Deep learning-based prediction of nodal metastasis in lung cancer using endobronchial ultrasound
Bibliographic record
Abstract
Objective: Endobronchial ultrasound-guided transbronchial needle aspiration is a vital tool for mediastinal and hilar lymph node staging in patients with lung cancer. Despite its high diagnostic performance and safety, it has a limited negative predictive value. Our objective was to evaluate the diagnostic performance of deep learning-based prediction of lung cancer lymph node metastases using convolutional neural networks developed from automatically extracted images of endobronchial ultrasound videos without supervision of the lymph node location. Methods: Patient and lymph node data were collected from a single-center database. The diagnosis of metastasis was confirmed with endobronchial ultrasound-guided transbronchial needle aspiration and/or surgically resected specimens; the diagnosis of normal lymph node was confirmed with surgically resected specimens only. An annotation system facilitated automated image extraction from endobronchial ultrasound videos. Image frames were randomly selected and split into training and validation datasets on a per-patient basis. A deep learning model with convolutional neural networks, SqueezeNet, was used for image classification via transfer learning based on pretraining from ImageNet. Adaptive moment estimation and stochastic gradient descent were applied as optimizers. Results: SqueezeNet, with adaptive moment estimation, achieved a sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 96.7% each after 300 epochs, whereas SqueezeNet with stochastic gradient descent achieved 91.1% each. However, SqueezeNet with stochastic gradient descent demonstrated more stable performance than with adaptive moment estimation. Conclusions: Deep learning-based image classification using convolutional neural networks showed promising diagnostic accuracy for lung cancer nodal metastasis. Future clinical trials are warranted to validate the algorithm's efficacy in a prospective, large-cohort study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".