Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. 2018;68(6):394–424.

PubMed  Google Scholar 

Roukos D. Current status and future perspectives in gastric cancer management. Cancer Treat Rev. 2000;26(4):243–55.

CAS  Article  Google Scholar 

Ang J, Hu L, Huang P-T, Wu J-X, Huang L-N, Cao C-H, Zheng Y-X, Chen L. Contrast-enhanced ultrasonography assessment of gastric cancer response to neoadjuvant chemotherapy. World J Gastroenterol WJG. 2012;18(47):7026.

CAS  Article  Google Scholar 

Wang X-Z, Zeng Z-Y, Ye X, Sun J, Zhang Z-M, Kang W-M. Interpretation of the development of neoadjuvant therapy for gastric cancer based on the vicissitudes of the NCCN guidelines. World J Gast Oncol. 2020;12(1):37.

CAS  Article  Google Scholar 

Fu J, Tang L, Li Z-Y, Li X-T, Zhu H-F, Sun Y-S, Ji J-F. Diffusion kurtosis imaging in the prediction of poor responses of locally advanced gastric cancer to neoadjuvant chemotherapy. Eur J Radiol. 2020;128: 108974.

Article  Google Scholar 

Li Z, Zhang D, Dai Y, Dong J, Wu L, Li Y, Cheng Z, Ding Y, Liu Z. Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: a pilot study. Chin J Cancer Res. 2018;30(4):406.

Article  Google Scholar 

Lauridsen CA, Falletin E, Hansen ML, Law I, Federspiel B, Bæksgaard L, Svendsen LB, Nielsen MB. Computed Tomography (CT) Perfusion as an early prognostic marker for treatment response to neoadjuvant chemotherapy in gastroesophageal junction cancer and gastric cancer-a prospective study. PLoS ONE. 2014;9:1–10.

Google Scholar 

Virendra K, Yuhua G, Satrajit B. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30(9):1234–48.

Article  Google Scholar 

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. https://doi.org/10.1007/s00330-021-07962-2.

Article  PubMed  PubMed Central  Google Scholar 

Wang W, Peng Y, Feng X, Zhao Y, Seeruttun SR, Zhang J, Cheng Z, Li Y, Liu Z, Zhou Z. Development and validation of a computed tomography-based radiomics signature to predict response to neoadjuvant chemotherapy for locally advanced gastric cancer. JAMA Netw Open. 2021;4(8):e2121143–e2121143.

Article  Google Scholar 

Sun K-Y, Hu H-T, Chen S-L, Ye J-N, Li G-H, Chen L-D, Peng J-J, Feng S-T, Yuan Y-J, Hou X. CT-based radiomics scores predict response to neoadjuvant chemotherapy and survival in patients with gastric cancer. BMC Cancer. 2020;20:1–11.

CAS  Article  Google Scholar 

Kather JN, Krisam J, Charoentong P, Luedde T, Herpel E, Weis C-A, Gaiser T, Marx A, Valous NA, Ferber D. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med. 2019;16(1): e1002730.

Article  Google Scholar 

Peng H, Dong D, Fang M-J, Li L, Tang L-L, Chen L, Li W-F, Mao Y-P, Fan W, Liu L-Z. Prognostic value of deep learning PET/CT-based radiomics: potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma. Clin Cancer Res. 2019;25(14):4271–9.

CAS  Article  Google Scholar 

Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Vega JEV, Brat DJ, Cooper LA. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci. 2018;115(13):E2970–9.

CAS  Article  Google Scholar 

Jiang Y, Liang X, Wang W, Chen C, Yuan Q, Zhang X, Li N, Chen H, Yu J, Xie Y. Noninvasive prediction of occult peritoneal metastasis in gastric cancer using deep learning. JAMA Netw Open. 2021;4(1):e2032269–e2032269.

Article  Google Scholar 

Jin C, Jiang Y, Yu H, Wang W, Li B, Chen C, Yuan Q, Hu Y, Xu Y, Zhou Z. Deep learning analysis of the primary tumour and the prediction of lymph node metastases in gastric cancer. Br J Surg. 2021;108(5):542–9.

CAS  Article  Google Scholar 

Gao Y, Zhang Z-D, Li S, Guo Y-T, Wu Q-Y, Liu S-H, Yang S-J, Ding L, Zhao B-C, Li S. Deep neural network-assisted computed tomography diagnosis of metastatic lymph nodes from gastric cancer. Chin Med J. 2019;132(23):2804.

Article  Google Scholar 

Amin MBES, Greene F, et al. AJCC Cancer Staging Manual. 8th ed. New York: Springer; 2017.

Book  Google Scholar 

NCCN duideline. https://www.nccn.org/patientresources/patient-resources/guidelines-for-patients.

