We present a novel application of Tensor Network methods in cancer treatment as a potential tool to solve the dose optimization problem in radiotherapy. In particular, the Intensity-Modulated Radiation Therapy (IMRT) technique - that allows treating irregular and inhomogeneous tumors while reducing the radiation toxicity on healthy organs - is based on the optimization of the radiation beamlets intensities. The optimization aims to maximize the delivery of the therapy dose to cancer while avoiding the organs at risk to prevent their damage by the radiation. Here, we map the dose optimization problem into the search of the ground state of an Ising-like Hamiltonian, describing a system of long-range interacting qubits. Finally, we apply a Tree Tensor Network algorithm to find the ground-state of the Hamiltonian. In particular, we present an anatomical scenario exemplifying a prostate cancer treatment. A similar approach can be applied to future hybrid classical-quantum algorithms, paving the way for the use of quantum technologies in future medical treatments.
Radiomics is a nascent field in quantitative imaging that uses advanced algorithms and considerable computing power to describe tumor phenotypes, monitor treatment response, and assess normal tissue toxicity quantifiably. Remarkable interest has been drawn to the field due to its noninvasive nature and potential for diagnosing and predicting patient prognosis. This review will attempt to comprehensively and critically discuss the various aspects of radiomics including its workflow, applications to different modalities, potential applications in cancer radiotherapy, and limitations.
Radiosensitizers can increase the local treatment efficacy under a relatively low and safe radiation dose, thereby facilitating tumor eradication and minimizing side effects. Here, we report a new class of radiosensitizers that contain several gold (Au) atoms embedded inside a peptide shell (e.g., Au10-12(SG)10-12) and can achieve ultrahigh tumor uptake (10.86 SUV at 24 h post injection) and targeting specificity, efficient renal clearance, and high radiotherapy enhancement.
It has been known for about sixty years that proton and heavy ion therapy is a very powerful radiation procedure for treating tumours. It has an innate ability to irradiate tumours with greater doses and spatial selectivity compared with electron and photon therapy and hence is a tissue sparing procedure. For more than twenty years powerful lasers have generated high energy beams of protons and heavy ions and hence it has been frequently speculated that lasers could be used as an alternative to RF accelerators to produce the particle beams necessary for cancer therapy. The present paper review…
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 aim of this work is to describe the technical characteristics of an AI-powered radiotherapy workflow that enables full-process automation (All-in-One), evaluate its performance implemented for on-couch initial treatment of rectal cancer, and provide insight into the behavior of full-workflow automation in the specialty of radiotherapy. The All-in-One workflow was developed based on a CT-integrated linear accelerator. It incorporates routine radiotherapy procedures from simulation, autosegmentation, autoplanning, image guidance, beam delivery, and in vivo quality assurance (QA) into one sch…
Radiation therapy with carbon ions is a novel technique of cancer radiotherapy, applicable in particular to treating radioresistant tumours at difficult localisations. Therapy planning, where the medical physicist, following the medical prescription, finds the optimum distribution of cancer cells to be inactivated by their irradiation over the tumour volume, is a basic procedure of cancer radiotherapy. The main difficulty encountered in therapy planning for ion radiotherapy is to correctly account for the enhanced radiobiological effectiveness of ions in the Spread Out Bragg Peak (SOBP) region…
Purpose: CBCT-based adaptive radiotherapy requires daily images for accurate dose calculations. This study investigates the feasibility of applying a single convolutional network to facilitate CBCT-to-CT synthesis for head-and-neck, lung, and breast cancer patients. Methods: Ninety-nine patients diagnosed with head-and-neck, lung or breast cancer undergoing radiotherapy with CBCT-based position verification were included in this study. CBCTs were registered to planning CTs according to clinical procedures. Three cycle-consistent generative adversarial networks (cycle-GANs) were trained in an u…
