# Artificial Intelligence in ALK-Rearranged NSCLC: Forecasting Response and Resistance

## TL;DR

Artificial intelligence is being explored to improve diagnosis and treatment of ALK-positive lung cancer, but most studies lack validation and real-world testing.

## Contribution

A systematic review of AI applications in ALK-rearranged NSCLC, highlighting predictive performance and research trends.

## Key findings

- AI models for predicting ALK status achieved area under the curve values between 0.73 and 0.99.
- Most studies lacked external validation and relied on retrospective, single-center data.
- Research themes focused on molecular characterization and computational methods, with a recent shift toward treatment-specific models.

## Abstract

ALK-positive non-small-cell lung cancer is a distinct molecular subtype for which targeted therapies have significantly enhanced patient outcomes. However, prediction of treatment response and understanding of resistance mechanisms remain clinically challenging. Artificial intelligence has been increasingly investigated as a tool to support these tasks by analyzing clinical data, imaging, pathology, and molecular features. In this systematic review, we summarize and critically appraise studies applying artificial intelligence to ALK-rearranged lung cancer, with a focus on diagnostic, prognostic, and treatment-related applications. We further explore methodological trends and research focus within the field. While many studies report promising predictive performance, most rely on retrospective, single-center data and lack external validation. This review highlights both the potential and current limitations of artificial intelligence in this setting and outlines key requirements for future clinically translatable research.

Background/Objectives: The management and prognosis of ALK-rearranged non-small-cell lung cancer have substantially improved over the past decade. However, challenges remain in timely molecular identification, prediction of treatment response, and understanding resistance mechanisms. This systematic review evaluates and synthesizes the evidence on artificial intelligence (AI) approaches leveraging imaging, pathology, molecular, and clinical data in this setting. Methods: A systematic search was conducted for peer-reviewed studies published between 2020 and 2025. Eligible studies involved human subjects and applied AI, machine learning, or deep learning methods to predict ALK status or treatment-related outcomes using imaging, pathology, molecular, or multimodal data. Study selection followed the PRISMA 2020 guidelines. Data were extracted on study design, data modality, AI methodology, clinical objectives, and performance metrics. Bibliometric co-occurrence analysis was performed to characterize thematic patterns and temporal trends. Results: Thirteen studies met the inclusion criteria, most of which were retrospective and single-center. AI approaches were applied to radiologic, pathologic, molecular, or multimodal data. Models predicting ALK status reported area under the curve values ranging from 0.73 to 0.99, while prognostic and treatment-response models reported moderate to high discriminative performance. Bibliometric analysis identified two dominant research themes focused on molecular characterization and computational methodology, with a recent shift toward treatment-specific and integrative analyses. External validation and clinical implementation remained limited across studies. Conclusions: AI shows promising potential to support diagnosis, prognostication, and treatment assessment in ALK-rearranged lung cancer. However, methodological heterogeneity, limited external validation, and a lack of prospective studies currently constrain clinical translation.