Search

Although several studies have been conducted on artificial intelligence (AI) use in mammography (MG), there is still a paucity of research on the diagnosis of metachronous bilateral breast cancer (BC), which is typically more challenging to diagnose. This study aimed to determine whether AI could enhance BC detection, achieving earlier or more accurate diagnoses than radiologists in cases of metachronous contralateral BC. We included patients who underwent unilateral BC surgery and subsequently developed contralateral BC. This retrospective study evaluated the AI-supported MG diagnostic system called FxMammo™. We evaluated the capability of FxMammo™ (FathomX Pte Ltd., Singapore) to diagnose BC more accurately or earlier than radiologists’ assessments. This evaluation was supplemented by reviewing MG readings made by radiologists. Out of 1101 patients who underwent surgery, 10 who had initially undergone a partial mastectomy and later developed contralateral BC were analyzed. The AI system identified malignancies in six cases (60%), while radiologists identified five cases (50%). Notably, two cases (20%) were diagnosed solely by the AI system. Additionally, for these cases, the AI system had identified malignancies a year before the conventional diagnosis. This study highlights the AI system’s effectiveness in diagnosing metachronous contralateral BC via MG. In some cases, the AI system consistently diagnosed cancer earlier than radiological assessments.

Background/aimAntiviral hepatitis and systemic therapies for hepatocellular carcinoma (HCC) remarkably progressed in the recent 10 years. This study aimed to reveal the actual transition and changes in the prognosis and background liver disease in non-advanced HCC in the past 20 years.MethodsThis retrospectively recruited 566 patients who were diagnosed with non-advanced HCC from February 2002 to February 2022. The prognosis was analyzed by subdividing according to the diagnosis date (period I: February 2002-April 2009 and period Ⅱ: May 2009-February 2022).ResultsPatients in period II (n = 351) were significantly older, with lower albumin-bilirubin (ALBI) scores and alpha-fetoprotein (AFP) and more anti-viral therapy, systemic therapy, and hepatic arterial infusion chemotherapy as compared with those in period I (n = 215). The etiology ratio of the background liver disease revealed decreased hepatitis C virus from 70.6% to 49.0% and increased non-B, non-C from 17.7% to 39.9% from periods I to Ⅱ. The multivariate analysis revealed older age and higher ALBI score in Barcelona Clinic Liver Cancer (BCLC) 0/A stage, AFP of >20 ng/mL, and higher ALBI score in BCLC B stage as independent prognosis factors. Fine-Gray competing risk model analysis revealed that liver-related deaths significantly decreased in period II as compared to period I, especially for BCLC stage 0/A (HR: 0.656; 95%CI: 0.442-0.972, P = 0.036).ConclusionThe characteristics of patients with non-advanced HCC have changed over time. Appropriate background liver management led to better liver-related prognoses in BCLC 0/A.

Autoimmune neutropenia (AIN) is an extremely rare condition, and there is no effective treatment option for this disorder. AIN can cause major complications in patients with perioperative infection. Herein, we present a 56-year-old female patient who was scheduled for breast cancer surgery. However, she was unexpectedly diagnosed with AIN. Thus, the surgery was postponed, and endocrine therapy was started. After 7 months of treatment, the surgery was performed. Granulocyte colony-stimulating factor was administered before the surgery, but the patient’s neutrophil count did not increase. Thus, levofloxacin was administered during the surgery. The patient had fever (38.6°C) 1 day after the surgery. Her surgical wound did not present with redness, and there were no other signs of infection. The fever subsided on the second day after the surgery. Nevertheless, antibiotics were administered for 5 days. The patient was discharged on the sixth day after the surgery.

A woman in her 70s, initially suspected of having fibroadenoma due to a well-defined mass in her breast, underwent regular mammography and ultrasound screenings. Over several years, no appreciable alterations in the mass were observed, maintaining the fibroadenoma diagnosis. However, in the fourth year, an ultrasound indicated slight enlargement and peripheral irregularities in the mass, even though the mammography images at that time showed no alterations. Interestingly, mammography images over time showed the gradual disappearance of previously observed arterial calcification around the mass. Pathological examination eventually identified the mass as invasive ductal carcinoma. Although the patient had breast tissue arterial calcification typical of atherosclerosis, none was present around the tumor-associated arteries. This case highlights the importance of monitoring arterial calcification changes in mammography, suggesting that they are crucial indicators in breast cancer diagnosis, beyond observing size and shape alterations.

