Jackknife Free-response Receiver Operating Characteristic Research Articles

Feasibility of thin-slice, low noise images created using multi-kernel synthesis to replace multiple image series in head CT.

SynthesiZed Improved Resolution and Concurrent nOise reductioN (ZIRCON) is a multi-kernel synthesis method that creates a single series of thin-slice computed tomography (CT) images displaying low noise and high spatial resolution, increasing reader efficiency and minimizing partial volume averaging. To compare the diagnostic performance of a single set of ZIRCON images to two routine clinical image series using conventional CT head and bone reconstruction kernels for diagnosing intracranial findings and fractures in patients with trauma or suspected acute neurologic deficit. In total, 50 patients underwent clinically indicated head CT in the ER (15 normal, 35 abnormal cases). A non-reader neuroradiologist established the reference standard. Three neuroradiologists reviewed two routine clinical series (head and bone kernels) and a single ZIRCON series, detecting intracranial findings or fractures and rating confidence (0-100). Sensitivity, specificity, and jackknife free-response receiver operating characteristic (JAFROC) figure of merit (FOM) were compared (limit of non-inferiority: -0.10). ZIRCON and conventional images demonstrated comparable performance for fractures (sensitivity: 51.5% vs. 54.5%; specificity: 40.2% vs. 34.2%) and intracranial findings (sensitivity: 88.2% vs. 91.4%; specificity: 77.2% vs. 73.7%).The estimated difference of JAFROC FOM demonstrated ZIRCON non-inferiority for acute pathologies overall (0.003 [95% CI=-0.051-0.057]) and fractures (0.048 [95% CI=-0.050-0.145]) but not for intracranial findings alone (-0.024 [95% CI=-0.100-0.052]). Thin-slice, low noise, and high spatial resolution images can be created to display intracranial findings and fractures replacing multiple images series in head CT with similar performance. Future studies in more patients and further algorithmic development are warranted.

Compressed sensing with deep learning reconstruction: Improving capability of gadolinium-EOB-enhanced 3D T1WI

PurposeThe purpose of this study was to determine the utility of compressed sensing (CS) with deep learning reconstruction (DLR) for improving spatial resolution, image quality and focal liver lesion detection on high-resolution contrast-enhanced T1-weighted imaging (HR-CE-T1WI) obtained by CS with DLR as compared with conventional CE-T1WI with parallel imaging (PI). MethodsSeventy-seven participants with focal liver lesions underwent conventional CE-T1WI with PI and HR-CE-T1WI, surgical resection, transarterial chemoembolization, and radiofrequency ablation, followed by histopathological or >2-year follow-up examinations in our hospital. Signal-to-noise ratios (SNRs) of liver, spleen and kidney were calculated for each patient, after which each SNR was compared by means of paired t-test. To compare focal lesion detection capabilities of the two methods, a 5-point visual scoring system was adopted for a per lesion basis analysis. Jackknife free-response receiver operating characteristic (JAFROC) analysis was then performed, while sensitivity and false positive rates (/data set) for consensus assessment of the two methods were also compared by using McNemar's test or the signed rank test. ResultsEach SNR of HR-CE-T1WI was significantly higher than that of conventional CE-T1WI with PI (p < 0.05). Sensitivities for consensus assessment showed that HR-CE-MRI had significantly higher sensitivity than conventional CE-T1WI with PI (p = 0.004). Moreover, there were significantly fewer FP/cases for HR-CE-T1WI than for conventional CE-T1WI with PI (p = 0.04). ConclusionCS with DLR are useful for improving spatial resolution, image quality and focal liver lesion detection capability of Gd-EOB-DTPA enhanced 3D T1WI without any need for longer breath-holding time.

Variations of image interpretations of radiologists from different populations in mammography and tomosynthesis with different levels of breast density.

