{"paper_id":"143b6e1a-4ab9-4e47-b387-2ee504004483","body_text":"Imaging in Gynecological Cancers\nÖzet\nGynecological 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.\nReferanslar\nZhu 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\nSingh 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\nZander 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\nFischerova 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\nChu 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\nArleo 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\nMiccò 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\nSiegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin. 2012;62(1):10–29. doi:10.3322/caac.20138\nEpstein 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\nOpolskiene 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\nNalaboff 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\nKaveh 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\nGreen 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\nWildenberg 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\nTesta 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\nSayasneh 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\nMurotsuki J. Contrast-enhanced ultrasound in obstetrics and gynecology. Donald Sch J Ultrasound Obstet Gynecol. 2007;1:16–19.\nBasha 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\nTimmerman 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\nAndreotti 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\nMiccò 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\nRe 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\nZhou 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\nBerek 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\nNeves 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\nRechichi 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\nSaleh 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\nSbarra 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\nBourgioti 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\nOtero-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\nKhan 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\nSadowski 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\nIyer 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\nSadowski 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\nFriedman 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\nDejanovic 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\nNarayanan 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\nBurger 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\nFruscio 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\nHynninen 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\nLi Z, Chu T. Recent advances on radionuclide labeled hypoxia-imaging agents. Curr Pharm Des. 2012;18(8):1084–1097. doi:10.2174/138161212799504774\nNguyen 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\nVirarkar 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\nZhou 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\nSone 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\nBi 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\nEmmert-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\nLo 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\nEl 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\nBhatla 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\nBerek 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\nThomassin-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\nChronas 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\nReferanslar\nZhu 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\nSingh 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\nZander 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\nFischerova 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\nChu 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\nArleo 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\nMiccò 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\nSiegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin. 2012;62(1):10–29. doi:10.3322/caac.20138\nEpstein 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\nOpolskiene 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\nNalaboff 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\nKaveh 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\nGreen 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\nWildenberg 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\nTesta 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\nSayasneh 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\nMurotsuki J. Contrast-enhanced ultrasound in obstetrics and gynecology. Donald Sch J Ultrasound Obstet Gynecol. 2007;1:16–19.\nBasha 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\nTimmerman 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\nAndreotti 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\nMiccò 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\nRe 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\nZhou 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\nBerek 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\nNeves 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\nRechichi 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\nSaleh 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\nSbarra 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\nBourgioti 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\nOtero-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\nKhan 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\nSadowski 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\nIyer 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\nSadowski 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\nFriedman 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\nDejanovic 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\nNarayanan 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\nBurger 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\nFruscio 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\nHynninen 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\nLi Z, Chu T. Recent advances on radionuclide labeled hypoxia-imaging agents. Curr Pharm Des. 2012;18(8):1084–1097. doi:10.2174/138161212799504774\nNguyen 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\nVirarkar 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\nZhou 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\nSone 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\nBi 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\nEmmert-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\nLo 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\nEl 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\nBhatla 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\nBerek 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\nThomassin-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\nChronas 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","source_license":"CC0","license_restricted":false}