|
1
|
Sung H, Ferlay J, Siegel RL, Laversanne M,
Soerjomataram I, Jemal A and Bray F: Global cancer statistics 2020:
GLOBOCAN estimates of incidence and mortality worldwide for 36
cancers in 185 countries. CA Cancer J Clin. 71:209–249. 2021.
View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Allemani C, Matsuda T, Di Carlo V,
Harewood R, Matz M, Nikšić M, Bonaventure A, Valkov M, Johnson CJ,
Estève J, et al: Global surveillance of trends in cancer survival
2000–14 (CONCORD-3): Analysis of individual records for 37 513 025
patients diagnosed with one of 18 cancers from 322 population-based
registries in 71 countries. Lancet. 391:1023–1075. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Sugawara K, Yamashita H, Urabe M, Uemura
Y, Okumura Y, Yagi K, Aikou S and Seto Y: Combining nutritional
status with TNM stage: A physiological update on gastric cancer
staging for improving prognostic accuracy in elderly patients. Int
J Clin Oncol. 27:1849–1858. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Song BI, Kim HW, Won KS, Ryu SW, Sohn SS
and Kang YN: Preoperative standardized uptake value of metastatic
lymph nodes measured by 18F-FDG PET/CT improves the prediction of
prognosis in gastric cancer. Medicine (Baltimore). 94:e10372015.
View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Mishra P: Practical explainable AI using
python: Artificial intelligence model explanations using
python-based libraries, extensions, and frameworks. Apress Media;
New York, NY: 2021
|
|
6
|
Ho TK: Random decision forests. In:
Proceedings of 3rd international conference on document analysis
and recognition. Volume 1. Montreal, QC: pp. 278–282. 1995
|
|
7
|
Friedman JH: Greedy function
approximation: A gradient boosting machine. Ann Statist.
29:1189–232. 2001. View Article : Google Scholar
|
|
8
|
Mori Y, Yokota H, Hoshino I, Iwatate Y,
Wakamatsu K, Uno T and Suyari H: Deep learning-based gene selection
in comprehensive gene analysis in pancreatic cancer. Sci Rep.
11:165212021. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Van Rossum G and Drake FL: Python 3
reference manual. CreateSpace Scotts Valley, CA: 2009
|
|
10
|
Pedregosa F, Varoquaux G, Gramfort A,
Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R,
Dubourg V, et al: Scikit-learn: Machine learning in python. J Mach
Learn Res. 12:2825–2830. 2011.
|
|
11
|
Chen T and Guestrin C: XGBoost: A scalable
tree boosting system. Proceedings of the 22nd ACM SIGKDD
international conference on knowledge discovery and data mining.
ACM; New York, NY: pp. 785–794. 2016, View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Chollet F: Others: Keras. GitHub.
2015.https://github.com/fchollet/kerasSeptember 1–2023
|
|
13
|
Ian G, Bengio Y and Courville A: Deep
learning. MIT Press; Cambridge, MA: 2016
|
|
14
|
Theodoridis S and Koutroumbas K: Pattern
recognition. 4th edition. Academic Press; Cambridge, MA: 2008
|
|
15
|
Piñeros M, Parkin DM, Ward K, Chokunonga
E, Ervik M, Farrugia H, Gospodarowicz M, O'Sullivan B,
Soerjomataram I, Swaminathan S, et al: Essential TNM: A registry
tool to reduce gaps in cancer staging information. Lancet Oncol.
20:e103–e111. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Lin JX, Lin JP, Xie JW, Wang JB, Lu J,
Chen QY, Cao LL, Lin M, Tu R, Zheng CH, et al: Complete blood
count-based inflammatory score (CBCS) is a novel prognostic marker
for gastric cancer patients after curative resection. BMC Cancer.
20:112020. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Kim EY, Lee JW, Yoo HM, Park CH and Song
KY: The platelet-to-lymphocyte ratio versus
neutrophil-to-lymphocyte ratio: Which is better as a prognostic
factor in gastric cancer? Ann Surg Oncol. 22:4363–4370. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
18
|
McMillan DC: The systemic
inflammation-based glasgow prognostic score: A decade of experience
in patients with cancer. Cancer Treat Rev. 39:534–540. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Shimura T, Shibata M, Gonda K, Okayama H,
Saito M, Momma T, Ohki S and Kono K: Serum transthyretin level is
associated with prognosis of patients with gastric cancer. J Surg
Res. 227:145–150. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Xiao J, He X, Wang Z, Hu J, Sun F, Qi F,
Yang S and Xiao Z: Serum carbohydrate antigen 19-9 and prognosis of
patients with gastric cancer. Tumour Biol. 35:1331–1334. 2014.
