PO-0621: Validation of tumor delineation on HE stained sections with cytokeratin staining as gold standard
Bibliographic record
Abstract
Purpose or ObjectiveA gene signature predicting loco-regional control (LRC) of locally advanced head and neck squamous cell carcinoma (HNSCC) after postoperative radiochemotherapy (PORT-C) will be evaluated using nanoString and RNA microarray data.The prognostic power of the signature as well as the correlation between both methods is evaluated to underline the robustness of the proposed signature. Material and MethodsGene expression analyses were performed using nanoString technology and the GeneChip® Human Transcriptome Array 2.0 (Affymetrix) on a multicentre retrospective patient cohort of 191 patients with HNSCC who received postoperative radiochemotherapy.The nanoString gene expression panel of 209 genes was composed hypothesis-driven, including genes which are involved in proliferation, invasion and metastasis as well as in radio(chemo)resistance associated with tumour hypoxia, cancer stem cell markers, cisplatin-resistance and DNA repair.A gene signature which optimally predicts LRC was extracted from the nanoString gene expression data.Different statistical methods for signature selection and outcome prediction were compared.In parallel, this gene signature was evaluated using gene expression data of the GeneChip® Human Transcriptome Array analyses.The prognostic performance of both methods, measured by the concordance index (CI), was compared. ResultsThe extracted nanoString gene signature contained genes related to cellular proliferation, migration, invasion, and tumour hypoxia.From the different statistical methods, Cox regression performed best and was chosen for outcome prediction.Internal 3-fold cross validation during model building showed a CI≈0.7,indicating a good performance of the model.Evaluating the signature using the gene expression data generated with the GeneChip® Human Transcriptome Array led to similar results.The expression values of each gene within the signature were significantly correlated between nanoString and RNA microarray data with R>0.4. ConclusionWe determined a gene signature for the prediction of LRC in a cohort of 191 patients with locally advanced HNSCC after postoperative radiochemotherapy based on nanoString gene expression data.The signature showed a good prognostic value and was validated by internal and external validation.Using gene expression data from the GeneChip® Human Transcriptome Array a similar prognostic value was obtained, underlining the robustness of the proposed signature.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".