RESUMEN

BACKGROUND AND AIMS: Preoperative prediction of axillary lymph node (ALN) burden in patients with early-stage breast cancer is pivotal for individualised treatment. This study aimed to develop a MRI radiomics model for evaluating the ALN burden in early-stage breast cancer and to provide biological interpretability to predictions by integrating radiogenomic data. METHODS: This study retrospectively analyzed 1211 patients with early-stage breast cancer from four centers, supplemented by data from The Cancer Imaging Archive (TCIA) and Duke University (DUKE). MRI radiomic features were extracted from dynamic contrast-enhanced MRI images and an ALN burden-related radscore was constructed by the backpropagation neural network algorithm. Clinical and combined models were developed, integrating ALN-related clinical variables and radscore. The Kaplan-Meier curve and log-rank test were used to assess the prognostic differences between the predicted high- and low-ALN burden groups in both Center I and DUKE cohorts. Gene set enrichment and immune infiltration analyses based on transcriptomic TCIA and TCIA Breast Cancer dataset were used to investigate the biological significance of the ALN-related radscore. RESULTS: The MRI radiomics model demonstrated an area under the curve of 0.781-0.809 in three validation cohorts. The predicted high-risk population demonstrated a poorer prognosis (log-rank P < .05 in both cohorts). Radiogenomic analysis revealed migration pathway upregulation and cell differentiation pathway downregulation in the high radscore groups. Immune infiltration analysis confirmed the ability of radiological features to reflect the heterogeneity of the tumor microenvironment. CONCLUSIONS: The MRI radiomics model effectively predicted the ALN burden and prognosis of early-stage breast cancer. Moreover, radiogenomic analysis revealed key cellular and immune patterns associated with the radscore.

Asunto(s)

RESUMEN

.

Asunto(s)

RESUMEN

OBJECTIVE: To investigate the potential association among preoperative breast MRI features, axillary nodal burden (ANB), and disease-free survival (DFS) in patients with early-stage breast cancer. MATERIALS AND METHODS: We retrospectively reviewed 297 patients with early-stage breast cancer (cT1-2N0M0) who underwent preoperative MRI between December 2016 and December 2018. Based on the number of positive axillary lymph nodes (LNs) determined by postoperative pathology, the patients were divided into high nodal burden (HNB; ≥3 positive LNs) and non-HNB (<3 positive LNs) groups. Univariable and multivariable logistic regression analyses were performed to identify independent risk factors associated with ANB. Predictive efficacy was evaluated using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Univariable and multivariable Cox proportional hazards regression analyses were performed to determine preoperative features associated with DFS. RESULTS: We included 47 and 250 patients in the HNB and non-HNB groups, respectively. Multivariable logistic regression analysis revealed that multifocality/multicentricity (adjusted odds ratio [OR] = 3.905, 95% confidence interval [CI]: 1.685-9.051, P = 0.001) and peritumoral edema (adjusted OR = 3.734, 95% CI: 1.644-8.479, P = 0.002) were independent risk factors for HNB. Combined peritumoral edema and multifocality/multicentricity achieved an AUC of 0.760 (95% CI: 0.707-0.807) for predicting HNB, with a sensitivity and specificity of 83.0% and 63.2%, respectively. During the median follow-up period of 45 months (range, 5-61 months), 26 cases (8.75%) of breast cancer recurrence were observed. Multivariable Cox proportional hazards regression analysis indicated that younger age (adjusted hazard ratio [HR] = 3.166, 95% CI: 1.200-8.352, P = 0.021), larger tumor size (adjusted HR = 4.370, 95% CI: 1.671-11.428, P = 0.002), and multifocality/multicentricity (adjusted HR = 5.059, 95% CI: 2.166-11.818, P < 0.001) were independently associated with DFS. CONCLUSION: Preoperative breast MRI features may be associated with ANB and DFS in patients with early-stage breast cancer.

