Development and validation of ultrasound-based radiomics model to predict germline BRCA mutations in patients with breast cancer.

BACKGROUND: Identifying breast cancer (BC) patients with germline breast cancer susceptibility gene (gBRCA) mutation is important. The current criteria for germline testing for BC remain controversial. This study aimed to develop a nomogram incorporating ultrasound radiomic features and clinicopathological factors to predict gBRCA mutations in patients with BC. MATERIALS AND METHODS: In this retrospective study, 497 women with BC who underwent gBRCA genetic testing from March 2013 to May 2022 were included, including 348 for training (84 with and 264 without a gBRCA mutation) and 149 for validation(36 patients with and 113 without a gBRCA mutation). Factors associated with gBRCA mutations were identified to establish a clinicopathological model. Radiomics features were extracted from the intratumoral and peritumoral regions (3 mm and 5 mm) of each image. The least absolute shrinkage and selection operator regression algorithm was used to select the features and logistic regression analysis was used to construct three imaging models. Finally, a nomogram that combined clinicopathological and radiomics features was developed. The models were evaluated based on the area under the receiver operating characteristic curve (AUC), calibration, and clinical usefulness. RESULTS: Age at diagnosis, family history of BC, personal history of other BRCA-related cancers, and human epidermal growth factor receptor 2 status were independent predictors of the clinicopathological model. The AUC of the imaging radiomics model combining intratumoral and peritumoral 3 mm areas in the validation set was 0.783 (95% confidence interval [CI]: 0.702-0.862), which showed the best performance among three imaging models. The nomogram yielded better performance than the clinicopathological model in validation sets (AUC: 0.824 [0.755-0.894] versus 0.659 [0.563-0.755], p = 0.007). CONCLUSION: The nomogram based on ultrasound images and clinicopathological factors performs well in predicting gBRCA mutations in BC patients and may help to improve clinical decisions about genetic testing.

Deep Learning-assisted Diagnosis of Breast Lesions on US Images: A Multivendor, Multicenter Study.

Purpose: To evaluate the diagnostic performance of a deep learning (DL) model for breast US across four hospitals and assess its value to readers with different levels of experience. Materials and Methods: In this retrospective study, a dual attention-based convolutional neural network was built and validated to discriminate malignant tumors from benign tumors by using B-mode and color Doppler US images (n = 45 909, March 2011-August 2018), acquired with 42 types of US machines, of 9895 pathologic analysis-confirmed breast lesions in 8797 patients (27 men and 8770 women; mean age, 47 years ± 12 [SD]). With and without assistance from the DL model, three novice readers with less than 5 years of US experience and two experienced readers with 8 and 18 years of US experience, respectively, interpreted 1024 randomly selected lesions. Differences in the areas under the receiver operating characteristic curves (AUCs) were tested using the DeLong test. Results: The DL model using both B-mode and color Doppler US images demonstrated expert-level performance at the lesion level, with an AUC of 0.94 (95% CI: 0.92, 0.95) for the internal set. In external datasets, the AUCs were 0.92 (95% CI: 0.90, 0.94) for hospital 1, 0.91 (95% CI: 0.89, 0.94) for hospital 2, and 0.96 (95% CI: 0.94, 0.98) for hospital 3. DL assistance led to improved AUCs (P < .001) for one experienced and three novice radiologists and improved interobserver agreement. The average false-positive rate was reduced by 7.6% (P = .08). Conclusion: The DL model may help radiologists, especially novice readers, improve accuracy and interobserver agreement of breast tumor diagnosis using US.Keywords: Ultrasound, Breast, Diagnosis, Breast Cancer, Deep Learning, Ultrasonography Supplemental material is available for this article. © RSNA, 2023.

Diagnostic value of ultrasound evaluation of major salivary glands for Sjögren's syndrome based on the novel OMERACT scoring system.

OBJECTIVE: The novel Outcome Measures in Rheumatology Clinical Trials (OMERACT) scoring system was used to evaluate the diagnostic effectiveness of major salivary gland ultrasonography (SGUS) for Sjögren's syndrome (SS). MATERIALS AND METHODS: SGUS was performed on the parotid glands (PGs) and submandibular glands of 242 patients (145 patients with SS and 97 patients without SS) using the OMERACT scoring system (grade 0-3). We also evaluated the association of SGUS scores with unstimulated whole salivary flow rate (UWSF), stimulated whole salivary flow rate (SWSF), and labial salivary gland biopsy (LSGB) results. RESULTS: The SGUS scores were significantly higher for the SS group than for the non-SS group (p < 0.001). Using a cutoff value of 8 for the total score yielded the highest sensitivity (76%), specificity (90%), and area under the receiver operating characteristic curve (AUC, 0.828). The correlation between SGUS scores and salivary gland function was moderate to good. A cutoff value of 10 for the total score was more effective in predicting SWSF outcomes than UWSF outcomes (sensitivity: 73% > 58%, specificity: 98% > 87%, and AUC: 0.856 > 0.723). The association of OMERACT scores with LSGB results was fair to moderate. Among 61 anti-SSA-negative patients, 17 had positive PG scores (including ten SS and seven non-SS patients), and 44 had negative PG scores (including 37 non-SS and seven SS patients). CONCLUSION: The OMERACT scoring system had good sensitivity and excellent specificity, demonstrating excellent diagnostic potential for SS and efficacy in assessing salivary gland function. Negative SGUS results may help reduce unnecessary biopsies in anti-SSA-negative patients.

