FDG-PET/MR Imaging for prediction of lung metastases in soft-tissue sarcomas of the extremities by texture analysis and wavelet image fusion
Notice bibliographique
Résumé
Soft-tissue sarcoma (STS) of the extremities forms a relatively uncommon yet aggressive group of neoplasms with high metastatic risk of the disease. The vast majority of STS metastases occur in the lungs. Due to the general poor prognosis of patients diagnosed with STS lung metastases, there is a clinical need to identify relevant prognostic factors as early as possible in the course of staging and treatment management. Recent evidence suggests that positron emission tomography (PET) using fluorodeoxyglucose (FDG) and magnetic resonance (MR) imaging texture features have the potential to predict the outcome of tumours through the assessment of their microenvironment heterogeneity characteristics. The goal of this work is therefore to investigate FDG-PET and MR texture features as potential early predictors of lung metastasis risk in STS cancer of the extremities.In this study, a dataset of 35 patients with histologically proven STS of the extremities was retrospectively analyzed. All patients received pre-treatment FDG-PET and MR scans. MR imaging data comprised of T1-weighted, T2 fat-saturation (T2FS) and short tau inversion recovery (STIR) sequences. The median follow-up period was 29 months (range: 4 to 85 months). Thirteen patients from the dataset developed lung metastases. Six texture features from the gray-level co-occurrence matrix (GLCM) were extracted from the FDG-PET, MR and fused FDG-PET/MR scans. In addition, the maximum standard uptake value (SUVmax) of the tumours was included in the feature set. The fusion of FDG-PET and MR scans was carried out using the discrete wavelet transform (DWT) and a band-pass frequencies enhancement technique. Statistical analysis was performed using Spearman's correlation (rho), and multivariable modeling using logistic regression. The prediction performance of the different multivariable models was assessed using bootstrap resampling by calculating the area under the receiver-operating characteristics curve (AUC) and Matthews' correlation coefficient (MCC). The highest univariate prediction of lung metastases was attributed to the SUVmax metric (rho=0.6382, p<0.0001). Most texture features extracted from fused scans had higher Spearman's correlation with lung metastases than those extracted from separate scans. On separate scans, FDG-PET texture features were generally dominant over MR texture features. The highest multivariable prediction of lung metastases was found using fused scans and the following 4-parameters model: 0.94*SUVmax − 0.401*PET-T2FS/STIR--Variance − 6.7*PET-T1--Contrast − 165*PET-T1--Homogeneity + 140. This model reached rho=0.8255, p<0.0001 on the entire dataset and AUC=0.956±0.002, MCC=0.829±0.002 in bootstrap testing sets. Overall, this work indicates the strong potential of FDG-PET and MR texture features for the prediction of lung metastases in STS cancer of the extremities. Substantial prediction improvements were found using texture features from fused scans and multivariable modeling strategies compared to texture features extracted from separate scans and univariate analysis. Potentially, this could improve patient outcomes by allowing better personalization of treatments and the application of pre-emptive strategies to mitigate disease spread.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».