This work aims to study the generalizability of a pre-developed deep learning (DL) dose prediction model for volumetric modulated arc therapy (VMAT) for prostate cancer and to adapt the model to three different internal treatment planning styles and one external institution planning style. We built the source model with planning data from 108 patients previously treated with VMAT for prostate cancer. For the transfer learning, we selected patient cases planned with three different styles from the same institution and one style from a different institution to adapt the source model to four target models. We compared the dose distributions predicted by the source model and the target models with the clinical dose predictions and quantified the improvement in the prediction quality for the target models over the source model using the Dice similarity coefficients (DSC) of 10% to 100% isodose volumes and the dose-volume-histogram (DVH) parameters of the planning target volume and the organs-at-risk. The source model accurately predicts dose distributions for plans generated in the same source style but performs sub-optimally for the three internal and one external target styles, with the mean DSC ranging between 0.81-0.94 and 0.82-0.91 for the internal and the external styles, respectively. With transfer learning, the target model predictions improved the mean DSC to 0.88-0.95 and 0.92-0.96 for the internal and the external styles, respectively. Target model predictions significantly improved the accuracy of the DVH parameter predictions to within 1.6%. We demonstrated model generalizability for DL-based dose prediction and the feasibility of using transfer learning to solve this problem. With 14-29 cases per style, we successfully adapted the source model into several different practice styles. This indicates a realistic way to widespread clinical implementation of DL-based dose prediction.
Grenoble University Hospital（格勒诺布尔大学医院）；TIMC-GMCAO Lab (CNRS UMR 5525)（TIMC-GMCAO实验室（CNRS UMR 5525））
Purpose: We report what is to our knowledge the initial experience with a new 3-dimensional ultrasound robotic system for prostate brachytherapy assistance, focal therapy and prostate biopsies. Its ability to track prostate motion intraoperatively allows it to manage motions and guide needles to predefined targets. Materials and Methods: A robotic system was created for transrectal ultrasound guided needle implantation combined with intraoperative prostate tracking. Experiments were done on 90 targets embedded in a total of 9 mobile, deformable, synthetic prostate phantoms. Experiments involve…
High-dose-rate brachytherapy is a tumor treatment method where a highly radioactive source is brought in close proximity to the tumor. In this paper we develop a simulated annealing algorithm to optimize the dwell times at preselected dwell positions to maximize tumor coverage under dose-volume constraints on the organs at risk. Compared to existing algorithms, our algorithm has advantages in terms of speed and objective value and does not require an expensive general purpose solver. Its success mainly depends on exploiting the efficiency of matrix multiplication and a careful selection of the…
During Stereotactic Body Radiotherapy (SBRT) for the treatment of prostate cancer, an inflatable endorectal balloon (ERB) may be used to reduce motion of the target and reduce the dose to the posterior rectal wall. This work assessed the dosimetric impact of manual interventions on ERB position in patients receiving prostate SBRT and investigated the impact of ERB interventions on prostate shape. Daily kilovoltage (kV) cone-beam computed tomography (CBCT) imaging was performed to localize the PTV, and an automated fusion with the planning images yielded displacements required for PTV re-locali…
Therapeutic ultrasound is an investigational modality which could potentially be used for minimally invasive treatment of prostate cancer. Computational simulations were used to study the effect of natural physiological variations in tissue parameters on the efficacy of therapeutic ultrasound treatment in the prostate. The simulations were conducted on a clinical ultrasound therapy system using patient computed tomography (CT) data. The values of attenuation, perfusion, specific heat capacity and thermal conductivity were changed within their biological ranges to determine their effect on peak…
Voxel-Based Conversion of Hypofractionated Radiotherapy Dose Distributions to 2 Gy-Equivalent OAR Constraints: Proof-of-Concept Demonstrating the Radiobiological Benefits of Hypofractionation in a Prostate Radiotherapy Case
