Assessment of clinical relevance of antigen improves diagnostic accuracy of hypersensitivity pneumonitis.

BACKGROUND: Exposure assessment is integral to the diagnosis of hypersensitivity pneumonitis (HP). Although the clinical relevance of exposed antigens is essential for the assessment, many of the previous guidelines or reports have only evaluated simple exposure histories or immunological tests. To overcome this problem, the Exposure Assessment Form (EAF) was developed as an assessment tool for classifying the exposure grade from G0 to G4. The EAF was modified from the description in the Japanese clinical practice guide 2022 for HP published by the Japanese Respiratory Society. METHODS: One hundred and seventy-two consecutive patients with interstitial lung disease who underwent multidisciplinary discussion (MDD) at our hospital were retrospectively examined. We assessed whether the use of the EAF improved the diagnostic performance of the international guideline of HP. We also evaluated whether the exposure grade affected the prognosis of HP. RESULTS: Even when a HP diagnosis was made with a confidence of 70% or higher according to the international guideline, less than half of these cases resulted in a final diagnosis of HP when the exposure grades were lower than G3. When the result of the EAF was integrated into the exposure definition of the international guideline, the specificity of the diagnostic performance improved, while sensitivity was maintained. Furthermore, HP patients with an exposure grade of G3 or higher showed a tendency to take a longer time to initiate medication. CONCLUSIONS: This is the first study to evaluate the clinical relevance of possible antigens using the EAF. Assessing the exposure grade prevents overdiagnosis and improves the diagnostic performance of the international guideline.

Clinical Utility of Breast Ultrasound Images Synthesized by a Generative Adversarial Network.

BACKGROUND AND OBJECTIVES: This study compares the clinical properties of original breast ultrasound images and those synthesized by a generative adversarial network (GAN) to assess the clinical usefulness of GAN-synthesized images. MATERIALS AND METHODS: We retrospectively collected approximately 200 breast ultrasound images for each of five representative histological tissue types (cyst, fibroadenoma, scirrhous, solid, and tubule-forming invasive ductal carcinomas) as training images. A deep convolutional GAN (DCGAN) image-generation model synthesized images of the five histological types. Two diagnostic radiologists (reader 1 with 13 years of experience and reader 2 with 7 years of experience) were given a reading test consisting of 50 synthesized and 50 original images (≥1-month interval between sets) to assign the perceived histological tissue type. The percentages of correct diagnoses were calculated, and the reader agreement was assessed using the kappa coefficient. RESULTS: The synthetic and original images were indistinguishable. The correct diagnostic rates from the synthetic images for readers 1 and 2 were 86.0% and 78.0% and from the original images were 88.0% and 78.0%, respectively. The kappa values were 0.625 and 0.650 for the synthetic and original images, respectively. The diagnoses made from the DCGAN synthetic images and original images were similar. CONCLUSION: The DCGAN-synthesized images closely resemble the original ultrasound images in clinical characteristics, suggesting their potential utility in clinical education and training, particularly for enhancing diagnostic skills in breast ultrasound imaging.

Prognosis of Subcutaneous Mastectomy for Special Types of Breast Cancer.

Background and objectives: In the treatment of the special type of breast cancer (STBC), the choice of chemotherapeutic agents is often based on the characteristic features of the histological type. On the other hand, the surgical strategy is usually determined by the tumor size and presence of lymph node metastasis, and the indication for immediate reconstruction is rarely discussed based on the histological type. The prognoses of STBC and invasive ductal carcinoma of the breast (IDC) patients who underwent subcutaneous mastectomy (SCM) with immediate reconstruction at our institution were compared. Materials and Methods: A total of 254 patients with SCM with immediate reconstruction from 1998 to 2018 were included; their tumor diameter or induration was less than 25 mm, and it was not in close proximity to the skin. Preoperative chemotherapy and non-invasive cancer cases were excluded. Results: The number of patients was 166 for skin-sparing mastectomy (SSM) and 88 for nipple-sparing mastectomy (NSM). The reconstructive techniques were deep inferior epigastric artery perforator flap (DIEP) reconstruction in 43 cases, latissimus dorsi flap reconstruction (LDflap) in 63 cases, tissue expander (TE) in 117 cases, and transverse rectus abdominis myocutaneous flap/vertical rectus abdominis myocutaneous flap (TRAM/VRAM) reconstruction in 31 cases. The histological types of breast cancer were 211 IDC and 43 STBC; 17 were mucinous carcinoma (MUC), 17 were invasive lobular carcinoma (ILC), 6 were apocrine carcinoma, 1 was tubular carcinoma, and 2 were invasive micropapillary carcinoma. There was no difference in local recurrence or disease-free survival (LRFS, DFS) between IDC and STBC, and overall survival (OS) was significantly longer in STBC. OS was better in the STBC group because SCM with immediate reconstruction was performed for STBC, which is a histological type with a relatively good prognosis. Highly malignant histological types, such as squamous cell carcinoma or metaplastic carcinoma, were totally absent in this study. Conclusions: The indications for SCM with immediate reconstruction for relatively common STBCs such as MUC and ILC can be the same as for IDC.

