Improved prognostication for lung cancer patients from computed tomography imaging using deep learning.
Bibliographic record
Abstract
2044 Background: Clinical TNM staging derived from computed tomography (CT) imaging is a key prognostic factor for lung cancer patients when making decisions about treatment, monitoring, and clinical trial eligibility. However, heterogeneity among patients, including by molecular subtypes, may result in variability of survival outcomes of patients with the same TNM stage that receive the same treatment. Artificial intelligence may offer additional, individualized prognostic information based on both known and unknown features present in CTs to facilitate more precise clinical decision making. We developed a novel deep learning-based technique to predict 2-year survival from pretreatment CTs of pathologically-confirmed lung cancer patients. Methods: A fully automated, end-to-end model was designed to localize the three-dimensional (3D) space comprising the lungs and heart, and to learn deep prognostic features using a 3D convolutional neural network (3DCNN). The 3DCNN was trained and validated using 1,841 CTs of 1,184 patients from five public datasets made available in The Cancer Imaging Archive. Spearman’s rank correlation (R) and concordance index (C-index) between the model output and survival status of each patient after 2-year follow-up from CT acquisition was assessed, in addition to sensitivity, specificity and accuracy stratified by staging. Results: 3DCNN showed an overall prediction accuracy of 75.0% (R = 0.32, C-index = 0.67, p < 0.0001), with higher performance achieved for stage I patients (Table) . 3DCNN showed better overall correlation with survival for 1,124 patients with available TNM staging, in comparison to TNM staging only (R = 0.19, C-index = 0.63, p < 0.0001); however, a weighted linear combination of both TNM staging and the 3DCNN yielded a superior correlation (R = 0.34, C-index = 0.73, p < 0.0001). Conclusions: Deep learning applied to pretreatment CT images provides personalized prognostic information that complements clinical staging and may help facilitate more precise prognostication of patients diagnosed with lung cancer. [Table: see text]
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".