A radiomics and genomics-derived model for predicting metastasis and prognosis in colorectal cancer.

Approximately 50% of colorectal cancer (CRC) patients would develop metastasis with poor prognosis, therefore, it is necessary to effectively predict metastasis in clinical treatment. In this study, we aimed to establish a machine-learning model for predicting metastasis in CRC patients by considering radiomics and transcriptomics simultaneously. Here, 1023 patients with CRC from three centers were collected and divided into five queues (Dazhou Central Hospital n = 517, Nanchong Central Hospital n = 120 and the Cancer Genome Atlas (TCGA) n = 386). A total of 854 radiomics features were extracted from tumor lesions on CT images, and 217 differentially expressed genes were obtained from non-metastasis and metastasis tumor tissues using RNA sequencing. Based on radiotranscriptomic (RT) analysis, a novel RT model was developed and verified through genetic algorithms (GA). Interleukin (IL)-26, a biomarker in RT model, was verified for its biological function in CRC metastasis. Furthermore, 15 radiomics variables were screened through stepwise regression, which was highly correlated with the IL26 expression level. Finally, a radiomics model (RA) was established by combining GA and stepwise regression analysis with radiomics features. The RA model exhibited favorable discriminatory ability and accuracy for metastasis prediction in two independent verification cohorts. We designed multicenter, multi-scale cohorts to construct and verify novel combined radiomics and genomics models for predicting metastasis in CRC. Overall, RT model and RA model might help clinicians in directing personalized diagnosis and therapeutic regimen selection for patients with CRC.

Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning.

BACKGROUND AND AIMS: The recurrence of papillary thyroid carcinoma (PTC) is not unusual and associated with risk of death. This study is aimed to construct a nomogram that combines clinicopathological characteristics and ultrasound radiomics signatures to predict the recurrence in PTC. METHODS: A total of 554 patients with PTC who underwent ultrasound imaging before total thyroidectomy were included. Among them, 79 experienced at least one recurrence. Then 388 were divided into the training cohort and 166 into the validation cohort. The radiomics features were extracted from the region of interest (ROI) we manually drew on the tumor image. The feature selection was conducted using Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. And multivariate Cox regression analysis was used to build the combined nomogram using radiomics signatures and significant clinicopathological characteristics. The efficiency of the nomogram was evaluated by receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Kaplan-Meier analysis was used to analyze the recurrence-free survival (RFS) in different radiomics scores (Rad-scores) and risk scores. RESULTS: The combined nomogram demonstrated the best performance and achieved an area under the curve (AUC) of 0.851 (95% CI: 0.788 to 0.913) in comparison to that of the radiomics signature and the clinical model in the training cohort at 3 years. In the validation cohort, the combined nomogram (AUC = 0.885, 95% CI: 0.805 to 0.930) also performed better. The calibration curves and DCA verified the clinical usefulness of combined nomogram. And the Kaplan-Meier analysis showed that in the training cohort, the cumulative RFS in patients with higher Rad-score was significantly lower than that in patients with lower Rad-score (92.0% vs. 71.9%, log rank P < 0.001), and the cumulative RFS in patients with higher risk score was significantly lower than that in patients with lower risk score (97.5% vs. 73.5%, log rank P < 0.001). In the validation cohort, patients with a higher Rad-score and a higher risk score also had a significantly lower RFS. CONCLUSION: We proposed a nomogram combining clinicopathological variables and ultrasound radiomics signatures with excellent performance for recurrence prediction in PTC patients.

Comparing Sonazoid contrast-enhanced ultrasound to contrast-enhanced CT and MRI for differentially diagnosing renal lesions: a prospective multicenter study.

