Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience

A Review of Artificial Intelligence in Breast Imaging

With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women's physical and mental health. Early breast cancer screening-through mammography, ultrasound, or magnetic resonance imaging (MRI)-can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.

Deep Learning-Based Computer-Aided Diagnosis for Breast Lesion Classification on Ultrasound: A Prospective Multicenter Study of Radiologists Without Breast Ultrasound Expertise

BACKGROUND. Computer-aided diagnosis (CAD) systems for breast ultrasound interpretation have been primarily evaluated at tertiary and/or urban medical centers by radiologists with breast ultrasound expertise. OBJECTIVE. The purpose of this study was to evaluate the usefulness of deep learning-based CAD software on the diagnostic performance of radiologists without breast ultrasound expertise at secondary or rural hospitals in the differentiation of benign and malignant breast lesions measuring up to 2.0 cm on ultrasound. METHODS. This prospective study included patients scheduled to undergo biopsy or surgical resection at any of eight participating secondary or rural hospitals in China of a breast lesion classified as BI-RADS category 3-5 on prior breast ultrasound from November 2021 to September 2022. Patients underwent an additional investigational breast ultrasound, performed and interpreted by a radiologist without breast ultrasound expertise (hybrid body/breast radiologists, either who lacked breast imaging subspecialty training or for whom the number of breast ultrasounds performed annually accounted for less than 10% of all ultrasounds performed annually by the radiologist), who assigned a BI-RADS category. CAD results were used to upgrade reader-assigned BI-RADS category 3 lesions to category 4A and to downgrade reader-assigned BI-RADS category 4A lesions to category 3. Histologic results of biopsy or resection served as the reference standard. RESULTS. The study included 313 patients (mean age, 47.0 ± 14.0 years) with 313 breast lesions (102 malignant, 211 benign). Of BI-RADS category 3 lesions, 6.0% (6/100) were upgraded by CAD to category 4A, of which 16.7% (1/6) were malignant. Of category 4A lesions, 79.1% (87/110) were downgraded by CAD to category 3, of which 4.6% (4/87) were malignant. Diagnostic performance was significantly better after application of CAD, in comparison with before application of CAD, in terms of accuracy (86.6% vs 62.6%, p < .001), specificity (82.9% vs 46.0%, p < .001), and PPV (72.7% vs 46.5%, p < .001) but not significantly different in terms of sensitivity (94.1% vs 97.1%, p = .38) or NPV (96.7% vs 97.0%, p > .99). CONCLUSION. CAD significantly improved radiologists' diagnostic performance, showing particular potential to reduce the frequency of benign breast biopsies. CLINICAL IMPACT. The findings indicate the ability of CAD to improve patient care in settings with incomplete access to breast imaging expertise.

Toward AI-supported US Triage of Women with Palpable Breast Lumps in a Low-Resource Setting

Background Most low- and middle-income countries lack access to organized breast cancer screening, and women with lumps may wait months for diagnostic assessment. Purpose To demonstrate that artificial intelligence (AI) software applied to breast US images obtained with low-cost portable equipment and by minimally trained observers could accurately classify palpable breast masses for triage in a low-resource setting. Materials and Methods This prospective multicenter study evaluated participants with at least one palpable mass who were enrolled in a hospital in Jalisco, Mexico, from December 2017 through May 2021. Orthogonal US images were obtained first with portable US with and without calipers of any findings at the site of lump and adjacent tissue. Then women were imaged with standard-of-care (SOC) US with Breast Imaging Reporting and Data System assessments by a radiologist. After exclusions, 758 masses in 300 women were analyzable by AI, with outputs of benign, probably benign, suspicious, and malignant. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were determined. Results The mean patient age ± SD was 50.0 years ± 12.5 (range, 18-92 years) and mean largest lesion diameter was 13 mm ± 8 (range, 2-54 mm). Of 758 masses, 360 (47.5%) were palpable and 56 (7.4%) malignant, including six ductal carcinoma in situ. AI correctly identified 47 or 48 of 49 women (96%-98%) with cancer with either portable US or SOC US images, with AUCs of 0.91 and 0.95, respectively. One circumscribed invasive ductal carcinoma was classified as probably benign with SOC US, ipsilateral to a spiculated invasive ductal carcinoma. Of 251 women with benign masses, 168 (67%) imaged with SOC US were classified as benign or probably benign by AI, as were 96 of 251 masses (38%, P < .001) with portable US. AI performance with images obtained by a radiologist was significantly better than with images obtained by a minimally trained observer. Conclusion AI applied to portable US images of breast masses can accurately identify malignancies. Moderate specificity, which could triage 38%-67% of women with benign masses without tertiary referral, should further improve with AI and observer training with portable US. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Slanetz in this issue.

The feasibility of a novel computer-aided classification system for the characterisation and diagnosis of breast masses on ultrasound: a single-centre preliminary test study

AIM

To introduce a novel computer-aided classification (CAC) system and investigate the feasibility of characterising and diagnosing breast masses on ultrasound (US).

MATERIALS AND METHODS

A total of 246 breast masses were included. US features and the final assessment categories of the breast masses were analysed by a radiologist and the CAC system according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon. The CAC system evaluated the BI-RADS assessment from the fusion of multi-view and colour Doppler US images without (SmartBreast) or with combining clinical variables (m-CAC system). The diagnostic performance and agreement of US characteristics between the radiologist and the CAC system were compared.

RESULTS

The agreement between the radiologist and the CAC system was substantial for mass shape (κ = 0.673), orientation (κ = 0.682), margin (κ = 0.622), posterior features (κ = 0.629), calcifications in a mass (κ = 0.709) and vascularity (κ = 0.745), fair for echo pattern (κ = 0.379), and moderate for BI-RADS assessment (κ = 0.575). With BI-RADS 4a as the cut-off value, the specificity (52.5% versus 25%, p<0.0001) and accuracy (73.98% versus 62.6%, p=0.0002) of the m-CAC system were improved without significant loss of sensitivity (94.44% versus 98.41%, p=0.1250) compared with the SmartBreast. The m-CAC system showed similar specificity (52.5% versus 45.83%, p=0.2430) and accuracy (73.98% versus 73.58%, p=1.0000) as the radiologist, but a lower sensitivity (94.44% versus 100%, p=0.0156).

CONCLUSION

The CAC system showed an acceptable agreement with the radiologist for characterisation of breast lesions. It has the potential to mimic the decision-making behaviour of radiologists for the classification of breast lesions.

The utilization of artificial intelligence applications to improve breast cancer detection and prognosis

Breast imaging faces challenges with the current increase in medical imaging requests and lesions that breast screening programs can miss. Solutions to improve these challenges are being sought with the recent advancement and adoption of artificial intelligent (AI)-based applications to enhance workflow efficiency as well as patient-healthcare outcomes. rtificial intelligent tools have been proposed and used to analyze different modes of breast imaging, in most of the published studies, mainly for the detection and classification of breast lesions, breast lesion segmentation, breast density evaluation, and breast cancer risk assessment. This article reviews the background of the Conventional Computer-aided Detection system and AI, AI-based applications in breast medical imaging for the identification, segmentation, and categorization of lesions, breast density and cancer risk evaluation. In addition, the challenges, and limitations of AI-based applications in breast imaging are also discussed.

Comparison of S-Detect and thyroid imaging reporting and data system classifications in the diagnosis of cytologically indeterminate thyroid nodules

PURPOSE

The aim of this study was to investigate the value of S-Detect for predicting the malignant risk of cytologically indeterminate thyroid nodules (CITNs).

METHODS

The preoperative prediction of 159 CITNs (Bethesda III, IV and V) were performed using S-Detect, Thyroid Imaging Reporting and Data System of American College of Radiology (ACR TI-RADS) and Chinese TI-RADS (C-TIRADS). First, Linear-by-Linear Association test and chi-square test were used to analyze the malignant risk of CITNs. McNemar's test and receiver operating characteristic curve were used to compare the diagnostic efficacy of S-Detect and the two TI-RADS classifications for CITNs. In addition, the McNemar's test was used to compare the diagnostic accuracy of the above three methods for different pathological types of nodules.

RESULTS

The maximum diameter of the benign nodules was significantly larger than that of malignant nodules [0.88(0.57-1.42) vs 0.57(0.46-0.81), P=0.002]. The risk of malignant CITNs in Bethesda system and the two TI-RADS classifications increased with grade (all P for trend<0.001). In all the enrolled CITNs, the diagnostic results of S-Detect were significantly different from those of ACR TI-RADS and C-TIRADS, respectively (P=0.021 and P=0.007). The sensitivity and accuracy of S-Detect [95.9%(90.1%-98.5%) and 88.1%(81.7%-92.5%)] were higher than those of ACR TI-RADS [87.6%(80.1%-92.7%) and 81.8%(74.7%-87.3%)] (P=0.006 and P=0.021) and C-TIRADS [84.3%(76.3%-90.0%) and 78.6%(71.3%-84.5%)] (P=0.001 and P=0.001). Moreover, the negative predictive value and the area under curve value of S-Detect [82.8% (63.5%-93.5%) and 0.795%(0.724%-0.855%)] was higher than that of C-TIRADS [54.8%(38.8%-69.8%) and 0.724%(0.648%-0.792%] (P=0.024 and P=0.035). However, the specificity and positive predictive value of S-Detect were similar to those of ACR TI-RADS (P=1.000 and P=0.154) and C-TIRADS (P=1.000 and P=0.072). There was no significant difference in all the evaluated indicators between ACR TI-RADS and C-TIRADS (all P>0.05). The diagnostic accuracy of S-Detect (97.4%) for papillary thyroid carcinoma (PTC) was higher than that of ACR TI-RADS (90.4%) and C-TIRADS (87.8%) (P=0.021 and P=0.003).

