Clinical sonochemotherapy of inoperable pancreatic cancer using diagnostic ultrasound and microbubbles: a multicentre, open-label, randomised, controlled trial.

OBJECTIVES: Sonochemotherapy, which uses microbubble (MB)-assisted ultrasound (US) to deliver chemotherapeutic agents, has the potential to enhance tumour chemotherapy. The combination of US and MB has been demonstrated to prolong the survival of patients with pancreatic cancer. This phase 2 clinical trial aimed to determine the clinical efficacy and safety of sonochemotherapy for inoperable pancreatic ductal adenocarcinoma by using US and MB. METHODS: Eighty-two patients with stage III or IV pancreatic cancer were recruited from July 2018 to March 2021 and followed up until September 2022. US treatment was performed with a modified diagnostic US scanner for 30 min after chemotherapeutic infusion. The primary endpoint was overall survival (OS), and the secondary endpoints were Eastern Cooperative Oncology Group (ECOG) status < 2, progression-free survival (PFS), disease control rate (DCR), and adverse events. RESULTS: Seventy-eight patients were randomly allocated (40 to chemotherapy and 38 to sonochemotherapy). The median OS was longer with sonochemotherapy than with chemotherapy (9.10 vs. 6.10 months; p = 0.037). The median PFS with sonochemotherapy was 5.50 months, compared with 3.50 months (p = 0.080) for chemotherapy. The time of ECOG status < 2 was longer with sonochemotherapy (7.20 months) than with chemotherapy (5.00 months; p = 0.029). The DCR was 73.68% for sonochemotherapy compared with 42.50% for the control (p = 0.005). The incidence of overall adverse events was balanced between the two groups. CONCLUSIONS: The use of sonochemotherapy can extend the survival and well-being time of stage III or IV pancreatic cancer patients without any increase in serious adverse events. TRIAL REGISTRATION: ChineseClinicalTrials.gov ChiCTR2100044721 CLINICAL RELEVANCE STATEMENT: This multicentre, randomised, controlled trial has proven that sonochemotherapy, namely, the combination of diagnostic ultrasound, microbubbles, and chemotherapy, could extend the overall survival of patients with end-stage pancreatic ductal adenocarcinoma from 6.10 to 9.10 months without increasing any serious adverse events. KEY POINTS: • This is the first multicentre, randomised, controlled trial of sonochemotherapy for clinical pancreatic cancer treatment using ultrasound and a commercial ultrasound contrast agent. • Sonochemotherapy extended the median overall survival from 6.10 (chemotherapy alone) to 9.10 months. • The disease control rate increased from 42.50% with chemotherapy to 73.68% with sonochemotherapy.

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.

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.

3-D Res-CapsNet convolutional neural network on automated breast ultrasound tumor diagnosis.

PURPOSE: We propose a 3-D tumor computer-aided diagnosis (CADx) system with U-net and a residual-capsule neural network (Res-CapsNet) for ABUS images and provide a reference for early tumor diagnosis, especially non-mass lesions. METHODS: A total of 396 patients with 444 tumors (226 malignant and 218 benign) were retrospectively enrolled from Sun Yat-sen University Cancer Center. In our CADx, preprocessing was performed first to crop and resize the tumor volumes of interest (VOIs). Then, a 3-D U-net and postprocessing were applied to the VOIs to obtain tumor masks. Finally, a 3-D Res-CapsNet classification model was executed with the VOIs and the corresponding masks to diagnose the tumors. Finally, the diagnostic performance, including accuracy, sensitivity, specificity, and area under the curve (AUC), was compared with other classification models and among three readers with different years of experience in ABUS review. RESULTS: For all tumors, the accuracy, sensitivity, specificity, and AUC of the proposed CADx were 84.9 %, 87.2 %, 82.6 %, and 0.9122, respectively, outperforming other models and junior reader. Next, the tumors were subdivided into mass and non-mass tumors to validate the system performance. For mass tumors, our CADx achieved an accuracy, sensitivity, specificity, and AUC of 85.2 %, 88.2 %, 82.3 %, and 0.9147, respectively, which was higher than that of other models and junior reader. For non-mass tumors, our CADx achieved an accuracy, sensitivity, specificity, and AUC of 81.6 %, 78.3 %, 86.7 %, and 0.8654, respectively, outperforming the two readers. CONCLUSION: The proposed CADx with 3-D U-net and 3-D Res-CapsNet models has the potential to reduce misdiagnosis, especially for non-mass lesions.

