Pretreatment Interstitial Lung Abnormalities Detected on Abdominal Computed Tomography Scans in Prostate Cancer Patients.

OBJECTIVE: Prostate cancer and interstitial lung abnormality (ILA) share similar risk factor, which is men and older age. The purpose of this study was to investigate the prevalence of pretreatment ILA among prostate cancer patients who underwent abdominal computed tomography (CT) within 1 year at their first visit to the urology department. In addition, we aimed to assess the association between pretreatment ILA and long-term survival in prostate cancer patients. METHODS: This study was conducted in patients who had a first visit for prostate cancer at urology department between 2005 and 2016 and underwent an abdominal CT within 1 year. A thoracic radiologist evaluated the presence of ILA through inspecting the lung base scanned on an abdominal CT. The association between pretreatment ILA and survival was assessed using Kaplan-Meier analysis with log-rank test. Specific survival rates at 12, 36, and 60 months according to the presence of ILA were evaluated using z -test. Cox regression analysis was used to assess the risk factors of mortality. RESULTS: A total of 173 patients were included (mean age, 70.23 ± 7.98 years). Pretreatment ILA was observed in 10.4% of patients. Patients with ILA were more likely to be older and current smokers. Pretreatment ILA was associated with poor survival ( P < 0.001). Age ≥70 years (hazards ratio [HR], 1.98; 95% confidence interval [CI], 1.24-3.16; P = 0.004), metastatic stage (HR, 2.26; 95% CI, 1.36-3.74; P = 0.002), and ILA (HR, 1.96; 95% CI, 1.06-3.60; P = 0.031) were the independent risk factors of mortality. An ILA (HR, 3.94; 95% CI, 1.78-8.72; P = 0.001) was the only independent risk factor of mortality in localized stage prostate cancer patients. CONCLUSIONS: This study provides important insights into the unexplored effect of pretreatment ILA in prostate cancer patients. Pretreatment ILAs were observed considerably in the lung bases scanned on the abdominal CT scans among prostate cancer patients. Furthermore, pretreatment ILAs were the risk factor of mortality. Therefore, lung bases should be routinely inspected in the abdominal CT scans of prostate cancer patients. This result may help clinicians in establishing personalized management strategy of prostate cancer patients.

Prediction of Residual Axillary Nodal Metastasis Following Neoadjuvant Chemotherapy for Breast Cancer: Radiomics Analysis Based on Chest Computed Tomography.

OBJECTIVE: To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer. MATERIALS AND METHODS: This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance. RESULTS: Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively. CONCLUSION: CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.

Torilis japonica Extract Suppresses the Induction of Atopic Inflammation.

As one of the major intractable allergic disorders, atopic inflammation is commonly accompanied by itching, dry skin, and inflammation. Atopic inflammation deteriorates the quality of life and has no fundamental cure, so it is crucial to urgently explore and develop natural resources for long-term treatment without any side effects. This study aimed to verify Torilis japonica extract (TJE)'s relieving effect and mechanism against atopic inflammation using skin cells and skin equivalent models, as well as to investigate torilin's effect (obtained from TJE) and other unknown components as marker compounds. Torilin concentration was verified in TJE using high-performance liquid chromatography and analyzed the unknown components using nuclear magnetic resonance spectroscopy. Furthermore, TJE's cytotoxicity, regenerative effect, and cell cycle regulation effects were confirmed using skin cells with atopic inflammation (human dermal fibroblasts and HaCaT keratinocytes) by using TNF-α and IFN-γ treatments. Consequently, TJE was demonstrated to regulate TARC and CTACK expressions as chemokines and those of interleukin-4, -5, and -13 as cytokines related to atopic inflammation. TJE was further confirmed to affect the matrix metalloproteinase-1, -2, and -9 expressions, which are essential in skin damage. Lastly, this study confirmed TJE's relieving effect against atopic inflammation through a 3D skin model and RhCE model using human dermal fibroblasts and HaCaT keratinocytes. These findings on atopic inflammation verified torilin's relieving effects and TJE's other components.

