Deep learning in CT image segmentation of cervical cancer: a systematic review and meta-analysis

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424. https://doi.org/10.3322/caac.21492.

Article  PubMed  Google Scholar 

Fidler MM, Gupta S, Soerjomataram I, Ferlay J, Steliarova-Foucher E, Bray F. Cancer incidence and mortality among young adults aged 20–39 years worldwide in 2012: a population-based study. Lancet Oncol. 2017;18(12):1579–89. https://doi.org/10.1016/S1470-2045(17)30677-0.

Article  PubMed  Google Scholar 

Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K, Cha KH, Summers RM, Giger ML. Deep learning in medical imaging and radiation therapy. Med Phys. 2019;46(1):e1–36. https://doi.org/10.1002/mp.13264.

Article  PubMed  Google Scholar 

Wang Z, Chang Y, Peng Z, Lv Y, Shi W, Wang F, Pei X, Xu XG. Evaluation of deep learning-based auto-segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients. J Appl Clin Med Phys. 2020;21(12):272–9. https://doi.org/10.1002/acm2.13097.

Article  PubMed  PubMed Central  Google Scholar 

Rhee DJ, Jhingran A, Rigaud B, Netherton T, Cardenas CE, Zhang L, Vedam S, Kry S, Brock KK, Shaw W, O’Reilly F, Parkes J, Burger H, Fakie N, Trauernicht C, Simonds H, Court LE. Automatic contouring system for cervical cancer using convolutional neural networks. Med Phys. 2020;47(11):5648–58. https://doi.org/10.1002/mp.14467.

Article  PubMed  Google Scholar 

Liu Z, Liu X, Guan H, Zhen H, Sun Y, Chen Q, Chen Y, Wang S, Qiu J. Development and validation of a deep learning algorithm for auto-delineation of clinical target volume and organs at risk in cervical cancer radiotherapy. Radiother Oncol. 2020;153:172–9. https://doi.org/10.1016/j.radonc.2020.09.060.

Article  PubMed  Google Scholar 

Shi J, Ding X, Liu X, Li Y, Liang W, Wu J. Automatic clinical target volume delineation for cervical cancer in CT images using deep learning. Med Phys. 2021;48(7):3968–81. https://doi.org/10.1002/mp.14898.

Article  PubMed  Google Scholar 

Liu Z, Liu X, Xiao B, Wang S, Miao Z, Sun Y, Zhang F. Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network. Phys Med. 2020;69:184–91. https://doi.org/10.1016/j.ejmp.2019.12.008.

Article  PubMed  Google Scholar 

Rigaud B, Anderson BM, Yu ZH, Gobeli M, Cazoulat G, Söderberg J, Samuelsson E, Lidberg D, Ward C, Taku N, Cardenas C, Rhee DJ, Venkatesan AM, Peterson CB, Court L, Svensson S, Löfman F, Klopp AH, Brock KK. Automatic segmentation using deep learning to enable online dose optimization during adaptive radiation therapy of cervical cancer. Int J Radiat Oncol Biol Phys. 2021;109(4):1096–110. https://doi.org/10.1016/j.ijrobp.2020.10.038.

Article  PubMed  Google Scholar 

Shal K, Choudhry MS. Evolution of deep learning algorithms for MRI-based brain tumor image segmentation. Crit Rev Biomed Eng. 2021;49(1):77–94. https://doi.org/10.1615/CritRevBiomedEng.2021035557.

Article  PubMed  Google Scholar 

Ju Z, Guo W, Gu S, Zhou J, Yang W, Cong X, Dai X, Quan H, Liu J, Qu B, Liu G. CT based automatic clinical target volume delineation using a dense-fully connected convolution network for cervical cancer radiation therapy. BMC Cancer. 2021;21(1):243. https://doi.org/10.1186/s12885-020-07595-6.

Article  PubMed  PubMed Central  Google Scholar 

Cai L, Gao J, Zhao D. A review of the application of deep learning in medical image classification and segmentation. Ann Transl Med. 2020;8(11):713. https://doi.org/10.21037/atm.2020.02.44.

Article  PubMed  PubMed Central  Google Scholar 

Cardenas CE, Yang J, Anderson BM, Court LE, Brock KB. Advances in auto-segmentation. Semin Radiat Oncol. 2019;29(3):185–97. https://doi.org/10.1016/j.semradonc.2019.02.001.