CONCLUSION

The diagnostic performance of S-Detect in differentiating CITNs was similar to ACR TI-RADS and superior to C-TIRADS, especially for PTC.

Evaluating breast ultrasound S-detect image analysis for small focal breast lesions

Background

S-Detect is a computer-assisted, artificial intelligence-based system of image analysis that has been integrated into the software of ultrasound (US) equipment and has the capacity to independently differentiate between benign and malignant focal breast lesions. Since the revision and upgrade in both the breast imaging-reporting and data system (BI-RADS) US lexicon and the S-Detect software in 2013, evidence that supports improved accuracy and specificity of radiologists' assessment of breast lesions has accumulated. However, such assessment using S-Detect technology to distinguish malignant from breast lesions with a diameter no greater than 2 cm requires further investigation.

Methods

The US images of focal breast lesions from 295 patients in our hospital from January 2019 to June 2022 were collected. The BI-RADS data were evaluated by the embedded program and as manually modified prior to the determination of a pathological diagnosis. The receiver operator characteristic (ROC) curves were constructed to compare the diagnostic accuracy between the assessments of the conventional US images, the S-Detect classification, and the combination of the two.

Results

There were 326 lesions identified in 295 patients, of which pathological confirmation demonstrated that 239 were benign and 87 were malignant. The sensitivity, specificity, and accuracy of the conventional imaging group were 75.86%, 93.31%, and 88.65%. The sensitivity, specificity, and accuracy of the S-Detect classification group were 87.36%, 88.28%, and 88.04%, respectively. The assessment of the amended combination of S-Detect with US image analysis (Co-Detect group) was improved with a sensitivity, specificity, and accuracy of 90.80%, 94.56%, and 93.56%, respectively. The diagnostic accuracy of the conventional US group, the S-Detect group, and the Co-Detect group using area under curves was 0.85, 0.88 and 0.93, respectively. The Co-Detect group had a better diagnostic efficiency compared with the conventional US group (Z = 3.882, p = 0.0001) and the S-Detect group (Z = 3.861, p = 0.0001). There was no significant difference in distinguishing benign from malignant small breast lesions when comparing conventional US and S-Detect techniques.

Conclusions

The addition of S-Detect technology to conventional US imaging provided a novel and feasible method to differentiate benign from malignant small breast nodules.

The added value of S-detect in the diagnostic accuracy of breast masses by senior and junior radiologist groups: a systematic review and meta-analysis

Background

S-detect is an emerging computer-aided diagnosis (CAD) technique that provides a reference for radiologists to identify breast cancer. Some studies have shown that US (ultrasound) + S-detect can improve the diagnostic accuracy of junior radiologists more than senior radiologists, but the results are inconsistent in various studies. Therefore, this meta-analysis aimed to assess the value of S-detect combined with the US outcomes from senior and junior radiologists for the diagnosis of breast cancer.

Methods

We searched the PubMed, Cochrane Library, Embase, Web of Science, and Wanfang databases, China Biology Medicine disc, China National Knowledge Infrastructure (CNKI), and VIP database for trials on the diagnostic accuracy of US + S-detect for the diagnosis of breast masses. The search time frame was from the date of establishment of the database to August 20, 2022. Two researchers independently screened the literature, extracted the information, and evaluated the quality of the included literature using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) scale. StataSE 15.1 software was utilized to assess pooled metrics, including sensitivity, specificity, and the area under the curve (AUC).

Results

A total of 19 articles with 3,349 patients and 3,895 breast masses were included in this meta-analysis. Of these, seventeen articles evaluated the diagnostic performance of senior radiologists' US + S-detect for breast cancer, while twelve articles reported junior radiologists' diagnostic performance. The risk of bias was primarily attributed to patient selection, flow and timing. In the senior radiologist group, the pooled sensitivity and specificity of US + S-detect were 0.93 [95% confidence interval (CI): 0.89-0.95] and 0.86 (95% CI: 0.80-0.90), respectively, with an AUC of 0.96. As for the junior radiologist group, the pooled sensitivity and specificity of US + S-detect were 0.89 (95% CI: 0.83-0.93) and 0.79 (95% CI: 0.72-0.84), respectively, and the AUC was 0.91.

Conclusions

The results of this meta-analysis showed that the pooled sensitivity and the AUC of both the senior and junior radiologist groups were high, with good diagnostic efficacy and high clinical application. However, the results of this study are highly heterogeneous and need to be validated by collecting more high-quality studies and accumulating a larger sample size.

An AI model of sonographer’s evaluation+ S-Detect + elastography + clinical information improves the preoperative identification of benign and malignant breast masses

Purpose

The purpose of the study was to build an AI model with selected preoperative clinical features to further improve the accuracy of the assessment of benign and malignant breast nodules.

Methods

Patients who underwent ultrasound, strain elastography, and S-Detect before ultrasound-guided biopsy or surgical excision were enrolled. The diagnosis model was built using a logistic regression model. The diagnostic performances of different models were evaluated and compared.

Results

A total of 179 lesions (101 benign and 78 malignant) were included. The whole dataset consisted of a training set (145 patients) and an independent test set (34 patients). The AI models constructed based on clinical features, ultrasound features, and strain elastography to predict and classify benign and malignant breast nodules had ROC AUCs of 0.87, 0.81, and 0.79 in the test set. The AUCs of the sonographer and S-Detect were 0.75 and 0.82, respectively, in the test set. The AUC of the combined AI model with the best performance was 0.89 in the test set. The combined AI model showed a better specificity of 0.92 than the other models. The sonographer’s assessment showed better sensitivity (0.97 in the test set).

Conclusion

The combined AI model could improve the preoperative identification of benign and malignant breast masses and may reduce unnecessary breast biopsies.

Evolution of research trends in artificial intelligence for breast cancer diagnosis and prognosis over the past two decades: A bibliometric analysis

Objective

In recent years, among the available tools, the concurrent application of Artificial Intelligence (AI) has improved the diagnostic performance of breast cancer screening. In this context, the present study intends to provide a comprehensive overview of the evolution of AI for breast cancer diagnosis and prognosis research using bibliometric analysis.

Methodology

Therefore, in the present study, relevant peer-reviewed research articles published from 2000 to 2021 were downloaded from the Scopus and Web of Science (WOS) databases and later quantitatively analyzed and visualized using Bibliometrix (R package). Finally, open challenges areas were identified for future research work.

Results

The present study revealed that the number of literature studies published in AI for breast cancer detection and survival prediction has increased from 12 to 546 between the years 2000 to 2021. The United States of America (USA), the Republic of China, and India are the most productive publication-wise in this field. Furthermore, the USA leads in terms of the total citations; however, hungry and Holland take the lead positions in average citations per year. Wang J is the most productive author, and Zhan J is the most relevant author in this field. Stanford University in the USA is the most relevant affiliation by the number of published articles. The top 10 most relevant sources are Q1 journals with PLOS ONE and computer in Biology and Medicine are the leading journals in this field. The most trending topics related to our study, transfer learning and deep learning, were identified.

Conclusion

The present findings provide insight and research directions for policymakers and academic researchers for future collaboration and research in AI for breast cancer patients.

Application of S-detect combined with virtual touch imaging quantification in ultrasound for diagnosis of breast mass

OBJECTIVES

Ultrasound is a safe and timely diagnosis method commonly used for breast lesion, however it depends on the operator to a certain degree. As an emerging technology, artificial intelligence can be combined with ultrasound in depth to improve the intelligence and precision of ultrasound diagnosis and avoid diagnostic errors caused by subjectivity of radiologists. This paper aims to investigate the value of artificial intelligence S-detect system combined with virtual touch imaging quantification (VTIQ) technique in the differential diagnosis of benign and malignant breast masses by conventional ultrasound (CUS). respectively, and AUCs for them were 0.74, 0.86, 0.79, and 0.94, respectively. The diagnostic accuracy of CUS+S-detect was higher than that of CUS (P<0.05). The diagnostic accuracy of CUS+S-detect was higher than that of CUS (P<0.05). The diagnostic specificity of CUS+VTIQ was higher than that of CUS (P<0.05). The diagnostic accuracy and AUC of CUS+S-detect+VTIQ were higher than those of S-detect or VTIQ applied to CUS alone (P<0.05). The sensitivities of CUS for senior radiologists, CUS for junior radiologists, CUS+S-detect+VTIQ for senior radiologists, and CUS+S-detect+VTIQ for junior radiologists were 60.00%, 80.00%, 72.73%, and 90.00%, respectively, when diagnosing benign and malignant breast masses in 50 randomly selected cases. The specificities for them was 66.67%, 76.67%, 80.00%, and 81.25%, respectively. The accuracies for them was 64.00%, 78.00%, 80.00%, and 88.00%, respectively. The AUCs for them were 0.63, 0.78, 0.88, and 0.80, respectively. Compared with the CUS for junior radiologists, the CUS+S-detect+VTIQ for junior radiologists had higher sensitivity, specificity, and accuracy (all P<0.05). The consistency of the combined application of S-detect and VTIQ for diagnosing breast masses at 2 different times was good among junior radiologists (Kappa=0.800).

