To analyze baseline CT/MR-based image features of salivary glands to predict radiation-induced xerostomia 3-months after head-and-neck cancer (HNC) radiotherapy. A retrospective analysis was performed on 266 HNC patients who were treated using radiotherapy at our institution between 2009 and 2018. CT and T1 post-contrast MR images along with NCI-CTCAE xerostomia grade (3-month follow-up) were prospectively collected at our institution. CT and MR images were registered on which parotid/submandibular glands were contoured. Image features were extracted for ipsilateral/contralateral parotid and submandibular glands relative to the location of the primary tumor. Dose-volume-histogram (DVH) parameters were also acquired. Features were pre-selected based on Spearman correlation before modelling by examining the correlation with xerostomia (p < 0.05). A shrinkage regression analysis of the pre-selected features was performed using LASSO. The internal validity of the variable selection was estimated by repeating the entire variable selection procedure using a leave-one-out-cross-validation. The most frequently selected variables were considered in the final model. A generalized linear regression with repeated ten-fold cross-validation was developed to predict radiation-induced xerostomia at 3-months after radiotherapy. This model was tested in an independent dataset (n = 50) of patients who were treated at the same institution in 2017–2018. We compared the prediction performances under eight conditions (DVH-only, CT-only, MR-only, CT + MR, DVH + CT, DVH + CT + MR, Clinical+CT + MR, and Clinical+DVH + CT + MR) using the area under the receiver operating characteristic curve (ROC-AUC). Among extracted features, 7 CT, 5 MR, and 2 DVH features were selected. The internal cohort (n = 216) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.73 ± 0.01, 0.69 ± 0.01, 0.70 ± 0.01, and 0.79 ± 0.01, respectively. The validation cohort (n = 50) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.63, 0.57, 0.66, and 0.68, respectively. The DVH-ROC was not significantly different than the CT-ROC (p = 0.8) or MR-ROC (p = 0.4). However, the CT + MR-ROC was significantly different than the CT-ROC (p = 0.03), but not the Clinical+DVH + CT + MR model (p = 0.5). Our results suggest that baseline CT and MR image features may reflect baseline salivary gland function and potential risk for radiation injury. The integration of baseline image features into prediction models has the potential to improve xerostomia risk stratification with the ultimate goal of truly personalized HNC radiotherapy.

Background To evaluate ultra-short-echo-time (UTE) MRI pulmonary signal-intensity measurements and reproducibility in chronic obstructive pulmonary disease (COPD). Methods A two-dimensional sequence (echo-time = 0.05 ms; acquisition-time = 13 s) with interleaved half-pulse excitation and radial ramp-sampling was used with compressed-sensing to reconstruct UTE images from under-sampled data. Five healthy volunteers and 15 subjects with COPD provided written informed consent to imaging and pulmonary-function-tests. Healthy volunteers underwent MRI at four lung volumes: full-expiration, functional-residual-capacity (FRC), FRC+1L, and full-inhalation; COPD patients underwent computed-tomography (CT) and MRI at FRC+1L. Three-week reproducibility was evaluated and the relative area of the density histogram ≤ −950 HU (RA₉₅₀) was compared with mean MRI signal-intensity. The 15th percentile of signal-intensity-histogram (SI₁₅) was compared with the 15th percentile of the CT-density-histogram (HU₁₅). Results In healthy subjects, signal-intensity correlated with the inverse of lung volume (r = 0.99; P = 0.007). Contrast-to-noise and signal-to-noise ratios were significantly improved for 32-channel UTE (P < 0.01). The coefficient of variation for 3-week repeated measurements was 4%. There were significant correlations for signal-intensity with RA₉₅₀ (r = −0.71; P = 0.005), FEV₁/FVC (r = 0.59; P = 0.02), and for SI₁₅ with HU₁₅ (r = 0.62; P = 0.01). Conclusion Pulmonary signal-intensity is reproducible and related to tissue density. In COPD subjects with and without bronchiectasis, signal-intensity was also related to pulmonary function and CT measurements. J. Magn. Reson. Imaging 2015;41:1465–1474.