Healthy Innovations；Holy Family University（圣家大学）；Tripoli Governmental Hospital and Doctors Center（的黎波里政府医院和医生中心）
Purpose: Automatic planning for focal low-dose-rate prostate brachytherapy (LDR-PB) boost. Methods: A simulated annealing approach was utilized for creating automatic focal LDR-PB boost plans using both geometric (i.e. adherence of isodose contours to structure contours) and dosimetric constraints. In 46 patients, four unilateral focal lesion scenarios were considered as the boost region, in the anterior/posterior and superior/inferior quadrants. Plans consisted of seeds with two air kerma strengths (0.740U and 0.351 or 0.354U) to achieve required dose coverage on these smaller focal targets. …
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR). We show that, as an example, we are able…
Automatic Treatment Planning using Reinforcement Learning for High-dose-rate Prostate Brachytherapy
Department of Medical Physics, Memorial Sloan Kettering Cancer Center（医学物理系，纪念斯隆-凯特琳癌症中心）；Department of Radiation Oncology and Winship Cancer Institute, Emory University（放射肿瘤学系和Winship癌症研究所，埃默里大学）；Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine（Wallace H. Coulter生物医学工程系，佐治亚理工学院和埃默里大学医学院）；Department of Biomedical Informatics, Emory University（生物医学信息学系，埃默里大学）
Purpose: Transurethral ultrasound therapy is an investigational treatment modality which could potentially be used for the localized treatment of prostate cancer. One of the limiting factors of this therapy is prostatic calcifications. These attenuate and reflect ultrasound and thus reduce the efficacy of the heating. The aim of this study is to investigate how prostatic calcifications affect therapeutic efficacy, and to identify the best sonication strategy when calcifications are present. Methods: Realistic computational models were used on clinical patient data in order to simulate differen…
The purpose of this study was to use various dosimetrical indices to determine the best IMRT modality technique for treating patients with prostate cancer. Ten patients with prostate cancer were included in this study. Intensity modulated radiation therapy plans were designed to include different modalities, including the linac step and shoot, Tomotherapy, RapidArc, and Proton systems. Various dosimetrical indices, like the prescription isodose to target volume (PITV) ratio, conformity index (CI), homogeneity index (HI), target coverage index (TCI), modified dose homogeneity index (MHI), confo…
Current inverse treatment planning methods that optimize both catheter positions and dwell times in prostate HDR brachytherapy use surrogate linear or quadratic objective functions that have no direct interpretation in terms of dose-volume histogram (DVH) criteria, do not result in an optimum or have long solution times. We decrease the solution time of existing linear and quadratic dose-based programming models (LP and QP, respectively) to allow optimizing over potential catheter positions using mixed integer programming. An additional average speed-up of 75% can be obtained by stopping the s…
Purpose. - Radiotherapy is an important treatment for prostate cancer.During treatment sessions, bladder and rectal repletion is difficult to quantify and cannot be measured with a single and initial CT scan acquisition. Some methods, such as image-guided radiation therapy and dose-guided radiation therapy, aimto compensate thismissing information through periodic CT acquisitions. The aimis to adapt patient's position, beam configuration or prescribed dose for a dosimetric compliance. Methods. -We evaluated organmotion (and repletion) for 54 patients after having computed the original ballisti…
Purpose: We present a framework for robust automated treatment planning using machine learning, comprising scenario-specific dose prediction and robust dose mimicking. Methods: The scenario dose prediction pipeline is divided into the prediction of nominal dose from input image and the prediction of scenario dose from nominal dose, each using a deep learning model with U-net architecture. By using a specially developed dose-volume histogram-based loss function, the predicted scenario doses are ensured sufficient target coverage despite the possibility of the training data being non-robust. Del…
Therapeutic protons acting on O18-substituted thymidine increase cytotoxicity in radio-resistant human cancer cells. We consider here the physics behind the irradiation during proton beam therapy and diagnosis using O18-enriched thymine in DNA, with attention to the effect of the presence of thymine-18 on cancer cell death.
