UNASSIGNED: To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer.
UNASSIGNED: A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3 mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA).
UNASSIGNED: A total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models.
UNASSIGNED: The intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.

OBJECTIVE: Accurate axillary evaluation plays an important role in prognosis and treatment planning for breast cancer. This study aimed to develop and validate a dynamic contrast-enhanced (DCE)-MRI-based radiomics model for preoperative evaluation of axillary lymph node (ALN) status in early-stage breast cancer.
METHODS: A total of 410 patients with pathologically confirmed early-stage invasive breast cancer (training cohort, N = 286; validation cohort, N = 124) from June 2018 to August 2022 were retrospectively recruited. Radiomics features were derived from the second phase of DCE-MRI images for each patient. ALN status-related features were obtained, and a radiomics signature was constructed using SelectKBest and least absolute shrinkage and selection operator regression. Logistic regression was applied to build a combined model and corresponding nomogram incorporating the radiomics score (Rad-score) with clinical predictors. The predictive performance of the nomogram was evaluated using receiver operator characteristic (ROC) curve analysis and calibration curves.
RESULTS: Fourteen radiomic features were selected to construct the radiomics signature. The Rad-score, MRI-reported ALN status, BI-RADS category, and tumour size were independent predictors of ALN status and were incorporated into the combined model. The nomogram showed good calibration and favourable performance for discriminating metastatic ALNs (N + (≥1)) from non-metastatic ALNs (N0) and metastatic ALNs with heavy burden (N + (≥3)) from low burden (N + (1-2)), with the area under the ROC curve values of 0.877 and 0.879 in the training cohort and 0.859 and 0.881 in the validation cohort, respectively.
CONCLUSIONS: The DCE-MRI-based radiomics nomogram could serve as a potential non-invasive technique for accurate preoperative evaluation of ALN burden, thereby assisting physicians in the personalized axillary treatment for early-stage breast cancer patients.
CONCLUSIONS: This study developed a potential surrogate of preoperative accurate evaluation of ALN status, which is non-invasive and easy-to-use.

BACKGROUND: Sentinel lymph node biopsy (SLNB) is the standard of care for axillary staging in early breast cancer patients with low-burden axillary metastasis (≤ 2 positive nodes). This study aimed to determine the diagnostic performances of 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and breast magnetic resonance imaging in detecting axillary lymph node (ALN) metastases and the reliability to predict ALN burden.
METHODS: A total of 275 patients with primary operable breast cancer receiving preoperative PET/CT and upfront surgery from January 2001 to December 2022 in a single institution were enrolled. A total of 244 (88.7%) of them also received breast MRI. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of PET/CT and breast MRI were assessed. The predictive values to determine ALN burden were evaluated using radio-histopathological concordance.
RESULTS: PET/CT demonstrated a sensitivity of 53.4%, specificity of 82.1%, PPV of 65.5%, NPV of 73.5%, and accuracy of 70.9% for detecting ALN metastasis, and the corresponding values for MRI were 71.8%, 67.8%, 56%, 80.8%, and 69.2%, respectively. Combining PET/CT and MRI showed a significantly higher PPV than MRI (72.7% vs 56% for MRI alone, p = 0.037) and a significantly higher NPV than PET/CT (84% vs 73.5% for PET/CT alone, p = 0.041). For predicting low-burden axillary metastasis (1-2 positive nodes), the PPVs were 35.9% for PET/CT, 36.7% for MRI, and 55% for combined PET/CT and MRI. Regarding patients with 0-2 positive ALNs in imaging, who were indicated for SLNB, the predictive correctness was 96.1% for combined PET/CT and MRI, 95.7% for MRI alone, and 88.6% for PET/CT alone.
CONCLUSIONS: PET/CT and breast MRI exhibit high predictive values for identifying low-burden axillary metastasis in patients with operable breast cancer with ≦ 2 positive ALNs on imaging.

