Prediction Model of Tumor Regression Grade for Advanced Gastric Cancer After Preoperative Chemotherapy
| Autor: | Zichen Hua, Runhua Feng, Lingquan Wang, Ming-Min Chen, Changyu He, Sheng Lu, Xuexin Yao, Wei Xu, Yanan Zheng, Qianchen Ma, Zhongyin Yang, Chao Yan, Zhenggang Zhu, Zhentian Ni, Min Yan, Zhenglun Zhu, Wentao Liu, Chen Li |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Oncology
medicine.medical_specialty Cancer Research Multivariate analysis medicine.medical_treatment tumor regression grade survival lcsh:RC254-282 03 medical and health sciences 0302 clinical medicine Internal medicine medicine Preoperative chemotherapy Pathological Lymph node advanced gastric cancer Original Research Tumor Regression Grade Chemotherapy preoperative chemotherapy business.industry medicine.disease lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens Primary tumor prediction model medicine.anatomical_structure 030220 oncology & carcinogenesis Cohort 030211 gastroenterology & hepatology business |
| Zdroj: | Frontiers in Oncology, Vol 11 (2021) Frontiers in Oncology |
| DOI: | 10.3389/fonc.2021.607640/full |
| Popis: | BackgroundPreoperative chemotherapy (PCT) has been considered an important treatment for advanced gastric cancer (AGC). The tumor regression grade (TRG) system is an effective tool for the assessment of patient responses to PCT. Pathological complete response (TRG = 0) of the primary tumor is an excellent predictor of better prognosis. However, which patients could achieve pathological complete response (TRG = 0) after chemotherapy is still unknown. The study aimed to find predictors of TRG = 0 in AGC.MethodsA total of 304 patients with advanced gastric cancer from July 2009 to November 2018 were enrolled retrospectively. All patients were randomly assigned (2:1) to training and internal validation groups. In addition, 124 AGC patients receiving PCT from December 2018 to June 2020 were included prospectively in the external validation cohort. A prediction model for TRG = 0 was established based on four predictors in the training group and was validated in the internal and external validation groups.ResultsThrough univariate and multivariate analyses, we found that CA199, CA724, tumor differentiation and short axis of the largest regional lymph node (LNmax) were independent predictors of TRG = 0. Based on the four predictors, we established a prediction model for TRG = 0. The AUC values of the prediction model in the training, internal and external validation groups were 0.84, 0.73 and 0.82, respectively.ConclusionsWe found that CA199, CA724, tumor differentiation and LNmax were associated with pathological response in advanced gastric cancer. The prediction model could provide guidance for clinical work. |
| Databáze: | OpenAIRE |
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