Sci Adv | CT上“看得见”的血管重塑：可解释AI助力肺癌抗血管治疗早期疗效预测
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TL;DR - A Science Advances study (Hubei Cancer Hospital / South-Central Minzu University, published 2026-07-24) builds an automated pipeline that extracts quantitative vascular morphometry features (QVMFs) from routine contrast-enhanced CT and uses SHAP-interpretable ML to predict early response to anti-angiogenic therapy in advanced lung cancer. It matters because it turns "vascular normalization" biology into a scalable, explainable biomarker from imaging clinics already acquire.
- Pipeline auto-segments tumor and pulmonary vasculature, then computes delta features from pre- vs. post-treatment scans (~4–6 weeks), targeting the drug's actual biological target (the vessel network) rather than generic radiomics texture or deep latent features.
- The delta-merge model (baseline + dynamic features) performed best: mean AUC 0.842 on internal 5-fold CV (163 patients, Hubei Cancer Hospital); external validation (82 patients, Wuhan Union Hospital) gave ~0.76 raw and ~0.81 after class balancing, despite slice-thickness (0.625–1.0 mm vs. 1.5–2.0 mm) and arterial/venous phase mismatch.
- Dynamic features outperformed pre-treatment static features alone; SHAP attributed decisions to reduced vessel endpoints, network simplification, and an "arterial-dominant, venous-adaptive" pattern, traceable back to 3D reconstructions showing improved intratumoral/peritumoral (~15 mm) contrast filling in responders.
- Limitations acknowledged: retrospective two-center design, heterogeneous follow-up timing and imaging phases, and SHAP-derived biology remains hypothesis-generating pending pathological/molecular cross-validation.
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Sci Adv | CT上“看得见”的血管重塑：可解释AI助力肺癌抗血管治疗早期疗效预测
TL;DR - A Science Advances study (Hubei Cancer Hospital / South-Central Minzu University, published 2026-07-24) builds an automated pipeline that extracts quantitative vascular morphometry features (QVMFs) from routine contrast-enhanced CT and uses SHAP-interpretable ML to predict early response to anti-angiogenic therapy in advanced lung cancer. It matters because it turns "vascular normalization" biology into a scalable, explainable biomarker from imaging clinics already acquire.
- Pipeline auto-segments tumor and pulmonary vasculature, then computes delta features from pre- vs. post-treatment scans (~4–6 weeks), targeting the drug's actual biological target (the vessel network) rather than generic radiomics texture or deep latent features.
- The delta-merge model (baseline + dynamic features) performed best: mean AUC 0.842 on internal 5-fold CV (163 patients, Hubei Cancer Hospital); external validation (82 patients, Wuhan Union Hospital) gave ~0.76 raw and ~0.81 after class balancing, despite slice-thickness (0.625–1.0 mm vs. 1.5–2.0 mm) and arterial/venous phase mismatch.
- Dynamic features outperformed pre-treatment static features alone; SHAP attributed decisions to reduced vessel endpoints, network simplification, and an "arterial-dominant, venous-adaptive" pattern, traceable back to 3D reconstructions showing improved intratumoral/peritumoral (~15 mm) contrast filling in responders.
- Limitations acknowledged: retrospective two-center design, heterogeneous follow-up timing and imaging phases, and SHAP-derived biology remains hypothesis-generating pending pathological/molecular cross-validation.