The uptake of 18F-fluorothymidine (18F-FLT) depends on cells’ proliferative rates. We compared the characteristics of 18F-FLT positron emission tomography/computed tomography (PET/CT) with those of 18F-fluorodeoxyglucose (18F-FDG) PET/CT for breast cancer. We prospectively diagnosed patients with breast cancer who underwent 18F-FLT PET/CT and 18F-FDG PET/CT. Subsequently, significant differences and correlation coefficients of the maximum standardized uptake value (SUVmax) in primary breast cancer and axillary lymph nodes were statistically evaluated. We enrolled eight patients with breast cancer. In six treatment-naive patients, the SUVmax for primary lesions showed a significant difference (mean, 2.1 vs. 4.1, p = 0.031) and a strong correlation (r = 0.969) between 18F-FLT and 18F-FDG. Further, although the SUVmax for the axillary lymph nodes did not show a significant difference between 18F-FLT and 18F-FDG (P = 0.246), there was a strong correlation between the two (r = 0.999). In a patient-by-patient study, there were cases in which only 18F-FDG uptake was observed in lymph nodes and normal breasts. Bone metastases demonstrated lower accumulation than bone marrow on the 18F-FLT PET/CT. In conclusion, a strong correlation was observed between the 18F-FLT PET/CT and 18F-FDG PET/CT uptake. Differences in the biochemical characteristics of 18F-FLT and 18F-FDG were reflected in the accumulation differences for breast cancer, metastatic lesions, and normal organs.

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.

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.

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.

Abstract 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.

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

Deep learning (DL) has become a remarkably powerful tool for image processing recently. However, the usefulness of DL in positron emission tomography (PET)/computed tomography (CT) for breast cancer (BC) has been insufficiently studied. This study investigated whether a DL model using images with multiple degrees of PET maximum-intensity projection (MIP) images contributes to increase diagnostic accuracy for PET/CT image classification in BC. We retrospectively gathered 400 images of 200 BC and 200 non-BC patients for training data. For each image, we obtained PET MIP images with four different degrees (0°, 30°, 60°, 90°) and made two DL models using Xception. One DL model diagnosed BC with only 0-degree MIP and the other used four different degrees. After training phases, our DL models analyzed test data including 50 BC and 50 non-BC patients. Five radiologists interpreted these test data. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Our 4-degree model, 0-degree model, and radiologists had a sensitivity of 96%, 82%, and 80–98% and a specificity of 80%, 88%, and 76–92%, respectively. Our 4-degree model had equal or better diagnostic performance compared with that of the radiologists (AUC = 0.936 and 0.872–0.967, p = 0.036–0.405). A DL model similar to our 4-degree model may lead to help radiologists in their diagnostic work in the future.

Ultrasound-guided, lymph node, fine-needle aspiration cytology is important in diagnosing axillary lymph node metastasis in breast cancer. However, poor needle visibility can render the procedure difficult. We describe a case in which state-of-the-art enhancement techniques using matrix linear probes can provide better needle visibility and improve the certainty and efficiency of the examination.

Background: Sarcopenia is associated with postoperative complications in patients undergoing digestive surgery. In this study, we investigated the impact of preoperative sarcopenia on postoperative complications in breast cancer patients who underwent total mastectomy. Methods: Patients with breast cancer who underwent total mastectomy were included in the analysis. The relationship between the presence of sarcopenia and postoperative complications (e.g., skin flap necrosis and seroma) and between the incidence of these complications as well as preoperative and surgical factors was investigated. Moreover, the effects of sarcopenia on recurrence-free survival and overall survival were evaluated. The psoas muscle index calculated using values measured on preoperative computed tomography images was used to diagnose sarcopenia. Results: In total, 43 (49%) of 88 patients presented with sarcopenia. The number of patients with a Geriatric Nutritional Risk Index score

