Sentinel Node Biopsy (SNB) is beneficial for reducing axillary functional impairment and lymphedema due to extended lymph node dissection. We used the Indigo Carmine dye instead of radioisotope, since it can simplify the complicated multistep identifying procedures and has economic benefit because it requires no radioisotope detection equipment. The operation for small breast cancer is continuously changing from a modified radical mastectomy to various type of breast conserving operations. Among these we performed a partial mastectomy with rotation flap using remnant breast tissue (RFB). This method needs small operation field, so we could reduce trauma to the patient, shorten the operation time, and use natural blood supplies and tissues without destructing other organ structures. The cosmetic effect is desirable to Korean women considering their relatively small breast size as to that of western people. In SNB, 5cc indigocarmine was injected intradermally just around main lesion. Sentinel node was able to be identified easily if a proper dose was used. Its approach was achieved in 15 to 20 minutes. Partial mastectomy (quadrantectomy) was done with cancer free margins. Rotation flap which is covering the defect included as much breast tissue as possible sparing the nipple areolar complex. Sentinel node biopsy and rotation flap brought out both satisfactory cosmetic result and cost effective outcome, so this breast conserving method is recommendable to small breast cancers.
Biopsy ; Breast Neoplasms* ; Breast* ; Equipment and Supplies ; Female ; Humans ; Indigo Carmine ; Lymph Node Excision ; Lymphedema ; Mastectomy, Modified Radical ; Mastectomy, Segmental ; Nipples

Ultrasonography-guided core needle biopsy has been standard of care for diagnosing suspicious breast lesion. The procedure is safe and has a low rate of complications. Most common complication might be bleeding or hematoma, which could be avoided by careful process or changing it to excisional biopsy or managed by proper management. Rarely, Post-procedural arteriovenous fistula is reported in almost all body fields, which is life quality threatening, not life-threatening. Most of them occur with obvious vessel injury, and their primary end-point of management is the obliteration of fistula by thrombosis. However, we experienced a case of iatrogenic arteriovenous fistula after core needle biopsy for breast lesion with small vessel injury which was not but small ones, and its thrill did not disappear even after thrombosis. We would like to share our clinical learnings from surgical management process of this rare complication.
Arteriovenous Fistula* ; Biopsy ; Biopsy, Large-Core Needle* ; Breast* ; Fistula ; Hematoma ; Hemorrhage ; Iatrogenic Disease ; Quality of Life ; Standard of Care ; Thrombosis

OBJECTIVE: In general, quadriplegic patients use their voices to call the caregiver. However, severe quadriplegic patients are in a state of tracheostomy, and cannot generate a voice. These patients require other communication tools to call caregivers. Recently, monitoring of eye status using artificial intelligence (AI) has been widely used in various fields. We made eye status monitoring system using deep learning, and developed a communication system for quadriplegic patients can call the caregiver. METHODS: The communication system consists of 3 programs. The first program was developed for automatic capturing of eye images from the face using a webcam. It continuously captured and stored 15 eye images per second. Secondly, the captured eye images were evaluated for open or closed status by deep learning, which is a type of AI. Google TensorFlow was used as a machine learning tool or library for convolutional neural network. A total of 18,000 images were used to train deep learning system. Finally, the program was developed to utter a sound when the left eye was closed for 3 seconds. RESULTS: The test accuracy of eye status was 98.7%. In practice, when the quadriplegic patient looked at the webcam and closed his left eye for 3 seconds, the sound for calling a caregiver was generated. CONCLUSION: Our eye status detection software using AI is very accurate, and the calling system for the quadriplegic patient was satisfactory.
Artificial Intelligence ; Caregivers ; Humans ; Learning ; Machine Learning ; Quadriplegia ; Tracheostomy ; Unsupervised Machine Learning ; Voice