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.

Evaluation of the Extent of Mesorectal Invasion and Mesorectal Fascia Involvement in Patients with T3 Rectal Cancer With 2-D and 3-D Transrectal Ultrasound: A Pilot Comparison Study With Magnetic Resonance Imaging Findings.

The aim of this study was to determine the value of 2-D and 3-D transrectal ultrasound (TRUS) in assessing the extent of mesorectal invasion (EMI) and mesorectal fascia involvement (MRF+) in patients with T3 rectal tumours. We retrospectively evaluated 80 patients with T3 stage rectal cancer who were pre-operatively evaluated by 2-D and 3-D TRUS before neoadjuvant chemoradiotherapy by using magnetic resonance imaging (MRI) as a reference standard. The T3 stage was subdivided into T3 ab (EMI ≤5 mm) and T3 cd (EMI >5 mm). The consistency assessment of the T3 sub-staging and MRF+ was compared between 2-D and 3-D TRUS using Cohen's kappa statistic. The concordance of the T3 sub-staging based on EMI was excellent between the 3-D TRUS and MRI (κ = 0.84) and good between the 2-D TRUS and MRI (κ = 0.67). For the assessment of MRF+ (κ = 0.82), 3-D TRUS and MRI showed excellent concordance. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 3-D TRUS for MRF+ assessment was 95.3%, 86.5%, 89.1% and 94.1%, respectively. The agreement between 3-D TRUS and MRI for the assessment of T3 sub-staging and MRF status was better in low rectal cancer (both κ = 0.85) than in middle (κ = 0.79 and 0.77) rectal cancer. Compared with MRI, 3-D TRUS has more advantages in the sub-staging of T3 rectal cancer and the assessment of MRF+ than those of 2-D TRUS, especially in low rectal cancer. For patients with T3 rectal cancer, 3-D TRUS may well complement MRI for selecting the appropriate treatment.

Risk model and risk stratification to preoperatively predict central lymph node metastasis in papillary thyroid carcinoma.

BACKGROUND: The central lymph node is the most common involvement for papillary thyroid carcinoma (PTC), which is correlated to recurrence and survival. But it is difficult to accurately evaluate lymph node prior to an operation. This retrospective study was designed to develop a risk model and risk stratification to preoperatively predict central lymph node metastasis (CLNM) in PTC and validate this model. METHODS: A series of 1,714 initial treatment PTC patients were enrolled. Among these patients, 1,001 patients were used to develop a predictive model and establish a stratification scoring system. This was validated through the remaining 713 patients. RESULTS: The multivariate analysis revealed that CLNM and lateral lymph node metastasis (LLNM) in ultrasound (US), tumor size, gender, capsule invasion in US, microcalcification and age were significant independent predictors for CLNM. The area under the curve (AUC) of the model was 0.778. Furthermore, the cutoff value to predict CLNM was 8 points, and the sensitivity and specificity were 77% and 65%, respectively. In the scoring system for CLNM, a score of ≤8, 8-18 and >18 were defined as low, intermediate and high risk, respectively. The risk of CLNM was approximately 30%, 60% and 80%, corresponding to the stratification. When validated, the model predicted the risk of CLNM with an AUC of 0.811, a sensitivity and specificity of 83% and 63%, respectively. CONCLUSIONS: This study presented a predictive model to preoperatively assess the risk of CLNM in PTC. The predictive model performed well, but needed to be prospectively validated in external center.