METHODS

CUS, S-detects, and VTIQ were used to differentially diagnose benign and malignant breast masses in 108 cases, and the final pathological results were referred to as the gold standard for classifying breast masses. The diagnostic efficacy were evaluated and compared, among the 3 methods and among S-detect applied to CUS (CUS+S-detect), VTIQ applied to CUS (CUS+VTIQ), and S-detect combined with VTIQ applied to CUS (CUS+S-detect+VTIQ). Fifty cases were acquired randomly from the collected breast masses, and 2 radiologists with different years of experience (2 and 8 years) used S-detect combined with VTIQ for the ultrasonic differential diagnosis of benign and malignant breast masses.

RESULTS

The differences in sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) of the 3 diagnostic methods of CUS, S-detect, and VTIQ were not statistically significant (all P>0.05). The sensitivities of CUS, CUS+Sdetect, CUS+VTIQ, and CUS+S-detect+VTIQ were 78.57%, 92.86%, 69.05%, and 95.24%, respectively, the specificities for them were 69.70%, 78.79%, 87.88%, and 92.42%, respectively, the accuracies for them were 73.15%, 84.26%, 80.56%, and 93.52%.

CONCLUSIONS

S-detect combined with VTIQ when applied to CUS can overcome the shortcomings of separate applications and complement each other, especially for junior radiologists, and can more effectively improve the diagnostic efficacy of ultrasound for benign and malignant breast masses.

Diagnostic accuracy of S-Detect to breast cancer on ultrasonography: A meta-analysis (PRISMA)

BACKGROUND

Computer-aided diagnosis (CAD) systems have shown great potential as an effective auxiliary diagnostic tool in breast imaging. Previous studies have shown that S-Detect technology has a high accuracy in the differential diagnosis of breast masses. However, the application of S-Detect in clinical practice remains controversial, and the results vary among different clinical trials. This meta-analysis aimed to determine the diagnostic accuracy of S-Detect for distinguishing between benign and malignant breast masses.

METHODS

We searched PubMed, Cochrane Library, and CBM databases from inception to April 1, 2021. Meta-analysis was conducted using STATA version 14.0 and Meta-Disc version 1.4 softwares. We calculated the summary statistics for sensitivity (Sen), specificity (Spe), positive, and negative likelihood ratio (LR+/LR-), diagnostic odds ratio(DOR), and summary receiver operating characteristic (SROC) curves. Cochran Q-statistic and I2 test were used to evaluate the potential heterogeneity between studies. Sensitivity analysis was performed to evaluate the influence of single studies on the overall estimate. We also performed meta-regression analyses to investigate potential sources of heterogeneity.

RESULTS

Eleven studies that met all the inclusion criteria were included in the meta-analysis. A total of 951 malignant and 1866 benign breast masses were assessed. All breast masses were histologically confirmed using S-Detect. The pooled Sen was 0.82 (95% confidence interval(CI) = 0.74-0.88); the pooled Spe was 0.83 (95%CI = 0.78-0.88). The pooled LR + was 4.91 (95%CI = 3.75-6.41); the pooled negative LR - was 0.21 (95%CI = 0.15-0.31). The pooled DOR of S-Detect in the diagnosis of breast nodules was 23.12 (95% CI = 14.53-36.77). The area under the SROC curve was 0.90 (SE = 0.0166). No evidence of publication bias was found (t = 0.54, P = .61).

CONCLUSIONS

Our meta-analysis indicates that S-Detect may have high diagnostic accuracy in distinguishing benign and malignant breast masses.

Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions

PURPOSE

To investigate the value of the combined diagnosis of multiparametric MRI-based deep learning models to differentiate triple-negative breast cancer (TNBC) from fibroadenoma magnetic resonance Breast Imaging-Reporting and Data System category 4 (BI-RADS 4) lesions and to evaluate whether the combined diagnosis of these models could improve the diagnostic performance of radiologists.

METHODS

A total of 319 female patients with 319 pathologically confirmed BI-RADS 4 lesions were randomly divided into training, validation, and testing sets in this retrospective study. The three models were established based on contrast-enhanced T1-weighted imaging, diffusion-weighted imaging, and T2-weighted imaging using the training and validation sets. The artificial intelligence (AI) combination score was calculated according to the results of three models. The diagnostic performances of four radiologists with and without AI assistance were compared with the AI combination score on the testing set. The area under the curve (AUC), sensitivity, specificity, accuracy, and weighted kappa value were calculated to assess the performance.

RESULTS

The AI combination score yielded an excellent performance (AUC = 0.944) on the testing set. With AI assistance, the AUC for the diagnosis of junior radiologist 1 (JR1) increased from 0.833 to 0.885, and that for JR2 increased from 0.823 to 0.876. The AUCs of senior radiologist 1 (SR1) and SR2 slightly increased from 0.901 and 0.950 to 0.925 and 0.975 after AI assistance, respectively.

CONCLUSION

Combined diagnosis of multiparametric MRI-based deep learning models to differentiate TNBC from fibroadenoma magnetic resonance BI-RADS 4 lesions can achieve comparable performance to that of SRs and improve the diagnostic performance of JRs.

A Reproducible Deep-Learning-Based Computer-Aided Diagnosis Tool for Frontotemporal Dementia Using MONAI and Clinica Frameworks

Despite Artificial Intelligence (AI) being a leading technology in biomedical research, real-life implementation of AI-based Computer-Aided Diagnosis (CAD) tools into the clinical setting is still remote due to unstandardized practices during development. However, few or no attempts have been made to propose a reproducible CAD development workflow for 3D MRI data. In this paper, we present the development of an easily reproducible and reliable CAD tool using the Clinica and MONAI frameworks that were developed to introduce standardized practices in medical imaging. A Deep Learning (DL) algorithm was trained to detect frontotemporal dementia (FTD) on data from the NIFD database to ensure reproducibility. The DL model yielded 0.80 accuracy (95% confidence intervals: 0.64, 0.91), 1 sensitivity, 0.6 specificity, 0.83 F1-score, and 0.86 AUC, achieving a comparable performance with other FTD classification approaches. Explainable AI methods were applied to understand AI behavior and to identify regions of the images where the DL model misbehaves. Attention maps highlighted that its decision was driven by hallmarking brain areas for FTD and helped us to understand how to improve FTD detection. The proposed standardized methodology could be useful for benchmark comparison in FTD classification. AI-based CAD tools should be developed with the goal of standardizing pipelines, as varying pre-processing and training methods, along with the absence of model behavior explanations, negatively impact regulators' attitudes towards CAD. The adoption of common best practices for neuroimaging data analysis is a step toward fast evaluation of efficacy and safety of CAD and may accelerate the adoption of AI products in the healthcare system.

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.

Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences

Breast cancer needs to be detected early to reduce mortality rate. Ultrasound imaging (US) could significantly enhance diagnosing cases with dense breasts. Most of the existing computer-aided diagnosis (CAD) systems employ a single ultrasound image for the breast tumor to extract features to classify it as benign or malignant. However, the accuracy of such CAD system is limited due to the large tumor size and shape variation, irregular and ambiguous tumor boundaries, and low signal-to-noise ratio in ultrasound images due to their noisy nature and the significant similarity between normal and abnormal tissues. To handle these issues, we propose a deep-learning-based radiomics method based on breast US sequences in this paper. The proposed approach involves three main components: radiomic features extraction based on a deep learning network, so-called ConvNeXt, a malignancy score pooling mechanism, and visual interpretations. Specifically, we employ the ConvNeXt network, a deep convolutional neural network (CNN) trained using the vision transformer style. We also propose an efficient pooling mechanism to fuse the malignancy scores of each breast US sequence frame based on image-quality statistics. The ablation study and experimental results demonstrate that our method achieves competitive results compared to other CNN-based methods.

Differing benefits of artificial intelligence-based computer-aided diagnosis (AI-CAD) for breast US according to workflow and experience level

Purpose

This study evaluated how artificial intelligence-based computer-assisted diagnosis (AI-CAD) for breast ultrasonography (US) influences diagnostic performance and agreement between radiologists with varying experience levels in different workflows.

Methods

Images of 492 breast lesions (200 malignant and 292 benign masses) in 472 women taken from April 2017 to June 2018 were included. Six radiologists (three inexperienced [<1 year of experience] and three experienced [10-15 years of experience]) individually reviewed US images with and without the aid of AI-CAD, first sequentially and then simultaneously. Diagnostic performance and interobserver agreement were calculated and compared between radiologists and AI-CAD.