Malignant Apocrine Lesions of the Breast: Multimodality Imaging Findings and Biologic Features.

The apocrine morphology of the breast is observed in a broad pathological spectrum, ranging from benign cysts to invasive carcinomas. However, the number of clinical research investigating malignant apocrine lesions is limited. This study retrospectively reviewed the data of patients with malignant apocrine lesions admitted in a tertiary center between January 2004 and December 2021, based on the radiology-pathology correlation and the recent advances in their status to enhance the therapeutic implications of androgen receptor (AR). Among the 37 patients with lesions, 27 (73.0%) had triple-negative subtypes with predominant AR expression. The radiological features of malignant apocrine lesions did not differ from those of typical invasive ductal carcinoma or ductal carcinoma in situ. This study demonstrated that knowledge on the imaging features of malignant apocrine lesions and their histological basis could enhance the adoption of new targeted therapies in patients with this particular type of breast cancer.

A Radiomics Approach on Chest CT Distinguishes Primary Lung Cancer from Solitary Lung Metastasis in Colorectal Cancer Patients.

PURPOSE: This study utilized a radiomics approach combined with a machine learning algorithm to distinguish primary lung cancer (LC) from solitary lung metastasis (LM) in colorectal cancer (CRC) patients with a solitary pulmonary nodule (SPN). MATERIALS AND METHODS: In a retrospective study, 239 patients who underwent chest computerized tomography (CT) at three different institutions between 2011 and 2019 and were diagnosed as primary LC or solitary LM were included. The data from the first institution were divided into training and internal testing datasets. The data from the second and third institutions were used as an external testing dataset. Radiomic features were extracted from the intra and perinodular regions of interest (ROI). After a feature selection process, Support vector machine (SVM) was used to train models for classifying between LC and LM. The performances of the SVM classifiers were evaluated with both the internal and external testing datasets. The performances of the model were compared to those of two radiologists who reviewed the CT images of the testing datasets for the binary prediction of LC versus LM. RESULTS: The SVM classifier trained with the radiomic features from the intranodular ROI and achieved the sensitivity/specificity of 0.545/0.828 in the internal test dataset, and 0.833/0.964 in the external test dataset, respectively. The SVM classifier trained with the combined radiomic features from the intra- and perinodular ROIs achieved the sensitivity/specificity of 0.545/0.966 in the internal test dataset, and 0.833/1.000 in the external test data set, respectively. Two radiologists demonstrated the sensitivity/specificity of 0.545/0.966 and 0.636/0.828 in the internal test dataset, and 0.917/0.929 and 0.833/0.929 in the external test dataset, which were comparable to the performance of the model trained with the combined radiomics features. CONCLUSION: Our results suggested that the machine learning classifiers trained using radiomics features of SPN in CRC patients can be used to distinguish the primary LC and the solitary LM with a similar level of performance to radiologists.

Prediction of the histologic upgrade of ductal carcinoma in situ using a combined radiomics and machine learning approach based on breast dynamic contrast-enhanced magnetic resonance imaging.