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.

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.

Development of prognostic index based on autophagy-related genes analysis in breast cancer.

BACKGROUND: Autophagy is a self-digesting process that can satisfy the metabolic needs of cells, and is closely related to development of cancer. However, the effect of autophagy-related genes (ARGs) on the prognosis of breast cancer remains unclear. RESULTS: We first found that 27 ARGs were significantly associated with overall survival in breast cancer. The prognosis-related ARGs signature established using the Cox regression model consists of 12 ARGs that can be divided patients into high-risk and low-risk groups. The overall survival of patients with high-risk scores (HR 3.652, 2.410-5.533; P < 0.001) was shorter than patients with low-risk scores. The area under the receiver operating characteristic (ROC) curve for 1-year, 3-year, and 5-year survival rates were 0.739, 0.727, and 0.742, respectively. CONCLUSION: The12-ARGs marker can predict the prognosis of breast cancer and thus help individualized treatment of patients at different risks. METHODS: Based on the TCGA dataset, we integrated the expression profiles of ARGs in 1,039 breast cancer patients. Differentially expressed ARGs and survival-related ARGs were evaluated by computational difference algorithm and COX regression analysis. In addition, we also explored the mutations in these ARGs. A new prognostic indicator based on ARGs was developed using multivariate COX analysis.

3D Inception U-net with Asymmetric Loss for Cancer Detection in Automated Breast Ultrasound.

PURPOSE: Breast cancer is the most common cancer and the leading cause of cancer-related deaths for women all over the world. Recently, automated breast ultrasound (ABUS) has become a new and promising screening modality for whole breast examination. However, reviewing volumetric ABUS is time-consuming and lesions could be missed during the examination. Therefore, computer-aided cancer detection in ABUS volume is extremely expected to help clinician for the breast cancer screening. METHODS: We develop a novel end-to-end 3D convolutional network for automated cancer detection in ABUS volume, in order to accelerate reviewing and meanwhile to provide high detection sensitivity with low false positives (FPs). Specifically, an efficient 3D Inception Unet-style architecture with fusion deep supervision mechanism is proposed to attain decent detection performance. In addition, a novel asymmetric loss is designed to help the network balancing false positive and false negative regions, thus improving detection sensitivity for small cancerous lesions. RESULTS: The efficacy of our network was extensively validated on a dataset including 196 patients with 661 cancer regions. Our network obtained a detection sensitivity of 95.1% with 3.0 FPs per ABUS volume. Furthermore, the average inference time of the network was 0.1 second per volume, which largely shortens the conventional reviewing time. CONCLUSIONS: The proposed network provides efficient and accurate cancer detection scheme using ABUS volume, and may assist clinicians for more efficient breast cancer screening.

Deeply-Supervised Networks With Threshold Loss for Cancer Detection in Automated Breast Ultrasound.

ABUS, or Automated breast ultrasound, is an innovative and promising method of screening for breast examination. Comparing to common B-mode 2D ultrasound, ABUS attains operator-independent image acquisition and also provides 3D views of the whole breast. Nonetheless, reviewing ABUS images is particularly time-intensive and errors by oversight might occur. For this study, we offer an innovative 3D convolutional network, which is used for ABUS for automated cancer detection, in order to accelerate reviewing and meanwhile to obtain high detection sensitivity with low false positives (FPs). Specifically, we offer a densely deep supervision method in order to augment the detection sensitivity greatly by effectively using multi-layer features. Furthermore, we suggest a threshold loss in order to present voxel-level adaptive threshold for discerning cancer vs. non-cancer, which can attain high sensitivity with low false positives. The efficacy of our network is verified from a collected dataset of 219 patients with 614 ABUS volumes, including 745 cancer regions, and 144 healthy women with a total of 900 volumes, without abnormal findings. Extensive experiments demonstrate our method attains a sensitivity of 95% with 0.84 FP per volume. The proposed network provides an effective cancer detection scheme for breast examination using ABUS by sustaining high sensitivity with low false positives. The code is publicly available at https://github.com/nawang0226/abus_code.

