Comparison of Thyroid Transcription Factor-1 Expression by 2 Monoclonal Antibodies in Pulmonary and Nonpulmonary Primary Tumors
Bibliographic record
Abstract
Thyroid transcription factor-1 (TTF-1) is a transcription factor that plays a role in the development and physiology of the thyroid and lungs. Expression of TTF-1 is used as a marker of lung and thyroid clinically. Commercially available clones of TTF-1 monoclonal antibodies, 8G7G3/1 and SPT24, have been reported to have different sensitivities for the detection of neoplasms of different origins. Although they are used extensively in daily practice, a comprehensive comparative study of these antibodies in a wide variety of neoplasms is lacking. We examined TTF-1 expression in primary tumors of the lung, prostrate, pancreas, stomach, salivary glands, breast, bladder, colon, and squamous cell carcinoma of the head and neck and compared the results obtained with both TTF-1 clones. The SPT24 clone detected more primary lung tumors of all histologic subtypes. Importantly, the SPT24 clone detected a significantly higher number of squamous cell carcinomas and carcinoid tumors of the lung. Among nonpulmonary primary tumors, a significant number of invasive urothelial carcinoma of the bladder (5.1%) was TTF-1 positive. In addition, a small proportion of prostate (1.2%), stomach (0.9%), salivary gland (1.8%), and colon (2.5%) carcinomas were positive with both clones. Of note, both clones stained the same nonpulmonary tumors with similar intensity and distribution. Carcinomas of the pancreas, breast, and squamous cell carcinomas of the head and neck were negative with both clones. In summary, the SPT24 clone detected a higher number of pulmonary non-small cell tumors of all histologic subtypes whereas both clones stained a similar proportion of nonpulmonary tumors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".