Artificial Intelligence and Machine Learning in Lung Cancer: Advances in Imaging, Detection, and Prognosis

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Lung cancer remains a leading cause of cancer mortality worldwide, necessitating advancements in early detection, staging, and treatment.
• Artificial intelligence (AI) and machine learning (ML) are emerging as transformative tools in oncology.
• These technologies offer potential for automated diagnosis, staging, and prognostic evaluation in lung cancer care.

Purpose of the Study:

• To provide a narrative review of current AI and ML applications across the lung cancer care continuum.
• To synthesize recent findings on AI-driven imaging, detection, and prognostic modeling in lung cancer.
• To highlight the potential of AI and ML in revolutionizing lung cancer treatment.

Main Methods:

• A comprehensive literature search was performed across major scientific databases.
• Peer-reviewed studies focusing on AI in lung cancer imaging, detection, and prognostic modeling were identified.
• Studies were thematically categorized into detection/screening, staging/diagnosis, and risk prediction/prognosis.

Main Results:

• Convolutional neural networks (CNNs) demonstrate high sensitivity and specificity for lung nodule detection, segmentation, and reducing false positives.
• AI algorithms consistently enhance the accuracy of non-small-cell lung cancer (NSCLC) staging, lymph node assessment, and malignancy classification, often matching or surpassing radiologist performance.
• Radiomics and multimodal AI models integrating imaging and clinical data show significant promise for predicting treatment outcomes and survival rates.

Conclusions:

• Despite significant progress, challenges remain in data heterogeneity, interpretability, repeatability, and clinical acceptance of AI tools.
• Future efforts should prioritize standardized datasets, ethical AI implementation, and transparent model evaluation.
• AI and ML hold revolutionary potential for intelligent, personalized, and real-time lung cancer treatment, bridging computational innovation with precision oncology.