Evidence map›Paper›PMID 42814122›Full record

ArticleEuropean radiology2026

DSDFF-YOLO: an automated computer-aided diagnostic method for post-therapy radioiodine whole-body scan.

Liuying Chen, Fan Yu, Junjuan Zhao, Quanyong Luo, Chentian Shen, Zihao Li, Shengnan Chen, Xuehai Ding

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Article in European radiology, 2026. 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

Authors and funding

8 authors.

Liuying Chen *School of Computer Engineering and Science, Shanghai University, Shanghai, China.
Fan Yu *Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Junjuan ZhaoSchool of Computer Engineering and Science, Shanghai University, Shanghai, China.
Quanyong LuoDepartment of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Chentian ShenDepartment of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zihao LiSchool of Computer Engineering and Science, Shanghai University, Shanghai, China.
Shengnan ChenSchool of Computer Engineering and Science, Shanghai University, Shanghai, China.
Xuehai DingSchool of Computer Engineering and Science, Shanghai University, Shanghai, China. dinghai@shu.edu.cn.ORCID http://orcid.org/0000-0001-6258-007X

Funding

Shanghai Sixth People's Hospital 2023ZZ2006
6 · The paper itself

Abstract

objectivesRadioiodine-131 (

methodsThis retrospective study analyzed data from 361 thyroid cancer patients treated with

resultsThe validation set included 62 patients (mean age 43.90 ± 13.80 years [SD], 36 female patients). The DSDFF-YOLO outperformed others in detecting thyroid remnants and mLNs, achieving a mAP₅₀ of 0.772, mean precision of 0.801, and recall of 0.731. On the independent test set (51 patients, mean age 44.51 ± 15.51 years [SD], 22 female patients), it surpassed nuclear medicine physicians in detecting thyroid remnants and mLNs, with sensitivities of 85.53% and 79.31%, and specificities of 91.11% and 93.52%, respectively.

conclusionThe DSDFF-YOLO enabled effective and accurate automated auxiliary diagnosis based on RxWBS. This method demonstrated excellent diagnostic performance and holds significant potential for clinical application. KEY POINTS: Question Can Deep Learning effectively assist clinicians in accurately diagnosing whole-body lesions on post-therapy radioiodine whole-body scans, rather than remaining confined to cervical lesions? Findings The proposed model achieved high sensitivity and specificity in diagnosing lesions on post-therapy radioiodine whole-body scans, outperforming nuclear medicine physicians. Clinical relevance The proposed model was a promising automated diagnostic tool that could assist clinicians in accurately diagnosing lesions on post-therapy radioiodine whole-body scans, potentially reducing patients' need for costly SPECT/CT imaging.

Indexed as

Computer-assisted diagnosesDeep LearningIodine-131Thyroid cancerWhole body imaging

Identifiers

PMID42814122

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