This study aims to investigate the diagnostic performances of Australian and Shanghai-based Chinese radiologists in reading full-field digital mammogram (FFDM) and digital breast tomosynthesis (DBT) with different levels of breast density. Eighty-two Australian radiologists interpreted a 60-case FFDM set, and 29 radiologists also reported a 35-case DBT set. Sixty Shanghai radiologists read the same FFDM set, and 32 radiologists read the DBT set. The diagnostic performances of Australian and Shanghai radiologists were assessed using truth data (cancer cases were biopsy proven) and compared overall in specificity, case sensitivity, lesion sensitivity, receiver operating characteristics (ROC) area under the curve, and jack-knife free-response receiver operating characteristics (JAFROC) figure of merit, and they were stratified by case characteristics using the Mann-Whitney U test. The Spearman rank test was used to explore the association between radiologists' performances and their work experience in mammogram interpretation. There were significantly higher performances of Australian radiologists compared with Shanghai radiologists in low breast density for case sensitivity, lesion sensitivity, ROC, and JAFROC in the FFDM set ( ); in high breast density, Shanghai radiologists' performances in lesion sensitivity and JAFROC were also lower than Australian radiologists ( ). In the DBT test set, Australian radiologists performed better than Shanghai radiologists in cancer detection in both low and high breast density. The work experience of Australian radiologists was positively linked to their diagnostic performances, whereas this association was not statistically significant in Shanghai radiologists. There were significant variations in reading performances between Australian and Shanghai radiologists in FFDM and DBT across different levels of breast density, lesion types, and lesion sizes. An effective training initiative tailored to suit local readers is essential to enhancing the diagnostic accuracy of Shanghai radiologists.

Development and performance evaluation of a deep learning lung nodule detection system

BackgroundLung cancer is the leading cause of cancer-related deaths throughout the world. Chest computed tomography (CT) is now widely used in the screening and diagnosis of lung cancer due to its effectiveness. Radiologists must identify each small nodule shadow from 3D volume images, which is very burdensome and often results in missed nodules. To address these challenges, we developed a computer-aided detection (CAD) system that automatically detects lung nodules in CT images.MethodsA total of 1997 chest CT scans were collected for algorithm development. The algorithm was designed using deep learning technology. In addition to evaluating detection performance on various public datasets, its robustness to changes in radiation dose was assessed by a phantom study. To investigate the clinical usefulness of the CAD system, a reader study was conducted with 10 doctors, including inexperienced and expert readers. This study investigated whether the use of the CAD as a second reader could prevent nodular lesions in lungs that require follow-up examinations from being overlooked. Analysis was performed using the Jackknife Free-Response Receiver-Operating Characteristic (JAFROC).ResultsThe CAD system achieved sensitivity of 0.98/0.96 at 3.1/7.25 false positives per case on two public datasets. Sensitivity did not change within the range of practical doses for a study using a phantom. A second reader study showed that the use of this system significantly improved the detection ability of nodules that could be picked up clinically (p = 0.026).ConclusionsWe developed a deep learning-based CAD system that is robust to imaging conditions. Using this system as a second reader increased detection performance.

Detection of acute rib fractures on CT images with convolutional neural networks: effect of location and type of fracture and reader’s experience

The evaluation of all ribs on thin-slice CT images is time consuming and it can be difficult to accurately assess the location and type of rib fracture in an emergency. The aim of our study was to develop and validate a convolutional neural network (CNN) algorithm for the detection of acute rib fractures on thoracic CT images and to investigate the effect of the CNN algorithm on radiologists' performance. The dataset for development of a CNN consisted of 539 thoracic CT scans with 4906 acute rib fractures. A three-dimensional faster region-based CNN was trained and evaluated by using tenfold cross-validation. For an observer performance study to investigate the effect of CNN outputs on radiologists' performance, 30 thoracic CT scans (28 scans with 90 acute rib fractures and 2 without rib fractures) which were not included in the development dataset were used. Observer performance study involved eight radiologists who evaluated CT images first without and second with CNN outputs. The diagnostic performance was assessed by using figure of merit (FOM) values obtained from the jackknife free-response receiver operating characteristic (JAFROC) analysis. When radiologists used the CNN output for detection of rib fractures, the mean FOM value significantly increased for all readers (0.759 to 0.819, P = 0.0004) and for displaced (0.925 to 0.995, P = 0.0028) and non-displaced fractures (0.678 to 0.732, P = 0.0116). At all rib levels except for the 1st and 12th ribs, the radiologists' true-positive fraction of the detection became significantly increased by using the CNN outputs. The CNN specialized for the detection of acute rib fractures on CT images can improve the radiologists' diagnostic performance regardless of the type of fractures and reader's experience. Further studies are needed to clarify the usefulness of the CNN for the detection of acute rib fractures on CT images in actual clinical practice.

