Arterial Calcification Disappearance in Breast Imaging: A Key Indicator for Transition to Invasive Ductal Carcinoma

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.

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.

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.

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 SUV_max and SUV_peak 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 SUV_max and SUV_peak 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. SUV_max and SUV_peak were significantly higher in DL-PET than in cPET. DL-PET and cPET exhibited comparable diagnostic abilities for ALN metastasis.

Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network

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.

18F-FDG-Detected Brown Tumor Confined to the Maxillary Bone With Parathyroid Adenoma

ABSTRACT

Brown tumor is a reactive osteolytic lesion associated with hyperparathyroidism and an extremely rare form of a single lesion in the maxilla. We report the case of a 57-year-old woman with renal dysfunction, nasal obstruction, and hypercalcemia. MRI and CT revealed a huge osteolytic lesion in the maxilla. 18F-FDG PET/CT demonstrated marked FDG uptake within the mass and the lower-left lobe of the thyroid gland. 99mTc-methoxy-isobutyl-isonitrile scintigraphy suggested that this accumulation was a parathyroid adenoma. Parathyroid adenoma resection was performed, and the maxillary tumor was diagnosed as brown tumor. FDG PET/CT was helpful in evaluating brown tumor and detecting parathyroid adenoma.

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.

A useful case of ultrasound-guided axillary lymph node aspiration in a breast cancer patient with improved needle visibility

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.

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.

Active Herpes Zoster Mimicking Worsening of Axillary Lymph Node Metastases of Breast Cancer after Chemotherapy on 18F-Fluorodeoxyglucose Positron-Emission Tomography/Computed Tomography

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.

Clinical usefulness of the fast protocol of breast diffusion-weighted imaging using 3T magnetic resonance imaging with a 16-channel breast coil

We aimed to evaluate the usefulness of a fast protocol of diffusion-weighted imaging (DWI) with one excitation using 3T magnetic resonance imaging (MRI) and a 16-channel breast coil. We analyzed 30 lesions from 27 women between February 2020 and June 2020. The visibility score (from 1 = extremely poor to 5 = excellent) and apparent diffusion coefficient (ADC) value between one and four excitations were evaluated by two readers. The image acquisition time was 40 s for one excitation and 1 min 52 s for four excitations. The visibility scores were 4.630 ± 0.718 and 4.267 ± 1.015 for one excitation and 4.730 ± 0.691 and 4.200 ± 1.000 for four excitations by the two readers. There was no significant difference in the visibility (P = 0.184 and P = 0.423), mean ADC value (P = 0.918 and P = 0.417), and minimum ADC value (P = 0.936 and P = 0.443) between one and four excitations by the two readers. Despite the short acquisition time, the visibility score and ADC values of one-excitation DWI were comparable to that with four excitations. Our fast DWI protocol could provide reproducible visibility and ADC value, potentially helping radiologists to efficiently diagnose patients.

Investigating the Image Quality and Utility of Synthetic MRI in the Breast

Purpose

Synthetic MRI reconstructs multiple sequences in a single acquisition. In the present study, we aimed to compare the image quality and utility of synthetic MRI with that of conventional MRI in the breast.

Methods

We retrospectively collected the imaging data of 37 women (mean age: 55.1 years; range: 20–78 years) who had undergone both synthetic and conventional MRI of T2-weighted, T1-weighted, and fat-suppressed (FS)-T2-weighted images. Two independent breast radiologists evaluated the overall image quality, anatomical sharpness, contrast between tissues, image homogeneity, and presence of artifacts of synthetic and conventional MRI on a 5-point scale (5 = very good to 1 = very poor). The interobserver agreement between the radiologists was evaluated using weighted kappa.

Results

For synthetic MRI, the acquisition time was 3 min 28 s. On the 5-point scale evaluation of overall image quality, although the scores of synthetic FS-T2-weighted images (4.01 ± 0.56) were lower than that of conventional images (4.95 ± 0.23; P < 0.001), the scores of synthetic T1- and T2-weighted images (4.95 ± 0.23 and 4.97 ± 0.16) were comparable with those of conventional images (4.92 ± 0.27 and 4.97 ± 0.16; P = 0.484 and 1.000, respectively). The kappa coefficient of conventional MRI was fair (0.53; P < 0.001), and that of conventional MRI was fair (0.46; P < 0.001).