Objective: To investigate whether support vector machine (SVM) trained with radiomics features based on breast magnetic resonance imaging (MRI) could predict the upgrade of ductal carcinoma in situ (DCIS) diagnosed by core needle biopsy (CNB) after surgical excision. Materials and methods: This retrospective study included a total of 349 lesions from 346 female patients (mean age, 54 years) diagnosed with DCIS by CNB between January 2011 and December 2017. Based on histological confirmation after surgery, the patients were divided into pure (n = 198, 56.7%) and upgraded DCIS (n = 151, 43.3%). The entire dataset was randomly split to training (80%) and test sets (20%). Radiomics features were extracted from the intratumor region-of-interest, which was semi-automatically drawn by two radiologists, based on the first subtraction images from dynamic contrast-enhanced T1-weighted MRI. A least absolute shrinkage and selection operator (LASSO) was used for feature selection. A 4-fold cross validation was applied to the training set to determine the combination of features used to train SVM for classification between pure and upgraded DCIS. Sensitivity, specificity, accuracy, and area under the receiver-operating characteristic curve (AUC) were calculated to evaluate the model performance using the hold-out test set. Results: The model trained with 9 features (Energy, Skewness, Surface Area to Volume ratio, Gray Level Non Uniformity, Kurtosis, Dependence Variance, Maximum 2D diameter Column, Sphericity, and Large Area Emphasis) demonstrated the highest 4-fold mean validation accuracy and AUC of 0.724 (95% CI, 0.619-0.829) and 0.742 (0.623-0.860), respectively. Sensitivity, specificity, accuracy, and AUC using the test set were 0.733 (0.575-0.892) and 0.7 (0.558-0.842), 0.714 (0.608-0.820) and 0.767 (0.651-0.882), respectively. Conclusion: Our study suggested that the combined radiomics and machine learning approach based on preoperative breast MRI may provide an assisting tool to predict the histologic upgrade of DCIS.

Relationship between Incidental Abnormalities on Screening Thoracic Computed Tomography and Mortality: A Long-Term Follow-Up Analysis.

OBJECTIVE: The present study aimed to assess the relationship between incidental abnormalities on thoracic computed tomography (CT) and mortality in a general screening population using a long-term follow-up analysis. MATERIALS AND METHODS: We retrospectively collected the medical records and CT images of 840 participants (mean age ± standard deviation [SD], 58.5 ± 6.7 years; 564 male) who underwent thoracic CT at a single health promotion center between 2007 and 2010. Two thoracic radiologists independently reviewed all CT images and evaluated any incidental abnormalities (interstitial lung abnormality [ILA], emphysema, coronary artery calcification [CAC], aortic valve [AV] calcification, and pulmonary nodules). Kaplan-Meier analysis with log-rank and z-tests was performed to assess the relationship between incidental CT abnormalities and all-cause mortality in the subsequent follow-up. Cox proportional hazards regression was performed to further identify risk factors of all-cause mortality among the incidental CT abnormalities and clinical factors. RESULTS: Among the 840 participants, 55 (6%), 171 (20%), 288 (34%), 396 (47%), and 97 (11%) had findings of ILA, emphysema, CAC, pulmonary nodule, and AV calcification, respectively, on initial CT. The participants were followed up for a mean period ± SD of 10.9 ± 1.4 years. All incidental CT abnormalities were associated with all-cause mortality in univariable analysis (p < 0.05). However, multivariable analysis further revealed fibrotic ILA as an independent risk factor for all-cause mortality (hazard ratio, 2.52 [95% confidence interval, 1.02-6.22], p = 0.046). ILA were also identified as an independent risk factor for lung cancer or respiratory disease-related deaths. CONCLUSION: Incidental abnormalities on screening thoracic CT were associated with increased mortality during the long-term follow-up. Among incidental CT abnormalities, fibrotic ILA were independently associated with increased mortality. Appropriate management and surveillance may be required for patients with fibrotic ILA on thoracic CT obtained for general screening purposes.

The p53-Driven Anticancer Effect of Ribes fasciculatum Extract on AGS Gastric Cancer Cells.

Cancer metastasis is directly related to the survival rate of cancer patients. Although cancer metastasis proceeds by the movement of cancer cells, it is fundamentally caused by its resistance to anoikis, a mechanism of apoptosis caused by the loss of adhesion of cancer cells. Therefore, it was found that inhibiting cancer migration and reducing anoikis resistance are important for cancer suppression, and natural compounds can effectively control it. Among them, Ribes fasciculatum, which has been used as a medicinal plant, was confirmed to have anticancer potential, and experiments were conducted to prove various anticancer effects by extracting Ribes fasciculatum (RFE). Through various experiments, it was observed that RFE induces apoptosis of AGS gastric cancer cells, arrests the cell cycle, induces oxidative stress, and reduces mobility. It was also demonstrated that anoikis resistance was attenuated through the downregulation of proteins, such as epidermal growth factor receptor (EGFR). Moreover, the anticancer effect of RFE depends upon the increase in p53 expression, suggesting that RFE is suitable for the development of p53-targeted anticancer materials. Moreover, through xenotransplantation, it was found that the anticancer effect of RFE confirmed in vitro was continued in vivo.

