Virtual dosimetry study with three cone-beam breast computed tomography scanners using a fast GPU-based Monte Carlo code

Objective. To compare the dosimetric performance of three cone-beam breast computed tomography (BCT) scanners, using real-time Monte Carlo-based dose estimates obtained with the virtual clinical trials (VCT)-BREAST graphical processing unit (GPU)-accelerated platform dedicated to VCT in breast imaging. Approach. A GPU-based Monte Carlo (MC) code was developed for replicatingin silicothe geometric, x-ray spectra and detector setups adopted, respectively, in two research scanners and one commercial BCT scanner, adopting 80 kV, 60 kV and 49 kV tube voltage, respectively. Our cohort of virtual breasts included 16 anthropomorphic voxelized breast phantoms from a publicly available dataset. For each virtual patient, we simulated exams on the three scanners, up to a nominal simulated mean glandular dose of 5 mGy (primary photons launched, in the order of 1011-1012per scan). Simulated 3D dose maps (recorded for skin, adipose and glandular tissues) were compared for the same phantom, on the three scanners. MC simulations were implemented on a single NVIDIA GeForce RTX 3090 graphics card.Main results.Using the spread of the dose distribution as a figure of merit, we showed that, in the investigated phantoms, the glandular dose is more uniform within less dense breasts, and it is more uniformly distributed for scans at 80 kV and 60 kV, than at 49 kV. A realistic virtual study of each breast phantom was completed in about 3.0 h with less than 1% statistical uncertainty, with 109primary photons processed in 3.6 s computing time.Significance. We reported the first dosimetric study of the VCT-BREAST platform, a fast MC simulation tool for real-time virtual dosimetry and imaging trials in BCT, investigating the dose delivery performance of three clinical BCT scanners. This tool can be adopted to investigate also the effects on the 3D dose distribution produced by changes in the geometrical and spectrum characteristics of a cone-beam BCT scanner.

Ceftriaxone efficacy for Mycobacterium avium complex lung disease in the hollow fiber and translation to sustained sputum culture conversion in patients

BACKGROUND

Only 35.6%-50.8% of patients with Mycobacterium avium complex (MAC) pulmonary disease achieve sustained sputum culture conversion (SSCC) on treatment with the azithromycin-ethambutol-rifabutin standard of care (SOC). We tested the efficacy of ceftriaxone, a β-lactam with a lung penetration ratio of 12.18-fold.

METHODS

We mimicked lung concentration-time profiles of seven ceftriaxone once-daily doses for 28 days in the hollow fiber system model of intracellular MAC (HFS- MAC). Monte Carlo experiments were used for dose selection.We also compared the once-daily ceftriaxone monotherapy to three-drug SOC against five MAC clinical isolates in HFS-MAC using γ (kill)-slopes. Results were translated to SSCC rates.

RESULTS

Ceftriaxone killed 1.02-3.82 log10 cfu/mL in dose-response studies. Ceftriaxone 2G once-daily was identified as the optimal dose. Ceftriaxone killed all five strains below day 0 versus 2/5 for SOC. The median γ (95% confidence interval) was 0.49(0.47-0.52) log10 cfu/mL/day for ceftriaxone and 0.38(0.34-0.43) log10 cfu/mL/day for SOC. In patients, the SOC was predicted to achieve SSCC rates of 39.3%(36%-42%) at 6 months (similar to meta-analyses results). The SOC SSCC was 50% at 8.18(3.64-27.66) months versus 3.58(2.20-7.23) months for ceftriaxone. Thus, ceftriaxone shortened time-to-SSCC 2.35-fold compared to SOC.

CONCLUSION

Ceftriaxone is a promising agent for creation of short-course chemotherapy.

Artificial Intelligence, Computational Simulations, and Extended Reality in Cardiovascular Interventions

Artificial intelligence, computational simulations, and extended reality, among other 21st century computational technologies, are changing the health care system. To collectively highlight the most recent advances and benefits of artificial intelligence, computational simulations, and extended reality in cardiovascular therapies, we coined the abbreviation AISER. The review particularly focuses on the following applications of AISER: 1) preprocedural planning and clinical decision making; 2) virtual clinical trials, and cardiovascular device research, development, and regulatory approval; and 3) education and training of interventional health care professionals and medical technology innovators. We also discuss the obstacles and constraints associated with the application of AISER technologies, as well as the proposed solutions. Interventional health care professionals, computer scientists, biomedical engineers, experts in bioinformatics and visualization, the device industry, ethics committees, and regulatory agencies are expected to streamline the use of AISER technologies in cardiovascular interventions and medicine in general.

