Diagnostic Accuracy of Artificial Intelligence Models for Differentiation of Squamous Cell Carcinoma and Adenocarcinoma of Lung—A Systematic Review

TL;DR

This study reviews how AI models can accurately distinguish between two types of lung cancer using imaging data.

Contribution

The study systematically evaluates the diagnostic accuracy of ML and deep learning models for differentiating lung SCC and ADC.

Findings

Deep learning models achieved 67–97% accuracy in differentiating lung SCC and ADC.

Machine learning models showed 75–87% accuracy in the same task.

Radiomic features improved diagnostic precision when combined with clinical data.

Abstract

Background/Objectives: Lung cancer remains the leading cause of cancer-related deaths worldwide, with Non-Small Cell Lung Cancer (NSCLC) accounting for the majority of cases, primarily Squamous Cell Carcinoma (SCC) and Adenocarcinoma (ADC). The aim of this systematic review is to summarise and critically appraise the performance of machine learning (ML)-based radiomics models in the differential diagnosis and overall survival analysis for lung SCC and ADC. Methods: PRISMA standards were followed in conducting the review. The quality of the studies was assessed using the Radiomics quality score (RQS) tool. Results: A total of 11 studies were included, demonstrating that deep learning and radiomics-based machine learning models significantly improve the non-invasive classification of lung squamous cell carcinoma and adenocarcinoma. Deep learning systems achieved an accuracy of 67–97%, and machine learning models showed an accuracy of 75–87%. The integration of radiomic features further enhanced diagnostic precision, showing strong potential for reliable histologic subtype differentiation. Conclusions: Machine learning-based radiomics models and deep learning significantly enhance the non-invasive, accurate differentiation of lung squamous and adenocarcinoma cell carcinoma when combined with clinical and pathological data.

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