Results

After implementing AI-CAD, the specificity, positive predictive value (PPV), and accuracy significantly improved, regardless of experience and workflow (all P<0.001, respectively). The overall area under the receiver operating characteristic curve significantly increased in simultaneous reading, but only for inexperienced radiologists. The agreement for Breast Imaging Reporting and Database System (BI-RADS) descriptors generally increased when AI-CAD was used (κ=0.29-0.63 to 0.35-0.73). Inexperienced radiologists tended to concede to AI-CAD results more easily than experienced radiologists, especially in simultaneous reading (P<0.001). The conversion rates for final assessment changes from BI-RADS 2 or 3 to BI-RADS higher than 4a or vice versa were also significantly higher in simultaneous reading than sequential reading (overall, 15.8% and 6.2%, respectively; P<0.001) for both inexperienced and experienced radiologists.

Conclusion

Using AI-CAD to interpret breast US improved the specificity, PPV, and accuracy of radiologists regardless of experience level. AI-CAD may work better in simultaneous reading to improve diagnostic performance and agreement between radiologists, especially for inexperienced radiologists.

Diagnostic performance improvement with combined use of proteomics biomarker assay and breast ultrasound

PURPOSE

To investigate the combined use of blood-based 3-protein signature and breast ultrasound (US) for validating US-detected lesions.

METHODS

From July 2011 to April 2020, women who underwent whole-breast US within at least 6 months from sampling period were retrospectively included. Blood-based 3-protein signature (Mastocheck®) value and US findings were evaluated. Following outcome measures were compared between US alone and the combination of Mastocheck® value with US: sensitivity, specificity, positive predictive value (PPV), negative predictive value, area under the receiver operating characteristic curve (AUC), and biopsy rate.

RESULTS

Among the 237 women included, 59 (24.9%) were healthy individuals and 178 (75.1%) cancer patients. Mean size of cancers was 1.2 ± 0.8 cm. Median value of Mastocheck® was significantly different between nonmalignant (- 0.24, interquartile range [IQR] - 0.48, - 0.03) and malignant lesions (0.55, IQR - 0.03, 1.42) (P < .001). Utilizing Mastocheck® value with US increased the AUC from 0.67 (95% confidence interval [CI] 0.61, 0.73) to 0.81 (95% CI 0.75, 0.88; P < .001), and specificity from 35.6 (95% CI 23.4, 47.8) to 64.4% (95% CI 52.2, 76.6; P < .001) without loss in sensitivity. PPV was increased from 82.2 (95% CI 77.1, 87.3) to 89.3% (95% CI 85.0, 93.6; P < .001), and biopsy rate was significantly decreased from 79.3 (188/237) to 72.1% (171/237) (P < .001). Consistent improvements in specificity, PPV, and AUC were observed in asymptomatic women, in women with dense breast, and in those with normal/benign mammographic findings.

CONCLUSION

Mastocheck® is an effective tool that can be used with US to improve diagnostic specificity and reduce false-positive findings and unnecessary biopsies.

Ultrasonographic morphological characteristics determined using a deep learning-based computer-aided diagnostic system of breast cancer

To investigate the correlations between ultrasonographic morphological characteristics quantitatively assessed using a deep learning-based computer-aided diagnostic system (DL-CAD) and histopathologic features of breast cancer.This retrospective study included 282 women with invasive breast cancer (<5 cm; mean age, 54.4 [range, 29-85] years) who underwent surgery between February 2016 and April 2017. The morphological characteristics of breast cancer on B-mode ultrasonography were analyzed using DL-CAD, and quantitative scores (0-1) were obtained. Associations between quantitative scores and tumor histologic type, grade, size, subtype, and lymph node status were compared.Two-hundred and thirty-six (83.7%) tumors were invasive ductal carcinoma, 18 (6.4%) invasive lobular carcinoma, and 28 (9.9%) micropapillary, apocrine, and mucinous. The mean size was 1.8 ± 1.0 (standard deviation) cm, and 108 (38.3%) cases were node positive. Irregular shape score was associated with tumor size (P < .001), lymph nodes status (P = .001), and estrogen receptor status (P = .016). Not-circumscribed margin (P < .001) and hypoechogenicity (P = .003) scores correlated with tumor size, and non-parallel orientation score correlated with histologic grade (P = .024). Luminal A tumors exhibited more irregular features (P = .048) with no parallel orientation (P = .002), whereas triple-negative breast cancer showed a rounder/more oval and parallel orientation.Quantitative morphological characteristics of breast cancers determined using DL-CAD correlated with histopathologic features and could provide useful information about breast cancer phenotypes.

Diagnostic Performance of an Artificial Intelligence System in Breast Ultrasound

OBJECTIVES

We study the performance of an artificial intelligence (AI) program designed to assist radiologists in the diagnosis of breast cancer, relative to measures obtained from conventional readings by radiologists.

METHODS

A total of 10 radiologists read a curated, anonymized group of 299 breast ultrasound images that contained at least one suspicious lesion and for which a final diagnosis was independently determined. Separately, the AI program was initialized by a lead radiologist and the computed results compared against those of the radiologists.

RESULTS

The AI program's diagnoses of breast lesions had concordance with the 10 radiologists' readings across a number of BI-RADS descriptors. The sensitivity, specificity, and accuracy of the AI program's diagnosis of benign versus malignant was above 0.8, in agreement with the highest performing radiologists and commensurate with recent studies.

CONCLUSION

The trained AI program can contribute to accuracy of breast cancer diagnoses with ultrasound.

Accuracy of ultrasonic artificial intelligence in diagnosing benign and malignant breast diseases: A protocol for systematic review and meta-analysis

BACKGROUND

Artificial intelligence system is a deep learning system based on computer-assisted ultrasonic image diagnosis, which can extract morphological features of breast mass and conduct objective and efficient image analysis, thus automatically intelligent classification of breast mass, avoiding subjective error and improving the accuracy of diagnosis.[1-2] A large number of studies have confirmed that artificial intelligence (AI) has high effectiveness and reliability in the differential diagnosis of benign and malignant breast diseases.[3-4] However, the results of these studies have been contradictory. Therefore, this meta-analysis tested the hypothesis that artificial intelligence system is accurate in distinguishing benign and malignant breast diseases.

METHODS

We will search PubMed, Web of Science, Cochrane Library, and Chinese biomedical databases from their inceptions to the November 20, 2021, without language restrictions. Two authors will independently carry out searching literature records, scanning titles and abstracts, full texts, collecting data, and assessing risk of bias. Review Manager 5.2 and Stata14.0 software will be used for data analysis.

RESULTS

This systematic review will determine the accuracy of AI in the differential diagnosis of benign and malignant breast diseases.

CONCLUSION

Its findings will provide helpful evidence for the accuracy of AI in the differential diagnosis of benign and malignant breast diseases.

SYSTEMATIC REVIEW REGISTRATION

INPLASY2021110087.

Evaluation of computer-aided diagnosis in breast ultrasonography: Improvement in diagnostic performance of inexperienced radiologists

PURPOSE

To evaluate if a computer-aided diagnosis (CAD) system on ultrasound (US) can improve the diagnostic performance of inexperienced radiologists.

METHODS

We collected ultrasound images of 256 breast lesions taken between March and May 2020. We asked two experienced and two inexperienced radiologists to retrospectively review the US features of each breast lesion according to the Breast Imaging Reporting and Data System (BI-RADS) categories. A CAD examination with S-Detect™ software (Samsung Healthcare, Seoul, South Korea) was conducted retrospectively by another uninvolved radiologist blinded to the BIRADS values previously attributed to the lesions. Diagnostic performances of experienced and inexperienced radiologists and CAD were compared and the inter-observer agreement among radiologists was calculated.

RESULTS

The diagnostic performance of the experienced group in terms of sensitivity was significantly higher than CAD (p < 0.001). Conversely, the diagnostic performance of inexperienced group in terms of both sensitivity and specificity was significantly lower than CAD (p < 0.001). We obtained an excellent agreement in the evaluation of the lesions among the two expert radiologists (Kappa coefficient: 88.7%), and among the two non-expert radiologists (Kappa coefficient: 84.9%).

CONCLUSION

The US CAD system is a useful additional tool to improve the diagnostic performance of the inexperienced radiologists, eventually reducing the number of unnecessary biopsies. Moreover, it is a valid second opinion in case of experienced radiologists.

Evaluation of Different Breast Cancer Screening Strategies for High-Risk Women in Beijing, China: A Real-World Population-Based Study

BACKGROUND

Mammography-based breast cancer screening has been widely implemented in many developed countries. Evidence was needed on participation and diagnostic performance of population-based breast cancer screening using ultrasound in China.

METHODS

We used data from the Cancer Screening Program in Urban China in Beijing from 2014 to 2019 and was followed up until July 2020 by matching with the Beijing Cancer Registry database. Eligible women between the ages of 45 and 69 years were recruited from six districts and assessed their risk of breast cancer through an established risk scoring system. Women evaluated to be at high risk of breast cancer were invited to undergo both ultrasound and mammography. Participation rates were calculated, and their associated factors were explored. In addition, the performance of five different breast cancer screening modalities was evaluated in this study.

