Oncosuppressor-Mutated Cell-Based Diagnostic Platform for Liquid Biopsy Diagnoses Benign Head and Neck Masses and Predicts Malignancy in Thyroid Nodules: Results from a Consecutive Cohort of Patients
Bibliographic record
Abstract
BACKGROUND: We reported that a novel oncosuppressor-mutated cell (OMC)-based platform has the potential for early cancer detection in healthy individuals and for identification of cancer patients at risk of developing metachronous metastases. OBJECTIVE: Herein, we sought to determine the diagnostic accuracy of this novel OMC-based platform in a consecutive cohort of patients operated for suspicious head and neck masses. METHODS: -deficient fibroblasts) were exposed to blood serum from patients with head and neck nodules before surgical removal. These cells were analyzed for their proliferation and survival. Treated OMCs were inoculated subcutaneously in NOD/SCID mice, and tumor growth was monitored over time. RESULTS: OMCs exposed to serum from patients with malignant lesions displayed increased proliferation compared to those exposed to serum from patients with benign lesions. Only OMCs exposed to serum from patients diagnosed with malignant thyroid neoplasia generated a cancerous mass. The sensitivity of the test was 92%, with only 1 false negative out of 34 patients. Immunohistochemical staining showed that the cancerous masses were poorly differentiated adenocarcinomas with high proliferative index. CONCLUSIONS: These data show that liquid biopsy combined with an OMC-based in vivo platform has the potential to diagnose benign head and neck masses and predict whether a thyroid nodule is malignant. These results strengthen the concept that OMCs can be used to detect circulating malignant factors in cancer patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".