Apatinib manifests an unexpectedly favorable outcome in the management of axillary lymph node follicular dendritic cell sarcoma: a case report.

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.

Additional confirmation of successful cell suspension fractionation of metastatic axillary node tissue.

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.

Combination of IVIM with DCE-MRI for diagnostic and prognostic evaluation of breast cancer.

PURPOSE: To identify the most effective combination of DCE-MRI (Ktrans,Kep) and IVIM (D,f) and analyze the correlations of these parameters with prognostic indicators (ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size) to improve the diagnostic and prognostic efficiency in breast cancer. METHODS: This is a prospective study. We performed T1WI, T2WI, IVIM, DCE-MRI at 3 T MRI examinations on benign and malignant breast lesions that met the inclusion criteria. We also collected pathological results of corresponding lesions, including ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size. The diagnostic efficacy of DCE-MRI, IVIM imaging, and their combination for benign and malignant breast lesions was assessed. Correlations between the DCE-MRI and IVIM parameters and prognostic indicators were assessed. RESULTS: Overall,59 female patients with 62 lesions (22 benign lesions and 40 malignant lesions) were included in this study. The malignant group showed significantly lower D values (p < 0.05) and significantly higher Ktrans, Kep, and f values (p < 0.05). The AUC values of DCE, IVIM, DCE + IVIM were 0.828, 0.882, 0.901. Ktrans, Kep, D and f values were correlated with the pathological grade (p < 0.05); Ktrans was negatively correlated with ER expression (r = -0.519, p < 0.05); Kep was correlated with PR expression and the Ki-67 index (r = -0.489, 0.330, p < 0.05); the DCE and IVIM parameters showed no significant correlations with the HER2 and ALN (p > 0.05). Tumor diameter was correlated with the Kep, D and f values (r = 0.246, -0.278, 0.293; p < 0.05). CONCLUSION: IVIM and DCE-MRI allowed differential diagnosis of benign and malignant breast lesions, and their combination showed significantly better diagnostic efficiency. DCE- and IVIM-derived parameters showed correlations with some prognostic factors for breast cancer.

Concurrent presence of primary hemangioma and breast cancer metastasis within a lymph node: a case report inspired by the legacy of Professor Juan Rosai.

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.

Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS.

Our study aims to address the methodological challenges frequently encountered in RNA-Seq data analysis within cancer studies. Specifically, it enhances the identification of key genes involved in axillary lymph node metastasis (ALNM) in breast cancer. We employ Generalized Linear Models with Quasi-Likelihood (GLMQLs) to manage the inherently discrete and overdispersed nature of RNA-Seq data, marking a significant improvement over conventional methods such as the t-test, which assumes a normal distribution and equal variances across samples. We utilize the Trimmed Mean of M-values (TMMs) method for normalization to address library-specific compositional differences effectively. Our study focuses on a distinct cohort of 104 untreated patients from the TCGA Breast Invasive Carcinoma (BRCA) dataset to maintain an untainted genetic profile, thereby providing more accurate insights into the genetic underpinnings of lymph node metastasis. This strategic selection paves the way for developing early intervention strategies and targeted therapies. Our analysis is exclusively dedicated to protein-coding genes, enriched by the Magnitude Altitude Scoring (MAS) system, which rigorously identifies key genes that could serve as predictors in developing an ALNM predictive model. Our novel approach has pinpointed several genes significantly linked to ALNM in breast cancer, offering vital insights into the molecular dynamics of cancer development and metastasis. These genes, including ERBB2, CCNA1, FOXC2, LEFTY2, VTN, ACKR3, and PTGS2, are involved in key processes like apoptosis, epithelial-mesenchymal transition, angiogenesis, response to hypoxia, and KRAS signaling pathways, which are crucial for tumor virulence and the spread of metastases. Moreover, the approach has also emphasized the importance of the small proline-rich protein family (SPRR), including SPRR2B, SPRR2E, and SPRR2D, recognized for their significant involvement in cancer-related pathways and their potential as therapeutic targets. Important transcripts such as H3C10, H1-2, PADI4, and others have been highlighted as critical in modulating the chromatin structure and gene expression, fundamental for the progression and spread of cancer.

