An Interactive System for Computer-Aided Diagnosis of Breast Masses

Although mammography is the only clinically accepted imaging modality for screening the general population to detect breast cancer, interpreting mammograms is difficult with lower sensitivity and specificity. To provide radiologists “a visual aid” in interpreting mammograms, we developed and tested an interactive system for computer-aided detection and diagnosis (CAD) of mass-like cancers. Using this system, an observer can view CAD-cued mass regions depicted on one image and then query any suspicious regions (either cued or not cued by CAD). CAD scheme automatically segments the suspicious region or accepts manually defined region and computes a set of image features. Using content-based image retrieval (CBIR) algorithm, CAD searches for a set of reference images depicting “abnormalities” similar to the queried region. Based on image retrieval results and a decision algorithm, a classification score is assigned to the queried region. In this study, a reference database with 1,800 malignant mass regions and 1,800 benign and CAD-generated false-positive regions was used. A modified CBIR algorithm with a new function of stretching the attributes in the multi-dimensional space and decision scheme was optimized using a genetic algorithm. Using a leave-one-out testing method to classify suspicious mass regions, we compared the classification performance using two CBIR algorithms with either equally weighted or optimally stretched attributes. Using the modified CBIR algorithm, the area under receiver operating characteristic curve was significantly increased from 0.865 ± 0.006 to 0.897 ± 0.005 (p < 0.001). This study demonstrated the feasibility of developing an interactive CAD system with a large reference database and achieving improved performance.     

Computer-Aided Diagnosis of Breast DCE-MRI Images Using Bilateral Asymmetry of Contrast Enhancement Between Two Breasts

Dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) of breasts is an important imaging modality in breast cancer diagnosis with higher sensitivity but relatively lower specificity. The objective of this study is to investigate a new approach to help improve diagnostic performance of DCE-MRI examinations based on the automated detection and analysis of bilateral asymmetry of characteristic kinetic features between the left and right breast. An image dataset involving 130 DCE-MRI examinations was assembled and used in which 80 were biopsy-proved malignant and 50 were benign. A computer-aided diagnosis (CAD) scheme was developed to segment breast areas depicted on each MR image, register images acquired from the sequential MR image scan series, compute average contrast enhancement of all pixels in one breast, and a set of kinetic features related to the difference of contrast enhancement between the left and right breast, and then use a multi-feature based Bayesian belief network to classify between malignant and benign cases. A leave-one-case-out validation method was applied to test CAD performance. The computed area under a receiver operating characteristic (ROC) curve is 0.78 ± 0.04. The positive and negative predictive values are 0.77 and 0.64, respectively. The study indicates that bilateral asymmetry of kinetic features between the left and right breasts is a potentially useful image biomarker to enhance the detection of angiogenesis associated with malignancy. It also demonstrates the feasibility of applying a simple CAD approach to classify between malignant and benign DCE-MRI examinations based on this new image biomarker.     

Semi-Automatic Region-of-Interest Segmentation Based Computer-Aided Diagnosis of Mass Lesions from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Breast Cancer Screening

Cancer screening with magnetic resonance imaging (MRI) is currently recommended for very high risk women. The high variability in the diagnostic accuracy of radiologists analyzing screening MRI examinations of the breast is due, at least in part, to the large amounts of data acquired. This has motivated substantial research towards the development of computer-aided diagnosis (CAD) systems for breast MRI which can assist in the diagnostic process by acting as a second reader of the examinations. This retrospective study was performed on 184 benign and 49 malignant lesions detected in a prospective MRI screening study of high risk women at Sunnybrook Health Sciences Centre. A method for performing semi-automatic lesion segmentation based on a supervised learning formulation was compared with the enhancement threshold based segmentation method in the context of a computer-aided diagnostic system. The results demonstrate that the proposed method can assist in providing increased separation between malignant and radiologically suspicious benign lesions. Separation between malignant and benign lesions based on margin measures improved from a receiver operating characteristic (ROC) curve area of 0.63 to 0.73 when the proposed segmentation method was compared with the enhancement threshold, representing a statistically significant improvement. Separation between malignant and benign lesions based on dynamic measures improved from a ROC curve area of 0.75 to 0.79 when the proposed segmentation method was compared to the enhancement threshold, also representing a statistically significant improvement. The proposed method has potential as a component of a computer-aided diagnostic system.     

