Search

Objectives/hypothesis: To assess the ability of specific positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) features to detect extracapsular extension (ECE) in head and neck squamous cell carcinoma (HNSCC) patients., Study Design: Retrospective study in a tertiary certified university cancer institute., Methods: We performed a review of patients with advanced HNSCC at Bern University Hospital between 2014 and 2018. Patients with pretherapeutic PET/CT and/or MRI who underwent neck dissection were included, with 212 patients fulfilling inclusion criteria. Blinded evaluation of specific PET/CT and MRI features with respect to presence of ECE was performed. Histopathological examination of neck dissection specimens was used as the gold standard to determine ECE status., Results: Out of the 212 included patients, 184 had PET/CT, 186 MRI, and 158 both modalities. Overall clinical stage IV (odds ratio [OR]: 2.26, 95% confidence interval [CI]: 2.25-11.74), ill-defined margins in both PET/CT and MRI (OR: 3.48, 95% CI: 1.21-9.98 and OR: 2.14, 95% CI: 0.94-4.89, respectively), and a maximum standardized uptake value ≥ 10 (OR: 5.44, 95% CI: 1.21-9.98) were all significant independent predictors of ECE. When combined, these four features led to a cumulative score able to predict ECE status with an accuracy of 91.43%., Conclusions: The current findings indicate specific features in PET/CT and MRI are potential predictors of ECE status and may help in pretherapeutic stratification in HNSCC., Level of Evidence: 4 Laryngoscope, 131:E163-E169, 2021., (© 2020 The American Laryngological, Rhinological and Otological Society, Inc.)

Purpose: To investigate and validate the potential role of a radiomics signature in predicting the side-specific probability of extracapsular extension (ECE) of prostate cancer (PCa)., Procedures: The preoperative magnetic resonance imaging data of 238 prostatic samples from 119 enrolled PCa patients were retrospectively assessed. The samples with were randomized in a two-to-one ratio into training (n = 74) and validation (n = 45) datasets. The radiomics features were derived from T2-weighted images (T2WIs). The optimal radiomics features were identified from the least absolute shrinkage and selection operator (LASSO) logistic regression model and were used to construct a predictive radiomics signature via dimension reduction and selection approaches. The association between the radiomics signatures and pathological ECE status was explored. Receiver operating characteristic (ROC) analysis was used to assess the discriminatory ability of the signature. The calibration performance and clinical usefulness of the radiomics signature were subsequently assessed by calibration curve and decision curve analyses., Results: The proposed radiomics signature that incorporated 17 selected radiomics features was significantly associated with pathological ECE outcomes (P < 0.001) in both the training and validation datasets. The constructed model displayed good discrimination, with areas under the curve (AUC) of 0.906 (95 % confidence interval (CI), 0.847, 0.948) and 0.821 (95 % CI, 0.726, 0.894) for the training and validation datasets, respectively, and had a good calibration performance. The clinical utility of this model was confirmed through decision curve analysis., Conclusions: The radiomics signature based on T2WIs showed the potential to predict the side-specific probability of pathological ECE status and can facilitate the preoperative individualized predictions for PCa patients.