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Updated: Jan 14, 2026
Using Micro-computed Tomography for the Assessment of Tumor Development and Follow-up of Response to Treatment in a Mouse Model of Lung Cancer
Published on: May 20, 2016
AI-based non-invasive profiling of the tumor immune microenvironment using longitudinal CT radiomics predicts
Guangjie Liu1, Xiaoyan Zhang1, Yutong He2
1Department of Thoracic Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Background:
Despite advances in immunotherapy, durable responses in lung cancer remain limited to a subset of patients, underscoring the need for biomarkers capturing spatial immune-tumor interactions. Current methods, such as PD-L1 immunohistochemistry, suffer from sampling bias and fail to decode dynamic immune evasion mechanisms non-invasively.
Methods:
We developed a radiomics framework integrating longitudinal tumor growth kinetics (log volume change rate, LVCR) with deep learning to: (1) delineate tumors via medical knowledge-guided segmentation; and (2) derive an Immune Evasion Score (IES) predicting immunosuppressive niches. The model employs immune-aware attention gates (IAAG) to prioritize regions associated with aggressive growth (high LVCR) and immune evasion.
Results:
Validated on 420 CT scans, our approach achieved superior segmentation accuracy (Dice=0.7728 ± 0.03; HD95 = 9.8 ± 1.5 mm) over existing models. Critically, the IES predicted PD-L1 expression (AUC = 0.85; *p*<0.001) and CD8+ T-cell exclusion (*p*<0.01). High IES correlated with rapid immunotherapy progression (HR = 2.3, *p*=0.004), and spatial analysis confirmed 72.3% concordance between IAAG-prioritized regions and pathological PD-L1+ niches.
Conclusion:
This work establishes a non-invasive paradigm for mapping immunosuppressive microenvironments, bridging precision radiotherapy with immunotherapy personalization. The IES provides a dynamic biomarker of immune evasion, potentially guiding patient stratification for checkpoint inhibitors.