Evaluation of Contrast-enhanced US LI-RADS version 2017: Application on 2020 Liver Nodules in Patients with Hepatitis B Infection.

Background Use of contrast material-enhanced (CE) US Liver Imaging Reporting and Data System (LI-RADS) version 2017 has not been validated in large populations where hepatitis B virus (HBV) is endemic. Purpose To evaluate the diagnostic performance of CE US LI-RADS version 2017 in a population with a high prevalence of HBV infection. Materials and Methods In this retrospective study, liver nodules in patients with HBV who were evaluated from January 2004 to December 2016 were categorized as CE US LR-1 to LR-5 through LR-M. A subgroup of LR-M nodules was reclassified as LR-5, and additional analysis was performed. The reference standard consisted of histologic evaluation or composite imaging and clinical follow-up findings. Diagnostic performance was assessed with sensitivity, specificity, positive predictive value (PPV), and negative predictive value. Results A total of 2020 nodules in 1826 patients (median age, 54 years ± 12 [standard deviation]; 1642 men) were included. Of the 1159 LR-5 lesions, 1141 were hepatocellular carcinoma (HCC); three, intrahepatic cholangiocarcinomas; six, other malignancies; six, atypical hyperplasia; and three, benign lesions. The PPV of LR-5 for HCC was 98% (95% confidence interval [CI]: 98%, 99%). In LR-M nodules, 153 showed arterial phase hyperenhancement, early washout, and absence of punched-out appearance within 5 minutes, and 142 of 153 (93%; 95% CI: 89%, 97%) were HCC. If these nodules were reclassified as LR-5, LR-M specificity and PPV as a predictor of non-HCC malignancy increased from 88% (95% CI: 87%, 89%) and 36% (95% CI: 31%, 41%) to 96% (95% CI: 95%, 97%) and 58% (95% CI: 51%, 65%), respectively (P < .001). Despite reclassification, LR-5 specificity and PPV remained high (94% [95% CI: 92%, 96%] and 98% [95% CI: 97%, 99%], respectively). Conclusion The contrast-enhanced US Liver Imaging Reporting and Data System version 2017 category LR-5 is effectively predictive of the presence of hepatocellular carcinoma. In patients with hepatitis B virus infection, performance may be further improved by reclassification of category LR-M nodules with arterial phase hyperenhancement, early washout, and no punched-out appearance to LR-5. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Sidhu in this issue.

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.

Diagnostic value, safety, and histopathologic discrepancy risk factors for endoscopic forceps biopsy and transrectal ultrasound-guided core needle biopsy in rectum lesions.

BACKGROUND: Accurate preoperative pathologic diagnosis is very important for making appropriate therapeutic decisions for patients with rectal lesions. This study aimed (I) to determine diagnostic value and safety of endoscopic forceps biopsy (EFB) and transrectal ultrasound (TRUS)-guided core needle biopsy (CNB), and (II) to analyze the risk factors for their histopathologic discrepancies, with a particular focus in identifying the indicators for re-biopsy using TRUS-guided CNB after EFB. METHODS: We retrospectively reviewed the records of 102 patients who received EFB and TRUS-guided CNB before surgery. The histopathologic concordance and risk factors for underdiagnosis by EFB and TRUS-guided CNB were analyzed. RESULTS: Compared with postoperative pathology, the histopathologic discrepancy rate of EFB and TRUS-guided CNB was 51.0% (52/102 lesions) and 8.8% (9/102 lesions), respectively. The kappa value for consistency with postoperative pathology findings was 0.420 for EFB and 0.876 for TRUS-guided CNB. The multivariate analyses and receiver operating characteristic (ROC) curve indicated that lesions thickness ≥13.5 mm [OR 1.080 (95% CI: 1.021-1.142), P=0.007] and flat/depressed shape [OR 0.206 (95% CI: 0.076-0.564), P=0.002] were significantly associated with histopathologic discrepancies in EFB. CONCLUSIONS: EFB was of limited clinical value in identifying the preoperative diagnosis of rectal lesions. Lesions thickness and flat/depressed shape at EFB were independent risk factors for pathologic discrepancies. TRUS-guided CNB may serve as a safe and effective supplement to routine EFB.