Prediction of the number of positive axillary lymph nodes according to sentinel lymph node involvement and biological subtypes in patients receiving neoadjuvant chemotherapy.

BACKGROUND: Sentinel lymph node biopsy (SLNB) has replaced axillary lymph node dissection (ALND) for assessing axillary lymph node status in clinically node-negative breast cancer patients. However, the approach to axillary surgery after neoadjuvant treatment is still controversial. In the present study, our objective was to predict the pathological nodal stage based on SLNB results and the clinicopathological characteristics of patients who initially presented with clinical N1 positivity but whose disease status was converted to clinical N0 after neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: After NAC, 150 clinically node-negative patients were included. The relationships between clinicopathologic parameters and the number of positive lymph nodes in SLNBs and ALNDs were assessed through binary/multivariate logistic regression analysis. RESULTS: Among 150 patients, 78 patients had negative SLNBs, and 72 patients had positive SLNBs. According to the ALND data of 21 patients with SLNB1+, there was no additional node involvement (80.8%), 1-2 lymph nodes were positive in 5 patients (19.2%), and no patient had ≥ 3 lymph nodes involved. Following the detection of SLNB1 + positivity, the rate of negative non-sentinel nodes were 75% in the luminal A/B subgroup, 100% in the HER-2-positive subgroup, and 100% in the triple-negative subgroup. Patients with a lower T stage (T1-3 vs. T4), fewer than 4 clinical nodes before NAC (< 4 vs. ≥4), and a decreased postoperative Ki-67 index (< 10% vs. stable/increase) were included. According to both univariate and multivariate analyses, being in the triple-negative or HER2-positive subgroup, compared to the luminal A/B subgroup (luminal A/B vs. HER2-positive/triple-negative), was found to be predictive of complete lymph node response. CONCLUSION: The number of SLNB-positive nodes, tumor-related parameters, and response to treatment may predict no additional nodes to be positive at ALND.

Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in invasive breast cancer.

OBJECTIVE: To develop a nomogram for predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer. METHODS: We included 307 patients with clinicopathologically confirmed invasive breast cancer. The cohort was divided into a training group (n=215) and a validation group (n=92). Ultrasound images were used to extract radiomics features. The least absolute shrinkage and selection operator (LASSO) algorithm helped select pertinent features, from which Radiomics Scores (Radscores) were calculated using the LASSO regression equation. We developed three logistic regression models based on Radscores and 2D image features, and assessed the models' performance in the validation group. A nomogram was created from the best-performing model. RESULTS: In the training set, the area under the curve (AUC) for the Radscore model, 2D feature model, and combined model were 0.76, 0.85, and 0.88, respectively. In the validation set, the AUCs were 0.71, 0.78, and 0.83, respectively. The combined model demonstrated good calibration and promising clinical utility. CONCLUSION: Our ultrasound-based radiomics nomogram can accurately and non-invasively predict ALNM in breast cancer, suggesting potential clinical applications to optimize surgical and medical strategies.

Integration of Radiomics and Immune-Related Genes Signatures for Predicting Axillary Lymph Node Metastasis in Breast Cancer.