RESULTS

A total of 49,161 eligible women were recruited in this study. Among them, 15,550 women were assessed as high risk for breast cancer, and 7,500 women underwent ultrasound and/or mammography as recommended, with a participation rate of 48.2%. The sensitivity of mammography alone, ultrasound alone, combined of ultrasound and mammography, ultrasound for primary screening followed by mammography for triage, and mammography for preliminary screening followed by ultrasound for triage were19.2%, 38.5%, 50.0%, 46.2%, and 19.2%, and the specificity were 96.1%, 98.6%, 94.7%, 97.6%, 95.7%, respectively. The sensitivity of combined ultrasound and mammography, ultrasound for primary screening followed by mammography for triage, was significantly higher than mammography alone (p=0.008 and p=0.039). Additionally, ultrasound alone (48,323 RMB ($7,550)) and ultrasound for primary screening followed by mammography for triage (55,927 RMB ($8,739)) were the most cost-effective methods for breast cancer screening than other modalities.

CONCLUSIONS

Ultrasound alone and ultrasound for primary screening and mammography are superior to mammography for breast cancer screening in high-risk Chinese women.

Axillary ultrasound and fine-needle aspiration cytology to predict clinically relevant nodal burden in breast cancer patients

Background

Axillary lymph node involvement is one important prognostic factor in breast cancer, but the way to access this information has been modified over the years. This study evaluated if axillary ultrasound (US) coupled with fine-needle aspiration cytology (FNAC) can accurately predict clinically relevant node metastasis in patients with breast cancer, and thus assist clinical decisions

Methods

This is a cross-sectional study with retrospective data collection of 241 individuals (239 women and 2 men) with unilateral operable breast cancer who were submitted to preoperative axillary assessment by physical exam, US and FNAC if suspicious nodes by imaging. We calculated sensitivity, specificity, and accuracy of the methods. We compared the patient's characteristics using chi-square test, parametrics and non-parametrics statistics according to the variable.

Results

The most sensible method was US (0.59; 95% CI, 0.50–0.69), and the most specific was US coupled with FNAC (0.97; 95% CI, 0.92–0.99). Only 2.7% of the patients with normal axillary US had more than 2 metastatic nodes in the axillary lymph node dissection, against 50% of the patients with suspicious lymph nodes in the US and positive FNAC.

Conclusions

Axillary US coupled with FNAC can sort patients who have a few metastatic nodes at most from those with heavy axillary burden and could be one more tool to initially evaluate patients and define treatment strategies.

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.

Added value of deep learning-based computer-aided diagnosis and shear wave elastography to b-mode ultrasound for evaluation of breast masses detected by screening ultrasound

Low specificity and operator dependency are the main problems of breast ultrasound (US) screening. We investigated the added value of deep learning-based computer-aided diagnosis (S-Detect) and shear wave elastography (SWE) to B-mode US for evaluation of breast masses detected by screening US.Between February 2018 and June 2019, B-mode US, S-Detect, and SWE were prospectively obtained for 156 screening US-detected breast masses in 146 women before undergoing US-guided biopsy. S-Detect was applied for the representative B-mode US image, and quantitative elasticity was measured for SWE. Breast Imaging Reporting and Data System final assessment category was assigned for the datasets of B-mode US alone, B-mode US plus S-Detect, and B-mode US plus SWE by 3 radiologists with varied experience in breast imaging. Area under the receiver operator characteristics curve (AUC), sensitivity, and specificity for the 3 datasets were compared using Delong's method and McNemar test.Of 156 masses, 10 (6%) were malignant and 146 (94%) were benign. Compared to B-mode US alone, the addition of S-Detect increased the specificity from 8%-9% to 31%-71% and the AUC from 0.541-0.545 to 0.658-0.803 in all radiologists (All P < .001). The addition of SWE to B-mode US also increased the specificity from 8%-9% to 41%-75% and the AUC from 0.541-0.545 to 0.709-0.823 in all radiologists (All P < .001). There was no significant loss in sensitivity when either S-Detect or SWE were added to B-mode US.Adding S-Detect or SWE to B-mode US improved the specificity and AUC without loss of sensitivity.

Artificial Intelligence in Medical Imaging of the Breast

Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.

Impact of Original and Artificially Improved Artificial Intelligence-based Computer-aided Diagnosis on Breast US Interpretation

OBJECTIVE

For breast US interpretation, to assess impact of computer-aided diagnosis (CADx) in original mode or with improved sensitivity or specificity.

METHODS

In this IRB approved protocol, orthogonal-paired US images of 319 lesions identified on screening, including 88 (27.6%) cancers (median 7 mm, range 1-34 mm), were reviewed by 9 breast imaging radiologists. Each observer provided BI-RADS assessments (2, 3, 4A, 4B, 4C, 5) before and after CADx in a mode-balanced design: mode 1, original CADx (outputs benign, probably benign, suspicious, or malignant); mode 2, artificially-high-sensitivity CADx (benign or malignant); and mode 3, artificially-high-specificity CADx (benign or malignant). Area under the receiver operating characteristic curve (AUC) was estimated under each modality and for standalone CADx outputs. Multi-reader analysis accounted for inter-reader variability and correlation between same-lesion assessments.

RESULTS

AUC of standalone CADx was 0.77 (95% CI: 0.72-0.83). For mode 1, average reader AUC was 0.82 (range 0.76-0.84) without CADx and not significantly changed with CADx. In high-sensitivity mode, all observers' AUCs increased: average AUC 0.83 (range 0.78-0.86) before CADx increased to 0.88 (range 0.84-0.90), P < 0.001. In high-specificity mode, all observers' AUCs increased: average AUC 0.82 (range 0.76-0.84) before CADx increased to 0.89 (range 0.87-0.92), P < 0.0001. Radiologists responded more frequently to malignant CADx cues in high-specificity mode (42.7% vs 23.2% mode 1, and 27.0% mode 2, P = 0.008).

CONCLUSION

Original CADx did not substantially impact radiologists' interpretations. Radiologists showed improved performance and were more responsive when CADx produced fewer false-positive malignant cues.

Application of ultrasonic dual-mode artificially intelligent architecture in assisting radiologists with different diagnostic levels on breast masses classification

PURPOSE

We aimed to compare the diagnostic performance and interobserver variability in breast tumor classification with or without the aid of an innovative dual-mode artificial intelligence (AI) architecture, which can automatically integrate information from ultrasonography (US) and shear-wave elastography (SWE).

METHODS

Diagnostic performance assessment was performed with a test subset, containing 599 images (from September 2018 to February 2019) from 91 patients including 64 benign and 27 malignant breast tumors. Six radiologists (three inexperienced, three experienced) were assigned to read images independently (independent diagnosis) and then make a secondary diagnosis with the knowledge of AI results. Sensitivity, specificity, accuracy, receiver-operator characteristics (ROC) curve analysis and Cohen's κ statistics were calculated.

RESULTS

In the inexperienced radiologists' group, the average area under the ROC curve (AUC) for diagnostic performance increased from 0.722 to 0.765 (p = 0.050) with secondary diagnosis using US-mode and from 0.794 to 0.834 (p = 0.019) with secondary diagnosis using dual-mode compared with independent diagnosis. In the experienced radiologists' group, the average AUC for diagnostic performance was significantly higher with AI system using the US-mode (0.812 vs. 0.833, p = 0.039), but not for dual-mode (0.858 vs. 0.866, p = 0.458). Using the US-mode, interobserver agreement among all radiologists improved from fair to moderate (p = 0.003). Using the dual-mode, substantial agreement was seen among the experienced radiologists (0.65 to 0.74, p = 0.017) and all radiologists (0.62 to 0.73, p = 0.001).

CONCLUSION

AI assistance provides a more pronounced improvement in diagnostic performance for the inexperienced radiologists; meanwhile, the experienced radiologists benefit more from AI in reducing interobserver variability.

[Artificial intelligence in medicine and gynecology-the wrong track or promise of cure?]

Artificial intelligence (AI) has attained a new level of maturity in recent years and is becoming the driver of digitalization in all areas of life. AI is a cross-sectional technology with great importance for all areas of medicine employing image data, text data and bio-data. There is no medical field that will remain unaffected by AI, with AI-assisted clinical decision-making assuming a particularly important role. AI methods are becoming established in medical workflow management and for prediction of treatment success or treatment outcome. AI systems are already able to lend support to imaging-based diagnosis and patient management, but cannot suggest critical decisions. The corresponding preventive or therapeutic measures can be more rationally assessed with the help of AI, although the number of diseases covered is currently too low to create robust systems for routine clinical use. Prerequisite for the widespread use of AI systems is appropriate training to enable physicians to decide when computer-assisted decision-making can be relied upon.