Article  PubMed  Google Scholar 

Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for Biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A, editors. Medical image computing and computer-assisted intervention– MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol. 9351. Cham: Springer; 2015. https://doi.org/10.1007/978-3-319-24574-4_28

Çiçek Ö, Abdulkadir A, Lienkamp SS, et al. 3D U-net Learning dense volumetric segmentation from sparse annotation. Lect Notes Comput Sci. 2016;9901:424–32.

Article  Google Scholar 

Liu X, Li KW, Yang R, Geng LS. Review of deep learning based automatic segmentation for lung cancer radiotherapy. Front Oncol. 2021;8(11):717039. https://doi.org/10.3389/fonc.2021.717039.

Article  Google Scholar 

Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39(4):640–51. https://doi.org/10.1109/TPAMI.2016.2572683.

Article  PubMed  Google Scholar 

van Kempen EJ, Post M, Mannil M, Witkam RL, Ter Laan M, Patel A, Meijer FJA, Henssen D. Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis. Eur Radiol. 2021;31(12):9638–53. https://doi.org/10.1007/s00330-021-08035-0.

Article  PubMed  PubMed Central  Google Scholar 

Badrigilan S, Nabavi S, Abin AA, Rostampour N, Abedi I, Shirvani A, Ebrahimi MM. Deep learning approaches for automated classification and segmentation of head and neck cancers and brain tumors in magnetic resonance images: a meta-analysis study. Int J Comput Assist Radiol Surg. 2021;16(4):529–42. https://doi.org/10.1007/s11548-021-02326-z.

Article  PubMed  Google Scholar 

Patzer RE, Kaji AH, Fong Y. TRIPOD reporting guidelines for diagnostic and prognostic studies. JAMA Surg. 2021;156(7):675–6. https://doi.org/10.1001/jamasurg.2021.0537.

Article  PubMed  Google Scholar 

Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26:297–302. https://doi.org/10.2307/1932409.

Article  Google Scholar 

Zhang D, Yang Z, Jiang S, Zhou Z, Meng M, Wang W. Automatic segmentation and applicator reconstruction for CT-based brachytherapy of cervical cancer using 3D convolutional neural networks. J Appl Clin Med Phys. 2020;21(10):158–69. https://doi.org/10.1002/acm2.13024.

Article  PubMed  PubMed Central  Google Scholar 

Sartor H, Minarik D, Enqvist O, Ulén J, Wittrup A, Bjurberg M, Trägårdh E. Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth. Clin Transl Radiat Oncol. 2020;14(25):37–45. https://doi.org/10.1016/j.ctro.2020.09.004.

Article  Google Scholar 

Liu Z, Chen W, Guan H, Zhen H, Shen J, Liu X, Liu A, Li R, Geng J, You J, Wang W, Li Z, Zhang Y, Chen Y, Du J, Chen Q, Chen Y, Wang S, Zhang F, Qiu J. An adversarial deep-learning-based model for cervical cancer CTV segmentation with multicenter blinded randomized controlled validation. Front Oncol. 2021;19(11):702270. https://doi.org/10.3389/fonc.2021.702270.

Article  Google Scholar 

Hu H, Yang Q, Li J, Wang P, Tang B, Wang X, Lang J. Deep learning applications in automatic segmentation and reconstruction in CT-based cervix brachytherapy. J Contemp Brachytherapy. 2021;13(3):325–30. https://doi.org/10.5114/jcb.2021.106118.

Article  PubMed  PubMed Central  Google Scholar 

Chang J-H, Lin K-H, Wang T-H, Zhou Y-K, Chung P-C. Image segmentation in 3D brachytherapy using convolutional LSTM. J Med Biol Eng. 2021. https://doi.org/10.1007/s40846-021-00624-0.

Article  Google Scholar 

Mohammadi R, Shokatian I, Salehi M, Arabi H, Shiri I, Zaidi H. Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer. Radiother Oncol. 2021;159:231–40. https://doi.org/10.1016/j.radonc.2021.03.030.

Article  PubMed  Google Scholar 

Ju Z, Wu Q, Yang W, Gu S, Guo W, Wang J, Ge R, Quan H, Liu J, Qu B. Automatic segmentation of pelvic organs-at-risk using a fusion network model based on limited training samples. Acta Oncol. 2020;59(8):933–9. https://doi.org/10.1080/0284186X.2020.1775290.