Background: Patients with non-small cell lung cancer may develop pneumonitis after thoracic radiotherapy (RT) and immune checkpoint inhibitors (ICIs). We hypothesized that distinct morphologic features are associated with different pneumonitis etiologies. Materials and Methods: We systematically compared computed tomography (CT) features of RT- versus ICI-pneumonitis. Clinical and imaging features were tested for association with pneumonitis severity. Lastly, we constructed an exploratory radiomics-based machine learning (ML) model to discern pneumonitis etiology. Results: Between 2009 and 2019, 82 patients developed pneumonitis: 29 after thoracic RT, 23 after ICI, and 30 after RT + ICI. Fifty patients had grade 2 pneumonitis, 22 grade 3, and 7 grade 4. ICI-pneumonitis was more likely bilateral (65% vs. 28%; p = .01) and involved more lobes (66% vs. 45% involving at least three lobes) and was less likely to have sharp border (17% vs. 59%; p = .004) compared with RT-pneumonitis. Pneumonitis morphology after RT + ICI was heterogeneous, with 47% bilateral, 37% involving at least three lobes, and 40% sharp borders. Among all patients, risk factors for severe pneumonitis included poor performance status, smoking history, worse lung function, and bilateral and multifocal involvement on CT. An ML model based on seven radiomic features alone could distinguish ICI- from RT-pneumonitis with an area under the receiver-operating curve of 0.76 and identified the predominant etiology after RT + ICI concordant with multidisciplinary consensus. Conclusion: RT- and ICI-pneumonitis exhibit distinct spatial features on CT. Bilateral and multifocal lung involvement is associated with severe pneumonitis. Integrating these morphologic features in the clinical management of patients who develop pneumonitis after RT and ICIs may improve treatment decision-making. Implications for Practice: Patients with non-small cell lung cancer often receive thoracic radiation and immune checkpoint inhibitors (ICIs), both of which can cause pneumonitis. This study identified similarities and differences in pneumonitis morphology on computed tomography (CT) scans among pneumonitis due to radiotherapy (RT) alone, ICI alone, and the combination of both. Patients who have bilateral CT changes involving at least three lobes are more likely to have ICI-pneumonitis, whereas those with unilateral CT changes with sharp borders are more likely to have radiation pneumonitis. After RT and/or ICI, severe pneumonitis is associated with bilateral and multifocal CT changes. These results can help guide clinicians in triaging patients who develop pneumonitis after radiation and during ICI treatment.

Despite decades of research, and the growing healthcare and societal burden of chronic obstructive pulmonary disease (COPD), therapeutic COPD breakthroughs have not occurred. Sub‐optimal COPD patient phenotyping, an incomplete understanding of COPD pathogenesis and a scarcity of sensitive tools that provide patient‐relevant intermediate endpoints likely all play a role in the lack of new, efficacious COPD interventions. In other words, COPD patients are still diagnosed based on the presence of persistent airflow limitation measured using spirometry. Spirometry measurements reflect the global sum of all the different possible COPD pathologies and perhaps because of this, we lose sight of the different contributions of airway and parenchymal abnormalities. With recent advances in thoracic X‐ray computed tomography (CT) and magnetic resonance imaging (MRI), lung structure and function abnormalities may be regionally identified and measured. These imaging endpoints may serve as biomarkers of COPD that can be used to better phenotype patients. Therefore, here we review novel CT and MRI measurements that help reveal COPD phenotypes and what COPD really ‘looks’ like, beyond spirometric indices. We discuss MR and CT imaging approaches for generating reproducible and sensitive measurements of COPD phenotypes related to pulmonary ventilation and perfusion as well as airway and parenchyma anatomical and morphological features. These measurements may provide a way to advance the development and testing of new COPD interventions and therapies.