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Discriminating Interpatient Variabilities of <i>RAS</i> Gene Variants for Precision Detection of Thyroid Cancer

2024· article· en· W4396988615 on OpenAlexaffabout
Guodong Fu, Ronald S. Chazen, Christina MacMillan, Ian Witterick

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsUniversity of TorontoLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
Fundersnot available
KeywordsHRASNeuroblastoma RAS viral oncogene homologMedicineThyroidKRASThyroid cancerThyroid nodulesMalignancyBiopsyInternal medicineConcordancePathologyOncologyCancerColorectal cancer

Abstract

fetched live from OpenAlex

Importance: Interpatient variabilities in genomic variants may reflect differences in tumor statuses among individuals. Objectives: To delineate interpatient variabilities in RAS variants in thyroid tumors based on the fifth World Health Organization classification of thyroid neoplasms and assess their diagnostic significance in cancer detection among patients with thyroid nodules. Design, Setting, and Participants: This prospective diagnostic study analyzed surgically resected thyroid tumors obtained from February 2016 to April 2022 and residual thyroid fine-needle aspiration (FNA) biopsies obtained from January 2020 to March 2021, at Mount Sinai Hospital, Toronto, Ontario, Canada. Data were analyzed from June 20, 2022, to October 15, 2023. Exposures: Quantitative detection of interpatient disparities of RAS variants (ie, NRAS, HRAS, and KRAS) was performed along with assessment of BRAF V600E and TERT promoter variants (C228T and C250T) by detecting their variant allele fractions (VAFs) using digital polymerase chain reaction assays. Main Outcomes and Measures: Interpatient differences in RAS, BRAF V600E, and TERT promoter variants were analyzed and compared with surgical histopathologic diagnoses. Malignancy rates, sensitivity, specificity, positive predictive values, and negative predictive values were calculated. Results: A total of 438 surgically resected thyroid tumor tissues and 249 thyroid nodule FNA biopsies were obtained from 620 patients (470 [75.8%] female; mean [SD] age, 50.7 [15.9] years). Median (IQR) follow-up for patients who underwent FNA biopsy analysis and subsequent resection was 88 (50-156) days. Of 438 tumors, 89 (20.3%) were identified with the presence of RAS variants, including 51 (11.6%) with NRAS, 29 (6.6%) with HRAS, and 9 (2.1%) with KRAS. The interpatient differences in these variants were discriminated at VAF levels ranging from 0.15% to 51.53%. The mean (SD) VAF of RAS variants exhibited no significant differences among benign nodules (39.2% [11.2%]), noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTPs) (25.4% [14.3%]), and malignant neoplasms (33.4% [13.8%]) (P = .28), although their distribution was found in 41.7% of NIFTPs and 50.7% of invasive encapsulated follicular variant papillary thyroid carcinomas (P < .001). RAS variants alone, regardless of a low or high VAF, were significantly associated with neoplasms at low risk of tumor recurrence (60.7% of RAS variants vs 26.9% of samples negative for RAS variants; P < .001). Compared with the sensitivity of 54.2% (95% CI, 48.8%-59.4%) and specificity of 100% (95% CI, 94.8%-100%) for BRAF V600E and TERT promoter variant assays, the inclusion of RAS variants into BRAF and TERT promoter variant assays improved sensitivity to 70.5% (95% CI, 65.4%-75.2%), albeit with a reduction in specificity to 88.8% (95% CI, 79.8%-94.1%) in distinguishing malignant neoplasms from benign and NIFTP tumors. Furthermore, interpatient differences in 5 gene variants (NRAS, HRAS, KRAS, BRAF, and TERT) were discriminated in 54 of 126 indeterminate FNAs (42.9%) and 18 of 76 nondiagnostic FNAs (23.7%), and all tumors with follow-up surgical pathology confirmed malignancy. Conclusions and Relevance: This diagnostic study delineated interpatient differences in RAS variants present in thyroid tumors with a variety of histopathological diagnoses. Discrimination of interpatient variabilities in RAS in combination with BRAF V600E and TERT promoter variants could facilitate cytology examinations in preoperative precision malignancy diagnosis among patients with thyroid nodules.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

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

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 head0.026
GPT teacher head0.328
Teacher spread0.302 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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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Citations9
Published2024
Admission routes2
Has abstractyes

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