Surveillance Mammography After Breast Conservation Therapy: Is Tomosynthesis Worth It?

INTRODUCTION: We investigated the downstream workup and costs associated with digital breast tomosynthesis (DBT) compared with 2-dimensional full field digital mammogram (FFDM) when employed as initial follow-up imaging in breast conservation therapy. METHODS: Between the years 2015 and 2017, 450 consecutive breast conservation therapy patients, ages 32 to 89, with a follow-up DBT (n=162) or FFDM (n=288) were retrospectively reviewed. The primary endpoints were further workup after follow-up mammogram and associated health care costs at 1 year. A single DBT costs an estimated $149 compared with $111 for FFDM, based on Centers for Medicare claims data from the Oncology Care Model. RESULTS: The first posttreatment mammogram was received within 3 (20%), 3 to 6 (32%), or after 6 months (48%) following radiation. Younger patients and those undergoing hypofractionated radiation were more likely to get DBT. There were no differences in stage, receptor status, or mammogram timing between those in the FFDM and DBT groups.The following downstream workup ensued for DBT compared with FFDM imaging: 18% versus 29% short-interval (6-mo) mammogram (odds ratio=1.83, P=0.01), 6% versus 11% breast magnetic resonance imaging (odds ratio=1.90, P=0.08), 4% ultrasound for each, and 3% biopsy for each (1 positive in the FFDM group). Including downstream workup, the estimated cost per patient in the DBT group was $216.14 compared with $237.83 in the FFDM group. Independent predictors for reduced downstream workup per multivariable analysis were the use of DBT and first follow-up mammogram at least 6 months after radiation (P<0.05). DISCUSSION: Excess workup was reduced with DBT compared with FFDM in the posttreatment setting, which translated to an improvement in cost efficiency in this study.

Characterization of radiologists' search strategies for lung nodule detection: slice-based versus volumetric displays.

The goal of this study was to assess whether radiologists' search paths for lung nodule detection in chest computed tomography (CT) between different rendering and display schemes have reliable properties that can be exploited as an indicator of ergonomic efficiency for the purpose of comparing different display paradigms. Eight radiologists retrospectively viewed 30 lung cancer screening CT exams, containing a total of 91 nodules, in each of three display modes [i.e., slice-by-slice, orthogonal maximum intensity projection (MIP) and stereoscopic] for the purpose of detecting and classifying lung nodules. Radiologists' search patterns in the axial direction were recorded and analyzed along with the location, size, and shape for each detected feature, and the likelihood that the feature is an actual nodule. Nodule detection performance was analyzed by employing free-response receiver operating characteristic methods. Search paths were clearly different between slice-by-slice displays and volumetric displays but, aside from training and novelty effects, not between MIP and stereographic displays. Novelty and training effects were associated with the stereographic display mode, as evidenced by differences between the beginning and end of the study. The stereo display provided higher detection and classification performance with less interpretation time compared to other display modes tested in the study; however, the differences were not statistically significant. Our preliminary results indicate a potential role for the use of radiologists' search paths in evaluating the relative ergonomic efficiencies of different display paradigms, but systematic training and practice is necessary to eliminate training curve and novelty effects before search strategies can be meaningfully compared.

Is maximum positive predictive value a good indicator of an optimal screening mammography practice?

OBJECTIVE: Positive predictive value (PPV1) has been used as one important indicator of the quality of screening mammography programs. We show how the relationship between sensitivity and recall rate may affect the operating point at which optimal (maximum) PPV1 occurs. CONCLUSION: Optimal (maximum) PPV1 can occur at any sensitivity level and should not be used as the sole indicator for practice optimization because it does not take into account the number of cancers that would be missed at that sensitivity.

Computer-aided detection performance in mammographic examination of masses: assessment.

PURPOSE: To compare performance of two computer-aided detection (CAD) systems and an in-house scheme applied to five groups of sequentially acquired screening mammograms. MATERIALS AND METHODS: Two hundred nineteen film-based mammographic examinations, classified into five groups, were included in this study. Group 1 included 58 examinations in which verified malignant masses were detected during screening; group 2, 39 in which all available latest examinations were performed prior to diagnosis of these malignant masses (subset of 39 women from group 1); group 3, 22 in which findings were interpreted as negative but were verified as cancer within 1 year from the negative interpretation (missed cancers); group 4, 50 in which findings were negative and patients were not recalled for additional procedures; and group 5, 50 in which patients were recalled for additional procedures and findings were negative for cancer. In all examinations, images were processed with two Food and Drug Administration-approved commercially available CAD systems and an in-house scheme. Performance levels in terms of true-positive detection rates and number of false-positive identifications per image and per examination were compared. RESULTS: Mass detection rates in positive examinations (group 1) were 67%-72%. Detection rates among three systems were not significantly different (P > .05). In 50 negative screening examinations (group 4), false-positive rates ranged from 1.08 to 1.68 per four-view examination. Performance level differences among systems were significant for false-positive rates (P = .008). Performance of all systems was at levels lower than publicly suggested in some retrospective studies. False-positive CAD cueing rates were significantly higher for negative examinations in which patients were recalled (group 5) than they were for those in which patients were not recalled (group 4) (P < or = .002). CONCLUSION: Performance of CAD systems for mass detection at mammography varies significantly, depending on examination and system used. Actual performance of all systems in clinical environment can be improved.

Computer-aided detection schemes: the effect of limiting the number of cued regions in each case.

OBJECTIVE: We assessed performance changes of a mammographic computer-aided detection scheme when we restricted the maximum number of regions that could be identified (cued) as showing positive findings in each case. MATERIALS AND METHODS: A computer-aided detection scheme was applied to 500 cases (or 2,000 images), including 300 cases in which mammograms showed verified malignant masses. We evaluated the overall case-based performance of the scheme using a free-response receiver operating characteristic approach, and we measured detection sensitivity at a fixed false-positive detection rate of 0.4 per image after gradually reducing the maximum number of cued regions allowed for each case from seven to one. RESULTS: The original computer-aided detection scheme achieved a maximum case-based sensitivity of 97% at 3.3 false-positive detected regions per image. For a detection decision score set at 0.565, the scheme had a 79% (237/300) case-based sensitivity, with 0.4 false-positive detected regions per image. After limiting the number of maximum allowed cued regions per case, the false-positive rates decreased faster than the true-positive rates. At a maximum of two cued regions per case, the false-positive rate decreased from 0.4 to 0.21 per image, whereas detection sensitivity decreased from 237 to 220 masses. To maintain sensitivity at 79%, we reduced the detection decision score to as low as 0.36, which resulted in a reduction of false-positive detected regions from 0.4 to 0.3 per image and a reduction in region-based sensitivity from 66.1% to 61.4%. CONCLUSION: Limiting the maximum number of cued regions per case can improve the overall case-based performance of computer-aided detection schemes in mammography.