chn123/astra-encoder-nlst-features

🫁 ASTRA-Sybil & ASTRA-Pillar: NLST 3D CT Survival Benchmark & Feature Archive

This repository hosts pre-extracted 3D spatial feature tensors and the canonical biostatistical validation benchmark for deep lung cancer risk assessment on the National Lung Screening Trial (NLST).

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📁 Repository Structure

.
├── 5D/                                         # 2,965 PyTorch 5D spatial feature tensors [2048, 10, 7, 7]
│   └── *.pt
├── manifest.csv                                # Full dataset manifest with clinical survival labels
├── reports/
│   ├── ASTRA_SYBIL_PILLAR_PROGRESS_REPORT.docx # Comprehensive scientific & clinical report (Word DOCX)
│   └── ASTRA_SYBIL_PILLAR_PROGRESS_REPORT.md   # Markdown progress report with full ablation tables
├── canonical_validation/                       # Canonical Biostatistical Validation Framework
│   ├── canonical_metrics.py                    # Harrell C, Uno IPCW C, t-AUC, Brier, Calibration, Bootstrap
│   ├── run_canonical_reevaluation.py           # Patient-level evaluation of V1, V2, V3 (1000 bootstraps)
│   ├── run_baselines_e4.py                     # Survival Baseline Ladder (KM, Cox, Astra 1D Linear Probe)
│   ├── run_ablations_e1_e2_e3.py               # GPU-Resident Ablation Suite (Pooling, Loss, Stratification)
│   └── results/
│       ├── canonical_v1_v2_v3_benchmark.json   # Full metric JSON with 95% CIs and paired p-values
│       ├── e4_survival_baselines_benchmark.json# Survival ladder JSON
│       └── e1_e2_e3_ablations_benchmark.json   # E1, E2, E3 ablations JSON
├── scripts/                                    # Training and extraction scripts
│   ├── train_sybil_vs_pillar_astra_v3.py       # V3 Model (Joint Temporal Stratification + Ranking Loss)
│   ├── train_sybil_vs_pillar_astra_v2.py       # V2 Model (Patient-Level Stratification)
│   └── train_sybil_vs_pillar_astra.py          # V1 Model (Series GroupKFold)
└── benchmarks/                                 # OOF predictions, metrics, and attention maps
    ├── sybil_oof_survival_v3_predictions.csv
    ├── pillar_oof_survival_v3_predictions.csv
    └── positive_cases_attention_maps.npz

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🔬 Clinical Cohort Overview

• Dataset: National Lung Screening Trial (NLST) Low-Dose CT (LDCT)
• Unique Patients ($N$): 1,201 (Zero patient overlap across folds)
• Total Scans / Series: 2,965
• Positive (Incident Cancer) Cases: 72 patients (140 series; 6.0% incidence)
• Censored / Cancer-Free Controls: 1,129 patients (2,825 series)
• Visual Encoder: Astra / Merlin (Inflated 3D ResNet-152, I3ResNet)
• Feature Tensor Dimensions: [2048, 10, 7, 7] (490 spatial tokens per scan)

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📊 Canonical Survival Benchmark Results (Patient-Level, $N=1,201$)

All evaluations are conducted at the independent patient level ($N=1,201$) with maximum risk aggregation across serial scans. Confidence intervals [95% CI] and paired $p$-values are computed via 1,000 cluster bootstrap resamples.

1. Survival Baseline Ladder (E4)

Baseline Model	Harrell's C-Index [95% CI]	Uno's IPCW C-Index	Year 1 AUC [95% CI]	Year 6 AUC [95% CI]	Integrated Brier Score (IBS)
Kaplan-Meier (Unconditional)	0.4924 [0.4263 - 0.5620]	0.5000	0.4171 [0.3340 - 0.5050]	0.5000 [0.5000 - 0.5000]	0.0700
Clinical Cox Model (Age, Gender, Smoking)	0.6446 [0.5786 - 0.7101]	0.6585	0.5993 [0.5008 - 0.6970]	0.6486 [0.5762 - 0.7188]	0.0685
Astra 1D Linear Probe (GAP + Ridge)	0.6764 [0.6108 - 0.7409]	0.7185	0.6540 [0.5606 - 0.7441]	0.6877 [0.6200 - 0.7513]	0.0641
Clinical + Astra 1D Combined	0.6893 [0.6262 - 0.7500]	0.7310	0.6594 [0.5663 - 0.7508]	0.7033 [0.6385 - 0.7645]	0.0636
Best Deep 3D Head (Astra-Pillar V3)	0.6241 [0.5625 - 0.6824]	0.6295	0.5689 [0.4687 - 0.6657]	0.6648 [0.5960 - 0.7288]	0.0716

Key Scientific Insight: Astra visual representations carry a genuine, strong prognostic signal (pushing concordance from 0.64 to 0.69). However, because the cohort contains only 72 incident events, heavily regularized linear probes on Astra GAP features outperform 3.1M-parameter deep spatial attention heads ($p \gg N_{events}$).