PURPOSE: To evaluate the diagnostic performance of contrast-enhanced (CE) ultrasound using Sonazoid (SNZ-CEUS) by comparing with contrast-enhanced computed tomography (CE-CT) and contrast-enhanced magnetic resonance imaging (CE-MRI) for differentiating benign and malignant renal masses. MATERIALS AND METHODS: 306 consecutive patients (from 7 centers) with renal masses (40 benign tumors, 266 malignant tumors) diagnosed by both SNZ-CEUS, CE-CT or CE-MRI were enrolled between September 2020 and February 2021. The examinations were performed within 7 days, but the sequence was not fixed. Histologic results were available for 301 of 306 (98.37%) lesions and 5 lesions were considered benign after at least 2 year follow-up without change in size and image characteristics. The diagnostic performances were evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and compared by McNemar's test. RESULTS: In the head-to-head comparison, SNZ-CEUS and CE-MRI had comparable sensitivity (95.60 vs. 94.51%, P = 0.997), specificity (65.22 vs. 73.91%, P = 0.752), positive predictive value (91.58 vs. 93.48%) and negative predictive value (78.95 vs. 77.27%); SNZ-CEUS and CE-CT showed similar sensitivity (97.31 vs. 96.24%, P = 0.724); however, SNZ-CEUS had relatively lower than specificity than CE-CT (59.09 vs. 68.18%, P = 0.683). For nodules > 4 cm, CE-MRI demonstrated higher specificity than SNZ-CEUS (90.91 vs. 72.73%, P = 0.617) without compromise the sensitivity. CONCLUSIONS: SNZ-CEUS, CE-CT, and CE-MRI demonstrate desirable and comparable sensitivity for the differentiation of renal mass. However, the specificity of all three imaging modalities is not satisfactory. SNZ-CEUS may be a suitable alternative modality for patients with renal dysfunction and those allergic to gadolinium or iodine-based agents.

Predicting the malignancy of extremity soft-tissue tumors by an ultrasound-based radiomics signature.

BACKGROUND: Accurate differentiation of extremity soft-tissue tumors (ESTTs) is important for treatment planning. PURPOSE: To develop and validate an ultrasound (US) image-based radiomics signature to predict ESTTs malignancy. MATERIAL AND METHODS: A dataset of US images from 108 ESTTs were retrospectively enrolled and divided into the training cohort (78 ESTTs) and validation cohort (30 ESTTs). A total of 1037 radiomics features were extracted from each US image. The most useful predictive radiomics features were selected by the maximum relevance and minimum redundancy method, least absolute shrinkage, and selection operator algorithm in the training cohort. A US-based radiomics signature was built based on these selected radiomics features. In addition, a conventional radiologic model based on the US features from the interpretation of two experienced radiologists was developed by a multivariate logistic regression algorithm. The diagnostic performances of the selected radiomics features, the US-based radiomics signature, and the conventional radiologic model for differentiating ESTTs were evaluated and compared in the validation cohort. RESULTS: In the validation cohort, the area under the curve (AUC), sensitivity, and specificity of the US-based radiomics signature for predicting ESTTs malignancy were 0.866, 84.2%, and 81.8%, respectively. The US-based radiomics signature had better diagnostic predictability for predicting ESTT malignancy than the best single radiomics feature and the conventional radiologic model (AUC = 0.866 vs. 0.719 vs. 0.681 for the validation cohort, all P <0.05). CONCLUSION: The US-based radiomics signature could provide a potential imaging biomarker to accurately predict ESTT malignancy.

EFSUMB Technical Review - Update 2023: Dynamic Contrast-Enhanced Ultrasound (DCE-CEUS) for the Quantification of Tumor Perfusion.

Dynamic contrast-enhanced ultrasound (DCE-US) is a technique to quantify tissue perfusion based on phase-specific enhancement after the injection of microbubble contrast agents for diagnostic ultrasound. The guidelines of the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) published in 2004 and updated in 2008, 2011, and 2020 focused on the use of contrast-enhanced ultrasound (CEUS), including essential technical requirements, training, investigational procedures and steps, guidance regarding image interpretation, established and recommended clinical indications, and safety considerations. However, the quantification of phase-specific enhancement patterns acquired with ultrasound contrast agents (UCAs) is not discussed here. The purpose of this EFSUMB Technical Review is to further establish a basis for the standardization of DCE-US focusing on treatment monitoring in oncology. It provides some recommendations and descriptions as to how to quantify dynamic ultrasound contrast enhancement, and technical explanations for the analysis of time-intensity curves (TICs). This update of the 2012 EFSUMB introduction to DCE-US includes clinical aspects for data collection, analysis, and interpretation that have emerged from recent studies. The current study not only aims to support future work in this research field but also to facilitate a transition to clinical routine use of DCE-US.

