Robustness of magnetic resonance imaging and positron emission tomography radiomic features in prostate cancer: Impact on recurrence prediction after radiation therapy.

Background and purpose: Radiomic features from MRI and PET are an emerging tool with potential to improve prostate cancer outcomes. However, feature robustness due to image segmentation variations is currently unknown. Therefore, this study aimed to evaluate the robustness of radiomic features with segmentation variations and their impact on predicting biochemical recurrence (BCR). Materials and methods: Multi-scanner, pre-radiation therapy imaging from 142 patients with localised prostate cancer was used. Imaging included T2-weighted (T2), apparent diffusion coefficient (ADC) MRI, and prostate-specific membrane antigen (PSMA)-PET. The prostate gland and intraprostatic tumours were manually and automatically segmented, and differences were quantified using Dice Coefficient (DC). Radiomic features including shape, first-order, and texture features were extracted for each segmentation from original and filtered images. Intraclass Correlation Coefficient (ICC) and Mean Absolute Percentage Difference (MAPD) were used to assess feature robustness. Random forest (RF) models were developed for each segmentation using robust features to predict BCR. Results: Prostate gland segmentations were more consistent (mean DC = 0.78) than tumour segmentations (mean DC = 0.46). 112 (3.6 %) radiomic features demonstrated 'excellent' robustness (ICC > 0.9 and MAPD < 1 %), and 480 features (15.4 %) demonstrated 'good' robustness (ICC > 0.75 and MAPD < 5 %). PET imaging provided more features with excellent robustness than T2 and ADC. RF models showed strong predictive power for BCR with a mean area under the receiver-operator-characteristics curve (AUC) of 0.89 (range 0.85-0.93). Conclusion: When using radiomic features for predictive modelling, segmentation variability should be considered. To develop BCR predictive models, radiomic features from the entire prostate gland are preferable over tumour segmentation-based features.

Towards optimal heterogeneous prostate radiotherapy dose prescriptions based on patient-specific or population-based biological features.

BACKGROUND: Escalation of prescribed dose in prostate cancer (PCa) radiotherapy enables improvement in tumor control at the expense of increased toxicity. Opportunities for reduction of treatment toxicity may emerge if more efficient dose escalation can be achieved by redistributing the prescribed dose distribution according to the known heterogeneous, spatially-varying characteristics of the disease. PURPOSE: To examine the potential benefits, limitations and characteristics of heterogeneous boost dose redistribution in PCa radiotherapy based on patient-specific and population-based spatial maps of tumor biological features. METHOD: High-resolution prostate histology images, from a cohort of 63 patients, annotated with tumor location and grade, provided patient-specific "maps" and a population-based "atlas" of cell density and tumor probability. Dose prescriptions were derived for each patient based on a heterogeneous redistribution of the boost dose to the intraprostatic lesions, with the prescription maximizing patient tumor control probability (TCP). The impact on TCP was assessed under scenarios where the distribution of population-based biological data was ignored, partially included, or fully included in prescription generation. Heterogeneous dose prescriptions were generated for three combinations of maps and atlas, and for conventional fractionation (CF), extreme hypo-fractionation (EH), moderate hypo-fractionation (MH), and whole Pelvic RT + SBRT Boost (WPRT + SBRT). The predicted efficacy of the heterogeneous prescriptions was compared with equivalent homogeneous dose prescriptions. RESULTS: TCPs for heterogeneous dose prescriptions were generally higher than those for homogeneous dose prescriptions. TCP escalation by heterogeneous dose prescription was the largest for CF. When only using population-based atlas data, the generated heterogeneous dose prescriptions of 55 to 58 patients (out of 63) had a higher TCP than for the corresponding homogeneous dose prescriptions. The TCPs of the heterogeneous dose prescriptions generated with the population-based atlas and tumor probability maps did not differ significantly from those using patient-specific biological information. The generated heterogeneous dose prescriptions achieved significantly higher TCP than homogeneous dose prescriptions in the posterior section of the prostate. CONCLUSION: Heterogeneous dose prescriptions generated via biologically-optimized dose redistribution can produce higher TCP than the homogeneous dose prescriptions for the majority of the patients in the studied cohort. For scenarios where patient-specific biological information was unavailable or partially available, the generated heterogeneous dose prescriptions can still achieve TCP improvement relative to homogeneous dose prescriptions.

Computational fluid dynamic modeling of the lymphatic system: a review of existing models and future directions.

