{
  "abstract": "Pathological complete response (pCR) and major pathological response (MPR) are early measures of treatment efficacy with prognostic relevance in patients with non-small cell lung cancer (NSCLC) receiving neoadjuvant chemoimmunotherapy. Individual biomarkers such as programmed cell death-ligand 1 (PD-L1) and tumor mutational burden (TMB), and conventional imaging interpretation, incompletely characterize treatment sensitivity. Integrating clinical biomarkers with radiomics may improve prediction by combining information on host factors and tumor biology with quantitative imaging measures of tumor heterogeneity. We aimed to develop and validate a model predicting pathological response. We retrospectively collected fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images and clinical data from 320 patients with NSCLC at Guangdong Provincial People's Hospital who received at least two cycles of chemoimmunotherapy followed by surgical resection and had postoperative pathological response assessments. Patients were randomly assigned to training (n=256) and validation (n=64) cohorts at an 8:2 ratio. A total of 1,834 CT and 179 PET radiomics features were extracted and integrated with demographic and tumor metabolic parameters, PD-L1 expression, TMB, and peripheral blood biomarkers. Following standardization and within-modality Pearson correlation filtering, features were selected using recursive feature elimination (RFE). A modality-aware attention model was developed to generate patient-specific scores for predicting MPR, including pCR. The model's performance was further validated in an external cohort of 31 patients from two independent hospitals. A total of 351 patients were included: 256 in the training cohort (160 responders and 96 non-responders), 64 in the internal testing cohort (35 responders and 29 non-responders), and 31 in the preliminary external test cohort. Our study proposes a modality-aware attention model, which was constructed upon RFE feature engineering technique. Our model integrates diverse data sources, including nine clinical characteristics, nine CT features, and nine PET features. Our model achieved an area under the receiver operating characteristic curve (AUC) of 0.7467 [95% confidence interval (CI): 0.6826 to 0.8159] in the training set. In the external independent validation cohort, the AUC was 0.7017 (95% CI: 0.5126 to 0.8715), with a sensitivity of 82% and a specificity of 86%. Compared to single-modality models based on clinical features (AUC =0.6931; 95% CI: 0.6232 to 0.7604), the modality-aware attention model significantly improved accuracy. The multimodal model showed potential for predicting MPR, including pCR, in patients with NSCLC receiving chemoimmunotherapy. Larger prospective multicenter studies are needed before clinical implementation.",
  "abstract_shown": true,
  "affiliations": [
    "Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.",
    "School of Artificial Intelligence, Beihang University, Beijing, China.",
    "Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.",
    "Department of Thoracic Surgery, The Third People's Hospital of Chengdu, Chengdu, China.",
    "School of Medicine, South China University of Technology, Guangzhou, China.",
    "PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.",
    "Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ao Li",
      "orcid": "0009-0007-5502-2193"
    },
    {
      "affs": [
        1
      ],
      "name": "Zi-Hao Mao",
      "orcid": "0009-0002-9703-4831"
    },
    {
      "affs": [
        2
      ],
      "name": "Hai-Long Wang"
    },
    {
      "affs": [
        3
      ],
      "name": "Yuan Wang"
    },
    {
      "affs": [
        0,
        4
      ],
      "name": "Qi-Hui Fu"
    },
    {
      "affs": [
        0
      ],
      "name": "Wen-Chao Feng"
    },
    {
      "affs": [
        5
      ],
      "name": "Yang Chen"
    },
    {
      "affs": [
        5
      ],
      "name": "Yin-Ting Hu"
    },
    {
      "affs": [
        6
      ],
      "name": "Zhi-Hua Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Wen-Zhao Zhong"
    },
    {
      "affs": [
        5
      ],
      "name": "Lei Jiang",
      "orcid": "0000-0002-9479-132X"
    },
    {
      "affs": [
        0
      ],
      "name": "Ben-Yuan Jiang",
      "orcid": "0009-0004-3998-5895"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3k2hzkb8am9zz.bib",
  "cited_by": 0,
  "doi": "10.21037/tlcr-2026-0739",
  "fulltext_url": "https://europepmc.org/articles/PMC13743465",
  "id": "3k2hzkb8am9zz",
  "issns": [
    "2218-6751",
    "2226-4477"
  ],
  "landing_url": "https://europepmc.org/article/MED/42999401",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7223918886",
  "pdf_url": "https://europepmc.org/articles/PMC13743465?pdf=render",
  "pmcid": "PMC13743465",
  "pmid": "42999401",
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3k2hzkb8am9zz.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T15:20:34Z",
      "native_id": "MED:42999401",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42999401"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Development and validation of a deep learning model integrating ¹⁸F-FDG PET/CT radiomics and clinical features to predict major pathological response in non-small cell lung cancer patients receiving chemoimmunotherapy.",
  "title_text": "Development and validation of a deep learning model integrating ¹⁸F-FDG PET/CT radiomics and clinical features to predict major pathological response in non-small cell lung cancer patients receiving chemoimmunotherapy.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T15:20:34Z",
  "url": "https://research.draconitic.com/paper/3k2hzkb8am9zz",
  "venue": "Translational lung cancer research",
  "versions": []
}