Artificial Intelligence in ALK -Rearranged NSCLC: Forecasting Response and Resistance.
Simple Summary: 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 intelligen...
| Publicado en: | Cancers Vol. 18; no. 6; pp. 973 - 989 |
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| Autores principales: | , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
MDPI
Mar2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=192599010&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192599010 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Mar2026 vid: 18 iid: 6 pid: 97109 pub: MDPI artinfo: ui: 192599010 192599010 192599010 10.3390/cancers18060973 192599010 ppf: 973 ppct: 16 formats: tig: atl: Artificial Intelligence in ALK -Rearranged NSCLC: Forecasting Response and Resistance. aug: au: Koulouris, Andreas Tsagkaris, Christos Kalaitzidis, Konstantinos Tsakonas, Georgios Mountzios, Giannis affil: Thoracic Oncology Center, Karolinska University Hospital, 171 76 Stockholm, Sweden sug: subj: Artificial Intelligence Utilization Carcinoma, Non-Small-Cell Lung Familial and Genetic Carcinoma, Non-Small-Cell Lung Diagnosis Lung Neoplasms Familial and Genetic Lung Neoplasms Diagnosis Forecasting Anaplastic Lymphoma Kinase Drug Resistance, Neoplasm Pathology, Molecular Carcinoma, Non-Small-Cell Lung Therapy Human Funding Source PubMed Medline Systematic Review Machine Learning Deep Learning Data Analysis Software Radiomics Positron-Emission Tomography Mutation Survival Analysis Disease Progression Bioinformatics Reproducibility of Results ab: Simple Summary: 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. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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