Impact of Tomosynthesis Acquisition on 3D Segmentations of Breast Outline and Adipose/Dense Tissue with AI: A Simulation-Based Study

Digital breast tomosynthesis (DBT) reconstructions introduce out-of-plane artifacts and false-tissue boundaries impacting the dense/adipose and breast outline (convex hull) segmentations. A virtual clinical trial method was proposed to segment both the breast tissues and the breast outline in DBT reconstructions. The DBT images of a representative population were simulated using three acquisition geometries: a left-right scan (conventional, I), a two-directional scan in the shape of a "T" (II), and an extra-wide range (XWR, III) left-right scan at a six-times higher dose than I. The nnU-Net was modified including two losses for segmentation: (1) tissues and (2) breast outline. The impact of loss (1) and the combination of loss (1) and (2) was evaluated using models trained with data simulating geometry I. The impact of the geometry was evaluated using the combined loss (1&2). The loss (1&2) improved the convex hull estimates, resolving 22.2% of the false classification of air voxels. Geometry II was superior to I and III, resolving 99.1% and 96.8% of the false classification of air voxels. Geometry III (Dice = (0.98, 0.94)) was superior to I (0.92, 0.78) and II (0.93, 0.74) for the tissue segmentation (adipose, dense, respectively). Thus, the loss (1&2) provided better segmentation, and geometries T and XWR improved the dense/adipose and breast outline segmentations relative to the conventional scan.

A systematic review of cardiac in-silico clinical trials

Computational models of the heart are now being used to assess the effectiveness and feasibility of interventions through in-silico clinical trials (ISCTs). As the adoption and acceptance of ISCTs increases, best practices for reporting the methodology and analysing the results will emerge. Focusing in the area of cardiology, we aim to evaluate the types of ISCTs, their analysis methods and their reporting standards. To this end, we conducted a systematic review of cardiac ISCTs over the period of 1 January 2012-1 January 2022, following the preferred reporting items for systematic reviews and meta-analysis (PRISMA). We considered cardiac ISCTs of human patient cohorts, and excluded studies of single individuals and those in which models were used to guide a procedure without comparing against a control group. We identified 36 publications that described cardiac ISCTs, with most of the studies coming from the US and the UK. In 75% of the studies, a validation step was performed, although the specific type of validation varied between the studies. ANSYS FLUENT was the most commonly used software in 19% of ISCTs. The specific software used was not reported in 14% of the studies. Unlike clinical trials, we found a lack of consistent reporting of patient demographics, with 28% of the studies not reporting them. Uncertainty quantification was limited, with sensitivity analysis performed in only 19% of the studies. In 97% of the ISCTs, no link was provided to provide easy access to the data or models used in the study. There was no consistent naming of study types with a wide range of studies that could potentially be considered ISCTs. There is a clear need for community agreement on minimal reporting standards on patient demographics, accepted standards for ISCT cohort quality control, uncertainty quantification, and increased model and data sharing.

Multiclass Segmentation of Breast Tissue and Suspicious Findings: A Simulation-Based Study for the Development of Self-Steering Tomosynthesis

In breast tomosynthesis, multiple low-dose projections are acquired in a single scanning direction over a limited angular range to produce cross-sectional planes through the breast for three-dimensional imaging interpretation. We built a next-generation tomosynthesis system capable of multidirectional source motion with the intent to customize scanning motions around "suspicious findings". Customized acquisitions can improve the image quality in areas that require increased scrutiny, such as breast cancers, architectural distortions, and dense clusters. In this paper, virtual clinical trial techniques were used to analyze whether a finding or area at high risk of masking cancers can be detected in a single low-dose projection and thus be used for motion planning. This represents a step towards customizing the subsequent low-dose projection acquisitions autonomously, guided by the first low-dose projection; we call this technique "self-steering tomosynthesis." A U-Net was used to classify the low-dose projections into "risk classes" in simulated breasts with soft-tissue lesions; class probabilities were modified using post hoc Dirichlet calibration (DC). DC improved the multiclass segmentation (Dice = 0.43 vs. 0.28 before DC) and significantly reduced false positives (FPs) from the class of the highest risk of masking (sensitivity = 81.3% at 2 FPs per image vs. 76.0%). This simulation-based study demonstrated the feasibility of identifying suspicious areas using a single low-dose projection for self-steering tomosynthesis.

Towards the simulation of bone-implant systems with a stratified material model

BACKGROUND

The clinical performance of medical devices is becoming increasingly important for the requirements of modern development processes and the associated regulations. However, the evidence for this performance can often only be obtained very late in the development process via clinical trials or studies.

OBJECTIVE

The purpose of the presented work is to show that the simulation of bone-implant-system has advanced in various aspects, including cloud-based execution, Virtual Clinical Trials and material modeling towards a point where and widespread utilization in healthcare for procedure planning and enhancing practices seems feasible. But this will only hold true if the virtual cohort data build from clinical Computer Tomography data are collected and analysed with care.

