. Womens Health (Lond). 2014;10(3):299–319. doi:10.2217/whe.14.13 Re GL, Cucinella G, Zaccaria G, et al. Role of MRI in the assessment of cervical cancer. Semin Ultrasound CT MR. 2023;44(3):228–237. doi:10.1053/j.sult.2023.04.003 Zhou L, Zhang Y, Li X, et al. Multi-model quantitative MRI of uterine cancers in precision medicine. Insights Imaging. 2025;16(1):1–12. doi:10.1186/s13244-024-01852-1 Berek JS, Matias-Guiu X, Creutzberg C, et al. FIGO staging of endometrial cancer: 2023. Int J Gynaecol Obstet. 2023;162(3):383–394. doi:10.1002/ijgo.14739 Neves TR, Correia MT, Serrado MA, et al. Staging of endometrial cancer using fusion T2-weighted images with diffusion-weighted images: a way to avoid gadolinium? Cancers (Basel). 2022;14(2):384. doi:10.3390/cancers14020384 Rechichi G, Galimberti S, Signorelli M, et al. Endometrial cancer: correlation of apparent diffusion coefficient with tumor grade, depth of myometrial invasion, and presence of lymph node metastases. AJR Am J Roentgenol. 2011;197(1):256–262. doi:10.2214/AJR.10.5583 Saleh M, Virarkar M, Bhosale P, El Sherif S, Javadi S, Faria SC. Endometrial cancer, the current International Federation of Gynecology and Obstetrics staging system, and the role of imaging. J Comput Assist Tomogr. 2020;44(5):714–729. doi:10.1097/RCT.0000000000001042 Sbarra M, Lupinelli M, Brook OR, Venkatesan AM, Nougaret S. Imaging of endometrial cancer. Radiol Clin North Am. 2023;61(4):609–625. doi:10.1016/j.rcl.2023.02.007 Bourgioti C, Chatoupis K, Antoniou A, et al. T2-weighted MRI findings predictive of parametrial involvement in patients with cervical cancer and histologically confirmed full thickness stromal invasion. Hell J Radiol. 2018;3(1):23–32. doi:10.36162/hjr.v3i1.13 Otero-García MM, Mesa-Álvarez A, Nikolic O, et al. Role of MRI in staging and follow-up of endometrial and cervical cancer: pitfalls and mimickers. Insights Imaging. 2019;10:19. doi:10.1186/s13244-019-0701-0 Khan SR, Arshad M, Wallitt K, Stewart V, Bharwani N, Barwick TD. What’s new in imaging for gynecologic cancer? Curr Oncol Rep. 2017;19(12):85. doi:10.1007/s11912-017-0648-3 Sadowski EA, Rockall AG, Maturen KE, Robbins JB, Thomassin-Naggara I. Adnexal lesions: imaging strategies for ultrasound and MR imaging. Diagn Interv Imaging. 2019;100(10):635–646. doi:10.1016/j.diii.2019.08.001 Iyer VR, Lee SI. MRI, CT, and PET/CT for ovarian cancer detection and adnexal lesion characterization. AJR Am J Roentgenol. 2010;194(2):311–321. doi:10.2214/AJR.09.3522 Sadowski EA, Thomassin-Naggara I, Rockall A, et al. O-RADS MRI risk stratification system: guide for assessing adnexal lesions from the ACR O-RADS Committee. Radiology. 2022;303(1):25–37. doi:10.1148/radiol.210437 Friedman SN, Itani M, Dehdashti F. PET imaging for gynecologic malignancies. Radiol Clin North Am. 2021;59(5):813–833. doi:10.1016/j.rcl.2021.05.006 Dejanovic D, Hansen NL, Loft A. PET/CT variants and pitfalls in gynecological cancers. Semin Nucl Med. 2021;51(6):593–610. doi:10.1053/j.semnuclmed.2021.06.002 Narayanan P, Sahdev A. The role of 18F-FDG PET/CT in common gynaecological malignancies. Br J Radiol. 