Diagnostic and prognostic markers for thyroid cancer
Bibliographic record
Abstract
Statement of the problem: The objectives of our study were to evaluate the debatable diagnostic role of FDG-PET/CT in predicting the risk of malignancy in follicular neoplasm and to build prognostic models to predict the risks of relapse and death from thyroid cancer, as there is no universally acceptable model valid for different histological types of thyroid cancers. Methods: The efficacy of FDG-PET/CT scan for predicting the risk of malignancy was assessed in a prospective cohort of 50 follicular neoplasms. Disease specific and relapse free survivals of a 2306-patient Manitoba thyroid cancer cohort were estimated by the Kaplan-Meier method and the independent influence of various prognostic factors was assessed by Cox Proportional Hazard models. Cumulative incidence of deaths and relapses from thyroid cancer was calculated by competing risk analysis, and was used to develop and validate prognostic nomograms, using R version 2.13.2 (www.r-project.org). A web-based prognostic model was developed to predict the disease specific survival, and validated internally and externally on an independent patient cohort from London, Ontario. Results: FDG PET/CT had an overall accuracy of 81% in predicting risk of malignancy in non-Hürthle follicular cell neoplasms and 87% accuracy in distinguishing follicular and Hürthle cell adenomas. The age standardized incidence of thyroid cancer in Manitoba increased by 373% from 1970 to 2010, with the proportion of papillary cancers increasing from 58% to 85.9%, and that of anaplastic cancer falling from 5.7% to 2.1% (p<0.001). The disease specific survival was adversely influenced by anaplastic histology, male gender, stage IV disease, incomplete surgical resection and age at diagnosis, during a median follow-up of 11.5 years. Prognostic nomograms were designed to predict the individualized 10-year risks of death and relapse from thyroid cancer, with their respective concordance indices of 0.92 and 0.76. A web-based model was successfully developed and externally validated with excellent discrimination. It compared favorably with the existing staging/risk stratification systems. Conclusions: FDGPET/CT has a very good accuracy of predicting risk of malignancy in non-Hürthle follicular cell neoplasms. We have successfully developed and validated prognostic nomograms and a web model for predicting oncological outcome of thyroid cancer.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".