A Computer-Aided Detection System for the Evaluation of Breast Cancer by Mammographic Appearance and Lesion Size

Responsible (use of) AI

Although there is a rich history of philosophical definitions of ethics when applied to human behavior, applying the same concepts and principles to AI may be fraught with problems. Anthropomorphizing AI to have characteristics such as "ethics" may promote a dangerous, unrealistic expectation that AI can be trained to have inherent, guaranteed ethical behavior. The authors instead advocate for increased research into the ethical use of AI from initial ideation and design through operational use and sustainment. The authors advocate for five key research areas: (1) education in ethics and core AI concepts for AI developers, leaders, and users, (2) development and use of model cards or datasheets for datasets to provide transparency into the strengths, limits, and potential biases of a trained model, (3) employing human-centered design that seeks to understand human value structures within a task context and enable effective human-machine interaction through intuitive and transparent interfaces, (4) targeted use of run time assurance that monitors and modifies the inputs or outputs of a trained model when necessary to enforce ethical principles such as safety or limiting bias, and (5) developing best practices for the use of a joint human-AI co-creation and training experience to enable a shared mental model and higher performance through potential emergent behavior.

Computational Imaging to Compensate for Soft-Tissue Deformations in Image-Guided Breast Conserving Surgery

OBJECTIVE

During breast conserving surgery (BCS), magnetic resonance (MR) images aligned to accurately display intraoperative lesion locations can offer improved understanding of tumor extent and position relative to breast anatomy. Unfortunately, even under consistent supine conditions, soft tissue deformation compromises image-to-physical alignment and results in positional errors.

METHODS

A finite element inverse modeling technique has been developed to nonrigidly register preoperative supine MR imaging data to the surgical scene for improved localization accuracy during surgery. Registration is driven using sparse data compatible with acquisition during BCS, including corresponding surface fiducials, sparse chest wall contours, and the intra-fiducial skin surface. Deformation predictions were evaluated at surface fiducial locations and subsurface tissue features that were expertly identified and tracked. Among n = 7 different human subjects, an average of 22 ± 3 distributed subsurface targets were analyzed in each breast volume.

RESULTS

The average target registration error (TRE) decreased significantly when comparing rigid registration to this nonrigid approach (10.4 ± 2.3 mm vs 6.3 ± 1.4 mm TRE, respectively). When including a single subsurface feature as additional input data, the TRE significantly improved further (4.2 ± 1.0 mm TRE), and in a region of interest within 15 mm of a mock biopsy clip TRE was 3.9 ± 0.9 mm.

CONCLUSION

These results demonstrate accurate breast deformation estimates based on sparse-data-driven model predictions.

SIGNIFICANCE

The data suggest that a computational imaging approach can account for image-to-surgery shape changes to enhance surgical guidance during BCS.

Breast mass detection in digital mammography based on anchor-free architecture

BACKGROUND AND OBJECTIVE

Accurate detection of breast masses in mammography images is critical to diagnose early breast cancer, which can greatly improve the patients' survival rate. However, it is still a big challenge due to the heterogeneity of breast masses and the complexity of their surrounding environment. Therefore, how to develop a robust breast mass detection framework in clinical practical applications to improve patient survival is a topic that researchers need to continue to explore.

METHODS

To address these problems, we propose a one-stage object detection architecture, called Breast Mass Detection Network (BMassDNet), based on anchor-free and feature pyramid which makes the detection of breast masses of different sizes well adapted. We introduce a truncation normalization method and combine it with adaptive histogram equalization to enhance the contrast between the breast mass and the surrounding environment. Meanwhile, to solve the overfitting problem caused by small data size, we propose a natural deformation data augmentation method and mend the train data dynamic updating method based on the data complexity to effectively utilize the limited data. Finally, we use transfer learning to assist the training process and to improve the robustness of the model ulteriorly.

RESULTS

On the INbreast dataset, each image has an average of 0.495 false positives whilst the recall rate is 0.930; On the DDSM dataset, when each image has 0.599 false positives, the recall rate reaches 0.943.

CONCLUSIONS

The experimental results on datasets INbreast and DDSM show that the proposed BMassDNet can obtain competitive detection performance over the current top ranked methods.

The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review

BACKGROUND

Early detection of breast cancer (BC) is crucial in lowering the mortality.

PURPOSE

To present an overview of studies concerning computer-aided detection (CAD) in screening mammography for early detection of BC and compare diagnostic accuracy and recall rates (RR) of single reading (SR) with SR + CAD and double reading (DR) with SR + CAD.

MATERIAL AND METHODS

PRISMA guidelines were used as a review protocol. Articles on clinical trials concerning CAD for detection of BC in a screening population were included. The literature search resulted in 1522 records. A total of 1491 records were excluded by abstract and 18 were excluded by full text reading. A total of 13 articles were included.

RESULTS

All but two studies from the SR vs. SR + CAD group showed an increased sensitivity and/or cancer detection rate (CDR) when adding CAD. The DR vs. SR + CAD group showed no significant differences in sensitivity and CDR. Adding CAD to SR increased the RR and decreased the specificity in all but one study. For the DR vs. SR + CAD group only one study reported a significant difference in RR.

