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Related Experiment Videos

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical

Sevim Sahin1, Adil Gursel Karacor2

  • 1Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences, Fenerbahce University, Istanbul 34758, Türkiye.

Diagnostics (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...

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This summary is machine-generated.

This study developed a data-efficient framework for non-small cell lung cancer (NSCLC) survival prediction using CT scans and clinical data. The model integrates imaging and clinical features, offering explainable predictions for better patient outcomes.

Area of Science:

  • Oncology
  • Radiology
  • Data Science

Background:

  • Non-small cell lung cancer (NSCLC) survival prediction is challenging, especially with limited data.
  • Deep learning models can struggle with generalization in small datasets.
  • A multimodal and explainable approach is needed to integrate imaging and clinical data.

Purpose of the Study:

  • To develop a data-efficient, multimodal, and explainable framework for NSCLC survival prediction.
  • To integrate computed tomography (CT)-derived imaging information with clinical variables.
  • To improve the generalization of survival prediction models in limited-sample settings.

Main Methods:

  • Utilized the NSCLC Radiomics (LUNG1) dataset with CT images, segmentations, and clinical data.
  • Extracted deep imaging embeddings using pretrained RadImageNet-InceptionV3 and fused them with engineered features and clinical variables.
Keywords:
CT radiomicsNSCLCdeep image embeddingsexplainable AIgradient-boosted treesmultimodal learningsmall datasetssurvival prediction

Related Experiment Videos

  • Employed principal component analysis for feature compression and trained gradient-boosted tree models (CatBoost, XGBoost, LightGBM) with SHAP for interpretability.

    • Main Results:

    • The CatBoost model achieved a C-index of 0.655 for three-class survival stratification.
    • The LightGBM model achieved a C-index of 0.576 for continuous survival regression.
    • Clinical variables were the primary prognostic indicators, with deep image embeddings offering complementary information, particularly for distinguishing short- and long-term survival.

    Conclusions:

    • The proposed framework demonstrates a feasible, data-efficient, and explainable image-to-tabular approach for NSCLC survival prediction.
    • Combining pretrained CT embeddings, clinical data, gradient-boosted trees, and SHAP analysis shows promise for limited-sample survival modeling.
    • External validation is crucial before clinical translation of this approach.