Parkinson’s disease (PD) is one of the late-onset neurodegenerative movement disorder. Major pathological markers of PD include progressive loss of dopaminergic neurons, Lewy body formation, genetic mutations, and environmental factors. Epigenetic regulation of specific gene expression via impaired histone acetylation is associated with neuronal dysfunction in various neurodegenerative diseases. In this study, we hypothesized that histone deacetylase (HDAC) inhibitor, valproic acid (VPA), can improve motor function by enhancing cell survival in PD genetic model mice with LRRK2 R1441G mutation. To address this question, we administered VPA in LRRK2 R1441G transgenic mice to determine whether VPA affects 1) histone acetylation and HDAC expression, 2) dopaminergic neuron survival, 3) inflammatory responses, 4) motor or non-motor symptoms. As results, VPA administration increased histone acetylation level and the number of tyrosine hydroxylase (TH) positive neurons in substantia nigra of LRRK2 R1441G mice. VPA reduced iba-1 positive activated microglia and the mRNA levels of pro-inflammatory marker genes in LRRK2 R1441G mice. In addition, VPA induced the improvement of PD-like motor and non-motor behavior in LRRK2 R1441G mice. These data suggest that the inhibition of HDAC can be further studied as potential future therapeutics for PD.
Acetylation ; Animals ; Cell Survival ; Dopaminergic Neurons ; Epigenomics ; Gene Expression ; Histone Deacetylases ; Histones ; Lewy Bodies ; Mice ; Mice, Transgenic ; Microglia ; Models, Genetic ; Movement Disorders ; Neurodegenerative Diseases ; Neurons ; Neuroprotection ; RNA, Messenger ; Substantia Nigra ; Tyrosine 3-Monooxygenase ; Valproic Acid

Objective: Various and accurate psychiatric assessments in patients with breast cancer who frequently suffer from psychological problems due to long-term survivors are warranted. This systematic review aimed to investigate the current evidence on psychometric properties of psychiatric assessment for evaluating psychological problems in breast cancer patients. Methods: This systematic review progressed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Four electronic databases such as Web of Science, PubMed, Embase, and Cumulative Index to Nursing and Allied Health Literature were searched. This study protocol was registered on Open Science Framework. Results: Of the 2,040 articles, 21 papers were finally included. Among them, only five studies showed the performance of psychiatric assessment tools. Among 13 assessment tools used in the selected articles, the Hospital Anxiety and Depression Scale (HADS), Distress Thermometer (DT), or Mini-Mental Adjustment to Cancer Scale was frequently used for the evaluation of psychological problems. The DT and Psychosocial Distress Questionnaire-Breast Cancer showed acceptable performances for the prediction of depression and anxiety assessed by the HADS. Conclusion This systematic review found psychiatric assessment tools with acceptable reliability and validity for breast cancer patients. However, comparative studies on reliability and validity of various scales are required to provide useful information for the selection of appropriate assessment tools based on the clinical settings and treatment stages of breast cancer. Joint research among the fields of psychiatry and breast surgery is needed for research to establish the convergent, concurrent, and predictive validity of psychiatric assessment tools in breast cancer patients.

The latissimus dorsi musculocutaneous flap (LDMCF) is widely used for breast reconstruction. However, it has the disadvantage of frequent seroma formation at the donor site, and late seroma has also been reported. The authors report histological findings after the surgical treatment of a late, repeatedly recurrent seroma at 10 years after breast reconstruction with LDMCF. In 2008, a 66-year-old female patient underwent immediate breast reconstruction with LDMCF. In 2015, a late seroma was found at the donor site. After aspiration and drainage, the seroma recurred again in 2018. Total surgical excision of the seroma was performed and bloody-appearing fluid was identified in the capsule. The excised tissue was biopsied. Histological examination revealed no evidence of blood in the fluid, and multinucleated giant cells with amorphous eosinophilic proteinaceous material were identified. The cyst was suggestive of chronic granulomatous inflammation. There was no recurrence at 8 months postoperatively. The patient described herein underwent surgical treatment of late seroma that recurred after immediate breast reconstruction with LDMCF, and histological findings were identified. These results may be helpful for other future studies regarding late seroma after breast reconstruction with LDMCF.