CONCLUSION

All but two studies showed an increase in RR, sensitivity and CDR when adding CAD to SR. Compared to DR no statistically significant differences in sensitivity or CDR were reported. Additional studies based on organized population-based screening programs, with longer follow-up time, high-volume readers, and digital mammography are needed to evaluate the efficacy of CAD.

Mammography to tomosynthesis: examining the differences between two-dimensional and segmented-three-dimensional visual search

BACKGROUND

Radiological techniques for breast cancer detection are undergoing a massive technological shift-moving from mammography, a process that takes a two-dimensional (2D) image of breast tissue, to tomosynthesis, a technique that creates a segmented-three-dimensional (3D) image. There are distinct benefits of tomosynthesis over mammography with radiologists having fewer false positives and more accurate detections; yet there is a significant and meaningful disadvantage with tomosynthesis in that it takes longer to evaluate each patient. This added time can dramatically impact workflow and have negative attentional and cognitive impacts on interpretation of medical images. To better understand the nature of segmented-3D visual search and the implications for radiology, the current study looked to establish a new testing platform that could reliably examine differences between 2D and segmented-3D search.

RESULTS

In Experiment 1, both professionals (radiology residents and certified radiologists) and non-professionals (undergraduate students) were found to have fewer false positives and were more accurate in segmented-3D displays, but at the cost of taking significantly longer in search. Experiment 2 tested a second group of non-professional participants, using a background that more closely resembled a mammogram, and replicated the results of Experiment 1-search was more accurate and there were fewer false alarms in segmented 3D displays but took more time.

CONCLUSION

The results of Experiments 1 and 2 matched the performance patterns found in previous radiology studies and in the clinic, suggesting this novel experimental paradigm potentially provides a flexible and cost-effective tool that can be utilized with non-professional populations to inform relevant visual search performance. From an academic perspective, this paradigm holds promise for examining the nature of segmented-3D visual search.

Evaluation of a computer-aided detection (CAD)-enhanced 2D synthetic mammogram: comparison with standard synthetic 2D mammograms and conventional 2D digital mammography

AIM

To evaluate the diagnostic performance of computer-aided detection (CAD)-enhanced synthetic mammograms in comparison with standard synthetic mammograms and full-field digital mammography (FFDM).

MATERIALS AND METHODS

A CAD-enhanced synthetic mammogram, a standard synthetic mammogram, and FFDM were available in 68 breast-screening cases recalled for soft-tissue abnormalities (masses, parenchymal deformities, and asymmetric densities). Two radiologists, blinded to image type and final assessment outcome, retrospectively read oblique and craniocaudal projections for each type of mammogram. The resulting 204 pairs of 2D images were presented in random order and scored on a five-point scale (1, normal to 5, malignant) without access to the Digital breast tomosynthesis (DBT) slices. Receiver operating characteristic (ROC) curve analysis was performed.

RESULTS

There were 34 biopsy-proven malignancies and 34 normal/benign cases. Diagnostic accuracy was significantly improved for the CAD-enhanced synthetic mammogram compared to the standard synthetic mammogram (area under the ROC curve [AUC]=0.846 and AUC=0.683 respectively, p=0.004) and compared to the conventional 2D FFDM (AUC=0.724, p=0.027). The CAD-enhanced synthetic mammogram had the highest diagnostic accuracy for all soft-tissue abnormalities, and for malignant lesions sensitivity was not affected by tumour size. For all 68 cases, there was an average of 3.2 areas enhanced per image. For the 34 cancer cases, 97.4% of lesions were correctly enhanced, with 2.1 false areas enhanced per image.

CONCLUSIONS

CAD enhancement significantly improves performance of synthetic 2D mammograms and also exhibits improved diagnostic accuracy compared to conventional 2D FFDM.

Investigation on slice direction dependent detectability of volumetric cone beam CT images

We investigate the detection performance of transverse and longitudinal planes for various signal sizes (i.e., 1 mm to 8 mm diameter spheres) in cone beam computed tomography (CBCT) images. CBCT images are generated by computer simulation and images are reconstructed using an FDK algorithm. For each slice direction and signal size, a human observer study is conducted with a signal-known-exactly/background-known-exactly (SKE/BKE) binary detection task. The detection performance of human observers is compared with that of a channelized Hotelling observer (CHO). The detection performance of an ideal linear observer is also calculated using a CHO with Laguerre-Gauss (LG) channels. The detectability of high contrast small signals (i.e., up to 4-mm-diameter spheres) is higher in the longitudinal plane than the transverse plane. It is also shown that CHO performance correlates well with human observer performance in both transverse and longitudinal plane images.

