Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer statistics 2020: GLOBOCAN estimates of incidence and Mortality Worldwide for 36 cancers in 185 countries. Cancer J Clin. 2021;71(3):209–49.
Cao W, Chen HD, Yu YW, Li N, Chen WQ. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020. Chin Med J (Engl). 2021;134(7):783–91.
Smyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F. Gastric cancer. Lancet (London England). 2020;396(10251):635–48.
Lordick F, Carneiro F, Cascinu S, Fleitas T, Haustermans K, Piessen G, Vogel A, Smyth EC. Gastric cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Annals Oncology: Official J Eur Soc Med Oncol. 2022;33(10):1005–20.
López Sala P, Leturia Etxeberria M, Inchausti Iguíñiz E, Astiazaran RodríguezA, Aguirre Oteiza MI, Zubizarreta Etxaniz M. Gastric adenocarcinoma: a review of the TNM classification system and ways of spreading. Radiologia. 2023;65(1):66–80.
Ye J, Ren Y, Wei Z, Hou X, Dai W, Cai S, Tan M, He Y, Yuan Y. External validation of a modified 8th AJCC TNM system for advanced gastric cancer: long-term results in southern China. Surg Oncol. 2018;27(2):146–53.
Lu J, Zheng CH, Cao LL, Li P, Xie JW, Wang JB, Lin JX, Chen QY, Lin M, Huang CM. The effectiveness of the 8th American Joint Committee on Cancer TNM classification in the prognosis evaluation of gastric cancer patients: a comparative study between the 7th and 8th editions. Eur J Surg Oncol. 2017;43(12):2349–56.
Peyroteo M, Martins PC, Canotilho R, Correia AM, Baia C, Sousa A, Brito D, Videira JF, Santos LL, de Sousa A. Impact of the 8th edition of the AJCC TNM classification on gastric cancer prognosis-study of a western cohort. Ecancermedicalscience. 2020;14:1124.
Zhu MH, Zhang KC, Yang ZL, Qiao Z, Chen L. Comparing prognostic values of the 7th and 8th editions of the American Joint Committee on Cancer TNM staging system for gastric cancer. Int J Biol Markers. 2020;35(1):26–32.
Zhang M, Ding C, Xu L, Ou B, Feng S, Wang G, Wang W, Liang Y, Chen Y, Zhou Z, et al. Comparison of a tumor-ratio-metastasis staging system and the 8th AJCC TNM staging system for gastric Cancer. Front Oncol. 2021;11:595421.
Yang K, Lu L, Liu H, Wang X, Gao Y, Yang L, Li Y, Su M, Jin M, Khan S. A comprehensive update on early gastric cancer: defining terms, etiology, and alarming risk factors. Expert Rev Gastroenterol Hepatol. 2021;15(3):255–73.
Johnston FM, Beckman M. Updates on management of gastric Cancer. Curr Oncol Rep. 2019;21(8):67.
Ma D, Zhang Y, Shao X, Wu C, Wu J. PET/CT for Predicting Occult Lymph Node Metastasis in Gastric Cancer. Curr Oncol (Toronto Ont). 2022;29(9):6523–39.
Zhao Q, Li Y, Hu Z, Tan B, Yang P, Tian Y. [Value of the preoperative TNM staging and the longest tumor diameter measurement of gastric cancer evaluated by MSCT]. Zhonghua Wei Chang Wai Ke Za Zhi = Chin J Gastrointest Surg. 2015;18(3):227–31.
Feng XY, Wang W, Luo GY, Wu J, Zhou ZW, Li W, Sun XW, Li YF, Xu DZ, Guan YX, et al. Comparison of endoscopic ultrasonography and multislice spiral computed tomography for the preoperative staging of gastric cancer – results of a single institution study of 610 Chinese patients. PLoS ONE. 2013;8(11):e78846.
Chen Q, Zhang L, Liu S, You J, Chen L, Jin Z, Zhang S, Zhang B. Radiomics in precision medicine for gastric cancer: opportunities and challenges. Eur Radiol. 2022;32(9):5852–68.
Xu Q, Sun Z, Li X, Ye C, Zhou C, Zhang L, Lu G. Advanced gastric cancer: CT radiomics prediction and early detection of downstaging with neoadjuvant chemotherapy. Eur Radiol. 2021;31(11):8765–74.
Wang Y, Liu W, Yu Y, Liu JJ, Xue HD, Qi YF, Lei J, Yu JC, Jin ZY. CT radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer. Eur Radiol. 2020;30(2):976–86.