METHODS

An overview of the principal steps necessary to perfor Finite Element Method-based structural mechanical simulations of bone-implant systems based on clinical imaging data is presented. Since these data form the baseline for virtual cohort construction, we present an enhancement method to make them more accurate and reliable.

RESULTS

The findings of our work comprise the initial step towards a virtual cohort for the evaluation of proximal femur implants. In addition, results of our proposed enhancement methodology for clinical Computer Tomography data that demonstrate the necessity for the usage of multiple image reconstructions are presented.

CONCLUSION

Simulation methodologies and pipelines nowadays are mature and have turnaround times that allow for a day-to-day use. However, small changes in the imaging and the pre-processing of data can have a significant impact on the obtaind results. Consequently, first steps towards virtual clinical trials, like collecting bone samples, are done, but the reliability of the input data remains subject to further research and development.

Emphysema Quantifications With CT Scan: Assessing the Effects of Acquisition Protocols and Imaging Parameters Using Virtual Imaging Trials

BACKGROUND

CT scan has notable potential to quantify the severity and progression of emphysema in patients. Such quantification should ideally reflect the true attributes and pathologic conditions of subjects, not scanner parameters. To achieve such an objective, the effects of the scanner conditions need to be understood so the influence can be mitigated.

RESEARCH QUESTION

How do CT scan imaging parameters affect the accuracy of emphysema-based quantifications and biomarkers?

STUDY DESIGN AND METHODS

Twenty anthropomorphic digital phantoms were developed with diverse anatomic attributes and emphysema abnormalities informed by a real COPD cohort. The phantoms were input to a validated CT scan simulator (DukeSim), modeling a commercial scanner (Siemens Flash). Virtual images were acquired under various clinical conditions of dose levels, tube current modulations (TCM), and reconstruction techniques and kernels. The images were analyzed to evaluate the effects of imaging parameters on the accuracy of density-based quantifications (percent of lung voxels with HU < -950 [LAA-950] and 15th percentile of lung histogram HU [Perc15]) across varied subjects. Paired t tests were performed to explore statistical differences between any two imaging conditions.

RESULTS

The most accurate imaging condition corresponded to the highest acquired dose (100 mAs) and iterative reconstruction (SAFIRE) with the smooth kernel of I31, where the measurement errors (difference between measurement and ground truth) were 35 ± 3 Hounsfield Units (HU), -4% ± 5%, and 26 ± 10 HU (average ± SD), for the mean lung HU, LAA-950, and Perc15, respectively. Without TCM and at the I31 kernel, increase of dose (20 to 100 mAs) improved the lung mean absolute error (MAE) by 4.2 ± 2.3 HU (average ± SD). TCM did not contribute to a systematic improvement of lung MAE.

INTERPRETATION

The results highlight that although CT scan quantification is possible, its reliability is impacted by the choice of imaging parameters. The developed virtual imaging trial platform in this study enables comprehensive evaluation of CT scan methods in reliable quantifications, an effort that cannot be readily made with patient images or simplistic physical phantoms.

Denoising digital breast tomosynthesis projections using deep learning with synthetic data as training set

Purpose

Image denoising based on deep neural networks (DNN) needs a big dataset containing digital breast tomosynthesis (DBT) projections acquired in different radiation doses to be trained, which is impracticable. Therefore, we propose extensively investigating the use of synthetic data generated by software for training DNNs to denoise DBT real data.

Approach

The approach consists of generating a synthetic dataset representative of the DBT sample space by software, containing noisy and original images. Synthetic data were generated in two different ways: (a) virtual DBT projections generated by OpenVCT and (b) noisy images synthesized from photography regarding noise models used in DBT (e.g., Poisson-Gaussian noise). Then, DNN-based denoising techniques were trained using a synthetic dataset and tested for denoising physical DBT data. Results were evaluated in quantitative (PSNR and SSIM measures) and qualitative (visual analysis) terms. Furthermore, a dimensionality reduction technique (t-SNE) was used for visualization of sample spaces of synthetic and real datasets.

Results

The experiments showed that training DNN models with synthetic data could denoise DBT real data, achieving competitive results to traditional methods in quantitative terms but showing a better balance between noise filtering and detail preservation in a visual analysis. T-SNE enables us to visualize if synthetic and real noises are in the same sample space.

Conclusion

We propose a solution for the lack of suitable training data to train DNN models for denoising DBT projections, showing that we just need the synthesized noise to be in the same sample space as the target image.