Article  PubMed  Google Scholar 

Noori M, Bahri A, Mohammadi K. Attention-guided version of 2D UNet for automatic brain tumor segmentation. In: 2019 9th international conference on computer and knowledge engineering (ICCKE), Mashhad, Iran; 2019. p. 269–75.

Akal O, Peng Z, Valadez GH. ComboNet: combined 2D & 3D architecture for aorta segmentation. arXiv:2006.05325

Shivdeo A, Lokwani R, Kulkarni V, Kharat A, Pant A. Comparative evaluation of 3D and 2D Deep learning techniques for semantic segmentation in CT scans. arXiv:2101.07612

Pellicer-Valero OJ, Marenco Jiménez JL, Gonzalez-Perez V, et al. Deep learning for fully automatic detection, segmentation, and Gleason Grade estimation of prostate cancer in multiparametric magnetic resonance images.arXiv:2103.12650

Tanderup K, Nielsen SK, Nyvang GB, et al. From point A to the sculpted pear: MR image guidance significantly improves tumour dose and sparing of organs at risk in brachytherapy of cervical cancer. Radiother Oncol. 2010;94:173–80.

Article  PubMed  Google Scholar 

Potter R, Georg P, Dimopoulos JC, et al. Clinical outcome of protocol based image (MRI) guided adaptive brachytherapy combined with 3D conformal radiotherapy with or without chemotherapy in patients with locally advanced cervical cancer. Radiother Oncol.

Simpson DR, Scanderbeg DJ, Carmona R, et al. Clinical outcomes of computed tomography-based volumetric brachytherapy planning for cervical cancer. Int J Radiat Oncol Biol Phys. 2015;93:150–7.

Article  PubMed  Google Scholar 

Charra-Brunaud C, Harter V, Delannes M, et al. Impact of 3D image-based PDR brachytherapy on outcome of patients treated for cervix carcinoma in France: results of the French STIC prospective study. Radiother Oncol. 2012;103:305–13.

Article  PubMed  Google Scholar 

Beller HL, Rapp DE, Zillioux J, Abdalla B, Duska LR, Showalter TN, Krupski TL, Cisu T, Congleton JY, Schenkman NS. Urologic complications requiring intervention following high-dose pelvic radiation for cervical cancer. Urology. 2021;151:107–12. https://doi.org/10.1016/j.urology.2020.09.011.

Article  PubMed  Google Scholar 

Spampinato S, Fokdal LU, Pötter R, Haie-Meder C, Lindegaard JC, Schmid MP, Sturdza A, Jürgenliemk-Schulz IM, Mahantshetty U, Segedin B, Bruheim K, Hoskin P, Rai B, Huang F, Cooper R, van der Steen-Banasik E, Van Limbergen E, Sundset M, Westerveld H, Nout RA, Jensen NBK, Kirisits C, Kirchheiner K, Tanderup K, EMBRACE Collaborative Group. Risk factors and dose-effects for bladder fistula, bleeding and cystitis after radiotherapy with imaged-guided adaptive brachytherapy for cervical cancer: an EMBRACE analysis. Radiother Oncol. 2021;158:312–20.

Article  PubMed  Google Scholar 

Fokdal L, Pötter R, Kirchheiner K, Lindegaard JC, Jensen NBK, Kirisits C, Chargari C, Mahantshetty U, Jürgenliemk-Schulz IM, Segedin B, Hoskin P, Tanderup K. Physician assessed and patient reported urinary morbidity after radio-chemotherapy and image guided adaptive brachytherapy for locally advanced cervical cancer. Radiother Oncol. 2018;127(3):423–30. https://doi.org/10.1016/j.radonc.2018.05.002.

Article  PubMed  Google Scholar 

Mansha MA, Sadaf T, Waheed A, Munawar A, Rashid A, Chaudry SJ. Long-term toxicity and efficacy of intensity-modulated radiation therapy in cervical cancers: experience of a cancer hospital in Pakistan. JCO Glob Oncol. 2020;6:1639–46. https://doi.org/10.1200/GO.20.00169.

Article  PubMed  Google Scholar 

Guo D, Jin D, Zhu Z, Ho T-Y, Harrison AP, Chao CH, Xiao J, Yuille A, Lin C-Y, Lu L. Organ at risk segmentation for head and neck cancer using stratified learning and neural architecture search. arXiv:2004.08426

Yamanakkanavar N, Choi JY, Lee B. MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer’s disease: a survey. Sensors. 2020;20(11):3243. https://doi.org/10.3390/s20113243.

Article  PubMed Central 

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