CAD May Not be Necessary for Microcalcifications in the Digital era, CAD May Benefit Radiologists for Masses

Objective:

The aim of this study was to evaluate the effectiveness of computer-aided detection (CAD) to mark the cancer on digital mammograms at the time of breast cancer diagnosis and also review retrospectively whether CAD marked the cancer if visible on any available prior mammograms, thus potentially identifying breast cancer at an earlier stage. We sought to determine why breast lesions may or may not be marked by CAD. In particular, we analyzed factors such as breast density, mammographic views, and lesion characteristics.

Materials and Methods:

Retrospective review from 2004 to 2008 revealed 3445 diagnosed breast cancers in both symptomatic and asymptomatic patients; 1293 of these were imaged with full field digital mammography (FFDM). After cancer diagnosis, in a retrospective review held by the radiologist staff, 43 of these cancers were found to be visible on prior-year mammograms (false-negative cases); these breast cancer cases are the basis of this analysis. All cases had CAD evaluation available at the time of cancer diagnosis and on prior mammography studies. Data collected included patient demographics, breast density, palpability, lesion type, mammographic size, CAD marks on current- and prior-year mammograms, needle biopsy method, pathology results (core needle and/or surgical), surgery type, and lesion size.

Results:

On retrospective review of the mammograms by the staff radiologists, 43 cancers were discovered to be visible on prior-year mammograms. All 43 cancers were masses (mass classification included mass, mass with calcification, and mass with architectural distortion); no pure microcalcifications were identified in this cohort. Mammograms with CAD applied at the time of breast cancer diagnosis were able to detect 79% (34/43) of the cases and 56% (24/43) from mammograms with CAD applied during prior year(s). In heterogeneously dense/extremely dense tissue, CAD marked 79% (27/34) on mammograms taken at the time of diagnosis and 56% (19/34) on mammograms with CAD applied during the prior year(s). At time of diagnosis, CAD marked lesions in 32% (11/34) on the craniocaudal (CC) view, 21% (7/34) on the mediolateral oblique (MLO) view. Lesion size of those marked by CAD or not marked were similar, the average being 15 and 12 mm, respectively.

Conclusion:

CAD marked cancers on mammograms at the time of diagnosis in 79% of the cases and in 56% of the cases from the mammograms with CAD applied in the prior year(s). Our review demonstrated that CAD can mark invasive breast carcinomas in even dense breast tissue. CAD marked a significant portion on the CC view only, which may be an indicator to radiologists to be especially vigilant when a lesion is marked on this view.

Evaluation of breast amorphous calcifications by a computer-aided detection system in full-field digital mammography

OBJECTIVES

The purpose of this study was to evaluate the performance of a direct computer-aided detection (d-CAD) system integrated with full-field digital mammography (FFDM) in assessment of amorphous calcifications.

METHODS

From 1438 consecutive stereotactic-guided biopsies, FFDM images with amorphous calcifications were selected for retrospective evaluation by d-CAD in 122 females (mean age, 56 years; range, 35-84 years). The sensitivity, specificity, accuracy and false-positive rate of the d-CAD system were calculated in the total group of 124 lesions and in the subgroups based on breast density, mammographic lesion distribution and extension. Logistic regression analysis was used to stratify the risk of malignancy by patient risk factors and age.

RESULTS

The d-CAD marked all (36/36) breast cancers, 85% (11/13) of the high-risk lesions and 80% (60/75) of benign amorphous calcifications (p<0.01) correctly. The sensitivity, specificity and diagnostic accuracy for the combined malignant and "high-risk" lesions was 96, 80 and 86%, respectively. The likelihood of malignancy was 29%. There was no significant difference between the marking of fatty or dense breasts (p>0.05); however, d-CAD marks showed differences for small (<7 mm) lesions (p=0.02) and clustered calcifications (p=0.03). The false-positive rate of d-CAD was 1.76 marks per full examination.

CONCLUSION

The d-CAD system correctly marked all biopsy-proven breast cancers and a large number of biopsy-proven high-risk lesions that presented as amorphous calcifications. Given our 29% likelihood of malignancy, imaging-guided biopsy appears to be a reasonable recommendation in cases of amorphous calcifications marked by d-CAD.

A STUDY ON GENETIC ALGORITHM BASED HYBRID SOFTCOMPUTING MODEL FOR BENIGNANCY/MALIGNANCY DETECTION OF MASSES USING DIGITAL MAMMOGRAM

In present works authors have developed a computerized classification procedure for tumor mass in breasts using digital mammogram. The process implements genetic algorithm and hybrid neuro-fuzzy approaches to classify tumor masses into benign and malignant group in order to assist the physicians for treatment planning. The classification process is based on accurate analysis of shape and margin of tumor mass appearing in breast. The shape features using Fourier descriptors introduce a large number of feature vectors. Thus, to classify different boundaries, a standard multilayer preceptor needs large number of inputs. Simultaneously, to train the network, a large number of training cycles and huge memory are also required. It is obvious that a complicated structure invites the problem of over learning and misclassification. In proposed methodology genetic algorithm (GA) has been used for the searching of effective input feature vectors. Adaptive neuro-fuzzy model has been used for final classification of different boundaries of tumor masses. The proposed technique is an innovative soft computing approach that removes the limitation of conventional neural networks and indicates a promising direction of adaptation in a changing environment. The classification system utilizes a Euclidean distance function to detect the belongingness of masses in benign and in malignant classes along with degree of benignancy/malignancy. Presently 200 digitized mammograms from MIAS and other databases have been considered for the experiment and which have shown an average of approximately 86% correct classification as compared with clinical data with a highest rate of 88.9%.