Nomogram for predicting lymph node metastasis in patients with ovarian cancer using ultrasonography: a multicenter retrospective study.

BACKGROUND: Ovarian cancer is a common cancer among women globally, and the assessment of lymph node metastasis plays a crucial role in the treatment of this malignancy. The primary objective of our study was to identify the risk factors associated with lymph node metastasis in patients with ovarian cancer and develop a predictive model to aid in the selection of the appropriate surgical procedure and treatment strategy. METHODS: We conducted a retrospective analysis of data from patients with ovarian cancer across three different medical centers between April 2014 and August 2022. Logistic regression analysis was employed to establish a prediction model for lymph node metastasis in patients with ovarian cancer. We evaluated the performance of the model using receiver operating characteristic (ROC) curves, calibration plots, and decision analysis curves. RESULTS: Our analysis revealed that among the 368 patients in the training set, 101 patients (27.4%) had undergone lymph node metastasis. Maximum tumor diameter, multifocal tumor, and Ki67 level were identified as independent risk factors for lymph node metastasis. The area under the curve (AUC) of the ROC curve in the training set was 0.837 (95% confidence interval [CI]: 0.792-0.881); in the validation set this value was 0.814 (95% CI: 0.744-0.884). Calibration plots and decision analysis curves revealed good calibration and clinical application value. CONCLUSIONS: We successfully developed a model for predicting lymph node metastasis in patients with ovarian cancer, based on ultrasound examination results and clinical data. Our model accurately identified patients at high risk of lymph node metastasis and may guide the selection of appropriate treatment strategies. This model has the potential to significantly enhance the precision and efficacy of clinical management in patients with ovarian cancer.

On-Site Diagnostic Ability of CEUS/CT/MRI for Hepatocellular Carcinoma (2019-2022): A Multicenter Study.

OBJECTIVES: To compare the on-site diagnostic performance of contrast-enhanced ultrasound (CEUS), computed tomography (CECT), and magnetic resonance imaging (CEMRI) for hepatocellular carcinoma (HCC) across diverse practice settings. METHODS: Between May 2019 and April 2022, a total of 2085 patients with 2320 pathologically confirmed focal liver lesions (FLLs) were enrolled. Imaging reports were compared with results from pathology analysis. Diagnostic performance was analyzed in defined size, high-risk factors for HCC, and hospital volume categories. RESULTS: Three images achieved similar diagnostic performance in classifying HCC from 16 types of FLLs, including HCC ≤2.0 cm. For HCC diagnosis at low-volume hospitals and HCC with high-risk factors, the accuracy and specificity of CEUS were comparable to CECT and CEMRI, while the sensitivity of CEUS (77.4 and 89.5%, respectively) was inferior to CEMRI (87.0 and 92.8%, respectively). The diagnostic accuracy of CEUS + CEMRI and CEUS + CECT increased by 7.8 and 6.2% for HCC ≤2.0 cm, 8.0 and 5.0% for HCC with high-risk factors, and 7.4 and 5.5% for HCC at low-volume hospitals, respectively, compared with CEMRI/CECT alone. CONCLUSIONS: Compared with CECT and CEMRI, CEUS provides adequate diagnostic performance in clinical first-line applications at high-volume hospitals. Moreover, a higher diagnostic performance for HCC is achieved by combining CEUS with CECT/CEMRI compared with any single imaging technique.

Prevalence of benign focal liver lesions and non-hepatocellular carcinoma malignant lesions in liver cirrhosis.

Liver cirrhosis is associated with an increased risk of developing hepatocellular carcinoma (HCC). However, other benign and malignant liver lesions may co-exist or may be the only focal liver lesion (FLL) detected. Compared to HCC, comparatively little is known about the frequency and natural history of benign FLL in patients with established liver cirrhosis.This review analyses the prevalence and frequency of benign and malignant FLL others than hepatocellular carcinoma (HCC) in liver cirrhosis including imaging and autopsy studies. Understanding these data should be helpful in avoiding misdiagnoses.

Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer.