Temporal subtraction CT with nonrigid image registration improves detection of bone metastases by radiologists: results of a large-scale observer study

To determine whether temporal subtraction (TS) CT obtained with non-rigid image registration improves detection of various bone metastases during serial clinical follow-up examinations by numerous radiologists. Six board-certified radiologists retrospectively scrutinized CT images for patients with history of malignancy sequentially. These radiologists selected 50 positive and 50 negative subjects with and without bone metastases, respectively. Furthermore, for each subject, they selected a pair of previous and current CT images satisfying predefined criteria by consensus. Previous images were non-rigidly transformed to match current images and subtracted from current images to automatically generate TS images. Subsequently, 18 radiologists independently interpreted the 100 CT image pairs to identify bone metastases, both without and with TS images, with each interpretation separated from the other by an interval of at least 30 days. Jackknife free-response receiver operating characteristics (JAFROC) analysis was conducted to assess observer performance. Compared with interpretation without TS images, interpretation with TS images was associated with a significantly higher mean figure of merit (0.710 vs. 0.658; JAFROC analysis, P = 0.0027). Mean sensitivity at lesion-based was significantly higher for interpretation with TS compared with that without TS (46.1% vs. 33.9%; P = 0.003). Mean false positive count per subject was also significantly higher for interpretation with TS than for that without TS (0.28 vs. 0.15; P < 0.001). At the subject-based, mean sensitivity was significantly higher for interpretation with TS images than that without TS images (73.2% vs. 65.4%; P = 0.003). There was no significant difference in mean specificity (0.93 vs. 0.95; P = 0.083). TS significantly improved overall performance in the detection of various bone metastases.

Does mammographic density remain a radiological challenge in the digital era?

The low subject contrast between cancerous and fibroglandular tissue could obscure breast abnormalities. To investigate radiologists' performance for detection of breast cancer in low and high mammographic density (MD) when cases are digitally acquired. A test set of 60 digital mammography cases, of which 20 were cancerous, were examined by 17 radiologists. Mammograms were categorized as low (≤50%) or high (>50%) MD and rated for suspicion of malignancy using the Royal Australian and New Zealand College of Radiology (RANZCR) classification system. Radiologist demographics including cases read per year, age, subspecialty, and years of reporting were recorded. Radiologist performance was analyzed by the following metrics: sensitivity; specificity; area under the receiver operating characteristic (ROC) curve (AUC), location sensitivity, and jackknife free-response ROC (JAFROC) figure of merit (FOM). Comparing high to low MD cases, radiologists showed a significantly higher sensitivity (P = 0.015), AUC (P = 0.003), location sensitivity (P = 0.002), and JAFROC FOM (P = 0.001). In high compared to low MD cases, radiologists with <1000 annual reads and radiologists with no mammographic subspecialty had significantly higher AUC, location sensitivity, and JAFROC FOM. Radiologists with ≥1000 annual reads and radiologists with mammography subspecialty demonstrated a significant increase in location sensitivity in high compared to low MD cases. In this experimental situation, radiologists' performance was higher when reading cases with high compared to low MD. Experienced radiologists were able to precisely localize lesions in breasts with higher MD. Further studies in unselected screening materials are needed to verify the results.