View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Kuroda D, Sawayama H, Kurashige J,
Iwatsuki M, Eto T, Tokunaga R, Kitano Y, Yamamura K, Ouchi M,
Nakamura K, et al: Controlling nutritional status (CONUT) score is
a prognostic marker for gastric cancer patients after curative
resection. Gastric Cancer. 21:204–212. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Takagi K, Domagala P, Polak WG, Buettner
S, Wijnhoven BPL and Ijzermans JNM: Prognostic significance of the
controlling nutritional status (CONUT) score in patients undergoing
gastrectomy for gastric cancer: A systematic review and
meta-analysis. BMC Surg. 19:1292019. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Namikawa T, Ishida N, Tsuda S, Fujisawa K,
Munekage E, Iwabu J, Munekage M, Uemura S, Tsujii S, Tamura T, et
al: Prognostic significance of serum alkaline phosphatase and
lactate dehydrogenase levels in patients with unresectable advanced
gastric cancer. Gastric Cancer. 22:684–691. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Cupp MA, Cariolou M, Tzoulaki I, Aune D,
Evangelou E and Berlanga-Taylor AJ: Neutrophil to lymphocyte ratio
and cancer prognosis: An umbrella review of systematic reviews and
meta-analyses of observational studies. BMC Med. 18:3602020.
View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Ding P, Guo H, Sun C, Yang P, Kim NH, Tian
Y, Liu Y, Liu P, Li Y and Zhao Q: Combined systemic
immune-inflammatory index (SII) and prognostic nutritional index
(PNI) predicts chemotherapy response and prognosis in locally
advanced gastric cancer patients receiving neoadjuvant chemotherapy
with PD-1 antibody sintilimab and XELOX: A prospective study. BMC
Gastroenterol. 22:1212022. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Ren F, Zhao Q, Zhao M, Zhu S, Liu B,
Bukhari I, Zhang K, Wu W, Fu Y, Yu Y, et al: Immune infiltration
profiling in gastric cancer and their clinical implications. Cancer
Sci. 112:3569–3584. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Que SJ, Chen QY, Qing-Zhong Liu ZY, Wang
JB, Lin JX, Lu J, Cao LL, Lin M, Tu RH, et al: Application of
preoperative artificial neural network based on blood biomarkers
and clinicopathological parameters for predicting long-term
survival of patients with gastric cancer. World J Gastroenterol.
25:6451–6464. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Kangi AK and Bahrampour A: Predicting the
survival of gastric cancer patients using artificial and bayesian
neural networks. Asian Pac J Cancer Prev. 19:487–490.
2018.PubMed/NCBI
|
|
29
|
Oh SE, Seo SW, Choi MG, Sohn TS, Bae JM
and Kim S: Prediction of overall survival and novel classification
of patients with gastric cancer using the survival recurrent
network. Ann Surg Oncol. 25:1153–1159. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Li Z, Wu X, Gao X, Shan F, Ying X, Zhang Y
and Ji J: Development and validation of an artificial neural
network prognostic model after gastrectomy for gastric carcinoma:
An international multicenter cohort study. Cancer Med. 9:6205–6215.
2020. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Afrash MR, Shanbehzadeh M and
Kazemi-Arpanahi H: Design and development of an intelligent system
for predicting 5-year survival in gastric cancer. Clin Med Insights
Oncol. 16:117955492211168332022. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Ahn HS, Lee HJ, Hahn S, Kim WH, Lee KU,
Sano T, Edge SB and Yang H-K: Evaluation of the seventh American
joint committee on cancer/international union against cancer
classification of gastric adenocarcinoma in comparison with the
sixth classification. Cancer. 116:5592–5598. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Deng Q, He B, Liu X, Yue J, Ying H, Pan Y,
Sun H, Chen J, Wang F, Gao T, et al: Prognostic value of
pre-operative inflammatory response biomarkers in gastric cancer
patients and the construction of a predictive model. J Transl Med.
13:662015. View Article : Google Scholar : PubMed/NCBI
|
