ReviewCancers2026
Radiological, Radiomics, and Metastatic Patterns Associated with Targetable Oncogenic Drivers on CT-Scan of Newly Diagnosed NSCLC Patients: A Comprehensive Radiogenomics Review.
Review in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial intelligence in non-small cell lung cancer: transforming diagnosis, treatment, and prognostic evaluation.Frontiers in medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The management of non-small cell lung cancer (NSCLC), including lung adenocarcinomas (LUAD), has been revolutionized with the advent of precision oncology. While advanced cancers often carry poor prognosis, those harboring specific molecular alterations sensitive to targeted therapy (notably tyrosine kinase inhibitor [TKI]) have experienced improved response to treatment and survival outcomes. Consequently, detecting these alterations through molecular screening panel has become standard in several countries, although this necessitates high-quality tissue sampling to inform optimal therapeutic decisions. Oncologic imaging occupies a pivotal role in the routine care of patients, in particular at diagnosis, with a wealth of information gathered but underutilized, as medical imaging reflects the disease in its entirety at a given time point. Moreover, recent advancements in imaging quantitative analysis, including radiomics and artificial intelligence, could aid in better integration and understanding of this information that has been overlooked for years. Several radiological phenotypes (or radiophenotypes) have been linked to tumor genomic alterations, both in standard radiology relying on semantic features and metastatic patterns, and in radiomics. Ultimately, understanding the relationships between imaging and targetable genomic alterations via accurate imaging biomarkers could complement ambiguous tumor or liquid biopsy, detect emerging new alterations, and even substitute biopsy through 'virtual biopsy'. During the past decade, there has been a surge in research focused on radiogenomic assessment of NSCLC and especially LUAD. However, due to the low prevalence of many oncogenic drivers, the scientific literature may lack clarity or present conflicting findings. This comprehensive review aims to provide a summary of the current state of this research, offering insights into the complex interplay between imaging and genomic alterations in lung adenocarcinoma.
Indexed as
Identifiers
What OpenQuestion holds
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.