Nie Xiuli, Chen Hua, Gao Peng, Yu Hairong, Sun Meili, Yan Peng
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引用次数: 0
Abstract
Purpose: The objective of this study was to evaluate the feasibility of using Artificial Intelligence (AI) to measure the long-diameter of tumors for evaluating treatment response.
Methods: Our study included 48 patients with lung-specific target lesions and conducted 277 measurements. The radiologists recorded the long-diameter in axial imaging plane of the target lesions for each measurement. Meanwhile, AI software was utilized to measure the long-diameter in both the axial imaging plane and in three dimensions (3D). Statistical analyses including the Bland-Altman plot, Spearman correlation analysis, and paired t-test to ascertain the accuracy and reliability of our findings.
Results: The Bland-Altman plot showed that the AI measurements had a bias of -0.28 mm and had limits of agreement ranging from - 13.78 to 13.22 mm (P = 0.497), indicating agreement with the manual measurements. However, there was no agreement between the 3D measurements and the manual measurements, with P < 0.001. The paired t-test revealed no statistically significant difference between the manual measurements and AI measurements (P = 0.497), whereas a statistically significant difference was observed between the manual measurements and 3D measurements (P < 0.001).
Conclusions: The application of AI in measuring the long-diameter of tumors had significantly improved efficiency and reduced the incidence of subjective measurement errors. This advancement facilitated more convenient and accurate tumor response evaluation.
期刊介绍:
BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.