ArticleAnnals of surgical oncology2026
Explainable PET-Based Habitat Modeling for Predicting Postoperative Recurrence Risk in Invasive Lung Adenocarcinoma.
Article in Annals of surgical oncology, 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.
- Preoperative CT-Based Habitat Radiomics Classifiers Predict Recurrence in Non-Small Cell Lung Cancer.medRxiv : the preprint server for health sciences · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
purposePostoperative recurrence remains a major clinical challenge in patients with resectable invasive adenocarcinoma of the lung (IAC). Conventional PET-based parameters and whole-tumor radiomics may insufficiently reflect the spatial heterogeneity relevant to recurrence risk. This study aimed to develop and validate an interpretable radiomics model based on [
methodsThis retrospective study included 156 patients with pathologically confirmed IAC who underwent preoperative [
resultsThe habitat-based combined model demonstrated the highest predictive performance, achieving an area under the curve of 0.823 in the test cohort, with good calibration and clinical utility. Stratification based on model output showed significant differences in DFS between high- and low-risk groups, with P < 0.0001 in the training cohort and P = 0.018 in the test cohort.
conclusionThis PET-based habitat radiomics model provides a noninvasive and interpretable tool for preoperative prediction of postoperative recurrence in IAC. By accurately identifying patients at high risk of recurrence and reduced DFS, the model may support risk-adapted decision-making for postoperative management.
Indexed as
Identifiers
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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 Socratic graph.