Evaluation of standard breast ultrasonography by adding two-dimensional and three-dimensional shear wave elastography: a prospective, multicenter trial.

OBJECTIVE: To reduce the number of biopsies performed on benign breast lesions categorized as BI-RADS 4-5, we investigated the diagnostic performance of combined two-dimensional and three-dimensional shear wave elastography (2D + 3D SWE) with standard breast ultrasonography (US) for the BI-RADS assessment of breast lesions. METHODS: A total of 897 breast lesions, categorized as BI-RADS 3-5, were subjected to standard breast US and supplemented by 2D SWE only and 2D + 3D SWE analysis. Based on the malignancy rate of less than 2% for BI-RADS 3, lesions assessed by standard breast US were reclassified with SWE assessment. RESULTS: After standard breast US evaluation, 268 (46.1%) participants underwent benign biopsies in BI-RADS 4-5 lesions. By using separated cutoffs for upstaging BI-RADS 3 at 120 kPa and downstaging BI-RADS 4a at 90 kPa in 2D + 3D SWE reclassification, 123 (21.2%) participants underwent benign biopsy, resulting in a 54.1% reduction (123 versus 268). CONCLUSION: Combining 2D + 3D SWE with standard breast US for reclassification of BI-RADS lesions may achieve a reduction in benign biopsies in BI-RADS 4-5 lesions without sacrificing sensitivity unacceptably. CLINICAL RELEVANCE STATEMENT: Combining 2D + 3D SWE with US effectively reduces benign biopsies in breast lesions with categories 4-5, potentially improving diagnostic accuracy of BI-RADS assessment for patients with breast lesions. TRIAL REGISTRATION: ChiCTR1900026556 KEY POINTS: • Reduce benign biopsy is necessary in breast lesions with BI-RADS 4-5 category. • A reduction of 54.1% on benign biopsies in BI-RADS 4-5 lesions was achieved using 2D + 3D SWE reclassification. • Adding 2D + 3D SWE to standard breast US improved the diagnostic performance of BI-RADS assessment on breast lesions: specificity increased from 54 to 79%, and PPV increased from 54 to 71%, with slight loss in sensitivity (97.2% versus 98.7%) and NPV (98.1% versus 98.7%).

Detection and diagnosis of automated breast ultrasound in patients with BI-RADS category 4 microcalcifications: a retrospective study.

BACKGROUND: Automated Breast Ultrasound (AB US) has shown good application value and prospects in breast disease screening and diagnosis. The aim of the study was to explore the ability of AB US to detect and diagnose mammographically Breast Imaging Reporting and Data System (BI-RADS) category 4 microcalcifications. METHODS: 575 pathologically confirmed mammographically BI-RADS category 4 microcalcifications from January 2017 to June 2021 were included. All patients also completed AB US examinations. Based on the final pathological results, analyzed and summarized the AB US image features, and compared the evaluation results with mammography, to explore the detection and diagnostic ability of AB US for these suspicious microcalcifications. RESULTS: 250 were finally confirmed as malignant and 325 were benign. Mammographic findings including microcalcifications morphology (61/80 with amorphous, coarse heterogeneous and fine pleomorphic, 13/14 with fine-linear or branching), calcification distribution (189/346 with grouped, 40/67 with linear and segmental), associated features (70/96 with asymmetric shadow), higher BI-RADS category with 4B (88/120) and 4 C (73/38) showed higher incidence in malignant lesions, and were the independent factors associated with malignant microcalcifications. 477 (477/575, 83.0%) microcalcifications were detected by AB US, including 223 malignant and 254 benign, with a significantly higher detection rate for malignant lesions (x2 = 12.20, P < 0.001). Logistic regression analysis showed microcalcifications with architectural distortion (odds ratio [OR] = 0.30, P = 0.014), with amorphous, coarse heterogeneous and fine pleomorphic morphology (OR = 3.15, P = 0.037), grouped (OR = 1.90, P = 0.017), liner and segmental distribution (OR = 8.93, P = 0.004) were the independent factors which could affect the detectability of AB US for microcalcifications. In AB US, malignant calcification was more frequent in a mass (104/154) or intraductal (20/32), and with ductal changes (30/41) or architectural distortion (58/68), especially with the both (12/12). BI-RADS category results also showed that AB US had higher sensitivity to malignant calcification than mammography (64.8% vs. 46.8%). CONCLUSIONS: AB US has good detectability for mammographically BI-RADS category 4 microcalcifications, especially for malignant lesions. Malignant calcification is more common in a mass and intraductal in AB US, and tend to associated with architectural distortion or duct changes. Also, AB US has higher sensitivity than mammography to malignant microcalcification, which is expected to become an effective supplementary examination method for breast microcalcifications, especially in dense breasts.