This study estimated the planning quality of intensity-modulated radiotherapy in 42 lung cancer cases to provide preliminary data for the development of a planning quality assurance algorithm. Organs in or near the thoracic cavity (ipsilateral lung, contralateral lung, heart, liver, esophagus, spinal cord, and bronchus) were selected as organs at risk (OARs). Radiotherapy plans were compared using the conformity index (CI), coverage index (CVI), and homogeneity index (HI) of the planning target volume (PTV), OAR-PTV distance and OAR-PTV overlap volume, and the V10Gy, V20Gy, and equivalent unif…
Adaptive Radiotherapy (ART) is an emerging technique for treating cancer patients which facilitates higher delivery accuracy and has the potential to reduce toxicity. However, ART is also resource-intensive, requiring extra human and machine time compared to standard treatment methods. In this analysis, we sought to predict the subset of node-negative cervical cancer patients who benefit the most from ART. CT images, initial plan data, and on-treatment Cone-Beam CT (CBCT) images for 20 retrospective cervical cancer patients were used to simulate doses from daily non-adaptive and adaptive techn…
In radiotherapy for head and neck cancer, the radiation dose delivered to the pharyngeal mucosa (mucosal lining of the throat) is thought to be a major contributing factor to dysphagia (swallowing dysfunction), the most commonly reported severe toxicity. There is a variation in the severity of dysphagia experienced by patients. Understanding the role of the dose distribution in dysphagia would allow improvements in the radiotherapy technique to be explored. The 3D dose distributions delivered to the pharyngeal mucosa of 249 patients treated as part of clinical trials were reconstructed. Pydico…
Current Views on Mechanisms of the FLASH Effect in Cancer Radiotherapy
Peking University（北京大学）；the Second Affiliated Hospital of Wenzhou Medical University（温州医科大学附属第二医院）；College of Chemistry and Molecular Engineering, Peking University（北京大学化学与分子工程学院）；Sun Yat-sen University（中山大学）；KIRI Precision Particle Therapy Flash Technologies Research Center（基锐精准粒子治疗闪速技术研究中心）；The University of Hong Kong-Shenzhen Hospital（香港大学深圳医院）
University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Duke University Medical Center（杜克大学医学中心）
This paper develops a method of biologically guided deep learning for post-radiation FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information. Based on the classic reaction-diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder-decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation F…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
In post-operative radiotherapy for prostate cancer, the cancerous prostate gland has been surgically removed, so the clinical target volume (CTV) to be irradiated encompasses the microscopic spread of tumor cells, which cannot be visualized in typical clinical images such as computed tomography or magnetic resonance imaging. In current clinical practice, physicians segment CTVs manually based on their relationship with nearby organs and other clinical information, per clinical guidelines. Automating post-operative prostate CTV segmentation with traditional image segmentation methods has been a…
Recently we have developed an algorithm for reconstructing volumetric images and extracting 3D tumor motion information from a single x-ray projection. We have demonstrated its feasibility using a digital respiratory phantom with regular breathing patterns. In this work, we present a detailed description and a comprehensive evaluation of the improved algorithm. The algorithm was improved by incorporating respiratory motion prediction. The accuracy and efficiency were then evaluated on 1) a digital respiratory phantom, 2) a physical respiratory phantom, and 3) five lung cancer patients. These e…
Purpose To develop and demonstrate a novel tumor shell feature for predicting distant failure in non-small cell lung cancer (NSCLC) and cervical cancer (CC) patients. Patients and Methods The shell predictive model was constructed using pretreatment positron emission tomography (PET) images from 48 NSCLC patients received stereotactic body radiation therapy (SBRT) and 52 CC patients underwent external beam radiation therapy and concurrent chemotherapy followed with high-dose-rate intracavitary brachytherapy. A shell feature, consisting of outer voxels around the tumor boundary, was extracted f…
