Scalable and accessible personalized photodynamic therapy optimization with FullMonte and PDT-SPACE.

SIGNIFICANCE: Open-source software packages have been extensively used in the past three decades in medical imaging and diagnostics, aiming to study the feasibility of the application ex vivo. Unfortunately, most of the existing open-source tools require some software engineering background to install the prerequisite libraries, choose a suitable computational platform, and combine several software tools to address different applications. AIM: To facilitate the use of open-source software in medical applications, enabling computational studies of treatment outcomes prior to the complex in-vivo setting. APPROACH: FullMonteWeb, an open-source, user-friendly web-based software with a graphical user interface for interstitial photodynamic therapy (iPDT) modeling, visualization, and optimization, is introduced. The software can perform Monte Carlo simulations of light propagation in biological tissues, along with iPDT plan optimization. FullMonteWeb installs and runs the required software and libraries on Amazon Web Services (AWS), allowing scalable computing without complex set up. RESULTS: FullMonteWeb allows simulation of large and small problems on the most appropriate compute hardware, enabling cost improvements of 10 × versus always running on a single platform. Case studies in optical property estimation and diffuser placement optimization highlight FullMonteWeb's versatility. CONCLUSION: The FullMonte open source suite enables easier and more cost-effective in-silico studies for iPDT.

Machine learning for real-time optical property recovery in interstitial photodynamic therapy: a stimulation-based study.

With the continued development of non-toxic photosensitizer drugs, interstitial photodynamic therapy (iPDT) is showing more favorable outcomes in recent clinical trials. IPDT planning is crucial to further increase the treatment efficacy. However, it remains a major challenge to generate a high-quality, patient-specific plan due to uncertainty in tissue optical properties (OPs), µ a and µ s . These parameters govern how light propagates inside tissues, and any deviation from the planning-assumed values during treatment could significantly affect the treatment outcome. In this work, we increase the robustness of iPDT against OP variations by using machine learning models to recover the patient-specific OPs from light dosimetry measurements and then re-optimizing the diffusers' optical powers to adapt to these OPs in real time. Simulations on virtual brain tumor models show that reoptimizing the power allocation with the recovered OPs significantly reduces uncertainty in the predicted light dosimetry for all tissues involved.

Optimizing Interstitial Photodynamic Therapy Planning With Reinforcement Learning-Based Diffuser Placement.

Interstitial photodynamic therapy (iPDT) has shown promising results recently as a minimally invasive stand-alone or intra-operative cancer treatment. The development of non-toxic photosensitizing drugs with improved target selectivity has increased its efficacy. However, personalized treatment planning that determines the number of photon emitters, their positions and their input powers while taking into account tissue anatomy and treatment response is still lacking to further improve outcomes. OBJECTIVE: To develop new algorithms that generate high-quality plans by optimizing over the light source positions, along with their powers, to minimize the damage to organs-at-risk while eradicating the tumor. The optimization algorithms should also accurately model the physics of light propagation through the use of Monte-Carlo simulators. METHODS: We use simulated-annealing as a baseline algorithm to place the sources. We propose different source perturbations that are likely to provide better outcomes and study their impact. To minimize the number of moves attempted (and effectively runtime) without degrading result quality, we use a reinforcement learning-based method to decide which perturbation strategy to perform in each iteration. We simulate our algorithm on virtual brain tumors modeling real glioblastoma multiforme cases, assuming a 5-ALA PpIX induced photosensitizer that is activated at [Formula: see text] wavelength. RESULTS: The algorithm generates plans that achieve an average of 46% less damage to organs-as-risk compared to the manual placement used in current clinical studies. SIGNIFICANCE: Having a general and high-quality planning system makes iPDT more effective and applicable to a wider variety of oncological indications. This paves the way for more clinical trials.

Modeling the efficiency of UV at 254 nm for disinfecting the different layers within N95 respirators.

The study presented a Monte Carlo simulation of light transport in eight commonly used filtered facepiece respirators (FFRs) to assess the efficacy of UV at 254 nm for the inactivation of SARS-CoV-2. The results showed different fluence rates across the thickness of the eight different FFRs, implying that some FFR models may be more treatable than others, with the following order being (from most to least treatable): models 1512, 9105s, 1805, 9210, 1870+, 8210, 8110s and 1860, for single side illumination. The model predictions did not coincide well with some previously reported experimental data on virus inactivation when applied to FFR surfaces. The simulations predicted that FFRs should experience higher log reductions (>>6-log) than those observed experimentally (often limited to ~5-log). Possible explanations are virus shielding by aggregation or soiling, and a lack of the Monte Carlo simulations considering near-field scattering effects that can create small, localized regions of low UV photon probability on the surface of the fiber material. If the latter is the main cause in limiting practical UV viral decontamination, improvement might be achieved by exposing the FFR to UV isotropically from all directions, such as by varying the UV source to the FFR surface angle during treatment.

