Volumetric changes of the parotid gland during IMRT based on mid-treatment imaging: implications for parotid stem cell sparing strategies in head and neck cancer.

BACKGROUND: To evaluate the change in parotid glands at mid-treatment during IMRT and the association between radiation dose to the parotid gland stem cell (PGSC) region and patient-reported xerostomia for patients with head and neck cancer (HNC). MATERIAL AND METHODS: Patients who were treated from 2006-2012 at our institution with patient-reported xerostomia outcomes available at least 9 months following RT were included. PG and PGSC regions were delineated and the dose was estimated from the treatment plan dose distribution, using contours from pre- and mid-treatment CT scans. The association between radiation dose and volumetric changes was assessed using linear regression. Univariable logistic regression, logistic dose-response curves, and receiver operating characteristics (ROC) were used to examine the relationship between radiation dose and patient-reported xerostomia. RESULTS: Sixty-three patients were included, most treated with 70 Gy in 33 fractions; 34 patients had mid-treatment CT scans. Both contralateral and ipsilateral PGs had considerable volume reduction from baseline to mid-treatment (25% and 27%, respectively, both p < .001), significantly associated with mean PG dose (-0.44%/Gy, p = .008 and -0.54%/Gy, p < .001, respectively). There was a > 5 Gy difference in mean PG and PGSC dose for 8/34 patients at mid-treatment, with 6/8 (75%) reporting severe xerostomia. Xerostomia prediction based on whole PG or PGSC region dose showed similar performance (ROC AUC 0.754 and 0.749, respectively). The corresponding dose-response models also predicted similar risk of patient-reported xerostomia with mean dose to the contralateral PG (32.5%) or PGSC region (31.4%) at the 20 Gy QUANTEC-recommended sparing level. CONCLUSIONS: The radiation dose to the PGSC region did not show stronger association with patient-reported xerostomia compared to that of whole PG, possibly due to considerable anatomical changes identified at mid-treatment. This shift in the size and position of the PG warrants adaptive planning strategies to evaluate the true benefit of parotid stem cell sparing.

Using Statistical Measures and Density Maps Generated From Chest Computed Tomography Scans to Identify and Monitor COVID-19 Cases in Radiation Oncology Rapidly.

Objectives This study aimed to evaluate quantitative and qualitative screening measures for anomalous computed tomography (CT) scans in cancer patients with potential coronavirus disease 2019 (COVID-19) as an automated detection tool in a radiation oncology treatment setting. Methods We identified a non-COVID-19 cohort and patients with suspected COVID-19 with chest CT scans from February 1, 2020 to June 30, 2020. Lungs were segmented, and a mean normal Hounsfield Unit (HU) histogram was generated for the non-COVID-19 CT scans; these were used to define thresholds for designating the COVID-19-suspected histograms as normal or abnormal. Statistical measures were computed and compared to the threshold levels, and density maps were generated to examine the difference between lungs with and without COVID-19 qualitatively. Results The non-COVID-19 cohort consisted of 70 patients with 70 CT scans, and the cohort of suspected COVID-19 patients consisted of 59 patients with 80 CT scans. Sixty-two patients were positive for COVID-19. The mean HUs and skewness of the intensity histogram discriminated between COVID-19 positive and negative cases, with an area under the curve of 0.948 for positive and 0.944 for negative cases. Skewness correctly identified 57 of 62 positive cases, whereas mean HUs correctly identified 17 of 18 negative cases. Density maps allowed for visualization of the temporal evolution of COVID-19 disease. Conclusions The statistical measures and density maps evaluated here could be employed in an automated screening algorithm for COVID-19 infection. The accuracy is high enough for a simple and rapid screening tool for early identification of suspected infection in patients treated with chemotherapy and radiation therapy already receiving CT scans as part of clinical care. This screening tool could also identify other infections that present critical risks for patients undergoing chemotherapy and radiation therapy, such as pneumonitis.