Artificial neural network model enhancing the accuracy of clinical evaluation for high-risk population of lymph node metastasis in non-intestinal type early gastric cancer: a multicenter real-world study in China.
Jiamei Guo, Kecheng Zhang, Gang Ji, Wei Wang, Gang Li, Zhiqiang Liu, Zuli Yang, Zaisheng Ye, Yantao Tian, Tao Zhang, Xiangyu Wang, Kun Yang, Tong Zhou, Qi You, Yong Li, Peng Ren, Rupeng Zhang, Jingyu Deng
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引用次数: 0
Abstract
Background: Recent years have witnessed a proliferation of studies aimed at developing clinical models capable of predicting lymph node metastasis (LNM) in early gastric cancer (EGC), yet tools for prediction grounded in the Lauren classification remain scarce.
Methods: Data of 6468 patients diagnosed with EGC from fifteen Chinese high-volume cancer centers between January 2005 and December 2015 were retrospectively analyzed. Utilizing multivariate logistic regression analysis and the multilayer perceptron (MLP) prediction algorithm, a nomogram and an artificial neural network (ANN) model were developed and validated, respectively, for predicting the likelihood of LNM in non-intestinal EGC cases. The models' performances were evaluated and a comparative analysis of their parameters was undertaken. Subsequently, in-depth risk stratification analyses were performed around the two models.
Results: Non-intestinal type EGC demonstrated an elevated LNM rate and inferior prognosis compared to the intestinal type. Both nomogram and ANN model were developed and performed well in discrimination, calibration and clinical utility. Notably, the ANN model surpassed the nomogram in specificity (95.8% vs. 71.3%, P < 0.001), positive predictive value (PPV) (62.0% vs. 36.2%, P < 0.001) and overall accuracy (82.7% vs. 70.5%, P < 0.001). Patients with different risk strata derived from the nomogram, ANN model, and their combined application exhibited significantly different outcomes. The extent of lymph node dissection significantly influenced prognoses in high-risk patients identified by the combined model, whereas the anatomical location of metastatic lymph nodes did not.
Conclusion: The ANN model established in this study can screen the patients at high risk of LNM in non-intestinal type EGC more accurately. Considering the high extragastric LNM rate observed in the high-risk stratum, radical gastrectomy combined with D2 lymph node dissection is strongly recommended.
背景:近年来,有大量研究旨在开发能够预测早期胃癌(EGC)淋巴结转移(LNM)的临床模型,但基于Lauren分类的预测工具仍然很少。方法:回顾性分析2005年1月至2015年12月来自中国15家肿瘤中心的6468例确诊为EGC的患者资料。利用多元逻辑回归分析和多层感知器(MLP)预测算法,分别建立了nomogram模型和人工神经网络(ANN)模型,并对其进行了验证,用于预测非肠道EGC病例发生LNM的可能性。对模型的性能进行了评价,并对其参数进行了对比分析。随后,围绕这两个模型进行了深入的风险分层分析。结果:与肠型相比,非肠型EGC具有较高的LNM发生率和较差的预后。建立了nomogram和ANN模型,在鉴别、校正和临床应用方面均取得了良好的效果。值得注意的是,ANN模型在特异性(95.8% vs. 71.3%, P < 0.001)、阳性预测值(PPV) (62.0% vs. 36.2%, P < 0.001)和总体准确性(82.7% vs. 70.5%, P < 0.001)方面均优于nomogram。由nomogram、ANN model及其联合应用得出的不同风险层患者的预后有显著差异。淋巴结清扫的程度显著影响联合模型确定的高危患者的预后,而转移性淋巴结的解剖位置则没有影响。结论:本研究建立的人工神经网络模型可以更准确地筛选非肠型EGC中LNM高危患者。考虑到高危层胃外淋巴结转移率高,强烈推荐根治性胃切除术联合D2淋巴结清扫术。
期刊介绍:
The International Journal of Surgery (IJS) has a broad scope, encompassing all surgical specialties. Its primary objective is to facilitate the exchange of crucial ideas and lines of thought between and across these specialties.By doing so, the journal aims to counter the growing trend of increasing sub-specialization, which can result in "tunnel-vision" and the isolation of significant surgical advancements within specific specialties.