Evidence map›Paper›PMID 34703393›Full record

ArticleWorld journal of nuclear medicine

Clinical predictors of I-131 therapy failure in differentiated thyroid cancer by machine learning: A single-center experience.

David J Lubin, Caleb Tsetse, Mohammad S Khorasani, Massoud Allahyari, Mary McGrath

Registry-linked trialAbstract read
In one paragraph

Article in World journal of nuclear medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06028282 (Post-RAI Therapy Dosimetry and Quality Assessment of Target Tissue Dosing), which is not on this map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06028282 unknown statusnot on this map

Post-RAI Therapy Dosimetry and Quality Assessment of Target Tissue Dosing

Typeobservational_patient_registrySponsorState University of New York - Upstate Medical UniversityRan2021 to 2025Enrolled10ConditionsThyroid DiseaseArmsSPECT
3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

David J LubinDepartment of Radiology, Nuclear Medicine, University Hospital, SUNY Upstate, Syracuse, NY, USA.
Caleb TsetseDepartment of Radiology, Nuclear Medicine, University Hospital, SUNY Upstate, Syracuse, NY, USA.
Mohammad S KhorasaniDepartment of Surgery, University Hospital, College of Medicine, Upstate Medical University, SUNY Upstate, Syracuse, NY, USA.
Massoud AllahyariDepartment of Radiology, Nuclear Medicine, University Hospital, SUNY Upstate, Syracuse, NY, USA.
Mary McGrathDepartment of Radiology, Nuclear Medicine, University Hospital, SUNY Upstate, Syracuse, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Well-differentiated thyroid carcinoma is predominantly a slow-growing malignancy, amendable to treatment, and has an excellent prognosis following thyroidectomy and radioiodine (RAI) therapy. However, patients who fail the initial RAI treatment attempt may require repeated RAI or other treatments and with this, comes an associated impact on patient quality of life. Therefore, the anticipation of patients in whom there is a higher risk of RAI failure may help in patient risk stratification and subsequent management. We conducted a retrospective review to determine the factors associated with initial RAI therapy failure in well-differentiated thyroid cancer patients. Using scikit-learn from Python, we implemented a machine-learning algorithm to determine the clinical patient factors associated with a higher likelihood of treatment resistance. We found that clinical factors such as tumor focality (

Indexed as

Machine learningradioiodine ablationrestratificationthyroglobulinthyroid cancer

Identifiers

PMID34703393
PMCPMC8488882

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Registered trials

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.