{
  "abstract": "Predicting response to induction chemotherapy (IC) and overall survival (OS) is critical for optimizing treatment in patients with locally advanced nasopharyngeal carcinoma (LANPC). This study aimed to develop and validate a multi-task deep learning model integrating pretreatment MRI and whole slide images (WSIs) to predict IC response and OS in LANPC. Pretreatment MRI and WSIs from 404 patients with LANPC were retrospectively collected to construct a multi-task model (MoEMIL) for the simultaneous prediction of early IC response and OS. In our cohort, no progressive disease cases were observed; therefore, the poor responder group consisted exclusively of patients with stable disease. MoEMIL employed multi-instance learning to process WSIs, PyRadiomics and a convolutional neural network (ResNet50) to extract MRI features, and fused multimodal features through a multi-gate mixture-of-experts architecture. Clustering-constrained attention multiple instance learning and gradient-weighted class activation mapping were applied for visualization and interpretation. MoEMIL effectively stratified patients into good and poor IC response groups, achieving areas under the curve of 0.917, 0.869, and 0.801 in the train, validation, and test sets, respectively. In the test set, MoEMIL significantly outperformed the deep learning radiomics model and TNM staging for both endpoints. Compared to the Pathomics model, MoEMIL achieved significant improvement for OS (P = 0.0049) and borderline improvement for IC response (P = 0.064). The model could stratify patients into high- and low-risk OS groups (P < 0.05). MoEMIL might be an adjunctive tool for early induction chemotherapy response prediction and prognostic stratification in LANPC. Author summary: We have developed a deep learning model that integrates two types of medical images, including magnetic resonance imaging (MRI) and digital pathological slices, to simultaneously predict response to induction chemotherapy and prognosis in patients with locally advanced nasopharyngeal carcinoma. Current treatment decisions primarily rely on traditional tumor staging (TNM), which often fails to comprehensively reflect the complexity of the disease. Our model, named MoEMIL, was trained and tested on data from 404 patients across two hospitals and consistently outperformed both single-model approaches and TNM staging methods. By identifying patients who exhibit poor response to induction chemotherapy or higher prognostic risk, our tool may assist clinicians in risk stratification and treatment planning, enabling intensified management for high-risk patients and avoiding unnecessary side effects for low-risk patients, but prospective validation is required before routine clinical adoption. Additionally, we visualize the model’s reasoning process through heat map generation, which highlights the image regions exerting the greatest influence on prediction outcomes. This work represents a step toward more precise treatment for nasopharyngeal carcinoma; however, larger-scale prospective studies are required before the model can be integrated into routine clinical practice.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, People's Republic of China",
    "Hunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, People's Republic of China",
    "Department of Health Service Center, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, People's Republic of China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 7,
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        0
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      "name": "Jing Hou"
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      "name": "Xiaochun Yi"
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        2
      ],
      "name": "Congrui Li"
    },
    {
      "name": "Junjian Li"
    },
    {
      "name": "Hui Cao"
    },
    {
      "name": "Qiang Lu"
    },
    {
      "name": "Xiaoping Yu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b4ypwkphvrww6.bib",
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  "doi": "10.1371/journal.pdig.0001378",
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
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      "2741|Radiomics and Machine Learning in Medical Imaging"
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      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
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    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Carcinoma/Nasopharyngeal carcinoma"
    ],
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      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Gastrointestinal tumors/Gastric cancer"
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      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Lung and intrathoracic tumors"
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      "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/Magnetic resonance imaging"
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  "pdf_url": "https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0001378&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/b4ypwkphvrww6.ris",
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  "subjects": [
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  "title": "Multi-task deep learning integrating pretreatment MRI and whole slide images predicts induction chemotherapy response and survival in locally advanced nasopharyngeal carcinoma",
  "title_text": "Multi-task deep learning integrating pretreatment MRI and whole slide images predicts induction chemotherapy response and survival in locally advanced nasopharyngeal carcinoma",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/b4ypwkphvrww6",
  "venue": "PLOS Digital Health",
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