Diagnostic challenges of lung biopsies in setting of metastatic female genital tract tumors; report of 2 cases
Bibliographic record
Abstract
Introduction: Lungs are one of the most common sites for metastatic tumors in the body. In addition, primary lung tumors are the most common cause of death due to neoplasms in both genders. Treatment strategies are completely different for primary and metastatic lung tumors making accurate diagnosis of lung tumors an effective factor in planning the correct treatment. The development of personalized medicine and targeted therapy, especially in the treatment of primary lung tumors, has highlighted the importance of correct diagnosis of these tumors. Case report: The Pathology Center of Imam Khomeini Hospital Cancer Institute in Tehran, Iran, as a referral center for cancer across the country, is faced with a large number of lung biopsies, and therefore, there will be numerous diagnostic challenges. In this article, we presented two cases of primary female genital tract (cervical) tumors that during follow up lung masses were detected. Diagnosis on lung biopsy assigned as primary lung adenocarcinoma. Both cases referred to our lab for second opinion accompanied with related resected sample and implementation of further supplementary markers documented metastatic origin of tumors. Discussion & conclusion: In both cases, similarity in immunohistochemical characteristics of metastatic tumors with primary lung adenocarcinoma, especially positive nuclear TTF1 staining led to misdiagnosis of lung tumor origin. This finding emphasizes on the use of other specific markers related to primary site of tumor to decrease possibility of incorrect diagnosis of the origin of the tumor in metastatic setting. Due to remarkable influence of primary versus metastatic origin of lung tumor on selection of treatment, pathologists should be considered correct diagnosis and notice to similarity of immunohistochemical markers of primary lung tumors to other organs and implementation of more specific markers are necessary.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".