• S Hahn, T Heusner, X Zhou, Y Zhan, Z Peng, M Hamami, M Forsting, A Bockisch, and G Antoch.
• Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Germany. steffen.hahn@uk-essen.de
• Rofo. 2010 Mar 1;182(3):243-7.

PurposeTo determine the feasibility of a PET/CT software tool (PET computer-aided detection: PET-CAD) for automated detection and assessment of pulmonary and hepatic lesions.Materials And Methods20 consecutive patients with colorectal liver metastases and 20 consecutive patients suffering from non-small cell lung cancer (NSCLC) were examined with FDG-PET/CT. In a first step the maximum standardized uptake values (SUV (max)) of non-tumorous liver and lung tissues were determined manually. This value was used as a threshold value for software-based lesion detection. The number of lesions detected, their SUV (max), and their sizes in the x, y, and z-planes, as automatically provided by PET-CAD, were compared to visual lesion detection and manual measurements on CT.ResultsThe sensitivity for automated detection was 96 % (86 - 99 %) for colorectal liver metastases and 90 % (70 - 99 %) for lung lesions. The positive predictive value was 80 % for liver and 68 % for lung lesions. The mean SUV (max) of all lung lesions was 9.3 and 8.8 for the liver lesions. When assessed by PET-CAD, the mean lesion sizes for liver lesions in the x, y, and z-planes were 4.3 cm, 4.6 cm, and 4.2 cm compared to 3.5 cm, 3.8 cm, and 3.6 cm for manual measurements. The mean lesion sizes of lung lesions were 7.4 cm, 7.7 cm, and 8.4 cm in the x, y, and z-planes when assessed by PET-CAD compared to 5.8 cm, 6.1 cm, and 7.1 cm when measured manually. Using manual assessment, the lesion sizes were significantly smaller in all planes (p < 0.005).ConclusionSoftware tools for automated lesion detection and assessment are expected to improve the clinical PET/CT workflow. Before implementation in the clinical routine, further improvements to the measurement accuracy are required.Georg Thieme Verlag KG Stuttgart New York.

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