Asunto(s)

RESUMEN

BACKGROUND: The tumor burden within the axillary lymph nodes (ALNs) constitutes a pivotal factor in breast cancer, serving as the primary determinant for treatment decisions and exhibiting a close correlation with prognosis. OBJECTIVE: This study aimed to investigate the potential of ultrasound-based radiomics and clinical characteristics in non-invasively distinguishing between low tumor burden (1-2 positive nodes) and high tumor burden (more than 2 positive nodes) in patients with node-positive breast cancer. METHODS: A total of 215 patients with node-positive breast cancer, who underwent preoperative ultrasound examinations, were enrolled in this study. Among these patients, 144 cases were allocated to the training set, 37 cases to the validation set, and 34 cases to the testing set. Postoperative histopathology was used to determine the status of ALN tumor burden. The region of interest for breast cancer was delineated on the ultrasound image. Nine models were developed to predict high ALN tumor burden, employing a combination of three feature screening methods and three machine learning classifiers. Ultimately, the optimal model was selected and tested on both the validation and testing sets. In addition, clinical characteristics were screened to develop a clinical model. Furthermore, Shapley additive explanations (SHAP) values were utilized to provide explanations for the machine learning model. RESULTS: During the validation and testing sets, the models demonstrated area under the curve (AUC) values ranging from 0.577 to 0.733 and 0.583 to 0.719, and accuracies ranging from 64.9% to 75.7% and 64.7% to 70.6%, respectively. Ultimately, the Boruta_XGB model, comprising five radiomics features, was selected as the final model. The AUC values of this model for distinguishing low from high tumor burden were 0.828, 0.715, and 0.719 in the training, validation, and testing sets, respectively, demonstrating its superiority over the clinical model. CONCLUSIONS: The developed radiomics models exhibited a significant level of predictive performance. The Boruta_XGB radiomics model outperformed other radiomics models in this study.

Asunto(s)

RESUMEN

OBJECTIVES: To investigate the value of conventional ultrasonography (US) combined with quantitative shear wave elastography (SWE) in evaluating and identifying target axillary lymph node (TALN) for fine needle aspiration biopsy (FNAB) of patients with early breast cancer. MATERIALS AND METHODS: A total of 222 patients with 223 ALNs were prospectively recruited from January 2018 to December 2021. All TALNs were evaluated by US, SWE and subsequently underwent FNAB. The diagnostic performances of US, SWE, UEor (either US or SWE was positive) and UEand (both US and SWE were positive), and FNAB guided by the above four methods for evaluating ALN status were assessed using receiver operator characteristic curve (ROC) analyses. Univariate and multivariate logistic regression analyses used to determine the independent predictors of axillary burden. RESULTS: The area under the ROC curve (AUC) for diagnosing ALNs using conventional US and SWE were 0.69 and 0.66, respectively, with sensitivities of 78.00% and 65.00% and specificities of 60.98% and 66.67%. The combined method, UEor, demonstrated significantly improved sensitivity of 86.00% (p < 0.001 when compared with US and SWE alone). The AUC of the UEor-guided FNAB [0.85 (95% CI, 0.80-0.90)] was significantly higher than that of US-guided FNAB [0.83 (95% CI, 0.78-0.88), p = 0.042], SWE-guided FNAB [0.79 (95% CI, 0.72-0.84), p = 0.001], and UEand-guided FNAB [0.77 (95% CI, 0.71-0.82), p < 0.001]. Multivariate logistic regression showed that FNAB and number of suspicious ALNs were found independent predictors of axillary burden in patients with early breast cancer. CONCLUSION: The UEor had superior sensitivity compared to US or SWE alone in ALN diagnosis. The UEor-guided FNAB achieved a lower false-negative rate compared to FNAB guided solely by US or SWE, which may be a promising tool for the preoperative diagnosis of ALNs in early breast cancer, and had the potential implication for the selection of axillary surgical modality.