The secondary neutron fields at the deep tumor therapy terminal at HIRFL (Heavy Ion Research Facility in Lanzhou) were investigated. The distributions of neutron ambient dose equivalent were measured with a FHT762 Wendi-II neutron ambient dose equivalent meter as the 12C ions with energies of 165, 207, 270, and 350 MeV/u bombarded on thick tissue-like targets. The thickness of targets used in experiments is larger than the range of the carbon ions. The neutron spectra and dose equivalent is simulated by using FLUKA code, and the results agree well with the experimental data. The experiment res…
Inverse planning algorithms for dwell time optimisation in interstitial high-dose-rate (HDR) brachytherapy may produce solutions with large dwell time variations within catheters, which may result in undesirable selective high-dose subvolumes. Extending the optimisation model with a dwell time modulation restriction (DTMR) that limits dwell time differences between neighboring dwell positions has been suggested to eliminate this issue. DTMRs may additionally reduce the sensitivity for uncertainties in dwell positions that result from catheter reconstruction errors and source positioning inaccu…
In this research, we presented the method of inhalation therapy with noble gas micro-doses and results of clinical studies in hospital of Russian Academy of Sciences (RAS). We have designed a device that allows dosed injection of noble gas with a volume of about 2-4 ml with peroxide vapors into the nasal cavity. After testing on the authors of the project and obtaining a positive impact of xenon and krypton, a study was conducted on a group of conditionally healthy volunteers in the amount of 20 people. We have noted a positive effect from 18 people. And also, in this article we briefly descri…
In this work, we propose a Machine Learning model that generates an adjustable 3D dose distribution for external beam radiation therapy for head-and-neck cancer treatments. In contrast to existing Machine Learning methods that provide a single model, we create pairs of models for each organ-at-risk, namely lower-extreme and upper-extreme models. These model pairs for an organ-at-risk propose doses that give lower and higher doses to that organ-at-risk, while also encapsulating the dose trade-off to other organs-at-risk. By weighting and combining the model pairs for all organs-at-risk, we are …
Mayo Clinic（梅奥诊所）；University of Georgia（佐治亚大学）；Technische Universität Dresden（德累斯顿工业大学）；Helmholtz-Zentrum Dresden - Rossendorf（德累斯顿-罗森多夫亥姆霍兹中心）；University of Virginia（弗吉尼亚大学）
University of British Columbia（不列颠哥伦比亚大学）；BC Cancer - Vancouver Centre（不列颠哥伦比亚癌症中心温哥华中心）
Treatment planning in low-dose-rate prostate brachytherapy (LDR-PB) aims to produce arrangement of implantable radioactive seeds that deliver a minimum prescribed dose to the prostate whilst minimizing toxicity to healthy tissues. There can be multiple seed arrangements that satisfy this dosimetric criterion, not all deemed 'acceptable' for implant from a physician's perspective. This leads to plans that are subjective to the physician's/centre's preference, planning style, and expertise. We propose a method that aims to reduce this variability by training a model to learn from a large pool of…
A new strategy for radiation therapy dosimetry planning (RTDP) used to reduce dose estimation errors due to respiratory motion in breast treatment was illustrated and evaluated in this study. On CT data set acquired for breast treatment, six different RTDP tangential techniques were performed: (i) three-dimensional conformal radiotherapy (3DCRT) with physical wedge, (ii) 3DCRT with virtual wedge, (iii) motion management technique (MMT) with physical wedge, (iv) MMT with virtual wedge, (v) 3DCRT with field-in-field, and (vi) intensity-modulated radiation therapy (IMRT) with direct aperture opti…
To accurately verify the dose of intensity-modulated radiation therapy (IMRT), we have used a global optimization method to investigate a new dose-verification algorithm. In practical application of this quality assurance (QA) procedure, verification of the dose using calculated and measured dose distributions involves a subtle problem in the region of high dose gradient. Consideration of systematic errors shows that the large dose differences in high-dose-gradient regions are due to the unexpected shift of measuring devices. We have proposed an optimization algorithm to correct this error, an…
We present a method of directly optimizing on deviations in clinical goal values in radiation therapy treatment planning. Using a new mathematical framework in which metrics derived from the dose-volume histogram are regarded as functionals of an auxiliary random variable, we are able to obtain volume-at-dose and dose-at-volume as infinitely differentiable functions of the dose distribution with easily evaluable function values and gradients. Motivated by the connection to risk measures in finance, which is formalized in this framework, we also derive closed-form formulas for mean-tail-dose an…
Variation of the relative biological effectiveness with fractionation in proton therapy: analysis of prostate cancer response
Instituto de Investigación Sanitaria de Santiago (IDIS)（圣地亚哥卫生研究所）；Complexo Hospitalario Universitario de Santiago de Compostela（圣地亚哥-德孔波斯特拉大学医院复合体）；Istituto Nazionale di Fisica Nucleare-Torino Section（意大利国家核物理研究所都灵分部）；Universidade de Santiago de Compostela（圣地亚哥-德孔波斯特拉大学）
Secondary radiation in a radiation therapy environment is considered a cause for post-treatment side effects including secondary carcinogenesis. In a proton-therapy setup, neutrons deliver a considerable extra dose to the patient. Various measurements and calculations of neutron dose suggest a wide range of values for this quantity. This paper provides a comparison between the previously published values and analyzes the Monte Carlo calculated neutron dose from the present work using the neutron cross-section data. The purpose is to provide a range for the convergence of several neutron-absorb…