Evaluation of liver parenchyma stiffness in patients with liver tumours: optimal strategy for shear wave elastography.

OBJECTIVES: To determine the methodology of non-invasive test for evaluation of liver stiffness (LS) with tumours using two-dimensional (2D) shear wave elastography (SWE). METHODS: One hundred and twenty-seven patients with liver tumours underwent 2D-SWE before surgery to measure liver and spleen stiffness (SS). Two-dimensional SWE values were obtained in the liver at 0-1 cm, 1-2 cm and >2 cm from the tumour edge (PLS-1, PLS-2 and RLS, respectively). The influence of tumour-associated factors was evaluated. The area under the receiver operating characteristic curve (AUC) for each value was analysed to diagnose cirrhosis. RESULTS: PLS-1 was higher than PLS-2, which was even higher than RLS (p < 0.001). The AUCs of PLS-1, PLS-2, RLS and SS for diagnosing cirrhosis were 0.760, 0.833, 0.940 and 0.676, with the specificity of 75.7%, 67.6%, 90.3% and 77.4%, respectively. Tumour sizes, locations or types showed no apparent influence on 2D-SWE values except for RLS, which was higher in patients with primary hepatic carcinomas (p < 0.05). CONCLUSIONS: LS with tumours is best measured at >2 cm away from the tumour edge. SS measurement could be used as an alternative to LS measurement in the event of no available liver for detection. KEY POINTS: • Tumour-associated factors impact background liver stiffness assessment. • Background liver stiffness is best measured at >2 cm from tumour edge. • Spleen stiffness can be an alternative to assess background liver stiffness.

Pretreatment anti-Mullerian hormone-based nomogram predicts menstruation status after chemotherapy for premenopausal women with hormone receptor-positive early breast cancer.

PURPOSE: Ovarian function is important for optimizing endocrine treatment in patients with hormone receptor-positive (HR+) early breast cancer (eBC). The aim of the study was to determine whether patients' pretreatment levels of anti-Mullerian hormone (AMH) were associated with menses status after chemotherapy and to build a predictive nomogram model for amenorrhea in women with HR+ eBC. METHODS: Between August 2013 and December 2014, 120 premenopausal patients with HR+ eBC were included retrospectively. The associations among age, prechemotherapy levels of AMH, follicle-stimulating hormone (FSH),and estradiol (E2) and the 2-year postchemotherapy menses status were analyzed. We determined the cutoff values of hormone levels by using the biostatistical tool (Cutoff Finder). A novel nomogram was established to predict the 2-year amenorrhea status based on the logistic analysis. Concordance index (C-index) was used to validate the capacity. RESULTS: One hundred nine women (90.8%) experienced amenorrhea after chemotherapy. AMH < 0.965 ng/ml predicted amenorrhea at 2 years (AUC 0.84, sensitivity 74% and specificity 81.8%), independent of age. The predictive nomogram based on age and pretreatment AMH and FSH levels was developed to predict the probability of 2-year postchemotherapy amenorrhea with a C-index of 0.88 (95% CI 0.84-0.91). CONCLUSIONS: In premenopausal patients with HR+ eBC, prechemotherapy AMH concentration was associated with the patient's 2-year amenorrhea status, independent of age. The nomogram model based on age and pretreatment AMH and FSH levels accurately predicted the 2-year amenorrhea status.