Predicting papillary thyroid carcinoma cervical lymph node metastases: an algorithm using the American College of Radiology Thyroid Imaging, Reporting and Data System.

BACKGROUND: It is important to predict lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) preoperatively; however, the relationship between the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) score and cervical LNM remains unclear. PURPOSE: To evaluate the association between the ACR TI-RADS score and cervical LNM in patients with PTC. MATERIAL AND METHODS: This retrospective study consisted of 474 patients with 548 PTCs. Cervical LNM including central LNM (CLNM) and lateral LNM (LLNM) were confirmed by pathology. Univariate and multivariate analyses were performed to investigate the risk factors of CLNM and LLNM. RESULTS: Multivariate logistic regression analyses indicated that younger age and multifocality were risk factors for CLNM in PTCs with TR5. In addition, younger age, larger tumor size, and Hashimoto's thyroiditis (HT) were risk factors for LLNM in PTCs ≥ 10 mm with TR5. In PTCs with TR4, ACR TI-RADS scores 5-6 conferred risks for LNM. In PTCs ≥ 10 mm with TR5, ACR TI-RADS scores ≥9 were risk factors for LLNM. CONCLUSION: A higher ACR TI-RADS score is a predictor for cervical LNM in PTCs with TR4 and PTCs ≥ 10 mm with TR5.

Value of multimodal imaging in the diagnosis of breast sclerosing adenosis associated with malignant lesions.

AIM: To explore the diagnostic value of multimodal imaging techniques, including automatic breast volume scanner (ABVS), mammography (MG), and magnetic resonance (MRI) in breast sclerosing adenosis (SA) associated with malignant lesions. METHODS: From January 2018 to October 2020, 76 patients (88 lesions) with pathologically confirmed as SA associated with malignant or benign lesions were retrospective analyzed. All patients completed ABVS examination, 58 patients (67 lesions) with MG and 50 patients (62 lesions) with MRI were also completed before biopsy or surgical excision, of which, six patients (eight lesions) diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 3 by all imaging examinations underwent surgical excision without biopsy, other 70 patients (80 lesions) with BI-RADS category 4 or above by any imaging examination completed biopsy, including 65 patients (75 lesions) were further surgical excised and the other five patients (five lesions) were just followed up. All lesions were retrospectively described and classified, and were divided into benign group and malignant group according to their pathological results. Image features of different examination methods between the two groups were compared and analyzed. A ROC curve was established using the sensitivity of BI-RADS categories to predict malignant lesions in different imaging techniques as the ordinate and 1-specificity as the abscissa. RESULTS: 88 lesions including 26 purely SA and 45 SA associated with benign lesions were classified as benign group, and the remaining 17 SA associated with malignant lesions were classified as malignant group. On ABVS, 40 mass lesions, their heterogeneous echo, not circumscribed margin and coronal convergence signs were statistically significant for malignant lesions (p < .05), but the remain 48 nonmass lesions lack specific sonographic features. On MG, 12 showed negative results, 55 showed with microcalcification, mass, structural distortion, and asymmetric density shadow, of which 11 lesions had the above two signs at the same time, but only microcalcification had statistical difference between the two groups. 35 mass enhanced lesions and 27 nonmass enhanced lesions on MRI, but there were no significant difference between their pathological results. Time signal intensity curves showed no differences, but ADC value <1.10 × 10-3  mm2 /s is more significant in malignant lesions (p < .05). The area under the ROC curve (AUC) of BI-RADS classification of ABVS, MG, and MRI in the diagnosis of malignant lesions were 0.611, 0.474, and 0.751, respectively, and the AUC of the combined diagnosis of the three was 0.761. CONCLUSION: Mass lesions with heterogeneous echo, not circumscribed margin and coronal convergence sign on ABVS, microcalcification on MG and the ADC value <1.10 × 10-3  mm2 /s on MRI are significant signs for SA associated with malignant lesions. The combined diagnosis of the three methods was the highest, and the following were MRI, ABVS, and MG. Therefore, be cognizant of significant characteristics in SA associated with malignancy showed in different imaging examinations can improve the preoperative evaluation of SA and better provide basis for subsequent clinical decision-making.