Virtual Interpolation Images of Tumor Development and Growth on Breast Ultrasound Image Synthesis With Deep Convolutional Generative Adversarial Networks.

OBJECTIVES: We sought to generate realistic synthetic breast ultrasound images and express virtual interpolation images of tumors using a deep convolutional generative adversarial network (DCGAN). METHODS: After retrospective selection of breast ultrasound images of 528 benign masses, 529 malignant masses, and 583 normal breasts, 20 synthesized images of each were generated by the DCGAN. Fifteen virtual interpolation images of tumors were generated by changing the value of the input vector. A total of 60 synthesized images and 20 virtual interpolation images were evaluated by 2 readers, who scored them on a 5-point scale (1, very good; to 5, very poor) and then answered whether the synthesized image was benign, malignant, or normal. RESULTS: The mean score of overall quality for synthesized images was 3.05, and that of the reality of virtual interpolation images was 2.53. The readers classified the generated images with a correct answer rate of 92.5%. CONCLUSIONS: A DCGAN can generate high-quality synthetic breast ultrasound images of each pathologic tissue and has the potential to create realistic virtual interpolation images of tumor development.

Clinical Usefulness of Ultrasound-Guided Fine Needle Aspiration and Core Needle Biopsy for Patients with Axillary Lymphadenopathy.

Background and Objectives: It is necessary to properly diagnose and manage axillary lymphadenopathy caused by a variety of diseases. This study aimed to evaluate the utility of ultrasound (US)-guided sampling in patients with axillary lymphadenopathy. Materials and Methods: Patients with axillary lymphadenopathy (excluding patients with newly diagnosed breast cancer) who underwent US-guided fine needle aspiration (FNA) or core needle biopsy (CNB) at a single center between February 2016 and September 2020 were retrospectively examined. The association between US imaging findings and malignancy was investigated and the diagnostic performance of US-guided sampling was assessed. Results: Fifty-five patients (including eight males) were included in the study; of these, 34 patients (61.8%) were finally diagnosed with a malignant lymph node lesion. Twenty-two patients (40.0%) had undergone FNA and 33 (60.0%) had undergone CNB. Larger short and long axis diameters, thicker lymph node cortex, and the absence of fatty hilum on the US were significantly associated with malignancy (p < 0.05). The diagnostic performance of FNA, CNB, and FNA + CNB was excellent (sensitivity, specificity, and accuracy of 0.909, 0.900, and 0.917 for FNA, 0.958, 1.000, and 0.970 for CNB, and 0.941, 0.952, and 0.945 for FNA + CNB, respectively). Conclusions: US-guided FNA and CNB play an important role in the diagnosis and management of patients with axillary lymphadenopathy.

Virtual Navigator Real-Time Ultrasound Fusion Imaging with Positron Emission Tomography/Computed Tomography for Preoperative Breast Cancer.

We used virtual navigator real-time ultrasound (US) fusion imaging with 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) to identify a lesion that could not be detected on the US alone in a preoperative breast cancer patient. Of the patient's two lesions of breast cancer, the calcified lesion could not be identified by US alone. By fusing US with 18F-FDG PET/CT, which had been performed in advance, the location of the lesion could be estimated and marked, which benefited planning an appropriate surgery. The fusion of US and 18F-FDG PET/CT was a simple and noninvasive method for identifying the lesions detected by 18F-FDG PET/CT.

