Khadija Sheikh

Purpose: To evaluate the relationship between delivered radiation (RT) and post-RT inversion-recovery ultrashort-echo-time (IR-UTE) MRI signal-intensity (SI) in gynecologic cancer patients treated with high-dose-rate (HDR) brachytherapy (BT). Methods: Seven patients underwent whole-pelvis RT (WPRT) followed by BT to the high-risk clinical target volume (HR-CTV). MR images were acquired at three time-points; pre-RT, post-WPRT/pre-BT, and 3–6 months post-BT. Diffuse-fibrosis (F_Diffuse) was imaged with a non-contrast dual-echo IR (inversion time [TI] = 60 ms) UTE research application, with image-subtraction of the later echo, only retaining the ultrashort-echo SI. Dense-fibrosis (F_Dense) imaging utilized single-echo Late-Gadolinium-Enhanced IR-UTE, acquired ∼ 15 min post-Gadavist injection. Resulting F_Diffuse and F_Dense SI were normalized to the corresponding gluteal-muscle SI. Images were deformably registered between time-points based on normal tissue anatomy. The remnant tumor at both time-points was segmented using multi-parametric MRI. Contours corresponding to the 50%, 100%, 150%, and 200% isodose lines (IDLs) of the prescription BT-dose were created. Mean F_Diffuse and F_Dense SI within (i) each IDL contour and (ii) the remnant tumor were calculated. Post-BT F_Diffuse and F_Dense SI were correlated with prescribed BT-dose. To determine the relationship between BT-dose and IR-UTE SI, the differences in the post-BT F_Dense across IDLs was determined using paired t-tests with Bonferroni correction. Results: F_Dense was higher in regions of higher dose for 6/7 patients, with mean ± SD values of 357 ± 103% and 331 ± 97% (p = .03) in the 100% and 50% IDL, respectively. F_Dense was higher in regions of higher dose in the responsive regions with mean ± SD values of 380 ± 122% and 356 ± 135% (p = .03) in the 150% and 50% IDL, respectively. Within the segmented remnant tumor, an increase in prescribed dose correlated with an increase in F_Dense post-BT (n = 5, r = .89, p = .04). Post-BT F_Diffuse inversely correlated (n = 7, r = -.83, p = .02) with prescribed BT-dose within the 100% IDL. Conclusions: Results suggest that F_Dense SI 3–6 months post-BT is a sensitive measure of tissue response to heterogeneous BT radiation-dose. Future studies will validate whether F_Diffuse and F_Dense are accurate biomarkers of fibrotic radiation response.

Purpose. To compare spatially fractionated radiation therapy (GRID) treatment planning techniques using proton pencil-beam-scanning (PBS) and photon therapy.Materials and Methods. PBS and volumetric modulated arc therapy (VMAT) GRID plans were retrospectively generated for 5 patients with bulky tumors. GRID targets were arranged along the long axis of the gross tumor, spaced 2 and 3 cm apart, and treated with a prescription of 18 Gy. PBS plans used 2- to 3-beam multiple-field optimization with robustness evaluation. Dosimetric parameters including peak-to-edge ratio (PEDR), ratio of dose to 90% of the valley to dose to 10% of the peak VPDR(D90/D10), and volume of normal tissue receiving at least 5 Gy (V5) and 10 Gy (V10) were calculated. The peak-to-valley dose ratio (PVDR), VPDR(D90/D10), and organ-at-risk doses were prospectively assessed in 2 patients undergoing PBS-GRID with pretreatment quality assurance computed tomography (QACT) scans.Results. PBS and VMAT GRID plans were generated for 5 patients with bulky tumors. Gross tumor volume values ranged from 826 to 1468 cm3. Peak-to-edge ratio for PBS was higher than for VMAT for both spacing scenarios (2-cm spacing, P = .02; 3-cm spacing, P = .01). VPDR(D90/D10) for PBS was higher than for VMAT (2-cm spacing, P = .004; 3-cm spacing, P = .002). Normal tissue V5 was lower for PBS than for VMAT (2-cm spacing, P = .03; 3-cm spacing, P = .02). Normal tissue mean dose was lower with PBS than with VMAT (2-cm spacing, P = .03; 3-cm spacing, P = .02). Two patients treated using PBS GRID and assessed with pretreatment QACT scans demonstrated robust PVDR, VPDR(D90/D10), and organs-at-risk doses.Conclusions. The PEDR was significantly higher for PBS than VMAT plans, indicating lower target edge dose. Normal tissue mean dose was significantly lower with PBS than VMAT. PBS GRID may result in lower normal tissue dose compared with VMAT plans, allowing for further dose escalation in patients with bulky disease.

