ASO Author Reflections: Deep-Learning Radiomics Nomogram Based on Enhanced CT to Predict the Effect of Neoadjuvant Chemotherapy on Metastatic Lymph Nodes in Locally Advanced Gastric Cancer
Author:
Funder
Natural Science Foundation of Shandong Province
Publisher
Springer Science and Business Media LLC
Subject
Oncology,Surgery
Link
https://link.springer.com/content/pdf/10.1245/s10434-023-14508-x.pdf
Reference5 articles.
1. Li Z, Zhang D, Dai Y, et al. Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: a pilot study. Chin J Cancer Res. 2018;30:406.
2. Wang W, Peng Y, Feng X, et al. 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:e2121143.
3. Cui Y, Zhang J, Li Z, et al. A CT-based deep-learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: a multicenter cohort study. Eclinicalmedicine. 2022. https://doi.org/10.1016/j.eclinm.2022.101348.
4. Sada YH, Smaglo BG, Tan JC, Cao HST, Musher BL, Massarweh NN. Prognostic value of nodal response after preoperative treatment of gastric adenocarcinoma. J Natl Comp Cancer Netw. 2019;17:161–8.
5. Zhong H, Wang T, Hou M, et al. Deep-learning radiomics nomogram based on enhanced CT to predict the response of neoadjuvant chemotherapy on metastatic lymph nodes in locally advanced gastric cancer. Ann Surg Oncol. 2023. https://doi.org/10.1245/s10434-023-14424-0.
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