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Record W3021880341 · doi:10.1210/jendso/bvaa046.889

MON-502 Clinical-Pathological and Molecular Prognostic Markers in Aggressive and Poorly Differentiated Thyroid Cancers; A Tertiary-Center Experience

2020· article· en· W3021880341 on OpenAlexaff
Suhaib Radi, Sabin Filimon, Michael Tamilia

Bibliographic record

VenueJournal of the Endocrine Society · 2020
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineThyroid cancerPathologicalInternal medicineThyroidPapillary thyroid cancerCancerGastroenterologyDiseasePathologyOncology

Abstract

fetched live from OpenAlex

Abstract Background: Aggressive variants of papillary thyroid cancer (AV-PTC) and poorly differentiated thyroid cancers (PDTC) are 2 malignancies that lie in between the well-differentiated and the undifferentiated anaplastic cancers. While management of those well-differentiated cancers is established in the literature, that of AV-PTC and PDTC is less clear as they behave different to their more benign counterparts. The aim of this study is to describe the clinico-pathologic characteristics and genotypic background of AV-PTC and PDTC and to assess their prognostic value. Methods: The charts of all patients with thyroid cancer in our center for the last 10 years were retrospectively reviewed. Those with AV-PTC and PDTC were selected and included in the analysis. Clinical presentation, pathologic characteristics, molecular markers, specific treatments and clinical outcomes were compared among groups. Results: Out of 3244 thyroid cancer charts reviewed, 87 patients met the criteria for AV-PTC (n=45) and PDTC (n=42). Mean age at diagnosis was 48.1 years (SD 17.8), with female predominance (64.4% vs 35.6%). Median duration of follow up was 3 years (0.1-30). Out of the 75 patients with follow up for more than a year, 42.7% had either persistent disease or recurrence (52.6% in AV-PTC and 32.4% in PDTC) and 4.1% died. Presence of vascular invasion was associated with higher rates of persistent or recurrent disease (74.1% in positive vascular invasion vs 20.5% in negative vascular invasion, p < 0.001). Recurrence rate was 0% in patients with Ki67 < 10% and 40% in those >= 10%. There was no difference in terms of recurrence based on presence of BRAF mutation (33% in BRAF+ & 29% in BRAF-, p=1), or percentage of aggressive/poorly differentiated tumor involvement (48% in > 30% involvement vs 28% in < 30%, p = 0.132). Discussion and conclusion: The prevalence of AV-PTC and PDTC in this cohort was low at 1.3% each, and the rate of patients with persistent or recurrent disease at 1 year after primary therapy was also similar to that reported (42.7%). The mortality rates, however, in our study is surprisingly lower than that expected elsewhere (4.1%), most likely attributed to a shorter follow up period. Patients with absent vascular invasion were less likely to have persistent or recurrent disease. Those with lower Ki67 (<10%) also had lower relapse rate, although, the p value was > 0.05. It is worth mentioning that even though there were higher rates of recurrence among those with > 30% tumor involvement, it did not reach statistical significance, supporting recent studies stating that even tumor involvement of > 10% can have adverse outcomes. In conclusion, AV-PTC and PDTC are relatively rare but aggressive tumors. Possible prognostic markers that can be used to guide therapy and monitoring include: vascular invasion, extra-thyroidal extension, response to primary therapy and the proliferative index Ki67.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.308
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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