Asunto(s)

RESUMEN

PURPOSE: A practical noninvasive method is needed to identify lymph node (LN) status in breast cancer patients diagnosed with a suspicious axillary lymph node (ALN) at ultrasound but a negative clinical physical examination. To predict ALN metastasis effectively and noninvasively, we developed an artificial intelligence-assisted ultrasound system and validated it in a retrospective study. METHODS: A total of 266 patients treated with sentinel LN biopsy and ALN dissection at Peking Union Medical College & Hospital(PUMCH) between the year 2017 and 2019 were assigned to training, validation and test sets (8:1:1). A deep learning model architecture named DeepLabV3 + was used together with ResNet-101 as the backbone network to create an ultrasound image segmentation diagnosis model. Subsequently, the segmented images are classified by a Convolutional Neural Network to predict ALN metastasis. RESULTS: The area under the receiver operating characteristic curve of the model for identifying metastasis was 0.799 (95% CI: 0.514-1.000), with good end-to-end classification accuracy of 0.889 (95% CI: 0.741-1.000). Moreover, the specificity and positive predictive value of this model was 100%, providing high accuracy for clinical diagnosis. CONCLUSION: This model can be a direct and reliable tool for the evaluation of individual LN status. Our study focuses on predicting ALN metastasis by radiomic analysis, which can be used to guide further treatment planning in breast cancer.

Asunto(s)

RESUMEN

BACKGROUND: The accuracy of traditional clinical methods for assessing the metastatic status of axillary lymph nodes (ALNs) is unsatisfactory. In this study, the authors propose the use of radiomic technology and three-dimensional (3D) visualization technology to develop an unsupervised learning model for predicting axillary lymph node metastasis in patients with breast cancer (BC), aiming to provide a new method for clinical axillary lymph node assessment in patients with this disease. METHODS: In this study, we retrospectively analyzed the data of 350 patients with invasive BC who underwent lung-enhanced computed tomography (CT) and axillary lymph node dissection surgery at the Department of Breast Surgery of the Second Xiangya Hospital of Central South University. The authors used 3D visualization technology to create a 3D atlas of ALNs and identified the region of interest for the lymph nodes. Radiomic features were subsequently extracted and selected, and a prediction model for ALNs was constructed using the K-means unsupervised algorithm. To validate the model, the authors prospectively collected data from 128 BC patients who were clinically evaluated as negative at our center. RESULTS: Using 3D visualization technology, we extracted and selected a total of 36 CT radiomics features. The unsupervised learning model categorized 1737 unlabeled lymph nodes into two groups, and the analysis of the radiomic features between these groups indicated potential differences in lymph node status. Further validation with 1397 labeled lymph nodes demonstrated that the model had good predictive ability for axillary lymph node status, with an area under the curve of 0.847 (0.825-0.869). Additionally, the model's excellent predictive performance was confirmed in the 128 axillary clinical assessment negative cohort (cN0) and the 350 clinical assessment positive (cN+) cohort, for which the correct classification rates were 86.72 and 87.43%, respectively, which were significantly greater than those of clinical assessment methods. CONCLUSIONS: The authors created an unsupervised learning model that accurately predicts the status of ALNs. This approach offers a novel solution for the precise assessment of ALNs in patients with BC.

Asunto(s)

RESUMEN

In this case report, a unique instance of delayed isolated anterior branch axillary nerve injury following shoulder dislocation is highlighted. The patient, a 55-year-old manual laborer, presented with severe deltoid wasting and reduced power 18 months postdislocation, necessitating a specialized treatment approach. The use of axillary nerve neurolysis and an innovative upper trapezius to anterior deltoid transfer via a subacromial path posterior to the clavicle, facilitated by an autologous semitendinosus graft, resulted in significant improvement with 160 degrees of abduction and Grade 4+ power Medical Research Council grading (MRC) at the 5-year follow-up.

Asunto(s)

RESUMEN

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.