CAD e mamografia: por que usar esta ferramenta?

A mamografia representa o melhor método de detecção precoce do câncer de mama, porém cerca de 10% a 30% das lesões mamárias são perdidas no rastreamento, devido a limitações próprias dos observadores humanos. A detecção auxiliada por computador (computer-aided detection - CAD) é uma tecnologia relativamente nova que tem sido implementada em alguns serviços de mamografia, com o intuito de prover uma dupla leitura. Estudos clínicos têm demonstrado que o CAD aumenta a sensibilidade de detecção do câncer da mama, por radiologistas, em até 21%. Um sistema CAD é útil em situações em que exista alta variabilidade interobservador, falta de observadores treinados, ou na impossibilidade de se realizar a dupla leitura com dois ou mais radiologistas. O objetivo desta revisão está baseado na necessidade de atualizar a comunidade médica acerca desta ferramenta, como um método auxiliar, quantitativo, não operador-dependente, e que visa a melhorar a qualidade do diagnóstico do câncer de mama.

Mammographic features of breast cancers at single reading with computer-aided detection and at double reading in a large multicenter prospective trial of computer-aided detection: CADET II

PURPOSE

To evaluate the mammographic features of breast cancer that favor lesion detection with single reading and computer-aided detection (CAD) or with double reading.

MATERIALS AND METHODS

The Computer Aided Detection Evaluation Trial II study was approved by the ethics committee, and all participants provided written informed consent. A total of 31,057 women were recruited from three screening centers between September 2006 and August 2007. They were randomly allocated to the double reading group, the single reading with CAD group, or the double reading and single reading with CAD group at a ratio of 1:1:28, respectively. In this study, cancers in the women whose mammograms were read with both single reading with CAD and double reading were retrospectively reviewed. The original mammograms were obtained for each case and reviewed by two of three experienced breast radiologists in consensus. The method of detection was noted. The size and predominant mammographic feature of the cancer were recorded, as was the breast density. CAD marking data were reviewed to determine if the cancer had been correctly marked.

RESULTS

A total of 227 cancers were detected in 28,204 women. A total of 170 cases were recalled with both reading regimens. Lesion types were masses (66%), microcalcifications (25%), parenchymal deformities (6%), and asymmetric densities (3%). The ability of the reading regimens to correctly prompt the reader to recall cases varied significantly by lesion type (P < .001). More parenchymal deformities were recalled with double reading, whereas more asymmetric densities were recalled with single reading with CAD. There was no difference in the ability of either reading regimen to prompt the reader to correctly recall masses or microcalcifications. CAD correctly prompted 100% of microcalcifications, 87% of mass lesions, 80% of asymmetric densities, and 50% of parenchymal deformities. CAD correctly marked 93% of spiculated masses compared with 80% of ill-defined masses (P = .054). There was a significant trend for cancers detected with double reading to occur only in women with a denser mammographic background pattern (P = .02). Size had no effect on lesion detection.

CONCLUSION

Readers using either single reading with CAD or double reading need to be aware of the strengths and weaknesses of reading regimens to avoid missing the more challenging cancer cases.

Sensitivity of a direct computer-aided detection system in full-field digital mammography for detection of microcalcifications not associated with mass or architectural distortion

PURPOSE

The purpose of this study was to evaluate the sensitivity of a direct computer-aided detection (CAD) system (d-CAD) in full-field digital mammography (FFDM) for the detection of microcalcifications not associated with mass or architectural distortion.

MATERIALS AND METHODS

A database search of 1063 consecutive stereotactic core biopsies performed between 2002 and 2005 identified 196 patients with Breast Imaging-Reporting and Data System (BI-RADS) 4 and 5 microcalcifications not associated with mass or distortion detected exclusively by bilateral FFDM. A commercially available CAD system (Second Look, version 7.2) was retrospectively applied to the craniocaudal and mediolateral oblique views in these patients (mean age, 59 years; range, 35-84 years). Breast density, location and mammographic size of the lesion, distribution, and tumour histology were recorded and analysed by using chi(2), Fisher exact, or McNemar tests, when applicable.

RESULTS

When using d-CAD, 71 of 74 malignant microcalcification cases (96%) and 101 of 122 benign microcalcifications (83%) were identified. There was a significant difference (P < .05) between CAD sensitivity on the craniocaudal view, 91% (68 of 75), vs CAD sensitivity on the mediolateral oblique view, 80% (60 of 75). The d-CAD sensitivity for dense breast tissue (American College of Radiology [ACR] density 3 and 4) was higher (97%) than d-CAD sensitivity (95%) for nondense tissue (ACR density 1 and 2), but the difference was not statically significant. All 28 malignant calcifications larger than 10 mm were detected by CAD, whereas the sensitivity for lesions small than or equal to 10 mm was 94%.