Classification of Breast Masses on Ultrasound Shear Wave Elastography using Convolutional Neural Networks.

We aimed to use deep learning with convolutional neural networks (CNNs) to discriminate images of benign and malignant breast masses on ultrasound shear wave elastography (SWE). We retrospectively gathered 158 images of benign masses and 146 images of malignant masses as training data for SWE. A deep learning model was constructed using several CNN architectures (Xception, InceptionV3, InceptionResNetV2, DenseNet121, DenseNet169, and NASNetMobile) with 50, 100, and 200 epochs. We analyzed SWE images of 38 benign masses and 35 malignant masses as test data. Two radiologists interpreted these test data through a consensus reading using a 5-point visual color assessment (SWEc) and the mean elasticity value (in kPa) (SWEe). Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. The best CNN model (which was DenseNet169 with 100 epochs), SWEc, and SWEe had a sensitivity of 0.857, 0.829, and 0.914 and a specificity of 0.789, 0.737, and 0.763 respectively. The CNNs exhibited a mean AUC of 0.870 (range, 0.844-0.898), and SWEc and SWEe had an AUC of 0.821 and 0.855. The CNNs had an equal or better diagnostic performance compared with radiologist readings. DenseNet169 with 100 epochs, Xception with 50 epochs, and Xception with 100 epochs had a better diagnostic performance compared with SWEc (P = 0.018-0.037). Deep learning with CNNs exhibited equal or higher AUC compared with radiologists when discriminating benign from malignant breast masses on ultrasound SWE.

Investigation of imaging features in contrast-enhanced magnetic resonance imaging of benign and malignant breast lesions.

PURPOSE: This study aimed to enhance the diagnostic accuracy of contrast-enhanced breast magnetic resonance imaging (MRI) using gadobutrol for differentiating benign breast lesions from malignant ones. Moreover, this study sought to address the limitations of current imaging techniques and criteria based on the Breast Imaging Reporting and Data System (BI-RADS). MATERIALS AND METHODS: In a multicenter retrospective study conducted in Japan, 200 women were included, comprising 100 with benign lesions and 100 with malignant lesions, all classified under BI-RADS categories 3 and 4. The MRI protocol included 3D fast gradient echo T1- weighted images with fat suppression, with gadobutrol as the contrast agent. The analysis involved evaluating patient and lesion characteristics, including age, size, location, fibroglandular tissue, background parenchymal enhancement (BPE), signal intensity, and the findings of mass and non-mass enhancement. In this study, univariate and multivariate logistic regression analyses were performed, along with decision tree analysis, to identify significant predictors for the classification of lesions. RESULTS: Differences in lesion characteristics were identified, which may influence malignancy risk. The multivariate logistic regression model revealed age, lesion location, shape, and signal intensity as significant predictors of malignancy. Decision tree analysis identified additional diagnostic factors, including lesion margin and BPE level. The decision tree models demonstrated high diagnostic accuracy, with the logistic regression model showing an area under the curve of 0.925 for masses and 0.829 for non-mass enhancements. CONCLUSION: This study underscores the importance of integrating patient age, lesion location, and BPE level into the BI-RADS criteria to improve the differentiation between benign and malignant breast lesions. This approach could minimize unnecessary biopsies and enhance clinical decision-making in breast cancer diagnostics, highlighting the effectiveness of gadobutrol in breast MRI evaluations.

Magnetic resonance imaging diagnosis of non-mass enhancement of the breast.

Breast Imaging Reporting and Data System magnetic resonance imaging (BI-RADS-MRI) classifies lesions as mass, non-mass enhancement (NME), or focus. BI-RADS ultrasound does not currently have the concept of non-mass. Additionally, knowing the concept of NME in MRI is significant. Thus, this study aimed to provide a narrative review of NME diagnosis in breast MRI. Lexicons are defined with distribution (focal, linear, segmental, regional, multiple regions, and diffuse) and internal enhancement patterns (homogenous, heterogeneous, clumped, and clustered ring) in the case of NME. Among these, linear, segmental, clumped, clustered ring, and heterogeneous are the terms that suggest malignancy. Hence, a hand search was conducted for reports of malignancy frequencies. The malignancy frequency in NME is widely distributed, ranging from 25 to 83.6%, and the frequency of each finding varies. Latest techniques, such as diffusion-weighted imaging and ultrafast dynamic MRI, are attempted to differentiate NME. Additionally, attempts are made in the preoperative setting to determine the concordance of lesion spread based on findings and the presence of invasion.