Efficiency and impact factors of anatomical intelligence for breast and hand-held ultrasound in lesion detection.

PURPOSE: To investigate the efficiency and impact factors of anatomical intelligence for breast (AI-Breast) and hand-held ultrasound (HHUS) in lesion detection. METHODS: A total of 172 outpatient women were randomly selected, underwent AI-Breast ultrasound (Group AI) once and HHUS twice. HHUS was performed by breast imaging radiologists (Group A) and general radiologists (Group B). For the AI-Breast examination, a trained technician performed the whole-breast scan and data acquisition, while other general radiologists performed image interpretation. The examination time and lesion detection rate were recorded. The impact factors for breast lesion detection, including breast cup size, number of lesions, and benign or malignant lesions were analyzed. RESULTS: The detection rates of Group AI, A, and B were 92.8 ± 17.0%, 95.0 ± 13.6%, and 85.0 ± 22.9%, respectively. Comparable lesion detection rates were observed in Group AI and Group A (P > 0.05), but a significantly lower lesion detection rate was observed in Group B compared to the other two (both P < 0.05). Regarding missed diagnosis rates of malignant lesions, comparable performance was observed in Group AI, Group A, and Group B (8% vs. 4% vs. 14%, all P > 0.05). Scan times of Groups AI, A, and B were 262.15 ± 40.4 s, 237.5 ± 110.3 s, 281.2 ± 86.1 s, respectively. The scan time of Group AI was significantly higher than Group A (P < 0.01), but was slightly lower than Group B (P > 0.05). We found a strong linear correlation between scan time and cup size in Group AI (r = 0.745). No impacts of cup size and number of lesions were found on the lesion detection rate in Group AI (P > 0.05). CONCLUSIONS: With the assist of AI-Breast system, the lesion detection rate of AI-Breast ultrasound was comparable to that of a breast imaging radiologist and superior to that of the general radiologist. AI-Breast ultrasound may be used as a potential approach for breast lesions surveillance.

Nodular fasciitis of the breast: the report of three cases.

BACKGROUND: Nodular Fasciitis is a benign fibroblastic proliferation in soft tissues, which mostly occurs in the upper extremities, trunk, head and neck region. It is rarely reported to occur in the breast. CASE PRESENTATION: Herein, we present sonograms of nodular fasciitis in the breast at different durations in three cases. In Case 1, we provided the longest follow-up time in all literatures. In Case 2 and Case 3, we provided the automated breast ultrasound finding of breast nodular fasciitis for the first time. CONCLUSION: Nodular Fasciitis shows clinical features and ultrasonography findings are similar to those of breast cancer. For superficially located breast lesions with a single and rapid growth, nodular fasciitis may be considered in the differential diagnosis of benign entities resembling malignant tumors on breast imaging.

The hidden breast lesions: A case report of bilateral breast cancer.

Bilateral breast cancer (BBC) is rare and is associated with an unfavorable prognosis. Consequently it is crucial to improve diagnostic performance of breast cancer in the clinical setting. We report a case of BBC in a 66-year-old woman and describe the imaging findings, including mammography, hand-held ultrasound, automated breast ultrasound, anatomical intelligence for breast ultrasound (AI-breast), and magnetic resonance imaging. Only AI-breast ultrasound successfully located the two tumors, while other imaging examinations failed to detect the tumor in the right breast.

The diagnostic performance of automated versus handheld breast ultrasound and mammography in symptomatic outpatient women: a multicenter, cross-sectional study in China.

