Simulation of sequential pathology images for the virtual clinical trials with rad-path correlation

A simulation of sequential breast pathology images is proposed, as a prerequisite for the development of virtual clinical trials (VCTs) with radiology-pathology (rad-path) correlation. The rad-path correlation of breast cancer findings is performed clinically to confirm concordance and increase confidence in diagnoses. VCTs have been used for optimization of breast imaging systems, based upon computer simulation of breast anatomy, imaging modalities, and image interpretation. Today, VCTs are used to optimize breast imaging at the “radiology” spatial scale, by simulating tissue structures seen in radiological images, namely, skin, adipose or dense tissue compartments, fibrous ligaments, and major ducts and blood vessels. We have extended this simulation to the microscopic (i.e., “pathology”) spatial scale, to allow for virtual rad-path correlation. Previously, we developed a manual simulation of adipose and dense tissue regions in pathology images, corresponding to a small region selected within a breast phantom simulated at the radiological scale. In this paper, we describe an automated simulation of adipocytes, epithelial and myoepithelial cells, collagen fibers, and fibroblasts. Adipocytes are simulated by recursive partitioning. Epithelial and myoepithelial cells are simulated radially around ductal or acinar lumen. Fibers and fibroblasts are simulated by an analogy with the electrostatic field. Our approach models the volumetric distributions of cells and various breast tissues, which allows the simulation of sequential pathology images at clinical inter-slice distances. The proposed simulation method has been evaluated by a clinical pathologist and medical physicists. The effect of the simulation approaches on the visual appearance of simulated pathology images has been evaluated.

[1]  Nooshin Kiarashi,et al.  Development and Application of a Suite of 4-D Virtual Breast Phantoms for Optimization and Evaluation of Breast Imaging Systems , 2014, IEEE Transactions on Medical Imaging.

[2]  Andrew D. A. Maidment,et al.  OpenVCT: a GPU-accelerated virtual clinical trial pipeline for mammography and digital breast tomosynthesis , 2018, Medical Imaging.

[3]  Darren Treanor,et al.  Toward routine use of 3D histopathology as a research tool. , 2012, The American journal of pathology.

[4]  Dan Wang,et al.  An improved processing method for breast whole-mount serial sections for three-dimensional histopathology imaging. , 2009, American journal of clinical pathology.

[5]  Barr,et al.  Superquadrics and Angle-Preserving Transformations , 1981, IEEE Computer Graphics and Applications.

[6]  Nasir M. Rajpoot,et al.  A model of the spatial tumour heterogeneity in colorectal adenocarcinoma tissue , 2016, BMC Bioinformatics.

[7]  Varsha Shankla,et al.  Automatic insertion of simulated microcalcification clusters in a software breast phantom , 2014, Medical Imaging.

[8]  Andrew D. A. Maidment,et al.  Simulation of Breast Anatomy: Bridging the Radiology-Pathology Scale Gap , 2016, Digital Mammography / IWDM.

[9]  Daniel Racoceanu,et al.  A model of tumor architecture and spatial interactions with tumor microenvironment in breast carcinoma , 2017, Medical Imaging.

[10]  Hilde Bosmans,et al.  The simulation of 3D mass models in 2D digital mammography and breast tomosynthesis. , 2014, Medical physics.

[11]  Andrew D. A. Maidment,et al.  Optimized simulation of breast anatomy for virtual clinical trials , 2018, Other Conferences.

[12]  D. Sanders Diagnosis and Differential Diagnosis of Breast Calcifications , 1988 .

[13]  Paolo P. Provenzano,et al.  Collagen reorganization at the tumor-stromal interface facilitates local invasion , 2006, BMC medicine.

[14]  D R Dance,et al.  Simulation and assessment of realistic breast lesions using fractal growth models , 2013, Physics in medicine and biology.

[15]  Nico Karssemeijer,et al.  3D volume reconstruction from serial breast specimen radiographs for mapping between histology and 3D whole specimen imaging , 2017, Medical physics.

[16]  Randy Heiland,et al.  PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems , 2017, bioRxiv.

[17]  Vittorio Cristini,et al.  Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): from microscopic measurements to macroscopic predictions of clinical progression. , 2012, Journal of theoretical biology.

[18]  Andrew D. A. Maidment,et al.  Optimized generation of high resolution breast anthropomorphic software phantoms. , 2012, Medical physics.

[19]  Frank W. Samuelson,et al.  In silico imaging clinical trials for regulatory evaluation: initial considerations for VICTRE, a demonstration study , 2017, Medical Imaging.

[20]  Etsuo A. Susaki,et al.  CUBIC pathology: three-dimensional imaging for pathological diagnosis , 2017, Scientific Reports.