Coronavirus disease 2019 (COVID-19) has become a major threat to public health since the outbreak in Wuhan in 2019. Chest computed tomography is recommended for COVID-19 cases for evaluation and follow up of pneumonia and related complication. We report the case of a 66-year-old man with underlying hypertension and a history of smoking 76 packs a year; he was frequently monitored by computed tomography for pulmonary changes during the period from early symptom onset to death. Furthermore, he developed a pneumothorax during the course. The occurrence of pneumothorax in COVID-19 patients is not common, and there have been only a few previous reports. This is a valuable case of pneumothorax in a patient with COVID-19 treated with a ventilator and extracorporeal membrane oxygenation. This case and previous reports suggest that pneumothorax occurs in COVID-19 with a relatively late onset (3–8 weeks). Long-term pneumonia morbidity, steroid therapy, positive pressure ventilation, and extracorporeal membrane oxygenation can cause pneumothorax, leading to capillary and alveolar damage.

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.

This study aimed to evaluate the ability of the pix2pix generative adversarial network (GAN) to improve the image quality of low-count dedicated breast positron emission tomography (dbPET). Pairs of full- and low-count dbPET images were collected from 49 breasts. An image synthesis model was constructed using pix2pix GAN for each acquisition time with training (3776 pairs from 16 breasts) and validation data (1652 pairs from 7 breasts). Test data included dbPET images synthesized by our model from 26 breasts with short acquisition times. Two breast radiologists visually compared the overall image quality of the original and synthesized images derived from the short-acquisition time data (scores of 1–5). Further quantitative evaluation was performed using a peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the visual evaluation, both readers revealed an average score of >3 for all images. The quantitative evaluation revealed significantly higher SSIM (p < 0.01) and PSNR (p < 0.01) for 26 s synthetic images and higher PSNR for 52 s images (p < 0.01) than for the original images. Our model improved the quality of low-count time dbPET synthetic images, with a more significant effect on images with lower counts.

A woman in her 70s was diagnosed with left breast cancer and left axillary lymph node metastasis by an ultrasound-guided biopsy. 18F-FDG-PET/CT showed strong FDG accumulation in the tumor in the left breast and a left axillary lymph node. Neoadjuvant chemotherapy (NAC) was administered in combination with a G-CSF injection to prevent febrile neutropenia. The post-treatment 18F-FDG-PET/CT showed the disappearance of the left breast tumor and left axillary lymph node and revealed a solitary new area of strong FDG accumulation in the sternum. To rule out the possibility of sternal metastasis, a sternal biopsy was performed at the same time as surgery, which revealed no malignant findings. Although very rare, focal uptake on 18F-FDG-PET/CT performed after anticancer drug therapy with G-CSF may mimic a solitary bone metastasis. A bone biopsy may be a useful technique to avoid an immediate misdiagnosis of bone metastasis.

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.

Purpose: Microwave radar-based breast imaging technology utilizes the principle of radar, in which radio waves reflect at the interface between target and normal tissues, which have different permittivities. This study aims to investigate the feasibility and safety of a portable microwave breast imaging device in clinical practice. Materials and methods: We retrospectively collected the imaging data of ten breast cancers in nine women (median age: 66.0 years; range: 37–78 years) who had undergone microwave imaging examination before surgery. All were Japanese and the tumor sizes were from 4 to 10 cm. Using a five-point scale (1 = very poor; 2 = poor; 3 = fair; 4 = good; and 5 = excellent), a radiologist specialized in breast imaging evaluated the ability of microwave imaging to detect breast cancer and delineate its location and size in comparison with conventional mammography and the pathological findings. Results: Microwave imaging detected 10/10 pathologically proven breast cancers, including non-invasive ductal carcinoma in situ (DCIS) and micro-invasive carcinoma, whereas mammography failed to detect 2/10 breast cancers due to dense breast tissue. In the five-point evaluation, median score of location and size were 4.5 and 4.0, respectively. Conclusion: The results of the evaluation suggest that the microwave imaging device is a safe examination that can be used repeatedly and has the potential to be useful in detecting 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.