OBJECTIVES: The purpose of this study was to evaluate the diagnostic performance of automated breast ultrasound (ABUS) for breast cancer by comparing it to handheld ultrasound (HHUS) and mammography (MG). METHODS: A multicenter cross-sectional study was conducted between February 2016 and March 2017 in five tertiary hospitals in China, and 1922 women aged 30-69 years old were recruited. Women aged 30-39 years (group A) underwent ABUS and HHUS, and women aged 40-69 (group B) underwent additional MG. Images were interpreted using the Breast Imaging Reporting and Data System (BI-RADS). All BI-RADS 4 and 5 cases were confirmed pathologically. Sensitivities and specificities of all modalities were compared. RESULTS: There were 83 cancers in 677 women in group A and 321 cancers in 1245 women in group B. In the whole study population, the sensitivities of ABUS and HHUS were 92.8% (375/404) and 96.3% (389/404), and the specificities were 93.0% (1411/1518) and 89.6% (1360/1518), respectively. ABUS had a significantly higher specificity to HHUS (p < 0.01), while HHUS had higher sensitivity (p = 0.01). In group B, the sensitivities of ABUS, HHUS, and MG were 93.5% (300/321), 96.6% (310/321), and 87.9% (282/321). The specificities were 93.0% (859/924), 89.9% (831/924), and 91.6% (846/924). ABUS had significantly higher sensitivity (p = 0.02) and comparable specificity compared with MG (p = 0.14). CONCLUSION: ABUS increased sensitivity and had similar specificity compared with mammography in the diagnosis of breast cancer. Additionally, ABUS has comparable performance to HHUS in women aged 30-69 years old. ABUS or HHUS is a suitable modality for breast cancer diagnosis. KEY POINTS: • In breast cancer diagnosis settings, automated breast ultrasound has a higher cancer detection rate, sensitivity, and specificity than mammography, especially in women with dense breasts. • Compared with handheld ultrasound, automated breast ultrasound has higher specificity, lower sensitivity, and comparable diagnostic performance. • Automated breast ultrasound is a suitable modality for breast cancer diagnosis, and may have a potential indication for its further use in the breast cancer early detection.

Diagnostic performance of automated breast ultrasound and handheld ultrasound in women with dense breasts.

PURPOSE: As an adjunct to mammography, ultrasound can improve the detection of breast cancer in women with dense breasts. We aimed to evaluate the diagnostic performance of automated breast ultrasound system (ABUS) and handheld ultrasound (HHUS) in Chinese women with dense breasts, both in combination with mammography and separately. METHODS: This is a cross-sectional multicenter clinical research study. Nine hundred and thirty-seven women with dense breasts underwent ABUS, HHUS, and mammography at one of five tertiary-care hospitals. The diagnostic performance of ABUS and HHUS was evaluated in combination with mammography, or separately in women with mammography-negative dense breasts. The agreement between ABUS and HHUS in breast cancer detection was also assessed. RESULTS: The sensitivity of the combination of ABUS or HHUS with mammography was 99.1% (219/221), and the specificities were 86.9% (622/716) and 84.9% (608/716), respectively. The area under the curve was 0.93 for ABUS combined with mammography and 0.92 for that of HHUS combined with mammography. Statistically significant agreement between ABUS and HHUS in breast cancer detection was observed (percent agreement = 0.94, κ = 0.85). The incremental cancer detection rate in mammography-negative dense breasts was 42.8 per 1000 ultrasound examinations. CONCLUSIONS: Both ABUS and HHUS as adjuncts to mammography can significantly improve the breast cancer detection rate in women with dense breasts, and there is a strong correlation between them. Given the high prevalence of dense breasts and the multiple advantages of ABUS over HHUS, such as less operator dependence and reproducibility, ABUS showed great potential for use in breast cancer early detection, especially in resource-limited areas.

Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning.