Wang Y, Liu W, Yu Y, Liu JJ, Jiang L, Xue HD, Lei J, Jin Z, Yu JC. Prediction of the depth of Tumor Invasion in Gastric Cancer: potential role of CT Radiomics. Acad Radiol. 2020;27(8):1077–84.
Liu S, Liang W, Huang P, Chen D, He Q, Ning Z, Zhang Y, Xiong W, Yu J, Chen T. Multi-modal analysis for accurate prediction of preoperative stage and indications of optimal treatment in gastric cancer. Radiol Med. 2023;128(5):509–19.
Wang R, Li J, Fang MJ, Dong D, Liang P, Gao JB. [The value of spectral CT-based radiomics in preoperative prediction of lymph node metastasis of advanced gastric cancer]. Zhonghua Yi Xue Za Zhi. 2020;100(21):1617–22.
Hallinan JT, Venkatesh SK, Peter L, Makmur A, Yong WP, So JB. CT volumetry for gastric carcinoma: association with TNM stage. Eur Radiol. 2014;24(12):3105–14.
Liu S, Shi H, Ji C, Zheng H, Pan X, Guan W, Chen L, Sun Y, Tang L, Guan Y et al. Preoperative CT texture analysis of gastric cancer: correlations with postoperative TNM staging. Clinical radiology 2018, 73(8):756.e751-756.e759.
Chen D, Fu M, Chi L, Lin L, Cheng J, Xue W, Long C, Jiang W, Dong X, Sui J, et al. Prognostic and predictive value of a pathomics signature in gastric cancer. Nat Commun. 2022;13(1):6903.
Hindson J. A novel pathomics signature for gastric cancer. Nat Rev Gastroenterol Hepatol. 2023;20(1):3.
Bankhead P, Loughrey MB, Fernandez JA, Dombrowski Y, McArt DG, Dunne PD, McQuaid S, Gray RT, Murray LJ, Coleman HG, et al. QuPath: open source software for digital pathology image analysis. Sci Rep. 2017;7(1):16878.
Roy S, Kumar Jain A, Lal S, Kini J. A study about color normalization methods for histopathology images. Micron. 2018;114:42–61.
van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S, Aerts H. Computational Radiomics System to Decode the Radiographic phenotype. Cancer Res. 2017;77(21):e104–7.
Cao R, Yang F, Ma SC, Liu L, Zhao Y, Li Y, Wu DH, Wang T, Lu WJ, Cai WJ, et al. Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer. Theranostics. 2020;10(24):11080–91.
Gurzu S, Sugimura H, Orlowska J, Szederjesi J, Szentirmay Z, Bara T, Bara T Jr., Fetyko A, Jung I. Proposal of a Dukes-MAC-like staging system for gastric cancer. J Investig Med. 2017;65(2):316–22.
Kuntz S, Krieghoff-Henning E, Kather JN, Jutzi T, Höhn J, Kiehl L, Hekler A, Alwers E, von Kalle C, Fröhling S, et al. Gastrointestinal cancer classification and prognostication from histology using deep learning: systematic review. Eur J cancer (Oxford England: 1990). 2021;155:200–15.
Wong ANN, He Z, Leung KL, To CCK, Wong CY, Wong SCC, Yoo JS, Chan CKR, Chan AZ, Lacambra MD et al. Current developments of Artificial Intelligence in Digital Pathology and its future clinical applications in gastrointestinal cancers. Cancers 2022, 14(15).
Li D, Li X, Li S, Qi M, Sun X, Hu G. Relationship between the deep features of the full-scan pathological map of mucinous gastric carcinoma and related genes based on deep learning. Heliyon. 2023;9(3):e14374.
Wang R, Dai W, Gong J, Huang M, Hu T, Li H, Lin K, Tan C, Hu H, Tong T, et al. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. J Hematol Oncol. 2022;15(1):11.
Wan L, Sun Z, Peng W, Wang S, Li J, Zhao Q, Wang S, Ouyang H, Zhao X, Zou S, et al. Selecting candidates for organ-preserving strategies after neoadjuvant chemoradiotherapy for rectal Cancer: development and validation of a Model Integrating MRI Radiomics and Pathomics. J Magn Reson Imaging: JMRI. 2022;56(4):1130–42.
Feng L, Liu Z, Li C, Li Z, Lou X, Shao L, Wang Y, Huang Y, Chen H, Pang X, et al. Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. Lancet Digit Health. 2022;4(1):e8–e17.
Add Comment