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整合正电子发射断层扫描（PET）和计算机断层扫描（CT）信息以提高肺结节诊断准确性：一种半自动计算机辅助方法。

Integrating PET and CT information to improve diagnostic accuracy for lung nodules: A semiautomatic computer-aided method.

作者信息

Nie Yongkang, Li Qiang, Li Feng, Pu Yonglin, Appelbaum Daniel, Doi Kunio

机构信息

Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, Chicago, Illinois 60637, USA.

出版信息

J Nucl Med. 2006 Jul;47(7):1075-80.

PMID:16818939

Abstract

UNLABELLED

Our objective was to develop and evaluate 3 semiautomatic computer-aided diagnostic (CAD) schemes for distinguishing between benign and malignant pulmonary nodules by use of features extracted from CT, 18F-FDG PET, and both CT and 18F-FDG PET.

METHODS

We retrospectively collected 92 consecutive cases of pulmonary nodules (<3 cm) in patients who underwent both thoracic CT and whole-body PET/CT. Forty-two of the nodules were malignant and 50 benign, as confirmed by pathologic examination and clinical follow-up. The interval between CT and PET was less than 1 mo. Four clinical parameters, including patient age, sex, smoking status, and history of previous malignancy, were used for the CAD schemes. Sixteen CT features based on size, shape, margin, and internal structure of nodules were independently rated subjectively by 2 chest radiologists. Four PET features were viewed on a PET/CT workstation. CAD schemes based on clinical parameters together with CT features, PET features, and both CT and PET features were then used to differentiate benign from malignant nodules. Finally, the output from the CAD schemes was evaluated by use of receiver-operating-characteristic analysis.

RESULTS

When we used clinical parameters and CT features as input units (CAD scheme 1), the area under the receiver-operating-characteristic curve (A(z) value) of the CAD scheme was 0.83. When we used clinical parameters and PET features as input units (CAD scheme 2), the A(z) value for the computer output was 0.91. However, when we used all data as input units (CAD scheme 3), the A(z) value for the computer output was 0.95. The performance of CAD scheme 3 was better than that of CAD scheme 1 or 2. A statistically significant difference existed between the A(z) values of CAD schemes 3 and 2 (P = 0.037) and between those of CAD schemes 3 and 1 (P = 0.015).

CONCLUSION

Our CAD scheme based on both PET and CT was better able to differentiate benign from malignant pulmonary nodules than were the CAD schemes based on PET alone and CT alone.

摘要

未标注

我们的目标是开发并评估3种半自动计算机辅助诊断（CAD）方案，通过利用从CT、18F-FDG PET以及CT和18F-FDG PET两者中提取的特征来区分肺结节的良恶性。

方法

我们回顾性收集了92例连续的肺结节（<3 cm）患者的病例，这些患者均接受了胸部CT和全身PET/CT检查。经病理检查和临床随访证实，其中42个结节为恶性，50个为良性。CT和PET检查的间隔时间小于1个月。CAD方案使用了4个临床参数，包括患者年龄、性别、吸烟状况和既往恶性肿瘤病史。基于结节大小、形状、边缘和内部结构的16个CT特征由2名胸部放射科医生独立主观评分。在PET/CT工作站上观察4个PET特征。然后使用基于临床参数以及CT特征、PET特征和CT与PET两者特征的CAD方案来区分良性和恶性结节。最后，通过使用受试者操作特征分析来评估CAD方案的输出。

结果

当我们使用临床参数和CT特征作为输入单元（CAD方案1）时，CAD方案的受试者操作特征曲线下面积（A(z)值）为0.83。当我们使用临床参数和PET特征作为输入单元（CAD方案2）时，计算机输出的A(z)值为0.91。然而，当我们使用所有数据作为输入单元（CAD方案3）时，计算机输出的A(z)值为0.95。CAD方案3的性能优于CAD方案1或2。CAD方案3与2的A(z)值之间（P = 0.037）以及CAD方案3与1的A(z)值之间（P = 0.015）存在统计学显著差异。