Background Deep learning (DL) algorithms are gaining extensive attention for their excellent performance in image recognition tasks. DL models can automatically make a quantitative assessment of complex medical image characteristics and achieve increased accuracy in diagnosis with higher efficiency. Purpose To determine the feasibility of using a DL approach to predict clinically negative axillary lymph node metastasis from US images in patients with primary breast cancer. Materials and Methods A data set of US images in patients with primary breast cancer with clinically negative axillary lymph nodes from Tongji Hospital (974 imaging studies from 2016 to 2018, 756 patients) and an independent test set from Hubei Cancer Hospital (81 imaging studies from 2018 to 2019, 78 patients) were collected. Axillary lymph node status was confirmed with pathologic examination. Three different convolutional neural networks (CNNs) of Inception V3, Inception-ResNet V2, and ResNet-101 architectures were trained on 90% of the Tongji Hospital data set and tested on the remaining 10%, as well as on the independent test set. The performance of the models was compared with that of five radiologists. The models' performance was analyzed in terms of accuracy, sensitivity, specificity, receiver operating characteristic curves, areas under the receiver operating characteristic curve (AUCs), and heat maps. Results The best-performing CNN model, Inception V3, achieved an AUC of 0.89 (95% confidence interval [CI]: 0.83, 0.95) in the prediction of the final clinical diagnosis of axillary lymph node metastasis in the independent test set. The model achieved 85% sensitivity (35 of 41 images; 95% CI: 70%, 94%) and 73% specificity (29 of 40 images; 95% CI: 56%, 85%), and the radiologists achieved 73% sensitivity (30 of 41 images; 95% CI: 57%, 85%; P = .17) and 63% specificity (25 of 40 images; 95% CI: 46%, 77%; P = .34). Conclusion Using US images from patients with primary breast cancer, deep learning models can effectively predict clinically negative axillary lymph node metastasis. Artificial intelligence may provide an early diagnostic strategy for lymph node metastasis in patients with breast cancer with clinically negative lymph nodes. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Bae in this issue.

Diagnostic Performance of Automated Breast Ultrasound in Differentiating Benign and Malignant Breast Masses in Asymptomatic Women: A Comparison Study With Handheld Ultrasound.

OBJECTIVES: Our aim was to investigate the diagnostic potential of an automated breast ultrasound (ABUS) system in differentiating benign and malignant breast masses compared with handheld ultrasound (HHUS). METHODS: Women were randomly and proportionally selected from outpatients and underwent both HHUS and ABUS examinations. Masses with final American College of Radiology Breast Imaging Reporting and Data System categories 2 and 3 were considered benign. Masses with final Breast Imaging Reporting and Data System categories 4 and 5 were considered malignant. The diagnosis was confirmed by pathologic results or at least a 1-year follow-up. Automated breast US and HHUS were compared on the basis of their sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Diagnostic consistency and areas under the receiver operating characteristic curves were analyzed. The maximum diameters of masses were compared among HHUS, ABUS, and pathologic results. RESULTS: A total of 599 masses in 398 women were confirmed by pathologic results or at least a 1-year follow-up; 103 of 599 masses were malignant, and 496 were benign. There were no significant differences between ABUS and HHUS in terms of diagnostic accuracy (80.1% versus 80.6%), specificity (77.62% versus 80.24%), positive predictive value (46.12% versus 46.46%), and negative predictive value (97.96% versus 95.67%). There were significant differences in sensitivity (92.23% versus 82.52%; P < .01) and areas under the curve (0.85 versus 0.81; P < .05) between ABUS and HHUS. The correlation of the maximum diameter was slightly higher between ABUS and pathologic results (r = 0.885) than between HHUS and pathologic results (r = 0.855), but the difference was not significant (P > .05). CONCLUSIONS: Automated breast US is better than HHUS in differentiating benign and malignant breast masses, especially with respect to specificity.

Progress in the Application of Lung Ultrasound for the Evaluation of Neonates with Respiratory Distress Syndrome.

Neonatal respiratory distress syndrome (NRDS) is a common critical disease in neonates. Early diagnosis and timely treatment are crucial. Historically, X-ray imaging was the primary method for diagnosing NRDS. However, this method carries radiation exposure risks, making it unsuitable for dynamic lung condition monitoring. In addition, neonates who are critically ill require bedside imaging, but diagnostic delays are often unavoidable due to equipment transportation and positioning limitations. These challenges have been resolved with the introduction of lung ultrasound (LUS) in neonatal intensive care. The diagnostic efficacy and specificity of LUS for NRDS is superior to that of X-ray. The non-invasive, dynamic, and real-time benefits of LUS also allow for real-time monitoring of lung changes throughout treatment for NRDS, yielding important insights for guiding therapy. In this paper, we examine the ultrasonographic characteristics of NRDS and the recent progress in the application of ultrasound in the diagnosis and treatment of NRDS while aiming to promote wider adoption of this method.