In Vitro/In Vivo Translation of Synergistic Combination of MDM2 and MEK Inhibitors in Melanoma Using PBPK/PD Modelling: Part III

The development of in vitro/in vivo translational methods and a clinical trial framework for synergistically acting drug combinations are needed to identify optimal therapeutic conditions with the most effective therapeutic strategies. We performed physiologically based pharmacokinetic-pharmacodynamic (PBPK/PD) modelling and virtual clinical trial simulations for siremadlin, trametinib, and their combination in a virtual representation of melanoma patients. In this study, we built PBPK/PD models based on data from in vitro absorption, distribution, metabolism, and excretion (ADME), and in vivo animals' pharmacokinetic-pharmacodynamic (PK/PD) and clinical data determined from the literature or estimated by the Simcyp simulator (version V21). The developed PBPK/PD models account for interactions between siremadlin and trametinib at the PK and PD levels. Interaction at the PK level was predicted at the absorption level based on findings from animal studies, whereas PD interaction was based on the in vitro cytotoxicity results. This approach, combined with virtual clinical trials, allowed for the estimation of PK/PD profiles, as well as melanoma patient characteristics in which this therapy may be noninferior to the dabrafenib and trametinib drug combination. PBPK/PD modelling, combined with virtual clinical trial simulation, can be a powerful tool that allows for proper estimation of the clinical effect of the above-mentioned anticancer drug combination based on the results of in vitro studies. This approach based on in vitro/in vivo extrapolation may help in the design of potential clinical trials using siremadlin and trametinib and provide a rationale for their use in patients with melanoma.

Virtual oncology research-different models and lessons learned

PURPOSE OF REVIEW

The COVID-19 pandemic posed several challenges to cancer research including halting of trials, reduced recruitment and protocol violations related to inflexible processes followed in clinical trials. Researchers adopted innovative measures to mitigate these problems and continue studies without compromising their quality. This review collates these adaptations that could well continue after the pandemic.

RECENT FINDINGS

The COVID-19 pandemic forced researchers globally to adopt innovative measures to overcome the challenges of the pandemic. These included protocol amendments to adjust to the pandemic and travel restrictions, and increased use of digital technologies. 'Virtual' clinical trials were conducted increasingly with adaptations in ethics and regulatory approvals, patient recruitment and consenting, study interventions and delivery of study medications, trial assessments, and monitoring. Many of these adaptations are safe and feasible, without compromising study quality and data integrity. Although these may not be universally applicable in all types of research, they bring many benefits including more diverse patient participation, less burden on patients for study procedures and reduced resources to conduct trials.

SUMMARY

The COVID-19 pandemic has affected cancer research adversely; however, learnings from the pandemic and adaptations from researchers are likely to improve the efficiency of clinical research beyond the pandemic.

Development and evaluation of a method for tumor growth simulation in virtual clinical trials of breast cancer screening

Purpose: Image-based analysis of breast tumor growth rate may optimize breast cancer screening and diagnosis by suggesting optimal screening intervals and guide the clinical discussion regarding personalized screening based on tumor aggressiveness. Simulation-based virtual clinical trials (VCTs) can be used to evaluate and optimize medical imaging systems and design clinical trials. This study aimed to simulate tumor growth over multiple screening rounds. Approach: This study evaluates a preliminary method for simulating tumor growth. Clinical data on tumor volume doubling time (TVDT) was used to fit a probability distribution ("clinical fit") of TVDTs. Simulated tumors with TVDTs sampled from the clinical fit were inserted into 30 virtual breasts ("simulated cohort") and used to simulate mammograms. Based on the TVDT, two successive screening rounds were simulated for each virtual breast. TVDTs from clinical and simulated mammograms were compared. Tumor sizes in the simulated mammograms were measured by a radiologist in three repeated sessions to estimate TVDT. Results: The mean TVDT was 297 days (standard deviation, SD, 169 days) in the clinical fit and 322 days (SD, 217 days) in the simulated cohort. The mean estimated TVDT was 340 days (SD, 287 days). No significant difference was found between the estimated TVDTs from simulated mammograms and clinical TVDT values ( p > 0.5 ). No significant difference ( p > 0.05 ) was observed in the reproducibility of the tumor size measurements between the two screening rounds. Conclusions: The proposed method for tumor growth simulation has demonstrated close agreement with clinical results, supporting potential use in VCTs of temporal breast imaging.

Computer Simulations of Case Difficulty in Digital Breast Tomosynthesis Using Virtual Clinical Trials

PURPOSE

Virtual clinical trials (VCTs) require computer simulations of representative patients and images to evaluate and compare changes in performance of imaging technologies. The simulated images are usually interpreted by model observers whose performance depends upon the selection of imaging cases used in training evaluation models. This work proposes an efficient method to simulate and calibrate soft tissue lesions, which matches the detectability threshold of virtual and human readings.