The Conceptual Independence of Health Status, Respiratory Symptoms and Dyspnea in Chronic Obstructive Pulmonary Disease in Real Clinical Practice.

The hypothesis that health status is the highest ranking concept, followed by respiratory symptoms and dyspnea as the lowest ranking concepts in subjects with chronic obstructive pulmonary disease (COPD) was tested in a real clinical setting with 157 subjects with stable COPD. Spearman's rank correlation coefficients for scores of health status using the COPD Assessment Test (CAT), respiratory symptoms using the COPD Evaluating Respiratory Symptoms (E-RS) and dyspnea using Dyspnea-12 (D-12) between any two were 0.6 to 0.7. Upon categorizing the patients as "abnormal" or "normal" according to the threshold, it was found that 30 patients (19.1%) had dyspnea, respiratory symptoms and impaired health status. Dyspnea was considered an important part of respiratory symptoms, though seven patients had dyspnea but no respiratory symptoms. There were 10 patients who had respiratory symptoms without dyspnea but without health status problems. Furthermore, there were six patients who had both dyspnea and respiratory symptoms but whose health status was classified as fine. Thus, the hypothesis was correct in approximately 85% of cases.

Prediction of breast cancer risk by automated volumetric breast density measurement.

PURPOSE: Dense breast (DB) is recognized as a breast cancer (BC) risk factor. Although DB is common in Japanese women, the incidence of BC is lower than in Caucasians. We evaluated whether DB is a risk factor or whether there are other risk factors for BC in Japanese women. MATERIALS AND METHODS: We retrospectively analyzed 635 BC patients and 999 controls who received a mammography at our hospital between February 2019 and March 2021. Volumetric breast density percentage (VBD%), breast volume (BV), and fibroglandular volume (FGV) were measured using Volpara™, an automated, three-dimensional image analysis program. A VBD% of 7.5% or higher was classified as DB. The association between the VBD%, BV, and FGV, and BC risk were assessed using logistic regression. RESULTS: Of the BC group and the control group, 77% and 79% had DB. The stratified FGV was positively associated with BC risk (odds ratio: 2.84; 95% confidence interval 1.58-5.12; P < 0.001). No significant association was found between either the VBD% or BV and BC risk. CONCLUSION: The proportion of Japanese women with DB was high, suggesting that DB might not be significantly associated with BC risk. However, our results also suggested that the FGV may be related to BC risk in Japanese women.

Comparison of Predictive Properties between Tools of Patient-Reported Outcomes: Risk Prediction for Three Future Events in Subjects with COPD.

BACKGROUND: Patient-reported outcome (PRO) measures must be evaluated for their discriminatory, evaluative, and predictive properties. However, the predictive capability remains unclear. We aimed to examine the predictive properties of several PRO measures of all-cause mortality, acute exacerbation of chronic obstructive pulmonary disease (COPD), and associated hospitalization. METHODS: A total of 122 outpatients with stable COPD were prospectively recruited and completed six self-administered paper questionnaires: the COPD Assessment Test (CAT), St. George's Respiratory Questionnaire (SGRQ), Baseline Dyspnea Index (BDI), Dyspnoea-12, Evaluating Respiratory Symptoms in COPD and Hyland Scale at baseline. Cox proportional hazards analyses were conducted to examine the relationships with future outcomes. RESULTS: A total of 66 patients experienced exacerbation, 41 were hospitalized, and 18 died. BDI, SGRQ Total and Activity, and CAT and Hyland Scale scores were significantly related to mortality (hazard ratio = 0.777, 1.027, 1.027, 1.077, and 0.951, respectively). The Hyland Scale score had the best predictive ability for PRO measures, but the C index did not reach the level of the most commonly used FEV1. Almost all clinical, physiological, and PRO measurements obtained at baseline were significant predictors of the first exacerbation and the first hospitalization due to it, with a few exceptions. CONCLUSIONS: Measurement of health status and the global scale of quality of life as well as some tools to assess breathlessness, were significant predictors of all-cause mortality, but their predictive capacity did not reach that of FEV1. In contrast, almost all baseline measurements were unexpectedly related to exacerbation and associated hospitalization.

