|
1
|
Bray F, Laversanne M, Sung H, Ferlay J,
Siegel RL, Soerjomataram I and Jemal A: Global cancer statistics
2022: GLOBOCAN estimates of incidence and mortality worldwide for
36 cancers in 185 countries. CA Cancer J Clin. 74:229–263.
2024.PubMed/NCBI View Article : Google Scholar
|
|
2
|
Tu Z, Wang Y, Liang J and Liu J:
Helicobacter pylori-targeted AI-driven vaccines: A paradigm shift
in gastric cancer prevention. Front Immunol.
15(1500921)2024.PubMed/NCBI View Article : Google Scholar
|
|
3
|
Rafiepoor H, Banoei MM, Ghorbankhanloo A,
Muhammadnejad A, Razavirad A, Soleymanjahi S and Amanpour S:
Exploring the potential of machine learning in gastric cancer:
Prognostic biomarkers, subtyping, and stratification. BMC Cancer.
25(809)2025.PubMed/NCBI View Article : Google Scholar
|
|
4
|
Yang L, Ding Y, Zhang D, Yang G, Dong X,
Zhang Z, Zhang C, Zhang W, Dai Y and Li Z: Predictive value of
enhanced CT and pathological indicators in lymph node metastasis in
patients with gastric cancer based on GEE model. BMC Med Imaging.
25(36)2025.PubMed/NCBI View Article : Google Scholar
|
|
5
|
Liu X, Han T, Wang Y, Liu H, Deng J, Xue
C, Li S and Zhou J: Prediction of Ki-67 expression in gastric
gastrointestinal stromal tumors using histogram analysis of
monochromatic and iodine images derived from spectral CT. Cancer
Imaging. 24(173)2024.PubMed/NCBI View Article : Google Scholar
|
|
6
|
Chen Z, Zhang G, Liu Y and Zhu K:
Radiomics analysis in predicting vascular invasion in gastric
cancer based on enhanced CT: A preliminary study. BMC Cancer.
24(1020)2024.PubMed/NCBI View Article : Google Scholar
|
|
7
|
Helal A, Hammam E, Ovenden CD, Candy NG,
Chaurasia B, Atallah O and Jukes A: A systematic review of
radiological prediction of ki 67 proliferation index of meningioma.
Neurosurg Rev. 47(881)2024.PubMed/NCBI View Article : Google Scholar
|
|
8
|
Peng C, Xu X, Ouyang Y, Li Y, Lu N, Zhu Y
and He C: Spatial variation of the gastrointestinal microbiota in
response to long-term administration of vonoprazan in mice with
high risk of gastric cancer. Helicobacter.
29(e13117)2024.PubMed/NCBI View Article : Google Scholar
|
|
9
|
Wang SN, Wang YK, Zhu CY, Jiang B, Ge DF
and Li YY: Significance of concurrent evaluation of HER2 gene
amplification and p53 and Ki67 expression in gastric cancer
tissues. Clin Transl Oncol. 27:126–134. 2025.PubMed/NCBI View Article : Google Scholar
|
|
10
|
Wu M, Zhu H, Han Z, Xu X, Liu Y, Cao H and
Zhu X: Prediction study of surrounding tissue invasion in clear
cell renal cell carcinoma based on multi-phase enhanced CT
radiomics. Abdom Radiol (NY). 50:2533–2548. 2025.PubMed/NCBI View Article : Google Scholar
|
|
11
|
Li C, Yue X, Chen S, Lin Y, Zhang Y, Liao
L and Zhang P: Preoperative prediction of Ki-67 expression in
hepatocellular carcinoma by spectral imaging on dual-energy
computed tomography (DECT). Quant Imaging Med Surg. 14:8402–8413.
2024.PubMed/NCBI View Article : Google Scholar
|
|
12
|
Gül S, Alberto M, Annika K, Pratschke J
and Rau B: Emerging treatment modalities for gastric cancer with
macroscopic peritoneal metastases: A systematic review. J Surg
Oncol. 130:1364–1377. 2024.PubMed/NCBI View Article : Google Scholar
|
|
13
|
Seo JW, Park KB, Chin HM and Jun KH: Does
Epstein-Barr virus-positive gastric cancer establish a significant
relationship with the multiple genes related to gastric
carcinogenesis? PLoS One. 18(e0283366)2023.PubMed/NCBI View Article : Google Scholar
|
|
14
|
Mao LT, Chen WC, Lu JY, Zhang HL, Ye YS,
Zhang Y, Liu B, Deng WW and Liu X: Quantitative parameters in novel
spectral computed tomography: Assessment of Ki-67 expression in
patients with gastric adenocarcinoma. World J Gastroenterol.