Objective: Given the long-term and severe distress experienced during breast cancer treatment, detecting depression among breast cancer patients is clinically crucial. This study aimed to explore a machine-learning model using self-report questionnaires to screen for depression in patients with breast cancer. Methods: A total of 327 patients who visited the breast cancer clinic were included in this study. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The depression was evaluated according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition. The prediction model’s performance based on supervised machine learning was conducted using MATLAB2022. Results: The BDI showed an area under the curve (AUC) of 0.785 when using the logistic regression (LR) classifier.The HADS and PHQ-9 showed an AUC of 0.784 and 0.756 when using the linear discriminant analysis, respectively.The combinations of BDI and HADS showed an AUC of 0.812 when using the LR. The combinations of PHQ-9, BDI, and HADS showed an AUC of 0.807 when using LR. Conclusion The combination model with BDI and HADS in breast cancer patients might be better than the method using a single scale. In future studies, it is necessary to explore strategies that can improve the performance of the model by integrating the method using questionnaires and other methods.

Objective: Given the long-term and severe distress experienced during breast cancer treatment, detecting depression among breast cancer patients is clinically crucial. This study aimed to explore a machine-learning model using self-report questionnaires to screen for depression in patients with breast cancer. Methods: A total of 327 patients who visited the breast cancer clinic were included in this study. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The depression was evaluated according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition. The prediction model’s performance based on supervised machine learning was conducted using MATLAB2022. Results: The BDI showed an area under the curve (AUC) of 0.785 when using the logistic regression (LR) classifier.The HADS and PHQ-9 showed an AUC of 0.784 and 0.756 when using the linear discriminant analysis, respectively.The combinations of BDI and HADS showed an AUC of 0.812 when using the LR. The combinations of PHQ-9, BDI, and HADS showed an AUC of 0.807 when using LR. Conclusion The combination model with BDI and HADS in breast cancer patients might be better than the method using a single scale. In future studies, it is necessary to explore strategies that can improve the performance of the model by integrating the method using questionnaires and other methods.

Objective: Due to the high frequency of depressive symptoms associated with breast cancer, it is crucial to screen for depression in breast cancer patients. While numerous screening tools are available for depression in this population, there is a need for a brief and convenient tool to enhance clinical use. This study aims to investigate the psychometric properties of the Patient Health Questionnaire-9 (PHQ-9) in patients with breast cancer. Methods: Patients with breast cancer (n=327) who visited the Breast Cancer Clinic were included in this study. The reliability of the PHQ-9 was analyzed by Cronbach’s α, and the construct validity of the PHQ-9 was explored by factor analysis. The concurrent validity of the PHQ-9 was evaluated by Pearson correlation analysis with the Hospital Anxiety and Depression Scale (HADS) and Perceived Stress Scale (PSS). Results: The values of Cronbach’s α ranged from 0.800 to 0.879 was acceptable. The exploratory factor analysis revealed that the one-factor model and two-factor model of the PHQ-9 explained 46% and 57% of the variance, respectively. The PHQ-9 were significantly correlated with those of HADS (r=0.702, p<0.001) and PSS (r=0.466, p<0.001). Consequently, the PHQ-9 demonstrated acceptable reliability and validity in breast cancer patients. Conclusion The findings of this study indicate that the PHQ-9 exhibits acceptable reliability and validity in patients with breast cancer. The convenience of this brief self-report questionnaire suggests its potential as a reliable and valid tool for assessing depression in breast cancer clinics.

Objective: Given the long-term and severe distress experienced during breast cancer treatment, detecting depression among breast cancer patients is clinically crucial. This study aimed to explore a machine-learning model using self-report questionnaires to screen for depression in patients with breast cancer. Methods: A total of 327 patients who visited the breast cancer clinic were included in this study. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The depression was evaluated according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition. The prediction model’s performance based on supervised machine learning was conducted using MATLAB2022. Results: The BDI showed an area under the curve (AUC) of 0.785 when using the logistic regression (LR) classifier.The HADS and PHQ-9 showed an AUC of 0.784 and 0.756 when using the linear discriminant analysis, respectively.The combinations of BDI and HADS showed an AUC of 0.812 when using the LR. The combinations of PHQ-9, BDI, and HADS showed an AUC of 0.807 when using LR. Conclusion The combination model with BDI and HADS in breast cancer patients might be better than the method using a single scale. In future studies, it is necessary to explore strategies that can improve the performance of the model by integrating the method using questionnaires and other methods.