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Purpose <p>In cancer therapy, tumor cell heterogeneity and dynamics influence gene sequencing and immunohistochemical staining. Importantly, patients treated with epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) have not demonstrated a favorable long-term prognosis. Therefore, this study proposes an integrated framework for artificial intelligence (IFAI) to explore new molecular detection methods.</p> Materials and methods <p>Our study integrated data from 506 non-small cell lung cancer (NSCLC) patients across three institutions in China and the USA. To fuse radiomics scores and deep network features from both tumors and surrounding tissues, we developed the IFAI with an attention-based DenseNet 121 as the backbone network. We also explored the synergy between IFAI and clinical factors (IFAI-C). Additionally, we gained further insights into the biological mechanisms of IFAI by analyzing patient RNA sequencing data.</p> Results <p>In independent test data, the IFAI-C demonstrated notable predictive performance, boasting an area under the curve of 0.912 for EGFR, 0.911 for exon 19 deletion (19Del), 0.905 for exon 21 mutation (L858R), 0.911 for T790M, and 0.904 for programmed cell death protein 1 (PD-1) or its ligand 1 (PD-L1). This capability is a crucial complement to traditional methods like gene sequencing and immunohistochemistry. Our analysis revealed that radiomics scores and deep network features in IFAI were significantly associated with EGFR genotypes, drug resistance mutations, and immune molecule expression. Furthermore, these features displayed robust connections with multiple genotypes associated with drug resistance and cancer progression mechanisms.</p> Conclusion <p>IFAI-C introduces a novel method with performance advantages, accompanied by biological analyses demonstrating the extraction of genotypic and immunomolecular information from both tumors and surrounding tissues. This discovery holds potential value in guiding therapeutic decisions for lung cancer.</p>

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Mining tumor and surrounding tissue information using artificial intelligence to predict responses to EGFR-targeted therapies and immunotherapy in lung cancer: a multicenter attribution analysis

  • 期刊论文

  • 发表日期：

    2025年10月4日

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  • Xingping Zhang,
  • Yuxin He,
  • TianXiang Rao,
  • Xingting Qiu,
  • Qingwen Lai,
  • Yanchun Zhang,
  • Guijuan Zhang

摘要

Purpose

In cancer therapy, tumor cell heterogeneity and dynamics influence gene sequencing and immunohistochemical staining. Importantly, patients treated with epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) have not demonstrated a favorable long-term prognosis. Therefore, this study proposes an integrated framework for artificial intelligence (IFAI) to explore new molecular detection methods.

Materials and methods

Our study integrated data from 506 non-small cell lung cancer (NSCLC) patients across three institutions in China and the USA. To fuse radiomics scores and deep network features from both tumors and surrounding tissues, we developed the IFAI with an attention-based DenseNet 121 as the backbone network. We also explored the synergy between IFAI and clinical factors (IFAI-C). Additionally, we gained further insights into the biological mechanisms of IFAI by analyzing patient RNA sequencing data.

Results

In independent test data, the IFAI-C demonstrated notable predictive performance, boasting an area under the curve of 0.912 for EGFR, 0.911 for exon 19 deletion (19Del), 0.905 for exon 21 mutation (L858R), 0.911 for T790M, and 0.904 for programmed cell death protein 1 (PD-1) or its ligand 1 (PD-L1). This capability is a crucial complement to traditional methods like gene sequencing and immunohistochemistry. Our analysis revealed that radiomics scores and deep network features in IFAI were significantly associated with EGFR genotypes, drug resistance mutations, and immune molecule expression. Furthermore, these features displayed robust connections with multiple genotypes associated with drug resistance and cancer progression mechanisms.

Conclusion

IFAI-C introduces a novel method with performance advantages, accompanied by biological analyses demonstrating the extraction of genotypic and immunomolecular information from both tumors and surrounding tissues. This discovery holds potential value in guiding therapeutic decisions for lung cancer.