Retinoblastoma: Insights on paradigms, clinicopathologic observations, clinical diagnostic procedures and technological interventions to render prognosis and cure to ailment

Aneesh Aneesh; V. Muneeswaran

doi:10.53730/ijhs.v6nS4.7793

Authors

Aneesh Department of ECE, Kalasilingam Academy of Research and Education, Anand Nagar, Krishnakoil, Tamilnadu-626126
V. Muneeswaran Department of ECE, Kalasilingam Academy of Research and Education, Anand Nagar, Krishnakoil, Tamilnadu-626126

Keywords:

Retinoblastoma (RB), Eye disease, Ultrasound (US), Computed Tomography (CT), Magnetic Resonance Imaging (MRI)

Abstract

Eye cancer is extremely rare. It can harm the eye's outer parts, such as the eyelid, which is made up of muscles, skin, and nerves. Intraocular cancer occurs when cancer begins inside the eyeball. Melanoma and lymphoma are perhaps the very familiar intraocular cancers in grown-up person. Retinoblastoma, which begins in the cells of the retina, seems to be the most common type of eye cancer in children. Cancer can also disperse from several other parts of the human body to the eye. Therapies for eye cancer vary depending on the type and stage of the disease. Radiation therapy, Surgery, laser therapy, and freezing or heat therapy are all the possibilities. Medical imaging techniques such as Ultrasound, Magnetic Resonance Imaging and Computed Tomography supports in obtaining the anatomical structure of the eye. By the introduction of the image segmentation algorithms, the infected portion in the eye image is segmented and the information such as the size and location of the tumor is also identified. This can assist the clinicians make more rapid and accurate diagnosis. The various studies using conventional segmentation methodologies on Retinoblastoma were reviewed in this paper.

Downloads

Download data is not yet available.

References

Parulekar, Manoj V., and Brenda L. Gallie. "Retinoblastoma." In Clinical Ophthalmic Genetics and Genomics, pp. 457-463. Academic Press, 2022.

Warda, Omar, ZishanNaeem, Kelsey A. Roelofs, Mandeep S. Sagoo, and M. Ashwin Reddy. "Retinoblastoma and vision." Eye (2022): 1-12.

Sheehan, Ann Patrice. "Retinoblastoma: Early Diagnosis is Crucial." Journal of Pediatric Health Care 34, no. 6 (2020): 601-605.

Wu, Cheng, Jiaqi Yang, Wei Xiao, Zehang Jiang, Shuxia Chen, DianleiGuo, Ping Zhang, Chunqiao Liu, Huasheng Yang, and ZhiXie. "Single-cell characterization of malignant phenotypes and microenvironment alteration in retinoblastoma." Cell Death & Disease 13, no. 5 (2022): 1-12.

Gu, Huali, Yizhuo Wang, Dongsheng Huang, XundaJi, Yi Zhang, Jianmin Ma, Mei Li et al. "Clinical Characteristics and Image Manifestations of a Rare Retinoblastoma with a Bone Metastasis." Cancer Management and Research 14 (2022): 1565.

Wang, Yi-Zhuo, Yi Zhang, Dong-Sheng Huang, Ji-Tong Shi, Jian-Min Ma, Bin Li, Xiao-Lin Xu, Yan Zhou, and Hua-Li Gu. "Clinical characteristics, treatment and prognosis of children with unilateral retinoblastoma and intracranial segment of Retrobulbar optic nerve invasion." BMC ophthalmology 21, no. 1 (2021): 1-8.

Tomar, Ankit Singh, Paul T. Finger, Brenda Gallie, Tero T. Kivelä, AshwinMallipatna, Chengyue Zhang, Junyang Zhao et al. "Metastatic Death Based on Presenting Features and Treatment for Advanced Intraocular Retinoblastoma: A Multicenter Registry-based Study." Ophthalmology (2022).

Li, Meng, XintongBian, Xu Chen, Ningke Fan, HongmiZou, YixiBao, and Yu Zhou. "Multifunctional liposome for photoacoustic/ultrasound imaging-guided chemo/photothermal retinoblastoma therapy." Drug Delivery 29, no. 1 (2022): 519-533.

Nath, Jyotiman, PranjalGoswami, ParthaPratimMedhi, GautamSarma, Apurba Kumar Kalita, and Mouchumee Bhattacharyya. "Dosimetric comparison of different radiotherapy techniques for the treatment of Retinoblastoma." Journal of Radiotherapy in Practice 21, no. 2 (2022): 159-164.

