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

Finnegan, Robert; Reynolds, Hayley M; Ebert, Martin A.; Sun, Yu; Holloway, Lois; Sykes, J.; Dowling, Jason; Mitchell, Catherine; Williams, Scott; Murphy, Declan; Haworth, Annette

doi:10.1016/j.phro.2022.02.011

Cited by 8 publications

(21 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Details of the co‐registration and framework for defining the 3D reference space are described by Reynolds et al 14 . and Finnegan et al 20 …”

Section: Methodsmentioning

confidence: 99%

“…This ex-vivo data was used to provide a 3D reference space to assemble the histology data and provide a framework for interpolation of data between histology images. Details of the co-registration and framework for defining the 3D reference space are described by Reynolds et al 14 and Finnegan et al 20 Cell density maps, representing the area cell density distribution, were computed by applying a previously developed image processing pipeline 21 to the histology images. As the histology slices in the present study were at a larger interval than the axial T2w slices (5 mm vs. 2.5 mm), the cell density maps and tumor annotation masks were linearly interpolated using adjacent slices to fill in for the missing slices.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…As a result, every voxel of each grade of tumor annotation mask was assigned a probabilistic value (between 0 and 1). 20 Following the interpolation, the areal cell density indicated by cell density maps was raised to the power of 3 2 to convert to the volumetric cell density distribution. 20 Finally, the cell density maps and tumor annotation masks were resampled to an isotropic 0.8 × 0.8 × 0.8 mm 3 resolution.…”

Section: Data Acquisitionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

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 voxelwise 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 ) 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∕𝛽 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.

show abstract

“…Details of the co‐registration and framework for defining the 3D reference space are described by Reynolds et al 14 . and Finnegan et al 20 …”

Section: Methodsmentioning

confidence: 99%

Section: Data Acquisitionmentioning

confidence: 99%

Section: Data Acquisitionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“… WH-guided deformable transformation. A novel WH-guided registration algorithm with distance-preserving regularisation was implemented, as developed by Finnegan et al (2022) [ 28 ]. This process also uses normalised distance maps to optimise a deformable transformation from the registered atlas WH delineation from step 1 to the automatic segmentation on the target image.…”

Section: Methodsmentioning

confidence: 99%

Open-source, fully-automated hybrid cardiac substructure segmentation: development and optimisation

et al. 2023

Self Cite

View full text Add to dashboard Cite

Radiotherapy for thoracic and breast tumours is associated with a range of cardiotoxicities. Emerging evidence suggests cardiac substructure doses may be more predictive of specific outcomes, however, quantitative data necessary to develop clinical planning constraints is lacking. Retrospective analysis of patient data is required, which relies on accurate segmentation of cardiac substructures. In this study, a novel model was designed to deliver reliable, accurate, and anatomically consistent segmentation of 18 cardiac substructures on computed tomography (CT) scans. Thirty manually contoured CT scans were included. The proposed multi-stage method leverages deep learning (DL), multi-atlas mapping, and geometric modelling to automatically segment the whole heart, cardiac chambers, great vessels, heart valves, coronary arteries, and conduction nodes. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD), and volume ratio. Performance was reliable, with no errors observed and acceptable variation in accuracy between cases, including in challenging cases with imaging artefacts and atypical patient anatomy. The median DSC range was 0.81–0.93 for whole heart and cardiac chambers, 0.43–0.76 for great vessels and conduction nodes, and 0.22–0.53 for heart valves. For all structures the median MDA was below 6 mm, median HD ranged 7.7–19.7 mm, and median volume ratio was close to one (0.95–1.49) for all structures except the left main coronary artery (2.07). The fully automatic algorithm takes between 9 and 23 min per case. The proposed fully-automatic method accurately delineates cardiac substructures on radiotherapy planning CT scans. Robust and anatomically consistent segmentations, particularly for smaller structures, represents a major advantage of the proposed segmentation approach. The open-source software will facilitate more precise evaluation of cardiac doses and risks from available clinical datasets. Graphical abstract

show abstract

“…Voxel-wise population analysis can provide powerful tools to assess the clinical impacts of image and dose difference across individuals ( 25 , 26 ). However, their application requires an accurate non-rigid registration of a whole population to a single coordinate system and the implementation of voxel-wise statistical tests.…”

Section: Introductionmentioning

confidence: 99%

Quality assurance for MRI-only radiation therapy: A voxel-wise population-based methodology for image and dose assessment of synthetic CT generation methods

et al. 2022

Self Cite

View full text Add to dashboard Cite

The quality assurance of synthetic CT (sCT) is crucial for safe clinical transfer to an MRI-only radiotherapy planning workflow. The aim of this work is to propose a population-based process assessing local errors in the generation of sCTs and their impact on dose distribution. For the analysis to be anatomically meaningful, a customized interpatient registration method brought the population data to the same coordinate system. Then, the voxel-based process was applied on two sCT generation methods: a bulk-density method and a generative adversarial network. The CT and MRI pairs of 39 patients treated by radiotherapy for prostate cancer were used for sCT generation, and 26 of them with delineated structures were selected for analysis. Voxel-wise errors in sCT compared to CT were assessed for image intensities and dose calculation, and a population-based statistical test was applied to identify the regions where discrepancies were significant. The cumulative histograms of the mean absolute dose error per volume of tissue were computed to give a quantitative indication of the error for each generation method. Accurate interpatient registration was achieved, with mean Dice scores higher than 0.91 for all organs. The proposed method produces three-dimensional maps that precisely show the location of the major discrepancies for both sCT generation methods, highlighting the heterogeneity of image and dose errors for sCT generation methods from MRI across the pelvic anatomy. Hence, this method provides additional information that will assist with both sCT development and quality control for MRI-based planning radiotherapy.

show abstract

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

Cited by 8 publications

References 59 publications

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

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

Open-source, fully-automated hybrid cardiac substructure segmentation: development and optimisation

Quality assurance for MRI-only radiation therapy: A voxel-wise population-based methodology for image and dose assessment of synthetic CT generation methods

Contact Info

Product

Resources

About