METHODS

Anthropomorphic breast phantoms were used to evaluate the simulation of four mass models (I-IV) that vary in shape and composition of soft tissue. Ellipsoidal (I) and spiculated (II-IV) masses were simulated using composite voxels with partial volumes. Digital breast tomosynthesis projections and reconstructions of a clinical system were simulated. Channelized Hotelling observers (CHOs) were evaluated using reconstructed slices of masses that varied in shape, composition, and density of surrounded tissue. The detectability threshold of each mass model was evaluated using receiver operating characteristic (ROC) curves calculated with the CHO's scores.

RESULTS

The area under the curve (AUC) of each calibrated mass model were within the 95% confidence interval (mean AUC [95% CI]) reported in a previous reader study (0.93 [0.89, 0.97]). The mean AUC [95% CI] obtained were 0.94 [0.93, 0.96], 0.92 [0.90, 0.93], 0.92 [0.90, 0.94], 0.93 [0.92, 0.95] for models I to IV, respectively. The mean AUC results varied substantially as a function of shape, composition, and density of surrounded tissue.

CONCLUSIONS

For successful VCTs, lesions composed of soft tissue should be calibrated to simulate imaging cases that match the case difficulty predicted by human readers. Lesion composition, shape, and size are parameters that should be carefully selected to calibrate VCTs. This article is protected by copyright. All rights reserved.

Stakeholder Perspectives on Barriers and Facilitators for the Adoption of Virtual Clinical Trials: Qualitative Study

Background

Conventional clinical trials are essential for generating high-quality evidence by measuring the efficacy of interventions in rigorously controlled clinical environments. However, their execution can be expensive and time-consuming. In addition, clinical trials face several logistical challenges regarding the identification, recruitment, and retention of participants; consistent data collection during trials; and adequate patient follow-up. This might lead to inefficient resource utilization. In order to partially address the current problems with conventional clinical trials, there exists the need for innovations. One such innovation is the virtual clinical trial (VCT). VCTs allow for the collection and integration of diverse data from multiple information sources, such as electronic health records, clinical and demographic data, patient-reported outcomes, anthropometric and activity measurements, and data collected by digital biomarkers or (small) samples that participants can collect themselves. Although VCTs have the potential to provide substantial value to clinical research and patients because they can lower clinical trial costs, increase the volume of data collected from patients’ daily environment, and reduce the burden of patient participation, so far VCT adoption is not commonplace.

Objective

This paper aims to better understand the barriers and facilitators to VCT adoption by determining the factors that influence individuals’ considerations regarding VCTs from the perspective of various stakeholders.

Methods

Based on online semistructured interviews, a qualitative study was conducted with pharmaceutical companies, food and health organizations, and an applied research organization in Europe. Data were thematically analyzed using Rogers’ diffusion of innovation theory.

Results

A total of 16 individuals with interest and experience in VCTs were interviewed, including persons from pharmaceutical companies (n=6), food and health organizations (n=4), and a research organization (n=6). Key barriers included a potentially low degree of acceptance by regulatory authorities, technical issues (standardization, validation, and data storage), compliance and adherence, and lack of knowledge or comprehension regarding the opportunities VCTs have to offer. Involvement of regulators in development processes, stakeholder exposure to the results of pilot studies, and clear and simple instructions and assistance for patients were considered key facilitators.

Conclusions

Collaboration among all stakeholders in VCT development is crucial to increase knowledge and awareness. Organizations should invest in accurate data collection technologies, and compliance of patients in VCTs needs to be ensured. Multicriteria decision analysis can help determine if a VCT is a preferred option by stakeholders. The findings of this study can be a good starting point to accelerate the development and widespread implementation of VCTs.

Validation of a mammographic image quality modification algorithm using 3D-printed breast phantoms

Purpose: To validate a previously proposed algorithm that modifies a mammogram to appear as if it was acquired with different technique factors using realistic phantom-based mammograms. Approach: Two digital mammography systems (an indirect- and a direct-detector-based system) were used to acquire realistic mammographic images of five 3D-printed breast phantoms with the technique factors selected by the automatic exposure control and at various other conditions (denoted by the original images). Additional images under other simulated conditions were also acquired: higher or lower tube voltages, different anode/filter combinations, or lower tube current-time products (target images). The signal and noise in the original images were modified to simulate the target images (simulated images). The accuracy of the image modification algorithm was validated by comparing the target and simulated images using the local mean, local standard deviation (SD), local variance, and power spectra (PS) of the image signals. The absolute relative percent error between the target and simulated images for each parameter was calculated at each sub-region of interest (local parameters) and frequency (PS), and then averaged. Results: The local mean signal, local SD, local variance, and PS of the target and simulated images were very similar, with a relative percent error of 5.5%, 3.8%, 7.8%, and 4.4% (indirect system), respectively, and of 3.7%, 3.8%, 7.7%, and 7.5% (direct system), respectively. Conclusions: The algorithm is appropriate for simulating different technique factors. Therefore, it can be used in various studies, for instance to evaluate the impact of technique factors in cancer detection using clinical images.

