ESTRO 2022 - Abstract Book

S685

Abstract book

ESTRO 2022

OC-0768 Stereotactic body radiotherapy for renal cell carcinoma: oncologic and renal function outcomes

R. Glicksman 1 , M. Niglas 2 , P. Cheung 3 , R. Korol 1 , D. Erler 1 , D. Vesprini 1 , H. Nusrat 4 , M. Davidson 5 , L. Zhang 1 , W. Chu 1

1 Sunnybrook Health Sciences Centre, University of Toronto, Radiation Oncology, Toronto, Canada; 2 Durham Regional Cancer Centre, Radiation Oncology, Oshawa, Canada; 3 Sunnybrook Health Sciences Centre, University of Toronto,, Radiation Oncology, Toronto, Canada; 4 Sunnybrook Health Sciences Centre, University of Toronto, , Radiation Oncology, Toronto, Canada; 5 Sunnybrook Health Sciences Centre, University of Toronto, Radiation Oncology, Toronto, Canada Purpose or Objective To evaluate oncologic and renal function outcomes of stereotactic body radiotherapy (SBRT) for medically inoperable patients with localized renal cell carcinoma (RCC). Materials and Methods With institutional ethics review board approval consecutive patients with medically inoperable localized RCC treated with curative intent SBRT (30-45 Gy in 5 fractions or 42 Gy in 3 fractions) were included. Local control (RECIST v1.1), distant metastasis, and impact on eGFR, ipsilateral and contralateral renal functions were collected. To compare pre- and post- SBRT renal function, general linear mixed model was performed. To adjust for multiplicity, Bonferroni adjusted p-value <0.007 was considered statistically significant. Univariate and multivariable linear mixed model was performed to search for predictive factors of each renal function metric over time. P-value <0.05 was considered statistically significant. Akaike Information Criterion was estimated for each model. Univariate and multivariable analyses were conducted to determine association of variables with oncologic and renal function outcomes. Results Seventy-four patients were analyzed. Median follow-up was 27.8 months (IQR 17.6-41.7). Fifty-seven percent of patients had tumours T1b or greater (median size 4.6 cm, 3.0-5.6cm). One, two and four-year cumulative incidence of local failure was 5.85%, 7.77% and 7.77%, respectively. Cumulative incidence of distant metastasis and survival at 2 years was 4.24% and 100%, respectively. On multivariable analysis, lower PTV mean dose (HR 0.68, 95% CI 0.49-0.94, p=0.019) and larger PTV volume (HR 6.93, 95% CI 1.82-26.45, p=0.005) were significantly associated with risk of developing local failure. Lower PTV maximum dose (HR 0.71, 95% CI 0.51-0.98, p=0.039) was significantly associated with risk of developing distant metastasis. The median change in eGFR (mL/min) from pre-SBRT levels was -7.0 (IQR -14.5 to -1.0) at 1-year and -11.5 (IQR -19.5 to - 4.0) at 2-years. The proportion of ipsilateral renal function decreased significantly over time from 47% pre-SBRT to 36% at 2-years, while the proportion of contralateral renal function correspondingly increased from 53% pre-SBRT to 64% at 2- years. On multivariable analysis, lower Charlson comorbidity score (p<0.0001), higher PTV mean dose (p=0.003) and higher uninvolved renal cortex volume (p<0.0001) were significantly associated with higher eGFR values over time. Conclusion Oncologic outcomes with RCC SBRT were favorable in this large institutional cohort. There was a longitudinal decline in renal function in the ipsilateral kidney with a compensatory increase in the contralateral kidney. Overall renal function decline over time is comparable to existing data. Clinical and dosimetric factors were significantly associated with oncologic and renal function outcomes. 1 Aarhus University Hospital, Danish Center for Particle Therapy, Aarhus, Denmark; 2 Aarhus University Hospital, Department of Experimental Clinical Oncology, Aarhus, Denmark; 3 Aarhus University Hospital, Department of Oncology, Aarhus, Denmark; 4 Aarhus University, Department of Clinical Medicine, Aarhus, Denmark Purpose or Objective Deep learning auto-segmentation of organs-at-risk (OAR) in head-neck cancer performs well for structures with high visual contrast such as parotid glands, mandibles, spinal cord and brain. However, OARs such as glottic larynx and pharyngeal constrictor muscles (PCMs) can have low or no visual contrast which results in inaccurate auto-segmentation. We aim to improve segmentation performance of these challenging OARs by including minimal manual delineations as input to deep learning auto-segmentation models. Materials and Methods We used a data set with planning CTs and manual delineations of OARs from 301 patients previously treated with radiotherapy for head-neck cancer at our institution. 30 cases were randomly selected for an external test set, and thus 271 cases were eligible for training. Our training data did not contain all OARs for all patients. Hence, models were trained and tested only on patients with the OARs of interest present (Table 1). To simulate minimal manual delineation (MMD) input, we extracted the most cranial and caudal slice of each OAR from our data set and input these along with CT in 3D full resolution nnUNets [Isensee, F et al. 2020] (CT+MMD). We included lower, middle and upper PCM, glottic larynx and parotid glands. For reference we also trained nnUNets without MMD (CT-only). All models consisted of a single fold (1000 epochs) nnUNet with default parameters. Parotid glands were trained and evaluated as one OAR to avoid segmentation of the contra lateral gland. Proffered Papers: Deep learning for image analysis OC-0769 Auto-segmentation of low contrast organs at risk improves with minimal prior delineation input M.E. Rasmussen 1,2,3,4 , J.A. Nijkamp 1,4 , J.G. Eriksen 2 , S.S. Korreman 1,3,4