Wyse, Emily, James T. Handa, Alan D. Friedman, and Monica S. Pearl. "A review of the literature for intra-arterial chemotherapy used to treat retinoblastoma." Pediatric radiology 46, no. 9 (2016): 1223-1233.

de Graaf, Pim, Sophia Göricke, FiraziaRodjan, Paolo Galluzzi, Philippe Maeder, Jonas A. Castelijns, and Hervé J. Brisse. "Guidelines for imaging retinoblastoma: imaging principles and MRI standardization." Pediatric radiology 42, no. 1 (2012): 2-14.

Ciller, Carlos, Sandro I. De Zanet, Michael B. Rüegsegger, Alessia Pica, Raphael Sznitman, Jean-Philippe Thiran, Philippe Maeder, Francis L. Munier, Jens H. Kowal, and Meritxell Bach Cuadra. "Automatic segmentation of the eye in 3D magnetic resonance imaging: a novel statistical shape model for treatment planning of retinoblastoma." International Journal of Radiation Oncology* Biology* Physics 92, no. 4 (2015): 794-802.

Munson, Micheal C., Devon L. Plewman, Katelyn M. Baumer, Ryan Henning, Collin T. Zahler, Alexander T. Kietzman, Alexandra A. Beard et al. "Autonomous early detection of eye disease in childhood photographs." Science advances 5, no. 10 (2019): eaax6363.

Badar, Maryam, Muhammad Haris, and Anam Fatima. "Application of deep learning for retinal image analysis: A review." Computer Science Review 35 (2020): 100203.

Li, Tao, Wang Bo, Chunyu Hu, Hong Kang, Hanruo Liu, Kai Wang, and Huazhu Fu. "Applications of deep learning in fundus images: A review." Medical Image Analysis 69 (2021): 101971.

Sundaram, Ramakrishnan, Ravichandran Ks, and PremaladhaJayaraman. "Extraction of blood vessels in fundus images of retina through hybrid segmentation approach." Mathematics 7, no. 2 (2019): 169.

Jansen, Robin W., Christiaan M. de Bloeme, Hervé J. Brisse, Paolo Galluzzi, LiesbethCardoen, Sophia Göricke, Philippe Maeder et al. "MR imaging features to differentiate retinoblastoma from Coats’ disease and persistent fetal vasculature." Cancers 12, no. 12 (2020): 3592.

Strijbis, Victor IJ, Christiaan M. de Bloeme, Robin W. Jansen, HamzaKebiri, Huu-Giao Nguyen, Marcus C. de Jong, Annette C. Moll, Merixtell Bach-Cuadra, Pim de Graaf, and Martijn D. Steenwijk. "Multi-view convolutional neural networks for automated ocular structure and tumor segmentation in retinoblastoma." Scientific reports 11, no. 1 (2021): 1-10.

Xu, Guan, YafangXue, ZeynepGürselÖzkurt, NazihaSlimani, Zizhong Hu, Xueding Wang, Kewen Xia, Teng Ma, Qifa Zhou, and HakanDemirci. "Photoacoustic imaging features of intraocular tumors: Retinoblastoma and uveal melanoma." PLoS One 12, no. 2 (2017): e0170752.

Khedekar, Amit, BharanidharanDevarajan, Kim Ramasamy, VeerappanMuthukkaruppan, and Usha Kim. "Smartphone-based application improves the detection of retinoblastoma." Eye 33, no. 6 (2019): 896-901.

Bernard, Alec, Shang Zhou Xia, SahalSaleh, TochukwuNdukwe, Joshua Meyer, Elliot Soloway, MandefroSintayehu et al. "EyeScreen: Development and Potential of a Novel Machine Learning Application to Detect Leukocoria." Ophthalmology Science (2022): 100158.

Li, Zhenwei, MengliJia, Xiaoli Yang, and MengyingXu. "Blood Vessel Segmentation of Retinal Image Based on Dense-U-Net Network." Micromachines 12, no. 12 (2021): 1478.

Ciurte, Anca, Xavier Bresson, Olivier Cuisenaire, NawalHouhou, SergiuNedevschi, Jean-Philippe Thiran, and Meritxell Bach Cuadra. "Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut." PloS one 9, no. 7 (2014): e100972.

Yang, Miin-Shen, Yu-Jen Hu, Karen Chia-Ren Lin, and Charles Chia-Lee Lin. "Segmentation techniques for tissue differentiation in MRI of ophthalmology using fuzzy clustering algorithms." Magnetic Resonance Imaging 20, no. 2 (2002): 173-179.

Ashwini, S., and R. I. Minu. "Consanguinity in Risk Assessment of Retinoblastoma Using Machine Learning." In Artificial Intelligence and Technologies, pp. 579-585. Springer, Singapore, 2022.