Secondary neoplastic lesions in lymph nodes are predominantly metastases from solid tumors, whereas primary lymph node hemangiomas are exceptionally uncommon, with only 24 well-documented cases in the literature. Histologically, they are characterized by endothelial cells that may appear flattened or enlarged, with variable vascular density, and the presence of stromal elements. Notably, the concurrent presence of a primary hemangioma and a metastasis from breast cancer - the latter being the most prevalent secondary lesion in axillary lymph nodes - represents an unprecedented observation. The unique case presented herein underscores the exceptional rarity of primary lymph node hemangiomas and demonstrates for the first time their possible coexistence with breast cancer metastasis within the same axillary lymph node. In sharing and discussing this case study, we pay homage to Professor Juan Rosai, whose work in redefining rare and complex diagnoses continues to enlighten our understanding of lymph node vascular lesions.

UNASSIGNED: Axillary lymph node involvement (ALNI) is associated with an increased risk of local recurrence and poor prognosis in early breast cancer. The determination of the risk of positive axillary lymph node contributes to therapeutic decisions.
UNASSIGNED: The aim of this study was to identify clinicopathological predictive factors of axillary lymph node metastases in patients with early breast cancer.
UNASSIGNED: We included patients with clinical T0, T1 andT2 invasive breast carcinoma who underwent resection of the primary tumor and axillary staging by sentinel lymph node biopsy and/or axillar lymph node dissection between 2012 and 2018.
UNASSIGNED: Of the 135patients included, 41.5% had ALNI. Regarding univariate analysis, clinical factors correlated with positive ALNM were clinical tumour size>30mm, clinical tumour stage, clinical number of tumours, clinical axillary nodal status and nodal status on ultrasound. Pathologic factors associated with nodal involvement were pathologic tumour stage, tumour grade SBR, number of foci, lymphovascular invasion, perineural invasion and Ki67>20%.In multivariate logistic regression, clinical axillary nodal status, pathologic tumour stage and lymphovascular invasion (LVI) remained as independent predictors of ALNI.
UNASSIGNED: Based on these results, we suggest that clinical axillary nodal status, pathologic tumour stage and LVI are predictive factors for ALNM in Tunisian women with early breast cancer.

We present herein an extension to our recently developed and published method termed \"Fractionation of Nodal Cell Suspension\" (FNCS). The method enables efficient subcellular fractionation into nuclear (N) and cytosolic (C) compartments of extremely fibrous and problematic metastatic axillary lymph node (mALN) tissue, using the entire nodule. For the purpose of the present study, a case of invasive lobular breast cancer (BC) patient with pT2N3aMx status and defined primary tumor markers (ERα 8, PR-B 8, and HER2 score 0) was available. Initially, the mALN tissue of this patient was analyzed by immunohistochemistry (IHC), and a positive correlation of nodal ERα, PR-B and HER2 biomarkers to those of the primary tumor was obtained. Subsequently, the mALN was FNCS fractionated into N and C, and Western blot (WB) analysis demonstrated a single band for ERα, PR-B and nuclear loading control (HDAC1) in nuclear, but not in the cytosolic compartments, confirming the efficiency of our fractionation protocol. At the same time, HER2 bands were not observed in either compartment, in accordance with HER2 negativity determined by IHC in both primary tumor and mALN tissue. In conclusion, by confirming the nuclear expression of ERα and PR-B biomarkers in metastatic loci, we demonstrate the purity of the FNCS-generated compartments - the protocol that offers a reliable tool for further analysis of nuclear versus cytosolic content in downstream analysis of novel biomarkers in the whole mALN of BC patients.

We present a case of follicular dendritic cell sarcoma in the axillary lymph node, which unexpectedly showed favorable outcomes after the application of apatinib. Follicular Dendritic Cell Sarcoma (FDCS) exhibits a rare incidence and an unclear pathogenic mechanism, contributing to the limited breakthroughs in its treatment to date within the medical field. The current mainstream therapeutic approaches include surgery, CHOP(cyclophosphamide, doxorubicin, vincristine, prednisone), ICE(ifosfamide, carboplatin, etoposide), ABVD(doxorubicin, bleomycin, vinblastine, dacarbazine), and immune checkpoint inhibitors. A 38-year-old male patient was admitted to the hospital due to a lump in the right axilla and underwent surgical treatment. Postoperative pathology confirmed the diagnosis of follicular dendritic cell sarcoma. Two months post-surgery, he faced a recurrence, prompting a subsequent surgical intervention complemented by tumor radiofrequency ablation. Despite these interventions, the treatment response was suboptimal. Subsequently, the patient was treated with the CHOP regimen, but after two cycles, he developed bone metastasis. Due to the patient\'s limited financial resources and refusal of immunotherapy, we switched to a regimen of gemcitabine and docetaxel, but the disease progressed again after two cycles. A one-cycle trial of albumin-bound paclitaxel yielded unsatisfactory results. Ultimately, the patient was treated with Apatinib, achieving a 10-month progression-free survival. Due to the patient\'s limited financial circumstances, we, in the absence of guideline recommendations and evidence from evidence-based medicine, achieved a 10-month progression-free survival (PFS) solely based on experiential use of the anti-angiogenic drug, Apatinib. The purpose of this case report is to provide additional therapeutic options for FDCS treatment and to pave the way for exploring the mechanism of action of Apatinib in FDCS.

