ArticleInsights into imaging2026
Radiomic subtypes predict survival and chemotherapy benefit in stage I lung adenocarcinoma: a multicenter study.
Article in Insights into imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
objectivesPostoperative survival outcomes vary substantially among patients diagnosed with stage I lung adenocarcinoma (LUAD). This study aimed to develop CT-based radiomic subtypes using unsupervised clustering to assess their association with overall survival (OS), systemic nutritional-inflammatory status, and adjuvant chemotherapy benefit. MATERIALS AND
methodsA total of 496 stage I LUAD patients from two independent centers were included. Preoperative CT radiomic features (n = 1218) were extracted, and subtypes were derived using the K-means clustering algorithm. The independent prognostic value of these subtypes, along with their capacity to predict the benefit of adjuvant chemotherapy, was evaluated through multivariable Cox regression and treatment-by-subtype interaction analyses.
resultsThree radiomic subtypes with significant prognostic differences in OS were identified. The high-risk subtype, Cluster 2, exhibited distinct clinical characteristics and was associated with markedly poorer OS (hazard ratio [HR] = 15.71, p < 0.001, compared to Cluster 0). Cluster 2 also showed an inflammatory imbalance, with elevated systemic immune-inflammation index and neutrophil-to-lymphocyte ratio, and a decreased lymphocyte-to-monocyte ratio. Notably, a significant interaction was found between subtypes and adjuvant chemotherapy (interaction p < 0.001, Cluster 2 vs Cluster 0). Subgroup analysis indicated that stage IB patients within Cluster 2 derived a significant survival benefit from adjuvant chemotherapy (interaction p = 0.003 vs Cluster 0).
conclusionsThis study developed a CT-based radiomic subtype system using unsupervised clustering that identifies high-risk stage I LUAD patients with systemic inflammatory imbalance. Notably, these subtypes predict differential survival benefits from adjuvant chemotherapy in high-risk stage IB patients, thereby supporting personalized postoperative treatment strategies. CRITICAL RELEVANCE STATEMENT: This CT-based radiomic subtype system stratifies prognosis and identifies stage I LUAD patients who may benefit from adjuvant chemotherapy, enabling personalized treatment decisions in radiology. KEY POINTS: Conventional tumor-node-metastasis (TNM) staging does not adequately capture tumor heterogeneity in stage I LUAD. Three CT-based radiomic subtypes were established, with the high-risk subgroup correlating with systemic inflammatory imbalance and poorer OS. CT-based radiomic stratification identifies stage IB patients who benefit from adjuvant chemotherapy, supporting personalized postoperative management.
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.