ArticleQuantitative imaging in medicine and surgery2026
Interpretable habitat radiomics based nomogram for predicting T790M mutation in non-small cell lung cancer with brain metastases.
Article in Quantitative imaging in medicine and surgery, 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Spatial heterogeneity within the tumor drives T790M resistance mutation. This study aimed to develop a habitat radiomics-based nomogram for predicting T790M resistance mutation for non-small cell lung cancer (NSCLC) patients with brain metastases (BM). Methods: A total of 301 patients from Beijing Chest Hospital, Capital Medical University and Shandong Cancer Hospital and Institute, Shandong First Medical University were retrospectively collected. Tumors were manually delineated on contrast-enhanced T1-weighted (T1-CE), and the segmentation was combined with an automatically generated 5 mm peritumoral region to form the whole region of interest (ROI). Predictive models were established using radiomics features extracted from intratumoral, peritumoral, whole ROI, and habitat-generated subregions within the whole ROI. A nomogram was developed by integrating a representative radiomics signature and meaningful clinical factors. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) was used to interpret feature distribution in the habitat model, and proteomics analysis was performed to explore the biological mechanisms of the nomogram. Results: The habitat model based on three subregions achieved higher AUCs of 0.924 and 0.902 in the training and test cohorts, respectively. The habitat radiomics-based nomogram yielded the highest AUCs of 0.943 and 0.931 in the two cohorts, separately. Meanwhile, clinical and other radiomics models showed moderate performance with AUCs ranging from 0.644 to 0.800 in the test cohort. Proteomics analysis revealed that RNA processing and splicing may represent the most critical pathway distinguishing high- and low-risk groups. Conclusions: Habitat imaging combining tumoral and peritumoral radiomics was beneficial for identifying T790M resistance mutation in NSCLC BM patients. Furthermore, a habitat radiomics-based nomogram can further improve the predictive ability.
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.