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2. Pooling Mechanism Ablation (E1)

Pooling Architecture	Trainable Parameters	Harrell's C-Index [95% CI]	Year 1 AUC [95% CI]	Integrated Brier Score (IBS)
Global MaxPool (GMP)	1,053,703	0.5745 [0.5056 - 0.6428]	0.5977 [0.5112 - 0.6817]	0.0674
Global AvgPool (GAP)	1,053,703	0.6159 [0.5454 - 0.6839]	0.5579 [0.4673 - 0.6422]	0.0668
Spatial Gated MIL	2,103,048	0.6060 [0.5364 - 0.6715]	0.5667 [0.4655 - 0.6632]	0.0669
Astra-Sybil Pooling	2,630,665	0.6423 [0.5768 - 0.7103]	0.5795 [0.4826 - 0.6767]	0.0721
Astra-Pillar Pooling	3,154,952	0.6121 [0.5462 - 0.6771]	0.5873 [0.4896 - 0.6810]	0.0716

Differences between Sybil and Pillar are not statistically significant ($p = 0.440$). Global AvgPool achieves significantly better Brier score calibration ($p = 0.010$).

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3. Loss Formulation Ablation (E2)

Loss Objective	Harrell C [95% CI]	Uno IPCW C	Year 1 AUC [95% CI]	IBS	Calibration Assessment
Masked BCE	0.5851 [0.5183 - 0.6506]	0.5682	0.5914 [0.4914 - 0.6900]	0.0811	Suboptimal Brier score
BCE + Pairwise Ranking	0.6191 [0.5566 - 0.6857]	0.6143	0.6447 [0.5484 - 0.7322]	0.0690	Best discrimination & calibration
Pure Pairwise Ranking	0.6091 [0.5367 - 0.6794]	0.6592	0.5381 [0.4375 - 0.6329]	0.1153	Uncalibrated (Poor Brier error)
Discrete Hazard NLL	0.5249 [0.4568 - 0.5951]	0.5319	0.5818 [0.4879 - 0.6637]	0.2499	Gradient sparsity collapse

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4. Gradient-Free Spatial Probing Benchmark (E5: Testing NN Optimization Failure)

Operator	Type	Dimension	Harrell C-Index [95% CI]	Uno IPCW C	Year 1 AUC	Paired ΔC vs GAP [95% CI]
Top-2 Pool (~0.4% vol)	Local Nodule Scale	2048	0.6832 [0.6126 - 0.7501]	0.6942	0.6840	+0.0059 [-0.0340, +0.0452]
GAP + Spatial Std	Hybrid (Global+Spread)	4096	0.6812 [0.6143 - 0.7459]	0.7082	0.6804	+0.0047 [-0.0191, +0.0269]
Top-1 GMP (Global Max)	Peak Activation	2048	0.6796 [0.6074 - 0.7480]	0.7042	0.6658	+0.0022 [-0.0405, +0.0437]
Spatial Std (Heterogeneity)	Dispersion Moment	2048	0.6789 [0.6097 - 0.7434]	0.7010	0.6881	+0.0024 [-0.0237, +0.0286]
GAP Baseline	Global Mean	2048	0.6769 [0.6151 - 0.7389]	0.7129	0.6422	0.0000 [REFERENCE]

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5. Controlled Focal-Spatial & Multimodal Clinical Fusion Benchmark (E6)

Evaluated under strict 5-Fold Stratified Cross-Validation at the Patient Level ($N=1,201$) with 3-fold nested inner-CV for hyperparameter tuning.

Model ID	Feature Set	Harrell C [95% CI]	Uno C	AUC 1y	AUC 2y	AUC 3y	AUC 4y	AUC 5y
M0	Clinical Demographics Only	0.6435 [0.5748 - 0.7110]	0.6411	0.5817	0.6167	0.6294	0.6417	0.6456
M1	GAP Imaging Alone	0.6278 [0.5577 - 0.6939]	0.6439	0.5766	0.6708	0.6001	0.6098	0.6273
M2	Clinical + GAP (Reference)	0.6258 [0.5562 - 0.6924]	0.6426	0.5760	0.5989	0.5818	0.6120	0.6245
M3	Top-2 Imaging Alone	0.6775 [0.6076 - 0.7491]	0.6961	0.6267	0.6777	0.6687	0.6910	0.6815
M4	Clinical + Top-2 (Primary)	0.6783 [0.6080 - 0.7501]	0.6968	0.6260	0.6778	0.6682	0.6912	0.6823
M6	Clinical + Spatial Std	0.6607 [0.5917 - 0.7294]	0.6777	0.6213	0.6118	0.6610	0.6817	0.6747
M8	Clinical + GAP + Std	0.6462 [0.5801 - 0.7159]	0.6640	0.5988	0.6021	0.6234	0.6533	0.6545
M10	Clinical + Top-2 + Std	0.6655 [0.5968 - 0.7376]	0.6824	0.6255	0.6058	0.6473	0.6791	0.6735

