Optical and Functional Imaging in Lung Cancer
Bibliographic record
Abstract
textabstractLung cancer is the second most common cancer in men and women, and is the \nleading cause of cancer related death. In industrialized countries the mortality rate \nof lung cancer is higher than the mortality rate of breast, colorectal and prostate \ncancer combined 1. When lung cancer is diagnosed at an early stage patients are \nconsidered to have the best overall survival rate 2. Unfortunately, only a minority of \npatients is currently diagnosed at a curable stage of disease. The lack of specific \nsymptoms at an early stage of the disease, the rapid growth of tumor cells and the \nmetastatic behavior of lung tumors are the main reasons for a diagnosis at an \nadvanced stage. \nNon-small-cell lung cancer (NSCLC) can be divided into three major histological \nsubtypes: squamouscell carcinoma, adenocarcinoma, and large-cell carcinoma 3. \nEighty-five percent of the lung cancer patients are diagnosed with NSCLC, and \n75% of the patients are diagnosed with an incurable stage IIIB or IV disease 4, 5. \nFifteen percent of the lung cancer patients have small-cell-lung cancer (SCLC) \nand the 5-year survival for them is even lower than for NSCLC 6. \nWhereas originally smoking is at the root of all types of lung cancer, the incidence \nof lung cancer in never smokers increases 7. Smoking is most strongly linked with \nSCLC and squamous-cell carcinoma 8, 9, although after the introduction of filter \ncigarets an increased incidence of adenocarcinomas was observed 10. This \nresulted in a change in ratio of adenocarcinomas-squamous cell carcinomas \ntowards adenocarcinomas 8, 11. In some countries squamous cell carcinoma is still \nthe most common histological type of lung cancer in male patients, e.g. France \n(41%) and United Kingdom (40%). In other countries adenocarcinoma is the most \ncommon type e.g. USA and Canada 12. In patients without a smoking history \nadenocarcinoma is most common 13-16. \nDespite new insights and improved medical treatments, lung cancer remains the \ntype of cancer with the highest mortality. Additional studies are needed to improve \ndetection of lung cancer in an early (pre)malignant stage to improve survival. \nImproved pretreatment staging of lung cancer is necessary to prevent under- or \nover treatment. Furthermore a better understanding of tumor behavior improves \ntreatment modalities. \nIn this introduction the histological subtypes of lung cancer, the microenvironment \nof lung cancer and systemic treatment modalities are described. Furthermore \nseveral imaging techniques to analyze the microenvironment of lung cancer tissue \nare discussed.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".