Assessing the Efficacy of Current Histopathological Tumor Reporting Systems for Evaluating the Response to Neoadjuvant Chemotherapy for Breast Carcinoma: Protocol for an Observational Study
Bibliographic record
Abstract
BACKGROUND: With a predicted 2 million new cases identified globally in 2018, breast carcinomas are the most common cancer in women and the primary cause of cancer-associated mortality. In the management of breast cancer, neoadjuvant chemotherapy treatment (NACT) has become a mainstay, particularly for patients with inflammatory and locally advanced breast cancer. It increases the possibility of breast-conserving surgery, facilitates tumor downstaging, and gives early indications of the effectiveness of treatment. Evaluating the histopathological response after NACT is crucial for prognosis and guiding subsequent treatment decisions. This study explores the various histopathological assessment systems used in breast carcinoma patients after NACT, focusing on the residual cancer burden (RCB) score, Miller-Payne system, Chevallier classification, Sataloff classification, National Surgical Adjuvant Breast and Bowel Project (NSABP) Protocol B-18 system, and the American Joint Committee on Cancer residual tumor size (R) categories. We compare their methodologies, strengths, limitations, and clinical significance, providing a detailed analysis of their roles in improving patient outcomes. OBJECTIVE: The aim of this study is to confirm the diagnosis of breast carcinoma based on histopathology, to evaluate various scoring systems through assessments of histomorphological features affecting post-NACT patients with breast carcinoma, to compare the various systems regarding the response to therapy and forming prognoses, and to develop an ideal histomorphological assessment system for breast carcinoma in post-NACT patients. The study also focused on how breast tumors respond to NACT and how this response can guide treatment decisions and improve the formulation of prognoses. METHODS: This observational study will be retrospective and prospective; it will include 128 patients diagnosed with breast carcinomas who have undergone NACT and were referred to a tertiary care hospital between January 2019 and December 2024. Following chemotherapy, a thorough examination of the histopathological specimens will be conducted to assess any changes in histomorphology. RESULTS: Data collection started in September 2021 and will be completed by December 2025. Data analysis began in January 2025, and the results are expected to be published in December 2025. Institutional ethics committee clearance was obtained prior to commencement of the study. This is a nonfunded academic study. CONCLUSIONS: This project aims to evaluate and compare histopathological assessment systems in patients with breast carcinoma after NACT. Various histopathological systems, such as the RCB score, the Miller-Payne grading system, and other systems, each provide valuable insights into how well tumors respond to chemotherapy. The aim is to reveal essential histopathological parameters, leading to the refinement and potential modification of grading systems to improve clinical decision-making, treatment outcomes, and personalized care; however, challenges persist in standardization and consensus. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56825.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.097 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".