🎯 Primary Endpoint Hypothesis Test:

$$\mathrm{\Delta }C=C_{extClinical+Top-2}-C_{extClinical+GAP}=+\mathbf{0.0529}[95\mathrm{\%}extCI:+\mathbf{0.0058},+\mathbf{0.1018}],p=\mathbf{0.0260}(extSTATISTICALLYSIGNIFICANT)\backslash Delta\; C\; =\; C\_\{\; ext\{Clinical\; +\; Top-2\}\}\; -\; C\_\{\; ext\{Clinical\; +\; GAP\}\}\; =\; \backslash mathbf\{+0.0529\}\; \backslash \; [95\backslash \%\; ext\{\; CI:\; \}\; \backslash mathbf\{+0.0058,\; \backslash \; +0.1018\}],\; \backslash \; p\; =\; \backslash mathbf\{0.0260\}\; \backslash \; (\; ext\{STATISTICALLY\; SIGNIFICANT\})$$

📉 PCA Dimensionality Compression (8 to 32 Dimensions Reaching C ≈ 0.72):

Representation	Raw 2048/4096D	PCA-8	PCA-16	PCA-32	PCA-64	PCA-128
Clinical + Top-2	0.6783	0.7188	0.6895	0.6993	0.6739	0.6548
Clinical + Top-2 + Std	0.6655	0.7203	0.6895	0.6921	0.6765	0.6330
Clinical + Spatial Std	0.6607	0.7186	0.6888	0.6920	0.6627	0.6140
Clinical + GAP	0.6258	0.6889	0.6702	0.6664	0.6720	0.6072

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🚀 Loading 5D Spatial Tensors

import torch
from huggingface_hub import hf_hub_download

# Download a sample 5D tensor
path = hf_hub_download(
    repo_id="chn123/astra-encoder-nlst-features",
    subfolder="5D",
    filename="100012_01-02-1999-NLST-LSS-56831_2.000000-0OPASEVZOOMB30f3002.012075.040.0null-00079.pt",
    repo_type="dataset"
)

tensor = torch.load(path, weights_only=True)
print("Shape:", tensor.shape)  # torch.Size([2048, 10, 7, 7])

---

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📖 Citation & References

If you use the Astra foundation features, 5D spatial representations, or the benchmark baselines, please cite:

@misc{wang2026astrageneralizablereportgeneration,
  title         = {Astra: a generalizable report generation foundation model for 3D computed tomography}, 
  author        = {Zhuhao Wang and Fang Chen and Chaohui Yu and Zihan Li and Yuchao Zheng and Jing Wang and Xuan Yang and Jia Guo and Zhenlu Yang and Xingju Zheng and Yihua Sun and Haojie Han and Xiaoxiao Qin and Zhan Feng and Wenbo Xiao and Chao Zhu and Yuehua Li and Shipeng Zhang and Hao Luo and Yunsong Peng and Fan Wang and Hongen Liao},
  year          = {2026},
  eprint        = {2605.31437},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2605.31437}
},
  author  = {{Astra Multimodal Research Team}},
  year    = {2024}
}

@article{nlst2011reduced,
  title   = {Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening},
  author  = {{National Lung Screening Trial Research Team}},
  journal = {New England Journal of Medicine},
  volume  = {365},
  number  = {5},
  pages   = {395--409},
  year    = {2011}
}

@article{mikhael2023sybil,
  title   = {Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography},
  author  = {Mikhael, Peter G. and Wohlwend, Jeremy and Yala, Adam and Karstens, Leslie and Xiang, Jing and Takigami, Amanda K. and others},
  journal = {Journal of Clinical Oncology},
  volume  = {41},
  number  = {12},
  pages   = {2191--2201},
  year    = {2023}
}

@misc{agrawal2025pillar0,
  title         = {Pillar-0: A New Frontier for Radiology Foundation Models}, 
  author        = {Kumar Krishna Agrawal and Longchao Liu and Long Lian and Michael Nercessian and Natalia Harguindeguy and Yufu Wu and Peter Mikhael and Gigin Lin and Lecia V. Sequist and Florian Fintelmann and Trevor Darrell and Yutong Bai and Maggie Chung and Adam Yala},
  year          = {2025},
  eprint        = {2511.17803},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2511.17803}
},
  author  = {Pillar Research Team and Collaborators},
  journal = {arXiv preprint arXiv:2511.17803},
  year    = {2025}
}