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Development of a Digital Image Database for Chest Radiographs With and Without a Lung Nodule

Receiver Operating Characteristic Analysis of Radiologists' Detection of Pulmonary Nodules

¹ Department of Radiology, Osaka City University Hospital, 1-5-7 Asahi-machi, Abeno-ku, Osaka, Osaka 545-8586 Japan.
² Department of Radiology, Iwate Medical University, 19-1 Uchimaru, Morioka, Iwate 020-8505 Japan.
³ Department of Radiology, Ehime University, Shizukawa, Shigenobu-cho, Onsen-gun, Ehime 791-0295 Japan.
⁴ Department of Radiology, Yamaguchi University, 1144 Ogushi, Ube, Yamaguchi 755-8506 Japan.
⁵ Department of Radiology, Kanazawa University, 5-11-80, Kotateno, Kanazawa, Ishikawa 920-0942 Japan.
⁶ Medical Engineering Division, Toshiba, 1-1-1 Shibaura, Minato-ku, Tokyo 105-8001 Japan.
⁷ Medical Imaging Division, Konica, 1 Sakura-cho, Hino, Tokyo 191-8511 Japan.
⁸ Department of Information Science, Faculty of Engineering, Gifu University, 1-1, Yanagido, Gifu, Gifu 501-1193 Japan.
⁹ Department of Radiological Technology, Nagoya University School of Health Sciences, 1-1-20 Taikouminami, Higashi-ku, Nagoya, Aichi 461-8673 Japan.
¹⁰ Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, 5841 S. Maryland Ave., MC2026, Chicago, IL 60637.

OBJECTIVE. We developed a digital image database (www.macnet.or.jp/jsrt2/cdrom_nodules.html) of 247 chest radiographs with and without a lung nodule. The aim of this study was to investigate the characteristics of image databases for potential use in various digital image research projects. Radiologists' detection of solitary pulmonary nodules included in the database was evaluated using a receiver operating characteristic (ROC) analysis.

MATERIALS AND METHODS. One hundred and fifty-four conventional chest radiographs with a lung nodule and 93 radiographs without a nodule were selected from 14 medical centers and were digitized by a laser digitizer with a 2048 x 2048 matrix size (0.175-mm pixels) and a 12-bit gray scale. Lung nodule images were classified into five groups according to the degrees of subtlety shown. The observations of 20 participating radiologists were subjected to ROC analysis for detecting solitary pulmonary nodules. Experimental results (areas under the curve, A_z) obtained from observer studies were used for characterization of five groups of lung nodules with different degrees of subtlety.

RESULTS. ROC analysis showed that the database included a wide range of various nodules yielding A_z values from 0.574 to 0.991 for the five categories of cases for different degrees of subtlety.

CONCLUSION. This database can be useful for many purposes, including research, education, quality assurance, and other demonstrations.

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