CONCLUSIONS

D-CAD had a high sensitivity in the depiction of asymptomatic breast cancers, which were seen as microcalcifications on FFDM screening, with a sensitivity of d-CAD on the craniocaudal view being significantly better. All malignant microcalcifications larger than 10 mm were detected by d-CAD.

Performance of computer-aided detection applied to full-field digital mammography in detection of breast cancers

OBJECTIVE

The aim of this retrospective study was to evaluate performance of computer-aided detection (CAD) with full-field digital mammography (FFDM) in detection of breast cancers.

MATERIALS AND METHODS

CAD was retrospectively applied to standard mammographic views of 127 cases with biopsy proven breast cancers detected with FFDM (Senographe 2000, GE Medical Systems). CAD sensitivity was assessed in total group of 127 cases and for subgroups based on breast density, mammographic lesion type, mammographic lesion size, histopathology and mode of presentation.

RESULTS

Overall CAD sensitivity was 91% (115 of 127 cases). There were no statistical differences (p > 0.1) in CAD detection of cancers in dense breasts 90% (53/59) versus non-dense breasts 91% (62/68). There was statistical difference (p < 0.05) in CAD detection of cancers that appeared mammographically as microcalcifications only versus other mammographic manifestations. CAD detected 100% (44/44) of cancers manifesting as microcalcifications, 89% (47/53) as no-calcified masses or asymmetries, 88% (14/16) as masses with associated calcifications, and 71% (10/14) as architectural distortions. CAD sensitivity for cancers 1-10mm was 84% (38/45); 11-20mm 93% (55/59); and >20mm 97% (22/23).

CONCLUSION

CAD applied to FFDM showed 100% sensitivity in identifying cancers manifesting as microcalcifications only and high sensitivity 86% (71/83) for other mammographic appearances of cancer. Sensitivity is influenced by lesion size. CAD in FFDM is an adjunct helping radiologist in early detection of breast cancers.

Automated detection of breast mass spiculation levels and evaluation of scheme performance

RATIONALE AND OBJECTIVES

Although the spiculation levels of breast mass boundaries are a primary sign of malignancy for masses detected on mammography, developing an automated computerized method to detect spiculation levels and quantitatively evaluating the performance of such a method is a difficult task. The objectives of this study were to (1) develop and test a new method to improve mass segmentation and detect mass boundary spiculation levels and (2) assess the performance of this method using a relatively large imaging data set.

MATERIALS AND METHODS

The fully automated method developed for this study includes three image-processing steps. In the first step, the principle of maximum entropy is applied in the selected region of interest (ROI) after correcting the background trend to enhance the initial outlines of a mass. In the second step, an active-contour model is used to refine the initial outlines. In the third step, spiculated lines connected to the mass boundary are detected and identified using a special line detector. A quantitative spiculation index is computed to assess the degree of spiculation. To develop and evaluate this automated method, 211 ROIs depicting masses were extracted from a publicly available image database. Among these ROIs, 106 depicted circumscribed mass regions and 105 involved spiculated mass regions. The performance of the method was evaluated using receiver-operating characteristic (ROC) analysis.

RESULTS

The computed area under the ROC curve, when applying the method to the data set, was 0.701 +/- 0.027. By setting up a threshold at a spiculation index of 5.0, the method achieved an overall classification accuracy of 66.4%, with 54.3% sensitivity and 78.3% specificity.

CONCLUSIONS

In this study, a new computerized method with a number of unique characteristics was developed to detect spiculated mass regions, and a simple spiculation index was applied to quantify mass spiculation levels. Although this quantitative index can be used to distinguish between spiculated and circumscribed masses, the results also suggest that the automated detection of mass spiculation levels remains a technical challenge.

Variable size computer-aided detection prompts and mammography film reader decisions

Introduction

The purpose of the present study was to investigate the effect of computer-aided detection (CAD) prompts on reader behaviour in a large sample of breast screening mammograms by analysing the relationship of the presence and size of prompts to the recall decision.

Methods

Local research ethics committee approval was obtained; informed consent was not required. Mammograms were obtained from women attending routine mammography at two breast screening centres in 1996. Films, previously double read, were re-read by a different reader using CAD. The study material included 315 cancer cases comprising all screen-detected cancer cases, all subsequent interval cancers and 861 normal cases randomly selected from 10,267 cases. Ground truth data were used to assess the efficacy of CAD prompting. Associations between prompt attributes and tumour features or reader recall decisions were assessed by chi-squared tests.

Results

There was a highly significant relationship between prompting and a decision to recall for cancer cases and for a random sample of normal cases (P < 0.001). Sixty-four per cent of all cases contained at least one CAD prompt. In cancer cases, larger prompts were more likely to be recalled (P = 0.02) for masses but there was no such association for calcifications (P = 0.9). In a random sample of 861 normal cases, larger prompts were more likely to be recalled (P = 0.02) for both mass and calcification prompts. Significant associations were observed with prompting and breast density (p = 0.009) for cancer cases but not for normal cases (P = 0.05).