Dataset of patient-derived digital breast phantoms for in silico studies in breast computed tomography, digital breast tomosynthesis, and digital mammography

PURPOSE

To present a dataset of computational digital breast phantoms derived from high-resolution three-dimensional (3D) clinical breast images for the use in virtual clinical trials in two-dimensional (2D) and 3D x-ray breast imaging.

ACQUISITION AND VALIDATION METHODS

Uncompressed computational breast phantoms for investigations in dedicated breast CT (BCT) were derived from 150 clinical 3D breast images acquired via a BCT scanner at UC Davis (California, USA). Each image voxel was classified in one out of the four main materials presented in the field of view: fibroglandular tissue, adipose tissue, skin tissue, and air. For the image classification, a semi-automatic software was developed. The semi-automatic classification was compared via manual glandular classification performed by two researchers. A total of 60 compressed computational phantoms for virtual clinical trials in digital mammography (DM) and digital breast tomosynthesis (DBT) were obtained from the corresponding uncompressed phantoms via a software algorithm simulating the compression and the elastic deformation of the breast, using the tissue's elastic coefficient. This process was evaluated in terms of glandular fraction modification introduced by the compression procedure. The generated cohort of 150 uncompressed computational breast phantoms presented a mean value of the glandular fraction by mass of 12.3%; the average diameter of the breast evaluated at the center of mass was 105 mm. Despite the slight differences between the two manual segmentations, the resulting glandular tissue segmentation did not consistently differ from that obtained via the semi-automatic classification. The difference between the glandular fraction by mass before and after the compression was 2.1% on average. The 60 compressed phantoms presented an average glandular fraction by mass of 12.1% and an average compressed thickness of 61 mm.

DATA FORMAT AND ACCESS

The generated digital breast phantoms are stored in DICOM files. Image voxels can present one out of four values representing the different classified materials: 0 for the air, 1 for the adipose tissue, 2 for the glandular tissue, and 3 for the skin tissue. The generated computational phantoms datasets were stored in the Zenodo public repository for research purposes (http://doi.org/10.5281/zenodo.4529852, http://doi.org/10.5281/zenodo.4515360).

POTENTIAL APPLICATIONS

The dataset developed within the INFN AGATA project will be used for developing a platform for virtual clinical trials in x-ray breast imaging and dosimetry. In addition, they will represent a valid support for introducing new breast models for dose estimates in 2D and 3D x-ray breast imaging and as models for manufacturing anthropomorphic physical phantoms.

Enhancing Patient Centricity and Advancing Innovation in Clinical Research with Virtual Randomized Clinical Trials (vRCTs)

The current public health crisis has highlighted the need to accelerate healthcare innovation. Despite unwavering levels of cooperation among academia, industry, and policy makers, it can still take years to bring a life-saving product to market. There are some obvious limitations, including lack of blinding or masking and small sample size, which render the results less applicable to the real world. Traditional randomized controlled trials (RCTs) are lengthy, expensive, and have a low success rate. There is a growing acknowledgement that the current process no longer fully meets the growing healthcare needs. Advances in technology coupled with proliferation of telehealth modalities, sensors, wearable and connected devices have paved the way for a new paradigm. Virtual randomized controlled trials (vRCTs) have the potential to drastically shorten the clinical trial cycle while maximizing patient-centricity, compliance, and recruitment. This new approach can inform clinical trials in real time and with a holistic view of a patient's health. This paper provides an overview of virtual clinical trials, addressing critical issues, including regulatory compliance, data security, privacy, and ownership.

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

A Virtual Home for the Virtual Clinical Trial

Virtual clinical trials (VCTs) can satisfy the need for rigorous clinical trials by using distributed technological solutions that eliminate the need for a physical trial site. This report explores potential benefits of using virtual reality (VR) to provide a "virtual site" for VCTs, a shared immersive hub in which VCT participants could experience elements of the trial and interact with the trial team. VR is a communication technology that has been emerging alongside the development of VCTs, although they have never been merged in a substantial way. Many of the gaps within the VCT paradigm are areas in which VR excels. VR environments are standardized and precisely uniform, the technology allows introduction of an almost endless set of stimuli to participants' visual and auditory systems, and VR systems are adept at capturing precise movement and behavioral data. Although VR has not yet found its way into VCTs, much of the groundwork for such integration has been laid through research and technological development achieved in the past few years. Future implementation of VR within VCTs could move us from site-less trials to those with a virtual site serving as a hub for trial information provision, interaction with trial representatives, administration of evaluations and assessments, and more.

Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.

