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Multimodal Learning for Lung Cancer Diagnosis and Management: A Deep Learning Pipeline for Classification, TNM Staging, and Treatment Protocol Generation

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Resumo:Accurate lung cancer diagnosis and staging are critical for personalized treatment and improved patient outcomes. Traditional diagnostic methods, such as manual image examination, are prone to variability and inefficiency. Recent advancements in Deep Learning (DL) have demonstrated potential in automating lung cancer classification and staging, thereby enhancing diagnostic accuracy and efficiency. However, existing solutions often address only isolated aspects of the diagnostic process, such as tumor detection, without offering a unified system for multi-target classification that integrates clinical tools for both clinicians and patients. This study presents a unified framework for lung cancer analysis that combines medical imaging, clinical data, and Large Language Models (LLMs) to support three key tasks: tumor type classification, TNM staging, and automated treatment protocol recommendation. Image-based classification was performed using YOLOv8n, trained on two CT datasets, achieving a maximum mean average precision (mAP50) of 0.418 and an F1 score of 0.44. TNM staging was addressed through a multimodal classifier, combining the ResNet50 model with a multilayer perceptron, which fused imaging and demographic inputs. This approach yielded an average F1 score of 0.389, with the M component showing the strongest performance. For treatment generation, a Retrieval-Augmented Generation (RAG) approach was employed, combining clinical prompts with relevant documents to produce personalized protocols using the Gemini 2.0 Flash LLM. The best-performing configuration achieved a stage match of 0.54 and a BERTScore of 0.83, along with high contextual fidelity across RAGAS metrics (Faithfulness: 0.68, Answer Relevancy: 0.81, Context Precision: 0.99, and Context Recall: 0.93). This framework demonstrates the potential to support both diagnosis and treatment planning within a single integrated system, contributing to more personalized and effective clinical decision-making in oncology.
Autores principais:Silva, Catarina Costa Pereira Nascimento da
Assunto:Deep Learning (DL) Large Language Models (LLMs) Lung Cancer Retrieval-Augmented Generation (RAG) Treatment Recommendation
Ano:2025
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

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