Conclusions

For both normal cases and cancer cases, prompted mammograms were more likely to be recalled and the prompt size was also associated with a recall decision.

Computer-aided diagnosis for the differentiation of malignant from benign thyroid nodules on ultrasonography

RATIONALE AND OBJECTIVES

We sought to evaluate the diagnostic performance of an artificial neural network (ANN) and binary logistic regression (BLR) in differentiating malignant from benign thyroid nodules on ultrasonography.

MATERIALS AND METHODS

Two experienced radiologists, who were unaware of the histopathological diagnosis, analyzed ultrasonographic (US) features of 109 pathologically proven thyroid lesions (49 malignant and 60 benign) in 96 patients. Each radiologist was asked to evaluate US findings and categorize nodules into one of the two groups (malignant vs. benign) in each case. The following 8 US parameters were assessed for each nodule: size, shape, margin, echogenicity, cystic change, microcalcification, macrocalcification, and halo sign. Statistically significant US findings were obtained with backward stepwise logistic regression and were used for training and testing of the ANN and the BLR. The performance of the ANN and BLR was compared to that of the radiologists using receiver-operating characteristic (ROC) analysis.

RESULTS

Statistically significant US findings were size, margin, echogenicity, cystic change, and macrocalcification of the nodules. The area under the ROC curve (Az) values of ANN and BLR were 0.9492 +/- 0.0195 and 0.9046 +/- 0.0289, respectively. The Az value was 0.8300 +/- 0.0359 for reader 1 and 0.7600 +/- 0.0409 for reader 2. The Az values for ANN and BLR were significantly higher than those for both radiologists (all p < .05).

CONCLUSION

The performance of the ANN and the BLR was better than that of the radiologists in the distinction of benign and malignant thyroid nodules.

Advances in computer-aided diagnosis for breast cancer

PURPOSE OF REVIEW

Computer-aided diagnosis (CAD) is a technology used for the detection and characterization of cancer. Although CAD is not limited to a single type of cancer, a large number of CAD systems to date have been designed and used for breast cancer. The aim of this review is to discuss the current state of the CAD systems for breast-cancer diagnosis, their application as a second reader in clinical practice, and studies that have evaluated the effect of CAD on radiologists' performance.

RECENT FINDINGS

A large number of CAD applications are being developed for different imaging modalities. Owing to commercially available Food and Drug Administration (FDA) approved systems, the main clinical use of CAD to date is for screen-film mammography. Many studies have shown that CAD improves radiologists' performance. A large number of academic institutions have devoted a substantial research effort to developing CAD methods.

SUMMARY

CAD systems will play an increasingly important role in the clinic as a second reader. Clinical trials have shown that CAD can improve the accuracy of breast-cancer detection. Preclinical studies have demonstrated the potential of CAD to improve the classification of malignant and benign lesions. An increased number of CAD systems are being developed for different breast-imaging modalities.

Results of a survey on digital screening mammography: prevalence, efficiency, and use of ancillary diagnostic AIDS

OBJECTIVE

As the use of full-field digital screening mammography grows rapidly, this study was conducted to determine the time required to interpret digital soft-copy (filmless) mammography compared with conventional film-screen screening mammography and to evaluate radiologists' use of ancillary diagnostic aids when interpreting digital mammography (DM) and conventional film-screen mammography (FSM).

MATERIALS AND METHODS

An 18-question survey was sent to 1,703 members of the Society of Breast Imaging, whose e-mail addresses were provided by the society. After subtracting those from whom out-of-office e-mail responses were received and three who wrote back to exclude themselves, there were 1,659 potential participants. Data from the respondents were collected and analyzed by tabulation and cross-tabulation.

RESULTS

In total, 396 members of the Society of Breast Imaging completed and returned surveys, for a 23.9% response rate. Of the respondents, 49.0% said that they had access to and interpreted DM. Their estimated average time to read a single digital mammographic study was 2.6 minutes, compared with 2.0 minutes for reading a single film-screen mammographic study. Therefore, the perceived time difference was 0.6 minutes. Magnification was the main ancillary diagnostic aid used in interpreting both DM and FSM: 74.2% of respondents used computer-based magnification at least half the time in interpreting DM, and 90.9% used optical magnification at least half the time in interpreting FSM. Optical magnification was also used by 28.5% of respondents at least half the time in interpreting DM. The respondents also used computer-aided detection frequently: 91.0% and 76.3% of those who had computer-aided detection available said that they used it at least 75% of the time in interpreting DM and FSM, respectively.

CONCLUSION

Digital mammography takes longer to interpret than FSM. Radiologists use various ancillary diagnostic aids, but magnification and computer-aided detection are the two most commonly used aids.

Evaluation of computer-aided detection systems in the detection of small invasive breast carcinoma

PURPOSE

To retrospectively compare two CAD systems for detecting invasive breast cancers manifesting as noncalcified masses smaller than 16 mm.

