PET/CT radiomics potentially improves progression-free survival (PFS) and overall survival (OS) prognostication beyond UICC TNM staging in oropharyngeal squamous cell carcinoma (OPSCC) patients
Bibliographic record
Abstract
Purpose Radiomic analysis of medical images enables automated comprehensive quantification of target lesion shape, texture, and signal intensity characteristics beyond visual assessment. We applied pre-treatment FDG-PET/non-contrast CT radiomics, UICC 8th edition TNM staging and machine learning for outcome prognostication in OPSCC. Methods Data from 311 OPSCC patients was retrieved from institutional archives and The Cancer Imaging Archive. Patients with known HPV-status, cM0-status at initial staging, PFS/death events or >18 months of uneventful follow-up, and treated with curative intent were included. After manual delineation of primary tumors and metastatic cervical lymph nodes, 1037 PET and 1037 CT radiomic features were extracted per lesion. In 33x-repeat 3-fold cross-validation, random survival forest (RSF) models for PFS and OS were trained using (1) radiomic features, (2) UICC T-, N- and overall stage concatenated with HPV-status, and (3) UICC staging combined with radiomics as input. In addition, random forest classifiers (RF) trained on radiomic features alone generated high- and low-risk groups. RSF and RF output was averaged across validation folds. Results In HPV+/HPV- OPSCC, RSF models for PFS yielded a mean Harrell’s C-index±SD of 0.54±0.06/0.50±0.06 (UICC), 0.62±0.05/0.55±0.07 (radiomics) and 0.62±0.05/0.56±0.07 (combined). RSF models for OS yielded 0.55±0.08/0.50±0.08 (UICC), 0.63±0.08/0.60±0.09 (radiomics) and 0.63±0.08/0.60±0.09 (combined). The Radiomics-based stratification of 3- to 5-year PFS and OS was significant in Kaplan-Meier analysis of HPV+ subjects, with similar trends in the smaller HPV- group. Conclusion PET/CT Radiomics may provide complementary value for prognostication in OPSCC. Publication History Article published online: 24 May 2022 © 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/). Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".