Dose painting of hypoxic tumour sub-volumes using positron-emission tomography (PET) has been shown to improve tumour control in silico in several sites. Pancreatic cancer presents a more stringent challenge, given its proximity to critical organs-at-risk (OARs) and anatomic motion. A radiobiological model was developed to estimate clonogen survival fraction (SF), using 18F-fluoroazomycin arabinoside PET (FAZA PET) images from ten patients with pancreatic cancer to quantify oxygen enhancement effects. For each patient, four simulated five-fraction stereotactic body radiotherapy (SBRT) plans we…
To enable magnetic resonance (MR)-only radiotherapy and facilitate modelling of radiation attenuation in humans, synthetic-CT (sCT) images need to be generated. Considering the application of MR-guided radiotherapy and online adaptive replanning, sCT generation should occur within minutes. This work aims at assessing whether an existing deep learning network can rapidly generate sCT images to be used for accurate MR-based dose calculations in the entire pelvis. A study was conducted on data of 91 patients with prostate, rectal and cervical cancer who underwent external beam radiotherapy acquir…
Using fiducial markers on patient's body surface to predict the tumor location is a widely used approach in lung cancer radiotherapy. The purpose of this work is to propose an algorithm that automatically identifies a sparse set of locations on the patient's surface with the optimal prediction power for the tumor motion. The sparse selection of markers on the external surface and the assumed linear relationship between the marker motion and the internal tumor motion are represented by a prediction matrix. Such a matrix is determined by solving an optimization problem, where the objective funct…
Causal Machine Learning Analysis of Empirical Relative Biological Effectiveness (RBE) for Mandible Osteoradionecrosis in Head and Neck Cancer Radiotherapy
A new type of metabolizable and efficient radiosensitizer for cancer radiotherapy is presented in this study by combining ultrasmall Au nanoclusters (NCs, <2 nm) with biocompatible coating ligands (glutathione, GSH). The new nano-construct (GSH-coated Au25 NCs) inherits attractive features of both the Au core (strong radiosensitizing effect) and GSH shell (good biocompatibility). It can preferentially accumulate in tumor via the improved EPR effect, which leads to strong enhancement for cancer radiotherapy. After the treatment, the small-sized GSH-Au25 NCs can be efficiently cleared by the kid…
TransAnaNet: Transformer-based Anatomy Change Prediction Network for Head and Neck Cancer Patient Radiotherapy
UT Southwestern Medical Center（德克萨斯大学西南医学中心）；University of Maryland Medical Center（马里兰大学医学中心）；Central Arkansas Radiation Therapy Institute（中央阿肯色放射治疗研究所）
Purpose: To develop an algorithm for real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. Methods: Given a set of volumetric images of a patient at N breathing phases as the training data, we perform deformable image registration between a reference phase and the other N-1 phases, resulting in N-1 deformation vector fields (DVFs). These DVFs can be represented efficiently by a few eigenvectors and coefficients obtained from principal component analysis (PCA). By varying the PCA coefficients, we can generate n…
We present a mechanistic biomathematical model of molecular radiotherapy of thyroid disease. The general model consists of a set of differential equations describing the dynamics of different populations of thyroid cells with varying degrees of damage caused by radiotherapy (undamaged cells, sub-lethally damaged cells, doomed cells, and dead cells), as well as the dynamics of thyroglobulin and antithyroglobulin autoantibodies, which are important surrogates of treatment response. The model is presented in two flavours: on the one hand, as a deterministic continuous model, which is useful to fi…
Purpose: To evaluate the clinical need for automated decision-support platforms for Adaptive Radiotherapy Therapy (ART) of Head & Neck cancer (HNC) patients. Methods: We tested RTapp (SegAna), a new decision-support software for ART, to investigate 22 HNC patients data retrospectively. For each fraction, RTapp estimated the daily and cumulative doses received by targets and OARs from daily 3D imaging in real-time. RTapp also included a prediction algorithm that analyzed dosimetric parameters (DP) trends against dosimetric endpoints (DE) to trigger adaptation up to 4 fractions ahead. Warning (V…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
