Validation of a radiobiological model for low-dose-rate prostate boost focal therapy treatment planning

A statistical, voxelised model of prostate cancer for biologically optimised radiotherapy

Background and purpose

Radiation therapy (RT) is commonly indicated for treatment of prostate cancer (PC). Biologicallyoptimised RT for PC may improve disease-free survival. This requires accurate spatial localisation and characterisation of tumour lesions. We aimed to generate a statistical, voxelised biological model to complement in vivomultiparametric MRI data to facilitate biologically-optimised RT.

Material and methods

Ex vivo prostate MRI and histopathological imaging were acquired for 63 PC patients. These data were co-registered to derive three-dimensional distributions of graded tumour lesions and cell density. Novel registration processes were used to map these data to a common reference geometry. Voxelised statistical models of tumour probability and cell density were generated to create the PC biological atlas. Cell density models were analysed using the Kullback–Leibler divergence to compare normal vs. lognormal approximations to empirical data.

Results

A reference geometry was constructed using ex vivo MRI space, patient data were deformably registered using a novel anatomy-guided process. Substructure correspondence was maintained using peripheral zone definitions to address spatial variability in prostate anatomy between patients. Three distinct approaches to interpolation were designed to map contours, tumour annotations and cell density maps from histology into ex vivo MRI space. Analysis suggests a log-normal model provides a more consistent representation of cell density when compared to a linear-normal model.

Conclusion

A biological model has been created that combines spatial distributions of tumour characteristics from a population into three-dimensional, voxelised, statistical models. This tool will be used to aid the development of biologically-optimised RT for PC patients.

Towards a Reduced In Silico Model Predicting Biochemical Recurrence After Radiotherapy in Prostate Cancer

OBJECTIVE

Purposes of this work were i) to develop an in silico model of tumor response to radiotherapy, ii) to perform an exhaustive sensitivity analysis in order to iii) propose a simplified version and iv) to predict biochemical recurrence with both the comprehensive and the reduced model.

METHODS

A multiscale computational model of tumor response to radiotherapy was developed. It integrated the following radiobiological mechanisms: oxygenation, including hypoxic death; division of tumor cells; VEGF diffusion driving angiogenesis; division of healthy cells and oxygen-dependent response to irradiation, considering, cycle arrest and mitotic catastrophe. A thorough sensitivity analysis using the Morris screening method was performed on 21 prostate computational tissues. Tumor control probability (TCP) curves of the comprehensive model and 15 reduced versions were compared. Logistic regression was performed to predict biochemical recurrence after radiotherapy on 76 localized prostate cancer patients using an output of the comprehensive and the reduced models.

RESULTS

No significant difference was found between the TCP curves of the comprehensive and a simplified version which only considered oxygenation, division of tumor cells and their response to irradiation. Biochemical recurrence predictions using the comprehensive and the reduced models improved those made from pre-treatment imaging parameters (AUC = 0.81 ± 0.02 and 0.82 ± 0.02 vs. 0.75 ± 0.03, respectively).

CONCLUSION

A reduced model of tumor response to radiotherapy able to predict biochemical recurrence in prostate cancer was obtained.

SIGNIFICANCE

This reduced model may be used in the future to optimize personalized fractionation schedules.

Automatic stratification of prostate tumour aggressiveness using multiparametric MRI: a horizontal comparison of texture features

Background: Previous studies have identified apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) can stratify prostate cancer into high- and low-grade disease (HG and LG, respectively). In this study, we consider the improvement of incorporating texture features (TFs) from T2-weighted (T2w) multiparametric magnetic resonance imaging (mpMRI) relative to mpMRI alone to predict HG and LG disease. Material and methods: In vivo mpMRI was acquired from 30 patients prior to radical prostatectomy. Sequences included T2w imaging, DWI and dynamic contrast enhanced (DCE) MRI. In vivo mpMRI data were co-registered with 'ground truth' histology. Tumours were delineated on the histology with Gleason scores (GSs) and classed as HG if GS ≥ 4 + 3, or LG if GS ≤ 3 + 4. Texture features based on three statistical families, namely the grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRLM) and the grey-level size zone matrix (GLSZM), were computed from T2w images. Logistic regression models were trained using different feature subsets to classify each lesion as either HG or LG. To avoid overfitting, fivefold cross validation was applied on feature selection, model training and performance evaluation. Performance of all models generated was evaluated using the area under the curve (AUC) method. Results: Consistent with previous studies, ADC was found to discriminate between HG and LG with an AUC of 0.76. Of the three statistical TF families, GLCM (plus select mpMRI features including ADC) scored the highest AUC (0.84) with GLRLM plus mpMRI similarly performing well (AUC  =  0.82). When all TFs were considered in combination, an AUC of 0.91 (95% confidence interval 0.87-0.95) was achieved. Conclusions: Incorporating T2w TFs significantly improved model performance for classifying prostate tumour aggressiveness. This result, however, requires further validation in a larger patient cohort.

