Radiation-induced parotid changes in oropharyngeal cancer patients: the role of early functional imaging and patient−/treatment-related factors

Abstract Background Functional magnetic resonance imaging may provide several quantitative indices strictly related to distinctive tissue signatures with radiobiological relevance, such as tissue cellular density and vascular perfusion. The role of Intravoxel Incoherent Motion Diffusion Weighted Imaging (IVIM-DWI) and Dynamic Contrast-Enhanced (DCE) MRI in detecting/predicting radiation-induced volumetric changes of parotids both during and shortly after (chemo)radiotherapy of oropharyngeal squamous cell carcinoma (SCC) was explored. Methods Patients with locally advanced oropharyngeal SCC were accrued within a prospective study offering both IVIM-DWI and DCE-MRI at baseline; IVIM-DWI was repeated at the 10th fraction of treatment. Apparent diffusion coefficient (ADC), tissue diffusion coefficient Dt, perfusion fraction f and perfusion-related diffusion coefficient D* were estimated both at baseline and during RT. Semi-quantitative and quantitative parameters, including the transfer constant Ktrans, were calculated from DCE-MRI. Parotids were contoured on T2-weighted images at baseline, 10th fraction and 8th weeks after treatment end and the percent change of parotid volume between baseline/10th fr (∆Vol10fr) and baseline/8th wk. (∆Volpost) computed. Correlations among volumetric changes and patient-, treatment- and imaging-related features were investigated at univariate analysis (Spearman’s Rho). Results Eighty parotids (40 patients) were analyzed. Percent changes were 18.2 ± 10.7% and 31.3 ± 15.8% for ∆Vol10fr and ∆Volpost, respectively. Among baseline characteristics, ∆Vol10fr was correlated to body mass index, patient weight as well as the initial parotid volume. A weak correlation was present between parotid shrinkage after the first 2 weeks of treatment and dosimetric variables, while no association was found after radiotherapy. Percent changes of both ADC and Dt at the 10th fraction were also correlated to ∆Vol10fr. Significant relationships were found between ∆Volpost and baseline DCE-MRI parameters. Conclusions Both IVIM-DWI and DCE-MRI can help to detect/predict early (during treatment) and shortly after treatment completion the parotid shrinkage. They may contribute to clarify the correlations between volumetric changes of parotid glands and patient−/treatment-related variables by assessing individual microcapillary perfusion and tissue diffusivity

Correlation between histogram-based DCE-MRI parameters and 18F-FDG PET values in oropharyngeal squamous cell carcinoma: Evaluation in primary tumors and metastatic nodes.

OBJECTIVES:To investigate the correlation between histogram-based Dynamic Contrast-Enhanced magnetic resonance imaging (DCE-MRI) parameters and positron emission tomography with 18F-fluorodeoxyglucose (18F-FDG-PET) values in oropharyngeal squamous cell carcinoma (OPSCC), both in primary tumors (PTs) and in metastatic lymph nodes (LNs). METHODS:52 patients with a new pathologically-confirmed OPSCC were included in the present retrospective cohort study. Imaging including DCE-MRI and 18F-FDG PET/CT scans were acquired in all patients. Both PTs and the largest LN, if present, were volumetrically contoured. Quantitative parameters, including the transfer constants, Ktrans and Kep, and the volume of extravascular extracellular space, ve, were calculated from DCE-MRI. The percentiles (P), P10, P25, P50, P75, P90, and skewness, kurtosis and entropy were obtained from the histogram-based analysis of each perfusion parameter. Standardized uptake values (SUV), SUVmax, SUVpeak, SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were calculated applying a SUV threshold of 40%. The correlations between all variables were investigated with the Spearman-rank correlation test. To exclude false positive results under multiple testing, the Benjamini-Hockberg procedure was applied. RESULTS:No significant correlations were found between any parameters in PTs, while significant associations emerged between Ktrans and 18F-FDG PET parameters in LNs. CONCLUSIONS:Evident relationships emerged between DCE-MRI and 18F-FDG PET parameters in OPSCC LNs, while no association was found in PTs. The complex relationships between perfusion and metabolic biomarkers should be interpreted separately for primary tumors and lymph-nodes. A multiparametric approach to analyze PTs and LNs before treatment is advisable in head and neck squamous cell carcinoma (HNSCC)

Deep Learning Staging of Liver Iron Content From Multiecho MR Images

Background MRI represents the most established liver iron content (LIC) evaluation approach by estimation of liver T2* value, but it is dependent on the choice of the measurement region and the software used for image analysis. Purpose To develop a deep-learning method for unsupervised classification of LIC from magnitude T2* multiecho MR images. Study Type Retrospective. Population/Subjects A total of 1069 thalassemia major patients enrolled in the core laboratory of the Myocardial Iron Overload in Thalassemia (MIOT) network, which were included in the training (80%) and test (20%) sets. Twenty patients from different MRI vendors included in the external test set. Field Strength/Sequence A5 T, T2* multiecho magnitude images. Assessment Four deep-learning convolutional neural networks (HippoNet-2D, HippoNet-3D, HippoNet-LSTM, and an ensemble network HippoNet-Ensemble) were used to achieve unsupervised staging of LIC using five classes (normal, borderline, middle, moderate, severe). The training set was employed to construct the deep-learning model. The performance of the LIC staging model was evaluated in the test set and in the external test set. The model's performances were assessed by evaluating the accuracy, sensitivity, and specificity with respect to the ground truth labels obtained by T2* measurements and by comparison with operator-induced variability originating from different region of interest (ROI) placements. Statistical Tests The network's performances were evaluated by single-class accuracy, specificity, and sensitivity and compared by one-way repeated measures analysis of variance (ANOVA) and one-way ANOVA. Results HippoNet-Ensemble reached an accuracy significantly higher than the other networks, and a sensitivity and specificity higher than HippoNet-LSTM. Accuracy, sensitivity, and specificity values for the LIC stages were: normal: 0.96/0.93/0.97, borderline: 0.95/0.85/0.98, mild: 0.96/0.88/0.98, moderate: 0.95/0.89/0.97, severe: 0.97/0.95/0.98. Correctly staging of cases was in the range of 85%-95%, depending on the LIC class. Multiclass accuracy was 0.90 against 0.92 for the interobserver variability. Data Conclusion The proposed HippoNet-Ensemble network can perform unsupervised LIC staging and achieves good prognostic performance. Evidence Level 4 Technical Efficacy Stage