Artificial Intelligence-based Motion Tracking in Cancer Radiotherapy: A Review

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Recently, artificial intelligence (AI) has demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review serves to present the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provide a literature summary on the topic. We will also discuss the limitations of these algorithms and propose potential improvements.Comment: 36 pages, 5 Figures, 4 Table

Advances in real-time thoracic guidance systems

Substantial tissue motion: \u3e1cm) arises in the thoracic/abdominal cavity due to respiration. There are many clinical applications in which localizing tissue with high accuracy: \u3c1mm) is important. Potential applications include radiation therapy, radio frequency ablation, lung/liver biopsies, and brachytherapy seed placement. Recent efforts have made highly accurate sub-mm 3D localization of discrete points available via electromagnetic: EM) position monitoring. Technology from Calypso Medical allows for simultaneous tracking of up to three implanted wireless transponders. Additionally, Medtronic Navigation uses wired electromagnetic tracking to guide surgical tools for image guided surgery: IGS). Utilizing real-time EM position monitoring, a prototype system was developed to guide a therapeutic linear accelerator to follow a moving target: tumor) within the lung/abdomen. In a clinical setting, electromagnetic transponders would be bronchoscopically implanted into the lung of the patient in or near the tumor. These transponders would ax to the lung tissue in a stable manner and allow real-time position knowledge throughout a course of radiation therapy. During each dose of radiation, the beam is either halted when the target is outside of a given threshold, or in a later study the beam follows the target in real-time based on the EM position monitoring. We present quantitative analysis of the accuracy and efficiency of the radiation therapy tumor tracking system. EM tracking shows promise for IGS applications. Tracking the position of the instrument tip allows for minimally invasive intervention and alleviates the trauma associated with conventional surgery. Current clinical IGS implementations are limited to static targets: e.g. craniospinal, neurological, and orthopedic intervention. We present work on the development of a respiratory correlated image guided surgery: RCIGS) system. In the RCIGS system, target positions are modeled via respiratory correlated imaging: 4DCT) coupled with a breathing surrogate representative of the patient\u27s respiratory phase/amplitude. Once the target position is known with respect to the surrogate, intervention can be performed when the target is in the correct location. The RCIGS system consists of imaging techniques and custom developed software to give visual and auditory feedback to the surgeon indicating both the proper location and time for intervention. Presented here are the details of the IGS lung system along with quantitative results of the system accuracy in motion phantom, ex-vivo porcine lung, and human cadaver environments

Improving Dose-Response Correlations for Locally Advanced NSCLC Patients Treated with IMRT or PSPT

The standard of care for locally advanced non-small cell lung cancer (NSCLC) is concurrent chemo-radiotherapy. Despite recent advancements in radiation delivery methods, the median survival time of NSCLC patients remains below 28 months. Higher tumor dose has been found to increase survival but also a higher rate of radiation pneumonitis (RP) that affects breathing capability. In fear of such toxicity, less-aggressive treatment plans are often clinically preferred, leading to metastasis and recurrence. Therefore, accurate RP prediction is crucial to ensure tumor coverage to improve treatment outcome. Current models have associated RP with increased dose but with limited accuracy as they lack spatial correlation between accurate dose representation and quantitative RP representation. These models represent lung tissue damage with radiation dose distribution planned pre-treatment, which assumes a fixed patient geometry and inevitably renders imprecise dose delivery due to intra-fractional breathing motion and inter-fractional anatomy response. Additionally, current models employ whole-lung dose metrics as the contributing factor to RP as a qualitative, binary outcome but these global dose metrics discard microscopic, voxel-(3D pixel)-level information and prevent spatial correlations with quantitative RP representation. To tackle these limitations, we developed advanced deformable image registration (DIR) techniques that registered corresponding anatomical voxels between images for tracking and accumulating dose throughout treatment. DIR also enabled voxel-level dose-response correlation when CT image density change (IDC) was used to quantify RP. We hypothesized that more accurate estimates of biologically effective dose distributions actually delivered, achieved through (a) dose accumulation using deformable registration of weekly 4DCT images acquired over the course or radiotherapy and (b) the incorporation of variable relative biological effectiveness (RBE), would lead to statistically and clinically significant improvement in the correlation of RP with biologically effective dose distributions. Our work resulted in a robust intra-4DCT and inter-4DCT DIR workflow, with the accuracy meeting AAPM TG-132 recommendations for clinical implementation of DIR. The automated DIR workflow allowed us to develop a fully automated 4DCT-based dose accumulation pipeline in RayStation (RaySearch Laboratories, Stockholm, Sweden). With a sample of 67 IMRT patients, our results showed that the accumulated dose was statistically different than the planned dose across the entire cohort with an average MLD increase of ~1 Gy and clinically different for individual patients where 16% resulted in difference in the score of the normal tissue complication probability (NTCP) using an established, clinically used model, which could qualify the patients for treatment planning re-evaluation. Lastly, we associated dose difference with accuracy difference by establishing and comparing voxel-level dose-IDC correlations and concluded that the accumulated dose better described the localized damage, thereby a closer representation of the delivered dose. Using the same dose-response correlation strategy, we plotted the dose-IDC relationships for both photon patients (N = 51) and proton patients (N = 67), we measured the variable proton RBE values to be 3.07–1.27 from 9–52 Gy proton voxels. With the measured RBE values, we fitted an established variable proton RBE model with pseudo-R2 of 0.98. Therefore, our results led to statistically and clinically significant improvement in the correlation of RP with accumulated and biologically effective dose distributions and demonstrated the potential of incorporating the effect of anatomical change and biological damage in RP prediction models

