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CAD Performance Analysis for Pulmonary Nodule Detection: Comparison of Thick- and Thin-Slice Helical CT Scans Randy D Ernst 1, Russell C Hardie 2, Metin.

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Presentation on theme: "CAD Performance Analysis for Pulmonary Nodule Detection: Comparison of Thick- and Thin-Slice Helical CT Scans Randy D Ernst 1, Russell C Hardie 2, Metin."— Presentation transcript:

1 CAD Performance Analysis for Pulmonary Nodule Detection: Comparison of Thick- and Thin-Slice Helical CT Scans 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

2 Introduction This study compares the performance of a CAD (QuickCue™, iCAD, Inc.) system in detecting lung nodules from thick- and thin-slice multi-detector row CT scans, and to evaluate the potential benefit of CAD on radiologist sensitivity.

3 Methods and Materials 57 reports 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 Have no significant breathing miss - registration, post surgical changes, pleural effusions & atelectasis

4 Methods and Materials 4-detector LightSpeed QX/I Scanner, GE systems HQ setting with 5.0 collimation, helical pitch of 0.75/1.0 Standard-dose (160 - 270 mA, 120 kVp) Images were reconstructed at 5 mm (thick) and 2.5 mm (thin) slice thicknesses.

5 Methods and Materials 140 nodules (3 mm - 25 mm) were identified pre-CAD by radiologists From thick-slice cases only. Cases with multiple nodules were excluded. Truth marks were mapped to the thin-slice data Mean nodule size 7.3 ± 4.2 mm (3 – 25 mm) Gold standard for nodule truth comes for post- CAD Radiologist review One gold standard for thick-slice and one for thin- slice

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

7 CAD detected 72.1% (101/140) of the pre-CAD 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 Review of Thick-Slice CAD Results

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

9 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 per case. 70 due to atelectasis 39 due to scarring Review of Thin-Slice CAD Results

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

11 Comparison Thick-slice cases Thin-slice cases CAD sensitivity 72.1%80.7% Radiologist sensitivity increase after CAD +25%+67.1% FPs5.64.6

12 FROC Curve for CAD

13 CAD detections in Thick-Slice Additional Detections

14 CAD detections in Thin-Slice Additional Detections

15 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 Case Follow-up

16 Example TPs Examples of nodules that are detected by both radiologist and CAD

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

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

31 Example FPs

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

37 Example FNs

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43 Conclusions Preliminary results indicate that both sensitivity and specificity of the CAD system increases when used with thin- slice scans versus thick-slice scans. The CAD system operating on both thick- and thin-slice scans improved radiologist sensitivity Improvement was greater for CAD operating on thin-slice scans


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