Extracting keyframes of breast ultrasound video using deep reinforcement learning.

Ultrasound (US) plays a vital role in breast cancer screening, especially for women with dense breasts. Common practice requires a sonographer to recognize key diagnostic features of a lesion and record a single or several representative frames during the dynamic scanning before performing the diagnosis. However, existing computer-aided diagnosis tools often focus on the final diagnosis process while neglecting the influence of the keyframe selection. Moreover, the lesions could have highly-irregular shapes, varying sizes, and locations during the scanning. The recognition of diagnostic characteristics associated with the lesions is challenging and also faces severe class imbalance. To address these, we proposed a reinforcement learning-based framework that can automatically extract keyframes from breast US videos of unfixed length. It is equipped with a detection-based nodule filtering module and a novel reward mechanism that can integrate anatomical and diagnostic features of the lesions into keyframe searching. A simple yet effective loss function was also designed to alleviate the class imbalance issue. Extensive experiments illustrate that the proposed framework can benefit from both innovations and is able to generate representative keyframe sequences in various screening conditions.

The lesion detection efficacy of deep learning on automatic breast ultrasound and factors affecting its efficacy: a pilot study.

OBJECTIVES: The aim of this study was to investigate the detection efficacy of deep learning (DL) for automatic breast ultrasound (ABUS) and factors affecting its efficacy. METHODS: Females who underwent ABUS and handheld ultrasound from May 2016 to June 2017 (N = 397) were enrolled and divided into training (n = 163 patients with breast cancer and 33 with benign lesions), test (n = 57) and control (n = 144) groups. A convolutional neural network was optimized to detect lesions in ABUS. The sensitivity and false positives (FPs) were evaluated and compared for different breast tissue compositions, lesion sizes, morphologies and echo patterns. RESULTS: In the training set, with 688 lesion regions (LRs), the network achieved sensitivities of 93.8%, 97.2% and 100%, based on volume, lesion and patient, respectively, with 1.9 FPs per volume. In the test group with 247 LRs, the sensitivities were 92.7%, 94.5% and 96.5%, respectively, with 2.4 FPs per volume. The control group, with 900 volumes, showed 0.24 FPs per volume. The sensitivity was 98% for lesions > 1 cm3, but 87% for those ≤1 cm3 (p < 0.05). Similar sensitivities and FPs were observed for different breast tissue compositions (homogeneous, 97.5%, 2.1; heterogeneous, 93.6%, 2.1), lesion morphologies (mass, 96.3%, 2.1; non-mass, 95.8%, 2.0) and echo patterns (homogeneous, 96.1%, 2.1; heterogeneous 96.8%, 2.1). CONCLUSIONS: DL had high detection sensitivity with a low FP but was affected by lesion size. ADVANCES IN KNOWLEDGE: DL is technically feasible for the automatic detection of lesions in ABUS.

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.

Pantoprazole (PPZ) Inhibits RANKL-Induced Osteoclast Formation and Function In Vitro and Prevents Lipopolysaccharide- (LPS-) Induced Inflammatory Calvarial Bone Loss In Vivo.

Bone remodeling is a process delicately balanced between osteoclastic bone resorption and osteoblastic bone formation. Osteoclasts (OCs) are multinucleated giant cells formed through the fusion of monocytic precursors of the hematopoietic stem cells lineage. OCs are the exclusive cells responsible for the resorption and degradation of the mineralized bone matrix. Pantoprazole (PPZ), a proton pump inhibitor (PPI), is commonly prescribed to reduce excess gastric acid production for conditions such as gastroesophageal reflux disease and peptic ulcer disease. Studies have found contradictory effects of PPI therapy on bone metabolism due to the lack of understanding of the exact underlying mechanism. In this study, we found that PPZ inhibits receptor activator of nuclear factor-κB (NF-κB) ligand- (RANKL-) induced osteoclastogenesis from bone marrow monocytic/macrophage (BMMs) precursors and the bone-resorbing activity of mature OCs. Correspondingly, the expression of OC marker genes was also attenuated. At the molecular level, PPZ treatment was associated with reduced activation of the ERK MAPK signaling pathways crucial to OC differentiation. Additionally, the in vivo administration of PPZ protected mice against lipopolysaccharide- (LPS-) induced inflammatory calvarial bone erosion, as a result of the reduced number and activity of OCs on the calvarial bone surface. Although PPI use is associated with increased risk of osteoporosis and bone fractures, our study provides evidence for the direct inhibitory effect of PPZ on OC formation and bone resorption in vitro and in vivo, suggesting a potential therapeutic use of PPZ in the treatment of osteolytic disease with localized bone destruction.