Fast and accurate dose predictions are one of the bottlenecks in treatment planning for microbeam radiation therapy (MRT). In this paper, we propose a machine learning (ML) model based on a 3D U-Net. Our approach predicts separately the large doses of the narrow high intensity synchrotron microbeams and the lower valley doses between them. For this purpose, a concept of macro peak doses and macro valley doses is introduced, describing the respective doses not on a microscopic level but as macroscopic quantities in larger voxels. The ML model is trained to mimic full Monte Carlo (MC) data. Comp…
Purpose: The presence of respiratory motion during radiation treatment leads to degradation of the expected dose distribution, both for target coverage and healthy-tissue sparing, particularly for techniques like pencil-beam scanning proton therapy which have dynamic delivery systems. While tools exist to estimate this degraded four-dimensional (4D) dose, they typically have one or more deficiencies such as ... Methods: To quickly compute the 4D-dose, the three main tasks of the calculator were run on graphics processing units (GPUs). These tasks were: simulating the delivery of the plan using…
Radiation therapy has remained as one of the main cancer treatment modalities and a highly cost-effective single modality treatment of cancer care. Typical regimens for fractionated external beam radiotherapy comprise a constant dose administered on weekdays, and no radiation on weekends. However, every patient has a tumor with distinct properties depending on intra-tumor heterogeneity, aggressiveness, and interactive properties with other cells that may make it more resistant or sensitive to radiation treatment. Accordingly, the concept of personalized cancer treatment is emerging to speciali…
The neutron dose distributions on observation distances and on observation angles were measured using a Wendi-II neutron dose-meter at the deep tumor therapy terminal at HIRFL (Heavy Ion Research Facility in Lanzhou) as 12C ions with energies from 165 to 350 MeV/u bombarding on thick solid water targets with different thickness according to the ion energies. The experimental results were compared with those calculated by FLUKA code. It is found that the experimental data was in good agreement with the calculated results. The neutron energy spectra were also studied by using the FLUKA code. The…
Cone beam CT (CBCT) has been widely used for patient setup in image guided radiation therapy (IGRT). Radiation dose from CBCT scans has become a clinical concern. The purposes of this study are 1) to commission a GPU-based Monte Carlo (MC) dose calculation package gCTD for Varian On-Board Imaging (OBI) system and test the calculation accuracy, and 2) to quantitatively evaluate CBCT dose from the OBI system in typical IGRT scan protocols. We first conducted dose measurements in a water phantom. X-ray source model parameters used in gCTD are obtained through a commissioning process. gCTD accurac…
Radiotherapy of pregnant cancer patients is not common, but when applied accurate assessment of the fetus dose is required, especially since the treatment planning systems are not able and do not allow accurate assessment of out-of-field doses. Proton therapy significantly reduces out-of-field doses, such as dose to the fetus, when compared to the photon radiotherapy techniques and as such could be promising for pregnant cancer patients. Within this study Monte Carlo calculations are performed on the three different computational phantoms representing pregnant women, all in second trimester of…
We demonstrate the application of mixture density networks (MDNs) in the context of automated radiation therapy treatment planning. It is shown that an MDN can produce good predictions of dose distributions as well as reflect uncertain decision making associated with inherently conflicting clinical tradeoffs, in contrast to deterministic methods previously investigated in literature. A two-component Gaussian MDN is trained on a set of treatment plans for postoperative prostate patients with varying extents to which rectum dose sparing was prioritized over target coverage. Examination on a test…
We propose a novel domain specific loss, which is a differentiable loss function based on the dose volume histogram, and combine it with an adversarial loss for the training of deep neural networks to generate Pareto optimal dose distributions. The mean squared error (MSE) loss, dose volume histogram (DVH) loss, and adversarial (ADV) loss were used to train 4 instances of the neural network model: 1) MSE, 2) MSE+ADV, 3) MSE+DVH, and 4) MSE+DVH+ADV. 70 prostate patients were acquired, and the dose influence arrays were calculated for each patient. 1200 Pareto surface plans per patient were gene…
Purpose: We propose a general framework for quantifying predictive uncertainties of dose-related quantities and leveraging this information in a dose mimicking problem in the context of automated radiation therapy treatment planning. Methods: A three-step pipeline, comprising feature extraction, dose statistic prediction and dose mimicking, is employed. In particular, the features are produced by a convolutional variational autoencoder and used as inputs in a previously developed nonparametric Bayesian statistical method, estimating the multivariate predictive distribution of a collection of p…
