Common genetic variation associated with increased susceptibility to prostate cancer does not increase risk of radiotherapy toxicity.

BACKGROUND: Numerous germline single-nucleotide polymorphisms increase susceptibility to prostate cancer, some lying near genes involved in cellular radiation response. This study investigated whether prostate cancer patients with a high genetic risk have increased toxicity following radiotherapy. METHODS: The study included 1560 prostate cancer patients from four radiotherapy cohorts: RAPPER (n=533), RADIOGEN (n=597), GenePARE (n=290) and CCI (n=150). Data from genome-wide association studies were imputed with the 1000 Genomes reference panel. Individuals were genetically similar with a European ancestry based on principal component analysis. Genetic risks were quantified using polygenic risk scores. Regression models tested associations between risk scores and 2-year toxicity (overall, urinary frequency, decreased stream, rectal bleeding). Results were combined across studies using standard inverse-variance fixed effects meta-analysis methods. RESULTS: A total of 75 variants were genotyped/imputed successfully. Neither non-weighted nor weighted polygenic risk scores were associated with late radiation toxicity in individual studies (P>0.11) or after meta-analysis (P>0.24). No individual variant was associated with 2-year toxicity. CONCLUSION: Patients with a high polygenic susceptibility for prostate cancer have no increased risk for developing late radiotherapy toxicity. These findings suggest that patients with a genetic predisposition for prostate cancer, inferred by common variants, can be safely treated using current standard radiotherapy regimens.

Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers.

Importance: Personalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and institutions. Objective: To explore clinically acceptable autocontouring solutions that can be integrated into existing workflows and used in different domains of radiotherapy. Design, Setting, and Participants: This quality improvement study used a multicenter imaging data set comprising 519 pelvic and 242 head and neck computed tomography (CT) scans from 8 distinct clinical sites and patients diagnosed either with prostate or head and neck cancer. The scans were acquired as part of treatment dose planning from patients who received intensity-modulated radiation therapy between October 2013 and February 2020. Fifteen different OARs were manually annotated by expert readers and radiation oncologists. The models were trained on a subset of the data set to automatically delineate OARs and evaluated on both internal and external data sets. Data analysis was conducted October 2019 to September 2020. Main Outcomes and Measures: The autocontouring solution was evaluated on external data sets, and its accuracy was quantified with volumetric agreement and surface distance measures. Models were benchmarked against expert annotations in an interobserver variability (IOV) study. Clinical utility was evaluated by measuring time spent on manual corrections and annotations from scratch. Results: A total of 519 participants' (519 [100%] men; 390 [75%] aged 62-75 years) pelvic CT images and 242 participants' (184 [76%] men; 194 [80%] aged 50-73 years) head and neck CT images were included. The models achieved levels of clinical accuracy within the bounds of expert IOV for 13 of 15 structures (eg, left femur, κ = 0.982; brainstem, κ = 0.806) and performed consistently well across both external and internal data sets (eg, mean [SD] Dice score for left femur, internal vs external data sets: 98.52% [0.50] vs 98.04% [1.02]; P = .04). The correction time of autogenerated contours on 10 head and neck and 10 prostate scans was measured as a mean of 4.98 (95% CI, 4.44-5.52) min/scan and 3.40 (95% CI, 1.60-5.20) min/scan, respectively, to ensure clinically accepted accuracy. Manual segmentation of the head and neck took a mean 86.75 (95% CI, 75.21-92.29) min/scan for an expert reader and 73.25 (95% CI, 68.68-77.82) min/scan for a radiation oncologist. The autogenerated contours represented a 93% reduction in time. Conclusions and Relevance: In this study, the models achieved levels of clinical accuracy within expert IOV while reducing manual contouring time and performing consistently well across previously unseen heterogeneous data sets. With the availability of open-source libraries and reliable performance, this creates significant opportunities for the transformation of radiation treatment planning.

Identifying risk factors for L'Hermitte's sign after IMRT for head and neck cancer.

BACKGROUND: L'Hermitte's sign (LS) after chemoradiotherapy for head and neck cancer appears related to higher spinal cord doses. IMRT plans limit spinal cord dose, but the incidence of LS remains high. METHODS: One hundred seventeen patients treated with TomoTherapy™ between 2008 and 2015 prospectively completed a side-effect questionnaire (VoxTox Trial Registration: UK CRN ID 13716). Baseline patient and treatment data were collected. Radiotherapy plans were analysed; mean and maximum spinal cord dose and volumes receiving 10, 20, 30 and 40 Gy were recorded. Dose variation across the cord was examined. These data were included in a logistic regression model. RESULTS: Forty two patients (35.9%) reported LS symptoms. Concurrent weekly cisplatin did not increase LS risk (p = 0.70, OR = 1.23 {95% CI 0.51-2.34}). Of 13 diabetic participants (9 taking metformin), only 1 developed LS (p = 0.025, OR = 0.13 {95% CI 0.051-3.27}). A refined binary logistic regression model showed that patients receiving unilateral radiation (p = 0.019, OR = 2.06 {95% CI 0.15-0.84}) were more likely to develop LS. Higher V40Gy (p = 0.047, OR = 1.06 {95% CI 1.00-1.12}), and younger age (mean age 56.6 vs 59.7, p = 0.060, OR = 0.96 {95% CI 0.92-1.00}) were associated with elevated risk of LS, with borderline significance. CONCLUSIONS: In this cohort, concomitant cisplatin did not increase risk, and LS incidence was lower in diabetic patients. Patient age and dose gradients across the spinal cord may be important factors.