ArticleTherapeutic advances in respiratory disease
A novel decision tree algorithm model based on chest CT parameters to predict the risk of recurrence and metastasis in surgically resected stage I synchronous multiple primary lung cancer.
Article in Therapeutic advances in respiratory disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Adenocarcinoma and squamous cell carcinoma in the same lobe of the lung with adenocarcinoma metastasis in the lymph nodes: a case report and literature review.Frontiers in oncology · 2025Article
- Functional assessment and clinical staging of patients undergoing lung resection.Therapeutic advances in respiratory diseaseArticle
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
No grant is acknowledged in the PubMed record.
Abstract
backgroundChest computed tomography (CT) may provide evidence to forecast unexpected recurrence and metastasis following radical surgery for stage I synchronous multiple primary lung cancer (SMPLC).
objectiveThis study aims to develop and validate a novel CT-based multi-parametric decision tree algorithm (CT-DTA) model capable of accurate risk assessment.
designA multicenter retrospective cohort study.
methodsThere were 209 patients with pathological stage I SMPLC from three tertiary centers included. We initially screened all of the CT-derived imaging parameters in the training cohort (130 patients from Center A) and then selected those showing statistical significance to construct a DTA model. The discriminative strength of the CT-DTA model for postoperative recurrence and metastasis was then validated in the validation cohort (79 patients from Centers B and C). Moreover, the performance of the CT-DTA model was further evaluated across different subgroups of the entire cohort.
resultsFive key imaging parameters measured on chest thin-section CT, including consolidation tumor ratio (CTR), long-axis diameter of the lesion, number of pure solid nodules, presence of spiculation and pleural indentation, constituted a CT-DTA model with nine leaf nodes, and CTR was the leading risk contributor of them. The CT-DTA model achieved a satisfactory predictive accuracy indicated by an area under the curve of more than 0.80 in both the training cohort and validation cohort. Meanwhile, this CT-DTA model was also exhaustively demonstrated to play as the only independent risk factor for postoperative recurrence and metastasis. Its promising predictive performance still remained stable across nearly all of the subgroups stratified by clinicopathological characteristics.
conclusionThis CT-DTA model could serve as a noninvasive, user-friendly, and practicable risk prediction tool to aid treatment decision-making in operable stage I SMPLC.
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.