Historically, research into the lymphatic system has been overlooked due to both a lack of knowledge and limited recognition of its importance. In the last decade however, lymphatic research has gained substantial momentum and has included the development of a variety of computational models to aid understanding of this complex system. This article reviews existing computational fluid dynamic models of the lymphatics covering each structural component including the initial lymphatics, pre-collecting and collecting vessels, and lymph nodes. This is followed by a summary of limitations and gaps in existing computational models and reasons that development in this field has been hindered to date. Over the next decade, efforts to further characterize lymphatic anatomy and physiology are anticipated to provide key data to further inform and validate lymphatic fluid dynamic models. Development of more comprehensive multiscale- and multi-physics computational models has the potential to significantly enhance the understanding of lymphatic function in both health and disease.

Toward Development of a Portable System for 3D Fluorescence Lymphography.

Lymphoedema is a debilitating disease that results in chronic swelling of a body region due to a dysfunctional lymphatic system. Since a cure is yet to be identified for this disease, management is currently the best option for preventing disease progression and improving patient outcomes. Fluorescence lymphography is a popular approach for mapping the lymphatic vessels to provide information about the underlying lymphatic dysfunction. However, current clinical fluorescence lymphography tools do not enable the creation of comprehensive 3D maps of lymphatics throughout affected limbs. This work presents the development toward multi-camera 3D reconstruction with fluorescence imaging to overcome the current limitations in clinical tools. Pilot studies have been performed that identify suitable instrumentation for this multi-camera approach and techniques for creating a 3D fluorescence lymphography device are discussed.Clinical Relevance- This paper presents development toward new low-cost and portable clinical tools for lymphoedema diagnosis and to facilitate personalised treatment and self-management of this disease.

Early-life microbiota-immune homeostasis.

As the prevalence of allergy and autoimmune disease in industrialized societies continues to rise, improving our understanding of the mechanistic roles behind microbiota-immune homeostasis has become critical for informing therapeutic interventions in cases of dysbiosis. Of particular importance, are alterations to intestinal microbiota occurring within the critical neonatal window, during which the immune system is highly vulnerable to environmental exposures. This review will highlight recent literature concerning mechanisms of early-life microbiota-immune homeostasis as well as discuss the potential for therapeutics in restoring dysbiosis in early life.

Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically targeted radiation therapy.

BACKGROUND: Prostate-Specific Membrane Antigen (PSMA) PET/CT and multiparametric MRI (mpMRI) are well-established modalities for identifying intra-prostatic lesions (IPLs) in localised prostate cancer. This study aimed to investigate the use of PSMA PET/CT and mpMRI for biologically targeted radiation therapy treatment planning by: (1) analysing the relationship between imaging parameters at a voxel-wise level and (2) assessing the performance of radiomic-based machine learning models to predict tumour location and grade. METHODS: PSMA PET/CT and mpMRI data from 19 prostate cancer patients were co-registered with whole-mount histopathology using an established registration framework. Apparent Diffusion Coefficient (ADC) maps were computed from DWI and semi-quantitative and quantitative parameters from DCE MRI. Voxel-wise correlation analysis was conducted between mpMRI parameters and PET Standardised Uptake Value (SUV) for all tumour voxels. Classification models were built using radiomic and clinical features to predict IPLs at a voxel level and then classified further into high-grade or low-grade voxels. RESULTS: Perfusion parameters from DCE MRI were more highly correlated with PET SUV than ADC or T2w. IPLs were best detected with a Random Forest Classifier using radiomic features from PET and mpMRI rather than either modality alone (sensitivity, specificity and area under the curve of 0.842, 0.804 and 0.890, respectively). The tumour grading model had an overall accuracy ranging from 0.671 to 0.992. CONCLUSIONS: Machine learning classifiers using radiomic features from PSMA PET and mpMRI show promise for predicting IPLs and differentiating between high-grade and low-grade disease, which could be used to inform biologically targeted radiation therapy planning.

Patient-specific voxel-level dose prescription for prostate cancer radiotherapy considering tumor cell density and grade distribution.

