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Randy D Ernst 1 , Russell C Hardie 2 , Metin N Gurcan 3 , Aytekin Oto 1 ,

Randy D Ernst 1 , Russell C Hardie 2 , Metin N Gurcan 3 , Aytekin Oto 1 , Steve K Rogers 3 , Jeffrey W Hoffmeister 3 1. Department of Radiology, The University of Texas Medical Branch, Galveston TX 2. iCAD Inc. and University of Dayton, Dayton OH 3. iCAD Inc., Beavercreek OH.

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Randy D Ernst 1 , Russell C Hardie 2 , Metin N Gurcan 3 , Aytekin Oto 1 ,

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  1. Randy D Ernst1, Russell C Hardie2, Metin N Gurcan3, Aytekin Oto1, Steve K Rogers3, Jeffrey W Hoffmeister3 1. Department of Radiology, The University of Texas Medical Branch, Galveston TX 2. iCAD Inc. and University of Dayton, Dayton OH 3. iCAD Inc., Beavercreek OH CAD Performance Analysis for Pulmonary Nodule Detection: Comparison of Thick- and Thin-Slice Multi-detector CT Scans

  2. Purpose • To compare the performance of a CAD (QuickCue™, Beavercreek, OH) system in detecting lung nodules from thick- and thin-slicemulti-detector row CT. • To evaluate the potential benefit of CAD on radiologist sensitivity.

  3. Methods and Materials • 57 studies reviewed retrospectively • Case selection: • Obtained during a 5-month period • Referred from multiple departments • Contain at least 1 pulmonary nodule but fewer than 10 nodules to localize • No significant miss-registration, breathing, surgical changes, pleural effusions & atelectasis

  4. Too many nodules to localize

  5. Methods and Materials • 4-slice multi-detector row CT (Lightspeed; GE Medical Systems) • HQ setting with 7.5 mm/rotation • Standard-dose (160 - 270 mA, 120 kVp) • Images reconstructed at 5-mm (thick) and 2.5 mm (thin) slice thicknesses

  6. Methods and Materials • 140 nodules (3 mm - 25 mm) were identified • pre-CAD by radiologists • from thick-slice cases only • mean nodule size 7.3 ± 4.2 mm • Truth marks were mapped to the thick-slice 5mm data. • Gold standards for nodule truth created from post-CAD radiologist review • One gold standard for thick-slice • Separate gold standard for thin-slice

  7. CAD System(QuickCue™, iCAD Inc.) DICOM Images 3D Lung Segmentation 3D Candidate Segmentation Calculate Features Classifier Detection Mask

  8. CAD System • Candidates segmented by thresholding and morphological processing • 2D and 3D features computed for each candidate • Anatomical information (hilus, airways, aorta, etc.) compared to reduce false positives • A classifier applied for final decision

  9. Review of Thick-Slice CAD Results • CAD detected 72.1% (101/140) of the thick gold standard truth nodules • CAD detected 35 additional radiologist-confirmed nodules, an increase of 25% (35/140) in sensitivity • 5.6 (317/57) false-positives per case • 55 due to atelectasis • 18 due to scarring

  10. Venn Diagram for Thick Pre-CAD Review CAD 0 0 317 101 35 39 3 Post-CAD Review Gold Standard

  11. Review of Thin-Slice CAD Results • CAD detected 80.7% (113/140) of the pre-CAD truth nodules. • CAD detected 94 additional radiologist-confirmed nodules, an increase of 67.1% (94/140). • 4.6 (262/57) false-positives reported per case. • 70 due to atelectasis • 39 due to scarring

  12. Venn Diagram for Thin Pre-CAD Review using thick-slice with detections mapped to thin-slice CAD using thin-slice 0 0 262 113 94 26 0 Post-CAD Review of thin-slice Gold Standard

  13. Comparison

  14. FROC Curve for CAD

  15. CAD detections - Thick-Slice

  16. CAD detections -Thin-Slice

  17. Case Follow-up • 5 primary lung cancers • 24 cases of metastatic cancer including • 7 lymphomas, 4 breast, 4 head and neck, 2 colon, 2 pancreas, 1 carcinoid, 1 seminoma,  1 ovarian, 1 melanoma and 1 tracheal papillomatosis • 23 cases of infection, including • 19 granulomatous disease either calcified, stable on follow-up or biopsy proven. 4 were presumed infection that resolved with follow-up • 1 case proved to be a thrombosed AVM • 4 cases lost to follow up

  18. Example TPs • Examples of nodules that are detected by both radiologist and CAD

  19. Example TPs • Examples of nodules that are initially missed by radiologists then detected after reviewing CAD

  20. Review of CAD Results • Sources of false positives • Vessel intersections • Inaccurate lung segmentation • Partial volume effects • Other lung abnormalities (scarring, atelectasis)

  21. Example FPs

  22. Review of CAD Results • Sources of false negatives (missed nodules) • Low density, irregular • Strong connectivity with vessels • Imperfect candidate segmentation • Inaccurate lung segmentation

  23. Example FNs 5mm Thick slice

  24. 5mm Thick slice

  25. 2.5mm Thin slice

  26. 5mm Thick slice

  27. 2.5mm Thin slice

  28. Conclusions • Sensitivity and specificity of the CAD system increased when used with thin-slice scans versus thick-slice scans. • CAD improved radiologist sensitivity on both thick- and thin-slice scans. • CAD improvement was greater for thin-slice scans.

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