BACKGROUND: To develop a radiogenomics nomogram for predicting axillary lymph node (ALN) metastasis in breast cancer and reveal underlying associations between radiomics features and biological pathways. MATERIALS AND METHODS: This study included 1062 breast cancer patients, 90 patients with both DCE-MRI and gene expression data. The optimal immune-related genes and radiomics features associated with ALN metastasis were firstly calculated, and corresponding feature signatures were constructed to further validate their performances in predicting ALN metastasis. The radiogenomics nomogram for predicting the risk of ALN metastasis was established by integrating radiomics signature, immune-related genes (IRG) signature, and critical clinicopathological factors. Gene modules associated with key radiomics features were identified by weighted gene co-expression network analysis (WGCNA) and submitted to functional enrichment analysis. Gene set variation analysis (GSVA) and correlation analysis were performed to investigate the associations between radiomics features and biological pathways. RESULTS: The radiogenomics nomogram showed promising predictive power for predicting ALN metastasis, with AUCs of 0.973 and 0.928 in the training and testing groups, respectively. WGCNA and functional enrichment analysis revealed that gene modules associated with key radiomics features were mainly enriched in breast cancer metastasis-related pathways, such as focal adhesion, ECM-receptor interaction, and cell adhesion molecules. GSVA also identified pathway activities associated with radiomics features such as glycogen synthesis, integration of energy metabolism. CONCLUSION: The radiogenomics nomogram can serve as an effective tool to predict the risk of ALN metastasis. This study provides further evidence that radiomics phenotypes may be driven by biological pathways related to breast cancer metastasis.

Implementation of Choosing Wisely® Recommendations for Lymph Node Surgery in Male Breast Cancer.

BACKGROUND: The Choosing Wisely® (CW) campaign recommended de-implementation of surgical management of axillary nodes in specified patients. This study aimed to assess trends in the application of CW guidelines for lymph node (LN) surgery in males with breast cancer. METHODS: The National Cancer Database was queried for males diagnosed with breast cancer from 2017 to 2020. Patients were categorized into two cohorts based on CW criteria. Cohort 1 included all T1-2, clinically node-negative patients who underwent breast-conserving therapy and with ≤ 2 positive nodes, and Cohort 2 included all T1-2, node-negative, hormone receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative patients aged ≥ 70 years. In Cohort 1, patients who underwent sentinel LN biopsy (SLNB) alone were compared with axillary LN dissection (ALND) or no LN surgery, while in Cohort 2, patients who underwent LN surgery were compared with those with no LN surgery. RESULTS: Of 617 patients who met the criteria for Cohort 1, 73.1% underwent SLNB alone compared with ALND (11.8%) or no LN surgery (15.1%). Those who received SLNB alone were younger (65 vs. 68 vs. 73 years; p < 0.001). The annual proportion of males who underwent SLNB alone remained stable from 2017 to 2020. Overall, 1565 patients met the criteria for Cohort 2, and 84.9% received LN surgery. LN surgery was omitted in older patients (81 vs. 77; p < 0.001). The proportion of elderly males with early-stage breast cancer who underwent LN surgery increased from 2017 to 2020. CONCLUSION: This study demonstrates that CW recommendations are not being routinely applied to males. These findings reinforce the need for additional studies and subsequent recommendations for optimal application of axillary surgery de-implementation for males diagnosed with breast cancer.

A nomogram model for predicting the risk of axillary lymph node metastasis in patients with early breast cancer and cN0 status.

