Trends in Immunotherapy

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Multimodal Deep Learning Framework for Decoding Treatment Response in NSCLC: Biomarker Discovery for Immune Checkpoint Inhibitors

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https://doi.org/10.54963/ti.v9i4.1403
Karegoudra, S., Ramasamy, V., Rajiv, S., Radhakrishnan, S., Makarand Jadhav, H., & Sudha Rani, T. (2025). Multimodal Deep Learning Framework for Decoding Treatment Response in NSCLC: Biomarker Discovery for Immune Checkpoint Inhibitors. Trends in Immunotherapy, 9(4), 260–283. https://doi.org/10.54963/ti.v9i4.1403
Volume 9 Issue 4 (2025)
Copyright (c) 2025 Shilpa Karegoudra, Viswanathan Ramasamy, S Rajiv, S. Radhakrishnan, Hemlata Makarand Jadhav, Tirukoti Sudha Rani
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Authors

  • Shilpa Karegoudra

    Department of Computer Science and Engineering, NMAM Institute of Technology (NMAMIT), Karkala, Karnataka 574110, India
  • Viswanathan Ramasamy

    Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh 522502, India
  • S Rajiv

    Department of Information Technology, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu 600062, India
  • S. Radhakrishnan

    Department of CSE‑AI, KKR & KSR Institute of Technology and Sciences, Vinjanampadu, Guntur 522017, India
  • Hemlata Makarand Jadhav

    Department of E&TC, Marathwada Mitra Mandal’s College of Engineering, Pune 411052, India
  • Tirukoti Sudha Rani

    Department of Computer Science & Engineering, Aditya University, Surampalem 533437, India

Received: 14 July 2025; Revised: 21 July 2025; Accepted: 6 August 2025; Published: 11 December 2025

Non-small cell lung cancer (NSCLC) remains one of the leading causes of cancer-related mortality worldwide, with therapeutic progress often limited by late-stage diagnosis and the variable effectiveness of existing treatments. Immune checkpoint inhibitors (ICIs) have emerged as a promising therapeutic approach, but their clinical success depends critically on accurate biomarker-based patient stratification. Current strategies for integrating biomarkers, however, remain fragmented and lack the robustness needed for reliable prediction of treatment outcomes. To address this gap, we propose a novel multimodal deep learning framework that integrates CT images, PET/CT scans, and curated biomarker profiles to improve prediction of ICI treatment response in NSCLC patients. Our model employs a pre-trained Xception encoder for advanced image feature extraction, an Encoder Attention Network for semantic representation learning, and a DenseNet-inspired metadata processor for structured biomarker data. Multimodal features are fused through a Block Dense Attention Convolutional Module with Self-Attention Multi-Head (BDAC-SAMH) mechanism, enabling richer interactions across modalities. Experimental evaluation demonstrates that our framework achieves 94.3% accuracy, 92.1% sensitivity, and 93.5% specificity, significantly outperforming conventional CNN-based unimodal methods. Importantly, the proposed system improves prediction of key ICI biomarkers, including PD-L1, TMB, and MSI, by 15.6% compared to unimodal baselines while revealing novel biomarker interactions. This highlights its potential to guide personalized immunotherapy strategies in NSCLC.

Keywords:

Multimodal Deep Learning; NSCLC; Immune Checkpoint Inhibitors; Biomarker Discovery; Lung Cancer Diagnosis

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