AUTHOR=Fatima Syeda Huda , Faisal Syeda Jazilah , Batool Syeda Verisha , Faisal Mehak , Aamir Talha , Shahid Zain Ul Abideen , Mahato Raghabendra Kumar 

TITLE=Evaluating the combined efficacy of Telisotuzumab Vedotin and artificial intelligence in the treatment of non-squamous non-small cell lung cancer: a narrative review focusing on pharmaceutical and technical insights

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1673586

DOI=10.3389/fonc.2025.1673586

ISSN=2234-943X

ABSTRACT=BackgroundNon-squamous Non-small Cell Lung Cancer (NSCLC) is among the most common lung cancers that are therapy-resistant. Telisotuzumab Vedotin (Teliso-V), an antibody-drug conjugate (ADC), targets mesenchymal-epithelial transition factor (c-MET) high cells, with minimum side effects. Additionally, Artificial Intelligence (AI) aids in enhancing diagnosis, detection of mutations and advancing personalized care. Teliso-V, with the assistance of AI technologies such as radiomics, enhances efficacy against cancer.ObjectiveTo assess the combined role of Teliso-V and AI in enhancing diagnosis, treatment, and outcomes in non-squamous NSCLC.MethodThis review emphasizes the value of Teliso-V and the contribution of AI in enhancing the diagnosis and therapy of NSCLC. It is based on PubMed and ClinicalTrials.gov trials over the past two decades.ResultTeliso-V is effective in MET-high non-squamous NSCLC, yielding a response of 34.6% in the LUMINOSITY trial. Moreover, the combination with epidermal growth factor receptor inhibitors like Osimertinib and Erlotinib enhances outcomes, but the combination with immunotherapy (Nivolumab) provided negligible benefit. Moreover, AI has emerged as a powerful agent in cancer management, helping with diagnosis, foretelling mutations, and refining treatment regimens. It also maximizes Teliso-V use in NSCLC with improved patient selection, the ability to predict MET status from imaging and pathology, and the combination of circulating tumor DNA with radiomics for real-time tracking. Additionally, in silico experiments and machine learning algorithms optimize the sequence of treatment and reduce toxicity. Consequently, AI-driven Clinical Decision Support Systems in electronic medical records facilitate precision prescribing. Though challenges such as data bias and black-box decision-making occur, there is potential for AI to optimize personalized NSCLC therapy.ConclusionTeliso-V is highly effective in MET-high NSCLC with tolerable side effects. Its combination with AI holds the hope of early diagnosis, individualized treatment, and intelligent ADCs of the future, but for this to manifest, clinical data and biomarker improvements must materialize.