Diagnosis of Solid Breast Tumors Using Vessel Analysis in Three-Dimensional Power Doppler Ultrasound Images

This study aims to evaluate whether the distribution of vessels inside and adjacent to tumor region at three-dimensional (3-D) power Doppler ultrasonography (US) can be used for the differentiation of benign and malignant breast tumors. 3-D power Doppler US images of 113 solid breast masses (60 benign and 53 malignant) were used in this study. Blood vessels within and adjacent to tumor were estimated individually in 3-D power Doppler US images for differential diagnosis. Six features including volume of vessels, vascularity index, volume of tumor, vascularity index in tumor, vascularity index in normal tissue, and vascularity index in surrounding region of tumor within 2 cm were evaluated. Neural network was then used to classify tumors by using these vascular features. The receiver operating characteristic (ROC) curve analysis and Student’s t test were used to estimate the performance. All the six proposed vascular features are statistically significant (p < 0.001) for classifying the breast tumors as benign or malignant. The A_Z (area under ROC curve) values for the classification result were 0.9138. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the diagnosis performance based on all six proposed features were 82.30 (93/113), 86.79 (46/53), 78.33 (47/60), 77.97 (46/59), and 87.04 % (47/54), respectively. The p value of A_Z values between the proposed method and conventional vascularity index method using z test was 0.04.     

Impact of a Computer-Aided Detection (CAD) System Integrated into a Picture Archiving and Communication System (PACS) on Reader Sensitivity and Efficiency for the Detection of Lung Nodules in Thoracic CT Exams

The objective of this study is to assess the impact on nodule detection and efficiency using a computer-aided detection (CAD) device seamlessly integrated into a commercially available picture archiving and communication system (PACS). Forty-eight consecutive low-dose thoracic computed tomography studies were retrospectively included from an ongoing multi-institutional screening study. CAD results were sent to PACS as a separate image series for each study. Five fellowship-trained thoracic radiologists interpreted each case first on contiguous 5 mm sections, then evaluated the CAD output series (with CAD marks on corresponding axial sections). The standard of reference was based on three-reader agreement with expert adjudication. The time to interpret CAD marking was automatically recorded. A total of 134 true-positive nodules, measuring 3 mm and larger were included in our study; with 85 ≥ 4 and 50 ≥ 5 mm in size. Readers detection improved significantly in each size category when using CAD, respectively, from 44 to 57 % for ≥3 mm, 48 to 61 % for ≥4 mm, and 44 to 60 % for ≥5 mm. CAD stand-alone sensitivity was 65, 68, and 66 % for nodules ≥3, ≥4, and ≥5 mm, respectively, with CAD significantly increasing the false positives for two readers only. The average time to interpret and annotate a CAD mark was 15.1 s, after localizing it in the original image series. The integration of CAD into PACS increases reader sensitivity with minimal impact on interpretation time and supports such implementation into daily clinical practice.     

Mammographic Image Denoising and Enhancement Using the Anscombe Transformation, Adaptive Wiener Filtering, and the Modulation Transfer Function

A new restoration methodology is proposed to enhance mammographic images through the improvement of contrast features and the simultaneous suppression of noise. Denoising is performed in the first step using the Anscombe transformation to convert the signal-dependent quantum noise into an approximately signal-independent Gaussian additive noise. In the Anscombe domain, noise is filtered through an adaptive Wiener filter, whose parameters are obtained by considering local image statistics. In the second step, a filter based on the modulation transfer function of the imaging system in the whole radiation field is applied for image enhancement. This methodology can be used as a preprocessing module for computer-aided detection (CAD) systems to improve the performance of breast cancer screening. A preliminary assessment of the restoration algorithm was performed using synthetic images with different levels of quantum noise. Afterward, we evaluated the effect of the preprocessing on the performance of a previously developed CAD system for clustered microcalcification detection in mammographic images. The results from the synthetic images showed an increase of up to 11.5 dB (p = 0.002) in the peak signal-to-noise ratio. Moreover, the mean structural similarity index increased up to 8.3 % (p < 0.001). Regarding CAD performance, the results suggested that the preprocessing increased the detectability of microcalcifications in mammographic images without increasing the false-positive rates. Receiver operating characteristic analysis revealed an average increase of 14.1 % (p = 0.01) in overall CAD performance when restored image sets were used.     