MATERIALS AND METHODS

Waiver of informed consent was granted by the Institutional Review Board that approved this HIPAA-compliant study. Mammograms obtained from two institutions providing consecutive invasive carcinomas manifesting as noncalcified masses smaller than 16 mm were evaluated by using two commercially available CAD systems (R2 ImageChecker M1000, version 5.0A and iCAD Second Look, version 6.0 mid operating point). To provide statistical power to test for a possible 10% difference in the sensitivity performance between the systems, 192 consecutive mammographic studies (182 unifocal, six multifocal, and four bilateral cancers) were collected. Masses were characterized using the Breast Imaging Reporting and Data System (BI-RADS). Per study specificity and mass false marker rate were determined by using 51 normal four-view studies, while scoring only the mass false-positive marks for noncalcified masses. Associations between mass characteristics and supplying institution were compared by using chi2 tests. A P value of .05 was considered to indicate a significant difference.

RESULTS

The respective per study sensitivity, per image (ie, per view) sensitivity, per study specificity, and mass false-positive marker rates were 81.8%, 64.7%, 39.2%, and 1.08 for the R2 ImageChecker M1000 system, and 60.9%, 42.6%, 31.4%, and 1.41 for the iCAD Second Look system. The overall per study and per image sensitivities were significantly better for R2 than for iCAD (McNemar test, all P<.001), with a nonsignificant higher per study specificity and lower mass false marker rate on normal studies. CAD results demonstrated at least a 20% variation between BI-RADS categories 4a and 5 for per study and per image sensitivity.

CONCLUSION

A statistically significant difference was observed in per study and per image sensitivity in our mammography data set with small (<16 mm), noncalcified invasive breast malignancies between two CAD systems. Differences in per study specificity and mass false marker rate were noted but were not statistically significant.

Interactive computer-aided diagnosis of breast masses: computerized selection of visually similar image sets from a reference library

RATIONALE AND OBJECTIVES

The clinical utility of interactive computer-aided diagnosis (ICAD) systems depends on clinical relevance and visual similarity between the queried breast lesions and the ICAD-selected reference regions. The objective of this study is to develop and test a new ICAD scheme that aims improve visual similarity of ICAD-selected reference regions.

MATERIALS AND METHODS

A large and diverse reference library involving 3,000 regions of interests was established. For each queried breast mass lesion by the observer, the ICAD scheme segments the lesion, classifies its boundary spiculation level, and computes 14 image features representing the segmented lesion and its surrounding tissue background. A conditioned k-nearest neighbor algorithm is applied to select a set of the 25 most "similar" lesions from the reference library. After computing the mutual information between the queried lesion and each of these initially selected 25 lesions, the scheme displays the six reference lesions with the highest mutual information scores. To evaluate the automated selection process of the six "visually similar" lesions to the queried lesion, we conducted a two-alternative forced-choice observer preference study using 85 queried mass lesions. Two sets of reference lesions selected by one new automated ICAD scheme and the other previously reported scheme using a subjective rating method were randomly displayed on the left and right side of the queried lesion. Nine observers were asked to decide for each of the 85 queried lesions which one of the two reference sets was "more visually similar" to the queried lesion.

RESULTS

In classification of mass boundary spiculation levels, the overall agreement rate between the automated scheme and an observer is 58.8% (Kappa = 0.31). In observer preference study, the nine observers preferred on average the reference lesion sets selected by the automated scheme as being more visually similar than the set selected by the subjective rating approach in 53.2% of the queried lesions. The results were not significantly different for the two methods (P = .128).

CONCLUSIONS

This study suggests that using the new automated ICAD scheme, the interobserver variability related issues can thus be avoided. Furthermore, the new scheme maintains the similar performance level as the previous scheme using the subjective rating method that can select reference sets that are significantly more visually similar (P < .05) than when using traditional ICAD schemes in which the mass boundary spiculation levels are not accurately detected and quantified.

Prospective Assessment of Computer-Aided Detection in Interpretation of Screening Mammography

OBJECTIVE

The purpose of this study was to prospectively assess the usefulness of computer-aided detection (CAD) in the interpretation of screening mammography and to provide the true sensitivity and specificity of this technique in a clinical setting.

SUBJECTS AND METHODS

Over a 26-month period, 5,016 screening mammograms were interpreted without, and subsequently with, the assistance of the iCAD MammoReader detection system. Data collected for actionable findings included dominant feature (calcification, mass, asymmetry, architectural distortion), detection method (radiologist only, CAD only, or both radiologist and CAD), BI-RADS assessment code, associated histopathology for those undergoing biopsy, and tumor stage for malignant lesions. The study population was cross-checked against an independent reference standard to identify false-negative cases.