Deep learning model with collage images for the segmentation of dedicated breast positron emission tomography images.

BACKGROUND: Dedicated breast positron emission tomography (dbPET) has high contrast and resolution optimized for detecting small breast cancers, leading to its noisy characteristics. This study evaluated the application of deep learning to the automatic segmentation of abnormal uptakes on dbPET to facilitate the assessment of lesions. To address data scarcity in model training, we used collage images composed of cropped abnormal uptakes and normal breasts for data augmentation. METHODS: This retrospective study included 1598 examinations between April 2015 and August 2020. A U-Net-based model with an uptake shape classification head was trained using either the original or augmented dataset comprising collage images. The Dice score, which measures the pixel-wise agreement between a prediction and its ground truth, of the models was compared using the Wilcoxon signed-rank test. Moreover, the classification accuracies were evaluated. RESULTS: After applying the exclusion criteria, 662 breasts were included; among these, 217 breasts had abnormal uptakes (mean age: 58 ± 14 years). Abnormal uptakes on the cranio-caudal and mediolateral maximum intensity projection images of 217 breasts were annotated and labeled as focus, mass, or non-mass. The inclusion of collage images into the original dataset yielded a Dice score of 0.884 and classification accuracy of 91.5%. Improvement in the Dice score was observed across all subgroups, and the score of images without breast cancer improved significantly from 0.750 to 0.834 (effect size: 0.76, P = 0.02). CONCLUSIONS: Deep learning can be applied for the automatic segmentation of dbPET, and collage images can improve model performance.

Comparison of the clinical and pathological characteristics of ultrasound-guided biopsy for breast masses and non-mass lesions between 16-gauge spring-loaded core needle biopsy and 12-gauge spring-loaded vacuum-assisted biopsy.

PURPOSE: To retrospectively compare the clinical and pathological characteristics of breast masses and non-mass lesions that underwent ultrasound (US)-guided 16-gauge spring-loaded core needle biopsy (CNB) or 12-gauge spring-loaded vacuum-assisted biopsy (VAB). METHODS: We retrospectively compared the results from US-guided diagnostic breast biopsy performed with a 16-gauge CNB (Magnum™) or a 12-gauge VAB (Celero®). The patients' backgrounds and pathological features for each device were examined. RESULTS: In 453 patients with 500 lesions, 373 lesions underwent CNB and 127 underwent VAB. The positive biopsy rate (positive predictive value 3) was significantly higher for VAB (92/127; 72.4%) than for CNB (231/373; 61.9%) (P = 0.032). Non-mass lesions were biopsied more frequently with VAB (57/127; 47.4%) than with CNB (27/378; 7.14%) (P = 0.000). The upgrade rate from high-risk to malignant lesions was significantly higher for CNB (5/19; 26.3%) than for VAB (1/8; 12.5%) (P = 0.043). There were five (1.34%) specimen failures with CNB and one (0.78%) with VAB, 18 (4.82%) re-biopsies with CNB and three (2.36%) with VAB, and 11/21 (52.4%) upgrades from ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) with CNB and 11/30 (36.7%) with VAB. Although these rates tended to be higher with CNB than with VAB, the difference was not significant. CONCLUSION: Although VAB had a significantly higher rate of non-mass lesion biopsies, the upgrade rate from high-risk to malignant lesions was significantly lower for VAB than for CNB. US-guided 12-gauge spring-loaded VAB may be more appropriate for biopsy of non-mass lesions.

Deep Learning-Based Image Quality Improvement in Digital Positron Emission Tomography for Breast Cancer.