Association analysis between quantitative MRI features and hypoxia-related genetic profiles in prostate cancer: a pilot study

OBJECTIVE

To investigate the association between multiparametric MRI (mpMRI) imaging features and hypoxia-related genetic profiles in prostate cancer.

METHODS

In vivo mpMRI was acquired from six patients prior to radical prostatectomy. Sequences included T₂ weighted (T₂W) imaging, diffusion-weighted imaging, dynamic contrast enhanced MRI and blood oxygen-level dependent imaging. Imaging data were co-registered with histology using three-dimensional deformable registration methods. Texture features were extracted from T₂W images and parametric maps from functional MRI. Full transcriptome genetic profiles were obtained using next generation sequencing from the prostate specimens. Pearson correlation coefficients were calculated between mpMRI data and hypoxia-related gene expression levels. Results were validated using glucose transporter one immunohistochemistry (IHC).

RESULTS

Correlation analysis identified 34 candidate imaging features (six from the mpMRI data and 28 from T₂W texture features). The IHC validation showed that 16 out of the 28 T₂W texture features achieved weak but significant correlations (p < 0.05).

CONCLUSIONS

Weak associations between mpMRI features and hypoxia gene expressions were found. This indicates the potential use of MRI in assessing hypoxia status in prostate cancer. Further validation is required due to the low correlation levels.

ADVANCES IN KNOWLEDGE

This is a pilot study using radiogenomics approaches to address hypoxia within the prostate, which provides an opportunity for hypoxia-guided selective treatment techniques.

Voxel-wise prostate cell density prediction using multiparametric magnetic resonance imaging and machine learning

BACKGROUND

There are currently no methods to estimate cell density in the prostate. This study aimed to develop predictive models to estimate prostate cell density from multiparametric magnetic resonance imaging (mpMRI) data at a voxel level using machine learning techniques.

MATERIAL AND METHODS

In vivo mpMRI data were collected from 30 patients before radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced imaging. Ground truth cell density maps were computed from histology and co-registered with mpMRI. Feature extraction and selection were performed on mpMRI data. Final models were fitted using three regression algorithms including multivariate adaptive regression spline (MARS), polynomial regression (PR) and generalised additive model (GAM). Model parameters were optimised using leave-one-out cross-validation on the training data and model performance was evaluated on test data using root mean square error (RMSE) measurements.

RESULTS

Predictive models to estimate voxel-wise prostate cell density were successfully trained and tested using the three algorithms. The best model (GAM) achieved a RMSE of 1.06 (± 0.06) × 10³ cells/mm² and a relative deviation of 13.3 ± 0.8%.

CONCLUSION

Prostate cell density can be quantitatively estimated non-invasively from mpMRI data using high-quality co-registered data at a voxel level. These cell density predictions could be used for tissue classification, treatment response evaluation and personalised radiotherapy.

Radiobiological parameters in a tumour control probability model for prostate cancer LDR brachytherapy