Jia F, Lei TA, Zyl C, Xtl A, Hfz B, Yss A, Jfj C. Diffusion kurtosis imaging in the prediction of poor responses of locally advanced gastric cancer to neoadjuvant chemotherapy—ScienceDirect. Eur J Radiol. 2020. https://doi.org/10.1016/j.ejrad.2020.108974.

Article  PubMed  Google Scholar 

Yang C, Jiang ZK, Liu LH, Zeng MS. Pre-treatment ADC image-based random forest classifier for identifying resistant rectal adenocarcinoma to neoadjuvant chemoradiotherapy. Int J Colorectal Dis. 2020. https://doi.org/10.1007/s00384-019-03455-3.

Article  PubMed  Google Scholar 

Zhou X, Yi Y, Liu Z, Cao W, Tian J. Radiomics-based pretherapeutic prediction of non-response to neoadjuvant therapy in locally advanced rectal cancer. Ann Surg Oncol. 2019. https://doi.org/10.1245/s10434-019-07300-3.

Article  PubMed  PubMed Central  Google Scholar 

Jiang Y, Jin C, Yu H, Wu J, Li R. Development and validation of a deep learning CT signature to predict survival and chemotherapy benefit in gastric cancer: a multicenter, retrospective study. Ann Surg. 2020. https://doi.org/10.1097/SLA.0000000000003778.

Article  PubMed  Google Scholar 

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016. pp 770–778

Dao T, Gu A, Ratner AJ, Smith V, Sa CD, Ré C. A Kernel Theory of Modern Data Augmentation. PMLR, 2019. pp 1528–1537

Pan SJ, Qiang Y. A Survey on Transfer Learning. IEEE Trans Knowl Data Eng. 2010;22(10):1345–59.

Article  Google Scholar 

Steiner B, Devito Z, Chintala S, Gross S, Paszke A, Massa F, Lerer A, Chanan G, Lin Z, Yang E. PyTorch: an imperative style, high-performance deep learning library. Neural information processing systems; 2019. p. 8026–37.

Google Scholar 

Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016. pp 2921–2929

Sauerbrei W, Boulesteix A-L, Binder H. Stability investigations of multivariable regression models derived from low-and high-dimensional data. J Biopharm Stat. 2011;21(6):1206–31.

Article  Google Scholar 

Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29–36.

CAS  Article  Google Scholar 

DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.

CAS  Article  Google Scholar 

Cheong J-H, Yang H-K, Kim H, Kim WH, Kim Y-W, Kook M-C, Park Y-K, Kim H-H, Lee HS, Lee KH. Predictive test for chemotherapy response in resectable gastric cancer: a multi-cohort, retrospective analysis. Lancet Oncol. 2018;19(5):629–38.

CAS  Article  Google Scholar 

Wu J, Li B, Sun X, Cao G, Rubin DL, Napel S, Ikeda DM, Kurian AW, Li R. Heterogeneous enhancement patterns of tumor-adjacent parenchyma at MR imaging are associated with dysregulated signaling pathways and poor survival in breast cancer. Radiology. 2017;285(2):401–13.

Article  Google Scholar 

Wu J, Cao G, Sun X, Lee J, Rubin DL, Napel S, Kurian AW, Daniel BL, Li R. Intratumoral spatial heterogeneity at perfusion MR imaging predicts recurrence-free survival in locally advanced breast cancer treated with neoadjuvant chemotherapy. Radiology. 2018;288(1):26–35.

Article  Google Scholar 

Feng F, Tian Y, Xu G, Liu Z, Liu S, Zheng G, Guo M, Lian X, Fan D, Zhang H. Diagnostic and prognostic value of CEA, CA19–9, AFP and CA125 for early gastric cancer. BMC Cancer. 2017;17(1):737.

Article  Google Scholar 

Chen XZ, Zhang WK, Yang K, Wang LL, Liu J, Wang L, Hu JK, Zhang B, Chen ZX, Chen JP. Correlation between serum CA724 and gastric cancer: multiple analyses based on Chinese population. Mol Biol Rep. 2012;39(9):9031–9.

CAS  Article  Google Scholar 

Yang AP, Liu J, Lei HY, Zhang QW, Zhao L, Yang GH. CA72–4 combined with CEA, \\ and CAl9–9 improves the sensitivity for the early diagnosis of gastric cancer. Clin Chim Acta. 2014. https://doi.org/10.1016/j.cca.2014.07.034.

Article  PubMed  Google Scholar 

Takekazu Y, Shunkichi K, Akira K, Koichi K, Takayoshi H, Norishige T, Masakazu M. Tumor markers CEA, CA19-9 and CA125 in monitoring of response to systemic chemotherapy in patients with advanced gastric cancer. Jpn J Clin Oncol. 1999;11:550–5.

Google Scholar 

Sun Z, Zhang N. Clinical evaluation of CEA, CA19-9, CA72-4 and CA125 in gastric cancer patients with neoadjuvant chemotherapy. World J Surg Oncol. 2014;12(1):397.

Article  Google Scholar