Dose outside of the prostate is associated with improved outcomes for high-risk prostate cancer patients treated with brachytherapy boost.

Background: One in three high-risk prostate cancer patients treated with radiotherapy recur. Detection of lymph node metastasis and microscopic disease spread using conventional imaging is poor, and many patients are under-treated due to suboptimal seminal vesicle or lymph node irradiation. We use Image Based Data Mining (IBDM) to investigate association between dose distributions, and prognostic variables and biochemical recurrence (BCR) in prostate cancer patients treated with radiotherapy. We further test whether including dose information in risk-stratification models improves performance. Method: Planning CTs, dose distributions and clinical information were collected for 612 high-risk prostate cancer patients treated with conformal hypo-fractionated radiotherapy, intensity modulated radiotherapy (IMRT), or IMRT plus a single fraction high dose rate (HDR) brachytherapy boost. Dose distributions (including HDR boost) of all studied patients were mapped to a reference anatomy using the prostate delineations. Regions where dose distributions significantly differed between patients that did and did-not experience BCR were assessed voxel-wise using 1) a binary endpoint of BCR at four-years (dose only) and 2) Cox-IBDM (dose and prognostic variables). Regions where dose was associated with outcome were identified. Cox proportional-hazard models with and without region dose information were produced and the Akaike Information Criterion (AIC) was used to assess model performance. Results: No significant regions were observed for patients treated with hypo-fractionated radiotherapy or IMRT. Regions outside the target where higher dose was associated with lower BCR were observed for patients treated with brachytherapy boost. Cox-IBDM revealed that dose response was influenced by age and T-stage. A region at the seminal vesicle tips was identified in binary- and Cox-IBDM. Including the mean dose in this region in a risk-stratification model (hazard ratio=0.84, p=0.005) significantly reduced AIC values (p=0.019), indicating superior performance, compared with prognostic variables only. The region dose was lower in the brachytherapy boost patients compared with the external beam cohorts supporting the occurrence of marginal misses. Conclusion: Association was identified between BCR and dose outside of the target region in high-risk prostate cancer patients treated with IMRT plus brachytherapy boost. We show, for the first-time, that the importance of irradiating this region is linked to prognostic variables.

Prediction of prostate tumour hypoxia using pre-treatment MRI-derived radiomics: preliminary findings.

PURPOSE: To develop a machine learning (ML) model based on radiomic features (RF) extracted from whole prostate gland magnetic resonance imaging (MRI) for prediction of tumour hypoxia pre-radiotherapy. MATERIAL AND METHODS: Consecutive patients with high-grade prostate cancer and pre-treatment MRI treated with radiotherapy between 01/12/2007 and 1/08/2013 at two cancer centres were included. Cancers were dichotomised as normoxic or hypoxic using a biopsy-based 32-gene hypoxia signature (Ragnum signature). Prostate segmentation was performed on axial T2-weighted (T2w) sequences using RayStation (v9.1). Histogram standardisation was applied prior to RF extraction. PyRadiomics (v3.0.1) was used to extract RFs for analysis. The cohort was split 80:20 into training and test sets. Six different ML classifiers for distinguishing hypoxia were trained and tuned using five different feature selection models and fivefold cross-validation with 20 repeats. The model with the highest mean validation area under the curve (AUC) receiver operating characteristic (ROC) curve was tested on the unseen set, and AUCs were compared via DeLong test with 95% confidence interval (CI). RESULTS: 195 patients were included with 97 (49.7%) having hypoxic tumours. The hypoxia prediction model with best performance was derived using ridge regression and had a test AUC of 0.69 (95% CI: 0.14). The test AUC for the clinical-only model was lower (0.57), but this was not statistically significant (p = 0.35). The five selected RFs included textural and wavelet-transformed features. CONCLUSION: Whole prostate MRI-radiomics has the potential to non-invasively predict tumour hypoxia prior to radiotherapy which may be helpful for individualised treatment optimisation.

Characterizing local dose perturbations due to gas cavities in magnetic resonance-guided radiotherapy.

