Evidence map›Paper›PMID 41686570›Full record

ArticleMedicine2026

Construction and evaluation of a SPECT-based radiomics nomogram for predicting the therapeutic response to radioactive iodine in patients with differentiated thyroid carcinoma.

Haiyang Yu, Yingnan Jia, Qian Wu, Peihui Liu

Abstract read
In one paragraph

Article in Medicine, 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Haiyang YuDepartment of Radiology, Yanbian University Affiliated Hospital (Yanbian Hospital), Yanji, Jilin Province, China.
Yingnan JiaDepartment of Nuclear Medicine, Southeast University Affiliated Xuzhou Central Hospital, Xuzhou, Jiangsu Province, China.ORCID 0009-0007-7476-8150
Qian WuDepartment of Nuclear Medicine, Southeast University Affiliated Xuzhou Central Hospital, Xuzhou, Jiangsu Province, China.
Peihui LiuDepartment of Neuro-Intervention, Huludao Central Hospital, Huludao, Liaoning Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Differentiated thyroid carcinoma (DTC) frequently requires radioactive iodine therapy, yet accurately predicting short-term therapeutic response remains challenging. Radiomics offers a means to quantify intratumoral heterogeneity from routine imaging. We aimed to develop and internally validate a Single-photon emission computed tomography (SPECT)-based radiomics nomogram for predicting 6-month response after RAI in DTC. We conducted a single-center retrospective study of 420 patients with DTC. Post-therapy 131I SPECT/CT images were processed to extract image biomarker standardisation initiative-aligned radiomic features in MATLAB; features passing reproducibility (ICC ≥ 0.80) and redundancy (|r|<0.85) filters were entered into a nested-cross-validation (CV) least absolute shrinkage and selection operator to derive a radiomics score, which was combined with clinicopathologic variables to build a nomogram. Model performance was assessed for discrimination (receiver operating characteristic area under the curve [AUC]), calibration (concordance index [C-index]; calibration intercept/slope), and clinical utility (decision-curve analysis); internal validation used outer 10-fold CV and 1000 bootstrap with permutation testing. Multicollinearity was checked by variance inflation factor. Interobserver reproducibility was high: 79/107 (73.8%) features achieved ICC ≥ 0.80 (median ICC 0.88), and the median mask-level Dice was 0.87. The nomogram showed AUC = 0.853 (95% confidence interval: 0.786-0.892) and C-index = 0.889 (95% confidence interval: 0.812-0.934) with good calibration. Internal validation yielded CV-AUC = 0.846 (SD 0.037) and bootstrap optimism-corrected C-index = 0.876/AUC = 0.848; permutation AUCs centered near 0.50. Decision-curve analysis indicated clinical net benefit across thresholds 0.15 to 0.95. All validation was internal; no external cohort was available. Radiomics score, age, regional lymph node metastasis, extrathyroidal extension, and tumor diameter independently influence the therapeutic outcome of RAI in DTC patients. In this exploratory, single-center study, the SPECT-based radiomics nomogram demonstrates promising internal performance; however, it must undergo external, multi-center validation and, if necessary, local recalibration before it can be considered for routine clinical use.

Indexed as

Iodine RadioisotopesNomogramsThyroid NeoplasmsTomography, Emission-Computed, Single-PhotonAdultAgedFemaleHumansMaleMiddle AgedRadiomicsReproducibility of ResultsRetrospective StudiesTreatment OutcomeIodine Radioisotopesclinical prognosislogistic regression analysisnomogram modelradiomicsthyroid neoplasms

Identifiers

PMID41686570
PMCPMC12908823

What Socratic holds

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LicenceCC BY
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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 Socratic graph.