UNASSIGNED: Non-mammary metastases to the breast and axilla are rare instances, and isolated axillary lymph node metastases are especially rare. We present a rare case of left axillary lymph node metastasis from a primary endometrial carcinosarcoma.
METHODS: We report a case of a 73-year-old woman who presented with a left breast tail palpable mass. Sonomammography and breast MRI revealed multiple enlarged left axillary lymph nodes (LN) showing malignant criteria without any suspected malignancy in either breast on imaging. The patient underwent a nodal excisional biopsy that diagnosed axillary lymph node metastasis from a gynecologic origin. Complementary abdominopelvic CT revealed a suspicious endometrial mass that was confirmed on MRI. She underwent D&C and the pathology revealed endometrial carcinosarcoma.
UNASSIGNED: Accurate detection of extramammary primary sites is crucial as their management and outcome differ significantly from primary breast cancer. To the best of our knowledge, our case could be the first reported case of isolated metastatic axillary LN from uterine carcinosarcoma presenting as the initial symptom without pelvic or abdominal LN involvement.
CONCLUSIONS: For these patients to avoid needless surgical procedures and therapies, a proper diagnosis made by a multidisciplinary team with precise radiologic and pathologic correlation is essential.

BACKGROUND: The most common route of breast cancer metastasis is through the mammary lymphatic network. An accurate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surgical complications. In this study, we aimed to develop a non-invasive prediction model incorporating breast specific gamma image (BSGI) features and ultrasonographic parameters to assess axillary lymph node status.
METHODS: Cohorts of breast cancer patients who underwent surgery between 2012 and 2021 were created (The training set included 1104 ultrasound images and 940 BSGI images from 235 patients, the test set included 568 ultrasound images and 296 BSGI images from 99 patients) for the development of the prediction model. six machine learning (ML) methods and recursive feature elimination were trained in the training set to create a strong prediction model. Based on the best-performing model, we created an online calculator that can make a linear predictor in patients easily accessible to clinicians. The receiver operating characteristic (ROC) and calibration curve are used to verify the model performance respectively and evaluate the clinical effectiveness of the model.
RESULTS: Six ultrasonographic parameters (transverse diameter of tumour, longitudinal diameter of tumour, lymphatic echogenicity, transverse diameter of lymph nodes, longitudinal diameter of lymph nodes, lymphatic color Doppler flow imaging grade) and one BSGI features (axillary mass status) were selected based on the best-performing model. In the test set, the support vector machines\' model showed the best predictive ability (AUC = 0.794, sensitivity = 0.641, specificity = 0.8, PPV = 0.676, NPV = 0.774 and accuracy = 0.737). An online calculator was established for clinicians to predict patients\' risk of ALN metastasis ( https://wuqian.shinyapps.io/shinybsgi/ ). The result in ROC showed the model could benefit from incorporating BSGI feature.
CONCLUSIONS: This study developed a non-invasive prediction model that incorporates variables using ML method and serves to clinically predict ALN metastasis and help in selection of the appropriate treatment option.

UNASSIGNED: This study aimed to investigate whether quantitative radiomics features extracted from conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) of primary breast lesions can help noninvasively predict axillary lymph nodes metastasis (ALNM) in breast cancer patients.
UNASSIGNED: A total of 111 breast cancer patients with 111 breast lesions were prospectively enrolled. All the included patients received presurgical CUS screening and CEUS examination and were randomly assigned to the training and validation sets at a ratio of 7:3 (n = 78 versus 33). Radiomics features were respectively extracted based on CUS and CEUS using the PyRadiomics package. The max-relevance and min-redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) analyses were used for feature selection and radiomics score calculation in the training set. The variance inflation factor (VIF) was performed to check the multicollinearity among selected predictors. The best performing model was selected to develop a nomogram using binary logistic regression analysis. The calibration and clinical utility of the nomogram were assessed.
UNASSIGNED: The model combining CUS reported ALN status, CUS radiomics score (CUS-radscore) and CEUS radiomics score (CEUS-radscore) exhibited the best performance. The areas under the curves (AUC) of our proposed nomogram in the training and external validation sets were 0.845 [95% confidence interval (CI), 0.739-0.950] and 0.901 (95% CI, 0.758-1). The calibration curves and decision curve analysis (DCA) demonstrated the nomogram\'s robust consistency and clinical utility.
UNASSIGNED: The established nomogram is a promising prediction tool for noninvasive prediction of ALN status. The radiomics features based on CUS and CEUS can help improve the predictive performance.