The treatment planning process for patients with head and neck (H&N) cancer is regarded as one of the most complicated due to large target volume, multiple prescription dose levels, and many radiation-sensitive critical structures near the target. Treatment planning for this site requires a high level of human expertise and a tremendous amount of effort to produce personalized high quality plans, taking as long as a week, which deteriorates the chances of tumor control and patient survival. To solve this problem, we propose to investigate a deep learning-based dose prediction model, Hierarchic…
Quantification of head and neck cancer patients' anatomical changes during radiotherapy: prediction of replanning need
McGill University（麦吉尔大学）；Research Institute of the McGill University Health Centre, McGill University（麦吉尔大学健康中心研究所，麦吉尔大学）；Department of Radiation Oncology, Hôpital Maisonneuve-Rosemont（Maisonneuve-Rosemont医院放射肿瘤科）；Department of Radiation Oncology, McGill University（麦吉尔大学放射肿瘤科）
In carbon-ion radiotherapy, single-beam delivery each day in alternate directions has been commonly practiced for operational efficiency, taking advantage of the Bragg peak and the relative biological effectiveness (RBE) for uniform dose conformation to a tumor. The treatment plans are usually evaluated with total RBE-weighted dose, which is however deficient in relevance to the biological effect in the linear-quadratic model due to its quadratic-dose term, or the dose-fractionation effect. In this study, we reformulate the extrapolated response dose (ERD), or synonymously BED, which normalize…
Metastasis is the process by which cells from a primary tumor disperse and form new tumors at distant anatomical locations. The treatment and prevention of metastatic cancer remains an extremely challenging problem. This work introduces a novel biologically motivated objective function to the radiation optimization community that takes into account metastatic risk instead of the status of the primary tumor. In this work, we consider the problem of developing fractionated irradiation schedules that minimize production of metastatic cancer cells while keeping normal tissue damage below an accept…
Background and Purpose: Stereotactic body radiotherapy (SBRT) for lung cancer accompanies a non-negligible risk of radiation pneumonitis (RP). This study presents a Bayesian network (BN) model that connects biological, dosimetric, and clinical RP risk factors. Material and Methods: 43 non-small-cell lung cancer patients treated with SBRT with 5 fractions or less were studied. Candidate RP risk factors included dose-volume parameters, previously reported clinical RP factors, 6 protein biomarkers at baseline and 6 weeks post-treatment. A BN ensemble model was built from a subset of the variables…
New Insights into Automatic Treatment Planning for Cancer Radiotherapy Using Explainable Artificial Intelligence
Department of Physics, The University of Texas at Arlington（德克萨斯大学阿灵顿分校物理系）；Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University（约翰霍普金斯大学放射肿瘤学与分子放射科学系）
Cancer is a primary cause of morbidity and mortality worldwide. The radiotherapy plays a more and more important role in cancer treatment. In the radiotherapy, the dose distribution maps in patient need to be calculated and evaluated for the purpose of killing tumor and protecting healthy tissue. Monte Carlo (MC) radiation transport calculation is able to account for all aspects of radiological physics within 3D heterogeneous media such as the human body and generate the dose distribution maps accurately. However, an MC calculation for doses in radiotherapy usually takes a great mass of time t…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Background and purpose: Radiation-induced erectile dysfunction (RiED) is commonly seen in prostate cancer patients. Clinical trials have been developed in multiple institutions to investigate whether dose-sparing to the internal-pudendal-arteries (IPA) will improve retention of sexual potency. The IPA is usually not considered a conventional organ-at-risk (OAR) due to segmentation difficulty. In this work, we propose a deep learning (DL)-based auto-segmentation model for the IPA that utilizes CT and MRI or CT alone as the input image modality to accommodate variation in clinical practice. Mate…
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）
Magnetic resonance delta radiomics to track radiation response in lung tumors receiving stereotactic MRI-guided radiotherapy
Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Maastricht University（马斯特里赫特大学）