Clinical implication of interstitial lung abnormality in elderly patients with early-stage non-small cell lung cancer.

BACKGROUND: Interstitial lung abnormality (ILA) is closely related to lung cancer. This study aimed to assess whether the presence of ILA is associated with the clinicoradiological features of elderly patients (≥70 years) with early-stage non-small cell lung cancer (NSCLC). METHODS: Elderly patients who underwent surgical resection for stage I or II NSCLC with preserved lung function between 2012 and 2019 were retrospectively identified. ILA was evaluated using a three-point scale. Univariate analyses were performed for clinicoradiological features based on the presence of ILA. Logistic and linear regression analyses were performed for cancer staging and tumor size, respectively. RESULTS: A total of 254 patients were evaluated. The presence of ILA (score = 2) was significantly associated with male sex, current or former smoker status, higher pack-years of smoking, low forced expiratory volume in one second/forced vital capacity ratios and diffusing capacity of the lung for carbon monoxide, and presence of emphysema (≥5%). Tumor characteristics, such as lower lobe and outer one-third location, squamous cell carcinoma, and higher cancer stage (stage II) were significantly associated with ILA. The presence of ILA independently predicted a higher cancer stage (adjusted odds ratio, 1.81; 95% confidence interval, 1.10-2.96; p = 0.02) and a larger tumor size in linear regression analysis (p = 0.04). CONCLUSIONS: Patients with ILA showed clinicoradiological features similar to those of idiopathic pulmonary fibrosis in elderly patients with early-stage NSCLC. Identifying the clinical implications of ILA in early-stage lung cancer will guide clinicians in providing appropriate management for these patients.

HER2-Positive Breast Cancer: Association of MRI and Clinicopathologic Features With Tumor-Infiltrating Lymphocytes.

BACKGROUND. Tumor-infiltrating lymphocytes (TILs) are associated with therapeutic outcomes and prognosis in patients with human epidermal growth factor receptor type 2 (HER2)-positive breast cancer. Identification of TIL levels is clinically relevant. OBJECTIVE. The purpose of our study was to explore associations of clinicopathologic and MRI features with TIL levels in patients with HER2-positive breast cancer. METHODS. A total of 212 consecutive women (mean age, 54.0 years) diagnosed with HER2-positive breast cancer between January 2017 and December 2019 were included in this retrospective study. Patients were divided into low-TIL (< 10%) and high-TIL (≥ 10%) groups. Three breast radiologists independently reviewed images; interreader agreement was assessed, and the first reader's findings were used for further analysis. Associations of clinicopathologic and MRI features with TIL levels were evaluated using multivariable logistic regression analysis. Subanalysis of TIL levels by hormone receptor (HR) status was also performed. RESULTS. A total of 115 (54.2%) patients had low TIL levels, and 97 (45.8%) patients had high TIL levels. A high TIL level was associated (all, p < .05) with histologic grade 3 (odds ratio [OR] = 3.98; frequency, 78.4% vs 52.2% in high- vs low-TIL groups, respectively), high tumor cellularity (OR = 4.59; median cellularity, 60% vs 50%), lower frequency of associated ductal carcinoma in situ (OR = 0.16; frequency, 86.6% vs 94.8%), and higher frequency of peritumoral edema on T2-weighted images (OR = 2.83; 71.1% vs 50.4%). In subgroup analysis by HR status, histologic grade 3 (OR = 5.03, p = .002) was a significant independent predictor of high TIL level in the HR-positive/HER2-positive group, whereas high tumor cellularity (OR = 9.06, p = .002), peritumoral edema (OR = 5.23, p = .03), and low ADC (OR = 11.69, p = .047) were independent predictors of high TIL level in the HR-negative/HER2-positive group. Interreader agreement for peritumoral edema was moderate among the three radiologists (к = 0.432-0.539). CONCLUSION. Peritumoral edema on MRI and the histopathologic feature of tumor aggressiveness help predict high TIL levels in patients with HER2-positive breast cancer. CLINICAL IMPACT. Pretreatment MRI features may serve as a useful tool for assessing TIL levels in patients with HER2-positive breast cancer and for helping to classify patients with variable clinical outcomes related to immune activity and to guide selection among neoadjuvant chemotherapy or HER2-targeted therapy or immunotherapy.