BACKGROUND: Metaplastic breast carcinomas are a rare variant group of breast carcinomas. They are usually high-grade and triple-negative tumors. They often present with large primary tumor sizes. However, the involvement of axillary lymph nodes is infrequent at the time of diagnosis. Metaplastic breast carcinomas are associated with a worse prognosis and a poorer response to chemotherapy in comparison with other non-metaplastic triple-negative breast cancers. Up until this point, there are no specific treatment recommendations for metaplastic breast carcinomas beyond those intended for invasive breast cancer in general.
METHODS: A 40-year-old woman complained of a palpable mass in her left axilla. On ultrasonography, the mass was solid, spindle-shaped, hypoechoic with regular borders, and exhibited decreased vascularity. At first, the mass appeared to be of a muscular origin. There was not any clinical nor ultrasonic evidence of a primary breast tumor. On magnetic resonance imaging, the axillary mass was a well-defined with regular borders, measuring 24 × 35 mm. Needle biopsy showed a spindle cell tumor with mild to moderate atypia. The subsequent surgical resection revealed a spindle cell neoplasm within a lymph node, favoring a metastatic origin of the tumor. The tumor cells lacked expression of estrogen, progesterone, and HER2 receptors. PET-CT scan indicated pathological uptake in the left breast. Accordingly, the patient was diagnosed with metaplastic breast cancer that had metastasized to the axillary lymph node. She commenced a combined chemotherapy regimen of doxorubicin and cyclophosphamide. After six treatment cycles, she underwent left modified radical mastectomy with axillary lymph node dissection. Pathological examination of the specimens revealed a total burn-out tumor in the breast due to excellent treatment response. There were no residual tumor cells. All dissected lymph nodes were free of tumor. At the one-year follow-up, the patient showed no signs of tumor recurrence.
CONCLUSIONS: This report sheds light on a distinctive presentation of metaplastic breast carcinoma, emphasizing the need for vigilance in diagnosing this rare and aggressive breast cancer variant. In addition, the patient\'s remarkable response to chemotherapy highlights potential treatment avenues for metaplastic breast cancer.

OBJECTIVE: Accurate identification of primary breast cancer and axillary positive-node response to neoadjuvant chemotherapy (NAC) is important for determining appropriate surgery strategies. We aimed to develop combining models based on breast multi-parametric magnetic resonance imaging and clinicopathologic characteristics for predicting therapeutic response of primary tumor and axillary positive-node prior to treatment.
METHODS: A total of 268 breast cancer patients who completed NAC and underwent surgery were enrolled. Radiomics features and clinicopathologic characteristics were analyzed through the analysis of variance and the least absolute shrinkage and selection operator algorithm. Finally, 24 and 28 optimal features were selected to construct machine learning models based on 6 algorithms for predicting each clinical outcome, respectively. The diagnostic performances of models were evaluated in the testing set by the area under the curve (AUC), sensitivity, specificity, and accuracy.
RESULTS: Of the 268 patients, 94 (35.1 %) achieved breast cancer pathological complete response (bpCR) and of the 240 patients with clinical positive-node, 120 (50.0 %) achieved axillary lymph node pathological complete response (apCR). The multi-layer perception (MLP) algorithm yielded the best diagnostic performances in predicting apCR with an AUC of 0.825 (95 % CI, 0.764-0.886) and an accuracy of 77.1 %. And MLP also outperformed other models in predicting bpCR with an AUC of 0.852 (95 % CI, 0.798-0.906) and an accuracy of 81.3 %.
CONCLUSIONS: Our study established non-invasive combining models to predict the therapeutic response of primary breast cancer and axillary positive-node prior to NAC, which may help to modify preoperative treatment and determine post-NAC surgery strategy.

BACKGROUND: Accurate assessment of axillary status after neoadjuvant therapy for breast cancer patients with axillary lymph node metastasis is important for the selection of appropriate subsequent axillary treatment decisions. Our objectives were to accurately predict whether the breast cancer patients with axillary lymph node metastases could achieve axillary pathological complete response (pCR).
METHODS: We collected imaging data to extract longitudinal CT image features before and after neoadjuvant chemotherapy (NAC), analyzed the correlation between radiomics and clinicopathological features, and developed models to predict whether patients with axillary lymph node metastasis can achieve axillary pCR after NAC. The clinical utility of the models was determined via decision curve analysis (DCA). Subgroup analyses were also performed. Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots.
RESULTS: A total of 549 breast cancer patients with metastasized axillary lymph nodes were enrolled in this study. 42 independent radiomics features were selected from LASSO regression to construct a logistic regression model with clinicopathological features (LR radiomics-clinical combined model). The AUC of the LR radiomics-clinical combined model prediction performance was 0.861 in the training set and 0.891 in the testing set. For the HR + /HER2 - , HER2 + , and Triple negative subtype, the LR radiomics-clinical combined model yields the best prediction AUCs of 0.756, 0.812, and 0.928 in training sets, and AUCs of 0.757, 0.777 and 0.838 in testing sets, respectively.
CONCLUSIONS: The combination of radiomics features and clinicopathological characteristics can effectively predict axillary pCR status in NAC breast cancer patients.