We investigated whether 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography images restored via deep learning (DL) improved image quality and affected axillary lymph node (ALN) metastasis diagnosis in patients with breast cancer. Using a five-point scale, two readers compared the image quality of DL-PET and conventional PET (cPET) in 53 consecutive patients from September 2020 to October 2021. Visually analyzed ipsilateral ALNs were rated on a three-point scale. The standard uptake values SUVmax and SUVpeak were calculated for breast cancer regions of interest. For "depiction of primary lesion", reader 2 scored DL-PET significantly higher than cPET. For "noise", "clarity of mammary gland", and "overall image quality", both readers scored DL-PET significantly higher than cPET. The SUVmax and SUVpeak for primary lesions and normal breasts were significantly higher in DL-PET than in cPET (p < 0.001). Considering the ALN metastasis scores 1 and 2 as negative and 3 as positive, the McNemar test revealed no significant difference between cPET and DL-PET scores for either reader (p = 0.250, 0.625). DL-PET improved visual image quality for breast cancer compared with cPET. SUVmax and SUVpeak were significantly higher in DL-PET than in cPET. DL-PET and cPET exhibited comparable diagnostic abilities for ALN metastasis.

Dose Reduction and Diagnostic Performance of Tin Filter-Based Spectral Shaping CT in Patients with Colorectal Cancer.

Routine CT examinations are crucial in colorectal cancer patients (CCPs); however, the high frequency of radiation exposure is a significant concern. This study investigated the radiation dose, image quality, and diagnostic performance of tin filter-based spectral shaping chest−abdominal−pelvic (CAP) CT for CCPs. We reviewed 44 CCPs who underwent single-phase enhanced tin-filtered 100 kV (TF100kV) and standard 120 kV (ST120kV) CAP CT on separate days. Radiation metrics including the volume CT dose index (CTDIvol), dose-length product (DLP), and effective dose (ED) were calculated for both protocols. Two radiologists assessed the presence of the following lesions: lung metastasis, liver metastasis, lymph node metastasis, peritoneal dissemination, and bone metastasis. The area under the receiver operating characteristic curve (AUC) was calculated for the diagnostic performance of each protocol. Radiation metrics of the TF100kV protocol were significantly lower than those of the ST120kV protocol (CDTIvol 1.60 ± 0.31 mGy vs. 14.4 ± 2.50, p < 0.0001; DLP 107.1 (95.9−125.5) mGy·cm vs. 996.7 (886.2−1144.3), p < 0.0001; ED 1.93 (1.73−2.26) mSv vs. 17.9 (16.0−20.6), p < 0.0001, respectively). TF100kV protocol achieved comparable diagnostic performance to that of the ST120kV protocol (AUC for lung metastasis: 1.00 vs. 0.94; liver metastasis: 0.88 vs. 0.83, respectively). TF100kV protocol could substantially reduce the radiation dose by 89% compared to that with the ST120kV protocol while maintaining good diagnostic performance in CCPs.

Higher body mass index is a more important risk factor than sarcopenia for complications in reconstruction of the deep inferior epigastric perforator.

BACKGROUND: In recent years, breast reconstruction using autologous tissue after breast cancer surgery has become a common procedure. This study investigated the association between the occurrence of complications in breast reconstruction using deep inferior epigastric perforator (DIEP) flaps and patient risk factors among Asian women. METHODS: This study included cases of breast reconstruction using DIEP flaps performed at our institution. We retrospectively investigated the relationship between preoperative and operative patient factors and postoperative complications by collecting data from medical records. Sarcopenia was also evaluated by calculating psoas muscle index from the area of the iliopsoas muscle at the level of the third lumbar vertebra using images from preoperative computed tomography. Postoperative complications were compared between a low-body mass index (BMI) group and a high-BMI group, defined using BMI values of <25 kg/m2 and ≥25 kg/m2, respectively. RESULTS: A total of 129 cases of breast reconstruction using DIEP flaps were included in this analysis. The frequency of postoperative complications was significantly higher in the high-BMI group, including for skin flap necrosis of the breast (p = 0.03), recipient-site infection (p = 0.03), and donor-site seroma (p = 0.003). Moreover, abdominal circumference correlated significantly with recipient-site infection (p = 0.01) and donor-site seroma (p = 0.002). Sarcopenia did not show significant correlations with any complications. CONCLUSION: BMI was identified as a risk factor for the occurrence of postoperative complications in breast reconstruction using the DIEP flap, but sarcopenia was not.