Asunto(s)

RESUMEN

OBJECTIVE: Preoperative detection of axillary lymph node metastases (ALNMs) from breast cancer is suboptimal; however, recent work suggests radiomics may improve detection of ALNMs. This study aims to develop a 3D CT radiomics model to improve detection of ALNMs compared to conventional imaging features in patients with locally advanced breast cancer. METHODS: Retrospective chart review was performed on patients referred to a specialty breast cancer center between 2015 and 2020 with US-guided biopsy-proven ALNMs and pretreatment chest CT. One hundred and twelve patients (224 lymph nodes) met inclusion and exclusion criteria and were assigned to discovery (n = 150 nodes) and testing (n = 74 nodes) cohorts. US-biopsy images were referenced in identifying ALNMs on CT, with contralateral nodes taken as negative controls. Positive and negative nodes were assessed for conventional features of lymphadenopathy as well as for 107 radiomic features extracted following 3D segmentation. Diagnostic performance of individual and combined radiomic features was evaluated. RESULTS: The strongest conventional imaging feature of ALNMs was short axis diameter ≥ 10 mm with a sensitivity of 64%, specificity of 95%, and area under the curve (AUC) of 0.89 (95% CI, 0.84-0.94). Several radiomic features outperformed conventional features, most notably energy, a measure of voxel density magnitude. This feature demonstrated a sensitivity, specificity, and AUC of 91%, 79%, and 0.94 (95% CI, 0.91-0.98) for the discovery cohort. On the testing cohort, energy scored 92%, 81%, and 0.94 (95% CI, 0.89-0.99) for sensitivity, specificity, and AUC, respectively. Combining radiomic features did not improve AUC compared to energy alone (P = .08). CONCLUSION: 3D radiomic analysis represents a promising approach for noninvasive and accurate detection of ALNMs.

Asunto(s)

Asunto(s)

RESUMEN

OBJECTIVE: To investigate the potential value of quantitative parameters derived from synthetic magnetic resonance imaging (syMRI) for discriminating axillary lymph nodes metastasis (ALNM) in breast cancer patients. MATERIALS AND METHODS: A total of 56 females with histopathologically proven invasive breast cancer who underwent both conventional breast MRI and additional syMRI examinations were enrolled in this study, including 30 patients with ALNM and 26 with non-ALNM. SyMRI has enabled quantification of T1 relaxation time (T1), T2 relaxation time (T2) and proton density (PD). The syMRI quantitative parameters of breast primary tumors before (T1tumor, T2tumor, PDtumor) and after (T1+tumor, T2+tumor, PD+tumor) contrast agent injection were obtained. Similarly, measurements were taken for axillary lymph nodes before (T1LN, T2LN, PDLN) and after (T1+LN, T2+LN, PD+LN) the injection, then theΔT1 (T1-T1+), ΔT2 (T2-T2+), ΔPD (PD-PD+), T1/T2 and T1+/T2+ were calculated. All parameters were compared between ANLM and non-ALNM group. Intraclass correlation coefficient for assessing interobserver agreement. The independent Student's t test or Mann-Whitney U test to determine the relationship between the mean quantitative values and the ALNM. Multivariate logistic regression analyses followed by receiver operating characteristics (ROC) analysis for discriminating ALN status. A P value < 0.05 was considered statistically significant. RESULTS: The short-diameter of lymph nodes (DLN) in ALNM group was significantly longer than that in the non-ALNM group (10.22 ± 3.58 mm vs. 5.28 ± 1.39 mm, P < 0.001). The optimal cutoff value was determined to be 5.78 mm, with an AUC of 0.894 (95 % CI: 0.838-0.939), a sensitivity of 86.7 %, and a specificity of 90.2 %. In syMRI quantitative parameters of breast tumors, T2tumor, ΔT2tumor and ΔPDtumor values showed statistically significant differences between the two groups (P < 0.05). T2tumor value had the best performance in discriminating ALN status (AUC = 0.712), and the optimal cutoff was 90.12 ms, the sensitivity and specificity were 65.0 % and 83.6 % respectively. In terms of syMRI quantitative parameters of lymph nodes, T1LN, T2LN, T1LN/T2LN, T2+LN and ΔT1LN values were significantly different between the two groups (P < 0.05), and their AUCs were 0.785, 0.840, 0.886, 0.702 and 0.754, respectively. Multivariate analyses indicated that the T1LN value was the only independent predictor of ALNM (OR=1.426, 95 % CI: 1.130-1.798, P = 0.039). The diagnostic sensitivity and specificity of T1LN was 86.7 % and 69.4 % respectively at the best cutoff point of 1371.00 ms. The combination of T1LN, T2LN, T1LN/T2LN, ΔT1LN and DLN had better performance for differentiating ALNM and non-ALNM, with AUCs of 0.905, 0.957, 0.964 and 0.897, respectively. CONCLUSION: The quantitative parameters derived from syMRI have certain value for discriminating ALN status in invasive breast cancer, with T2tumor showing the highest diagnostic efficiency among breast lesions parameters. Moreover, T1LN acted as an independent predictor of ALNM.