OBJECTIVES: To develop and validate an ultrasound elastography radiomics nomogram for preoperative evaluation of the axillary lymph node (ALN) burden in early-stage breast cancer. METHODS: Data of 303 patients from hospital #1 (training cohort) and 130 cases from hospital #2 (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomics features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. The minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select ALN status-related features. Proportional odds ordinal logistic regression was performed using the radiomics signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated its performance using C-index and calibration. RESULTS: SWE signature, US-reported LN status, and molecular subtype were independent risk factors associated with ALN status. The nomogram based on these variables showed good discrimination in the training (overall C-index: 0.842; 95%CI, 0.773-0.879) and the validation set (overall C-index: 0.822; 95%CI, 0.765-0.838). For discriminating between disease-free axilla (N0) and any axillary metastasis (N + (≥ 1)), it achieved a C-index of 0.845 (95%CI, 0.777-0.914) for the training cohort and 0.817 (95%CI, 0.769-0.865) for the validation cohort. The tool could also discriminate between low (N + (1-2)) and heavy metastatic ALN burden (N + (≥ 3)), with a C-index of 0.827 (95%CI, 0.742-0.913) in the training cohort and 0.810 (95%CI, 0.755-0.864) in the validation cohort. CONCLUSION: The radiomics model shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for decision-making. KEY POINTS: • Radiomics analysis helps radiologists to evaluate the axillary lymph node status of breast cancer with accuracy. • This multicentre retrospective study showed that radiomics nomogram based on shear-wave elastography provides incremental information for risk stratification. • Treatment can be given with more precision based on the model.

The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study.

OBJECTIVES: To evaluate the diagnostic value of computer-aided diagnosis (CAD) software on ultrasound in distinguishing benign and malignant breast masses and avoiding unnecessary biopsy. METHODS: This prospective, multicenter study included patients who were scheduled for pathological diagnosis of breast masses between April 2019 and November 2020. Ultrasound images, videos, CAD analysis, and BI-RADS were obtained. The AUC, accuracy, sensitivity, specificity, PPV, and NPV were calculated and compared with radiologists. RESULTS: Overall, 901 breast masses in 901 patients were enrolled in this study. The accuracy, sensitivity, specificity, PPV and NPV of CAD software were 89.6%, 94.2%, 87.0%, 80.4%, and 96.3, respectively, in the long-axis section; 89.0%, 91.4%, 87.7%, 80.8%, and 94.7%, respectively, in the short-axis section. With BI-RADS 4a as the cut-off value, CAD software has a higher AUC (0.906 vs 0.734 vs 0.696, all p < 0.001) than both experienced and less experienced radiologists. With BI-RADS 4b as the cut-off value, CAD software showed better AUC than less experienced radiologists (0.906 vs 0.874, p < 0.001), but not superior to experienced radiologists (0.906 vs 0.883, p = 0.057). After the application of CAD software, the unnecessary biopsy rate of BI-RADS categories 4 and 5 was significantly decreased (33.0% vs 11.9%, 37.8% vs 14.5%), and the malignant rate of biopsy in category 4a was significantly increased (11.6% vs 40.7%, 7.4% vs 34.9%, all p < 0.001). CONCLUSIONS: CAD software on ultrasound can be used as an effective auxiliary diagnostic tool for differential diagnosis of benign and malignant breast masses and reducing unnecessary biopsy. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov (NCT03887598) KEY POINTS: • Prospective multicenter study showed that computer-aided diagnosis software provides greater diagnostic confidence for differentiating benign and malignant breast masses. • Computer-aided diagnosis software can help radiologists reduce unnecessary biopsy. • The management of patients with breast masses becomes more appropriate.

Ultrasound, CT, and MR Imaging for Evaluation of Cystic Renal Masses.

Cystic renal masses are often encountered during abdominal imaging. Although most of them are benign simple cysts, some cystic masses have malignant characteristics. The Bosniak classification system provides a useful way to classify cystic masses. The Bosniak classification is based on the results of a well-established computed tomography protocol. Over the past 30 years, the classification system has been refined and improved. This paper reviews the literature on this topic and compares the advantages and disadvantages of different screening and classification methods. Patients will benefit from multimodal diagnosis for lesions that are difficult to classify after a single examination.

The Added Value of a Computer-Aided Diagnosis System in Differential Diagnosis of Breast Lesions by Radiologists With Different Experience.

