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Record W2756123689

Optical and Functional Imaging in Lung Cancer

2010· article· en· W2756123689 on OpenAlexaboutno aff
K.H. van deLeest

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLung cancerCancerAdenocarcinomaInternal medicineOncologyStage (stratigraphy)Survival rateCarcinomaColorectal cancerPathology
DOInot available

Abstract

fetched live from OpenAlex

textabstractLung cancer is the second most common cancer in men and women, and is the leading cause of cancer related death. In industrialized countries the mortality rate of lung cancer is higher than the mortality rate of breast, colorectal and prostate cancer combined 1. When lung cancer is diagnosed at an early stage patients are considered to have the best overall survival rate 2. Unfortunately, only a minority of patients is currently diagnosed at a curable stage of disease. The lack of specific symptoms at an early stage of the disease, the rapid growth of tumor cells and the metastatic behavior of lung tumors are the main reasons for a diagnosis at an advanced stage. Non-small-cell lung cancer (NSCLC) can be divided into three major histological subtypes: squamouscell carcinoma, adenocarcinoma, and large-cell carcinoma 3. Eighty-five percent of the lung cancer patients are diagnosed with NSCLC, and 75% of the patients are diagnosed with an incurable stage IIIB or IV disease 4, 5. Fifteen percent of the lung cancer patients have small-cell-lung cancer (SCLC) and the 5-year survival for them is even lower than for NSCLC 6. Whereas originally smoking is at the root of all types of lung cancer, the incidence of lung cancer in never smokers increases 7. Smoking is most strongly linked with SCLC and squamous-cell carcinoma 8, 9, although after the introduction of filter cigarets an increased incidence of adenocarcinomas was observed 10. This resulted in a change in ratio of adenocarcinomas-squamous cell carcinomas towards adenocarcinomas 8, 11. In some countries squamous cell carcinoma is still the most common histological type of lung cancer in male patients, e.g. France (41%) and United Kingdom (40%). In other countries adenocarcinoma is the most common type e.g. USA and Canada 12. In patients without a smoking history adenocarcinoma is most common 13-16. Despite new insights and improved medical treatments, lung cancer remains the type of cancer with the highest mortality. Additional studies are needed to improve detection of lung cancer in an early (pre)malignant stage to improve survival. Improved pretreatment staging of lung cancer is necessary to prevent under- or over treatment. Furthermore a better understanding of tumor behavior improves treatment modalities. In this introduction the histological subtypes of lung cancer, the microenvironment of lung cancer and systemic treatment modalities are described. Furthermore several imaging techniques to analyze the microenvironment of lung cancer tissue are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0530.012

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.

Opus teacher head0.008
GPT teacher head0.300
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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