Axillary staging is commonly performed via sentinel lymph node biopsy for patients with early breast cancer (EBC) presenting with clinically negative axillary lymph nodes (cN0). The present study aimed to investigate the association between axillary lymph node metastasis (ALNM), clinicopathological characteristics of tumors and results from axillary ultrasound (US) scanning. Moreover, a nomogram model was developed to predict the risk for ALNM based on relevant factors. Data from 998 patients who met the inclusion criteria were retrospectively reviewed. These patients were then randomly divided into a training and validation group in a 7:3 ratio. In the training group, receiver operating characteristic curve analysis was used to identify the cutoff values for continuous measurement data. R software was used to identify independent ALNM risk variables in the training group using univariate and multivariate logistic regression analysis. The selected independent risk factors were incorporated into a nomogram. The model differentiation was assessed using the area under the curve (AUC), while calibration was evaluated through calibration charts and the Hosmer-Lemeshow test. To assess clinical applicability, a decision curve analysis (DCA) was conducted. Internal verification was performed via 1000 rounds of bootstrap resampling. Among the 998 patients with EBC, 228 (22.84%) developed ALNM. Multivariate logistic analysis identified lymphovascular invasion, axillary US findings, maximum diameter and molecular subtype as independent risk factors for ALNM. The Akaike Information Criterion served as the basis for both nomogram development and model selection. Robust differentiation was shown by the AUC values of 0.855 (95% CI, 0.817-0.892) and 0.793 (95% CI, 0.725-0.857) for the training and validation groups, respectively. The Hosmer-Lemeshow test yielded P-values of 0.869 and 0.847 for the training and validation groups, respectively, and the calibration chart aligned closely with the ideal curve, affirming excellent calibration. DCA showed that the net benefit from the nomogram significantly outweighed both the 'no intervention' and the 'full intervention' approaches, falling within the threshold probability interval of 12-97% for the training group and 17-82% for the validation group. This underscores the robust clinical utility of the model. A nomogram model was successfully constructed and validated to predict the risk of ALNM in patients with EBC and cN0 status. The model demonstrated favorable differentiation, calibration and clinical applicability, offering valuable guidance for assessing axillary lymph node status in this population.

Assessing the Efficacy of Radioactive Iodine Seed Localisation in Targeted Axillary Dissection for Node-Positive Early Breast Cancer Patients Undergoing Neoadjuvant Systemic Therapy: A Systematic Review and Pooled Analysis.

Targeted axillary dissection (TAD), employing marked lymph node biopsy (MLNB) alongside sentinel lymph node biopsy (SLNB), is increasingly recognised for its efficacy in reducing false negative rates (FNRs) in node-positive early breast cancer patients receiving neoadjuvant systemic therapy (NST). One such method, 125I radioactive seed localisation (RSL), involves implanting a seed into a biopsy-proven lymph node either pre- or post-NST. This systematic review and pooled analysis aimed to assess the performance of RSL in TAD among node-positive patients undergoing NST. Six studies, encompassing 574 TAD procedures, met the inclusion criteria. Results showed a 100% successful deployment rate, with a 97.6% successful localisation rate and a 99.8% retrieval rate. Additionally, there was a 60.0% concordance rate between SLNB and MLNB. The FNR of SLNB alone was significantly higher than it was for MLNB (18.8% versus 5.3%, respectively; p = 0.001). Pathological complete response (pCR) was observed in 44% of cases (248/564). On average, the interval from 125I seed deployment to surgery was 75.8 days (range: 0-272). These findings underscore the efficacy of RSL in TAD for node-positive patients undergoing NST, enabling precise axillary pCR identification and facilitating the safe omission of axillary lymph node dissection.

Isolated left axillary nodal metastasis from endometrial carcinosarcoma: A case report and literature review.

INTRODUCTION AND IMPORTANCE: 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. CASE PRESENTATION: 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. CLINICAL DISCUSSION: 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. CONCLUSION: 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.

The Feasibility and Reliability of Sentinel Lymph Node Biopsy After Neoadjuvant Chemotherapy in Breast Cancer Patients With Negative Axillary Lymph Nodes-A Meta-analysis.