BACKGROUND: Accurate diagnosis of breast lesions and discrimination of axillary lymph node (ALN) metastases largely depend on radiologist experience.
OBJECTIVE: To develop a deep learning-based whole-process system (DLWPS) for segmentation and diagnosis of breast lesions and discrimination of ALN metastasis.
METHODS: Retrospective.
METHODS: 1760 breast patients, who were divided into training and validation sets (1110 patients), internal (476 patients), and external (174 patients) test sets.
UNASSIGNED: 3.0T/dynamic contrast-enhanced (DCE)-MRI sequence.
RESULTS: DLWPS was developed using segmentation and classification models. The DLWPS-based segmentation model was developed by the U-Net framework, which combined the attention module and the edge feature extraction module. The average score of the output scores of three networks was used as the result of the DLWPS-based classification model. Moreover, the radiologists\' diagnosis without and with the DLWPS-assistance was explored. To reveal the underlying biological basis of DLWPS, genetic analysis was performed based on RNA-sequencing data.
METHODS: Dice similarity coefficient (DI), area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and kappa value.
RESULTS: The segmentation model reached a DI of 0.828 and 0.813 in the internal and external test sets, respectively. Within the breast lesions diagnosis, the DLWPS achieved AUCs of 0.973 in internal test set and 0.936 in external test set. For ALN metastasis discrimination, the DLWPS achieved AUCs of 0.927 in internal test set and 0.917 in external test set. The agreement of radiologists improved with the DLWPS-assistance from 0.547 to 0.794, and from 0.848 to 0.892 in breast lesions diagnosis and ALN metastasis discrimination, respectively. Additionally, 10 breast cancers with ALN metastasis were associated with pathways of aerobic electron transport chain and cytoplasmic translation.
CONCLUSIONS: The performance of DLWPS indicates that it can promote radiologists in the judgment of breast lesions and ALN metastasis and nonmetastasis.
METHODS: 4 TECHNICAL EFFICACY STAGE: 3.

UNASSIGNED: Cat Intensive care unit (ICU) delirium is a significant complication that increases the mortality, morbidity, and length of stay for critically ill patient.
UNASSIGNED: The aim of this study was to assess the critical care nurse\'s knowledge of ICU delirium and the effectiveness of an educational program about the recognition and assessment of ICU delirium on critical care nurse\'s knowledge.
UNASSIGNED: A quasi-experimental single group pre-test-post-test design was conducted using delirium knowledge assessment questionnaires.
UNASSIGNED: The median post test score of overall nurses\' knowledge was 76.2 (range 19.1-95.2) compared to the median pre-test score of 38.1 (range 14.3 - 61.9) indicating a significant change in nurses\' knowledge after conducting the educational program (p<0.001).
UNASSIGNED: Critical care nurses\' knowledge of ICU delirium was low before the intervention and increased significantly after delivering an educational program.