Predicting Underestimation of Invasive Cancer in Patients with Core-Needle-Biopsy-Diagnosed Ductal Carcinoma In Situ Using Deep Learning Algorithms.

The prediction of an occult invasive component in ductal carcinoma in situ (DCIS) before surgery is of clinical importance because the treatment strategies are different between pure DCIS without invasive component and upgraded DCIS. We demonstrated the potential of using deep learning models for differentiating between upgraded versus pure DCIS in DCIS diagnosed by core-needle biopsy. Preoperative axial dynamic contrast-enhanced magnetic resonance imaging (MRI) data from 352 lesions were used to train, validate, and test three different types of deep learning models. The highest performance was achieved by Recurrent Residual Convolutional Neural Network using Regions of Interest (ROIs) with an accuracy of 75.0% and area under the receiver operating characteristic curve (AUC) of 0.796. Our results suggest that the deep learning approach may provide an assisting tool to predict the histologic upgrade of DCIS and provide personalized treatment strategies to patients with underestimated invasive disease.

Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning.

OBJECTIVE: This study was conducted in order to investigate the feasibility of using radiomics analysis (RA) with machine learning algorithms based on breast magnetic resonance (MR) images for discriminating malignant from benign MR-detected additional lesions in patients with primary breast cancer. MATERIALS AND METHODS: One hundred seventy-four MR-detected additional lesions (benign, n = 86; malignancy, n = 88) from 158 patients with ipsilateral primary breast cancer from a tertiary medical center were included in this retrospective study. The entire data were randomly split to training (80%) and independent test sets (20%). In addition, 25 patients (benign, n = 21; malignancy, n = 15) from another tertiary medical center were included for the external test. Radiomics features that were extracted from three regions-of-interest (ROIs; intratumor, peritumor, combined) using fat-saturated T1-weighted images obtained by subtracting pre- from postcontrast images (SUB) and T2-weighted image (T2) were utilized to train the support vector machine for the binary classification. A decision tree method was utilized to build a classifier model using clinical imaging interpretation (CII) features assessed by radiologists. Area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity were used to compare the diagnostic performance. RESULTS: The RA models trained using radiomics features from the intratumor-ROI showed comparable performance to the CII model (accuracy, AUROC: 73.3%, 69.6% for the SUB RA model; 70.0%, 75.1% for the T2 RA model; 73.3%, 72.0% for the CII model). The diagnostic performance increased when the radiomics and CII features were combined to build a fusion model. The fusion model that combines the CII features and radiomics features from multiparametric MRI data demonstrated the highest performance with an accuracy of 86.7% and an AUROC of 91.1%. The external test showed a similar pattern where the fusion models demonstrated higher levels of performance compared with the RA- or CII-only models. The accuracy and AUROC of the SUB+T2 RA+CII model in the external test were 80.6% and 91.4%, respectively. CONCLUSION: Our study demonstrated the feasibility of using RA with machine learning approach based on multiparametric MRI for quantitatively characterizing MR-detected additional lesions. The fusion model demonstrated an improved diagnostic performance over the models trained with either RA or CII alone.

Axillary metastasis from occult breast cancer and synchronous contralateral breast cancer initially suspected to be cancer with contralateral axillary metastasis: a case report.