29:1602–1613. 2023.PubMed/NCBI View Article : Google Scholar
|
|
15
|
Yuan L, Lin X, Zhao P, Ma H, Duan S and
Sun S: Correlations between DKI and DWI with Ki-67 in gastric
adenocarcinoma. Acta Radiol. 64:1792–1798. 2023.PubMed/NCBI View Article : Google Scholar
|
|
16
|
Kock Am Brink M, Dunst LS, Behrens HM,
Krüger S, Becker T and Röcken C: Intratumoral heterogeneity affects
tumor regression and Ki67 proliferation index in perioperatively
treated gastric carcinoma. Br J Cancer. 128:375–386.
2023.PubMed/NCBI View Article : Google Scholar
|
|
17
|
Chen XS, Shan YC, Dong SY, Wang WT, Yang
YT, Liu LH, Xu ZH, Zeng MS and Rao SX: Utility of preoperative
computed tomography features in predicting the Ki-67 labeling index
of gastric gastrointestinal stromal tumors. Eur J Radiol.
142(109840)2021.PubMed/NCBI View Article : Google Scholar
|
|
18
|
Huang S, Nie X, Pu K, Wan X and Luo J: A
flexible deep learning framework for liver tumor diagnosis using
variable multi-phase contrast-enhanced CT scans. J Cancer Res Clin
Oncol. 150(443)2024.PubMed/NCBI View Article : Google Scholar
|
|
19
|
Gao Y, Yang X, Li H and Ding DW: A
knowledge-enhanced interpretable network for early recurrence
prediction of hepatocellular carcinoma via multi-phase CT imaging.
Int J Med Inform. 189(105509)2024.PubMed/NCBI View Article : Google Scholar
|
|
20
|
Yu Z, Liu Y, Dai X, Cui E, Cui J and Ma C:
Enhancing preoperative diagnosis of microvascular invasion in
hepatocellular carcinoma: Domain-adaptation fusion of multi-phase
CT images. Front Oncol. 14(1332188)2024.PubMed/NCBI View Article : Google Scholar
|
|
21
|
Zhang G, Man Q, Shang L, Zhang J, Cao Y,
Li S, Qian R, Ren J, Pu H, Zhou J, et al: Using multi-phase CT
radiomics features to predict EGFR mutation status in lung
adenocarcinoma patients. Acad Radiol. 31:2591–2600. 2024.PubMed/NCBI View Article : Google Scholar
|
|
22
|
Du M, Wang X, Zhuang S, Lou K, Li G, Xie
X, Wang M, Zang H, Wang M and Shen W: Quantitative parameters in
novel spectral computed tomography for assessing gastric cancer and
cell proliferation. Eur J Radiol. 167(111052)2023.PubMed/NCBI View Article : Google Scholar
|
|
23
|
Wu L, Wang H, Chen Y, Zhang X, Zhang T,
Shen N, Tao G, Sun Z, Ding Y, Wang W and Bu J: Beyond
radiologist-level liver lesion detection on multi-phase
contrast-enhanced CT images by deep learning. iScience.
26(108183)2023.PubMed/NCBI View Article : Google Scholar
|
|
24
|
Ananda S, Jain RK, Li Y, Iwamoto Y, Han
XH, Kanasaki S, Hu H and Chen YW: A boundary-enhanced liver
segmentation network for multi-phase CT images with unsupervised
domain adaptation. Bioengineering (Basel). 10(899)2023.PubMed/NCBI View Article : Google Scholar
|
|
25
|
Chen D, Zhou R and Li B: Preoperative
prediction of Her-2 and Ki-67 status in gastric cancer using
18F-FDG PET/CT radiomics features of visceral adipose
tissue. Br J Hosp Med (Lond). 85:1–18. 2024.PubMed/NCBI View Article : Google Scholar
|
|
26
|
Yang C, Zhu F, Yang J, Wang M, Zhang S and
Zhao Z: DCE-MRI quantitative analysis and MRI-based radiomics for
predicting the early efficacy of microwave ablation in lung
cancers. Cancer Imaging. 25(26)2025.PubMed/NCBI View Article : Google Scholar
|
|
27
|
Hou J, Zhang S, Li S, Zhao Z, Zhao L,
Zhang T and Liu W: CT-based radiomics models using intralesional
and different perilesional signatures in predicting the
microvascular density of hepatic alveolar echinococcosis. BMC Med
Imaging. 25(84)2025.PubMed/NCBI View Article : Google Scholar
|
|
28
|
Lin J, Ou Y, Luo M, Jiang X, Cen S and
Zeng G: Combining clinical characteristics with CT radiomics to
predict Ki67 expression level of small renal mass based on
artificial intelligence algorithms. Front Oncol.
15(1541143)2025.PubMed/NCBI View Article : Google Scholar
|
|
29
|
Sun S, Li L, Xu M, Wei Y, Shi F and Liu S:
Epstein-Barr virus positive gastric cancer: The pathological basis
of CT findings and radiomics models prediction. Abdom Radiol (NY).
49:1779–1791. 2024.PubMed/NCBI View Article : Google Scholar
|