PURPOSE: Due to differences in attenuation and the electron return effect (ERE), the presence of gas can increase the risk of toxicity in organs at risk (OAR) during magnetic resonance-guided radiotherapy (MRgRT). Current adaptive MRgRT workflows using density overrides negate gas from the dose calculation, meaning that the effects of ERE around gas are not taken into account. In order to achieve an accurate adaptive MRgRT treatment, we should be able to quickly evaluate whether gas present during treatment causes dose constraint violation during an MRgRT fraction. We propose an analytic method for predicting dose perturbations caused by air cavities in OARs during MRgRT. METHOD: Ten virtual water phantoms were created: nine containing a centrally located spherical air cavity and a reference phantom without an air cavity. Monte Carlo dose calculations were produced to irradiate the phantoms with a single 7 MV photon beam under the influence of a 1.5 T transverse magnetic field (Monaco 5.19.02 Treatment Panning System (TPS) (Elekta AB, Stockholm, Sweden)). Dose distributions of the phantoms with and without air cavities were compared. We used a spherical coordinate system originating in the center of the cavity to sample the dose distributions and calculate the dose perturbation as a result of the presence of each air cavity, ∆D%(θ,Φ)calc . . Dose effects due to ERE and differences in attenuation due to density changes were considered separately. Least squared analysis was used to fit the calculated dose perturbations to mathematical functions. Effects due to ERE were fit to a modulated sinusoidal function and those due to attenuation differences were fit to a 2D Gaussian function. We used the fits to derive a single equation describing dose perturbations around spherical air cavities as a function of angles, θ, Φ, distance from cavity surface, d, and cavity radius, r. We measured the fitting error by calculating the residual error (RE); the difference between the calculated and fitted dose perturbation. RESULTS: Both ERE and differences in attenuation contribute toward the total dose effects of air cavities in MRgRT. Whereas ERE dominates close to the surface of the cavities, attenuation effects dominate at distances >0.5 cm from the cavities. We showed that dose effects around a spherical air cavity (≤1 cm from the surface) due to ERE fit a modulated sinusoidal function with mean (RE) ≤-1.4E-5% and root mean square error (rms) (RE) ≤4.1%. Effects due to attenuation differences fit a Gaussian function with mean (RE) ≤0.7% and rms (RE) ≤1.8%. Our general equation, which we verified using multiple sizes of spherical and cylindrical air cavity, fits Monte Carlo simulated data with mean (RE) ≤±0.9% and rms (RE) ≤6.9%. CONCLUSION: We show that local dose perturbations around unplanned spherical air cavities during MRgRT can be well characterized analytically. We present an equation that can be incorporated into the clinical workflow to allow for fast evaluation of dose effects of unplanned gas. We also envision this method contributing to the clinical implementation of real time adaptive radiotherapy (ART) for MRgRT using MRI planning.

Assessing localized dosimetric effects due to unplanned gas cavities during pelvic MR-guided radiotherapy using Monte Carlo simulations.

PURPOSE: It has been proposed that beam modulation and opposing beam configurations can cancel effects of the Electron Return Effect (ERE) during MR-guided radiotherapy (MRgRT). However, this may not always be the case for unplanned gas cavities outside of the target in the pelvic region. We evaluate dosimetric effects, including effects in the rectal wall, due to unplanned spherical air cavities during MRgRT. METHODS: Nine virtual cuboid water phantoms containing spherical air cavities (0.5-7.5 cm diameter) and a reference phantom without air were created. Monte Carlo dose calculations of 7 MV photons under the influence of a 1.5 T transverse magnetic field were produced using Monaco 5.19.02 Treatment Planning System (TPS) (Elekta AB, Stockholm, Sweden). Cavities in the path of a single and multiple beam plans were considered. Dose distributions of phantoms with and without air cavities were compared (ΔD% ) using a spherical coordinate system originating in the center of the cavity. Effects in the rectal wall were quantified by comparing dose volume histogram (DVH) parameters for solid and gaseous filling from simulated rectal wall structures. RESULTS: Max(ΔD% ) of ~70% and 20% were observed around large cavities in the path of a single and multiple beam plans, respectively. Approximately 45 cm3 of phantom surrounding the largest cavity in a single beam received dose changes of >10%. Dmean in the rectal wall was unchanged when comparing gaseous and solid filling in the path of a single beam; however, D1cc and Dmax increased by up to ~45% and ~63%, respectively. CONCLUSIONS: Unplanned gas cavities in the path of a single beam during pelvic MRgRT with a 1.5 T transverse magnetic field cause dose changes which may impact toxicity in the rectal wall, depending on local dose and fractionation. Effects are reduced but not eliminated with a five-beam plan.