UNASSIGNED: To compare vaccinated-side axillary lymph node uptake on 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) after coronavirus disease-2019 (COVID-19) and influenza vaccination.
UNASSIGNED: We retrospectively analyzed 177 patients who underwent 18F-FDG PET/CT after COVID-19 or influenza vaccination. We compared the uptake of the vaccinated-side axillary lymph nodes of 109 COVID-19 vaccinated patients with those of a lot of influenza-vaccinated patients. We also compared the uptake between 66 patients who received the first COVID-19 vaccination with 43 who received the second COVID-19 vaccination.
UNASSIGNED: 18F-FDG-avid axillary lymph nodes on the vaccinated side were significantly more frequently observed in the COVID-19 group (45%) than in the influenza group (19%) (p<0.001). When the interval between vaccination to PET/CT was within 7 days, there was no significant difference in the frequency of 18F-FDG-avid vaccinated-side axillary lymph nodes between the groups (COVID-19 group: 41% vs. influenza group: 45%, p=0.724). When the interval was over 7 days, 18F-FDG-avid lymph nodes were much more frequent in the COVID-19 group (47%) than in the influenza group (7%) (p<0.001). Comparing the first and second COVID-19 groups, 18F-FDG-avid lymph nodes were more frequent in the second vaccination group than in the first vaccination group, but the difference was not significant.
UNASSIGNED: 18F-FDG-avid vaccinated-side axillary lymph nodes were more frequently observed in the COVID-19 group than in the influenza group. In the case of the COVID-19 vaccine, a delay of 18F-FDG PET/CT examination is recommended by a longer interval from vaccination than in the influenza vaccine.
UNASSIGNED: Koronavirüs hastalığı-2019 (COVID-19) aşısı ve grip aşısı sonra 18F-florodeoksiglukoz (FDG) pozitron emisyon tomografisi/bilgisayarlı tomografi (PET/BT) ile aşılanan taraf aksiller lenf nodu tutulumunu karşılaştırmaktır.
UNASSIGNED: COVID-19 veya influenzaya yönelik aşılamadan sonra 18F-FDG PET/BT uygulanan 177 hastayı retrospektif olarak inceledik. COVID-19 aşısı yapılan 109 hastanın aşılanmış taraftaki aksiller lenf nodlarındaki 18F-FDG tutulumunu, grip aşısı yapılan hastalardaki tutulum ile karşılaştırdık. Ayrıca ilk COVID-19 aşısını olan 66 hasta ile ikinci COVID-19 aşısını olan 43 hasta arasındaki alımı karşılaştırdık.
UNASSIGNED: Aşılanan taraftaki 18F-FDG tutulumu olan aksiller lenf nodları, COVID-19 grubunda (%45) influenza grubuna (%19) göre anlamlı derecede daha sık gözlendi (p<0,001). PET/BT ile aşılama arasındaki aralık 7 gün içinde olduğunda, gruplar arasında 18F-FDG tutulumu olan aşılı taraf aksiller lenf nodu sıklığında anlamlı bir fark yoktu (COVID-19 grubu: %41 vs. influenza grubu: %45, p=0,724). Aralık 7 günden fazla olduğunda, 18F-FDG tutulumlu lenf nodları, COVID-19 grubunda (%47) influenza grubuna (%7) göre çok daha sıktı (p<0,001). Birinci ve ikinci COVID-19 grupları karşılaştırıldığında, 18F-FDG tutulumlu lenf nodları ikinci aşılama grubunda birinci aşılama grubuna göre daha sıktı ancak aradaki fark anlamlı değildi.
UNASSIGNED: 18F-FDG tutulumlu aşılanmış taraf aksiller lenf nodları, COVID-19 grubunda influenza grubuna göre daha sık gözlendi. COVID-19 aşısı söz konusu olduğunda, 18F-FDG PET/BT incelemesinin, grip aşısına göre daha uzun bir aralıkla ertelenmesi önerilir.

The diagnostic effectiveness of traditional imaging techniques is insufficient to assess the response of lymph nodes (LNs) to neoadjuvant chemotherapy (NAC), especially for pathological complete response (pCR). A radiomics model based on computed tomography (CT) could be helpful.
Prospective consecutive breast cancer patients with positive axillary LNs initially were enrolled, who received NAC prior to surgery. Chest contrast-enhanced thin-slice CT scan was performed both before and after the NAC (recorded as the first and the second CT respectively), and on both of them, the target metastatic axillary LN was identified and demarcated layer by layer. Using pyradiomics-based software that was independently created, radiomics features were retrieved. A pairwise machine learning workflow based on Sklearn (https://scikit-learn.org/) and FeAture Explorer was created to increase diagnostic effectiveness. An effective pairwise auto encoder model was developed by the improvement of data normalization, dimensionality reduction, and features screening scheme as well as the comparison of the prediction effectiveness of the various classifiers.
A total of 138 patients were enrolled, and 77 (58.7%) in the overall group achieved pCR of LN after NAC. Nine radiomics features were finally chosen for modeling. The AUCs of the training group, validation group, and test group were 0.944 (0.919-0.965), 0.962 (0.937-0.985), and 1.000 (1.000-1.000), respectively, and the corresponding accuracies were 0.891, 0.912, and 1.000.
The pCR of axillary LNs in breast cancer following NAC can be precisely predicted using thin-sliced enhanced chest CT-based radiomics.