Automated Breast Ultrasound: Interobserver Agreement, Diagnostic Value, and Associated Clinical Factors of Coronal-Plane Image Features.

OBJECTIVE: To evaluate the interobserver agreement, diagnostic value, and associated clinical factors of automated breast ultrasound (ABUS) coronal features in differentiating breast lesions. MATERIALS AND METHODS: This study enrolled 457 pathologically confirmed lesions in 387 female (age, 46.4 ± 10.3 years), including 377 masses and 80 non-mass lesions (NMLs). The unique coronal features, including retraction phenomenon, hyper- or hypoechoic rim (continuous or discontinuous), skipping sign, and white wall sign, were defined and recorded. The interobserver agreement on image type and coronal features was evaluated. Furthermore, clinical factors, including the lesion size, distance to the nipple or skin, palpability, and the histological grade were analyzed. RESULTS: Among the 457 lesions, 296 were malignant and 161 were benign. The overall interobserver agreement for image type and all coronal features was moderate to good. For masses, the retraction phenomenon was significantly associated with malignancies (p < 0.001) and more frequently presented in small and superficial invasive carcinomas with a low histological grade (p = 0.027, 0.002, and < 0.001, respectively). Furthermore, continuous hyper- or hypoechoic rims were predictive of benign masses (p < 0.001), whereas discontinuous rims were predictive of malignancies (p < 0.001). A hyperechoic rim was more commonly detected in masses more distant from the nipple (p = 0.027), and a hypoechoic rim was more frequently found in large superficial masses (p < 0.001 for both). For NMLs, the skipping sign was a predictor of malignancies (p = 0.040). CONCLUSION: The coronal plane of ABUS may provide useful diagnostic value for breast lesions.

Different Ultrasound Scoring Methods for Assessing Medial Arterial Calcification: Association with Diabetic Complications.

The aims of the study described here were to evaluate medial arterial calcification (MAC) of the lower limbs, identified on ultrasound, in patients with type 2 diabetes, and to analyze the association of MAC with diabetic complications including peripheral arterial disease, peripheral neuropathy, retinopathy, and nephropathy. Ultrasound was performed in 359 patients, and the severity of MAC was assessed by the length of MAC (score range: 0-8) and the number of arterial segmentations with MAC (score range: 0-6). Our results revealed that MAC scoring based on the segmentation method was an independent predictor of peripheral arterial disease and nephropathy, but not an independent predictor of peripheral neuropathy or retinopathy. MAC scoring based on the length method was not an independent predictor of any complication. The segmentation method for assessing MAC on ultrasound may be a valuable tool in clinical work.

Value of Hand-held Ultrasound in the Differential Diagnosis and Accurate Breast Imaging Reporting and Data System Subclassification of Complex Cystic and Solid Breast Lesions.

To explore the value of hand-held ultrasound (HHUS) for diagnosing complex cystic and solid breast lesions, 472 pathologically proven lesions were analyzed. The lesions were divided into four types based on ultrasound features. Positive predictive values (PPVs) for lesion types and risk factor performances were assessed. Furthermore, HHUS and mammography (MAM) performances were compared: 27 lesions missed in MAM were detected in HHUS. Ultrasound feature analysis revealed higher PPVs for type III and IV lesions than for type I and II lesions. In patients older than 51 y, a type III or IV lesion with a diameter greater than 18 mm, an irregular shape, a non-parallel orientation, an uncircumscribed margin, calcification, vascularity and abnormal axillary lymph nodes were suggestive of malignancy; the area under the curve reached 0.869. Thus, ultrasound is useful in diagnosing complex cystic and solid breast lesions, which should be categorized as Breast Imaging Reporting and Data System (BI-RADS) 4B or 4C.

Adenoid cystic carcinoma of the breast: a study of five cases.

Adenoid cystic carcinoma of the breast is a rare type of breast cancer. Most patients present with a bilateral palpable mass. Ultrasound and mammography are non-specific and can sometimes lead to misdiagnosis because of their variable imaging features. Pathological examination is the standard reference. Surgery is the mainstay of treatment for patients. Although adenoid cystic carcinoma has an excellent prognosis, metastatic cases have been reported. This report aims to discuss the clinical and imaging features of one case of adenoid cystic carcinoma with a poor prognosis and four cases with a good prognosis at our center.

