Therapy-induced histopathological changes in breast cancers: The changing role of pathology in breast cancer diagnosis and treatment
Bibliographic record
Abstract
Aim: Breast cancer therapy causes morphological alterations in the cancerous as well as the surrounding healthy tissue. The histopathological interpretation in such cases, thus, requires a thorough knowledge of the cytological and stromal changes rendered by the therapy during and posttherapy. The aim of the present study was to evaluate such cytological and stromal changes rendered by the therapy in breast cancer cases.\nMethods: The present study was a combined retrospective and prospective study, wherein clinical and histopathological details were collected from a total of 39 cases of breast carcinoma before and posttherapy, and the changes induced by the therapy were correlated.\nResults: Stage II breast carcinoma was found to be the most predominant stage, while invasive ductal carcinoma-not otherwise specified (IDC-NOS) of tumor was the most common histologic type both before (94.87%) and after (76.92%) therapy. Pathologic complete response (pCR) was observed in 18% of the cases while 15% showed pathologic partial response (pPR) and 66.7% cases had a stable disease. Intracellular changes commonly noted after chemotherapy included nuclear enlargement, hyperchromasia, and increased nuclear: cytoplasmic ratio while predominant stromal changes included necrosis (74.4%), fibrosis (64.1%), and desmoplasia (59%).\nConclusion: Breast cancer therapy causes morphological alterations in the cancerous as well as the surrounding healthy tissue. The histopathological interpretation in such cases, thus, requires a thorough knowledge of the cytological and stromal changes rendered by the therapy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".