An analysis of the risk factors for invasive fungal infections in preterm infants and a discussion of prevention strategies.

BACKGROUND: Although the success rate of resuscitation in preterm infants is increasing, the long length of hospital stay in preterm infants and the need for more invasive operations, coupled with the widespread use of empirical antibiotics, have increased the prevalence of fungal infections in preterm infants in neonatal intensive care units (NICUs) year on year. OBJECTIVE: The present study aims to explore the risk factors of invasive fungal infections (IFI) in preterm infants and to identify some prevention strategies. METHODS: A total of 202 preterm infants with a gestational age of 26 weeks to 36+6 weeks and a birth weight of less than 2,000 g, admitted to our neonatal unit during the 5-year period from January 2014 to December 2018, were selected for the study. Among these preterm infants, six cases that developed fungal infections during hospitalization were enrolled as the study group, and the remaining 196 infants who did not develop fungal infections during hospitalization were the control group. The gestational age, length of hospital stay, duration of antibiotic therapy, duration of invasive mechanical ventilation, indwelling duration of the central venous catheter, and duration of intravenous nutrition of the two groups were compared and analyzed. RESULTS: There were statistically significant differences between the two groups in the gestational age, length of hospital stay, and duration of antibiotic therapy. CONCLUSION: A small gestational age, a lengthy hospital stay, and long-term use of broad-spectrum antibiotics are the high-risk factors for fungal infections in preterm infants. Medical and nursing measures to address the high-risk factors might reduce the incidence of fungal infections and improve the prognosis in preterm infants.

Densely connected convolutional networks for ultrasound image based lesion segmentation.

Delineating lesion boundaries play a central role in diagnosing thyroid and breast cancers, making related therapy plans and evaluating therapeutic effects. However, it is often time-consuming and error-prone with limited reproducibility to manually annotate low-quality ultrasound (US) images, given high speckle noises, heterogeneous appearances, ambiguous boundaries etc., especially for nodular lesions with huge intra-class variance. It is hence appreciative but challenging for accurate lesion segmentations from US images in clinical practices. In this study, we propose a new densely connected convolutional network (called MDenseNet) architecture to automatically segment nodular lesions from 2D US images, which is first pre-trained over ImageNet database (called PMDenseNet) and then retrained upon the given US image datasets. Moreover, we also designed a deep MDenseNet with pre-training strategy (PDMDenseNet) for segmentation of thyroid and breast nodules by adding a dense block to increase the depth of our MDenseNet. Extensive experiments demonstrate that the proposed MDenseNet-based method can accurately extract multiple nodular lesions, with even complex shapes, from input thyroid and breast US images. Moreover, additional experiments show that the introduced MDenseNet-based method also outperforms three state-of-the-art convolutional neural networks in terms of accuracy and reproducibility. Meanwhile, promising results in nodular lesion segmentation from thyroid and breast US images illustrate its great potential in many other clinical segmentation tasks.

Mammography and Ultrasonography Manifestations of Sclerosing Lymphocytic Lobulitis of the Breast: A Series of Seven Case Reports.

OBJECTIVE: The present study aimed to analyze mammography and ultrasonography (US) manifestations of sclerosing lymphocytic lobulitis (SLL) of the breast. METHODS: A total of 8 pathologically confirmed SLL lesions from seven women (with one patient having bilateral breast lesions) were included in the study. All patients underwent preoperative mammography and US examinations. The findings from both modalities were classified and compared to their corresponding clinical data. RESULTS: Four patients were diagnosed with diabetes mellitus. Mammography results revealed that seven lesions presented as focal asymmetry or asymmetry. Seven lesions were observed as non-mass lesions on US examination. The most commonly observed US lesion features were as follows: seven lesions had focal non-ductal hypoechoic areas (87.5%), seven lesions exhibited posterior shadowing (87.5%), all lesions showed no vascularity or vessels in the rim (100%), no lesion had calcifications (0%), five lesions had an elasticity score of 3 (100%), one lesion showed retraction on the coronal plane (20%), and one lesion displayed a skipping sign on the coronal plane (20%). Based on these US findings, seven lesions (87.5%) were classified as BI-RADS 4. CONCLUSION: The mammography findings for SLL are often nonspecific. However, the US features of SLL typically present as non-mass lesions. The absence of calcification and vascularity and no retraction on the coronal plane inside the lesion may help to differentiate this disease from the conventional forms of breast carcinoma.