BACKGROUND: In prostate radiation therapy, recent studies have indicated a benefit in increasing the dose to intraprostatic lesions (IPL) compared with standard whole gland radiation therapy. Such approaches typically aim to deliver a target dose to the IPL(s) with no deliberate effort to modulate the dose within the IPL. Prostate cancers demonstrate intra-tumor heterogeneity and hence it is hypothesized that further gains in the optimal delivery of radiation therapy can be achieved through modulation of the dose distribution within the tumor. To account for tumor heterogeneity, biologically targeted radiation therapy (BiRT) aims to utilize a voxel-wise approach to IPL dose prescription by incorporating knowledge of the spatial distribution of tumor characteristics. PURPOSE: The aim of this study was to develop a workflow for generating voxel-wise optimal dose prescriptions that maximize patient tumor control probability (TCP), and evaluate the feasibility and benefits of applying this workflow on a cohort of 62 prostate cancer patients. METHOD: The source data for this proof-of-concept study included high resolution histology images annotated with tumor location and grade. Image processing techniques were used to compute voxel-level cell density distribution maps. An absolute tumor cell distribution was calculated via linearly scaling according to published estimated tumor cell numbers. For the IPLs of each patient, optimal dose prescriptions were obtained via three alternative methods for redistribution of IPL boost doses according to maximization of TCP. The radiosensitivity uncertainties were considered using a truncated log-normally distributed linear radiosensitivity parameter ( α k ${\alpha }_k$ ) and compared with Gleason pattern (GP) dependent radiosensitivity parameters that were derived based on previously published methods. An ensemble machine learning method was implemented to identify patient-specific features that predict the TCP improvement resulting from dose redistribution relative to a uniform dose distribution. RESULTS: The Gleason pattern-dependent radiosensitivity parameters were calculated for 20 published prostate cancer α / ß ${{\alpha}}/{{\beta}}$ ratios. Optimal voxel-level dose prescriptions were generated for all 62 PCa patients. For all dose redistribution scenarios, the optimal dose distribution always shows a higher (or equivalent) TCP level than the uniform dose distribution. The applied random forest regressor could predict patient-specific TCP improvement with low root mean square error (≤1.5%) by using total tumor number, volume of IPLs and the standard deviation of tumor cell number among all voxels. CONCLUSION: Biologically-optimized redistribution of a boost dose can yield TCP improvement relative to a uniform-boost dose distribution. Patient-specific tumor characteristics can be used to predict the likelihood of benefit from a redistribution approach for the individual patient.

Semi-quantitative and quantitative dynamic contrast-enhanced (DCE) MRI parameters as prostate cancer imaging biomarkers for biologically targeted radiation therapy.

BACKGROUND: Biologically targeted radiation therapy treatment planning requires voxel-wise characterisation of tumours. Dynamic contrast enhanced (DCE) DCE MRI has shown promise in defining voxel-level biological characteristics. In this study we consider the relative value of qualitative, semi-quantitative and quantitative assessment of DCE MRI compared with diffusion weighted imaging (DWI) and T2-weighted (T2w) imaging to detect prostate cancer at the voxel level. METHODS: Seventy prostate cancer patients had multiparametric MRI prior to radical prostatectomy, including T2w, DWI and DCE MRI. Apparent Diffusion Coefficient (ADC) maps were computed from DWI, and semi-quantitative and quantitative parameters computed from DCE MRI. Tumour location and grade were validated with co-registered whole mount histology. Kolmogorov-Smirnov tests were applied to determine whether MRI parameters in tumour and benign voxels were significantly different. Cohen's d was computed to quantify the most promising biomarkers. The Parker and Weinmann Arterial Input Functions (AIF) were compared for their ability to best discriminate between tumour and benign tissue. Classifier models were used to determine whether DCE MRI parameters improved tumour detection versus ADC and T2w alone. RESULTS: All MRI parameters had significantly different data distributions in tumour and benign voxels. For low grade tumours, semi-quantitative DCE MRI parameter time-to-peak (TTP) was the most discriminating and outperformed ADC. For high grade tumours, ADC was the most discriminating followed by DCE MRI parameters Ktrans, the initial rate of enhancement (IRE), then TTP. Quantitative parameters utilising the Parker AIF better distinguished tumour and benign voxel values than the Weinmann AIF. Classifier models including DCE parameters versus T2w and ADC alone, gave detection accuracies of 78% versus 58% for low grade tumours and 85% versus 72% for high grade tumours. CONCLUSIONS: Incorporating DCE MRI parameters with DWI and T2w gives improved accuracy for tumour detection at a voxel level. DCE MRI parameters should be used to spatially characterise tumour biology for biologically targeted radiation therapy treatment planning.

Rapid genome sequencing identifies a novel de novo SNAP25 variant for neonatal congenital myasthenic syndrome.