Bone Marrow Carcinomatosis in a Stage IV Breast Cancer Patient Treated by Letrozole as First-Line Endocrine Therapy.

Bone marrow carcinomatosis (BMC) associated with breast cancer is a rare but often difficult-to-treat condition; we report a case of a female stage IV breast cancer patient in her seventies with BMC that improved with endocrine monotherapy. The patient had hemoglobinopenia and thrombocytopenia at the time of diagnosis. The diagnosis of BMC due to estrogen receptor-positive invasive lobular carcinoma was confirmed. After transfusion of 4 units of concentrated red blood cells, endocrine treatment with letrozole improved the hematopenia. Ten months after the treatment started, bone metastases worsened, so the patient was changed to combination therapy with palbociclib and fulvestrant, after which there was no worsening of the disease.

Spontaneous regression of breast lymphoproliferative disorders after withdrawal of methotrexate in rheumatoid arthritis patients with Epstein-Barr virus infection: a case report and review of the literature.

BACKGROUND: Lymphoproliferative disorder (LPD) has been shown to occur after treatment with methotrexate (MTX). Currently, MTX-LPD has become widely recognized, but its mechanism and prognostic factors remain unclear. CASE PRESENTATION: We report the first case of Epstein-Barr virus (EBV)-associated MTX-LPD of the breast. A 63-year-old Asian woman with long-term rheumatoid arthritis presented to our facility with intermittent fever. A physical examination revealed a 3-cm lump in her left breast. She had been taking MTX for the past 15 years. Laboratory studies revealed slightly elevated levels of EBV-viral capsid antigen antibody immunoglobulin G and EBV nuclear antibody. Contrast-enhanced computer tomography revealed a mass in the left breast, a subcutaneous nodule in the abdomen, a mass in the left lung, and a nodule in the left retroperitoneum. The definitive diagnosis was consistent with MTX-LPD merging into an EBV-positive, diffuse large B-cell lymphoma. Six months following the withdrawal of MTX, the breast mass had markedly shrunk and the patient remained in good health for 1 year with no evidence of relapse of LPD. CONCLUSION: MTX-LPD rarely occurs in the breast, and it is difficult to diagnose because there have only been six reported cases of breast MTX-LPD reported in the literature. EBV-positive MTX-LPD tends to regress spontaneously after MTX withdrawal, and our case also had similar results. It is important to make an appropriate diagnosis of MTX-LPD of the breast based on imaging and pathology to determine the appropriate treatment protocol for this rare disorder.

Deep learning for image classification in dedicated breast positron emission tomography (dbPET).

OBJECTIVE: This study aimed to investigate and determine the best deep learning (DL) model to predict breast cancer (BC) with dedicated breast positron emission tomography (dbPET) images. METHODS: Of the 1598 women who underwent dbPET examination between April 2015 and August 2020, a total of 618 breasts on 309 examinations for 284 women who were diagnosed with BC or non-BC were analyzed in this retrospective study. The Xception-based DL model was trained to predict BC or non-BC using dbPET images from 458 breasts of 109 BCs and 349 non-BCs, which consisted of mediallateral and craniocaudal maximum intensity projection images, respectively. It was tested using dbPET images from 160 breasts of 43 BC and 117 non-BC. Two expert radiologists and two radiology residents also interpreted them. Sensitivity, specificity, and area under the receiver operating characteristic curves (AUCs) were calculated. RESULTS: Our DL model had a sensitivity and specificity of 93% and 93%, respectively, while radiologists had a sensitivity and specificity of 77-89% and 79-100%, respectively. Diagnostic performance of our model (AUC = 0.937) tended to be superior to that of residents (AUC = 0.876 and 0.868, p = 0.073 and 0.073), although not significantly different. Moreover, no significant differences were found between the model and experts (AUC = 0.983 and 0.941, p = 0.095 and 0.907). CONCLUSIONS: Our DL model could be applied to dbPET and achieve the same diagnostic ability as that of experts.