OBJECTIVES: To evaluate the value of the computer-aided diagnosis system, S-Detect (based on deep learning algorithm), in distinguishing benign and malignant breast masses and reducing unnecessary biopsy based on the experience of radiologists. METHODS: From February 2018 to March 2019, 266 breast masses in 192 women were included in our study. Ultrasound (US) examination, including S-Detect technique, was performed by the radiologist with about 10 years of clinical experience in breast US imaging. US images were analyzed by four other radiologists with different experience in breast imaging (radiologists 1, 2, 3, and 4 with 1, 4, 9, and 20 years, respectively) according to their clinical experience (with and without the results of S-Detect). Diagnostic capabilities and unnecessary biopsy of radiologists and radiologists combined with S-Detect were compared and analyzed. RESULTS: After referring to the results of S-Detect, the changes made by less experienced radiologists were greater than experienced radiologists (benign or malignant, 44 vs 22 vs 14 vs 2; unnecessary biopsy, 34 vs 25 vs 10 vs 5). When combined with S-Detect, less experienced radiologists showed significant improvement in accuracy, specificity, positive predictive value, negative predictive value, and area under curve (P < .05), but not for experienced radiologists (P > .05). Similarly, the unnecessary biopsy rate of less experienced radiologists decreased significantly (44.4% vs 32.7%, P = .006; 36.8% vs 28.2%, P = .033), but not for experienced radiologists (P > .05). CONCLUSIONS: Less experienced radiologists rely more on S-Detect software. And S-Detect can be an effective decision-making tool for breast US, especially for less experienced radiologists.

Conventional ultrasound for diagnosis of hepatic steatosis is better than believed.

BACKGROUND: Hepatic steatosis is a condition frequently encountered in clinical practice, with potential progression towards fibrosis, cirrhosis, and hepatocellular carcinoma. Detection and staging of hepatic steatosis are of most importance in nonalcoholic fatty liver disease (NAFLD), a disease with a high prevalence of more than 1 billion individuals affected. Ultrasound (US) is one of the most used noninvasive imaging techniques used in the diagnosis of hepatic steatosis. Detection of hepatic steatosis with US relies on several conventional US parameters, which will be described. US is the first-choice imaging in adults at risk for hepatic steatosis. The use of some scoring systems may add additional accuracy especially in assessing the severity of hepatic steatosis. SUMMARY: In the presented paper, we discuss screening and risk stratification, ultrasound features for diagnosing hepatic steatosis, B-mode criteria, focal fatty patterns and Doppler features of the hepatic vessels, and the value of the different US signs for the diagnosis of liver steatosis including classifying the severity of steatosis using different US scores. Limitations of conventional B-mode and Doppler features in the evaluation of hepatic steatosis are also discussed, including those in grading and assessing the complications of steatosis, namely fibrosis and nonalcoholic steatohepatitis. KEY MESSAGES: Ultrasound is the first-line imaging examination for the screening and follow-up of patients with liver steatosis. The use of some scoring systems may add additional accuracy in assessing the severity of steatosis. Conventional B-mode and Doppler ultrasound have limitations in grading and assessing the complications of steatosis.

Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study.

OBJECTIVES: To evaluate the prediction performance of deep convolutional neural network (DCNN) based on ultrasound (US) images for the assessment of breast cancer molecular subtypes. METHODS: A dataset of 4828 US images from 1275 patients with primary breast cancer were used as the training samples. DCNN models were constructed primarily to predict the four St. Gallen molecular subtypes and secondarily to identify luminal disease from non-luminal disease based on the ground truth from immunohistochemical of whole tumor surgical specimen. US images from two other institutions were retained as independent test sets to validate the system. The models' performance was analyzed using per-class accuracy, positive predictive value (PPV), and Matthews correlation coefficient (MCC). RESULTS: The model achieved good performance in identifying the four breast cancer molecular subtypes in the two test sets, with accuracy ranging from 80.07% (95% CI, 76.49-83.23%) to 97.02% (95% CI, 95.22-98.16%) and 87.94% (95% CI, 85.08-90.31%) to 98.83% (95% CI, 97.60-99.43) for the two test cohorts for each sub-category, respectively. In terms of 4-class weighted average MCC, the model achieved 0.59 for test cohort A and 0.79 for test cohort B. Specifically, the DCNN also yielded good diagnostic performance in discriminating luminal disease from non-luminal disease, with a PPV of 93.29% (95% CI, 90.63-95.23%) and 88.21% (95% CI, 85.12-90.73%) for the two test cohorts, respectively. CONCLUSION: Using pretreatment US images of the breast cancer, deep learning model enables the assessment of molecular subtypes with high diagnostic accuracy. TRIAL REGISTRATION: Clinical trial number: ChiCTR1900027676 KEY POINTS: • Deep convolutional neural network (DCNN) helps clinicians assess tumor features with accuracy. • Multicenter retrospective study shows that DCNN derived from pretreatment ultrasound imagine improves the prediction of breast cancer molecular subtypes. • Management of patients becomes more precise based on the DCNN model.

