Computer-Aided Detection and Diagnosis of Breast Cancer

Diagnostic Accuracy of Opportunistic Breast Cancer Screening Based on Mammography in Iran

Background: Although breast cancer is the most prevalent type of cancer among Iranian women, its screening is opportunistic in Iran. The available guidelines for breast cancer screening are based on mammography. A screening modality should have adequate diagnostic accuracy and be widely available at reasonable cost. Although mammography is highly accessible in Iran, its accuracy has not been investigated. Objectives: This study aimed to evaluate the accuracy of mammography in opportunistic screening regarding the current rate of patient attendance. Patients and Methods: A total of 491 women undergoing screening mammography were followed-up based on their medical records. They were divided into positive and negative screening groups, based on the breast imaging-reporting and data system (BI-RADS) categories and approaches. To evaluate the disease status of positive cases, pathology reports were investigated, and negative cases were followed-up for stability over time. Results: In the study sample, sensitivity was estimated at 73.08% (95% CI: 55.21 - 88.93), specificity was estimated at 94.41% (95% CI: 91.91 - 96.32), and accuracy was 93.28% (95% CI: 90.69 - 95.33). These test accuracy indices were not significantly different between the groups regarding age, family history, breast density, and history of breast interventions. Conclusion: The test’s sensitivity or ability to detect a disease was relatively low in opportunistic screening; it was found to be similar to the results of studies of first time implementation of screeninng. In both settings, a test needs to diagnose both incident and prevalent cases. The overall accuracy of mammography was acceptable, even in opportunistic screening.

The actionable imaging report

The goal of a diagnostic imaging examination is to provide the referring provider with an actionable imaging report that can be used to impart information to determine optimal clinical management for the patient. An actionable imaging report not only conveys the findings of the examination accurately, but does so in a timely and safe manner for an imaging examination that was performed appropriately and using the correct technique. The use of information technology tools has been paramount in improving the value of the imaging report and continues to play a prominent role in this process. The diversity of abdominal imaging, in both the variety of imaging modalities available and the organ systems evaluated, makes it well-suited to adopt these information technology solutions to improve report quality, including increased consistency in reports and in follow-up recommendations. This review discusses the components of the imaging chain involved in optimizing the imaging report with specific emphasis on the role of information technology applications to address the challenges that are frequently encountered. Specific abdominal imaging examples are presented to provide practical guidance and clinical context.

Evaluation of the accuracy of a computer-aided diagnosis (CAD) system in breast ultrasound according to the radiologist's experience

RATIONALE AND OBJECTIVES

The aim of this study was to evaluate the performance of a computer-aided diagnosis (CAD) system for breast ultrasound to improve the characterization of breast lesions detected on ultrasound by junior and senior radiologists.

MATERIALS AND METHODS

One hundred sixty ultrasound breast lesions were randomly reviewed blindly by four radiologists with different levels of expertise (from 20 years [radiologist A] to 4 months [radiologist D]), with and without the help of an ultrasound CAD system (B-CAD version 2). All lesions had been biopsied. Sensitivity and specificity with and without CAD were calculated for each radiologist for the following evaluation criteria: Breast Imaging Reporting and Data System category and the final diagnosis (benign or malignant). Intrinsic sensitivity, specificity, and predictive values of CAD alone were also calculated.

RESULTS

CAD detected all cancers, and its use increased radiologists' sensitivity scores when this was possible (with vs without CAD: radiologist A, 99% vs 99%; radiologist B, 96% vs 87%; radiologist C, 95% vs 88%; radiologist D, 91% vs 88%). Seven additional cancers were diagnosed. However, the low specificity of CAD (48%) decreased the specificity of radiologists, especially of the more experienced among them (with vs without CAD: radiologist A, 46% vs 70%; radiologist B, 58% vs 80%; radiologist C, 57% vs 69%; radiologist D, 71% vs 71%).

CONCLUSIONS

CAD for breast ultrasound appears to be a useful tool for improving the diagnosis of malignant lesions for junior radiologists. Nevertheless, its low specificity must be taken into account to limit biopsies of benign lesions.

Strategies for digital mammography interpretation in a clinical patient population

Mammography is the basic imaging modality for early detection of breast cancer. The aim of this prospective study was to evaluate the impact of different mammogram reading strategies on the diagnostic yield in a consecutive patient population referred for digital mammography to a hospital. First, the effect of using computer-aided detection (CAD) software on the performance of mammogram readers was studied. Furthermore, the impact of employing technologists as either prereaders or double readers was assessed, as compared to the conventional strategy of single reading by a radiologist. Digital mammograms of 1,048 consecutive patients were evaluated by a radiologist and 3 technologists with and without the use of CAD software. ROC analysis was used to study the effects of the different strategies. In the conventional strategy, an overall area under the curve (AUC) of 0.92 was found, corresponding to a sensitivity of 84% and specificity of 94%. When applying CAD software, the AUCs were similar before and after CAD for all readers (mean of 0.95). Employing technologists in prereading and double reading of mammograms resulted in a mean AUC of 0.91 and 0.96, respectively. In the prereading strategy, the corresponding sensitivity and specificity were 81 and 96%; in the double reading strategy they were 96 and 79%, respectively. Concluding, in this clinical population, systematic application of CAD software by either radiologist or technologists failed to improve the diagnostic yield. Furthermore, employing technologists as double readers of mammograms was the most effective strategy in improving breast cancer detection in daily clinical practice.