Congenital myasthenic syndrome (CMS) is a group of 32 disorders involving genetic dysfunction at the neuromuscular junction resulting in skeletal muscle weakness that worsens with physical activity. Precise diagnosis and molecular subtype identification are critical for treatment as medication for one subtype may exacerbate disease in another (Engel et al., Lancet Neurol 14: 420 [2015]; Finsterer, Orphanet J Rare Dis 14: 57 [2019]; Prior and Ghosh, J Child Neurol 36: 610 [2021]). The SNAP25-related CMS subtype (congenital myasthenic syndrome 18, CMS18; MIM #616330) is a rare disorder characterized by muscle fatigability, delayed psychomotor development, and ataxia. Herein, we performed rapid whole-genome sequencing (rWGS) on a critically ill newborn leading to the discovery of an unreported pathogenic de novo SNAP25 c.529C > T; p.Gln177Ter variant. In this report, we present a novel case of CMS18 with complex neonatal consequence. This discovery offers unique insight into the extent of phenotypic severity in CMS18, expands the reported SNAP25 variant phenotype, and paves a foundation for personalized management for CMS18.

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.

Furin cleavage of the SARS-CoV-2 spike is modulated by O-glycosylation.

The SARS-CoV-2 coronavirus responsible for the global pandemic contains a novel furin cleavage site in the spike protein (S) that increases viral infectivity and syncytia formation in cells. Here, we show that O-glycosylation near the furin cleavage site is mediated by members of the GALNT enzyme family, resulting in decreased furin cleavage and decreased syncytia formation. Moreover, we show that O-glycosylation is dependent on the novel proline at position 681 (P681). Mutations of P681 seen in the highly transmissible alpha and delta variants abrogate O-glycosylation, increase furin cleavage, and increase syncytia formation. Finally, we show that GALNT family members capable of glycosylating S are expressed in human respiratory cells that are targets for SARS-CoV-2 infection. Our results suggest that host O-glycosylation may influence viral infectivity/tropism by modulating furin cleavage of S and provide mechanistic insight into the role of the P681 mutations found in the highly transmissible alpha and delta variants.

Biologically Targeted Radiation Therapy: Incorporating Patient-Specific Hypoxia Data Derived from Quantitative Magnetic Resonance Imaging.

PURPOSE: Hypoxia has been linked to radioresistance. Strategies to safely dose escalate dominant intraprostatic lesions have shown promising results, but further dose escalation to overcome the effects of hypoxia require a novel approach to constrain the dose in normal tissue.to safe levels. In this study, we demonstrate a biologically targeted radiotherapy (BiRT) approach that can utilise multiparametric magnetic resonance imaging (mpMRI) to target hypoxia for favourable treatment outcomes. METHODS: mpMRI-derived tumour biology maps, developed via a radiogenomics study, were used to generate individualised, hypoxia-targeting prostate IMRT plans using an ultra- hypofractionation schedule. The spatial distribution of mpMRI textural features associated with hypoxia-related genetic profiles was used as a surrogate of tumour hypoxia. The effectiveness of the proposed approach was assessed by quantifying the potential benefit of a general focal boost approach on tumour control probability, and also by comparing the dose to organs at risk (OARs) with hypoxia-guided focal dose escalation (DE) plans generated for five patients. RESULTS: Applying an appropriately guided focal boost can greatly mitigate the impact of hypoxia. Statistically significant reductions in rectal and bladder dose were observed for hypoxia-targeting, biologically optimised plans compared to isoeffective focal DE plans. CONCLUSION: Results of this study suggest the use of mpMRI for voxel-level targeting of hypoxia, along with biological optimisation, can provide a mechanism for guiding focal DE that is considerably more efficient than application of a general, dose-based optimisation, focal boost.

Furin cleavage of the SARS-CoV-2 spike is modulated by O-glycosylation.

The SARS-CoV-2 coronavirus responsible for the global pandemic contains a unique furin cleavage site in the spike protein (S) that increases viral infectivity and syncytia formation. Here, we show that O-glycosylation near the furin cleavage site is mediated by specific members of the GALNT enzyme family and is dependent on the novel proline at position 681 (P681). We further demonstrate that O-glycosylation of S decreases furin cleavage. Finally, we show that GALNT family members capable of glycosylating S are expressed in human respiratory cells that are targets for SARS-CoV-2 infection. Our results suggest that O-glycosylation may influence viral infectivity/tropism by modulating furin cleavage of S and provide mechanistic insight into the potential role of P681 mutations in the recently identified, highly transmissible B.1.1.7 variant.

