Abstract SP097: Local Regional Management Following Neoadjuvant Chemotherapy: Minding the Knowledge Gaps
Bibliographic record
Abstract
Abstract Neoadjuvant chemotherapy is often used in managing breast cancer patients, particular those with HER2-positive (HER2+) or triple negative disease. Among the known benefits of administering chemotherapy prior to surgery is the opportunity to assess response which informs prognosis. The CTneoBC analysis, which included 12 trials enrolling over 11,950 patients, showed that patients experiencing a pathologic complete response (pCR; ypT0ypN0 or ypT0/isN0) have improved event-free and overall survival with the association between pCR and longterm outcomes being strongest in patients with triple negative breast cancer (TNBC) and in those with HER2+/hormone receptor-negative breast cancer receiving trastuzumab. In triple negative and HER2+ breast cancer, the response to neoadjuvant chemotherapy also informs the need for additional adjuvant therapy. For patients with TNBC and significant residual disease after neoadjuvant chemotherapy, the CREATE-X trial showed benefit for adjuvant xeloda. For patients with HER2+ breast cancer not experiencing a pCR to neoadjuvant chemotherapy plus HER2-targeted therapy, the KATHERINE trial showed a significant improvement in invasive disease-free survival and overall survival in patients receiving adjuvant T-DM1 versus adjuvant trastuzuamb. With respect to considerations for local regional therapy, it is known that neoadjuvant chemotherapy decreases the primary tumor size such that tumors thought to be inoperable become operable and tumors thought to require mastectomy downstage to be eligible for lumpectomy. More recently it has been shown that neoadjuvant chemotherapy can decrease the extent of axillary surgery required. While historically patients with clinically node positive disease at presentation were thought to require axillary lymph node dissection (ALND), the American College of Surgeons Oncology Group Z1071 trial demonstrated that sentinel lymph node biopsy (SLNB) was feasible in those who convert to clinically node negative disease following neoadjuvant chemotherapy. These findings were confirmed in the European SENTINA trial and Canadian SN-FNAC study. All three of these studies suggested that technical aspects of the SLNB procedure were critical to ensure a sufficiently low false negative rate. While these are the “known knowns”, there are many “known unknowns” and evolving data, particularly as it relates to local regional therapy following neoadjuvant chemotherapy. Included among the “known unknowns” are the optimal imaging modality to assess response to neoadjuvant chemotherapy thereby facilitating surgical planning, optimal technique for performing SLNB for those patients converting from clinical N1 to clinical N0 disease after neoadjuvant chemotherapy, the definition of negative margins for a lumpectomy, the role of routine use of immunohistochemistry for cytokeratins in pathologic evaluation of the sentinel lymph nodes, the optimal method for pathologic staging following neoadjuvant chemotherapy, and the role of ALND versus radiation therapy for patients with residual nodal metastases in the sentinel nodes. More recently, it has been questioned whether patients experiencing a complete radiographic response can undergo image guided biopsy in lieu of surgery; yet data from 3 prospective series demonstrate unacceptably high false negative rates with this approach. Given the significant implications of accurately assessing response to therapy and the presence of residual disease, optimizing local regional management is a critical component of the multidisciplinary care of these patients. In this lecture, the available data guiding local regional management after neoadjuvant chemotherapy will be reviewed with attention focused on areas in which there is limited data, therefore knowledge gaps. Citation Format: E Mittendorf. Local Regional Management Following Neoadjuvant Chemotherapy: Minding the Knowledge Gaps [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr SP097.
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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".