Purpose:To assess how computer-aided detection (CAD) affects reader performance in detecting early lung cancer on chest radiographs. Materials and Methods:In this ethics committee-approved study, 46 individuals with 49 computed tomographically (CT)-detected and histologically proved lung cancers and 65 patients without nodules at CT were retrospectively included. All subjects participated in a lung cancer screening trial. Chest radiographs were obtained within 2 months after screening CT. Four radiology residents and two experienced radiologists were asked to identify and localize potential cancers on the chest radiographs, fi rst without and subsequently with the use of CAD software. A fi gure of merit was calculated by using free-response receiver operating characteristic analysis. Results:Tumor diameter ranged from 5.1 to 50.7 mm (median, 11.8 mm). Fifty-one percent (22 of 49) of lesions were subtle and detected by two or fewer readers. Stand-alone CAD sensitivity was 61%, with an average of 2.4 falsepositive annotations per chest radiograph. Average sensitivity was 63% for radiologists at 0.23 false-positive annotations per chest radiograph and 49% for residents at 0.45 false-positive annotations per chest radiograph.Figure of merit did not change signifi cantly for any of the observers after using CAD. CAD marked between fi ve and 16 cancers that were initially missed by the readers. These correctly CAD-depicted lesions were rejected by radiologists in 92% of cases and by residents in 77% of cases. Conclusion:The sensitivity of CAD in identifying lung cancers depicted with CT screening was similar to that of experienced radiologists. However, CAD did not improve cancer detection because, especially for subtle lesions, observers were unable to suffi ciently differentiate true-positive from false-positive annotations.q RSNA, 2010
This study evaluated the accuracy of ultrasound-guided fine-needle aspiration cytology of the sonographically most suspicious axillary lymph node (US/FNAC) to select early breast cancer patients with three or more tumour-positive axillary lymph nodes. Between 2004 and 2014, a total of 2130 patients with histologically proven early breast cancer were evaluated and treated in the Noordwest Clinics Alkmaar. US/FNAC was performed preoperatively in all these patients. We analysed the results of US/FNAC retrospectively. Pathological axillary node status (sentinel node biopsy and/or axillary lymph node dissection) was used as reference standard. A total of 634 (29.8 %) of 2130 patients had axillary lymph node metastases on final histology. 248 node positive patients (11.6 %) had three or more positive lymph nodes. The accuracy of US/FNAC to detect three or more positive lymph nodes was 89.8 %, sensitivity was 44.8 %, specificity was 95.7 %, PPV was 58.1 %, and NPV was 92.9 %. This study shows a more than adequate accuracy of preoperative US/FNAC to detect three or more positive lymph nodes (89.8 %). However, when US/FNAC was chosen as the only axillary staging method, 6.4 % of all patients (false negative group) would have been undertreated and 3.8 % of all patients (false positive group) would have been overtreated according to the ACOSOG Z0011 criteria.
ObjectiveChest radiographs (CXR) are an important diagnostic tool for the detection of invasive pulmonary aspergillosis (IPA) in critically ill patients, but their diagnostic value is limited by a poor sensitivity. By using advanced image processing, the aim of this study was to increase the value of chest radiographs in the diagnostic work up of neutropenic patients who are suspected of IPA.MethodsThe frontal CXRs of 105 suspected cases of IPA were collected from four institutions. Radiographs could contain single or multiple sites of infection. CT was used as reference standard. Five radiologists and two residents participated in an observer study for the detection of IPA on CXRs with and without bone suppressed images (ClearRead BSI 3.2; Riverain Technologies). The evaluation was performed separately for the right and left lung, resulting in 78 diseased cases (or lungs) and 132 normal cases (or lungs). For each image, observers scored the likelihood of focal infectious lesions being present on a continuous scale (0–100). The area under the receiver operating characteristics curve (AUC) served as the performance measure. Sensitivity and specificity were calculated by considering only the lungs with a suspiciousness score of greater than 50 to be positive.ResultsThe average AUC for only CXRs was 0.815. Performance significantly increased, to 0.853, when evaluation was aided with BSI (p = 0.01). Sensitivity increased from 49% to 66% with BSI, while specificity decreased from 95% to 90%.ConclusionThe detection of IPA in CXRs can be improved when their evaluation is aided by bone suppressed images. BSI improved the sensitivity of the CXR examination, outweighing a small loss in specificity.
ObjectivesTo assess whether short-term feedback helps readers to increase their performance using computer-aided detection (CAD) for nodule detection in chest radiography.MethodsThe 140 CXRs (56 with a solitary CT-proven nodules and 84 negative controls) were divided into four subsets of 35; each were read in a different order by six readers. Lesion presence, location and diagnostic confidence were scored without and with CAD (IQQA-Chest, EDDA Technology) as second reader. Readers received individual feedback after each subset. Sensitivity, specificity and area under the receiver-operating characteristics curve (AUC) were calculated for readings with and without CAD with respect to change over time and impact of CAD.ResultsCAD stand-alone sensitivity was 59 % with 1.9 false-positives per image. Mean AUC slightly increased over time with and without CAD (0.78 vs. 0.84 with and 0.76 vs. 0.82 without CAD) but differences did not reach significance. The sensitivity increased (65 % vs. 70 % and 66 % vs. 70 %) and specificity decreased over time (79 % vs. 74 % and 80 % vs. 77 %) but no significant impact of CAD was found.ConclusionShort-term feedback does not increase the ability of readers to differentiate true- from false-positive candidate lesions and to use CAD more effectively.Key Points• Computer-aided detection (CAD) is increasingly used as an adjunct for many radiological techniques.• Short-term feedback does not improve reader performance with CAD in chest radiography.• Differentiation between true- and false-positive CAD for low conspicious possible lesions proves difficult.• CAD can potentially increase reader performance for nodule detection in chest radiography.
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