To provide recommendations for the selection of radiobiological parameters for prostate cancer treatment planning. Recommendations were based on validation of the previously published values, parameter estimation and a consideration of their sensitivity within a tumour control probability (TCP) model using clinical outcomes data from low-dose-rate (LDR) brachytherapy. The proposed TCP model incorporated radiosensitivity (α) heterogeneity and a non-uniform distribution of clonogens. The clinical outcomes data included 849 prostate cancer patients treated with LDR brachytherapy at four Australian centres between 1995 and 2012. Phoenix definition of biochemical failure was used. Validation of the published values from four selected literature and parameter estimation was performed with a maximum likelihood estimation method. Each parameter was varied to evaluate the change in calculated TCP to quantify the sensitivity of the model to its radiobiological parameters. Using a previously published parameter set and a total clonogen number of 196 000 provided TCP estimates that best described the patient cohort. Fitting of all parameters with a maximum likelihood estimation was not possible. Variations in prostate TCP ranged from 0.004% to 0.67% per 1% change in each parameter. The largest variation was caused by the log-normal distribution parameters for α (mean, [Formula: see text], and standard deviation, σ _α ). Based on the results using the clinical cohort data, we recommend a previously published dataset is used for future application of the TCP model with inclusion of a patient-specific, non-uniform clonogen density distribution which could be derived from multiparametric imaging. The reduction in uncertainties in these parameters will improve the confidence in using biological models for clinical radiotherapy planning.

Understanding the Relationship Between Interactive Optimisation and Visual Analytics in the Context of Prostate Brachytherapy

The fields of operations research and computer science have long sought to find automatic solver techniques that can find high-quality solutions to difficult real-world optimisation problems. The traditional workflow is to exactly model the problem and then enter this model into a general-purpose "black-box" solver. In practice, however, many problems cannot be solved completely automatically, but require a "human-in-the-loop" to iteratively refine the model and give hints to the solver. In this paper, we explore the parallels between this interactive optimisation workflow and the visual analytics sense-making loop. We assert that interactive optimisation is essentially a visual analytics task and propose a problem-solving loop analogous to the sense-making loop. We explore these ideas through an in-depth analysis of a use-case in prostate brachytherapy, an application where interactive optimisation may be able to provide significant assistance to practitioners in creating prostate cancer treatment plans customised to each patient's tumour characteristics. However, current brachytherapy treatment planning is usually a careful, mostly manual process involving multiple professionals. We developed a prototype interactive optimisation tool for brachytherapy that goes beyond current practice in supporting focal therapy - targeting tumour cells directly rather than simply seeking coverage of the whole prostate gland. We conducted semi-structured interviews, in two stages, with seven radiation oncology professionals in order to establish whether they would prefer to use interactive optimisation for treatment planning and whether such a tool could improve their trust in the novel focal therapy approach and in machine generated solutions to the problem.

Focal therapy for prostate cancer: the technical challenges

Focal therapy for prostate cancer has been proposed as an alternative treatment to whole gland therapy, offering the opportunity for tumor dose escalation and/or reduced toxicity. Brachytherapy, either low-dose-rate or high-dose-rate, provides an ideal approach, offering both precision in dose delivery and opportunity for a highly conformal, non-uniform dose distribution. Whilst multiple consensus documents have published clinical guidelines for patient selection, there are insufficient data to provide clear guidelines on target volume delineation, treatment planning margins, treatment planning approaches, and many other technical issues that should be considered before implementing a focal brachytherapy program. Without consensus guidelines, there is the potential for a diversity of practices to develop, leading to challenges in interpreting outcome data from multiple centers. This article provides an overview of the technical considerations for the implementation of a clinical service, and discusses related topics that should be considered in the design of clinical trials to ensure precise and accurate methods are applied for focal brachytherapy treatments.

Predicting prostate tumour location from multiparametric MRI using Gaussian kernel support vector machines: a preliminary study

The performance of a support vector machine (SVM) algorithm was investigated to predict prostate tumour location using multi-parametric MRI (mpMRI) data. The purpose was to obtain information of prostate tumour location for the implementation of bio-focused radiotherapy. In vivo mpMRI data were collected from 16 patients prior to radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast enhanced imaging. In vivo mpMRI was registered with 'ground truth' histology, using ex vivo MRI as an intermediate registration step to improve accuracy. Prostate contours were delineated by a radiation oncologist and tumours were annotated on histology by a pathologist. Five patients with minimal imaging artefacts were selected for this study. A Gaussian kernel SVM was trained and tested on different patient data subsets. Parameters were optimised using leave-oneout cross validation. Signal intensities of mpMRI were used as features and histology annotations as true labels. Prediction accuracy, as well as area under the curve (AUC) of the receiver operating characteristics (ROC) curve, were used to assess performance. Results demonstrated the prediction accuracy ranged from 70.4 to 87.1% and AUC of ROC ranged from 0.81 to 0.94. Additional investigations showed the apparent diffusion coefficient map from diffusion weighted imaging was the most important imaging modality for predicting tumour location. Future work will incorporate additional patient data into the framework to increase the sensitivity and specificity of the model, and will be extended to incorporate predictions of biological characteristics of the tumour which will be used in bio-focused radiotherapy optimisation.