Autophagy blockade synergistically enhances nanosonosensitizer-enabled sonodynamic cancer nanotherapeutics.

Ultrasound-triggered sonodynamic therapy (SDT) represents an emerging therapeutic modality for cancer treatment based on its specific feature of noninvasiveness, high tissue-penetrating depth and desirable therapeutic efficacy, but the SDT-induced pro-survival cancer-cell autophagy would significantly lower the SDT efficacy for cancer treatment. Here we propose an "all-in-one" combined tumor-therapeutic strategy by integrating nanosonosensitizers-augmented noninvasive SDT with autophagy inhibition based on the rationally constructed nanoliposomes that co-encapsulates clinically approved sonosensitizers protoporphyrin IX (PpIX) and early-phase autophagy-blocking agent 3-methyladenine (3-MA). It has been systematically demonstrated that nanosonosensitizers-augmented SDT induced cytoprotective pro-survival autophagy through activation of MAPK signaling pathway and inhibition of AMPK signaling pathway, and this could be efficaciously inhibited by 3-MA in early-phase autophagy, which significantly decreased the cell resistance to intracellular oxidative stress and complied a remarkable synergistic effect on SDT medicated cancer-cell apoptosis both in vitro at cellular level and in vivo on tumor-bearing animal model. Therefore, our results provide a proof-of-concept combinatorial tumor therapeutics based on nanosonosensitizers for the treatment of ROS-resistant cancer by autophagy inhibition-augmented SDT.

Diagnosis of Prostate Cancer in Patients with Prostate-Specific Antigen (PSA) in the Gray Area: Construction of 2 Predictive Models.

BACKGROUND Two diagnostic models of prostate cancer (PCa) and clinically significant prostate cancer (CS-PCa) were established using clinical data of among patients whose prostate-specific antigen (PSA) levels are in the gray area (4.0-10.0 ng/ml). MATERIAL AND METHODS Data from 181 patients whose PSA levels were in the gray area were retrospectively analyzed, and the following data were collected: age, digital rectal examination, total PSA, PSA density (PSAD), free/total PSA (f/t PSA), transrectal ultrasound, multiparametric magnetic resonance imaging (mpMRI), and pathological reports. Patients were diagnosed with benign prostatic hyperplasia (BPH) and PCa by pathology reports, and PCa patients were separated into non-clinically significant PCa (NCS-PCa) and CS-PCa by Gleason score. Afterward, predictor models constructed by above parameters were researched to diagnose PCa and CS-PCa, respectively. RESULTS According to the analysis of included clinical data, there were 109 patients with BPH, 44 patients with NCS-PCa, and 28 patients with CS-PCa. Regression analysis showed PCa was correlated with f/t PSA, PSAD, and mpMRI (P<0.01), and CS-PCa was correlated with PSAD and mpMRI (P<0.01). The area under the receiver operating characteristic curves of 2 models for PCa (sensitivity=73.64%, specificity=64.23%) and for CS-PCa (sensitivity=71.41%, specificity=81.82%) were 0.79 and 0.87, respectively. CONCLUSIONS The prediction models had satisfactory diagnostic value for PCa and CS-PCa among patients with PSA in the gray area, and use of these models may help reduce overdiagnosis.

