Evidence map›Paper›PMID 42708557›Full record

ArticleTechnology in cancer research & treatment

Evaluating Diagnostic Strategies for Thyroid Nodule Assessment Using S-Detect in Transverse and Longitudinal Ultrasound Planes.

Ziman Chen, Mengting Ye, Jieyi Liang, Fei Chen, Michael Tin Cheung Ying

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Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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5 authors.

Ziman ChenDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong.ORCID 0000-0002-2765-5002
Mengting YeDepartment of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
Jieyi LiangDepartment of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
Fei ChenDepartment of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
Michael Tin Cheung YingDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

IntroductionThe influence of ultrasound imaging plane on the diagnostic performance of artificial intelligence-based computer-aided diagnosis systems for thyroid nodules remains unclear. This study evaluated S-Detect in transverse and longitudinal ultrasound planes and compared four plane-based interpretation strategies.MethodsThis prospective cross-sectional study included 157 patients with 207 surgically confirmed thyroid nodules. Each nodule was independently analyzed by S-Detect in transverse (S-Detect_T) and longitudinal (S-Detect_L) planes. Four diagnostic strategies were evaluated: transverse-plane assessment, longitudinal-plane assessment, a serial strategy classifying a nodule as possibly malignant only when both planes indicated possible malignancy, and a parallel strategy classifying a nodule as possibly malignant when either plane indicated possible malignancy. Diagnostic performance, agreement, and interplane discordance were assessed.ResultsOf the 207 nodules, 140 were malignant and 67 were benign. S-Detect_L showed numerically higher sensitivity and specificity than S-Detect_T (93.6% vs 92.1% and 62.7% vs 59.7%, respectively), but neither difference was statistically significant. Transverse- and longitudinal-plane classifications agreed in 89.4% of nodules, whereas 10.6% showed interplane discordance. The parallel strategy achieved the highest sensitivity (97.9%) and negative predictive value (92.5%) but the lowest specificity (55.2%). The serial strategy achieved the highest specificity (67.2%) and the lowest false-positive rate (32.8%) but had lower sensitivity (87.9%). Compared with the serial strategy, the parallel strategy identified 14 additional malignant nodules while producing eight additional false-positive classifications. A sensitivity analysis restricted to one nodule per patient showed the same overall pattern.ConclusionsTransverse- and longitudinal-plane S-Detect assessments showed similar diagnostic performance, although interplane discordance occurred in a subset of nodules. The parallel strategy favored sensitivity, whereas the serial strategy favored specificity and fewer false-positive classifications. No imaging plane or combination rule was consistently superior across all diagnostic measures.

Indexed as

Image Interpretation, Computer-AssistedThyroid NoduleAdultAgedArtificial IntelligenceCross-Sectional StudiesDiagnosis, Computer-AssistedFemaleHumansMaleMiddle AgedProspective StudiesSensitivity and SpecificityUltrasonographyartificial intelligencecomputer-aided diagnosisS-Detectthyroid noduleultrasound imaging

Identifiers

PMID42708557
PMCPMC13554603

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.