OBJECTIVE: To develop and validate a simplified scoring system by integrating MRI and clinicopathologic features for preoperative prediction of axillary pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in clinically node-positive breast cancer.
METHODS: A total of 389 patients from three hospitals were retrospectively analyzed. To identify independent predictors for axillary pCR, univariable and multivariable logistic regression analyses were performed on pre- and post-NAC MRI and clinicopathologic features. Then, a simplified scoring system was constructed based on regression coefficients of predictors in the multivariable model, and its predictive performance was assessed with the receiver operating characteristic curve and calibration curve. The added value of the scoring system for reducing false-negative rate (FNR) of the sentinel lymph node biopsy (SLNB) was also evaluated.
RESULTS: The simplified scoring system including seven predictors: progesterone receptor-negative (Three points), HER2-positive (Two points), post-NAC clinical T0-1 stage (Two points), pre-NAC higher ADC value of breast tumor (One point), absence of perinodal infiltration at pre-NAC (One point) and post-NAC MRI (Two points), and absence of enhancement in the tumor bed at post-NAC MRI (Two points), showed good calibration and discrimination, with AUCs of 0.835, 0.828 and 0.798 in the training, internal and external validation cohorts, respectively. The axillary pCR rates were increased with the total points of the scoring system, and patients with a score of ≥11 points had a pCR rate of 86%-100%. In test cohorts for simulating clinical application, the diagnostic accuracy for axillary pCR was 80%-90% among four different radiologists. Compared to standalone SLNB, combining the scoring system with SLNB reduced the FNR from 14.5% to 4.8%.
CONCLUSIONS: The clinicopathologic-image scoring system with good predictive performance for axillary pCR in clinically node-positive breast cancer, may guide axillary management after NAC and improve patient selection for de-escalating axillary surgery to reduce morbidity.

OBJECTIVE: Reactive FDG uptake in the axillary lymph nodes (ALN) and deltoid muscle (DM) after COVID-19 mRNA vaccination has been recognized, although the actual situation in the Japanese population remains unknown. To determine the incidence of reactive FDG uptake and its contributing factors, we retrospectively studied a cohort of subjects who were vaccinated at our hospital.
METHODS: Whole-body FDG-PET/CT examinations performed in 237 subjects out of 240 subjects with a definite history of COVID-19 vaccination (BNT162b2; BioNTech-Pfizer) were analyzed. Positivity and SUVmax of FDG uptake in the ALN and DM ipsilateral to vaccination, various subject characteristics, and the grade of the pathological FDG-PET/CT findings were evaluated using a multivariate analysis.
RESULTS: FDG uptake in the ALN and DM ipsilateral to vaccination was seen in about 60% of the subjects even soon (0-4 days) after the first vaccination, with percentages reaching 87.5% and 75.0%, respectively, after the second vaccination. DM uptake had almost disappeared at around 2 weeks, while ALN uptake persisted for 3 weeks or longer. A multivariate analysis showed that a short duration since vaccination, a younger age, a female sex, and a low FDG-PET/CT grade (minimal pathological FDG uptake) contributed significantly to positive ALN uptake, while a short duration since vaccination and a female sex were the only significant contributors to positive DM uptake. This study is the first to identify factors contributing to positive FDG uptake in ALN and DM after COVID-19 vaccination.
CONCLUSIONS: A high incidence of FDG uptake in ALN and DM was observed after vaccination. ALN uptake seemed to be associated with a younger age, a female sex, and minimal pathological FDG uptake. After vaccination, an acute inflammatory reaction in DM followed by immune reaction in ALN linked to humoral immunity may be speculated.

OBJECTIVE: To compare the effectiveness of two methods of extracorporeal shock-wave therapy (ESWT) in a rat model of forelimb lymphedema, induced by axillary lymph node dissection.
METHODS: Sprague-Dawley rats were randomly allocated to a group that received 500 ESWT shocks only in the lymphedematous forelimb (Forelimb/ESWT) and a group that received 300 ESWT shocks in the axilla and 200 shocks in the lymphedematous forelimb (Axilla+Forelimb/ESWT). The circumferences of each limb were then measured. Immunohistochemistry for a pan-endothelial marker (cluster of differentiation [CD]31) and lymphatic vessel endothelial hyaluronan receptor-1, and western blot analysis for vascular endothelial growth factor receptor-3 (VEGFR3) and VEGF-C were performed.
RESULTS: The circumferences of the limbs showed significant effects of group and time following surgery. The circumferences at the carpal joint and 2.5 cm above were smallest in the naïve limbs, larger in the Axilla+Forelimb/ESWT group, and the largest in the control group. VEGFR3 tended to be expressed at a higher level in the Axilla+Forelimb/ESWT group (1.96-fold) than in the Forelimb/ESWT group (1.20-fold) versus the opposite non-edematous forelimbs, although this difference was not statistically significant.
CONCLUSIONS: These data suggest that ESWT protocols have differential effects on angiogenesis and lymphangiogenesis in lymphedematous limbs.