Optimization strategies for respiratory motion management in stereotactic body radiation therapy

Various challenges arise during the treatment of lung tumors with stereotactic body radiation therapy (SBRT), which is a form of hypofractionated high precision conformal radiation therapy delivered to small targets. The dose is applied in only a few fractions and respiratory organ and tumor motion is a source of uncertainty additional to interfractional set-up errors. Respiratory organ and tumor motion is highly patient-specific and it affects the whole radiotherapy treatment chain. In this thesis, motion management techniques for SBRT are evaluated and improved in a clinical setting. A clinical need for improvement has been present at the LMU university hospital for each issue addressed in this thesis: Initially, the usage of respiratory correlated computed tomography (4DCT), which is vital for SBRT treatment, was seen as impractical and prone to uncertainties in the data reconstruction in its current form. Therefore, the 4DCT reconstruction workflow has been improved to minimize these potential error sources. Secondly, treatment planning for tumors affected by respiratory motion was evaluated and subsequently improved. Finally, the treatment technique of respiratory gating was implemented at the clinic, which led to the need of evaluating the respiratory gating characteristics of the novel system configuration. At first, the 4DCT reconstruction workflow used in clinical practice was investigated, as in the presence of respiratory motion the knowledge of tumor position over time is essential in SBRT treatments. Using 4DCT, the full motion range of the individual tumor can be determined. However, certain 4DCT reconstruction methods can under- or overestimate tumor motion due to limitations in the data acquisition scheme and due to the incorrect sorting of certain X-ray computed tomography (CT) image slices into different respiratory phases. As the regular clinical workflow of cycle-based sorting (CBS) without maximum inspiration detection (and therefore no clear starting point for the individual breathing cycles) seemed to be affected by these potential errors, the usage of CBS with correct maximum detection and another sorting algorithm of the respiration states, so-called local amplitude-based sorting (LAS), both have been implemented for a reduction of image artifacts and improved 4DCT quality. The three phase binning algorithms have been investigated in a phantom study (using 10 different breathing waveforms) and in a patient study (with 10 different patients). The mis-representation of the tumor volume was reduced in both implemented sorting algorithms compared to the previously used CBS approach (without correct maximum detection) in the phantom and the patient study. The clinical recommendation was the use of CBS with improved maximum detection, as too many manual interventions would be needed for the LAS workflow. Secondly, a combination of the actual patient breathing trace during treatment, the log files generated by the linear accelerator (LINAC), and Monte Carlo (MC) four-dimensional (4D) dose calculations for each individual fraction was implemented as a 4D dose evaluation tool. This workflow was tested in a clinical environment for SBRT treatment planning on multiple CT datasets featuring: a native free-breathing 3DCT, an average intensity projection (AIP) as well as a maximum intensity projection (MIP), both obtained from the patient's 4DCT, and density overrides (DOs) in a 3DCT. This study has been carried out for 5 SBRT patients for three-dimensional conformal radiation therapy (3D-CRT) and volumetric modulated arc therapy (VMAT) treatment plans. The dose has been recalculated on each 4DCT breathing phase according the the patient's breathing waveform and accumulated to the gross tumor volume (GTV) at the end-of-exhale (EOE) breathing phase using deformable image registration. Even though the least differences in planned and recalculated dose were found for AIP and MIP treatment planning, the results indicate a strong dependency on individual tumor motion due to the variability of breathing motion in general, and on tumor size. The combination of the patient's individual breathing trace during each SBRT fraction with 4D MC dose calculation based on the LINAC log file information leads to a good approximation of actual dose delivery. Finally, in order to ensure precise and accurate treatment for respiratory gating techniques, the technical characteristics of the LINAC in combination with a breathing motion monitoring system as s surrogate for tumor motion have to be identified. The dose delivery accuracy and the latency of a surface imaging system in connection with a modern medical LINAC were investigated using a dynamic breathing motion phantom. The dosimetric evaluation has been carried out using a static 2D-diode array. The measurement of the dose difference between gated and ungated radiation delivery was found to be below 1% (for clinical relevant gating levels of about 30%). The beam-on latency, or time delay, determined using radiographic films was found to be up to 851 ms±100 ms. With these known parameters, an adjustment of the pre-selected gating level or the internal target volume (ITV) margins could be made. With the highly patient-specific character of respiratory motion, lung SBRT faces many additional challenges besides the specific issues addressed in this thesis. However, the findings of this thesis have improved clinical workflows at the Department of Radiation Oncology of the LMU University hospital. In a future perspective, a workflow using evaluation of the actual 4D dose in combination with accurate 4DCT image acquisition and specialized treatment delivery (such as respiratory gating) has the potential for a safe further reduction of treatment margins and increased sparing of organs-at-risk (OARs) in SBRT without compromising tumor dose targeting accuracy