Asunto(s)

RESUMEN

OBJECTIVE: To determine the incidence, timing, and long-term outcomes of unilateral axillary lymphadenopathy ipsilateral to vaccine site (UIAL) on screening mammography after COVID-19 vaccination. METHODS: This retrospective, multisite study included consecutive patients undergoing screening mammography February 8, 2021, to January 31, 2022, with at least 1 year of follow-up. UIAL was typically considered benign (BI-RADS 1 or 2) in the setting of recent (≤6 weeks) vaccination or BI-RADS 0 (ultrasound recommended) when accompanied by a breast finding or identified >6 weeks postvaccination. Vaccination status and manufacturer were obtained from regional registries. Lymphadenopathy rates in vaccinated patients with and without UIAL were compared using Pearson's χ2 test. RESULTS: There were 44,473 female patients (mean age 60.4 ± 11.4 years) who underwent screening mammography at five sites, and 40,029 (90.0%) received at least one vaccine dose. Ninety-four (0.2%) presented with UIAL, 1 to 191 days postvaccination (median 13.5 [interquartile range: 5.0-31.0]). Incidence declined from 2.1% to 0.9% to ≤0.5% after 1, 2, and 3 weeks and persisted up to 36 weeks (P < .001). UIAL did not vary across manufacturer (P = .15). Of 94, 77 (81.9%) were BI-RADS 1 or 2 at screening. None were diagnosed with malignancy at 1-year follow-up. Seventeen (18.1%) were BI-RADS 0 at screening. At diagnostic workup, 13 (76.5%) were BI-RADS 1 or 2, 2 (11.8%) were BI-RADS 3, and 2 (11.8%) were BI-RADS 4. Both BI-RADS 4 patients had malignant status and ipsilateral breast malignancies. Of BI-RADS 3 patients, at follow-up, one was biopsied yielding benign etiology, and one was downgraded to BI-RADS 2. DISCUSSION: Isolated UIAL on screening mammography performed within 6 months of COVID-19 vaccination can be safely assessed as benign.

Asunto(s)

RESUMEN

BACKGROUND: The unexpected detection of axillary lymphadenopathy (AxL) in cancer patients (pts) represents a real concern during the COVID-19 vaccination era. Benign reactions may take place after vaccine inoculation, which can mislead image interpretation in patients undergoing F-18-FDG, F-18-Choline, and Ga-68-DOTATOC PET/CT. They may also mimic loco-regional metastases or disease. We assessed PET/CT findings after COVID-19 first dose vaccination in cancer patients and the impact on their disease course management. METHODS: We evaluated 333 patients undergoing PET/CT (257 F-18-FDG, 54 F-18-Choline, and 23 Ga-68 DOTATOC) scans after the first vaccination with mRNA vaccine (Pfizer-BioNTech) (study group; SG). The uptake index (SUVmax) of suspected AxL was defined as significant when the ratio was > 1.5 as compared to the contralateral lymph nodes. Besides, co-registered CT (Co-CT) features of target lymph nodes were evaluated. Nodes with aggregate imaging positivity were further investigated. RESULTS: Overall, the prevalence of apparently positive lymph nodes on PET scans was 17.1% during the vaccination period. 107 pts of the same setting, who had undergone PET/CT before the COVID-19 pandemic, represented the control group (CG). Only 3 patients of CG showed reactive lymph nodes with a prevalence of 2.8% (p < 0.001 as compared to the vaccination period). 84.2% of SG patients exhibited benign characteristics on co-CT images and only 9 pts needed thorough appraisal. CONCLUSION: The correct interpretation of images is crucial to avoid unnecessary treatments and invasive procedures in vaccinated cancer pts. A detailed anamnestic interview and the analysis of lymph nodes' CT characteristics, after performing PET/CT, may help to clear any misleading diagnosis.