Background: The application of sentinel lymph node biopsy (SLNB) has expanded from early breast cancer to locally advanced breast cancer with neoadjuvant chemotherapy (NAC). For patients with negative axillary lymph nodes, performing SLNB before or after NAC remains controversial. Objectives: To evaluate the diagnostic feasibility and reliability of SLNB after NAC in breast cancer patients with negative axillary nodes at initial diagnosis. Design: To calculate pooled identification rate (IR) and false negative rate (FNR) of SLNB after NAC on breast cancer patients with initially negative axillary nodes by enrolling relevant studies and perform subgroup analysis by the type of tracer and the number of biopsied sentinel lymph nodes in average. Data sources and methods: The PubMed, Embase, Cochrane, Web of Science, and Scopus databases from January 1, 2002, to March 1, 2022, were searched for studies. The QUADAS-2 tool and MINORS item were employed to evaluate the quality of the included studies. I2 and Q tests were used to evaluate the heterogeneity among the studies. Random-effects model and fixed-effects model were employed to calculate the pooled IR, FNR, and 95% confidence interval (CI). Publication bias was evaluated, and sensitivity analysis was performed. Subgroup analysis was performed according to the type of tracer (single/double) and the number of biopsied sentinel lymph nodes in average (⩽2/>2). Results: A total of 21 studies covering 1716 patients were enrolled in this study (IR = 93%, 95% CI = 90-96; FNR = 8%, 95% CI = 6-11). Conclusion: The SLNB after NAC can serve as a feasible and reliable approach in breast cancer patients with negative axillary lymph node. In our study, no significant impact of tracer was found on the IR and FNR of SLNB, and the number of biopsy nodes >2 leads to the decreased FNR of SLNB.

Value of Contrast-Enhanced Ultrasound Combined with Immune-Inflammatory Markers in Predicting Axillary Lymph Node Metastasis of Breast Cancer.

RATIONALE AND OBJECTIVES: To evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) combined with immune-inflammatory markers in predicting axillary lymph node metastasis (ALNM) in breast cancer patients. METHODS: From January 2020 to June 2023, the clinicopathological data and ultrasound features of 401 breast cancer patients who underwent biopsy or surgery were recorded. Patients were randomly divided into a training set (321 patients) and a validation set (80 patients). The risk factors for ALNM were determined using univariate, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis, and prediction models were constructed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess their diagnostic performance. RESULTS: Logistic regression analysis demonstrated that systemic immunoinflammatory index (SII), CA125, Ki67, pathological type, lesion size, enhancement pattern and Breast Imaging Reporting and Data System (BI-RADS) category were significant risk factors for ALNM. Three different models were constructed, and the combined model yielded an AUC of 0.903, which was superior to the clinical model (AUC=0.790) and ultrasound model (AUC=0.781). A nomogram was constructed based on the combined model, calibration curves and DCA demonstrated its satisfactory performance in predicting ALNM. CONCLUSION: The nomogram combining ultrasound features and immune-inflammatory markers could serve as a valuable instrument for predicting ALNM in breast cancer patients. DATA AVAILABILITY STATEMENT: The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer: a multicenter study.

OBJECTIVES: Developing a deep learning radiomics model from longitudinal breast ultrasound and sonographer's axillary ultrasound diagnosis for predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer. METHODS: Breast cancer patients undergoing NAC followed by surgery were recruited from three centers between November 2016 and December 2022. We collected ultrasound images for extracting tumor-derived radiomics and deep learning features, selecting quantitative features through various methods. Two machine learning models based on random forest were developed using pre-NAC and post-NAC features. A support vector machine integrated these data into a fusion model, evaluated via the area under the curve (AUC), decision curve analysis, and calibration curves. We compared the fusion model's performance against sonographer's diagnosis from pre-NAC and post-NAC axillary ultrasonography, referencing histological outcomes from sentinel lymph node biopsy or axillary lymph node dissection. RESULTS: In the validation cohort, the fusion model outperformed both pre-NAC (AUC: 0.899 vs. 0.786, p < 0.001) and post-NAC models (AUC: 0.899 vs. 0.853, p = 0.014), as well as the sonographer's diagnosis of ALN status on pre-NAC and post-NAC axillary ultrasonography (AUC: 0.899 vs. 0.719, p < 0.001). Decision curve analysis revealed patient benefits from the fusion model across threshold probabilities from 0.02 to 0.98. The model also enhanced sonographer's diagnostic ability, increasing accuracy from 71.9% to 79.2%. CONCLUSION: The deep learning radiomics model accurately predicted the ALN response to NAC in breast cancer. Furthermore, the model will assist sonographers to improve their diagnostic ability on ALN status before surgery. CLINICAL RELEVANCE STATEMENT: Our AI model based on pre- and post-neoadjuvant chemotherapy ultrasound can accurately predict axillary lymph node metastasis and assist sonographer's axillary diagnosis. KEY POINTS: Axillary lymph node metastasis status affects the choice of surgical treatment, and currently relies on subjective ultrasound. Our AI model outperformed sonographer's visual diagnosis on axillary ultrasound. Our deep learning radiomics model can improve sonographers' diagnosis and might assist in surgical decision-making.

