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

Abstract 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.

Download Full-text

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

JMIR Medical Informatics ◽

10.2196/16334 ◽

2020 ◽

Vol 8 (3) ◽

pp. e16334 ◽

Cited By ~ 2

Author(s):

Liang Yongping ◽

Ping Zhou ◽

Zhang Juan ◽

Zhao Yongfeng ◽

Wengang Liu ◽

...

Keyword(s):

Breast Imaging ◽

Characteristic Curve ◽

Computer Aided Diagnosis ◽

Breast Lesions ◽

Paired Data ◽

Clinical Trial Registry ◽

Experienced Reader ◽

Experienced Radiologist ◽

Computer Aided ◽

Aided Diagnosis

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

Download Full-text

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

10.2196/preprints.16334 ◽

2019 ◽

Author(s):

Liang Yongping ◽

Ping Zhou ◽

Zhang Juan ◽

Zhao Yongfeng ◽

Wengang Liu ◽

...

Keyword(s):

Breast Imaging ◽

Characteristic Curve ◽

Computer Aided Diagnosis ◽

Breast Lesions ◽

Paired Data ◽

Clinical Trial Registry ◽

Experienced Reader ◽

Experienced Radiologist ◽

Computer Aided ◽

Aided Diagnosis

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

Download Full-text

2. CAD of Breast Imaging : an Expectation of a Radiologist(Symposium III Computer-aided Diagnosis : Benefits and Challenges in Diagnostic Radiology)(The 59th Annual Scientific Congress)

Japanese Journal of Radiological Technology ◽

10.6009/jjrt.kj00003174058 ◽

2003 ◽

Vol 59 (11) ◽

pp. 1343-1344

Author(s):

TOKIKO ENDO

Keyword(s):

Breast Imaging ◽

Computer Aided Diagnosis ◽

Diagnostic Radiology ◽

Annual Scientific ◽

Computer Aided ◽

Scientific Congress ◽

Aided Diagnosis

Download Full-text

A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

BioMed Research International ◽

10.1155/2020/7695207 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Said Boumaraf ◽

Xiabi Liu ◽

Chokri Ferkous ◽

Xiaohong Ma

Keyword(s):

Feature Selection ◽

Predictive Value ◽

Breast Imaging ◽

Feature Selection Method ◽

Computer Aided Diagnosis ◽

Screening Mammography ◽

Breast Masses ◽

Computer Aided ◽

Aided Diagnosis

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient’s age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

Download Full-text

Computer-Aided Diagnosis of Pancreas Serous Cystic Neoplasms: A Radiomics Method on Preoperative MDCT Images

Technology in Cancer Research & Treatment ◽

10.1177/1533033818824339 ◽

2019 ◽

Vol 18 ◽

pp. 153303381882433 ◽

Cited By ~ 11

Author(s):

Ran Wei ◽

Kanru Lin ◽

Wenjun Yan ◽

Yi Guo ◽

Yuanyuan Wang ◽

...

Keyword(s):

Validation Cohort ◽

Characteristic Curve ◽

Computer Aided Diagnosis ◽

Cystic Neoplasm ◽

Multidetector Row Computed Tomography ◽

Cystic Neoplasms ◽

Independent Validation ◽

Computer Aided ◽

Selection Operator ◽

Aided Diagnosis

Objective: Our aim was to propose a preoperative computer-aided diagnosis scheme to differentiate pancreatic serous cystic neoplasms from other pancreatic cystic neoplasms, providing supportive opinions for clinicians and avoiding overtreatment. Materials and Methods: In this retrospective study, 260 patients with pancreatic cystic neoplasm were included. Each patient underwent a multidetector row computed tomography scan and pancreatic resection. In all, 200 patients constituted a cross-validation cohort, and 60 patients formed an independent validation cohort. Demographic information, clinical information, and multidetector row computed tomography images were obtained from Picture Archiving and Communication Systems. The peripheral margin of each neoplasm was manually outlined by experienced radiologists. A radiomics system containing 24 guideline-based features and 385 radiomics high-throughput features was designed. After the feature extraction, least absolute shrinkage selection operator regression was used to select the most important features. A support vector machine classifier with 5-fold cross-validation was applied to build the diagnostic model. The independent validation cohort was used to validate the performance. Results: Only 31 of 102 serous cystic neoplasm cases in this study were recognized correctly by clinicians before the surgery. Twenty-two features were selected from the radiomics system after 100 bootstrapping repetitions of the least absolute shrinkage selection operator regression. The diagnostic scheme performed accurately and robustly, showing the area under the receiver operating characteristic curve = 0.767, sensitivity = 0.686, and specificity = 0.709. In the independent validation cohort, we acquired similar results with receiver operating characteristic curve = 0.837, sensitivity = 0.667, and specificity = 0.818. Conclusion: The proposed radiomics-based computer-aided diagnosis scheme could increase preoperative diagnostic accuracy and assist clinicians in making accurate management decisions.

Download Full-text

The Establishment of Transformer-Based Computer-Aided Diagnosis Model to Improve the Classification Consistency of BI-RADS-US3–5 Nodules Among Radiologists: A Multi-Center Study

10.21203/rs.3.rs-1179922/v1 ◽

2021 ◽

Author(s):

Hongtao Ji ◽

Qiang Zhu ◽

Teng Ma ◽

Yun Cheng ◽

Shuai Zhou ◽

...

Keyword(s):

Breast Imaging ◽

Computer Aided Diagnosis ◽

Image Features ◽

Cad Model ◽

Data Systems ◽

Test Set ◽

Classification Consistency ◽

Computer Aided ◽

Sensitivity Specificity ◽

Aided Diagnosis

Abstract Background: Significant differences exist in classification outcomes for radiologists using ultrasonography-based breast imaging-reporting and data systems for diagnosing category 3–5 (BI-RADS-US 3–5) breast nodules, due to a lack of clear and distinguishing image features. As such, this study investigates the use of a transformer-based computer-aided diagnosis (CAD) model for improved BI-RADS-US 3–5 classification consistency.Methods: Five radiologists independently performed BI-RADS-US annotations on a breast ultrasonography image set collected from 20 hospitals in China. The data were divided into training, validation, testing, and sampling sets. The trained transformer-based CAD model was then used to classify test images, for which sensitivity, specificity, and accuracy were calculated. Variations in these metrics among the 5 radiologists were analyzed by referencing BI-RADS-US classification results for the sampling test set, provided by CAD, to determine whether classification consistency (the kappa value)，sensitivity, specificity, and accuracy had improved.Results: Classification accuracy for the CAD model applied to the test set was 95.7% for category 3 nodules, 97.6% for category 4A nodules, 95.60% for category 4B nodules, 94.2% for category 4C nodules, and 97.5% for category 5 nodules. Adjustments were made to 1,583 nodules, as 905 were classified to a higher category and 678 to a lower category in the sampling test set. As a result, the accuracy, sensitivity, and specificity of classification by each radiologist improved, with the consistency (kappa values) for all radiologists increasing to >0.60.Conclusions: The proposed transformer-based CAD model improved BI-RADS-US 3–5 nodule classification by individual radiologists and increased diagnostic consistency.

Download Full-text