ArticleBMC cancer2025
A survival prediction model for leptomeningeal metastasis patients with non-small cell lung cancer based on deep learning.
Article in BMC cancer, 2025. 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.
- Survival outcomes and a prognostic nomogram in EGFR-mutant non-small cell lung cancer with leptomeningeal metastasis: a real-world cohort study.Journal of neuro-oncology · 2026Article
- Application of deep learning in prognostic prediction of non-small cell lung cancer.Frontiers in cell and developmental biology · 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
5 authors.
Funding
Abstract
backgroundLeptomeningeal metastasis (LM) in non-small cell lung cancer (NSCLC) is a terminal complication with limited prognostic tools, especially after failure of tyrosine kinase inhibitor treatment. This study aimed to develop and validate a multi-modal model to predict overall survival (OS) in LM patients. The model utilizes deep learning (DL) and radiomic features extracted from T1-weighted contrast-enhanced MRI, combined with clinical variables.
methodsWe retrospectively enrolled 174 NSCLC patients with LM confirmed via cerebrospinal fluid cytology. Regions of interest were manually segmented on MRI, and features were extracted following IBSI guidelines. Robust radiomic features were selected using reproducibility metrics and LASSO-Cox regression. Deep learning signatures were generated using pre-trained convolutional networks with transfer learning and Cox proportional hazards loss. An integrated prognostic model was constructed by combining significant clinical, radiomic, and DL-based predictors. Performance was assessed using the C-index, time-dependent ROC, calibration curves, and decision curve analysis.
resultsThe integrated model demonstrated superior predictive performance, achieving a C-index of 0.709 (95% CI: 0.628–0.790) in the training cohort and 0.629 (0.499–0.759) in validation, outperforming clinical, radiomic, and DL-only models. Time-dependent AUC reached 0.807 at 18 months. Calibration and decision curves confirmed clinical utility and accuracy. Intrathecal chemotherapy was identified as a significant prognostic factor (HR = 0.654, p < 0.05).
conclusionThe multi-modal model integrating DL, radiomics, and clinical data shows potential for improving OS prediction in NSCLC patients with LM, providing a preliminary reference for personalized prognostic assessment and therapeutic strategy development.
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.