Improvement of Cancer Detection on Mammograms via BREAST Test Sets

Breast Screen Reader Assessment Strategy (BREAST) is an innovative training and research program for radiologists in Australia and New Zealand. The aim of this study is to evaluate the efficacy of BREAST test sets in improving readers' performance in detecting cancers on mammograms. Between 2011 and 2018, 50 radiologists (40 fellows, 10 registrars) completed three BREAST test sets and 17 radiologists completed four test sets. Each test set contained 20 biopsy-proven cancer and 40 normal cases. Immediate image-based feedback was available to readers after they completed each test set which allowed the comparison of their selections with the truth. Case specificity, case sensitivity, lesion sensitivity, the Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and Jackknife Free-Response Receiver Operating Characteristic (JAFROC) Figure of Merit (FOM) were calculated for each reader. Kruskal-Wallis test was utilized to compare scores of the radiologist and registrars across all test-sets whilst Wilcoxon signed rank test was to compare the scores between pairs of test sets. Significant improvements in lesion sensitivity ranging from 21% to 31% were found in radiologists completing later test sets compared to first test set (p ≤ 0.01). Eighty three percent of radiologists achieved higher performance in lesion sensitivity after they completed the first read. Registrars had significantly better scores in the third test set compared to the first set with mean increases of 79% in lesion sensitivity (p = 0.005) and 37% in JAFROC (p = 0.02). Sixty percent and 100% of registrars increased their scores in lesion sensitivity in the second and third reads compared to the first read while the percentage of registrars with higher scores in JAFROC was 80%. Introduction of BREAST into national training programs appears to have an important impact in promoting diagnostic efficacy amongst radiologists and radiology registrars undergoing mammographic readings.

Arterial spin-labeling is useful for the diagnosis of residual or recurrent meningiomas

ASL is useful in evaluating tumour blood flow and in detecting hypervascular tumours. The purpose of this study was to assess the additive value of ASL to non-contrast and contrast-enhanced (NC/CE)-T1WI for diagnosing residual or recurrent meningiomas. This retrospective study included 25 postoperative patients (20 women, 5 men; median age, 65 [32-85] years) with and 25 gender- and age-matched postoperative patients without residual or recurrent meningiomas. ASL was performed using a pseudocontinuous method. Seven independent observers (two radiology residents, two general radiologists and three neuroradiologists) participated in two reading sessions consisting of only NC/CE-T1WI (first session) or NC/CE-T1WI with ASL (second session). We evaluated the sensitivity and diagnostic performance for the detection of residual or recurrent meningiomas. The diagnostic performance was assessed using a figure of merit (FOM) calculated via jackknife free-response receiver-operating characteristics. Statistical analysis was performed with paired t tests, with a significance level of p < .05. The sensitivities were as follows (NC/CE-T1WI vs. NC/CE-T1WI with ASL): residents (62.1% vs. 70.7%), general radiologists (75.9% vs. 87.9%), neuroradiologists (97.7% vs. 100%) and all observers (81.3% vs. 88.2%). The FOMs were as follows (NC/CE-T1WI vs. NC/CE-T1WI with ASL): residents (0.76 vs. 0.83), general radiologists (0.83 vs. 0.93), neuroradiologists (0.95 vs. 0.99) and all observers (0.86 vs. 0.93). The addition of ASL significantly improved the diagnostic parameters for all observers except neuroradiologists (p <. 05). ASL improved the detection rate of residual or recurrent meningiomas on NC/CE-T1WI among both radiology residents and general radiologists. • ASL improved diagnostic performance for residual/recurrent meningioma compare to NC/CE-T1WI alone. • Diagnostic sensitivity was increased after adding ASL compared with NC/CE-T1WI. • FOM was increased after adding ASL compared with NC/CE-T1WI.

Detection and Characterization of Solid Pulmonary Nodules at Digital Chest Tomosynthesis: Data from a Cohort of the Pilot Swedish Cardiopulmonary Bioimage Study.