A Review of the Role of the S-Detect Computer-Aided Diagnostic Ultrasound System in the Evaluation of Benign and Malignant Breast and Thyroid Masses.

Computer-aided diagnosis (CAD) systems have attracted extensive attention owing to their performance in the field of image diagnosis and are rapidly becoming a promising auxiliary tool in medical imaging tasks. These systems can quantitatively evaluate complex medical imaging features and achieve efficient and high-diagnostic accuracy. Deep learning is a representation learning method. As a major branch of artificial intelligence technology, it can directly process original image data by simulating the structure of the human brain neural network, thus independently completing the task of image recognition. S-Detect is a novel and interactive CAD system based on a deep learning algorithm, which has been integrated into ultrasound equipment and can help radiologists identify benign and malignant nodules, reduce physician workload, and optimize the ultrasound clinical workflow. S-Detect is becoming one of the most commonly used CAD systems for ultrasound evaluation of breast and thyroid nodules. In this review, we describe the S-Detect workflow and outline its application in breast and thyroid nodule detection. Finally, we discuss the difficulties and challenges faced by S-Detect as a precision medical tool in clinical practice and its prospects.

Tai Chi exercise improves age-associated decline in cerebrovascular function: a cross-sectional study.

BACKGROUND: Tai Chi exercise has been reported to enhance physical and mental health in the older adults; however, the mechanism remains elusive. TRIAL DESIGN: We recruited 289 older adults practicing Tai Chi for over 3 years, together with 277 age-matched older and 102 young adults as controls. 168 Tai Chi practitioners were successfully matched to 168 older controls aged 60-69 based on a propensity score for statistics. METHODS: Cerebrovascular function was evaluated by measuring the hemodynamics of the carotid artery. Spearman correlation was performed to validate the age-associated physiological parameters. RESULTS: Cerebrovascular function in older adults significantly degenerated compared with the young, and was substantially correlated with age. Compared with the older control group, Tai Chi practitioners showed significant improvements in CVHI (cerebral vascular hemodynamics indices) Score (P = 0.002), mean blood flow velocity (P = 0.014), maximal blood flow velocity (P = 0.04) and minimum blood flow velocity (P < 0.001), whereas the age-related increases in pulse wave velocity (P = 0.022), characteristic impedance (P = 0.021) and peripheral resistance (P = 0.044) were lowered. CONCLUSIONS: These data demonstrate a rejuvenation role of Tai Chi in improving the age-related decline of the cerebrovascular function. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR1900025187).

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.

Usefulness of the Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System in Diagnosing Focal Liver Lesions by Inexperienced Radiologists.

OBJECTIVES: To evaluate the usefulness of the contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) in diagnosing focal liver lesions (FLLs) by inexperienced radiologists. METHODS: Images and clinical data from 258 patients at risk for hepatocellular carcinoma who underwent CEUS were collected retrospectively. Two trained inexperienced radiologists and 2 experienced radiologists reviewed all CEUS clips. Each inexperienced radiologist assigned a CEUS LI-RADS category for each observation and labeled it benign or malignant independently. Each experienced radiologist labeled each lesion malignant or benign independently using a conventional diagnostic method. Interobserver agreement of CEUS LI-RADS was analyzed by the κ test. The overall diagnostic accuracy of the LI-RADS category and conventional diagnosis was described by the sensitivity, specificity, positive predictive value, and negative predictive value. All test results were considered significant at P < .05. RESULTS: A κ value of 0.774 indicated that the CEUS LI-RADS algorithm resulted in substantial consistency between the inexperienced radiologists. For the diagnosis of hepatocellular carcinoma, the sensitivity, specificity, positive predictive value, and negative predictive value were improved significantly in inexperienced radiologists using the CEUS LI-RADS compared to conventional methods. The overall diagnostic accuracy of the experienced radiologists was almost equal to that of CEUS LI-RADS categories assigned by the inexperienced radiologists. CONCLUSIONS: The CEUS LI-RADS algorithm can not only obtain substantial consistency among inexperienced radiologists but also have excellent diagnostic efficacy in the differentiation of benign from malignant FLLs compared to conventional methods. As a comprehensive algorithm, the CEUS LI-RADS can act as a guide for trainees in learning how to diagnose FLLs.