CAD Performance Analysis for Pulmonary Nodule Detection: Comparison of Thick- and Thin-Slice Multi- detector 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 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

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

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

Too many nodules to localize

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

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

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

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

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

Venn Diagram for Thick CAD Pre-CAD Review Post-CAD Review Gold Standard

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

Venn Diagram for Thin CAD using thin-slice Pre-CAD Review using thick-slice with detections mapped to thin-slice Post-CAD Review of thin-slice Gold Standard

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

FROC Curve for CAD

CAD detections - Thick-Slice

CAD detections -Thin-Slice

5 primary lung cancers 5 primary lung cancers 24 cases of metastatic cancer including 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 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 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 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 1 case proved to be a thrombosed AVM 4 cases lost to follow up 4 cases lost to follow up Case Follow-up

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

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

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

Example FPs

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

5mm Thick slice Example FNs

5mm Thick slice

2.5mm Thin slice

5mm Thick slice

2.5mm Thin slice

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