Ultrasound Imaging for Detecting Metastasis to Level II and III Axillary Lymph Nodes after Axillary Lymph Node Dissection for Invasive Breast Cancer.

OBJECTIVES: The diagnostic value of axillary ultrasound (US) for level II and III axillary lymph node metastasis after axillary lymph node dissection for invasive breast cancer is currently not clear. The objectives of this study were to retrospectively analyze the diagnostic value of axillar US for level II and III axillary lymph node metastasis and compare it with palpation and to analyze the US features of level II and III axillary lymph nodes that are predictive of metastatic recurrence during follow-up. METHODS: Cases with level II or III axillary lymph nodes detected by US between January 2005 and December 2017 at a cancer center were divided into 2 groups according to a retrospective analysis of US findings: potential malignancy group and follow-up group. Biopsy was performed in all patients in the potential malignancy group. In the follow-up group, the patients were followed for at least 2 years, and biopsy was performed if suspicious US features were detected. RESULTS: The 401 enrolled cases were followed by axillary US and physical examination (PE) for comparison. Finally, 55 axillary metastases were pathologically confirmed (14%). The sensitivity, specificity, and area under the receiver operating characteristic curve for axillary US were 92.7%, 93.9%, and 0.933, respectively, and the corresponding values for PE were 49.1%, 91.3%, and 0.702 (P < .001). An increase in the major or minor axis diameter of the lymph nodes of greater than 2 mm, a Solbiati index value of less than 1.5, and the presence of new suspicious lesions in other regions were significant predictors of lymph node metastasis based on the US findings (P = .013, .006, .015, and .036). CONCLUSIONS: Axillary is helpful in the follow-up of level II and III axillary lymph nodes after axillary lymph node dissection for invasive breast cancer and can detect cancer recurrence earlier than PE.

A multicenter hospital-based diagnosis study of automated breast ultrasound system in detecting breast cancer among Chinese women.

OBJECTIVE: The automated breast ultrasound system (ABUS) is a potential method for breast cancer detection; however, its diagnostic performance remains unclear. We conducted a hospital-based multicenter diagnostic study to evaluate the clinical performance of the ABUS for breast cancer detection by comparing it to handheld ultrasound (HHUS) and mammography (MG). METHODS: Eligible participants underwent HHUS and ABUS testing; women aged 40-69 years additionally underwent MG. Images were interpreted using the Breast Imaging Reporting and Data System (BI-RADS). Women in the BI-RADS categories 1-2 were considered negative. Women classified as BI-RADS 3 underwent magnetic resonance imaging to distinguish true- and false-negative results. Core aspiration or surgical biopsy was performed in women classified as BI-RADS 4-5, followed by a pathological diagnosis. Kappa values and agreement rates were calculated between ABUS, HHUS and MG. RESULTS: A total of 1,973 women were included in the final analysis. Of these, 1,353 (68.6%) and 620 (31.4%) were classified as BI-RADS categories 1-3 and 4-5, respectively. In the older age group, the agreement rate and Kappa value between the ABUS and HHUS were 94.0% and 0.860 (P<0.001), respectively; they were 89.2% and 0.735 (P<0.001) between the ABUS and MG, respectively. Regarding consistency between imaging and pathology results, 78.6% of women classified as BI-RADS 4-5 based on the ABUS were diagnosed with precancerous lesions or cancer; which was 7.2% higher than that of women based on HHUS. For BI-RADS 1-2, the false-negative rates of the ABUS and HHUS were almost identical and were much lower than those of MG. CONCLUSIONS: We observed a good diagnostic reliability for the ABUS. Considering its performance for breast cancer detection in women with high-density breasts and its lower operator dependence, the ABUS is a promising option for breast cancer detection in China.

DEPDC1 promotes cell proliferation and tumor growth via activation of E2F signaling in prostate cancer.

DEP domain containing 1 (DEPDC1) is recently reported to be overexpressed in several types of human cancer; however the role of DEPDC1 in prostate cancer remains to be investigated. Herein, we identified that the DEPDC1 mRNA and protein expression levels were dramatically increased in prostate cancer tissues and cell lines. Overexpression of DEPDC1 promoted, but depletion of DEPDC1 inhibited cell proliferation by regulating the G1-S phase cell cycle transition. Importantly, we found that DEPDC1 was essential for the tumor growth and formation of bone metastases of prostate cancer cells in vivo. Finally, we demonstrated that DEPDC1 interacted with E2F1 and increased its transcriptional activity, leading to hyper-activation of E2F signaling in prostate cancer cells. Our findings reveal an oncogenic role of DEPDC1 in prostate cancer progression via activation of E2F signaling, and suggest DEPDC1 might be a potential therapeutic target against the disease.