Deep learning classification of uveal melanoma based on histopathological images and identification of a novel indicator for prognosis of patients

Bishop KD, Olszewski AJ. Epidemiology and survival outcomes of ocular and mucosal melanomas: a population-based analysis. Int J Cancer. 2014;134(12):2961–71.

Article  CAS  PubMed  Google Scholar 

Houtzagers LE, Wierenga APA, Ruys AAM, Luyten GPM, Jager MJ. Iris colour and the risk of developing uveal melanoma. Int J Mol Sci. 2020;21(19):7172.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Tomizuka T, Namikawa K, Higashi T. Characteristics of melanoma in Japan: a nationwide registry analysis 2011–2013. Melanoma Res. 2017;27(5):492–7.

Article  PubMed  Google Scholar 

Mallone F, Sacchetti M, Lambiase A, Moramarco A. Molecular insights and emerging strategies for treatment of metastatic uveal melanoma. Cancers (Basel). 2020;12(10):2761.

Article  CAS  PubMed  Google Scholar 

Sugase T, Lam BQ, Danielson M, Terai M, Aplin AE, Gutkind JS, Sato T. Development and optimization of orthotopic liver metastasis xenograft mouse models in uveal melanoma. J Transl Med. 2020;18(1):208.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Smidt-Nielsen I, Bagger M, Heegaard S, Andersen KK, Kiilgaard JF. Posterior uveal melanoma incidence and survival by AJCC tumour size in a 70-year nationwide cohort. Acta Ophthalmol. 2021;99(8):e1474–82.

Article  PubMed  PubMed Central  Google Scholar 

Fallico M, Raciti G, Longo A, Reibaldi M, Bonfiglio V, Russo A, Caltabiano R, Gattuso G, Falzone L, Avitabile T. Current molecular and clinical insights into uveal melanoma (Review). Int J Oncol. 2021;58(4):10.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Griewank KG, van de Nes J, Schilling B, Moll I, Sucker A, Kakavand H, Haydu LE, Asher M, Zimmer L, Hillen U, et al. Genetic and clinico-pathologic analysis of metastatic uveal melanoma. Mod Pathol. 2014;27(2):175–83.

Article  CAS  PubMed  Google Scholar 

Damato B, Eleuteri A, Taktak AF, Coupland SE. Estimating prognosis for survival after treatment of choroidal melanoma. Prog Retin Eye Res. 2011;30(5):285–95.

Article  PubMed  Google Scholar 

Chen R, Zheng D, Li Q, Xu S, Ye C, Jiang Q, Yan F, Jia Y, Zhang X, Ruan J. Immunotherapy of cholangiocarcinoma: therapeutic strategies and predictive biomarkers. Cancer Lett. 2022;546:215853.

Article  CAS  PubMed  Google Scholar 

Luo N, Sun X, Ma S, Li X, Zhu W, Fu M, Yang F, Chen Z, Li Q, Zhang Y, et al. Development of a novel prognostic model of glioblastoma based on m6A-associated immune genes and identification of a new biomarker. Front Oncol. 2022;12: 868415.

Article  PubMed  PubMed Central  Google Scholar 

Wang N, Gu Y, Li L, Chi J, Liu X, Xiong Y, Jiang S, Zhang W, Zhong C. Identification of novel prognostic risk signature of breast cancer based on ferroptosis-related genes. Sci Rep. 2022;12(1):13766.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Falzone L, Romano GL, Salemi R, Bucolo C, Tomasello B, Lupo G, Anfuso CD, Spandidos DA, Libra M, Candido S. Prognostic significance of deregulated microRNAs in uveal melanomas. Mol Med Rep. 2019;19(4):2599–610.

CAS  PubMed  PubMed Central  Google Scholar 

Ferrier ST, Burnier JV. Novel methylation patterns predict outcome in uveal melanoma. Life (Basel). 2020;10(10):248.

PubMed  Google Scholar 

Zhao DD, Zhao X, Li WT. Identification of differentially expressed metastatic genes and their signatures to predict the overall survival of uveal melanoma patients by bioinformatics analysis. Int J Ophthalmol. 2020;13(7):1046–53.

Article  PubMed  PubMed Central  Google Scholar 

Jager MJ, Shields CL, Cebulla CM, Abdel-Rahman MH, Grossniklaus HE, Stern MH, Carvajal RD, Belfort RN, Jia R, Shields JA, et al. Uveal melanoma Nat Rev Dis Primers. 2020;6(1):24.

Article  PubMed  Google Scholar 

Singh AD, Shields CL, Shields JA. Prognostic factors in uveal melanoma. Melanoma Res. 2001;11(3):255–63.