Photodynamic therapy outcome modelling for patients with spinal metastases: a simulation-based study.

Spinal metastases often occur in the advanced stages of breast, lung or prostate cancer, resulting in a significant impact on the patient's quality of life. Current treatment modalities for spinal metastases include both systemic and localized treatments that aim to decrease pain, improve mobility and structural stability, and control tumour growth. With the development of non-toxic photosensitizer drugs, photodynamic therapy (PDT) has shown promise as a minimally invasive non-thermal alternative in oncology, including for spinal metastases. To apply PDT to spinal metastases, predictive algorithms that optimize tumour treatment and minimize the risk of spinal cord damage are needed to assess the feasibility of the treatment and encourage a broad acceptance of PDT in clinical trials. This work presents a framework for PDT modelling and planning, and simulates the feasibility of using a BPD-MA mediated PDT to treat bone metastases at two different wavelengths (690 nm and 565 nm). An open-source software for PDT planning, PDT-SPACE, is used to evaluate different configurations of light diffusers (cut-end and cylindrical) fibres with optimized power allocation in order to minimize the damage to spinal cord or maximize tumour destruction. The work is simulated on three CT images of metastatically involved vertebrae acquired from three patients with spinal metastases secondary to colorectal or lung cancer. Simulation results show that PDT at a 565 nm wavelength has the ability to treat 90% of the metastatic lesion with less than 17% damage to the spinal cord. However, the energy required, and hence treatment time, to achieve this outcome with the 565 nm is infeasible. The energy required and treatment time for the longer wavelength of 690 nm is feasible ([Formula: see text] min), but treatment aimed at 90% of the metastatic lesion would severely damage the proximal spinal cord. PDT-SPACE provides a simulation platform that can be used to optimize PDT delivery in the metastatic spine. While this work serves as a prospective methodology to analyze the feasibility of PDT for tumour ablation in the spine, preclinical studies in an animal model are ongoing to elucidate the spinal cord damage extent as a function of PDT dose, and the resulting short and long term functional impairments. These will be required before there can be any consideration of clinical trials.

Optimizing interstitial photodynamic therapy with custom cylindrical diffusers.

Interstitial photodynamic therapy (iPDT) has shown promise recently as a minimally invasive cancer treatment, partially due to the development of non-toxic photosensitizers in the absence of activation light. However, a major challenge in iPDT is the pre-treatment planning process that specifies the number of diffusers needed, along with their positions and allocated powers, to confine the light distribution to the target volume as much as possible. In this work, a new power allocation algorithm for cylindrical light diffusers including those that can produce customized longitudinal (tailored) emission profiles is introduced. The proposed formulation is convex to guarantee the minimum over-dose possible on the surrounding organs-at-risk. The impact of varying the diffuser lengths and penetration angles on the quality of the plan is evaluated. The results of this study are demonstrated for different photosensitizers activated at different wavelengths and simulated on virtual tumors modeling virtual glioblastoma multiforme cases. Results show that manufacturable cylindrical diffusers with tailored emission profiles can significantly outperform those with conventional flat profiles with an average damage reduction on white matter of 15% to 55% and on gray matter of 23% to 58%.

Automatic interstitial photodynamic therapy planning via convex optimization.

Finding a high-quality treatment plan is an essential, yet difficult, stage of Photodynamic therapy (PDT) as it will determine the therapeutic efficacy in eradicating malignant tumors. A high-quality plan is patient-specific, and provides clinicians with the number of fiber-based spherical diffusers, their powers, and their interstitial locations to deliver the required light dose to destroy the tumor while minimizing the damage to surrounding healthy tissues. In this work, we propose a general convex light source power allocation algorithm that, given light source locations, guarantees optimality of the resulting solution in minimizing the over/under-dosage of volumes of interest. Furthermore, we provide an efficient framework for source selection with concomitant power reallocation to achieve treatment plans with a clinically feasible number of sources and comparable quality. We demonstrate our algorithms on virtual test cases that model glioblastoma multiforme tumors, and evaluate the performance of four different photosensitizers with different activation wavelengths and specific tissue uptake ratios. Results show an average reduction of the damage to organs-at-risk (OAR) by 29% to 31% with comparable runtime to existing power allocation techniques.