Purpose To investigate the performance of digital tomosynthesis (DTS) for detection and characterization of incidental solid lung nodules. Materials and Methods This prospective study was based on a population study with 1111 randomly selected participants (age range, 50-64 years) who underwent a medical evaluation that included chest computed tomography (CT). Among these, 125 participants with incidental nodules 5 mm or larger were included in this study, which added DTS in conjunction with the follow-up CT and was performed between March 2012 and October 2014. DTS images were assessed by four thoracic radiologists blinded to the true number of nodules in two separate sessions according to the 5-mm (125 participants) and 6-mm (55 participants) cut-off for follow-up of incidental nodules. Pulmonary nodules were directly marked on the images by the readers and graded regarding confidence of presence and recommendation for follow-up. Statistical analyses included jackknife free-response receiver operating characteristic, receiver operating characteristic, and Cohen κ coefficient. Results Overall detection rate ranges of CT-proven nodules 5 mm or larger and 6 mm or larger were, respectively, 49%-58% and 48%-62%. Jackknife free-response receiver operating characteristics figure of merit for detection of CT-proven nodules 5 mm or larger and 6 mm or larger was 0.47 and 0.51, respectively, and area under the receiver operating characteristic curve regarding recommendation for follow-up was 0.62 and 0.65, respectively. Conclusion Routine use of DTS would result in lower detection rates and reduced number of small nodules recommended for follow-up. © RSNA, 2018.

Observer Variability in Breast Cancer Diagnosis between Countries with and without Breast Screening

Image reporting is a vital component of patient management depending on individual radiologists' performance. Our objective was to explore mammographic diagnostic efficacy in a country where breast cancer screening does not exist. Two mammographic test sets were used: a typical screening (TS) and high-difficulty (HD) test set. Nonscreening (NS) radiologists (n = 11) read both test sets, while 52 and 49 screening radiologists read the TS and HD test sets, respectively. The screening radiologists were classified into two groups: a less experienced (LE) group with ≤5 years' experience and a more experienced (ME) group with ≥5 years' experience. A Kruskal-Wallis and Tukey-Kramer post hoc test were used to compare reading performance among reader groups, and the Wilcoxon matched pairs tests was used to compare TS and ND test sets for the NS radiologists. Across the three reader groups, there were significant differences in case sensitivity (χ2 [2] = 9.4, P = .008), specificity (χ2 [2] = 10.3, P = .006), location sensitivity (χ2 [2] = 19.8, P < .001), receiver operating characteristics, area under the curve (χ2 [2] = 19.7, P < .001) and jack-knife free-response receiver operating characteristics (JAFROCs) (χ2 [2] = 18.1, P < .001). NS performance for all measured scores was significantly lower than those for the ME readers (P < .006), while only location sensitivity was lower (χ2 [2] = 17.5, P = .026) for the NS compared to the LE group. No other significant differences were observed. Large variations in mammographic performance exist between radiologists from screening and nonscreening countries.

Temporal Subtraction of Serial CT Images with Large Deformation Diffeomorphic Metric Mapping in the Identification of Bone Metastases

Purpose To determine the improvement of radiologist efficiency and performance in the detection of bone metastases at serial follow-up computed tomography (CT) by using a temporal subtraction (TS) technique based on an advanced nonrigid image registration algorithm. Materials and Methods This retrospective study was approved by the institutional review board, and informed consent was waived. CT image pairs (previous and current scans of the torso) in 60 patients with cancer (primary lesion location: prostate, n = 14; breast, n = 16; lung, n = 20; liver, n = 10) were included. These consisted of 30 positive cases with a total of 65 bone metastases depicted only on current images and confirmed by two radiologists who had access to additional imaging examinations and clinical courses and 30 matched negative control cases (no bone metastases). Previous CT images were semiautomatically registered to current CT images by the algorithm, and TS images were created. Seven radiologists independently interpreted CT image pairs to identify newly developed bone metastases without and with TS images with an interval of at least 30 days. Jackknife free-response receiver operating characteristics (JAFROC) analysis was conducted to assess observer performance. Reading time was recorded, and usefulness was evaluated with subjective scores of 1-5, with 5 being extremely useful and 1 being useless. Significance of these values was tested with the Wilcoxon signed-rank test. Results The subtraction images depicted various types of bone metastases (osteolytic, n = 28; osteoblastic, n = 26; mixed osteolytic and blastic, n = 11) as temporal changes. The average reading time was significantly reduced (384.3 vs 286.8 seconds; Wilcoxon signed rank test, P = .028). The average figure-of-merit value increased from 0.758 to 0.835; however, this difference was not significant (JAFROC analysis, P = .092). The subjective usefulness survey response showed a median score of 5 for use of the technique (range, 3-5). Conclusion TS images obtained from serial CT scans using nonrigid registration successfully depicted newly developed bone metastases and showed promise for their efficient detection. © RSNA, 2017 Online supplemental material is available for this article.

Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study.

PurposeTo assess the effect of computer-aided detection (CAD) of brain metastasis (BM) on radiologists’ diagnostic performance in interpreting three-dimensional brain magnetic resonance (MR) imaging using follow-up imaging and consensus as the reference standard.Materials and methodsThe institutional review board approved this retrospective study. The study cohort consisted of 110 consecutive patients with BM and 30 patients without BM. The training data set included MR images of 80 patients with 450 BM nodules. The test set included MR images of 30 patients with 134 BM nodules and 30 patients without BM. We developed a CAD system for BM detection using template-matching and K-means clustering algorithms for candidate detection and an artificial neural network for false-positive reduction. Four reviewers (two neuroradiologists and two radiology residents) interpreted the test set images before and after the use of CAD in a sequential manner. The sensitivity, false positive (FP) per case, and reading time were analyzed. A jackknife free-response receiver operating characteristic (JAFROC) method was used to determine the improvement in the diagnostic accuracy.ResultsThe sensitivity of CAD was 87.3% with an FP per case of 302.4. CAD significantly improved the diagnostic performance of the four reviewers with a figure-of-merit (FOM) of 0.874 (without CAD) vs. 0.898 (with CAD) according to JAFROC analysis (p < 0.01). Statistically significant improvement was noted only for less-experienced reviewers (FOM without vs. with CAD, 0.834 vs. 0.877, p < 0.01). The additional time required to review the CAD results was approximately 72 sec (40% of the total review time).ConclusionCAD as a second reader helps radiologists improve their diagnostic performance in the detection of BM on MR imaging, particularly for less-experienced reviewers.

Analysis of computer-aided detection techniques and signal characteristics for clustered microcalcifications on digital mammography and digital breast tomosynthesis

With IRB approval, digital breast tomosynthesis (DBT) images of human subjects were collected using a GE GEN2 DBT prototype system. Corresponding digital mammograms (DMs) of the same subjects were collected retrospectively from patient files. The data set contained a total of 237 views of DBT and equal number of DM views from 120 human subjects, each included 163 views with microcalcification clusters (MCs) and 74 views without MCs. The data set was separated into training and independent test sets. The pre-processing, object prescreening and segmentation, false positive reduction and clustering strategies for MC detection by three computer-aided detection (CADe) systems designed for DM, DBT, and a planar projection image generated from DBT were analyzed. Receiver operating characteristic (ROC) curves based on features extracted from microcalcifications and free-response ROC (FROC) curves based on scores from MCs were used to quantify the performance of the systems. Jackknife FROC (JAFROC) and non-parametric analysis methods were used to determine the statistical difference between the FROC curves. The difference between the CADDM and CADDBT systems when the false positive rate was estimated from cases without MCs did not reach statistical significance. The study indicates that the large search space in DBT may not be a limiting factor for CADe to achieve similar performance as that observed in DM.

Detection of masses in digital breast tomosynthesis using complementary information of simulated projection.