BACKGROUND: Initial detection of axillary metastasis without known ipsilateral breast cancer could be a challenging diagnostic problem. Four options could be considered for the primary site of the malignancy: ipsilateral occult breast cancer, contralateral breast cancer, tumors in other distant organs, and primary axillary malignancy itself. Although breast cancer is known as the most common primary cancer of axillary metastasis, both occult breast cancer and breast cancer with contralateral axillary metastasis (CAM) are rare. CASE PRESENTATION: A 63-year-old woman presented with palpable right axillary metastasis, and a tiny contralateral breast cancer was detected by breast magnetic resonance imaging. No lesion was found in the ipsilateral right breast and contralateral left axillary region. Both right axillary metastasis and contralateral breast cancer were positive for estrogen receptor. The diagnostic issue was to determine whether the axillary metastasis was derived from the contralateral breast cancer or not. Right axillary dissection and left breast conserving surgery were performed. The final diagnosis was occult breast cancer that presented with axillary lymph node metastasis and early-stage synchronous contralateral breast cancer, based on clinical evidence and postoperative pathologic results. After surgery, systemic treatment and whole breast irradiation were administered. No recurrence or metastasis was observed 15 months postoperatively. CONCLUSION: For accurate diagnosis of axillary metastasis without detectable ipsilateral breast cancer, multifaceted diagnostic approach considering clinical, radiological, and pathological evidences is required.

Anti-Obesity Effect of Hot Water Extract of Barley Sprout through the Inhibition of Adipocyte Differentiation and Growth.

Barley sprouts are known to have several effective physiological activities. In this study, the anti-obesity effect of a barley sprout hot water extract (BSE) was confirmed. Saponarin was quantitatively analyzed in BSE using HPLC, and the inhibitory effect on 3T3-L1 pre-adipocyte differentiation into adipocytes was confirmed by Oil Red O staining, TG assay, and Western blotting. In addition, the inhibitory effect of BSE on adipocyte growth was confirmed through glucose uptake and lipolysis of adipocytes. C57/BL/6N mice were induced to obesity with a high-fat diet, and BSE was administered to confirm the effect on an animal model. Weight gain, morphological changes in adipose tissue, changes in the food efficiency ratio, and blood biochemical changes were observed, and an improvement effect on fatty liver was confirmed. As a result, the anti-obesity effect of BSE was confirmed in vitro, and it was confirmed that this effect was also effective in vivo and that it could be helpful in the treatment of obesity-related diseases.

Blastic plasmacytoid dendritic cell neoplasm of the breast: A case report and review of the literature.

RATIONALE: Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is an uncommon and aggressive hematologic malignancy that arises from plasmacytoid dendritic cells. BPDCN typically presents with skin lesions with or without involvement of lymph nodes, peripheral blood, or bone marrow. However, breast involvement of BPDCN is rare and there has been no report describing the radiologic features of BPDCN within breast parenchyma. PATIENT CONCERNS: We report a case of a 47-year-old woman who presented with an incidentally detected hypermetabolic breast lesion on PET/CT with concurrent right cheek plaque. DIAGNOSES: Skin biopsy was performed for the right cheek plaque. Mammography and breast ultrasonography were performed to evaluate the breast lesion. The lesion was depicted as a 2.5 cm sized focal asymmetry on mammogram and an irregular heterogeneous echoic mass within the mammary zone of the right upper outer breast. Core needle biopsy was performed for the breast lesion. Histologic diagnosis of the two lesions was BPDCN. INTERVENTIONS: The patient was treated with induction and consolidation chemotherapy and received allogenic peripheral blood stem cell transplantation. OUTCOMES: The patient remains in complete remission state without relapse at 34 months since initial diagnosis. LESSONS: This is the first case of BPDCN manifested as a breast parenchymal mass and assessed by diagnostic breast imaging tools (mammography and ultrasonography). This case report is significant for BPDCN within the breast parenchyma and presenting rare radiologic description of BPDCN.

[Invasive Ductal Carcinoma Originating from a Borderline Phyllodes Tumor in a Young Female: A Case Report]. / ì Šì€ 여성의 경계성 ì—½ìƒì¢ ì–‘ì—ì„œ 생긴 침윤성 ìœ ê´€ì•”: 증례 ë³´ê³ .