Background and Objectives: This study aimed to investigate whether predictive indicators for the deterioration of respiratory status can be derived from the deep learning data analysis of initial chest computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19). Materials and Methods: Out of 117 CT scans of 75 patients with COVID-19 admitted to our hospital between April and June 2020, we retrospectively analyzed 79 CT scans that had a definite time of onset and were performed prior to any medication intervention. Patients were grouped according to the presence or absence of increased oxygen demand after CT scan. Quantitative volume data of lung opacity were measured automatically using a deep learning-based image analysis system. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the opacity volume data were calculated to evaluate the accuracy of the system in predicting the deterioration of respiratory status. Results: All 79 CT scans were included (median age, 62 years (interquartile range, 46–77 years); 56 (70.9%) were male. The volume of opacity was significantly higher for the increased oxygen demand group than for the nonincreased oxygen demand group (585.3 vs. 132.8 mL, p < 0.001). The sensitivity, specificity, and AUC were 76.5%, 68.2%, and 0.737, respectively, in the prediction of increased oxygen demand. Conclusion: Deep learning-based quantitative analysis of the affected lung volume in the initial CT scans of patients with COVID-19 can predict the deterioration of respiratory status to improve treatment and resource management.

A 74 year old Japanese woman was diagnosed with invasive breast carcinoma. Her axillary lymph node was slightly swollen and had a short-axis diameter of 8 mm, but fine-needle aspiration did not lead to the diagnosis of metastasis. Subsequent 18F-fluorodeoxyglucose positron emission tomography/computed tomography showed no abnormal accumulation on the lymph node. Ultrafast dynamic magnetic resonance imaging yielded a very fast contrast enhancement like that of the primary lesion based on which we suspected lymph node metastasis. To our knowledge, this is the first report that shows that ultrafast imaging has contributed to the diagnosis of axillary lymph node metastasis.

Objective: To determine the phase that facilitates flap observation of the ascending aorta in Stanford type A acute aortic dissection with perfused false lumen. Methods: We reconstructed retrospective Electrocardiogram-gated Computed Tomography Angiography images of the ascending aorta of all 20 patients to 20 phases of curved-multiplanar reconstruction in 5% increment. One radiologist created and randomized 10 cross-sectional images of each phase for every patient and two radiologists scored these images on a 5-point scale depending on the degree of flap stoppage. We calculated the average score for each phase of each case and compared them among the three groups. Results: Image scores were significantly better in the 65 %–100 % R-R interval group than those in the 5%–30 % (p < 2e-16) and 35 %–60 % R-R interval groups(p = 7.2e-10). Similar scores were observed in the Heart Rate > 70 group (p = 0.00039, 2.2e-14). Moreover a similar tendency was observed in the arrhythmia group (p = 0.0035, 0.294). No difference was found in the degree of flap stoppage in the 65 %–100 % R-R interval group between the Heart Rate > 70 and Heart Rate ≤ 70 groups (p = 0.466) and between the arrhythmia and non-arrhythmia groups (p = 0.1240). Conclusion: In observing the ascending aorta, We obtained a good image at 65 %–100 % R-R interval and similar tendency was observed in the patients with arrhythmia.

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.

A woman in her 60s presented to our hospital with a left breast mass that was diagnosed as breast cancer. 18F-Fluorodeoxyglucose positron-emission tomography/computed tomography (18F-FDG PET/CT) revealed intense, hot uptake in the cancerous mass and left axillary lymph node metastasis. After chemotherapy, another PET/CT scan was performed. Although the mass and left axillary lymph nodes shrank and FDG uptake decreased, enlarged lymph nodes with high FDG uptake appeared in the right axilla. The patient had a painful vesicular eruption on the front to the back of the right upper hemithorax, which was diagnosed as active herpes zoster. Active herpes zoster mimics a worsening axillary lymph node metastasis on the PET/CT scan.

