FLT-PET And FDG-PET/CT For The Detection Of Relapse Following Definitive Radiotherapy In Non-small Cell Lung Cancer: Preliminary Results
Bibliographic record
Abstract
Background: To identify radiomics features as prognostic factors in patients with adenocarcinoma of lung and assess its incremental value to the traditional staging system and clinical-pathologic risk factors.Method: Total 1085 patients who underwent surgery for lung adenocarcinoma were enrolled in this study (from March 2010 to December 2014, training cohort: n ¼ 749; from January 2015 to February 2016, temporal validation cohort: n ¼ 336).A subset of 80/94 reproducible radiomics features including shape, first order statistics and texture features were identified reproducible and selected for analysis.A radiomics signature to predict (overall survival, OS and recurrence free survival, RFS) was generated by using the least absolute shrinkage and selection operator, or LASSO in training cohort.Association between the radiomics signature and prognosis was explored.Prognostic models incorporating radiomics signature alone and combined clinical-pathologic risk factors including staging system, age, sex, smoking status and adenocarcinoma subtype were tested in the temporal validation cohort.Result: s: The radiomics signatures (constructed from 5 features identified from LASSO) were significantly associated with OS and RFS.Compared with traditional staging, the radiomics signature resulted in better performance for the estimation of OS (C-index for radiomic signature vs TNM staging ¼ 0.726 vs 0.689 in training set, 0.798 vs 0.766 in validation set) and RFS (0.760 vs 0.722 in training set, 0.773 vs 0.751 in validation set) in both training and validation cohorts.The combined model of radiomic signature and clinical-pathologic risk factors showed a significant improvement of predictive performance over the TNM staging system in both training and validation cohorts (OS, C-index ¼ 0.774 in training set and 0.857 in validation set; RFS, Cindex ¼ 0.796 in training set and 0.837 in validation set; for all, p < 0.05).Conclusion: Contrast enhanced CT-based radiomics provided improved prognostic prediction in resectable lung adenocarcinoma, which might enable a step forward precision medicine and affect personalized postoperative treatment strategies.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".