Comparative diagnostic accuracy of multiparametric magnetic resonance imaging-ultrasound fusion-guided biopsy versus systematic biopsy for clinically significant prostate cancer.

Purpose: To examine the accuracy of transperineal magnetic resonance imaging (MRI)-ultrasound (US) fusion biopsy (FB) in identifying men with prostate cancer (PCa) that has reached a clinically relevant stage. Methods: This investigation enrolled 459 males. In 210 of these patients (FB group), transperineal MRI/US fusion-guided biopsies were performed on the suspicious region, and in 249 others, a systematic biopsy (SB) was performed (SB group). We compared these groups using Gleason scores and rates of cancer detection. Results: PCa cases counted 198/459 (43.1%), including 94/249 (37.8%) in the SB group and 104/210 (49.5%) in the FB group. FB was associated with higher overall diagnostic accuracy relative to SB (88.5% and 72.3%, P = 0.024). FB exhibited greater sensitivity than SB (88.9% and 71.2%, P = 0.025). The area under the curve for FB and SB approaches was 0.837 and 0.737, respectively, such that FB was associated with an 11.9% increase in accuracy as determined based upon these AUC values. Relative to SB, FB was better able to detect high-grade tumors (GS ≥ 7) (78.85% vs. 60.64%, P = 0.025). Conclusion: Transperineal MRI-US fusion targeted biopsy is superior to the systematic one as an approach to diagnosing clinically significant PCa, as it is a viable technical approach to prostate biopsy.

Multi-modal artificial intelligence for the combination of automated 3D breast ultrasound and mammograms in a population of women with predominantly dense breasts.

OBJECTIVES: To assess the stand-alone and combined performance of artificial intelligence (AI) detection systems for digital mammography (DM) and automated 3D breast ultrasound (ABUS) in detecting breast cancer in women with dense breasts. METHODS: 430 paired cases of DM and ABUS examinations from a Asian population with dense breasts were retrospectively collected. All cases were analyzed by two AI systems, one for DM exams and one for ABUS exams. A selected subset (n = 152) was read by four radiologists. The performance of AI systems was based on analysis of the area under the receiver operating characteristic curve (AUC). The maximum Youden's index and its associated sensitivity and specificity were also reported for each AI systems. Detection performance of human readers in the subcohort of the reader study was measured in terms of sensitivity and specificity. RESULTS: The performance of the AI systems in a multi-modal setting was significantly better when the weights of AI-DM and AI-ABUS were 0.25 and 0.75, respectively, than each system individually in a single-modal setting (AUC-AI-Multimodal = 0.865; AUC-AI-DM = 0.832, p = 0.026; AUC-AI-ABUS = 0.841, p = 0.041). The maximum Youden's index for AI-Multimodal was 0.707 (sensitivity = 79.4%, specificity = 91.2%). In the subcohort that underwent human reading, the panel of four readers achieved a sensitivity of 93.2% and specificity of 32.7%. AI-multimodal achieves superior or equal sensitivity as single human readers at the same specificity operating points on the ROC curve. CONCLUSION: Multimodal (ABUS + DM) AI systems for detecting breast cancer in women with dense breasts are a potential solution for breast screening in radiologist-scarce regions.

RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease.

Digital health data used in diagnostics, patient care, and oncology research continue to accumulate exponentially. Most medical information, and particularly radiology results, are stored in free-text format, and the potential of these data remains untapped. In this study, a radiological repomics-driven model incorporating medical token cognition (RadioLOGIC) is proposed to extract repomics (report omics) features from unstructured electronic health records and to assess human health and predict pathological outcome via transfer learning. The average accuracy and F1-weighted score for the extraction of repomics features using RadioLOGIC are 0.934 and 0.934, respectively, and 0.906 and 0.903 for the prediction of breast imaging-reporting and data system scores. The areas under the receiver operating characteristic curve for the prediction of pathological outcome without and with transfer learning are 0.912 and 0.945, respectively. RadioLOGIC outperforms cohort models in the capability to extract features and also reveals promise for checking clinical diagnoses directly from electronic health records.