Neoadjuvant therapy in gynaecological malignancies: What pathologists need to know
Bibliographic record
Abstract
In recent times, there has been a growing tendency to treat advanced gynaecological malignancies with neoadjuvant chemotherapy (NACT), with the goal of reducing tumour volume and enhancing operability resulting in optimal cytoreduction. This approach is used in particular for patients with advanced high-grade serous carcinoma of the ovary, fallopian tube or peritoneum. Pathology plays a crucial role in the management of these patients, both before and after NACT. Prior to initiation of NACT, a biopsy should be performed, usually of the omental cake, to confirm that a malignancy is present, to identify the site of origin of the tumour and to type and grade the tumour. Histopathologists must be aware of the resultant morphological effects of NACT when examining specimens following interval cytoreduction surgery. Tumour typing and grading, and even the identification of residual neoplasia, are particular challenges. Immunohistochemistry, when used judiciously, can be a useful adjunct in certain scenarios. A pathological assessment of the response to chemotherapy, and the pathological stage should be provided in the pathology report, as these may inform prognosis and subsequent management. We present a comprehensive overview of the relevant clinical and pathological aspects pertaining to NACT for gynaecological malignancies for the practicing surgical pathologist.
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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 | 0.004 |
| 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 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".