Tango1 coordinates the formation of endoplasmic reticulum/Golgi docking sites to mediate secretory granule formation.

Regulated secretion is a conserved process occurring across diverse cells and tissues. Current models suggest that the conserved cargo receptor Tango1 mediates the packaging of collagen into large coat protein complex II (COPII) vesicles that move from the endoplasmic reticulum (ER) to the Golgi apparatus. However, how Tango1 regulates the formation of COPII carriers and influences the secretion of other cargo remains unknown. Here, through high-resolution imaging of Tango1, COPII, Golgi, and secretory cargo (mucins) in Drosophila larval salivary glands, we found that Tango1 forms ring-like structures that mediate the formation of COPII rings rather than vesicles. These COPII rings act as docking sites for the cis-Golgi. Moreover, we observed nascent secretory mucins emerging from the Golgi side of these Tango1-COPII-Golgi complexes, suggesting that these structures represent functional docking sites/fusion points between the ER exit sites and the Golgi. Loss of Tango1 disrupted the formation of COPII rings, the association of COPII with the cis-Golgi, mucin O-glycosylation, and secretory granule biosynthesis. Additionally, we identified a Tango1 self-association domain that is essential for formation of this structure. Our results provide evidence that Tango1 organizes an interaction site where secretory cargo is efficiently transferred from the ER to Golgi and then to secretory vesicles. These findings may explain how the loss of Tango1 can influence Golgi/ER morphology and affect the secretion of diverse proteins across many tissues.

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.

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.

Multiparametric MRI and radiomics in prostate cancer: a review.

Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. The application of radiomics approaches in prostate cancer has not only enabled automatic localisation of the disease but also provided a non-invasive solution to assess tumour biology (e.g. aggressiveness and the presence of hypoxia). This article reviews mpMRI and its expanding role in PCa detection, staging and patient management. Following that, an overview of prostate radiomics will be provided, with a special focus on its current applications as well as its future directions.

Voxel-wise correlation of positron emission tomography/computed tomography with multiparametric magnetic resonance imaging and histology of the prostate using a sophisticated registration framework.

OBJECTIVES: To develop a registration framework for correlating positron emission tomography/computed tomography (PET/CT) images with multiparametric magnetic resonance imaging (mpMRI) and histology of the prostate, thereby enabling voxel-wise analysis of imaging parameters. PATIENTS AND METHODS: In this prospective proof-of-concept study, nine patients scheduled for radical prostatectomy underwent mpMRI and PET/CT imaging before surgery. One had PET imaging using 18 F-fluoromethylcholine, five using 68 Ga-labelled prostate-specific membrane antigen (PSMA)-HBED-CC (PMSA-11), and three using a trial 68 Ga-labelled THP-PSMA tracer. PET/CT data were co-registered with mpMRI via the CT scan and an in vivo three-dimensional T2-weighted (T2w) MRI, and then co-registered with ground truth histology data using ex vivo MRI of the prostate specimen. Maximum and mean standardised uptake values (SUVmax and SUVmean ) were extracted from PET data using tumour annotations from histology, and Kolmogorov-Smirnov tests were used to compare between tumour- and benign-voxel values. Correlation analysis was performed between mpMRI and PET SUV tumour voxel values using Pearson's correlation coefficient and R2 statistics. RESULTS: PET/CT data from all nine patients were successfully registered with mpMRI and histology data. SUVmax and SUVmean ranged from 2.21 to 12.11 and 1.08 to 4.21, respectively. All patients showed the PET SUV values in benign and tumour voxels were from statistically different distributions. Correlation analysis showed no consistent trend between the T2w or apparent diffusion coefficient values and PET SUV. However, parameters from dynamic contrast-enhanced (DCE) MRI including the maximum enhancement, volume transfer constant (Ktrans ), and the initial area under the contrast agent concentration curve for the first 60 s after injection (iAUGC60), showed consistent positive correlations with PET SUV. Furthermore, R2* values from blood oxygen level-dependent (BOLD) MRI showed consistent negative correlations with PET SUV-voxel values. CONCLUSION: We have developed a novel framework for registering and correlating PET/CT data at a voxel-level with mpMRI and histology. Despite registration uncertainties, perfusion and oxygenation parameters from DCE MRI and BOLD imaging showed correlations with PET SUV. Further analysis will be performed on a larger patient cohort to quantify these proof-of-concept findings. Improved understanding of the correlation between mpMRI and PET will provide supportive information for focal therapy planning of the prostate.