Article  CAS  PubMed  Google Scholar 

Wei JW, Tafe LJ, Linnik YA, Vaickus LJ, Tomita N, Hassanpour S. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep. 2019;9(1):3358.

Article  PubMed  PubMed Central  Google Scholar 

Gehrung M, Crispin-Ortuzar M, Berman AG, O’Donovan M, Fitzgerald RC, Markowetz F. Triage-driven diagnosis of Barrett’s esophagus for early detection of esophageal adenocarcinoma using deep learning. Nat Med. 2021;27(5):833–41.

Article  CAS  PubMed  Google Scholar 

Hou L, Samaras D, Kurc TM, Gao Y, Davis JE, Saltz JH. Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2016;2016:2424–33.

PubMed  PubMed Central  Google Scholar 

Kraus OZ, Ba JL, Frey BJ. Classifying and segmenting microscopy images with deep multiple instance learning. Bioinformatics. 2016;32(12):i52–9.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Madabhushi A, Lee G. Image analysis and machine learning in digital pathology: challenges and opportunities. Med Image Anal. 2016;33:170–5.

Article  PubMed  PubMed Central  Google Scholar 

Yu KH, Zhang C, Berry GJ, Altman RB, Re C, Rubin DL, Snyder M. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun. 2016;7:12474.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Brinker TJ, Hekler A, Enk AH, Berking C, Haferkamp S, Hauschild A, Weichenthal M, Klode J, Schadendorf D, Holland-Letz T, et al. Deep neural networks are superior to dermatologists in melanoma image classification. Eur J Cancer. 2019;119:11–7.

Article  PubMed  Google Scholar 

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chen PL, Roh W, Reuben A, Cooper ZA, Spencer CN, Prieto PA, Miller JP, Bassett RL, Gopalakrishnan V, Wani K, et al. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov. 2016;6(8):827–37.

Article  PubMed  PubMed Central  Google Scholar 

Prat A, Navarro A, Pare L, Reguart N, Galvan P, Pascual T, Martinez A, Nuciforo P, Comerma L, Alos L, et al. Immune-related gene expression profiling after PD-1 blockade in non-small cell lung carcinoma, head and neck squamous cell carcinoma, and melanoma. Cancer Res. 2017;77(13):3540–50.

Article  CAS  PubMed  Google Scholar 

Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, Berent-Maoz B, Pang J, Chmielowski B, Cherry G, et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell. 2016;165(1):35–44.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yang C, Zhang H, Chen M, Wang S, Qian R, Zhang L, Huang X, Wang J, Liu Z, Qin W, et al. A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer. Elife. 2022;11:e71880.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hekler A, Utikal JS, Enk AH, Berking C, Klode J, Schadendorf D, Jansen P, Franklin C, Holland-Letz T, Krahl D, et al. Pathologist-level classification of histopathological melanoma images with deep neural networks. Eur J Cancer. 2019;115:79–83.

Article  PubMed  Google Scholar 

Zhang H, Kalirai H, Acha-Sagredo A, Yang X, Zheng Y, Coupland SE. Piloting a deep learning model for predicting nuclear BAP1 immunohistochemical expression of uveal melanoma from hematoxylin-and-eosin sections. Transl Vis Sci Technol. 2020;9(2):50.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Farquhar N, Thornton S, Coupland SE, Coulson JM, Sacco JJ, Krishna Y, Heimann H, Taktak A, Cebulla CM, Abdel-Rahman MH, et al. Patterns of BAP1 protein expression provide insights into prognostic significance and the biology of uveal melanoma. J Pathol Clin Res. 2018;4(1):26–38.

Article  CAS  PubMed  Google Scholar 

Ewens KG, Kanetsky PA, Richards-Yutz J, Purrazzella J, Shields CL, Ganguly T, Ganguly A. Chromosome 3 status combined with BAP1 and EIF1AX mutation profiles are associated with metastasis in uveal melanoma. Invest Ophthalmol Vis Sci. 2014;55(8):5160–7.

Article  CAS  PubMed  Google Scholar 

Yavuzyigitoglu S, Koopmans AE, Verdijk RM, Vaarwater J, Eussen B, van Bodegom A, Paridaens D, Kilic E, de Klein A. Rotterdam ocular melanoma study g: uveal melanomas with SF3B1 mutations: a distinct subclass associated with late-onset metastases. Ophthalmology. 2016;123(5):1118–28.

Article  PubMed  Google Scholar 

Asnaghi L,

留言 (0)

沒有登入
gif