Intrasystem Repeatability of S-Detect for Breast Ultrasound Classification With Identical Static Images: Single-Center Retrospective Repeatability Study.
Computer-aided diagnostic systems such as S-Detect (Samsung Medison) are increasingly integrated into breast ultrasound workflows. Notwithstanding extensive past evaluation of S-Detect's diagnostic accuracy, its intrasystem repeatability at the software level with identical static images, a fundamental prerequisite for clinical reliability, has not been systematically investigated.
This study aimed to evaluate the intrasystem repeatability of the S-Detect computer-aided diagnostic system in classifying breast nodules in identical static ultrasound images.
This retrospective, registered, blinded repeatability study analyzed 398 breast nodules from 261 women (mean age 43.10, SD 12.57 years) who underwent surgery between February 2019 and March 2020 at a single institution. Identical stored static ultrasound images, acquired by a single experienced sonographer on 1 Samsung RS80A ultrasound system, were each analyzed twice using the same S-Detect workstation: immediately after acquisition (S-Detect 1) and again at least 4 weeks later under blinded conditions with manual cursor repositioning (S-Detect 2). Repeatability was assessed using concordance rate and Cohen κ. The diagnostic performance of each run was compared against surgical histopathology.
Of 398 nodules, 156 (39.2%) were initially classified as possibly benign, and 242 (60.8%) were initially classified as possibly malignant. On repeat analysis, 37.4% (149/398) and 62.6% (249/398) of the nodules were classified as possibly benign and malignant, respectively. A total of 4.5% (7/156) of the nodules initially classified as benign were reclassified as malignant, whereas no malignant-to-benign changes occurred. The overall concordance rate was 98.2% (391/398), with a Cohen κ of 0.95 (95% CI 0.94-0.99; P<.001). Diagnostic performance remained stable across runs (area under the curve=0.913 vs 0.902; P=0.702).
Under controlled conditions with identical static images, S-Detect showed high intrasystem repeatability, underscoring strong software-level consistency, although its translation to real-world clinical reproducibility requires further validation.
This study aimed to evaluate the intrasystem repeatability of the S-Detect computer-aided diagnostic system in classifying breast nodules in identical static ultrasound images.
This retrospective, registered, blinded repeatability study analyzed 398 breast nodules from 261 women (mean age 43.10, SD 12.57 years) who underwent surgery between February 2019 and March 2020 at a single institution. Identical stored static ultrasound images, acquired by a single experienced sonographer on 1 Samsung RS80A ultrasound system, were each analyzed twice using the same S-Detect workstation: immediately after acquisition (S-Detect 1) and again at least 4 weeks later under blinded conditions with manual cursor repositioning (S-Detect 2). Repeatability was assessed using concordance rate and Cohen κ. The diagnostic performance of each run was compared against surgical histopathology.
Of 398 nodules, 156 (39.2%) were initially classified as possibly benign, and 242 (60.8%) were initially classified as possibly malignant. On repeat analysis, 37.4% (149/398) and 62.6% (249/398) of the nodules were classified as possibly benign and malignant, respectively. A total of 4.5% (7/156) of the nodules initially classified as benign were reclassified as malignant, whereas no malignant-to-benign changes occurred. The overall concordance rate was 98.2% (391/398), with a Cohen κ of 0.95 (95% CI 0.94-0.99; P<.001). Diagnostic performance remained stable across runs (area under the curve=0.913 vs 0.902; P=0.702).
Under controlled conditions with identical static images, S-Detect showed high intrasystem repeatability, underscoring strong software-level consistency, although its translation to real-world clinical reproducibility requires further validation.
Authors
Yongping Yongping, Zhou Zhou, Wang Wang, Zhang Zhang, Zhou Zhou, Zhang Zhang, Cai Cai, Juan Juan
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