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Digital histopathological images of biopsy predict response to neoadjuvant chemotherapy for locally advanced gastric cancer

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Abstract

Background

Neoadjuvant chemotherapy (NAC) has been recognized as an effective therapeutic option for locally advanced gastric cancer as it is expected to reduce tumor size, increase the resection rate, and improve overall survival. However, for patients who are not responsive to NAC, the best operation timing may be missed together with suffering from side effects. Therefore, it is paramount to differentiate potential respondents from non-respondents. Histopathological images contain rich and complex data that can be exploited to study cancers. We assessed the ability of a novel deep learning (DL)-based biomarker to predict pathological responses from images of hematoxylin and eosin (H&E)-stained tissue.

Methods

In this multicentre observational study, H&E-stained biopsy sections of patients with gastric cancer were collected from four hospitals. All patients underwent NAC followed by gastrectomy. The Becker tumor regression grading (TRG) system was used to evaluate the pathologic chemotherapy response. Based on H&E-stained slides of biopsies, DL methods (Inception-V3, Xception, EfficientNet-B5, and ensemble CRSNet models) were employed to predict the pathological response by scoring the tumor tissue to obtain a histopathological biomarker, the chemotherapy response score (CRS). The predictive performance of the CRSNet was evaluated.

Results

69,564 patches from 230 whole-slide images of 213 patients with gastric cancer were obtained in this study. Based on the F1 score and area under the curve (AUC), an optimal model was finally chosen, named the CRSNet model. Using the ensemble CRSNet model, the response score derived from H&E staining images reached an AUC of 0.936 in the internal test cohort and 0.923 in the external validation cohort for predicting pathological response. The CRS of major responders was significantly higher than that of minor responders in both internal and external test cohorts (both p < 0.001).

Conclusion

In this study, the proposed DL-based biomarker (CRSNet model) derived from histopathological images of the biopsy showed potential as a clinical aid for predicting the response to NAC in patients with locally advanced GC. Therefore, the CRSNet model provides a novel tool for the individualized management of locally advanced gastric cancer.

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Data availability

Data is available under request.

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Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 81602049 and 81802342), the Natural Science Foundation of Guangdong Province, China (Grant No. 2018A030313978), Shenzhen Science and Technology Program (No. JCYJ20220530145001002), and the Kelin New Star of the First Affiliated Hospital of Sun Yat‐Sen University (Grant Nos. R08011 and R08010).

Author information

Author notes

  1. Zhihao Zhou, Yong Ren and Zhimei Zhang are co-first authors.

Authors and Affiliations

  1. Department of Gastrointestinal Surgery, First Affiliated Hospital of Sun Yat-sen University, Zhongshan 2nd Street, No. 58, Guangzhou, 510080, Guangdong, China

    Zhihao Zhou, Zhixiong Wang & Guanghua Li

  2. Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Pazhou Lab, No.70 Yuean Road, Haizhu District, Guangzhou, Guangdong, China

    Yong Ren

  3. Department of Pathology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China

    Zhimei Zhang

  4. Department of Gastrointestinal Surgery, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, Guangdong, China

    Tianpei Guan

  5. Department of Pathology, Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China

    Wei Chen

  6. Department of Gastrointestinal Surgery, First People’s Hospital of Foshan, Foshan, Guangdong, China

    Tedong Luo

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  1. Zhihao Zhou
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Correspondence to Guanghua Li.

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Zhou, Z., Ren, Y., Zhang, Z. et al. Digital histopathological images of biopsy predict response to neoadjuvant chemotherapy for locally advanced gastric cancer. Gastric Cancer 26, 734–742 (2023). https://doi.org/10.1007/s10120-023-01407-z

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  • DOI: https://doi.org/10.1007/s10120-023-01407-z

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