An automatic detection model for spread through air spaces in postoperative pathological sections based on deep learning in NSCLC
Clinical Snapshot
PICO Framework
| P — Population | 91 NSCLC patients with STAS-positive pathological slides (129 slides total) |
| I — Intervention | Deep learning-based AI framework for automated STAS detection (tumor segmentation + object detection algorithms) |
| C — Comparator | Manual pathologist assessment and other object detection methods |
| O — Outcomes | Diagnostic accuracy metrics (precision, recall, AP, F1 score), Jaccard similarity for segmentation, interclass correlation with pathologists, association with disease-free survival |
Bottom Line
This study presents a deep learning model for automated detection of spread through air spaces (STAS) in NSCLC pathology slides, achieving reasonable accuracy metrics (precision 0.738, recall 0.747). However, the study has significant methodological limitations that limit clinical applicability. Most critically, it suffers from spectrum bias by including only STAS-positive cases, making it impossible to assess the model's ability to distinguish STAS-positive from STAS-negative specimens - the key clinical question. The lack of sensitivity/specificity data and confidence intervals further hampers clinical interpretation. While the correlation with survival outcomes is encouraging and the technology shows promise for assisting pathologists, the current evidence is insufficient to support clinical implementation. A properly designed diagnostic accuracy study including both STAS-positive and STAS-negative cases with appropriate blinding and statistical analysis is needed before this tool can be considered for routine clinical use.
Key Findings
P Value: p < 0.05 for survival association
Effect Size: Precision 0.738, Recall 0.747, F1 score 0.742 (internal validation)
Primary Outcome: AI model diagnostic accuracy for STAS detection
Nnt Or Sensitivity: External validation: Precision 0.670, Recall 0.705, F1 score 0.687
Confidence Interval: Not reported
Clinical Application
Requires digital pathology infrastructure and integration with existing workflow; computational requirements not specified Could support pathology services in Australia where STAS assessment influences staging and treatment decisions; would need TGA approval as medical device software; relevant to RACGP lung cancer guidelines NSCLC patients undergoing surgical resection where STAS assessment is clinically relevant
Abstract
Spread through air spaces (STAS) is recognized as an aggressive pattern of invasion in lung cancer and has been associated with poorer survival outcomes. However, STAS is frequently overlooked or misdiagnosed during routine pathological diagnosis. We analyzed 129 pathological slides from 91 STAS-positive patients with non-small cell lung cancer (NSCLC). An artificial intelligence framework consisting of a tumor region segmentation algorithm and an object detection algorithm was developed. The segmentation algorithm was first used to isolate the main tumor region, followed by STAS detection using an improved object detection model. The segmentation module achieved a Jaccard similarity of 0.846. The proposed object detection algorithm demonstrated superior performance with a precision of 0.738, recall of 0.747 and average precision (AP) of 0.784 and F1 score of 0.742, outperforming other object detection methods. Both segmentation and detection results met diagnostic requirements, with higher accuracy observed in lung adenocarcinoma (LUAD) than in lung squamous cell carcinoma (LUSC). In external validation, the model achieved a precision of 0.670, recall of 0.705 and AP of 0.732 and F1 score of 0.687. Additionally, the AI-derived STAS counts showed substantial agreement with pathologists (interclass correlation coefficient = 0.703). Kaplan-Meier survival analysis revealed that the number of STAS events was significantly associated with disease-free survival (DFS) in stage I LUAD (p < .05). We propose a deep learning-based artificial intelligence framework for automated STAS detection and quantification in digital whole-slide images of NSCLC. This model holds promising potential to assist pathologists in achieving more comprehensive and accurate diagnosis of STAS.
References
- 1.Zhang, Y., Deng, S., Guo, M., Kang, Y., Lin, E., Meng, H., Du, F., & Zhao, Y. (2026). An automatic detection model for spread through air spaces in postoperative pathological sections based on deep learning in NSCLC. Computer Assisted Surgery, Article 2674321. https://doi.org/10.1080/24699322.2026.2674321
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