Finnegan Robert N, Reynolds Hayley M, Ebert Martin A, Sun Yu, Holloway Lois, Sykes Jonathan R, Dowling Jason, Mitchell Catherine, Williams Scott G, Murphy Declan G, Haworth Annette
Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia.
Liverpool Cancer Therapy Centre, South Western Sydney Local Health District, Liverpool, New South Wales, Australia.
Phys Imaging Radiat Oncol. 2022 Mar 6;21:136-145. doi: 10.1016/j.phro.2022.02.011. eCollection 2022 Jan.
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 multiparametric MRI data to facilitate biologically-optimised RT.
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.
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.
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.
放射治疗（RT）常用于前列腺癌（PC）的治疗。针对PC的生物优化放疗可能会提高无病生存率。这需要对肿瘤病变进行精确的空间定位和特征描述。我们旨在生成一个统计性的、体素化的生物模型，以补充多参数MRI数据，促进生物优化放疗。
对63例PC患者进行了离体前列腺MRI和组织病理学成像。对这些数据进行配准，以得出分级肿瘤病变和细胞密度的三维分布。采用新的配准方法将这些数据映射到一个共同的参考几何结构上。生成肿瘤概率和细胞密度的体素化统计模型，以创建PC生物图谱。使用Kullback-Leibler散度分析细胞密度模型，以比较经验数据的正态近似与对数正态近似。
利用离体MRI空间构建了一个参考几何结构，采用一种新的解剖学引导方法对患者数据进行变形配准。利用外周区定义来维持亚结构对应，以解决患者之间前列腺解剖结构的空间变异性。设计了三种不同的插值方法，将组织学中的轮廓、肿瘤标注和细胞密度图映射到离体MRI空间。分析表明，与线性正态模型相比，对数正态模型能更一致地表示细胞密度。
已经创建了一个生物模型，该模型将人群中肿瘤特征的空间分布整合到三维体素化统计模型中。该工具将用于辅助为PC患者开发生物优化放疗。
