A Rare Case of Non-Hodgkin B-Cell Lymphoma Following Invasive Lobular Carcinoma of the Breast: A Case Report
Bibliographic record
Abstract
The association between breast cancer and non-Hodgkin lymphoma of the spleen is extremely rare, with very few cases documented in the medical literature. We present the case of a 39-year-old woman in good health but with a family history of breast cancer, who, in 2017, developed invasive lobular carcinoma in her right breast, which was treated with mastectomy followed by hormonal therapy. In 2024, she presented with a suspicious right axillary mass, suspected of recurrence, which was confirmed by fine-needle aspiration biopsy. The patient received neoadjuvant chemotherapy, followed by axillary lymph node dissection and bilateral adnexectomy. CT and PET scans showed suspicious splenic lesions suggestive of metastases. Infectious and hematological tests were negative, leading to the decision to perform laparoscopic splenectomy. Histological examination revealed follicular B-cell non-Hodgkin lymphoma. The patient is now in good general condition and is on a biannual follow-up. The case highlights the diagnostic complexity of tumor recurrences and the need to consider alternative diagnoses other than metastasis in oncological patients.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".