Asunto(s)

RESUMEN

BACKGROUND: Among women with early invasive breast cancer and 1-2 positive sentinel nodes, sentinel lymph node biopsy (SLNB) is non-inferior to axillary lymph node dissection (ALND).1-3 However, preoperative axillary ultrasonography (AxUS) may not be sensitive enough to discriminate burden of nodal metastasis in these patients, potentially leading to overtreatment.4-6 This study compares axillary operation rates in patients who did and did not receive preoperative AxUS, assessing its utility and risks for overtreatment. METHODS: This is a retrospective cohort study of patients with clinical T1/T2 breast tumors who were clinically node negative and underwent an axillary operation. RESULTS: Patients who had preoperative AxUS received more ALND compared to patients who did not (5.6% vs. 1.4%, p â€‹< â€‹0.001). There was no significant difference in the number of additional axillary operations following SLNB (2.1% vs. 2.3%, p â€‹= â€‹0.77). CONCLUSION: Eliminating preoperative AxUS is associated with fewer invasive ALND procedures, without increased rate of axillary reoperations.

Asunto(s)

RESUMEN

BACKGROUND: In HR+ /HER2- breast cancer patients with ≤ 3 positive axillary lymph nodes (ALNs), genomic tests can streamline chemotherapy decisions. Current studies, centered on tumor metrics, miss broader patient insights. Automated Breast Volume Scanning (ABVS) provides advanced 3D imaging, and its potential synergy with radiomics for ALN evaluation is untapped. OBJECTIVE: This study sought to combine ABVS radiomics and clinical characteristics in a nomogram to predict ≤ 3 positive ALNs in HR+ /HER2- breast cancer patients with 1-2 positive sentinel lymph nodes (SLNs), guiding clinicians in genetic test candidate selection. METHODS: We enrolled 511 early-stage breast cancer patients: 362 from A Hospital for training and 149 from B Hospital for validation. Using LASSO logistic regression, primary features were identified. A clinical-radiomics nomogram was developed to predict the likelihood of ≤ 3 positive ALNs in HR+ /HER2- patients with 1-2 positive SLNs. We assessed the discriminative capability of the nomogram using the ROC curve. The model's calibration was confirmed through a calibration curve, while its fit was evaluated using the Hosmer-Lemeshow (HL) test. To determine the clinical net benefits, we employed the Decision Curve Analysis (DCA). RESULTS: In the training group, 81.2% patients had ≤ 3 metastatic ALNs, and 83.2% in the validation group. We developed a clinical-radiomics nomogram by analyzing clinical characteristics and rad-scores. Factors like positive SLNs (OR=0.077), absence of negative SLNs (OR=11.138), lymphovascular invasion (OR=0.248), and rad-score (OR=0.003) significantly correlated with ≤ 3 positive ALNs. The clinical-radiomics nomogram, with an AUC of 0.910 in training and 0.882 in validation, outperformed the rad-score-free clinical nomogram (AUCs of 0.796 and 0.782). Calibration curves and the HL test (P values 0.688 and 0.691) confirmed its robustness. DCA showed the clinical-radiomics nomogram provided superior net benefits in predicting ALN burden across specific threshold probabilities. CONCLUSION: We developed a clinical-radiomics nomogram that integrated radiomics from ABVS images and clinical data to predict the presence of ≤ 3 positive ALNs in HR+ /HER2- patients with 1-2 positive SLNs, aiding oncologists in identifying candidates for genomic tests, bypassing ALND. In the era of precision medicine, combining genomic tests with SLN biopsy refines both surgical and systemic patient treatments.