Technical Note: Noise models for virtual clinical trials of digital breast tomosynthesis

PURPOSE

To investigate the use of an affine-variance noise model, with correlated quantum noise and spatially dependent quantum gain, for the simulation of noise in virtual clinical trials (VCT) of digital breast tomosynthesis (DBT).

METHODS

Two distinct technologies were considered: an amorphous-selenium (a-Se) detector with direct conversion and a thallium-doped cesium iodide (CsI(Tl)) detector with indirect conversion. A VCT framework was used to generate noise-free projections of a uniform three-dimensional simulated phantom, whose geometry and absorption match those of a polymethyl methacrylate (PMMA) uniform physical phantom. The noise model was then used to generate noisy observations from the simulated noise-free data, while two clinically available DBT units were used to acquire projections of the PMMA physical phantom. Real and simulated projections were then compared using the signal-to-noise ratio (SNR) and normalized noise power spectrum (NNPS).

RESULTS

Simulated images reported errors smaller than 4.4% and 7.0% in terms of SNR and NNPS, respectively. These errors are within the expected variation between two clinical units of the same model. The errors increase to 65.8% if uncorrelated models are adopted for the simulation of systems featuring indirect detection. The assumption of spatially independent quantum gain generates errors of 11.2%.

CONCLUSIONS

The investigated noise model can be used to accurately reproduce the noise found in clinical DBT. The assumption of uncorrelated noise may be adopted if the system features a direct detector with minimal pixel crosstalk.

Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

Protecting and promoting public health is the mission of the U.S. Food and Drug Administration (FDA). FDA's Center for Devices and Radiological Health (CDRH), which regulates medical devices marketed in the U.S., envisions itself as the world's leader in medical device innovation and regulatory science-the development of new methods, standards, and approaches to assess the safety, efficacy, quality, and performance of medical devices. Traditionally, bench testing, animal studies, and clinical trials have been the main sources of evidence for getting medical devices on the market in the U.S. In recent years, however, computational modeling has become an increasingly powerful tool for evaluating medical devices, complementing bench, animal and clinical methods. Moreover, computational modeling methods are increasingly being used within software platforms, serving as clinical decision support tools, and are being embedded in medical devices. Because of its reach and huge potential, computational modeling has been identified as a priority by CDRH, and indeed by FDA's leadership. Therefore, the Office of Science and Engineering Laboratories (OSEL)-the research arm of CDRH-has committed significant resources to transforming computational modeling from a valuable scientific tool to a valuable regulatory tool, and developing mechanisms to rely more on digital evidence in place of other evidence. This article introduces the role of computational modeling for medical devices, describes OSEL's ongoing research, and overviews how evidence from computational modeling (i.e., digital evidence) has been used in regulatory submissions by industry to CDRH in recent years. It concludes by discussing the potential future role for computational modeling and digital evidence in medical devices.

Synthetic breast phantoms from patient based eigenbreasts

PURPOSE

The limited number of 3D patient-based breast phantoms available could be augmented by synthetic breast phantoms in order to facilitate virtual clinical trials (VCTs) using model observers for breast imaging optimization and evaluation.

METHODS

These synthetic breast phantoms were developed using Principal Component Analysis (PCA) to reduce the number of dimensions needed to describe a training set of images. PCA decomposed a training set of M breast CT volumes (with millions of voxels each) into an M-1-dimensional space of eigenvectors, which we call eigenbreasts. Each of the training breast phantoms was compactly represented by the mean image plus a weighted sum of eigenbreasts. The distribution of weights observed from training was then sampled to create new synthesized breast phantoms.

RESULTS

The resulting synthesized breast phantoms demonstrated a high degree of realism, as supported by an observer study. Two out of three experienced physicist observers were unable to distinguish between the synthesized breast phantoms and the patient-based phantoms. The fibroglandular density and noise power law exponent of the synthesized breast phantoms agreed well with the training data.

CONCLUSIONS

Our method extends our series of digital breast phantoms based on breast CT data, providing the capability to generate new, statistically varying ensembles consisting of tens of thousands of virtual subjects. This work represents an important step toward conducting future virtual trials for task-based assessment of breast imaging, where it is vital to have a large ensemble of realistic phantoms for statistical power as well as clinical relevance.

Location- and lesion-dependent estimation of mammographic background tissue complexity

We specify a notion of perceived background tissue complexity (BTC) that varies with lesion shape, lesion size, and lesion location in the image. We propose four unsupervised BTC estimators based on: perceived pre and postlesion similarity of images, lesion border analysis (LBA; conspicuous lesion should be brighter than its surround), tissue anomaly detection, and local energy. The latter two are existing methods adapted for location- and lesion-dependent BTC estimation. For evaluation, we ask human observers to measure BTC (threshold visibility amplitude of a given lesion inserted) at specified locations in a mammogram. As expected, both human measured and computationally estimated BTC vary with lesion shape, size, and location. BTCs measured by different human observers are correlated ([Formula: see text]). BTC estimators are correlated to each other ([Formula: see text]) and less so to human observers ([Formula: see text]). With change in lesion shape or size, LBA estimated BTC changes in the same direction as human measured BTC. Proposed estimators can be generalized to other modalities (e.g., breast tomosynthesis) and used as-is or customized to a specific human observer, to construct BTC-aware model observers with applications, such as optimization of contrast-enhanced medical imaging systems and creation of a diversified image dataset with characteristics of a desired population.