Primary breast osteosarcoma (PBO) is very rare. This report presents a case of POB that was evaluated by multiple modalities. A woman in her 70s presented with a mass of increasing size in her right breast. A mammogram and an ultrasound visualized a lobulated mass containing coarse calcification in the right breast. Magnetic resonance imaging showed a strong enhancement effect and high signal on diffusion-weighted imaging. Further imaging on 18F-fluorodeoxyglucose positron-emission tomography and computed tomography exhibited a high uptake. A right total mastectomy was performed. Histologic examination revealed abundant periosteal formation, areas of calcification and moderately pleomorphic oval to spindle-shaped stromal cells, leading to the diagnosis of PBO. The presence of calcified breast tumors exhibiting aggressive growth indicates that PBO should be added to the differential diagnosis.

Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women’s health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound.

The purpose of this study was to use the Coronavirus Disease 2019 (COVID-19) Reporting and Data System (CO-RADS) to evaluate the chest computed tomography (CT) images of patients suspected of having COVID-19, and to investigate its diagnostic performance and interobserver agreement. The Dutch Radiological Society developed CO-RADS as a diagnostic indicator for assessing suspicion of lung involvement of COVID-19 on a scale of 1 (very low) to 5 (very high). We investigated retrospectively 154 adult patients with clinically suspected COVID-19, between April and June 2020, who underwent chest CT and reverse transcription-polymerase chain reaction (RT-PCR). The patients’ average age was 61.3 years (range, 21–93), 101 were male, and 76 were RT-PCR positive. Using CO-RADS, four radiologists evaluated the chest CT images. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Interobserver agreement was calculated using the intraclass correlation coefficient (ICC) by comparing the individual reader’s score to the median of the remaining three radiologists. The average sensitivity was 87.8% (range, 80.2–93.4%), specificity was 66.4% (range, 51.3–84.5%), and AUC was 0.859 (range, 0.847–0.881); there was no significant difference between the readers (p > 0.200). In 325 (52.8%) of 616 observations, there was absolute agreement among observers. The average ICC of readers was 0.840 (range, 0.800–0.874; p < 0.001). CO-RADS is a categorical taxonomic evaluation scheme for COVID-19 pneumonia, using chest CT images, that provides outstanding performance and from substantial to almost perfect interobserver agreement for predicting COVID-19.

We aimed to use generative adversarial network (GAN)-based anomaly detection to diagnose images of normal tissue, benign masses, or malignant masses on breast ultrasound. We retrospectively collected 531 normal breast ultrasound images from 69 patients. Data augmentation was performed and 6372 (531 × 12) images were available for training. Efficient GAN-based anomaly detection was used to construct a computational model to detect anomalous lesions in images and calculate abnormalities as an anomaly score. Images of 51 normal tissues, 48 benign masses, and 72 malignant masses were analyzed for the test data. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of this anomaly detection model were calculated. Malignant masses had significantly higher anomaly scores than benign masses (p < 0.001), and benign masses had significantly higher scores than normal tissues (p < 0.001). Our anomaly detection model had high sensitivities, specificities, and AUC values for distinguishing normal tissues from benign and malignant masses, with even greater values for distinguishing normal tissues from malignant masses. GAN-based anomaly detection shows high performance for the detection and diagnosis of anomalous lesions in breast ultrasound images.

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

Deep convolutional generative adversarial networks (DCGANs) are newly developed tools for generating synthesized images. To determine the clinical utility of synthesized images, we generated breast ultrasound images and assessed their quality and clinical value. After retrospectively collecting 528 images of 144 benign masses and 529 images of 216 malignant masses in the breasts, synthesized images were generated using a DCGAN with 50, 100, 200, 500, and 1000 epochs. The synthesized (n = 20) and original (n = 40) images were evaluated by two radiologists, who scored them for overall quality, definition of anatomic structures, and visualization of the masses on a five-point scale. They also scored the possibility of images being original. Although there was no significant difference between the images synthesized with 1000 and 500 epochs, the latter were evaluated as being of higher quality than all other images. Moreover, 2.5%, 0%, 12.5%, 37.5%, and 22.5% of the images synthesized with 50, 100, 200, 500, and 1000 epochs, respectively, and 14% of the original images were indistinguishable from one another. Interobserver agreement was very good (|r| = 0.708−0.825, p < 0.001). Therefore, DCGAN can generate high-quality and realistic synthesized breast ultrasound images that are indistinguishable from the original images.

Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field. [ABSTRACT FROM AUTHOR]