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

A rapid and highly accurate diagnostic tool for distinguishing benign tumors from malignant ones is required owing to the high incidence of breast cancer. Although various computer-aided diagnosis (CAD) systems have been developed to interpret ultrasound images of breast tumors, feature selection and the setting of parameters are still essential to classification accuracy and the minimization of computational complexity. This work develops a highly accurate CAD system that is based on a support vector machine (SVM) and the artificial immune system (AIS) algorithm for evaluating breast tumors. Experiments demonstrate that the accuracy of the proposed CAD system for classifying breast tumors is 96.67 %. The sensitivity, specificity, PPV, and NPV of the proposed CAD system are 96.67, 96.67, 95.60, and 97.48 %, respectively. The receiver operator characteristic (ROC) area index A_z is 0.9827. Hence, the proposed CAD system can reduce the number of biopsies and yield useful results that assist physicians in diagnosing breast tumors.     

An Investigation into the Consistency in Mammographic Density Identification by Radiologists: Effect of Radiologist Expertise and Mammographic Appearance

The aim of this work is to investigate how radiologist expertise and image appearance may have an impact on inter-reader variability of mammographic density (MD) identification. Seventeen radiologists, divided into three expertise groups, were asked to manually segment the areas they consider to be MD in 40 clinical images. The variation in identification of MD for each image was quantified by finding the range of segmentation areas. The impact of radiologist expertise and image appearance on this variation was explored. The range of areas chosen by participating radiologists varied from 7 to 73 % across the 40 images, with a mean range of 35 ± 13 %. Participants with high expertise were more likely to choose similar areas to one another, compared to participants with medium and low expertise levels (mean range were 19 ± 10 %, 29 ± 13 % and 25 ± 14 %, respectively, p < 0.0001). There was a significantly higher average grey level for the area segmented by all radiologists as MD compared to the area of variation, with mean grey level value for 8-bit images being 146 ± 19 vs. 99 ± 14, respectively. MD segmentation borders were consistent in areas where there was a sharp intensity change within a short distance. In conclusion, radiologists with high expertise tend to have a higher agreement when identifying MD. Tissues which have a lower contrast and a less visually sharp gradient change at the interface between high density tissue and adipose background lead to inter-reader variation in choosing mammographic density.     

Automated 3D Ιnterstitial Lung Disease Εxtent Quantification: Performance Evaluation and Correlation to PFTs

In this study, the performance of a recently proposed computer-aided diagnosis (CAD) scheme in detection and 3D quantification of reticular and ground glass pattern extent in chest computed tomography of interstitial lung disease (ILD) patients is evaluated. CAD scheme performance was evaluated on a dataset of 37 volumetric chest scans, considering five representative axial anatomical levels per scan. CAD scheme reliability analysis was performed by estimating agreement (intraclass correlation coefficient, ICC) of automatically derived ILD pattern extent to semi-quantitative disease extent assessment in terms of 29-point rating scale provided by two expert radiologists. Receiver operating characteristic (ROC) analysis was employed to assess CAD scheme accuracy in ILD pattern detection in terms of area under ROC curve (A_z). Correlation of reticular and ground glass volumetric pattern extent to pulmonary function tests (PFTs) was also investigated. CAD scheme reliability was substantial for ILD extent (ICC = 0.809) and distinct reticular pattern extent (0.806) and moderate for distinct ground glass pattern extent (0.543), performing within inter-observer agreement. CAD scheme demonstrated high accuracy in detecting total ILD (A_z = 0.950 ± 0.018), while accuracy in detecting distinct reticular and ground glass patterns was 0.920 ± 0.023 and 0.883 ± 0.024, respectively. Moderate and statistically significant negative correlation was found between reticular volumetric pattern extent and diffusing capacity, forced expiratory volume in 1 s, forced vital capacity, and total lung capacity (R = −0.581, −0.513, −0.494, and −0.446, respectively), similar to correlations found between radiologists’ semi-quantitative ratings with PFTs. CAD-based quantification of disease extent is in agreement with radiologists’ semi-quantitative assessment and correlates to specific PFTs, suggesting a potential imaging biomarker for ILD staging and management.     