Differential diagnosis of breast cancer assisted by S-Detect artificial intelligence system

Objective Traditional breast ultrasound relies too much on the operation skills of diagnostic doctors, and the repeatability in different doctors was low. This study aimed to evaluate the assistant diagnostic value of S-Detect artificial intelligence (AI) system in differentiating benign from malignant breast masses. Methods The ultrasound images of 40 patients who underwent ultrasound examination in our hospital were collected. The conventional ultrasound images, elastic images, and S-Detect mode of breast lesions were analyzed. The breast imaging reporting and data system recommended by the American Society of Radiology (BI-RADS) classification for each breast mass was evaluated both by the doctor and AI. The receiver operator characteristics (ROC) curves were drawn to compare the diagnostic efficiency. Result Among the 40 lesions, 16 were benign, and 24 were malignant. The S-Detect AI system had a high diagnostic efficiency for malignant mass, with sensitivity, specificity, and accuracy of 95.8%, 93.8%, and 89.6%. The accuracy of AI was higher than the elastic image and then than the conventional gray-scale image. With the assistance of the S-Detect AI system, the accuracy of BI-RADS classification was improved significantly. Conclusion The S-Detect AI system will enhance breast cancer diagnostic accuracy and improve ultrasound examination quality.

Application of deep learning to establish a diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging

PURPOSE

There are currently few specific artificial intelligence (AI) studies for Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions. This study aimed to establish an AI diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging and to compare its performance with that of radiologists.

METHODS

The ultrasound images of 1311 lesions were evaluated by radiologists according to the BI-RADS categories, using pathology results as reference. Two classification standards (standards 1 and 2) for benign and malignant lesions were defined and used to calculate the diagnostic performance of radiologists, altogether and individually. The breast lesion images were also used to develop an AI diagnostic model.

RESULTS

The diagnostic performance of AI and that of the radiologists were compared using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). All parameters of diagnostic performance, except for sensitivity and NPV, improved with standard 2. For the 202 lesions in the test set, the diagnostic performance of the AI model had 77.0% accuracy, 82.0% sensitivity, 71.7% specificity, 79.3% PPV, 75.1% NPV, and an AUC of 0.846. When the AI model was used to analyze category 4A lesions, the PPV was 9.3%, which was better than that of the radiologists, although not significantly.

CONCLUSIONS

Deep learning technology shows a good performance in classifying benign and malignant breast lesions. It may be potentially used in practice to improve diagnostic accuracy and reduce unnecessary biopsies of breast lesions.

Artificial intelligence for breast ultrasound: An adjunct tool to reduce excessive lesion biopsy

PURPOSE

To determine whether adding an artificial intelligence (AI) system to breast ultrasound (US) can reduce unnecessary biopsies.

METHODS

Conventional US and AI analyses were prospectively performed on 173 suspicious breast lesions before US-guided core needle biopsy or vacuum-assisted excision. Conventional US images were retrospectively reviewed according to the BI-RADS 2013 lexicon and categories. Two downgrading stratifications based on AI assessments were manually used to downgrade the BI-RADS category 4A lesions to category 3. Stratification A was used to downgrade if the assessments of both orthogonal sections of a lesion from AI were possibly benign. Stratification B was used to downgrade if the assessment of any of the orthogonal sections was possibly benign. The effects of AI-based diagnosis on lesions to reduce unnecessary biopsy were analyzed using histopathological results as reference standards.

RESULTS

Forty-three lesions diagnosed as BI-RADS category 4A by conventional US received AI-based hypothetical downgrading. While downgrading with stratification A, 14 biopsies were correctly avoided. The biopsy rate for BI-RADS category 4A lesions decreased from 100 % to 67.4 % (P <  0.001). While downgrading with stratification B, 27 biopsies could be avoided with two malignancies missed, and the biopsy rate would decrease to 37.2 % (P <  0.05, compared with conventional US and stratification A).

CONCLUSION

Adding an AI system to breast US could reduce unnecessary lesion biopsies. Downgrading stratification A was recommended for its lower misdiagnosis rate.

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.

Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review

IMPORTANCE

An increasing number of machine learning (ML)-based clinical decision support systems (CDSSs) are described in the medical literature, but this research focuses almost entirely on comparing CDSS directly with clinicians (human vs computer). Little is known about the outcomes of these systems when used as adjuncts to human decision-making (human vs human with computer).

OBJECTIVES

To conduct a systematic review to investigate the association between the interactive use of ML-based diagnostic CDSSs and clinician performance and to examine the extent of the CDSSs' human factors evaluation.

EVIDENCE REVIEW

A search of MEDLINE, Embase, PsycINFO, and grey literature was conducted for the period between January 1, 2010, and May 31, 2019. Peer-reviewed studies published in English comparing human clinician performance with and without interactive use of an ML-based diagnostic CDSSs were included. All metrics used to assess human performance were considered as outcomes. The risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Risk of Bias in Non-Randomised Studies-Intervention (ROBINS-I). Narrative summaries were produced for the main outcomes. Given the heterogeneity of medical conditions, outcomes of interest, and evaluation metrics, no meta-analysis was performed.

FINDINGS

A total of 8112 studies were initially retrieved and 5154 abstracts were screened; of these, 37 studies met the inclusion criteria. The median number of participating clinicians was 4 (interquartile range, 3-8). Of the 107 results that reported statistical significance, 54 (50%) were increased by the use of CDSSs, 4 (4%) were decreased, and 49 (46%) showed no change or an unclear change. In the subgroup of studies carried out in representative clinical settings, no association between the use of ML-based diagnostic CDSSs and improved clinician performance could be observed. Interobserver agreement was the commonly reported outcome whose change was the most strongly associated with CDSS use. Four studies (11%) reported on user feedback, and, in all but 1 case, clinicians decided to override at least some of the algorithms' recommendations. Twenty-eight studies (76%) were rated as having a high risk of bias in at least 1 of the 4 QUADAS-2 core domains, and 6 studies (16%) were considered to be at serious or critical risk of bias using ROBINS-I.

CONCLUSIONS AND RELEVANCE

This systematic review found only sparse evidence that the use of ML-based CDSSs is associated with improved clinician diagnostic performance. Most studies had a low number of participants, were at high or unclear risk of bias, and showed little or no consideration for human factors. Caution should be exercised when estimating the current potential of ML to improve human diagnostic performance, and more comprehensive evaluation should be conducted before deploying ML-based CDSSs in clinical settings. The results highlight the importance of considering supported human decisions as end points rather than merely the stand-alone CDSSs outputs.

Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses

A major limitation of screening breast ultrasound (US) is a substantial number of false-positive biopsy. This study aimed to develop a deep learning-based computer-aided diagnosis (DL-CAD)-based diagnostic model to improve the differential diagnosis of screening US-detected breast masses and reduce false-positive diagnoses. In this multicenter retrospective study, a diagnostic model was developed based on US images combined with information obtained from the DL-CAD software for patients with breast masses detected using screening US; the data were obtained from two hospitals (development set: 299 imaging studies in 2015). Quantitative morphologic features were obtained from the DL-CAD software, and the clinical findings were collected. Multivariable logistic regression analysis was performed to establish a DL-CAD-based nomogram, and the model was externally validated using data collected from 164 imaging studies conducted between 2018 and 2019 at another hospital. Among the quantitative morphologic features extracted from DL-CAD, a higher irregular shape score (P = .018) and lower parallel orientation score (P = .007) were associated with malignancy. The nomogram incorporating the DL-CAD-based quantitative features, radiologists' Breast Imaging Reporting and Data Systems (BI-RADS) final assessment (P = .014), and patient age (P < .001) exhibited good discrimination in both the development and validation cohorts (area under the receiver operating characteristic curve, 0.89 and 0.87). Compared with the radiologists' BI-RADS final assessment, the DL-CAD-based nomogram lowered the false-positive rate (68% vs. 31%, P < .001 in the development cohort; 97% vs. 45% P < .001 in the validation cohort) without affecting the sensitivity (98% vs. 93%, P = .317 in the development cohort; each 100% in the validation cohort). In conclusion, the proposed model showed good performance for differentiating screening US-detected breast masses, thus demonstrating a potential to reduce unnecessary biopsies.

Reducing the number of unnecessary biopsies of US-BI-RADS 4a lesions through a deep learning method for residents-in-training: a cross-sectional study

OBJECTIVE

The aim of the study is to explore the potential value of S-Detect for residents-in-training, a computer-assisted diagnosis system based on deep learning (DL) algorithm.

METHODS

The study was designed as a cross-sectional study. Routine breast ultrasound examinations were conducted by an experienced radiologist. The ultrasonic images of the lesions were retrospectively assessed by five residents-in-training according to the Breast Imaging Report and Data System (BI-RADS) lexicon, and a dichotomic classification of the lesions was provided by S-Detect. The diagnostic performances of S-Detect and the five residents were measured and compared using the pathological results as the gold standard. The category 4a lesions assessed by the residents were downgraded to possibly benign as classified by S-Detect. The diagnostic performance of the integrated results was compared with the original results of the residents.

PARTICIPANTS

A total of 195 focal breast lesions were consecutively enrolled, including 82 malignant lesions and 113 benign lesions.

