The value of artificial intelligence-assisted technology in HER2 assessmentof gastric cancer patients receiving neoadjuvant chemotherapy

Yang LIU; Yiyang HU; Yueping LIU; Shuyao NIU; Pingan DING; Yuan TIAN; Honghai GUO; Peigang YANG; Ze ZHANG; Tao ZHENG; Bibo TAN; Liqiao FAN; Yong LI; Qun ZHAO

doi:10.19401/j.cnki.1007-3639.2023.04.008

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The value of artificial intelligence-assisted technology in HER2 assessmentof gastric cancer patients receiving neoadjuvant chemotherapy

Article | 更新时间：2025-12-31

- The value of artificial intelligence-assisted technology in HER2 assessmentof gastric cancer patients receiving neoadjuvant chemotherapy
- China Oncology Vol. 33, Issue 4, Pages: 377-387(2023)
- 作者机构：
  
  1. 河北医科大学第四医院外三科，河北石家庄 050011
  2. 河北医科大学第四医院病理科，河北石家庄 050011
- 作者简介：
  
  ZHAO Qun.
- 基金信息：
- DOI：10.19401/j.cnki.1007-3639.2023.04.008
  CLC： R735.2
- Received：27 May 2022，
  
  Revised：2023-03-13，
  
  Published：30 April 2023
- 稿件说明：
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Yang LIU, Yiyang HU, Yueping LIU, et al. The value of artificial intelligence-assisted technology in HER2 assessmentof gastric cancer patients receiving neoadjuvant chemotherapy[J]. China Oncology, 2023, 33(4): 377-387.
DOI：

Yang LIU, Yiyang HU, Yueping LIU, et al. The value of artificial intelligence-assisted technology in HER2 assessmentof gastric cancer patients receiving neoadjuvant chemotherapy[J]. China Oncology, 2023, 33(4): 377-387. DOI： 10.19401/j.cnki.1007-3639.2023.04.008.

摘要

背景与目的：

胃癌存在较高的肿瘤异质性，部分胃癌患者新辅助化疗（neoadjuvant chemotherapy，NAC）前后人表皮生长因子受体2（human epidermal growth factor receptor 2，HER2）表达明显差异化。为了进一步研究HER2表达的异质性，并减少人为评估的视觉误差，本研究采用人工智能（artificial intelligence，AI）辅助显微镜对胃癌患者NAC前后HER2表达进行评估，评价AI的实用性及可行性，并研究NAC对胃癌患者HER2表达的影响，为后续治疗提供参考。

方法：

收集来自以河北医科大学第四医院为主的多中心、前瞻性、随机对照Ⅲ期临床试验（NCT01516944）中接受NAC的397例胃癌患者临床资料，NAC前胃镜咬检及NAC后手术标本HER2分别采用传统视觉评估及AI辅助显微镜评估，并设定3名高年资病理科医师综合评估结果为金标准。分析两种评估结果与金标准的一致性，探讨影响NAC前后HER2表达变化的临床病理学特征及表达改变对患者预后的影响。

结果：

相较于视觉评估，AI评估结果与金标准的一致性更佳（0.766

0.853，0.773

0.876）。通过AI评估，97例（24.43%）患者的HER2在NAC后出现表达下调，而27例（6.80%）患者出现HER2表达上调。单因素分析显示HER2表达改变与淋巴结转移（ypN）（

= 0.019）、肿瘤退缩分级（

= 0.003）明显相关。肿瘤退缩不佳是影响HER2表达上调的独立危险因素（

= 0.032）。预后方面，病理学完全缓解（pathologic complete response，pCR）患者5年总生存（overall survival，OS）率、无病生存（disease-free survival，DFS）率均明显优于非pCR患者（92.9%

42.5%，

= 0.002；92.9%

36.0%，

= 0.001）。而在非pCR患者中，NAC后HER2表达下调者5年OS率和DFS率均优于上调者（56.7%

30.5%，

0.001；56.1%

23.0%，

0.001）；且在非pCR的HER2 2 + /3 + 患者中，NAC后HER2表达下调者5年OS率和DFS率均优于HER2表达无改变者（56.7%

33.5%，

= 0.003；56.1%

32.0%，

= 0.002）。

结论：

AI技术应用于胃癌NAC患者HER2评估能减少人为视觉测量误差，可以成为高效、精准评估HER2表达的有力工具。NAC后pCR及HER2表达下调的胃癌患者具有更好的远期生存。若患者HER2表达上调可能提示预后不佳，临床上更应关注此类患者出现复发转移情况，并及时调整治疗方案。

Abstract

Background and purpose:

Tumor heterogeneity exists in gastric cancer. The expression of human epidermal growth factor receptor 2 (HER2) is significantly different in some gastric cancer patients before and af

ter neoadjuvant chemotherapy (NAC). In order to further study the heterogeneity of HER2 expression and reduce the visual error of human interpretation

we used phase Ⅲ artificial intelligence (AI) to assess the HER2 status of gastric cancer patients pre-NAC and post-NAC to evaluate the practicability and feasibility of AI

and studied the effect of NAC on HER2 expression in gastric cancer patients

so as to provide reference for subsequent treatment.

Methods:

Clinical data of 397 gastric cancer patients receiving NAC were collected from a multicenter

prospective

randomized controlled phase Ⅲ clinical trial (NCT01516944) mainly based on on the Fourth Hospital of Hebei Medical University. HER2 expression in samples of pre-NAC endoscopic biopsies and post-NAC surgical specimens was first assessed visually by 2 pathologists using optical microscope followed by AI-assisted microscope respectively. The comprehensive results of three senior pathologists were set as the gold standard. The consistency between the two assessment results and the gold standard was analyzed

and the clinicopathological features affecting the expression changes of HER2 pre- and post-NAC and the effect of HER2 change on prognosis were also explored.

Results:

Compared with visual assessment

the consistency between AI assessment results and gold standard was better (0.766

0.853

0.773

0.876). HER2 expression assessed by AI was down-regulated in 97 patients (24.43%) post-NAC and up-regulated in 27 patients (6.80%). Change of HER2 expression was significantly correlated with lymph node metastasis (ypN

=0.019) and tumor regression grade (

=0.003). Poor tumor regression was an independent risk factor for the upregulation of HER2 (

=0.032). The 5-year overall survival (OS) rate and disease-free survival (DFS) rate of pathologic complete response (pCR) patients were significantly better compared with non-pCR patients (92.9%

42.5%

=0.002; 92.9%

36.0%

=0.001). In non-pCR patients

the 5-year OS and DFS rates of HER2 down-regulated patients post-NAC were better compared with HER2 up-regulated patients(56.7%

30.5%

0.001; 56.1%

23.0%

0.001). And for those HER2 2+/3+ non-pCR patients

the 5-year OS and DFS rates of HER2 down-regulated patients post-NAC were better compared with HER2 unchanged patients (56.7%

33.5%

=0.003; 56.1%

32.0%

=0.002).

Conclusion:

The application of AI technology in HER2 assessment of NAC patients with gastric cancer can reduce visual measurement error

and AI could be a powerful tool to assess HER2 expression efficiently and accurately. Gastric cancer patients with pCR and down-regulated HER2 post-NAC are more likely to achieve better long-term survival. If upregulation of HER2 status may suggest poor prognosis

attention should be paid closely to the recurrence and metastasis of such patients

and the treatment should be adjusted in time.

关键词

Keywords

references

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