Assessing the performance of morphological parameters in distinguishing breast tumors on ultrasound images

This work aims at investigating seven morphological parameters in distinguishing malignant and benign breast tumors on ultrasound images. Linear discriminant analysis was applied to sets of up to five parameters and then the performances were assessed using the area Az (+/- standard error) under the ROC curve, accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value and negative predictive value. The most relevant individual parameters were the normalized residual value (nrv) and overlap ratio (RS), both calculated from the convex polygon technique, and the circularity (C). When nrv and C were taken together with roughness (R), calculated from normalized radial length (NRL), a performance slightly over 83% in distinguishing malignant and benign breast tumors was achieved.

Use of a computer-aided detection system to detect missed lung cancer at chest radiography

PURPOSE

To study the ability of a computer-aided detection (CAD) system to detect lung cancer overlooked at initial interpretation by the radiologist.

MATERIALS AND METHODS

Institutional review board approval was given for this study. Patient consent was not required; a HIPAA waiver was granted because of the retrospective nature of the data collection. In patients with lung cancer diagnosed from 1995 to 2006 at two institutions, each chest radiograph obtained prior to tumor discovery was evaluated by two radiologists for an overlooked lesion. The size and location of the nodules were documented and graded for subtlety (grades 1-4, 1 = very subtle). Each radiograph with a missed lesion was analyzed by a commercial CAD system, as was the follow-up image at diagnosis. An age- and sex-matched control group was used to assess CAD false-positive rates.

RESULTS

Missed lung cancer was found in 89 patients (age range, 51-86 years; mean age, 65 years; 80 men, nine women) on 114 radiographs. Lesion size ranged from 0.4 to 5.5 cm (mean, 1.8 cm). Lesions were most commonly peripheral (n = 63, 71%) and in upper lobes (n = 67, 75%). Lesion subtlety score was 1, 2, 3, or 4 on 43, 49, 17, and five radiographs, respectively. CAD identified 53 (47%) and 46 (52%) undetected lesions on a per-image and per-patient basis, respectively. The average size of lesions detected with CAD was 1.73 cm compared with 1.85 cm for lesions that were undetected (P = .47). A significant difference (P = .017) was found in the average subtlety score between detected lesions (score, 2.06) and undetected lesions (score, 1.68). An average of 3.9 false-positive results occurred per radiograph; an average of 2.4 false-positive results occurred per radiograph for the control group.

CONCLUSION

CAD has the potential to detect approximately half of the lesions overlooked by human readers at chest radiography.

Computer-aided detection in full-field digital mammography in a clinical population: performance of radiologist and technologists

The purpose of the study was to evaluate the impact of a computer-aided detection (CAD) system on the performance of mammogram readers in interpreting digital mammograms in a clinical population. Furthermore, the ability of a CAD system to detect breast cancer in digital mammography was studied in comparison to the performance of radiologists and technologists as mammogram readers. Digital mammograms of 1,048 consecutive patients were evaluated by a radiologist and three technologists. Abnormalities were recorded and an imaging conclusion was given as a BI-RADS score before and after CAD analysis. Pathology results during 12 months follow up were used as a reference standard for breast cancer. Fifty-one malignancies were found in 50 patients. Sensitivity and specificity were computed before and after CAD analysis and provided with 95% CIs. In order to assess the detection rate of malignancies by CAD and the observers, the pathological locations of these 51 breast cancers were matched with the locations of the CAD marks and the mammographic locations that were considered to be suspicious by the observers. For all observers, the sensitivity rates did not change after application of CAD. A mean sensitivity of 92% was found for all technologists and 84% for the radiologist. For two technologists, the specificity decreased (from 84 to 83% and from 77 to 75%). For the radiologist and one technologist, the application of CAD did not have any impact on the specificity rates (95 and 83%, respectively). CAD detected 78% of all malignancies. Five malignancies were indicated by CAD without being noticed as suspicious by the observers. In conclusion, the results show that systematic application of CAD in a clinical patient population failed to improve the overall sensitivity of mammogram interpretation by the readers and was associated with an increase in false-positive results. However, CAD marked five malignancies that were missed by the different readers.

Computer-aided diagnosis using morphological features for classifying breast lesions on ultrasound

OBJECTIVES

To develop and evaluate a computer-aided diagnosis (CAD) system with automatic contouring and morphological analysis to aid in the classification of breast tumors using ultrasound.

METHODS

We evaluated 118 breast lesions (34 malignant and 84 benign tumors). Each tumor contour was automatically extracted from the digitized ultrasound image. Nineteen practical morphological features from the extracted contour were calculated and principal component analysis (PCA) was applied to find independent features. A support vector machine (SVM) classifier utilized the selected principal vectors to identify the breast tumor as benign or malignant. In this study, all the cases were sampled with k-fold cross-validation (k = 10) to evaluate the performance by receiver-operating characteristics (ROC) curve analysis.

RESULTS

The areas under the ROC curves for the proposed CAD systems using all morphological features and the lower-dimensional principal vector were 0.91 and 0.90, respectively. The classification ability for breast tumors using morphological information was good.

CONCLUSIONS

This system differentiates benign from malignant breast tumors well and therefore provides a clinically useful second opinion. Moreover, the morphological features are nearly setting-independent and thus available to various ultrasound machines.