Guohua Shen1, Zhiyun Jia2, Houfu Deng3. 1. Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, People's Republic of China. shengh1990@126.com. 2. Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, People's Republic of China. shengh1990@hotmail.com. 3. Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, People's Republic of China.
Abstract
OBJECTIVES: The potential performance of apparent diffusion coefficient (ADC) values for distinguishing malignant and benign pulmonary lesions, further characterizing the subtype of lung cancer was assessed. METHODS: PubMed, EMBASE, Cochrane Library, EBSCO, and three Chinese databases were searched to identify eligible studies on diffusion-weighted imaging (DWI) of focal pulmonary lesions. ADC values of malignant and benign lesions were extracted by lesion type and statistically pooled based on a linear mixed model. Further analysis for subtype of lung cancer was also performed. The methodological quality was assessed using the quality assessment of diagnostic accuracy studies tool. RESULTS: Thirty-four articles involving 2086 patients were included. Malignant pulmonary lesions have significantly lower ADC values than benign lesions [1.21 (95% CI, 1.19-1.22) mm(2)/s vs. 1.76 (95% CI, 1.72-1.80) mm(2)/s; P < 0.05]. There is a significant difference between ADC values of small cell lung cancer and non-small cell lung cancer (P < 0.05), while the differences were not significant among histological subtypes of lung cancer. The methodological quality was relatively high, and the data points from Begg's test indicated that there was probably no obvious publication bias. CONCLUSIONS: The ADC value is helpful for distinguishing malignant and benign pulmonary lesions and provides a promising method for differentiation of SCLC from NSCLC. KEY POINTS: • This meta-analysis assesses the role of DWI in pulmonary lesions. • Differentiation and classification subtype of lung cancer is essential for treatment decision-making. • ADC values can help distinguish between malignant and benign lesions. • ADC values might help characterize the subtype of lung cancer.
OBJECTIVES: The potential performance of apparent diffusion coefficient (ADC) values for distinguishing malignant and benign pulmonary lesions, further characterizing the subtype of lung cancer was assessed. METHODS: PubMed, EMBASE, Cochrane Library, EBSCO, and three Chinese databases were searched to identify eligible studies on diffusion-weighted imaging (DWI) of focal pulmonary lesions. ADC values of malignant and benign lesions were extracted by lesion type and statistically pooled based on a linear mixed model. Further analysis for subtype of lung cancer was also performed. The methodological quality was assessed using the quality assessment of diagnostic accuracy studies tool. RESULTS: Thirty-four articles involving 2086 patients were included. Malignant pulmonary lesions have significantly lower ADC values than benign lesions [1.21 (95% CI, 1.19-1.22) mm(2)/s vs. 1.76 (95% CI, 1.72-1.80) mm(2)/s; P < 0.05]. There is a significant difference between ADC values of small cell lung cancer and non-small cell lung cancer (P < 0.05), while the differences were not significant among histological subtypes of lung cancer. The methodological quality was relatively high, and the data points from Begg's test indicated that there was probably no obvious publication bias. CONCLUSIONS: The ADC value is helpful for distinguishing malignant and benign pulmonary lesions and provides a promising method for differentiation of SCLC from NSCLC. KEY POINTS: • This meta-analysis assesses the role of DWI in pulmonary lesions. • Differentiation and classification subtype of lung cancer is essential for treatment decision-making. • ADC values can help distinguish between malignant and benign lesions. • ADC values might help characterize the subtype of lung cancer.
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