The purpose of this study is to develop a computer-aided detection system that combines the detection results in 3D digital breast tomosynthesis (DBT) volume and 2D simulated projection (synthesized image which is not provided by the vendor but generated from DBT volume in this study) to improve the accuracy of mass detection in DBT. The 3D DBT volume has a problem of blurring in the out-of-focus plane because it is reconstructed from a limited number of projection view images acquired over a limited angular range. To solve the problem, the simulated projection is generated by measuring the blurriness of voxels in the DBT volume and adopting conspicuity voxels. A contour-based detection algorithm is applied to detecting masses in the simulated projection. The DBT volume is analyzed by using an unsupervised mass detection algorithm, which results in mass candidates in the DBT volume. The mass likelihood scores estimated for mass candidates on the DBT volume and the simulated projection are merged in a probabilistic manner through a Bayesian network model to differentiate masses and false positives (FPs). Experiments were conducted on a clinical data set of 320 DBT volumes. In 90 volumes, at least one biopsy-proven malignant mass was presented. The longest diameter of masses ranged from 7.0 to 56.4 mm (mean = 25.4 mm). The sizes of masses in the data set were relatively large compared to the sizes of the masses reported in other detection studies. Three image quality measurements (overall sharpness, sharpness of mass boundary, and contrast) were used to evaluate the image quality of the simulated projection compared to the DBT central slice where the mass was most conspicuous and other projection methods (maximum intensity projection and average projection). A free-response receiver operating characteristic (FROC) analysis was adopted for evaluating the accuracy of mass detection in the DBT volume, the simulated projection, and the combined approach. A jackknife FROC analysis (JAFROC) was used to estimate the statistical significance of the difference between two FROC curves. The overall sharpness and the sharpness of mass boundary in the simulated projection are higher than those in the DBT central slice and other projection methods. The contrast of the simulated projection is lower than the DBT central slice. The mass detection in the DBT volume achieved region-based sensitivities of 80% and 85% with 1.75 and 2.11 FPs per DBT volume. The proposed combined mass detection approach achieved same sensitivities with reduced FPs of 1.33 and 1.93 per DBT volume. The difference of the FROC curves between the combined approach and the mass detection in the DBT volume was statistically significant (p < 0.01) by JAFROC analysis. This study indicates that the combined approach that merges the detection results in the DBT volume and the simulated projection is a promising approach to improve the accuracy of mass detection in DBT.

In the digital era, architectural distortion remains a challenging radiological task

To compare readers' performance in detecting architectural distortion (AD) compared with other breast cancer types using digital mammography. Forty-one experienced breast screen readers (20 US and 21 Australian) were asked to read a single test set of 30 digitally acquired mammographic cases. Twenty cases had abnormal findings (10 with AD, 10 non-AD) and 10 cases were normal. Each reader was asked to locate and rate any abnormalities. Lesion and case-based performance was assessed. For each collection of readers (US; Australian; combined), jackknife free-response receiver operating characteristic (JAFROC), figure of merit (FOM), and inferred receiver operating characteristic (ROC), area under curve (Az) were calculated using JAFROC v.4.1 software. Readers' sensitivity, location sensitivity, JAFROC, FOM, ROC, Az scores were compared between cases groups using Wilcoxon's signed ranked test statistics. For lesion-based analysis, significantly lower location sensitivity (p=0.001) was shown on AD cases compared with non-AD cases for all reader collections. The case-based analysis demonstrated significantly lower ROC Az values (p=0.02) for the first collection of readers, and lower sensitivity for the second collection of readers (p=0.04) and all-readers collection (p=0.008), for AD compared with non-AD cases. The current work demonstrates that AD remains a challenging task for readers, even in the digital era.

Evaluation of the effect of zoom function on lesion detection by soft-copy reading of screening mammograms.

This study aims to evaluate the effectiveness of zooming in improving screen-reader performance in reporting digital mammograms. Two experiments were conducted. In the first experiment, 5 readers were asked to report 59 two-view bilateral mammograms retrospectively with zooming function turned off. The second session was similar to the first one except that zooming was enabled. The task of readers was to assess if the mammograms were normal or abnormal and rate the confidence levels for each of the lesion they detected. The reader performances were evaluated via case sensitivity, lesion sensitivity, specificity, receiver operating characteristics (ROC) area under the curve (AUC) and jackknife free-response receiver operating characteristics (JAFROC) figure of merit (FOM). There was no significant improvement in overall reader performance in detecting abnormalities in zooming condition compared with no zooming in terms of case sensitivity (96% and 87%, P = 0.285) or lesion sensitivity (88% and 81%, P = 0.224). However, differences in ROC AUC and JAFROC FOM (P ≤ 0.05) were found in two readers when they performed the test set with zooming function. The results suggested that the use of the zooming function did improve the performance of some readers in detecting abnormal cases.

Mammographic Density and Cancer Detection: Does Digital Imaging Challenge our Current Understanding?