RESULTS

S-Detect presented higher specificity (77.88%) and area under the curve (AUC) (0.82) than the residents (specificity: 19.47%-48.67%, AUC: 0.62-0.74). A total of 24, 31, 38, 32 and 42 identified as BI-RADS 4a lesions by residents 1, 2, 3, 4 and 5 were downgraded to possibly benign lesions by S-Detect, respectively. Among these downgraded lesions, 24, 28, 35, 30 and 40 lesions were proven to be pathologically benign, respectively. After combining the residents' results with the results of the software in category 4a lesions, the specificity and AUC of the five residents significantly improved (specificity: 46.02%-76.11%, AUC: 0.71-0.85, p<0.001). The intraclass correlation coefficient of the five residents also increased after integration (from 0.480 to 0.643).

CONCLUSIONS

With the help of the DL software, the specificity, overall diagnostic performance and interobserver agreement of the residents greatly improved. The software can be used as adjunctive tool for residents-in-training, downgrading 4a lesions to possibly benign and reducing unnecessary biopsies.

S-Detect characterization of focal solid breast lesions: a prospective analysis of inter-reader agreement for US BI-RADS descriptors

BACKGROUND

To assess inter-reader agreement for US BI-RADS descriptors using S-Detect: a computer-guided decision-making software assisting in US morphologic analysis.

METHODS

73 solid focal breast lesions (FBLs) (mean size: 15.9 mm) in 73 consecutive women (mean age: 51 years) detected at US were randomly and independently assessed according to the BI-RADS US lexicon, without and with S-Detect, by five independent reviewers. US-guided core-biopsy and 24-month follow-up were considered as standard of reference. Kappa statistics were calculated to assess inter-operator agreement, between the baseline and after S-Detect evaluation. Agreement was graded as poor (≤ 0.20), moderate (0.21-0.40), fair (0.41-0.60), good (0.61-0.80), or very good (0.81-1.00).

RESULTS

33/73 (45.2%) FBLs were malignant and 40/73 (54.8%) FBLs were benign. A statistically significant improvement of inter-reader agreement from fair to good with the use of S-Detect was observed for shape (from 0.421 to 0.612) and orientation (from 0.417 to 0.7) (p < 0.0001) and from moderate to fair for margin (from 0.204 to 0.482) and posterior features (from 0.286 to 0.522) (p < 0.0001). At baseline analysis isoechoic (0.0485) and heterogeneous (0.1978) echo pattern, microlobulated (0.1161) angular (0.1204) and spiculated (0.1692) margins and combined pattern (0.1549) for posterior features showed the worst agreement rate (poor). After S-Detect evaluation, all variables but isoechoic pattern showed an agreement class upgrade with a statistically significant improvement of inter-reader agreement (p < 0.0001).

CONCLUSIONS

S-Detect significantly improved inter-reader agreement in the assessment of FBLs according to the BI-RADS US lexicon but evaluation of margin and echo pattern needs to be further improved, particularly isoechoic pattern.

Evaluation of the Quadri-Planes Method in Computer-Aided Diagnosis of Breast Lesions by Ultrasonography: Prospective Single-Center Study

Background

Computer-aided diagnosis (CAD) is a tool that can help radiologists diagnose breast lesions by ultrasonography. Previous studies have demonstrated that CAD can help reduce the incidence of missed diagnoses by radiologists. However, the optimal method to apply CAD to breast lesions using diagnostic planes has not been assessed.

Objective

The aim of this study was to compare the performance of radiologists with different levels of experience when using CAD with the quadri-planes method to detect breast tumors.

Methods

From November 2018 to October 2019, we enrolled patients in the study who had a breast mass as their most prominent symptom. We assigned 2 ultrasound radiologists (with 1 and 5 years of experience, respectively) to read breast ultrasonography images without CAD and then to perform a second reading while applying CAD with the quadri-planes method. We then compared the diagnostic performance of the readers for the 2 readings (without and with CAD). The McNemar test for paired data was used for statistical analysis.

Results

A total of 331 patients were included in this study (mean age 43.88 years, range 17-70, SD 12.10), including 512 lesions (mean diameter 1.85 centimeters, SD 1.19; range 0.26-9.5); 200/512 (39.1%) were malignant, and 312/512 (60.9%) were benign. For CAD, the area under the receiver operating characteristic curve (AUC) improved significantly from 0.76 (95% CI 0.71-0.79) with the cross-planes method to 0.84 (95% CI 0.80-0.88; P<.001) with the quadri-planes method. For the novice reader, the AUC significantly improved from 0.73 (95% CI 0.69-0.78) for the without-CAD mode to 0.83 (95% CI 0.80-0.87; P<.001) for the combined-CAD mode with the quadri-planes method. For the experienced reader, the AUC improved from 0.85 (95% CI 0.81-0.88) to 0.87 (95% CI 0.84-0.91; P=.15). The kappa indicating consistency between the experienced reader and the novice reader for the combined-CAD mode was 0.63. For the novice reader, the sensitivity significantly improved from 60.0% for the without-CAD mode to 79.0% for the combined-CAD mode (P=.004). The specificity, negative predictive value, positive predictive value, and accuracy improved from 84.9% to 87.8% (P=.53), 76.8% to 86.7% (P=.07), 71.9% to 80.6% (P=.13), and 75.2% to 84.4% (P=.12), respectively. For the experienced reader, the sensitivity improved significantly from 76.0% for the without-CAD mode to 87.0% for the combined-CAD mode (P=.045). The NPV and accuracy moderately improved from 85.8% and 86.3% to 91.0% (P=.27) and 87.0% (P=.84), respectively. The specificity and positive predictive value decreased from 87.4% to 81.3% (P=.25) and from 87.2% to 93.0% (P=.16), respectively.

Conclusions

S-Detect is a feasible diagnostic tool that can improve the sensitivity, accuracy, and AUC of the quadri-planes method for both novice and experienced readers while also improving the specificity for the novice reader. It demonstrates important application value in the clinical diagnosis of breast cancer.

Trial Registration

ChiCTR.org.cn 1800019649; http://www.chictr.org.cn/showproj.aspx?proj=33094

Performance of Computer-Aided Diagnosis in Ultrasonography for Detection of Breast Lesions Less and More Than 2 cm: Prospective Comparative Study

Background

Computer-aided diagnosis (CAD) is used as an aid tool by radiologists on breast lesion diagnosis in ultrasonography. Previous studies demonstrated that CAD can improve the diagnosis performance of radiologists. However, the optimal use of CAD on breast lesions according to size (below or above 2 cm) has not been assessed.

Objective

The aim of this study was to compare the performance of different radiologists using CAD to detect breast tumors less and more than 2 cm in size.

Methods

We prospectively enrolled 261 consecutive patients (mean age 43 years; age range 17-70 years), including 398 lesions (148 lesions>2 cm, 79 malignant and 69 benign; 250 lesions≤2 cm, 71 malignant and 179 benign) with breast mass as the prominent symptom. One novice radiologist with 1 year of ultrasonography experience and one experienced radiologist with 5 years of ultrasonography experience were each assigned to read the ultrasonography images without CAD, and then again at a second reading while applying the CAD S-Detect. We then compared the diagnostic performance of the readers in the two readings (without and combined with CAD) with breast imaging. The McNemar test for paired data was used for statistical analysis.

Results

For the novice reader, the area under the receiver operating characteristic curve (AUC) improved from 0.74 (95% CI 0.67-0.82) from the without-CAD mode to 0.88 (95% CI 0.83-0.93; P<.001) at the combined-CAD mode in lesions≤2 cm. For the experienced reader, the AUC improved from 0.84 (95% CI 0.77-0.90) to 0.90 (95% CI 0.86-0.94; P=.002). In lesions>2 cm, the AUC moderately decreased from 0.81 to 0.80 (novice reader) and from 0.90 to 0.82 (experienced reader). The sensitivity of the novice and experienced reader in lesions≤2 cm improved from 61.97% and 73.23% at the without-CAD mode to 90.14% and 97.18% (both P<.001) at the combined-CAD mode, respectively.

Conclusions

S-Detect is a feasible diagnostic tool that can improve the sensitivity for both novice and experienced readers, while also improving the negative predictive value and AUC for lesions≤2 cm, demonstrating important application value in the clinical diagnosis of breast cancer.

Trial Registration

Chinese Clinical Trial Registry ChiCTR1800019649; http://www.chictr.org.cn/showprojen.aspx?proj=33094

A combined ultrasonic B-mode and color Doppler system for the classification of breast masses using neural network

OBJECTIVES

To develop a dual-modal neural network model to characterize ultrasound (US) images of breast masses.

MATERIALS AND METHODS

A combined US B-mode and color Doppler neural network model was developed to classify US images of the breast. Three datasets with breast masses were originally detected and interpreted by 20 experienced radiologists according to Breast Imaging-Reporting and Data System (BI-RADS) lexicon ((1) training set, 103212 masses from 45,433 + 12,519 patients. (2) held-out validation set, 2748 masses from 1197 + 395 patients. (3) test set, 605 masses from 337 + 78 patients). The neural network was first trained on training set. Then, the trained model was tested on a held-out validation set to evaluate agreement on BI-RADS category between the model and the radiologists. In addition, the model and a reader study of 10 radiologists were applied to the test set with biopsy-proven results. To evaluate the performance of the model in benign or malignant classifications, the receiver operating characteristic curve, sensitivities, and specificities were compared.