Purpose: To evaluate dosimetric changes detected using synthetic computed tomography (sCT) derived from online cone‐beam CTs (CBCT) in pediatric patients treated using intensity‐modulated proton therapy (IMPT).Methods: Ten pediatric patients undergoing IMPT and aligned daily using proton gantry‐mounted CBCT were identified for retrospective analysis with treated anatomical sites fully encompassed in the CBCT field of view. Dates were identified when the patient received both a CBCT and a quality assurance CT (qCT) for routine dosimetric evaluation. sCTs were generated based on a deformable registration between the initial plan CT (pCT) and CBCT. The clinical IMPT plans were re‐computed on the same day qCT and sCT, and dosimetric changes due to tissue change or response from the initial plan were computed using each image. Linear regression analysis was performed to determine the correlation between dosimetric changes detected using the qCT and the sCT. Gamma analysis was also used to compare the dose distributions computed on the qCT and sCT.Results: The correlation coefficients (p‐values) between qCTs and sCTs for changes detected in target coverage, overall maximum dose, and organ at risk dose were 0.97 (< .001), 0.84 (.002) and 0.91 (< .001), respectively. Mean ± SD gamma pass rates of the sCT‐based dose compared to the qCT‐based dose at 3%/3 mm, 3%/2 mm, and 2%/2 mm criteria were 96.5%±4.5%, 93.2%±6.3%, and 91.3%±7.8%, respectively. Pass rates tended to be lower for targets near lung.Conclusion: While insufficient for re‐planning, sCTs provide approximate dosimetry without administering additional imaging dose in pediatric patients undergoing IMPT. Dosimetric changes detected using sCTs are correlated with changes detected using clinically‐standard qCTs; however, residual differences in dosimetry remain a limitation. Further improvements in sCT image quality may both improve online dosimetric evaluation and reduce imaging dose for pediatric patients by reducing the need for routine qCTs.

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.

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.

Purpose: The purpose of this work was to compare the dosimetry and delivery times of 3D‐conformal (3DCRT)‐, volumetric modulated arc therapy (VMAT)‐, and tomotherapy‐based approaches for spatially fractionated radiation therapy for deep tumor targets.Methods: Two virtual GRID phantoms were created consisting of 7 “target” cylinders (1‐cm diameter) aligned longitudinally along the tumor in a honey‐comb pattern, mimicking a conventional GRID block, with 2‐cm center‐to‐center spacing (GRID_2 cm) and 3‐cm center‐to‐center spacing (GRID_3 cm), all contained within a larger cylinder (8 and 10 cm in diameter for the GRID_2 cm and GRID_3 cm, respectively). In a single patient, a GRID_3 cm structure was created within the gross tumor volume (GTV). Tomotherapy, VMAT (6 MV + 6 MV‐flattening‐filter‐free) and multi‐leaf collimator segment 3DCRT (6 MV) plans were created using commercially available software. Two tomotherapy plans were created with field widths (TOMO_2.5 cm) 2.5 cm and (TOMO_5 cm) 5 cm. Prescriptions for all plans were set to deliver a mean dose of 15 Gy to the GRID targets in one fraction. The mean dose to the GRID target and the heterogeneity of the dose distribution (peak‐to‐valley and peak‐to‐edge dose ratios) inside the GRID target were obtained. The volume of normal tissue receiving 7.5 Gy was determined.Results: The peak‐to‐valley ratios for GRID_2 cm/GRID_3 cm/Patient were 2.1/2.3/2.8, 1.7/1.5/2.8, 1.7/1.9/2.4, and 1.8/2.0/2.8 for the 3DCRT, VMAT, TOMO_5 cm, and TOMO_2.5 cm plans, respectively. The peak‐to‐edge ratios for GRID_2 cm/GRID_3 cm/Patient were 2.8/3.2/5.4, 2.1/1.8/5.4, 2.0/2.2/3.9, 2.1/2.7/5.2 and for the 3DCRT, VMAT, TOMO_5 cm, and TOMO_2.5 cm plans, respectively. The volume of normal tissue receiving 7.5 Gy was lowest in the TOMO_2.5 cm plan (GRID_2 cm/GRID_3 cm/Patient = 54 cm³/19 cm³/10 cm³). The VMAT plans had the lowest delivery times (GRID_2 cm/GRID_3 cm/Patient = 17 min/8 min/9 min).Conclusion: Our results present, for the first time, preliminary evidence comparing IMRT‐GRID approaches which result in high‐dose “islands” within a target, mimicking what is achieved with a conventional GRID block but without high‐dose “tail” regions outside of the target. These approaches differ modestly in their ability to achieve high peak‐to‐edge ratios and also differ in delivery times.