Asunto(s)

RESUMEN

PURPOSE: The aim of this study was to evaluate the rate of axillary node-positive disease in patients with early breast cancer who had a suspicious axillary lymph node on radiation planning computed tomography (CT). METHODS AND MATERIALS: A retrospective review was conducted of the medical records of all patients with breast cancer who were referred for axillary ultrasound from the radiation unit to the breast imaging unit at the Meirav Breast Center, Sheba Medical Center, from 2012 to 2022. Ethics approval was obtained. Only the records of patients who were referred due to an abnormal axillary lymph node seen on radiation planning CT were further evaluated. RESULTS: During the study period, a total of 21 patients were referred to the breast imaging unit for evaluation of suspicious nodes seen on radiation planning CT. Of these, 3 cases were excluded. A total of 15 out of the 18 (83%) patients included had an abnormal lymph node in the ultrasound, and an ultrasound-guided biopsy was recommended (BI-RADS 4). Of these, 3 (out of 15, 20%) had a positive biopsy for tumor cells from the axillary lymph node. Two were cases after primary systemic therapy without complete pathologic response. Thickening of the lymph node cortex and complete loss of the central fatty hilum were associated with pathologic lymph node. CONCLUSION: Sonar had limited ability to differentiate reactive nodes from involved nodes. The presence of lymph nodes with loss of cortical-hilum differentiation on ultrasound together with clinical features are parameters that can help guide the need of further biopsy. Histopathology evaluation is important to make the diagnosis of residual axillary disease. Future studies and guidelines are needed to improve the diagnostic abilities and reduce the number of patients who are undergoing biopsy for noninvolved nodes.

Asunto(s)

Asunto(s)

RESUMEN

OBJECTIVE: Automated breast ultrasound (ABUS) is a relevant imaging technique for early breast cancer diagnosis and is increasingly being used as a supplementary tool for mammography. This study compared the performance of ABUS and handheld ultrasound (HHUS) in detecting and characterizing the axillary lymph nodes (LNs) in patients with breast cancer. MATERIALS AND METHODS: We retrospectively reviewed the medical records of women with recently diagnosed early breast cancer (≤ T2) who underwent both ABUS and HHUS examinations for axilla (September 2017-May 2018). ABUS and HHUS findings were compared using pathological outcomes as reference standards. Diagnostic performance in predicting any axillary LN metastasis and heavy nodal-burden metastases (i.e., ≥ 3 LNs) was evaluated. The ABUS-HHUS agreement for visibility and US findings was calculated. RESULTS: The study included 377 women (53.1 ± 11.1 years). Among 385 breast cancers in 377 patients, 101 had axillary LN metastases and 30 had heavy nodal burden metastases. ABUS identified benign-looking or suspicious axillary LNs (average, 1.4 ± 0.8) in 246 axillae (63.9%, 246/385). According to the per-breast analysis, the sensitivity, specificity, positive and negative predictive values, and accuracy of ABUS in predicting axillary LN metastases were 43.6% (44/101), 95.1% (270/284), 75.9% (44/58), 82.6% (270/327), and 81.6% (314/385), respectively. The corresponding results for HHUS were 41.6% (42/101), 95.1% (270/284), 75.0% (42/56), 82.1% (270/329), and 81.0% (312/385), respectively, which were not significantly different from those of ABUS (P ≥ 0.53). The performance results for heavy nodal-burden metastases were 70.0% (21/30), 89.6% (318/355), 36.2% (21/58), 97.3% (318/327), and 88.1% (339/385), respectively, for ABUS and 66.7% (20/30), 89.9% (319/355), 35.7% (20/56), 97.0% (319/329), and 88.1% (339/385), respectively, for HHUS, also not showing significant difference (P ≥ 0.57). The ABUS-HHUS agreement was 95.9% (236/246; Cohen's kappa = 0.883). CONCLUSION: Although ABUS showed limited sensitivity in diagnosing axillary LN metastasis in early breast cancer, it was still useful as the performance was comparable to that of HHUS.

Asunto(s)

RESUMEN

ABSTRACT: Solitary axillary lymph node metastasis from bladder cancer is rare. A 65-year-old woman with a history of bladder urothelial carcinoma presented to our hospital with an axillary mass. No abnormal lesion in FDG PET/CT was identified except a solitary soft tissue mass with significant FDG uptake in the right axilla. Puncture pathology of the mass confirmed the metastasis of differentiated urothelial carcinoma.

Asunto(s)