Validation of a Projection-domain Insertion of Liver Lesions into CT Images

RATIONALE AND OBJECTIVES

The aim of this study was to validate a projection-domain lesion-insertion method with observer studies.

MATERIALS AND METHODS

A total of 51 proven liver lesions were segmented from computed tomography images, forward projected, and inserted into patient projection data. The images containing inserted and real lesions were then reconstructed and examined in consensus by two radiologists. First, 102 lesions (51 original, 51 inserted) were viewed in a randomized, blinded fashion and scored from 1 (absolutely inserted) to 10 (absolutely real). Statistical tests were performed to compare the scores for inserted and real lesions. Subsequently, a two-alternative-forced-choice test was conducted, with lesions viewed in pairs (real vs. inserted) in a blinded fashion. The radiologists selected the inserted lesion and provided a confidence level of 1 (no confidence) to 5 (completely certain). The number of lesion pairs that were incorrectly classified was calculated.

RESULTS

The scores for inserted and proven lesions had the same median (8) and similar interquartile ranges (inserted, 5.5-8; real, 6.5-8). The mean scores were not significantly different between real and inserted lesions (P value = 0.17). The receiver operating characteristic curve was nearly diagonal, with an area under the curve of 0.58 ± 0.06. For the two-alternative-forced-choice study, the inserted lesions were incorrectly identified in 49% (25 out of 51) of pairs; radiologists were incorrect in 38% (3 out of 8) of pairs even when they felt very confident in identifying the inserted lesion (confidence level ≥4).

CONCLUSIONS

Radiologists could not distinguish between inserted and real lesions, thereby validating the lesion-insertion technique, which may be useful for conducting virtual clinical trials to optimize image quality and radiation dose.

Impact of breast structure on lesion detection in breast tomosynthesis, a simulation study

This study aims to characterize the effect of background tissue density and heterogeneity on the detection of irregular masses in breast tomosynthesis, while demonstrating the capability of the sophisticated tools that can be used in the design, implementation, and performance analysis of virtual clinical trials (VCTs). Twenty breast phantoms from the extended cardiac-torso (XCAT) family, generated based on dedicated breast computed tomography of human subjects, were used to extract a total of 2173 volumes of interest (VOIs) from simulated tomosynthesis images. Five different lesions, modeled after human subject tomosynthesis images, were embedded in the breasts and combined with the lesion absent condition yielded a total of [Formula: see text] VOIs. Effects of background tissue density and heterogeneity on the detection of the lesions were studied by implementing a composite hypothesis signal detection paradigm with location known exactly, lesion known exactly or statistically, and background known statistically. Using the area under the receiver operating characteristic curve, detection performance deteriorated as density was increased, yielding findings consistent with clinical studies. A human observer study was performed on a subset of the simulated tomosynthesis images, confirming the detection performance trends with respect to density and serving as a validation of the implemented detector. Performance of the implemented detector varied substantially across the 20 breasts. Furthermore, background tissue density and heterogeneity affected the log-likelihood ratio test statistic differently under lesion absent and lesion present conditions. Therefore, considering background tissue variability in tissue models can change the outcomes of a VCT and is hence of crucial importance. The XCAT breast phantoms have the potential to address this concern by offering realistic modeling of background tissue variability based on a wide range of human subjects, comprising various breast shapes, sizes, and densities.

Predictive Modeling of Drug Treatment in the Area of Personalized Medicine

Despite a growing body of knowledge on the mechanisms underlying the onset and progression of cancer, treatment success rates in oncology are at best modest. Current approaches use statistical methods that fail to embrace the inherent and expansive complexity of the tumor/patient/drug interaction. Computational modeling, in particular mechanistic modeling, has the power to resolve this complexity. Using fundamental knowledge on the interactions occurring between the components of a complex biological system, large-scale in silico models with predictive capabilities can be generated. Here, we describe how mechanistic virtual patient models, based on systematic molecular characterization of patients and their diseases, have the potential to shift the theranostic paradigm for oncology, both in the fields of personalized medicine and targeted drug development. In particular, we highlight the mechanistic modeling platform ModCell™ for individualized prediction of patient responses to treatment, emphasizing modeling techniques and avenues of application.