Enhanced CT Images by the Wavelet Transform Improving Diagnostic Accuracy of Chest Nodules

The objective of this study was to compare the diagnostic accuracy in the interpretation of chest nodules using original CT images versus enhanced CT images based on the wavelet transform. The CT images of 118 patients with cancers and 60 with benign nodules were used in this study. All images were enhanced through an algorithm based on the wavelet transform. Two experienced radiologists interpreted all the images in two reading sessions. The reading sessions were separated by a minimum of 1 month in order to minimize the effect of observer’s recall. The Mann–Whitney U nonparametric test was used to analyze the interpretation results between original and enhanced images. The Kruskal–Wallis H nonparametric test of K independent samples was used to investigate the related factors which could affect the diagnostic accuracy of observers. The area under the ROC curves for the original and enhanced images was 0.681 and 0.736, respectively. There is significant difference in diagnosing the malignant nodules between the original and enhanced images (z = 7.122, P < 0.001), whereas there is no significant difference in diagnosing the benign nodules (z = 0.894, P = 0.371). The results showed that there is significant difference between original and enhancement images when the size of nodules was larger than 2 cm (Z = −2.509, P = 0.012, indicating the size of the nodules is a critical evaluating factor of the diagnostic accuracy of observers). This study indicated that the image enhancement based on wavelet transform could improve the diagnostic accuracy of radiologists for the malignant chest nodules.Key words: Wavelet transform, chest nodules, enhanced CT     

MRI-Based Breast Volumetry—Evaluation of Three Different Software Solutions

As lipofilling of the female breast is becoming more popular in plastic surgery, the use of MRI to assess breast volume has been employed to control postoperative results. Therefore, we sought to evaluate the accuracy of magnetic resonance imaging (MRI)-based breast volumetry software tools by comparing the measurements of silicone implant augmented breasts with the actual implant volume specified by the manufacturer. MRI-based volume analysis was performed in eight bilaterally augmented patients (46 ± 9 years) with three different software programs (Brainlab© I plan 2.6 neuronavigation software; mass analysis, version 5.3, Medis©; and OsiriX© v.3.0.2. 32-bit). The implant volumes analysed by the BrainLab© software had a mean deviation of 2.2 ± 1.7% (r = 0.99) relative to the implanted prosthesis. OsiriX© software analysis resulted in a mean deviation of 2.8 ± 3.0% (r = 0.99) and the Medis© software had a mean deviation of 3.1 ± 3.0% (r = 0.99). Overall, the volumes of all analysed breast implants correlated very well with the real implant volumes. Processing time was 10 min per breast with each system and 30 s (OsiriX©) to 5 min (BrainLab© and Medis©) per silicone implant. MRI-based volumetry is a powerful tool to calculate both native breast and silicone implant volume in situ. All software solutions performed well and the measurements were close to the actual implant sizes. The use of MRI breast volumetry may be helpful in: (1) planning reconstructive and aesthetic surgery of asymmetric breasts, (2) calculating implant size in patients with missing documentation of a previously implanted device and (3) assessing post-operative results objectively.Key words: MRI, volumetry, mamma, breast, lipofilling, silicone implant, BrainLab, OsiriX, Medis     

Observer Performance Using Virtual Pathology Slides: Impact of LCD Color Reproduction Accuracy

The use of color LCDs in medical imaging is growing as more clinical specialties use digital images as a resource in diagnosis and treatment decisions. Telemedicine applications such as telepathology, teledermatology, and teleophthalmology rely heavily on color images. However, standard methods for calibrating, characterizing, and profiling color displays do not exist, resulting in inconsistent presentation. To address this, we developed a calibration, characterization, and profiling protocol for color-critical medical imaging applications. Physical characterization of displays calibrated with and without the protocol revealed high color reproduction accuracy with the protocol. The present study assessed the impact of this protocol on observer performance. A set of 250 breast biopsy virtual slide regions of interest (half malignant, half benign) were shown to six pathologists, once using the calibration protocol and once using the same display in its “native” off-the-shelf uncalibrated state. Diagnostic accuracy and time to render a decision were measured. In terms of ROC performance, Az (area under the curve) calibrated = 0.8570 and Az uncalibrated = 0.8488. No statistically significant difference (p = 0.4112) was observed. In terms of interpretation speed, mean calibrated = 4.895 s; mean uncalibrated = 6.304 s which is statistically significant (p = 0.0460). Early results suggest a slight advantage diagnostically for a properly calibrated and color-managed display and a significant potential advantage in terms of improved workflow. Future work should be conducted using different types of color images that may be more dependent on accurate color rendering and a wider range of LCDs with varying characteristics.     

