Page 44 - ARNM-3-2

Advances in Radiotherapy
& Nuclear Medicine Radiomics for gastric cancer

doi: 10.1007/s00330-019-06398-z analysis for predicting lymphovascular and perineural
invasion. Diagn Interv Radiol. 2020;26(6):515-522.
42. Yardimci AH, Sel I, Bektas CT, et al. Computed tomography
texture analysis in patients with gastric cancer: A quantitative doi: 10.5152/dir.2020.19507
imaging biomarker for preoperative evaluation before 52. Zheng H, Zheng Q, Jiang M, et al. Contrast-enhanced CT
neoadjuvant chemotherapy treatment. Jpn J Radiol. based radiomics in the preoperative prediction of perineural
2020;38(6):553-560.
invasion for patients with gastric cancer. Eur J Radiol.
doi: 10.1007/s11604-020-00936-2 2022;154:110393.
43. Yang J, Wu Q, Xu L, et al. Integrating tumor and nodal doi: 10.1016/j.ejrad.2022.110393
radiomics to predict lymph node metastasis in gastric 53. Fan L, Li J, Zhang H, et al. Machine learning analysis for
cancer. Radiother Oncol. 2020;150:89-96. the noninvasive prediction of lymphovascular invasion
doi: 10.1016/j.radonc.2020.06.004 in gastric cancer using PET/CT and enhanced CT-based
radiomics and clinical variables. Abdom Radiol (NY).
44. Wang L, Gong J, Huang X, et al. CT-based radiomics 2022;47(4):1209-1222.
nomogram for preoperative prediction of No.10 lymph
nodes metastasis in advanced proximal gastric cancer. Eur J doi: 10.1007/s00261-021-03315-1
Surg Oncol. 2021;47(6):1458-1465. 54. Yang L, Chu W, Li M, et al. Radiomics in gastric cancer: First
doi: 10.1016/j.ejso.2020.11.132 clinical investigation to predict lymph vascular invasion and
survival outcome using F-FDG PET/CT images. Front
18
45. Sun Z, Jiang Y, Chen C, et al. Radiomics signature based Oncol. 2022;12:836098.
on computed tomography images for the preoperative
prediction of lymph node metastasis at individual stations doi: 10.3389/fonc.2022.836098
in gastric cancer: A multicenter study. Radiother Oncol. 55. Bando E, Makuuchi R, Tokunaga M, Tanizawa Y,
2021;165:179-190. Kawamura T, Terashima M. Impact of clinical tumor-node-
doi: 10.1016/j.radonc.2021.11.003 metastasis staging on survival in gastric carcinoma patients
receiving surgery. Gastric Cancer. 2017;20(3):448-456.
46. Wang X, Li C, Fang M, et al. Integrating No.3 lymph
nodes and primary tumor radiomics to predict lymph doi: 10.1007/s10120-016-0637-x
node metastasis in T1-2 gastric cancer. BMC Med Imaging. 56. Sohn BH, Hwang JE, Jang HJ, et al. Clinical significance
2021;21(1):58. of four molecular subtypes of gastric cancer identified
doi: 10.1186/s12880-021-00587-3 by the cancer genome atlas project. Clin Cancer Res.
2017;23(15):4441-4449.
47. Liu Q, Li J, Xin B, et al. 18F-FDG PET/CT radiomics for
preoperative prediction of lymph node metastases and doi: 10.1158/1078-0432.CCR-16-2211
nodal staging in gastric cancer. Front Oncol. 2021;11:723345. 57. Bortolotto C, Lancia A, Stelitano C, et al. Radiomics features
doi: 10.3389/fonc.2021.723345 as predictive and prognostic biomarkers in NSCLC. Expert
Rev Anticancer Ther. 2021;21(3):257-266.
48. Xue XQ, Yu WJ, Shao XL, et al. Radiomics model based on
preoperative 18F-fluorodeoxyglucose PET predicts N2-3b doi: 10.1080/14737140.2021.1852935
lymph node metastasis in gastric cancer patients. Nucl Med 58. Giganti F, Antunes S, Salerno A, et al. Gastric cancer: Texture
Commun. 2022;43(3):340-349. analysis from multidetector computed tomography as a
doi: 10.1097/MNM.0000000000001523 potential preoperative prognostic biomarker. Eur Radiol.
2017;27(5):1831-1839.
49. Montagnani F, Crivelli F, Aprile G, et al. Long-term survival
after liver metastasectomy in gastric cancer: Systematic doi: 10.1007/s00330-016-4540-y
review and meta-analysis of prognostic factors. Cancer Treat 59. Wang X, Sun J, Zhang W, et al. Use of radiomics to extract
Rev. 2018;69:11-20. splenic features to predict prognosis of patients with gastric
doi: 10.1016/j.ctrv.2018.05.010 cancer. Eur J Surg Oncol. 2020;46(10 Pt A):1932-1940.
doi: 10.1016/j.ejso.2020.06.021
50. Chen X, Yang Z, Yang J, et al. Radiomics analysis of contrast-
enhanced CT predicts lymphovascular invasion and disease 60. Wang S, Feng C, Dong D, et al. Preoperative computed
outcome in gastric cancer: A preliminary study. Cancer tomography-guided disease-free survival prediction in
Imaging. 2020;20(1):24. gastric cancer: A multicenter radiomics study. Med Phys.
doi: 10.1186/s40644-020-00302-5 2020;47(10):4862-4871.
doi: 10.1002/mp.14350
51. Yardımcı AH, Koçak B, Turan Bektaş C, et al. Tubular gastric
adenocarcinoma: Machine learning-based CT texture 61. Li W, Zhang L, Tian C, et al. Prognostic value of computed

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