Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer.

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.

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.

Diagnosis and treatment approach of delayed presentation of anterior branch axillary nerve injury following shoulder dislocation: case report.

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.

3D CT Radiomic Analysis Improves Detection of Axillary Lymph Node Metastases Compared to Conventional Features in Patients With Locally Advanced Breast Cancer.

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.

Investigation of synthetic MRI with quantitative parameters for discriminating axillary lymph nodes status in invasive breast cancer.

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.

Axillary ultrasonography for early-stage invasive breast cancer.

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.

Challenging Axillary Lymph Nodes on PET/CT in Cancer Patients throughout COVID-19 Vaccination Era.

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.

Nomogram Utilizing ABVS Radiomics and Clinical Factors for Predicting ≤ 3 Positive Axillary Lymph Nodes in HR+ /HER2- Breast Cancer with 1-2 Positive Sentinel Nodes.

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.

Incidental Axillary Lymphadenopathy Found on Radiation Planning Computed Tomography.

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.

Automated Versus Handheld Breast Ultrasound for Evaluating Axillary Lymph Nodes in Patients With Breast Cancer.

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.

Diagnóstico de precisão das lesões mamárias / Accurate diagnosis of breast lesions

PONTOS-CHAVE As lesões mamárias compreendem uma ampla variedade de diagnósticos que apresentam comportamentos diversos. As lesões mamárias podem ser classificadas como lesões benignas, de potencial de malignidade indeterminado (B3), carcinoma in situ e carcinoma invasor. Na era da medicina personalizada, individualizar e obter um diagnóstico preciso faz grande diferença no desfecho final da paciente, principalmente no caso do câncer de mama. Exames de imagem direcionados e de qualidade, métodos de biópsia adequadamente selecionados e análises de anatomopatologia convencional, imuno-histoquímica e até molecular são determinantes no diagnóstico e no manejo das pacientes.

Evaluation of a Surgical Navigation System for Localization and Excision of Nonpalpable Lesions in Breast and Axillary Surgery.

Introduction: Elucent Medical has introduced a novel EnVisio™ Surgical Navigation system which uses SmartClips™ that generate a unique electromagnetic signal triangulated in 3 dimensions for real-time navigation. The purpose of this study was to evaluate the efficacy and feasibility of the EnVisio Surgical Navigation system in localizing and excising nonpalpable lesions in breast and axillary surgery. Methods: This pilot study prospectively examined patients undergoing breast and nodal localization using the EnVisio Surgical Navigation system. SmartClips were placed by designated radiologists using ultrasound (US) or mammographic (MMG) guidance. The technical evaluation focused on successful deployment and subsequent excision of all localized lesions including SmartClips and biopsy clips. Results: Eleven patients underwent localization using 27 SmartClips which included bracketed multifocal disease (n = 4) and clipped lymph node (n = 1). The bracketed cases were each localized with 2 SmartClips. Mammography and ultrasound were used (n = 8 and n = 19, respectively) to place the SmartClips. All 27 devices were successfully deployed within 5 mm of the targeted lesion or biopsy clip. All SmartClip devices were identified and retrieved intraoperatively. No patients required a second operation for margin excision. Conclusion: In a limited sample, the EnVisio Surgical Navigation system was a reliable technology for the localization of breast and axillary lesions planned for surgical excision. Further comparative studies are required to evaluate its efficacy in relation to the other existing localization modalities.

Calcified Axillary Lesions on Mammography: Review and Management.

When interpreting mammography, breast radiologists may identify radiopaque densities in the axilla on the mediolateral oblique or lateral projections. When such densities are encountered, true calcifications must be differentiated from pseudocalcifications (artifact). Using imaging, breast radiologists should be able to localize the finding as being dermal, within the soft tissues, within a lymph node, or intramuscular. By combining the anatomic location with the clinical presentation and any other imaging findings, breast radiologists will be able to determine the most appropriate management.

Carcinoma neuroendocrino primario de mama / Primary neuroendocrine carcinoma of the breast

No disponible

Diagnóstico de cáncer de pulmón a raíz de un «absceso» complicado / Diagnosis of Lung Cancer Due to a Complicated Abscess

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Seudoaneurisma postraumático de la arteria axilar presentado como tumoración axilar / Postraumatic pseudoaneurysm of the axillary artery presenting as an axillary tumor

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