ReviewJournal of thoracic disease2024
Artificial intelligence applications in personalizing lung cancer management: state of the art and future perspectives.
Review in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions.Chinese medical journal pulmonary and critical care medicine · 2026Review
- Review
- Radiogenomics: transforming lung cancer care through non-invasive imaging and genomic integration.Medical oncology (Northwood, London, England) · 2025Review
- Artificial Intelligence as a Diagnostic Tool in Preoperative Surgical Planning for Early Non-Small Cell Lung Cancer: A Single-Center Experience.Journal of clinical medicine · 2025Article
- Computed tomography-based quantification of intra-tumoral heterogeneity for predicting treatment response to concurrent chemoradiotherapy in non-small cell lung cancer: a multicenter retrospective study.Translational cancer research · 2025Article
- Artificial Intelligence in Clinical Nutrition: Bridging Data Analytics and Nutritional Care.Current nutrition reports · 2025Review
- Illuminating the black box: Machine learning enhances preoperative prediction in intrahepatic cholangiocarcinoma.World journal of gastroenterology · 2025Article
- Editorial: Artificial intelligence and imaging for oncology.Frontiers in oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is still a leading cause of cancer-related deaths worldwide. Vital to ameliorating patient survival rates are early detection, precise evaluation, and personalized treatments. Recent years have witnessed a profound transformation in the field, marked by intricate diagnostic processes and intricate therapeutic protocols that integrate diverse omics domains, heralding a paradigm shift towards personalized and preventive healthcare. This dynamic landscape has embraced the incorporation of advanced machine learning and deep learning techniques, particularly artificial intelligence (AI), into the realm of precision medicine. These groundbreaking innovations create fertile ground for the development of AI-based models adept at extracting valuable insights to inform clinical decisions, with the potential to quantitatively interpret patient data and impact overall patient outcomes significantly. In this comprehensive narrative review, a synthesis of various studies is presented, with a specific focus on three core areas aimed at providing clinicians with a practical understanding of AI-based technologies' potential applications in the diagnosis and management of non-small cell lung cancer (NSCLC). The emphasis is placed on methods for diagnosing malignancy in lung lesions, approaches to predicting histology and other pathological characteristics, and methods for predicting NSCLC gene mutations. The review culminates in a discussion of current trends and future perspectives within the domain of AI-based models, all directed toward enhancing patient care and outcomes in NSCLC. Furthermore, the review underscores the synthesis of diverse studies, accentuating AI applications in NSCLC diagnosis and management. It concludes with a forward-looking discussion on current trends and future perspectives, highlighting the LANTERN Study as a pioneering force set to elevate patient care and outcomes to unprecedented levels.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.