Confirmed value of shear wave elastography for ultrasound characterization of breast masses using a conservative approach in Chinese women: a large-size prospective multicenter trial.

PURPOSE: This study aimed to investigate the value of shear wave elastography (SWE) for characterization of breast masses in a Chinese population. PATIENTS AND METHODS: Two thousand two hundred seventy-three women consented to be prospectively enrolled for the characterization of breast masses with ultrasound and SWE. Breast masses were known from symptoms, palpability, and/or previous imaging screening with mammography and/or ultrasound. Correlation of SWE qualitative and quantitative features with malignancy risk and impact on diagnostic performance of combining SWE features were assessed, and the Breast Imaging Reporting and Data System (BI-RADS) scoring was calculated using histopathology as reference. RESULTS: Data of 2,262 masses (median size: 13 mm; range: 1.3-50) from 2,262 patients (median age: 43 years; range: 18-91) were investigated, of which 752 (33.3%) were malignant. Sensitivity and specificity of BI-RADS diagnosis were 97.5% (733/752) and 54.8% (827/1,510), respectively. By logistic regression, the combination of maximum elasticity (E max) measurements with BI-RADS assessments increased the area under the receiver operating characteristic curve from 0.908 (95% CI: 0.896-0.920) to 0.954 (95% CI: 0.944-0.962). Using E max of 30 kPa or lower to selectively downgrade BI-RADS 4a masses to follow-up, and E max of 160 kPa or higher to selectively upgrade BI-RADS 3 lesions to biopsy, specificity significantly increased from 54.8% (827/1,510) to 66.1% (998/1,510) (P<0.001) while sensitivity decreased nonsignificantly from 97.5% (733/752) to 96.9% (729/752) (P=0.2891). Positive predictive value for biopsy recommendation increased from 51.7% (733/1,417) to 58.7% (729/1,241) (P<0.001). CONCLUSION: Adding SWE maximum stiffness to BI-RADS 3 and BI-RADS 4a breast masses in a Chinese population increased significantly the specificity of breast ultrasonography, without significant change in sensitivity.

Diagnostic value of intraoperative ultrasonography in assessing thoracic recurrent laryngeal nerve lymph nodes in patients with esophageal cancer.

BACKGROUD: The incidence of recurrent laryngeal nerve (RLN) injury has increased due to RLN lymph node dissection. The aim of this study was to evaluate the ability of intraoperative ultrasonography (IU) to detect RLN nodal metastases in esophageal cancer patients. METHODS: Sixty patients with esophageal cancer underwent IU, computed tomography (CT), and endoscopic ultrasonography (EUS) to assess for RLN nodal metastasis. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were compared. RESULTS: The sensitivities of IU, CT, and EUS in diagnosing right RLN nodal metastases were 71.4, 14.3, and 30.0%, respectively, and a significant difference among these three examinations was observed (χ2 = 10.077, P = .006). The specificities of IU, CT, and EUS for diagnosing right RLN nodal metastasis were 67.4, 97.8, and 95.0%, respectively, and a significant difference was observed (χ2 = 21.725, P < .001). No significant differences in either PPV or NPV were observed when diagnosing right RLN nodal metastases. For diagnosis of left RLN lymph nodal metastases, the sensitivities of IU, CT, and EUS were 91.7, 16.7, and 40.0% respectively. There was a significant difference among these diagnostic sensitivities (χ2 = 14.067, P = .001). The specificities of IU, CT, and EUS for diagnosis of left RLN nodal metastases were 79.2, 100, and 82.5%, respectively and a significant difference was observed (χ2 = 10.819, P = .004). No significant differences were observed in PPV or NPV for these examinations when diagnosing left RLN nodal metastases. CONCLUSION: Intraoperative ultrasonography showed superior sensitivity compared with preoperative CT or EUS in detecting RLN lymph node metastasis in patients with thoracic esophageal cancer.

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.