Conventional cancer therapies include surgery, radiation therapy, chemotherapy, and, more recently, immunotherapy. These modalities are often combined to improve the therapeutic index. The general concept of radiation therapy is to increase the therapeutic index by creating a physical dose differential between tumors and normal tissues through precision dose targeting, image guidance, and high radiation beams that deliver radiation dose with high conformality, e.g., protons and ions. However, treatment and cure are still limited by normal tissue radiation toxicity, with many patients experienc…
Charged Particle Therapy is a technique for cancer treatment that exploits hadron beams, mostly protons and carbons. A critical issue is the monitoring of the dose released by the beam to the tumor and to the surrounding tissues. We present the design of a new tracking device for monitoring on-line the dose in ion therapy through the detection of secondary charged particles produced by the beam interactions in the patient tissues. In fact, the charged particle emission shape can be correlated with the spatial dose release and the Bragg peak position. The detector uses the information provided …
We propose to develop deep learning models that can predict Pareto optimal dose distributions by using any given set of beam angles, along with patient anatomy, as input to train the deep neural networks. We implement and compare two deep learning networks that predict with two different beam configuration modalities. We generated Pareto optimal plans for 70 patients with prostate cancer. We used fluence map optimization to generate 500 IMRT plans that sampled the Pareto surface for each patient, for a total of 35,000 plans. We studied and compared two different models, Model I and Model II. M…
For the in vivo range verification in proton therapy, it has been tried to measure the spatial distribution of the prompt gammas generated by the proton-induced interactions with the close relationship with the proton dose distribution. However, the high energy of the prompt gammas and background gammas are still problematic in measuring the distribution. In this study, we suggested a new method determining the in vivo range by utilizing the time structure of the prompt gammas formed with the rotation of a range modulation wheel (RMW) in the passive scattering proton therapy. To validate the M…
Purpose: To verify dose delivery and quality assurance of volumetric modulated arc therapy (VMAT) for head and neck cancer. Method: The Imaging and Radiation Oncology Core Houston (IROC-H) head and neck phantom with thermo- luminescent dosimeters (TLDs) and films, were imaged with computed tomography scan and the reconstructed image was transferred to pinnacle treatment planning system (TPS). On TPS the planning target volume (PTV), secondary target volume (STV) and organ at risk (OAR) were delineated manually and a treatment plan was made. The dose constraints were determined for the concerne…
UT Southwestern Medical Center（德克萨斯大学西南医学中心）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Inter-observer variation is a significant problem in clinical target volume(CTV) segmentation in postoperative settings, where there is no gross tumor present. In this scenario, the CTV is not an anatomically established structure, but one determined by the physician based on the clinical guideline used, the preferred tradeoff between tumor control and toxicity, their experience and training background, and other factors. This results in high inter-observer variability between physicians. This variability has been considered an issue, but the absence of multiple physician CTV contours for each…
Cao et al (2018) published an article on inverse planning based on dose-averaged linear energy transfer (LET). Their claim that the problem can be cast as a linear optimization model relies on an incorrect application of the Charnes-Cooper transformation. In this comment we show that their linear model is simlar to one where dose-averaged LET is multiplied with dose, explaining why their model was nonetheless able to improve the LET distribution.
We give a scenario-based treatment plan optimization formulation that is equivalent to planning with geometric margins if the scenario doses are calculated using the static dose cloud approximation. If the scenario doses are instead calculated more accurately, then our formulation provides a novel robust planning method that overcomes many of the difficulties associated with previous scenario-based robust planning methods. In particular, our method protects only against uncertainties that can occur in practice, it gives a sharp dose fall-off outside high dose regions, and it avoids underdosage…
A split feasibility formulation for the inverse problem of intensity-modulated radiation therapy (IMRT) treatment planning with dose-volume constraints (DVCs) included in the planning algorithm is presented. It involves a new type of sparsity constraint that enables the inclusion of a percentage-violation constraint in the model problem and its handling by continuous (as opposed to integer) methods. We propose an iterative algorithmic framework for solving such a problem by applying the feasibility-seeking CQ-algorithm of Byrne combined with the automatic relaxation method (ARM) that uses cycl…
We study the propagation of nucleons and nuclei in tissue-like media within a Monte Carlo Model for Heavy-ion Therapy (MCHIT) based on the GEANT4 toolkit (version 8.2). The model takes into account fragmentation of projectile nuclei and secondary interactions of produced nuclear fragments. Model predictions are validated with available experimental data obtained for water and PMMA phantoms irradiated by monoenergetic carbon-ion beams. The MCHIT model describes well (1) the depth-dose distributions in water and PMMA, (2) the doses measured for fragments of certain charge, (3) the distributions …