Conclusion

The image quality of synthetic T1- and T2-weighted images was similar to that of conventional images and diagnostically acceptable, whereas the quality of synthetic T2-weighted FS images was inferior to conventional images. Although synthetic MRI images of the breast have the potential to provide efficient image diagnosis, further validation and improvement are required for clinical application.

The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review

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.

Deep-learning approach with convolutional neural network for classification of maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging

PURPOSE

We aimed to evaluate deep learning approach with convolutional neural networks (CNNs) to discriminate between benign and malignant lesions on maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging (MRI).

METHODS

We retrospectively gathered maximum intensity projections of dynamic contrast-enhanced breast MRI of 106 benign (including 22 normal) and 180 malignant cases for training and validation data. CNN models were constructed to calculate the probability of malignancy using CNN architectures (DenseNet121, DenseNet169, InceptionResNetV2, InceptionV3, NasNetMobile, and Xception) with 500 epochs and analyzed that of 25 benign (including 12 normal) and 47 malignant cases for test data. Two human readers also interpreted these test data and scored the probability of malignancy for each case using Breast Imaging Reporting and Data System. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated.

RESULTS

The CNN models showed a mean AUC of 0.830 (range, 0.750-0.895). The best model was InceptionResNetV2. This model, Reader 1, and Reader 2 had sensitivities of 74.5%, 72.3%, and 78.7%; specificities of 96.0%, 88.0%, and 80.0%; and AUCs of 0.895, 0.823, and 0.849, respectively. No significant difference arose between the CNN models and human readers (p > 0.125).

CONCLUSION

Our CNN models showed comparable diagnostic performance in differentiating between benign and malignant lesions to human readers on maximum intensity projection of dynamic contrast-enhanced breast MRI.

Late-onset pneumothorax in a COVID-19 patient treated with ventilation and ECMO: A case report and literature review

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.

Feasibility of ultrafast dynamic magnetic resonance imaging for the diagnosis of axillary lymph node metastasis: A case report

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.

Evaluation of the Usefulness of CO-RADS for Chest CT in Patients Suspected of Having COVID-19

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.

Efficient Anomaly Detection with Generative Adversarial Network for Breast Ultrasound Imaging

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.

Clinical usefulness of 2-deoxy-2-[18F] fluoro-d-glucose-positron emission tomography/computed tomography for assessing early oral squamous cell carcinoma (cT1-2N0M0)

Background

Positron emission tomography with 2-deoxy-2-[18F] fluoro-d-glucose integrated with computed tomography (FDG-PET/CT) is a useful method to evaluate patients with oral squamous cell carcinoma (OSCC). However, the prognostic significance of FDG-PET/CT for assessing early OSCC remains unclear.

Methods

Pretreatment FDG-PET/CT of 205 consecutive patients (125 men, 80 women, mean age 59.7 year old) with early OSCC (cT1-2N0M0) between June 2010 and December 2014 were retrospectively analyzed. FDG avidity in primary lesions was assessed by visual interpretation. Thereafter, maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were measured in primary lesions. The relationship between each parameter and recurrence free survival (RFS) was assessed using the log-rank test. The performance of FDG-PET/CT for diagnosing metastatic lesions and synchronous cancer was also assessed.

Results

During the follow-up period (mean 32.9 months), 43 patients developed recurrences (21.0%). Patients with visually positive FDG uptake in primary lesions showed significantly shorter RFS than the others (63.0 months vs. 52.9 months, P = 0.005). In those patients, greater SUVmax, MTV, and TLG did not significantly predict shorter RFS. The sensitivity and specificity of FDG-PET/CT for cervical nodal metastases detection were 32.3% and 77.6%, respectively. FDG-PET/CT detected eight synchronous cancers (3.9%) and overlooked six synchronous cancers (2.9%).

Conclusions

Although its utility for detecting cervical nodal metastases and synchronous cancers is limited, FDG-PET/CT is a potentially prognostic indicator in early OSCC.