Classification of Hypervascular Liver Lesions Based on Hepatic Artery and Portal Vein Blood Supply Coefficients Calculated from Triphasic CT Scans

Perfusion CT of the liver typically involves scanning the liver at least 20 times, resulting in a large radiation dose. We developed and validated a simplified model of tumor blood supply that can be applied to standard triphasic scans and evaluated whether this can be used to distinguish benign and malignant liver lesions. Triphasic CTs of 46 malignant and 32 benign liver lesions were analyzed. For each phase, regions of interest were drawn in the arterially enhancing portion of each lesion, as well as the background liver, aorta, and portal vein. Hepatic artery and portal vein blood supply coefficients for each lesion were then calculated by expressing the enhancement curve of the lesion as a linear combination of the enhancement curves of the aorta and portal vein. Hepatocellular carcinoma (HCC) and hypervascular metastases, on average, both had increased hepatic artery coefficients compared to the background liver. Compared to HCC, benign lesions, on average, had either a greater hepatic artery coefficient (hemangioma) or a greater portal vein coefficient (focal nodular hyperplasia or transient hepatic attenuation difference). Hypervascularity with washout is a key diagnostic criterion for HCC, but it had a sensitivity of 72 % and specificity of 81 % for diagnosing malignancy in our diverse set of liver lesions. The sensitivity for malignancy was increased to 89 % by including enhancing lesions that were hypodense on all phases. The specificity for malignancy was increased to 97 % (p = 0.039) by also examining hepatic artery and portal vein blood supply coefficients, while maintaining a sensitivity of 76 %.     

Effect of Dose Reduction on the Ability of Digital Mammography to Detect Simulated Microcalcifications

The purpose of this article was to report the relationship between radiation dose and the ability of sentence digital mammography to detect microcalcifications. All images were acquired by computed radiography and an anthropomorphic breast phantom. The tube voltage and anode/filter combination used were 28 kVp and Mo/Mo. Simulated microcalcifications with an approximate diameter of 250–350 μm were positioned on the phantom. Groups of six microcalcifications were arranged in one of two patterns, a line cluster 1 cm long or a hexagonal cluster 4 mm wide. One of the six microcalcifications was removed to create a negative control. Each cluster was placed on 25 different points. Four levels of milliampere-second (mAs) values were applied: 100%, 50%, 25%, and 12.5%. Five staff radiologists participated in an observer performance test. All observers used a workstation with a 3-megapixel monochrome LCD monitor. The areas under the receiver-operating characteristics curves (AUC) were used to compare diagnostic performance among the four doses. The overall AUC scores were 0.97 with 100% mAs, 0.93 (n.s.) with 50%, 0.90 (p < 0.05) with 25%, and 0.81 (p < 0.01) with 12.5% mAs. Among the negative series, the percentage of images on which observers were able to identify the removed microcalcification point decreased from 88.8% with 100% mAs to 83.6% (n.s.) with 50%, 74.8% (p < 0.001) with 25%, and 67.2% (p < 0.001) with 12.5% mAs. A certain level of dose reduction in digital mammography may be an option.Key words: Digital mammography, computed radiography, observer performance, radiation dose, ROC-based analysis, phantoms, imaging     

乳腺钼靶摄影和增强磁共振在乳腺病变 诊断中的应用比较

目的 探讨乳腺钼靶摄影和增强磁共振（MRI）在乳腺病变诊断中的应用。方法 回顾性分析2015年9月~2017年5月我院收治的经乳腺钼靶摄影和增强MRI诊断,最终病理确诊的90例乳腺疾病患者,共104个病灶,对比两种诊断技术的诊断价值。结果 104个病灶中良性60例、恶性44例。MRI诊断灵敏度、阴性预测值、符合率高于钼靶,差异有统计学意义（P＜0.05）。MRI诊断灵敏度、特异度、阳性预测值、阴性预测值、符合率分别为88.64%、91.67%、88.64%、91.67%、90.38%。恶性病灶粗大不均、多形性、簇状、不均匀强化、增强Ⅲ型比重高于良性病灶,差异有统计学意义（P＜0.05）。按照OR比从高到底分别为增强Ⅲ型、多形性、不均匀强化、簇状、粗大不均。恶性病灶ADC低于良性病灶,差异有统计学意义（P＜0.05）。结论 MRI诊断乳腺病变的灵敏度、特异度、阳性预测值、阴性预测值、符合率分别高于钼靶,因此增强MRI诊断乳腺病变的效用更好。     

Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry

Breast cancer screening is central to early breast cancer detection. Identifying and monitoring process measures for screening is a focus of the National Cancer Institute’s Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) initiative, which requires participating centers to report structured data across the cancer screening continuum. We evaluate the accuracy of automated information extraction of imaging findings from radiology reports, which are available as unstructured text. We present prevalence estimates of imaging findings for breast imaging received by women who obtained care in a primary care network participating in PROSPR (n = 139,953 radiology reports) and compared automatically extracted data elements to a “gold standard” based on manual review for a validation sample of 941 randomly selected radiology reports, including mammograms, digital breast tomosynthesis, ultrasound, and magnetic resonance imaging (MRI). The prevalence of imaging findings vary by data element and modality (e.g., suspicious calcification noted in 2.6 % of screening mammograms, 12.1 % of diagnostic mammograms, and 9.4 % of tomosynthesis exams). In the validation sample, the accuracy of identifying imaging findings, including suspicious calcifications, masses, and architectural distortion (on mammogram and tomosynthesis); masses, cysts, non-mass enhancement, and enhancing foci (on MRI); and masses and cysts (on ultrasound), range from 0.8 to1.0 for recall, precision, and F-measure. Information extraction tools can be used for accurate documentation of imaging findings as structured data elements from text reports for a variety of breast imaging modalities. These data can be used to populate screening registries to help elucidate more effective breast cancer screening processes.     

Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG)

In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and threshold selection methodologies. Prior to that, some pre-processing methodologies are involved. Breast skin is detected and deleted using the integration of two algorithms, namely the level set active contour and morphological thinning. The system is applied and tested on 40 test images from the RIDER breast MRI dataset, the results are evaluated and presented in comparison to the ground truths of the dataset. The analysis of variance (ANOVA) test shows that there is a statistically significance in the performance compared to the previous segmentation approaches that have been tested on the same dataset where ANOVA p values for the evaluation measures’ results are less than 0.05, such as: relative overlap (p = 0.0002), misclassification rate (p = 0.045), true negative fraction (p = 0.0001) and sum of true volume fraction (p = 0.0001).     

Improving Radiology Report Quality by Rapidly Notifying Radiologist of Report Errors

Radiology report errors occur for many reasons including the use of pre-filled report templates, wrong-word substitution, nonsensical phrases, and missing words. Reports may also contain clinical errors that are not specific to the speech recognition including wrong laterality and gender-specific discrepancies. Our goal was to create a custom algorithm to detect potential gender and laterality mismatch errors and to notify the interpreting radiologists for rapid correction. A JavaScript algorithm was devised to flag gender and laterality mismatch errors by searching the text of the report for keywords and comparing them to parameters within the study’s HL7 metadata (i.e., procedure type, patient sex). The error detection algorithm was retrospectively applied to 82,353 reports 4 months prior to its development and then prospectively to 309,304 reports 15 months after implementation. Flagged reports were reviewed individually by two radiologists for a true gender or laterality error and to determine if the errors were ultimately corrected. There was significant improvement in the number of flagged reports (pre, 198/82,353 [0.24 %]; post, 628/309,304 [0.20 %]; P = 0.04) and reports containing confirmed gender or laterality errors (pre, 116/82,353 [0.014 %]; post, 285/309,304 [0.09 %]; P < 0.0001) after implementing our error notification system. The number of flagged reports containing an error that were ultimately corrected improved dramatically after implementing the notification system (pre, 17/116 [15 %]; post, 239/285 [84 %]; P < 0.0001). We developed a successful automated tool for detecting and notifying radiologists of potential gender and laterality errors, allowing for rapid report correction and reducing the overall rate of report errors.