RESULTS

The trained dual-modal model showed favorable agreement with the assessment performed by the radiologists (κ = 0.73; 95% confidence interval, 0.71-0.75) in classifying breast masses into four BI-RADS categories in the validation set. For the binary categorization of benign or malignant breast masses in the test set, the dual-modal model achieved the area under the ROC curve (AUC) of 0.982, while the readers scored an AUC of 0.948 in terms of the ROC convex hull.

CONCLUSION

The dual-modal model can be used to assess breast masses at a level comparable to that of an experienced radiologist.

KEY POINTS

• A neural network model based on ultrasonic imaging can classify breast masses into different Breast Imaging-Reporting and Data System categories according to the probability of malignancy. • A combined ultrasonic B-mode and color Doppler neural network model achieved a high level of agreement with the readings of an experienced radiologist and has the potential to automate the routine characterization of breast masses.

Screening Breast Ultrasound Using Handheld or Automated Technique in Women with Dense Breasts

In women with dense breasts (heterogeneously or extremely dense), adding screening ultrasound to mammography increases detection of node-negative invasive breast cancer. Similar incremental cancer detection rates averaging 2.1-2.7 per 1000 have been observed for physician- and technologist-performed handheld ultrasound (HHUS) and automated ultrasound (AUS). Adding screening ultrasound (US) for women with dense breasts significantly reduces interval cancer rates. Training is critical before interpreting examinations for both modalities, and a learning curve to achieve optimal performance has been observed. On average, about 3% of women will be recommended for biopsy on the prevalence round because of screening US, with a wide range of 2%-30% malignancy rates for suspicious findings seen only on US. Breast Imaging Reporting and Data System 3 lesions identified only on screening HHUS can be safely followed at 1 year rather than 6 months. Computer-aided detection and diagnosis software can augment performance of AUS and HHUS; ongoing research on machine learning and deep learning algorithms will likely improve outcomes and workflow with screening US.

Computer-Aided Diagnosis of Solid Breast Lesions With Ultrasound: Factors Associated With False-negative and False-positive Results

OBJECTIVES

To investigate factors that may lead to false-positive or false-negative results in a computer-aided diagnostic system (S-Detect; Samsung Medison Co, Ltd, Seoul, Korea) for ultrasound (US) examinations of solid breast lesions.

METHODS

This prospective study was approved by the Institutional Review Board of Sun Yat-sen Memorial Hospital. All patients signed and provided written informed consent before biopsy or surgery. From September 2017 to May 2018, 269 consecutive women with 338 solid breast lesions were included. All lesions were examined with US and S-Detect before biopsy or surgical excision. The final US assessments made by radiologists and S-Detect were matched to the pathologic results. Patient and lesion factors in the "true" and "false" S-Detect groups were compared, and multivariate logistic regression analyses were used to identify the factors associated with false S-Detect results.

RESULTS

The mean age of the patients ± SD was 42.6 ± 12.9 years (range, 18-77 years). Of the 338 lesions, 209 (61.8%) were benign, and 129 (38.2%) were malignant. Larger lesions, the presence of lesion calcifications detected by B-mode US, and grades of 2 and 3 according to Adler et al (Ultrasound Med Biol 1990; 16:553-559) were significantly associated with false-positive S-Detect results (odds ratio [OR], 1.071; P = .006; OR, 5.851; P = .001; OR, 1.726; P = .009, respectively). Smaller lesions and the absence of calcifications detected by B-mode US in malignant solid breast lesions were significantly associated with false-negative S-Detect results (OR, 1.141; P = .015; OR, 7.434; P = .016).

CONCLUSIONS

Larger benign lesions, the presence of lesion calcifications, and high degrees of vascularity are likely to show false-positive S-Detect results. Smaller malignant lesions and the absence of calcifications are likely to show false-negative S-Detect results.

Novel insights into breast cancer progression and metastasis: A multidisciplinary opportunity to transition from biology to clinical oncology

According to the most recent epidemiological studies, breast cancer shows the highest incidence and the second leading cause of death in women. Cancer progression and metastasis are the main events related to poor survival of breast cancer patients. This can be explained by the presence of highly resistant to chemo- and radiotherapy stem cells in many breast tumor tissues. In this context, numerous studies highlighted the possible involvement of epithelial to mesenchymal transition phenomenon as biological program to generate cancer stem cells, and thus participate to both metastatic and drug resistance process. Therefore, the comprehension of mechanisms (both cellular and molecular) involved in breast cancer occurrence and progression can lay the foundation for the development of new diagnostic and therapeutical protocols. In this review, we reported the most important findings in the field of breast cancer highlighting the most recent data concerning breast tumor biology, diagnosis and therapy.

[Artificial intelligence in medicine-the wrong track or promise of cure?]

Artificial intelligence (AI) has attained a new level of maturity in recent years and is developing into the driver of digitalization in all areas of life. AI is a cross-sectional technology with great importance for all branches of medicine employing imaging as well as text and biodata. There is no field of medicine that remains unaffected by AI, with AI-assisted clinical decision-making assuming a particularly important role. AI methods are becoming established in medial workflow management and for prediction of therapeutic success or treatment outcome. AI systems are already able to lend support to imaging-based diagnosis and patient management, but cannot suggest critical decisions. The corresponding preventive or therapeutic measures can be more rationally assessed with the help of AI, although the number of diseases covered is currently far too low for the creation of robust systems for clinical routine. Prerequisite for the comprehensive use of AI systems is appropriate training to enable physicians to decide when computer-assisted decision-making can be relied upon.

Performance and Reading Time of Automated Breast US with or without Computer-aided Detection

BackgroundComputer-aided detection (CAD) systems may be used to help radiologists interpret automated breast (AB) US images. However, the optimal use of CAD with AB US has, to the knowledge of the authors, not been determined.PurposeTo compare the performance and reading time of different readers by using AB US CAD system to detect breast cancer in different reading modes.Materials and MethodsIn this retrospective study, 1485 AB US images (282 with malignant lesions, 695 with benign lesions, and 508 healthy) in 1452 women (mean age, 43.7 years; age range, 19-82 years) including 529 (36.4%) women who were asymptomatic were collected between 2016 and 2017. A CAD system was used to interpret the images. Three novice readers with 1-3 years of US experience and three experienced readers with 5-10 years of US experience were assigned to read AB US images without CAD, at a second reading (after the reader completed a full unaided interpretation), and at concurrent reading (use of CAD at the start of the assessment). Diagnostic performances and reading times were compared by using analysis of variance.ResultsFor all readers, the mean area under the receiver operating characteristic curve improved from 0.88 (95% confidence interval [CI]: 0.85, 0.91) at without-CAD mode to 0.91 (95% CI: 0.89, 0.92; P < .001) at the second-reading mode and 0.90 (95% CI: 0.89, 0.92; P = .002) at the concurrent-reading mode. The mean sensitivity of novice readers in women who were asymptomatic improved from 67% (95% CI: 63%, 74%) at without-CAD mode to 88% (95% CI: 84%, 89%) at both the second-reading mode and the concurrent-reading mode (P = .003). Compared with the without-CAD and second-reading modes, the mean reading time per volume of concurrent reading was 16 seconds (95% CI: 11, 22; P < .001) and 27 seconds (95% CI: 21, 32; P < .001) shorter, respectively.ConclusionComputer-aided detection (CAD) was helpful for novice readers to improve cancer detection at automated breast US in women who were asymptomatic. CAD was more efficient when used concurrently for all readers.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by Slanetz in this issue.

Feasibility of computer-assisted diagnosis for breast ultrasound: the results of the diagnostic performance of S-detect from a single center in China

Objective

To investigate the feasibility of a CAD system S-detect on a database from a single Chinese medical center.

Materials and methods

An experienced radiologist performed breast US examinations and made assessments of 266 consecutive breast lesions in 227 patients. S-detect classified the lesions automatically in a dichotomous form. An in-training resident who was blind to both the US diagnostic results and histological results reviewed the images afterward. The final histological results were considered as the diagnostic gold standard. The diagnostic performances and interrater agreements were analyzed.

Results

A total of 266 focal breast lesions (161 benign lesions and 105 malignant lesions) were assessed in this study. S-detect had a lower sensitivity (87.07%) and a higher specificity (72.27%) compared with the experienced radiologist (sensitivity 98.1% and specificity 65.43%). The sensitivity and specificity of S-detect were better than that of the resident (sensitivity 82.86% and specificity 68.94%). The AUC value of S-detect (0.807) showed no significant difference with the experienced radiologist (0.817) and was higher than that of the resident (0.758). S-detect had moderate agreement with the experienced radiologist.

Conclusion

In this single-center study, a high level of diagnostic performance of S-detect on 266 breast lesions of Chinese women was observed. S-detect had almost equal diagnostic capacity with an experienced radiologist and performed better than a novice reader. S-detect was also distinguished for its high specificity. These results supported the feasibility of S-detect in aiding the diagnosis of breast lesions on an independent database.