To investigate the impact of breast density on the performance of radiologists when mammograms are digitally acquired and displayed. A total of 150 craniocaudal digital mammograms including 75 cases with cancer were examined by 14 radiologists divided into two groups: those who read more (six) and less (eight) than 2000 mammograms per year. Cases were classified as low or high mammographic density. For both types of cases, detection of cancers within and outside the dense fibroglandular tissue was investigated. The performance of radiologist was measured using jack-knife free-response receiver operating characteristic (JAFROC) figure of merit (FOM). Radiologists with over 2000 annual reads had significantly higher JAFROC FOM (P = .03) for high (0.76) mammographic density compared to low (0.70) mammographic density cases. When lesions overlaid the fibroglandular tissue, cases with high mammographic density compared to low mammographic density displayed increased location sensitivity for all radiologists (P = .03) and for those radiologists reading more than 2000 mammograms annually (P = .04), whereas JAFROC FOMs increased for all radiologists (P = .05). No significant changes were observed when the lesion was outside the fibroglandular region. Increased mammographic density improves the performance of experienced radiologists when using digital mammograms. This finding, which does not align with those previously reported for film screen systems, may be because of windowing/leveling opportunities available with digital images.

Assessing the effect of a true-positive recall case in screening mammography: does perceptual priming alter radiologists' performance?

To measure the effect of the insertion of less-difficult malignant cases on subsequent breast cancer detection by breast imaging radiologists. The research comprises two studies. Study 1: 8 radiologists read 2 sets of images each consisting of 40 mammographic cases. Set A contained four abnormal cases, and Set B contained six abnormal cases, including two priming cases (less difficult malignancies) placed at intervals of three and five subsequent cases before a subtle cancer. Study 2: 16 radiologists read a third condition of the same cases, known as Set C, containing six abnormal cases and two priming cases immediately preceding the subtle cancer cases. The readers were asked to localize malignancies and give confidence ratings on decisions. Although not significant, a decrease in performance was observed in Set B compared with in Set A. There was a significant increase in the receiver operating characteristic (ROC) area under the curve (z = -2.532; p = 0.0114) and location sensitivity (z = -2.128; p = 0.0333) between the first and second halves of Set A and a marginal improvement in jackknife free-response ROC figure of merit (z = -1.89; p = 0.0587) between the first and second halves of Set B. In Study 2, Set C yielded no significant differences between the two halves of the study. Overall findings show no evidence that priming with lower difficulty malignant cases affects the detection of higher difficulty cancers; however, performance may decrease with priming. This research suggests that inserting additional malignant cases in screening mammography sets as an audit tool may potentially lead to a decrease in performance of experienced breast radiologists.

The Effect of JPEG2000 Compression on Detection of Skull Fractures

To investigate the effect of the Joint Photographic Experts Group (JPEG2000) 30:1 and 60:1 lossy compression on the detection of cranial vault fractures when compared to JPEG2000 lossless compression. Fifty cranial computed tomography (CT) images were processed with three different level of JPEG2000 compression (lossless, 30:1 lossy, and 60:1 lossy) creating three sets of images. These were presented to five musculoskeletal specialists and five neuroradiologists. Each reader read at two of the three compression levels. Twenty-two cases contained a single fracture; the remaining 28 cases contained no fractures. Observers were asked to identify the presence or absence of a fracture, to locate its site, and rate their degree of confidence. Receiver operating characteristic (ROC), jackknife free-response receiver operating characteristic (JAFROC) and the Dorfman-Berbaum-Metz multiple reader multiple case (DBM-MRMC) analyses were used to explore differences between the lossless and lossy compressed images. JPEG2000 lossless and 30:1 lossy compression demonstrated no significant difference in their performance with JAFROC and DBM-MRMC analysis (P < .416); however, JPEG2000 30:1 lossy compression demonstrated significantly better performance than 60:1 lossy compression (P < .016). A significant increase in misplaced confidence ratings was also seen with 60:1 (P < .037) over 30:1 lossy and lossless compression. JPEG2000 60:1 compression degrades the detection of skull fractures significantly while increasing the confidence with which readers rate fractures compared with 30:1 lossy and lossless compression. JPEG2000 30:1 lossy compression does not significantly change performance when compared to JPEG2000 lossless for the detection of skull fractures on CT.