TROG 15.03 phase II clinical trial of Focal Ablative STereotactic Radiosurgery for Cancers of the Kidney - FASTRACK II.

BACKGROUND: Stereotactic ablative body radiotherapy (SABR) is a non-invasive alternative to surgery to control primary renal cell cancer (RCC) in patients that are medically inoperable or at high-risk of post-surgical dialysis. The objective of the FASTRACK II clinical trial is to investigate the efficacy of SABR for primary RCC. METHODS: FASTRACK II is a single arm, multi-institutional phase II study. Seventy patients will be recruited over 3 years and followed for a total of 5 years. Eligible patients must have a biopsy confirmed diagnosis of primary RCC with a single lesion within a kidney, have ECOG performance ≤2 and be medically inoperable, high risk or decline surgery. Radiotherapy treatment planning is undertaken using four dimensional CT scanning to incorporate the impact of respiratory motion. Treatment must be delivered using a conformal or intensity modulated technique including IMRT, VMAT, Cyberknife or Tomotherapy. The trial includes two alternate fractionation schedules based on tumour size: for tumours ≤4 cm in maximum diameter a single fraction of 26Gy is delivered; and for tumours > 4 cm in maximum diameter 42Gy in three fractions is delivered. The primary outcome of the study is to estimate the efficacy of SABR for primary RCC. Secondary objectives include estimating tolerability, characterising overall survival and cancer specific survival, estimating the distant failure rate, describing toxicity and renal function changes after SABR, and assessment of cost-effectiveness of SABR compared with current therapies. DISCUSSION: The present study design allows for multicentre prospective validation of the efficacy of SABR for primary RCC that has been observed from prior single institutional and retrospective series. The study also allows assessment of treatment related toxicity, overall survival, cancer specific survival, freedom from distant failure and renal function post therapy. TRIAL REGISTRATION: Clinicaltrials.gov NCT02613819 , registered Nov 25th 2015.

Diffusion weighted and dynamic contrast enhanced MRI as an imaging biomarker for stereotactic ablative body radiotherapy (SABR) of primary renal cell carcinoma.

PURPOSE: To explore the utility of diffusion and perfusion changes in primary renal cell carcinoma (RCC) after stereotactic ablative body radiotherapy (SABR) as an early biomarker of treatment response, using diffusion weighted (DWI) and dynamic contrast enhanced (DCE) MRI. METHODS: Patients enrolled in a prospective pilot clinical trial received SABR for primary RCC, and had DWI and DCE MRI scheduled at baseline, 14 days and 70 days after SABR. Tumours <5cm diameter received a single fraction of 26 Gy and larger tumours received three fractions of 14 Gy. Apparent diffusion coefficient (ADC) maps were computed from DWI data and parametric and pharmacokinetic maps were fitted to the DCE data. Tumour volumes were contoured and statistics extracted. Spearman's rank correlation coefficients were computed between MRI parameter changes versus the percentage tumour volume change from CT at 6, 12 and 24 months and the last follow-up relative to baseline CT. RESULTS: Twelve patients were eligible for DWI analysis, and a subset of ten patients for DCE MRI analysis. DCE MRI from the second follow-up MRI scan showed correlations between the change in percentage voxels with washout contrast enhancement behaviour and the change in tumour volume (ρ = 0.84, p = 0.004 at 12 month CT, ρ = 0.81, p = 0.02 at 24 month CT, and ρ = 0.89, p = 0.001 at last follow-up CT). The change in mean initial rate of enhancement and mean Ktrans at the second follow-up MRI scan were positively correlated with percent tumour volume change at the 12 month CT onwards (ρ = 0.65, p = 0.05 and ρ = 0.66, p = 0.04 at 12 month CT respectively). Changes in ADC kurtosis from histogram analysis at the first follow-up MRI scan also showed positive correlations with the percentage tumour volume change (ρ = 0.66, p = 0.02 at 12 month CT, ρ = 0.69, p = 0.02 at last follow-up CT), but these results are possibly confounded by inflammation. CONCLUSION: DWI and DCE MRI parameters show potential as early response biomarkers after SABR for primary RCC. Further prospective validation using larger patient cohorts is warranted.