RESULTS

Of the 5,016 cases, the recall rate increased from 12% to 14% with the addition of CAD. Of the 107 (2%) patients who underwent biopsy, 101 (94%) were prompted by the radiologist and six (6%) were prompted by CAD. Of the 124 biopsies performed on actionable findings in the 107 patients, findings in 79 (64%) were benign and in 45 (36%) were in situ or invasive carcinoma. Three study participants who were not recalled by the radiologist with the assistance of CAD developed cancer within 1 year of the screening mammogram and were considered to be false-negative cases. The radiologist detected 43 (90%) of the 48 total malignancies and 45 (94%) of the 48 malignancies with the assistance of CAD. CAD missed eight cancers that were detected by the radiologist, which presented as architectural distortions (n = 3), irregular masses (n = 4), and a circumscribed mass (n = 1). CAD detected two in situ cancers as a faint cluster of calcifications that had not been perceived by the radiologist and one mass that was dismissed by the radiologist, accounting for at least a 4.7% increase in cancer detection rate. Sensitivity of screening mammography with the use of CAD (94%) represented an absolute and relative 4% increase over the sensitivity of the radiologist alone (90%). Specificity of screening mammography with and without the use of CAD was 99%.

CONCLUSION

Routine use of CAD while interpreting screening mammograms significantly increases recall rates, has no significant effect on positive predictive value for biopsy, and can increase cancer detection rate by at least 4.7% and sensitivity by at least 4%. This study provides "true" values for sensitivity and specificity for use of CAD in interpretation of screening mammography as measured prospectively in the context of a working clinical setting.

Multiview-based computer-aided detection scheme for breast masses

In this study, we developed and tested a new multiview-based computer-aided detection (CAD) scheme that aims to maintain the same case-based sensitivity level as a single-image-based scheme while substantially increasing the number of masses being detected on both ipsilateral views. An image database of 450 four-view examinations (1800 images) was assembled. In this database, 250 cases depicted malignant masses, of which 236 masses were visible on both views and 14 masses were visible only on one view. First, we detected suspected mass regions depicted on each image in the database using a single-image-based CAD. For each identified region (with detection score > or = 0.55), we then identified a matching strip of interest on the ipsilateral view based on the projected distance to the nipple along the centerline. By lowering CAD operating threshold inside the matching strip, we searched for a region located inside the strip and paired it with the original region. A multifeature-based artificial neural network scored the likelihood of the paired "matched" regions representing true-positive masses. All single (unmatched) regions except for those either with very high detection scores (> or = 0.85) or those located near the chest wall that cannot be matched on the other view were discarded. The original single-image-based CAD scheme detected 186 masses (74.4% case-based sensitivity) and 593 false-positive regions. Of the 186 identified masses, 91 were detected on two views (48.9%) and 95 were detected only on one view (51.1%). Of the false-positive detections, 54 were paired on the ipsilateral view inside the corresponding matching strips and the remaining 485 were not, which represented 539 case-based false-positive detections (0.3 per image). Applying the multiview-based CAD scheme, the same case-based sensitivity was maintained while cueing 169 of 186 masses (90.9%) on both views and at the same time reducing the case-based false-positive detection rate by 23.7% (from 539 to 411). The study demonstrated that the new multiview-based CAD scheme could substantially increase the number of masses being cued on two ipsilateral views while reducing the case-based false-positive detection rate.

Computer-Aided Detection and Diagnosis of Breast Cancer

The use of computer-aided detection (CAD) with film or digital mammography is now widely regarded as the standard of practice in mammography and has been shown to increase the rate of breast cancer detection. There are inherent limitations in 2D mammography, and new technologies involving 2D and 3D imaging with X-rays, ultrasound, and MRI are in use or under investigation. CAD can aid in the reduction of oversight error for these modalities and has the potential to assist the physician in unifying the interpretation across alternative modalities. We believe the result will be improved sensitivity and specificity due to both improved detection and diagnosis.

A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment

The purpose of this study was to develop and test a method for selecting "visually similar" regions of interest depicting breast masses from a reference library to be used in an interactive computer-aided diagnosis (CAD) environment. A reference library including 1000 malignant mass regions and 2000 benign and CAD-generated false-positive regions was established. When a suspicious mass region is identified, the scheme segments the region and searches for similar regions from the reference library using a multifeature based k-nearest neighbor (KNN) algorithm. To improve selection of reference images, we added an interactive step. All actual masses in the reference library were subjectively rated on a scale from 1 to 9 as to their "visual margins speculations". When an observer identifies a suspected mass region during a case interpretation he/she first rates the margins and the computerized search is then limited only to regions rated as having similar levels of spiculation (within +/-1 scale difference). In an observer preference study including 85 test regions, two sets of the six "similar" reference regions selected by the KNN with and without the interactive step were displayed side by side with each test region. Four radiologists and five nonclinician observers selected the more appropriate ("similar") reference set in a two alternative forced choice preference experiment. All four radiologists and five nonclinician observers preferred the sets of regions selected by the interactive method with an average frequency of 76.8% and 74.6%, respectively. The overall preference for the interactive method was highly significant (p < 0.001). The study demonstrated that a simple interactive approach that includes subjectively perceived ratings of one feature alone namely, a rating of margin "spiculation," could substantially improve the selection of "visually similar" reference images.