Phyllodes tumors of the breast are relatively rare fibroepithelial tumors that account for less than 1% of primary breast neoplasms. Phyllodes tumors have epithelial and stromal components and they originate from the periductal stroma. They are classified as benign, borderline, or malignant. Carcinomatous differentiation of the epithelial components of phyllodes tumors is rare, and their imaging features have not been accurately described. Herein, we report a rare case of invasive ductal carcinoma originating from a borderline phyllodes tumor in a 21-year-old female with radiologic and pathologic findings.

Tumor Seeding after Percutaneous Transthoracic Needle Biopsy of Metastatic Pulmonary Ameloblastoma.

Percutaneous transthoracic needle biopsy (PTNB) is a minimally-invasive procedure that is an indispensable tool for evaluating pulmonary lesions. Though extremely rare, tumor seeding of the pleura and chest wall can occur as a complication. Given that the breast is located anterior to the thorax, needle tracking through the breast is inevitable when PTNB is performed using the anterior approach. We describe tumor seeding of metastatic pulmonary ameloblastoma in the pectoralis muscle layer of the breast along the needle track of PTNB in a 51-year-old female presenting with a palpable lump in the right breast.

Pictorial Review of Mediastinal Masses with an Emphasis on Magnetic Resonance Imaging.

Magnetic resonance imaging (MRI) has become a crucial tool for evaluating mediastinal masses considering that several lesions that appear indeterminate on computed tomography and radiography can be differentiated on MRI. Using a three-compartment model to localize the mass and employing a basic knowledge of MRI, radiologists can easily diagnose mediastinal masses. Here, we review the use of MRI in evaluating mediastinal masses and present the images of various mediastinal masses categorized using the International Thymic Malignancy Interest Group's three-compartment classification system. These masses include thymic hyperplasia, thymic cyst, pericardial cyst, thymoma, mediastinal hemangioma, lymphoma, mature teratoma, bronchogenic cyst, esophageal duplication cyst, mediastinal thyroid carcinoma originating from ectopic thyroid tissue, mediastinal liposarcoma, mediastinal pancreatic pseudocyst, neurogenic tumor, meningocele, and plasmacytoma.

18F-fluorodeoxyglucose uptake on PET/computed tomography in association with androgen receptor expression and other clinicopathologic factors in surgically resected triple-negative breast cancer.

OBJECTIVE: To evaluate whether androgen receptor expression in triple-negative breast cancer (TNBC) is associated with F-fluorodeoxyglucose (FDG) uptake and to identify predictive factors of F-FDG uptake in TNBC. METHODS: We retrospectively assessed data of 156 surgically resected primary TNBC in 156 consecutive patients who underwent F-FDG PET/computed tomography between July 2013 and May 2017. Clinicopathologic features, including androgen receptor expression, were categorized and the distribution of maximum standardized uptake value (SUVmax) was compared between the groups. We also evaluated the correlations between the predictive factors and SUVmax. RESULTS: Median SUVmax was higher in the androgen receptor-negative group than in the androgen receptor-positive group (8.8 vs. 7.1, P = 0.026) with negative correlation between androgen receptor and SUVmax (P = 0.041, correlation coefficient (r) = -0.163). On multivariate regression analysis, tumor size (>20 mm), histological grade, Ki-67 (≥14%), and the presence of ductal carcinoma in situ (DCIS) were significantly associated with SUVmax (P < 0.001, P = 0.012, P = 0.017 and P = 0.021, respectively). Tumor size, histological grade and Ki-67 were positively correlated with SUVmax (P < 0.001, r = 0.450; P = 0.004, r = 0.228; P = 0.001, r = 0.269, respectively), while the presence of DCIS showed negative correlation with SUVmax (P < 0.001, r = -0.292). CONCLUSION: Androgen receptor-positive TNBC showed lower F-FDG uptake than androgen receptor-negative triple-TNBC. Tumor size, histological grade, Ki-67 and the presence of DCIS significantly influenced F-FDG uptake in TNBC.