Digital Quantification of Tumor Cellularity as a Novel Prognostic Feature of Non–Small Cell Lung Carcinoma
Bibliographic record
Abstract
Non–small cell lung carcinoma is currently staged based on the size and involvement of other structures. Tumor size may be a surrogate measure of the total number of tumor cells. A recently revised reporting system for adenocarcinoma incorporates high-risk histologic patterns, which may have increased cellular density. Modern digital image analysis tools can be utilized to automate the quantification of cells. In this study, we tested the hypothesis that tumor cellularity can be used as a novel prognostic tool for lung cancer. Digital slides from The Cancer Genome Atlas lung adenocarcinoma (ADC) data set (n = 213) and lung squamous cell carcinoma (SCC) data set (n = 90) were obtained and analyzed using QuPath. The number of tumor cells was normalized with the surface area of the tumor to provide a measure of tumor cell density. Tumor cellularity was calculated by multiplying the size of the tumor with the cell density. Major histologic patterns and grade were compared with the tumor density of the lung ADC and lung SCC cases. The overall and progression-free survival were compared between groups of high and low tumor cellularity. High-grade histologic patterns in the ADC and SCC cases were associated with greater tumor densities compared with low-grade patterns. Cases with lower tumor cellularity had improved overall and progression-free survival compared with cases with higher cellularity. These results support tumor cellularity as a novel prognostic tool for non–small cell lung carcinoma that considers tumor stage and grade elements. Non–small cell lung carcinoma is currently staged based on the size and involvement of other structures. Tumor size may be a surrogate measure of the total number of tumor cells. A recently revised reporting system for adenocarcinoma incorporates high-risk histologic patterns, which may have increased cellular density. Modern digital image analysis tools can be utilized to automate the quantification of cells. In this study, we tested the hypothesis that tumor cellularity can be used as a novel prognostic tool for lung cancer. Digital slides from The Cancer Genome Atlas lung adenocarcinoma (ADC) data set (n = 213) and lung squamous cell carcinoma (SCC) data set (n = 90) were obtained and analyzed using QuPath. The number of tumor cells was normalized with the surface area of the tumor to provide a measure of tumor cell density. Tumor cellularity was calculated by multiplying the size of the tumor with the cell density. Major histologic patterns and grade were compared with the tumor density of the lung ADC and lung SCC cases. The overall and progression-free survival were compared between groups of high and low tumor cellularity. High-grade histologic patterns in the ADC and SCC cases were associated with greater tumor densities compared with low-grade patterns. Cases with lower tumor cellularity had improved overall and progression-free survival compared with cases with higher cellularity. These results support tumor cellularity as a novel prognostic tool for non–small cell lung carcinoma that considers tumor stage and grade elements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".