The use of longitudinal CT-based radiomics and clinicopathological features predicts the pathological complete response of metastasized axillary lymph nodes in breast cancer.

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.

Predicting axillary lymph node metastasis in breast cancer patients: A radiomics-based multicenter approach with interpretability analysis.

PURPOSE: To develop a MRI-based radiomics model, integrating the intratumoral and peritumoral imaging information to predict axillary lymph node metastasis (ALNM) in patients with breast cancer and to elucidate the model's decision-making process via interpretable algorithms. METHODS: This study included 376 patients from three institutions who underwent contrast-enhanced breast MRI between 2021 and 2023. We used multiple machine learning algorithms to combine peritumoral, intratumoral, and radiological characteristics with the building of radiological, radiomics, and combined models. The model's performance was compared based on the area under the curve (AUC) obtained from the receiver operating characteristic analysis and interpretable machine learning techniques to analyze the operating mechanism of the model. RESULTS: The radiomics model, incorporating features from both intratumoral tissue and the 3 mm peritumoral region and utilizing the backpropagation neural network (BPNN) algorithm, demonstrated superior diagnostic efficacy, achieving an AUC of 0.820. The AUC of the combination of the RAD score, clinical T stage, and spiculated margin was as high as 0.855. Furthermore, we conducted SHapley Additive exPlanations (SHAP) analysis to evaluate the contributions of RAD score, clinical T stage, and spiculated margin in ALNM status prediction. CONCLUSIONS: The interpretable radiomics model we propose can better predict the ALNM status of breast cancer and help inform clinical treatment decisions.

Evaluating the role of level 3 axillary lymph node dissection in metastatic melanoma: Can we predict involvement?

BACKGROUND AND OBJECTIVES: MSLT-2 and DECOG-SLT established that immediate complete axillary lymph node dissection (CLND) did not correlate with an increase in melanoma-specific survival when compared with active ultrasound observation in patients with sentinel lymph node (SLN)-positive disease. After those trials, there was a shift toward performing CLND only for clinically node-positive disease. With these changes, we sought to determine the role of level III axillary lymph nodes in bulky disease and how the use of neoadjuvant therapy may impact the rate of positivity in level III axillary nodes. METHODS: We performed a retrospective chart review on all patients who underwent axillary CLND for cutaneous melanoma by one surgeon at an academic center from 2014 to 2022. These patients underwent CLND based on either having SLN+ disease or having clinically palpable or radiographically bulky disease. RESULTS: Of 95 patients included, there were 7 (7.3%) patients with level III positivity. One was SLN+ (1.0%), while 3 (3.1%) had bulky disease and neoadjuvant therapy, and 3 (3.1%) had bulky disease without neoadjuvant therapy. No preoperative factors were identified that predicted level III involvement. After performing CLND, the patients who had clinically palpable or radiographically bulky disease and neoadjuvant therapy had higher percent necrosis of nodes in levels I and II but not III. At 5 years, overall survival and recurrence-free survival were improved in those without level III involvement (58% and 64%, respectively) when compared to those with level III involvement (41% and 50%), though this was not statistically significant. CONCLUSIONS: Further study may identify better prognostic factors for level III positivity, allowing for the possibility of dissecting only levels I and II or even replacing CLND with targeted node dissections.