Translational Research of Audiovisual Biofeedback: An investigation of respiratory-guidance in lung and liver cancer patient radiation therapy

Through the act of breathing, thoracic and abdominal anatomy is in constant motion and is typically irregular. This irregular motion can exacerbate errors in radiation therapy, breathing guidance interventions operate to minimise these errors. However, much of the breathing guidance investigations have not directly quantified the impact of regular breathing on radiation therapy accuracy. The first aim of this thesis was to critically appraise the literature in terms of the use of breathing guidance interventions via systematic review. This review found that 21 of the 27 identified studies yielded significant improvements from the use of breathing guidance. None of the studies were randomised and no studies quantified the impact on 4DCT image quality. The second aim of this thesis was to quantify the impact of audiovisual biofeedback breathing guidance on 4DCT. This study utilised data from an MRI study to program the motion of a digital phantom prior to then simulating 4DCT imaging. Audiovisual biofeedback demonstrated to significantly improved 4DCT image quality over free breathing. The third aim of this thesis was to assess the impact of audiovisual biofeedback on liver cancer patient breathing over a course of stereotactic body radiation therapy (SBRT). The findings of this study demonstrated the effectiveness of audiovisual biofeedback in producing consistent interfraction respiratory motion over a course of SBRT. The fourth aim of this thesis was to design and implement a phase II clinical trial investigating the use and impact of audiovisual biofeedback in lung cancer radiation therapy. The findings of a retrospective analysis were utilised to design and determine the statistics of the most comprehensive breathing guidance study to date: a randomised, stratified, multi-site, phase II clinical trial.. The fifth aim of this thesis was to explore the next stages of audiovisual biofeedback in terms of translating evidence into broader clinical use through commercialisation. This aim was achieved by investigating the the product-market fit of the audiovisual biofeedback technology. The culmination of these findings demonstrates the clinical benefit of the audiovisual biofeedback respiratory guidance system and the possibility to make breathing guidance systems more widely available to patients

Optimization strategies for respiratory motion management in stereotactic body radiation therapy