Ten-year outcomes using low dose rate brachytherapy for localised prostate cancer: An update to the first Australian experience

INTRODUCTION

To report long-term prostate-specific antigen (PSA) and toxicity outcomes for patients with localised prostate cancer treated with Iodine-125 permanent implantation at a single Australian centre.

METHODS

Between September 1994 and November 2007, 207 patients at Sir Charles Gairdner Hospital with localised prostate cancer were consecutively treated with Iodine-125 permanent interstitial implantation. Post-therapy assessment was performed three times a month and included clinical review and biochemical (PSA) evaluation. PSA progression was evaluated using the Phoenix (nadir + 2.0) definition. Treatment-related morbidity was assessed using the Common Terminology Criteria for Adverse Events (CTCAE) version 3.0 guidelines. The rate of biochemical failure was calculated by Kaplan-Meier plots. Univariate and multivariate analyses were performed to evaluate outcomes by pre-treatment clinical prognostic factors and radiation dosimetry.

RESULTS

Median follow-up was 7.8 years. The 10-year biochemical disease-free survival (bDFS) for the entire cohort was 89%. Ten-year bDFS estimates by pre-treatment risk group were 96% for low-risk, 83% for intermediate-risk and 50% for high-risk disease. On multivariate analysis, pre-treatment PSA was an independent predictor of bDFS. D90 dose did not show a statistically significant effect on bDFS. The peak incidences of late grade 3 or higher urinary and rectal toxicities were 10.7% and 1.1% respectively.

CONCLUSION

Excellent long-term biochemical control was demonstrated with Iodine-125 permanent interstitial implantation in appropriately selected patients with prostate cancer. The results of our single centre experience are comparable with those of other single institutions.

A radiobiology-based inverse treatment planning method for optimisation of permanent l-125 prostate implants in focal brachytherapy

Treatment plans for ten patients, initially treated with a conventional approach to low dose-rate brachytherapy (LDR, 145 Gy to entire prostate), were compared with plans for the same patients created with an inverse-optimisation planning process utilising a biologically-based objective. The 'biological optimisation' considered a non-uniform distribution of tumour cell density through the prostate based on known and expected locations of the tumour. Using dose planning-objectives derived from our previous biological-model validation study, the volume of the urethra receiving 125% of the conventional prescription (145 Gy) was reduced from a median value of 64% to less than 8% whilst maintaining high values of TCP. On average, the number of planned seeds was reduced from 85 to less than 75. The robustness of plans to random seed displacements needs to be carefully considered when using contemporary seed placement techniques. We conclude that an inverse planning approach to LDR treatments, based on a biological objective, has the potential to maintain high rates of tumour control whilst minimising dose to healthy tissue. In future, the radiobiological model will be informed using multi-parametric MRI to provide a personalised medicine approach.

Recent developments and best practice in brachytherapy treatment planning

Brachytherapy has evolved over many decades, but more recently, there have been significant changes in the way that brachytherapy is used for different treatment sites. This has been due to the development of new, technologically advanced computer planning systems and treatment delivery techniques. Modern, three-dimensional (3D) imaging modalities have been incorporated into treatment planning methods, allowing full 3D dose distributions to be computed. Treatment techniques involving online planning have emerged, allowing dose distributions to be calculated and updated in real time based on the actual clinical situation. In the case of early stage breast cancer treatment, for example, electronic brachytherapy treatment techniques are being used in which the radiation dose is delivered during the same procedure as the surgery. There have also been significant advances in treatment applicator design, which allow the use of modern 3D imaging techniques for planning, and manufacturers have begun to implement new dose calculation algorithms that will correct for applicator shielding and tissue inhomogeneities. This article aims to review the recent developments and best practice in brachytherapy techniques and treatments. It will look at how imaging developments have been incorporated into current brachytherapy treatment and how these developments have played an integral role in the modern brachytherapy era. The planning requirements for different treatments sites are reviewed as well as the future developments of brachytherapy in radiobiology and treatment planning dose calculation.