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一种用于个体化预测胃癌血管侵犯的新型列线图。

A Novel Nomogram for Individually Predicting of Vascular Invasion in Gastric Cancer.

机构信息

Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China.

Guangxi Clinical Research Center for Colorectal Cancer, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China.

出版信息

Technol Cancer Res Treat. 2021 Jan-Dec;20:15330338211004924. doi: 10.1177/15330338211004924.

Abstract

PURPOSE

Vascular invasion (VI) is associated with recurrence and is an indicator of poor prognosis in gastric cancer (GC). Pre-operative identification of VI may guide the selection of the optimal surgical approach and assess the requirement for neoadjuvant therapy.

METHODS

A total of 271 patients were retrospectively collected and randomly allocated into the training and validation datasets. The least absolute shrinkage and selection operator regression model was used to select potentially relevant features, and multivariable logistic regression analysis was used to develop the nomogram.

RESULTS

The nomogram consisted of pre-operative serum complement C3 levels, duration of symptoms, pre-operative computed tomography stage, abdominal distension and undifferentiated carcinoma. The nomogram provided good calibration for both the training and the validation set, with area under the curve values of 0.792 and 0.774. Decision curve analysis revealed that the nomogram was clinically useful.

CONCLUSION

The present study constructed a nomogram for the pre-operative prediction of VI in patients with GC. The nomogram may aid the identification of high-risk patients and aid the optimization of pre-operative decision-making.

摘要

目的

血管侵犯(VI)与复发相关,是胃癌(GC)预后不良的指标。术前识别 VI 可指导选择最佳手术方式,并评估新辅助治疗的需求。

方法

共回顾性收集了 271 例患者,并将其随机分配到训练集和验证集中。最小绝对收缩和选择算子回归模型用于选择潜在相关特征,多变量逻辑回归分析用于建立列线图。

结果

该列线图由术前血清补体 C3 水平、症状持续时间、术前 CT 分期、腹胀和未分化癌组成。该列线图在训练集和验证集上均具有良好的校准度,曲线下面积值分别为 0.792 和 0.774。决策曲线分析表明该列线图具有临床实用性。

结论

本研究构建了一个用于预测 GC 患者术前 VI 的列线图。该列线图有助于识别高危患者,并有助于优化术前决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfaa/8111553/fafde47f5f3b/10.1177_15330338211004924-fig1.jpg

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