Various challenges arise during the treatment of lung tumors with stereotactic body radiation therapy (SBRT), which is a form of hypofractionated high precision conformal radiation therapy delivered to small targets. The dose is applied in only a few fractions and respiratory organ and tumor motion is a source of uncertainty additional to interfractional set-up errors. Respiratory organ and tumor motion is highly patient-specific and it affects the whole radiotherapy treatment chain. In this thesis, motion management techniques for SBRT are evaluated and improved in a clinical setting. A clinical need for improvement has been present at the LMU university hospital for each issue addressed in this thesis: Initially, the usage of respiratory correlated computed tomography (4DCT), which is vital for SBRT treatment, was seen as impractical and prone to uncertainties in the data reconstruction in its current form. Therefore, the 4DCT reconstruction workflow has been improved to minimize these potential error sources. Secondly, treatment planning for tumors affected by respiratory motion was evaluated and subsequently improved. Finally, the treatment technique of respiratory gating was implemented at the clinic, which led to the need of evaluating the respiratory gating characteristics of the novel system configuration. At first, the 4DCT reconstruction workflow used in clinical practice was investigated, as in the presence of respiratory motion the knowledge of tumor position over time is essential in SBRT treatments. Using 4DCT, the full motion range of the individual tumor can be determined. However, certain 4DCT reconstruction methods can under- or overestimate tumor motion due to limitations in the data acquisition scheme and due to the incorrect sorting of certain X-ray computed tomography (CT) image slices into different respiratory phases. As the regular clinical workflow of cycle-based sorting (CBS) without maximum inspiration detection (and therefore no clear starting point for the individual breathing cycles) seemed to be affected by these potential errors, the usage of CBS with correct maximum detection and another sorting algorithm of the respiration states, so-called local amplitude-based sorting (LAS), both have been implemented for a reduction of image artifacts and improved 4DCT quality. The three phase binning algorithms have been investigated in a phantom study (using 10 different breathing waveforms) and in a patient study (with 10 different patients). The mis-representation of the tumor volume was reduced in both implemented sorting algorithms compared to the previously used CBS approach (without correct maximum detection) in the phantom and the patient study. The clinical recommendation was the use of CBS with improved maximum detection, as too many manual interventions would be needed for the LAS workflow. Secondly, a combination of the actual patient breathing trace during treatment, the log files generated by the linear accelerator (LINAC), and Monte Carlo (MC) four-dimensional (4D) dose calculations for each individual fraction was implemented as a 4D dose evaluation tool. This workflow was tested in a clinical environment for SBRT treatment planning on multiple CT datasets featuring: a native free-breathing 3DCT, an average intensity projection (AIP) as well as a maximum intensity projection (MIP), both obtained from the patient's 4DCT, and density overrides (DOs) in a 3DCT. This study has been carried out for 5 SBRT patients for three-dimensional conformal radiation therapy (3D-CRT) and volumetric modulated arc therapy (VMAT) treatment plans. The dose has been recalculated on each 4DCT breathing phase according the the patient's breathing waveform and accumulated to the gross tumor volume (GTV) at the end-of-exhale (EOE) breathing phase using deformable image registration. Even though the least differences in planned and recalculated dose were found for AIP and MIP treatment planning, the results indicate a strong dependency on individual tumor motion due to the variability of breathing motion in general, and on tumor size. The combination of the patient's individual breathing trace during each SBRT fraction with 4D MC dose calculation based on the LINAC log file information leads to a good approximation of actual dose delivery. Finally, in order to ensure precise and accurate treatment for respiratory gating techniques, the technical characteristics of the LINAC in combination with a breathing motion monitoring system as s surrogate for tumor motion have to be identified. The dose delivery accuracy and the latency of a surface imaging system in connection with a modern medical LINAC were investigated using a dynamic breathing motion phantom. The dosimetric evaluation has been carried out using a static 2D-diode array. The measurement of the dose difference between gated and ungated radiation delivery was found to be below 1% (for clinical relevant gating levels of about 30%). The beam-on latency, or time delay, determined using radiographic films was found to be up to 851 ms±100 ms. With these known parameters, an adjustment of the pre-selected gating level or the internal target volume (ITV) margins could be made. With the highly patient-specific character of respiratory motion, lung SBRT faces many additional challenges besides the specific issues addressed in this thesis. However, the findings of this thesis have improved clinical workflows at the Department of Radiation Oncology of the LMU University hospital. In a future perspective, a workflow using evaluation of the actual 4D dose in combination with accurate 4DCT image acquisition and specialized treatment delivery (such as respiratory gating) has the potential for a safe further reduction of treatment margins and increased sparing of organs-at-risk (OARs) in SBRT without compromising tumor dose targeting accuracy

Inverse-Consistent Determination of Young\u27s Modulus of Human Lung

Human lung undergoes respiration-induced deformation due to sequential inhalation and exhalation. Accurate determination of lung deformation is crucial for tumor localization and targeted radiotherapy in patients with lung cancer. Numerical modeling of human lung dynamics based on underlying physics and physiology enables simulation and virtual visualization of lung deformation. Dynamical modeling is numerically complicated by the lack of information on lung elastic behavior, structural heterogeneity as well as boundary constrains. This study integrates physics-based modeling and image-based data acquisition to develop the patient-specific biomechanical model and consequently establish the first consistent Young\u27s modulus (YM) of human lung. This dissertation has four major components: (i) develop biomechanical model for computation of the flow and deformation characteristics that can utilize subject-specific, spatially-dependent lung material property; (ii) develop a fusion algorithm to integrate deformation results from a deformable image registration (DIR) and physics-based modeling using the theory of Tikhonov regularization; (iii) utilize fusion algorithm to establish unique and consistent patient specific Young\u27s modulus and; (iv) validate biomechanical model utilizing established patient-specific elastic property with imaging data. The simulation is performed on three dimensional lung geometry reconstructed from four-dimensional computed tomography (4DCT) dataset of human subjects. The heterogeneous Young\u27s modulus is estimated from a linear elastic deformation model with the same lung geometry and 4D lung DIR. The biomechanical model adequately predicts the spatio-temporal lung deformation, consistent with data obtained from imaging. The accuracy of the numerical solution is enhanced through fusion with the imaging data beyond the classical comparison of the two sets of data. Finally, the fused displacement results are used to establish unique and consistent patient-specific elastic property of the lung

Evaluation of Tumor Localization in Respiration Motion-Corrected Stereotactic Body Radiotherapy Patients Treated with TomoTherapy.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017