2017;90(1071):20170283. doi:10.1259/bjr.20170283 Burger IA, Vargas HA, Donati OF, et al. The value of 18F-FDG PET/CT in recurrent gynecologic malignancies prior to pelvic exenteration. Gynecol Oncol. 2013;129(3):586–592. doi:10.1016/j.ygyno.2013.03.014 Fruscio R, Sina F, Dolci C, et al. Preoperative 18F-FDG PET/CT in the management of advanced epithelial ovarian cancer. Gynecol Oncol. 2013;131(3):689–693. doi:10.1016/j.ygyno.2013.09.002 Hynninen J, Kemppainen J, Lavonius M, et al. A prospective comparison of integrated FDG-PET/contrast-enhanced CT and contrast-enhanced CT for pretreatment imaging of advanced epithelial ovarian cancer. Gynecol Oncol. 2013;131(2):389–394. doi:10.1016/j.ygyno.2013.07.100 Li Z, Chu T. Recent advances on radionuclide labeled hypoxia-imaging agents. Curr Pharm Des. 2012;18(8):1084–1097. doi:10.2174/138161212799504774 Nguyen NC, Beriwal S, Moon CH, et al. Diagnostic value of FDG PET/MRI in females with pelvic malignancy: a systematic review of the literature. Front Oncol. 2020;10:519440. doi:10.3389/fonc.2020.519440 Virarkar M, Viswanathan C, Iyer R, et al. The role of positron emission tomography/magnetic resonance imaging in gynecological malignancies. J Comput Assist Tomogr. 2019;43(6):825–834. doi:10.1097/RCT.0000000000000919 Zhou J, Zeng ZY, Li L. Progress of artificial intelligence in gynecological malignant tumors. Cancer Manag Res. 2020;12:12823–12840. doi:10.2147/CMAR.S284906 Sone K, Toyohara Y, Taguchi A, et al. Application of artificial intelligence in gynecologic malignancies: a review. J Obstet Gynaecol Res. 2021;47(7):2577–2585. doi:10.1111/jog.14838 Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin. 2019;69(2):127–157. doi:10.3322/caac.21552 Emmert-Streib F, Yang Z, Feng H, Tripathi S, Dehmer M. An introductory review of deep learning for prediction models with big data. Front Artif Intell. 2020;3:4. doi:10.3389/frai.2020.00004 Lo Gullo R, Daimiel I, Morris EA, Pinker K. Combining molecular and imaging metrics in cancer: radiogenomics. Insights Imaging. 2020;11(1):1. doi:10.1186/s13244-019-0836-0 El Naqa I, Napel S, Zaidi H. Radiogenomics is the future of treatment response assessment in clinical oncology. Med Phys. 2018;45(11):4325–4328. doi:10.1002/mp.13136 Bhatla N, Berek JS, Cuello Fredes M, et al. Revised FIGO staging for carcinoma of the cervix uteri, 2018. Int J Gynaecol Obstet. 2019;145(1):129–135. doi:10.1002/ijgo.12749 Berek JS, Matias-Guiu X, Creutzberg C, et al. Revised FIGO staging for carcinoma of the corpus uteri, 2023. Int J Gynaecol Obstet. 2023;162(2):383–394. doi:10.1002/ijgo.14739 Thomassin-Naggara I, Poncelet E, Jalaguier-Coudray A, et al. O-RADS MRI risk stratification system: a consensus guideline from the ACR Ovarian-Adnexal Reporting and Data System Committee. Radiology. 2020;294(1):199–209. doi:10.1148/radiol.2019191151 Chronas D, Jörg I, Bolten K, et al. First Case Report of Uterine Leiomyosarcoma Diagnosed After Transcervical Fibroid Ablation. J Clin Med. 2025;14(1):88. doi:10.3390/jcm14010088