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Objectives: This study aims to evaluate the patellar height changes after distal femur (DF) endoprosthetic replacement (EPR) and its impact on anterior knee pain (AKP) and range of motion (ROM). Methods: A retrospective review of three institutions' databases was performed. The patellar height was determined using the modified Insall-Salvati ratio (MIS), the Blackburne-Peel (BP) and the Caton-Deschamps (CD) indexes. Data regarding AKP and ROM were collected. Results: A total of 199 patients were included. The mean age at presentation was 37.9 ± 23.1 years. The mean one-year follow-up MIS, BP and CD were 1.52 (sd: 0.41), 0.82 (sd: 0.33) and 0.93 (sd: 0.33). Patellar height decreased significantly compared to the pre-operative values according to all three scores (p < 0.001). AKP was reported by 34 (17.1%) patients at 1 year follow-up. Patients with patella baja (MIS < 1.2) or pseudo patella baja (CD < 0.6) had a higher incidence of AKP (p = 0.037 and p = 0.024, respectively). The mean flexion ROM was 91°, with a direct correlation with patellar height (MIS p = 0.020, BP p = 0.036 and CD p = 0.036). Conclusion: The restoration of the native position of the joint line in DF EPR is important to maintain optimal patellofemoral biomechanics. Despite surgeons' tendency toward a reduction in patellar height with respect to pre-operative values, an increase in patellar height might help to achieve better knee flexion and reduce AKP.
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PURPOSE: To investigate the role of dosiomics features extracted from physical dose (DPHYS), RBE-weighted dose (DRBE) and dose-averaged Linear Energy Transfer (LETd), to predict the risk of local recurrence (LR) in skull base chordoma (SBC) treated with Carbon Ion Radiotherapy (CIRT). Thus, define and evaluate dosiomics-driven tumor control probability (TCP) models. MATERIALS AND METHODS: 54 SBC patients were retrospectively selected for this study. A regularized Cox proportional hazard model (r-Cox) and Survival Support Vector Machine (s-SVM) were tuned within a repeated Cross Validation (CV) and patients were stratified in low/high risk of LR. Models' performance was evaluated through Harrell's concordance statistic (C-index), and survival was represented through Kaplan-Meier (KM) curves. A multivariable logistic regression was fit to the selected feature sets to generate a dosiomics-driven TCP model for each map. These were compared to a reference model built with clinical parameters in terms of f-score and accuracy. RESULTS: The LETd maps reached a test C-index of 0.750 and 0.786 with r-Cox and s-SVM, and significantly separated KM curves. DPHYS maps and clinical parameters showed promising CV outcomes with C-index above 0.8, despite a poorer performance on the test set and patients stratification. The LETd-based TCP showed a significatively higher f-score (0.67[0.52-0.70], median[IQR]) compared to the clinical model (0.4[0.32-0.63], p < 0.025), while DPHYS achieved a significatively higher accuracy (DPHYS: 0.73[0.65-0.79], Clinical: 0.6 [0.52-0.72]). CONCLUSION: This analysis supports the role of LETd as relevant source of prognostic factors for LR in SBC treated with CIRT. This is reflected in the TCP modeling, where LETd and DPHYS showed an improved performance with respect to clinical models.
Subject(s)
Chordoma , Heavy Ion Radiotherapy , Skull Base Neoplasms , Chordoma/radiotherapy , Skull Base Neoplasms/radiotherapy , Humans , Treatment Outcome , Male , Female , Middle Aged , Retrospective Studies , Radiometry , Adult , Aged , Radiotherapy Dosage , Linear Energy Transfer , Proportional Hazards Models , Neoplasm Recurrence, Local/radiotherapy , Support Vector MachineABSTRACT
The measurement of partial pressures of oxygen (O2) and carbon dioxide (CO2) is fundamental for evaluating a patient's conditions in clinical practice. There are many ways to retrieve O2/CO2 partial pressures and concentrations. Arterial blood gas (ABG) analysis is the gold standard technique for such a purpose, but it is invasive, intermittent, and potentially painful. Among all the alternative methods for gas monitoring, non-invasive transcutaneous O2 and CO2 monitoring has been emerging since the 1970s, being able to overcome the main drawbacks of ABG analysis. Clark and Severinghaus electrodes enabled the breakthrough for transcutaneous O2 and CO2 monitoring, respectively, and in the last twenty years, many innovations have been introduced as alternatives to overcome their limitations. This review reports the most recent solutions for transcutaneous O2 and CO2 monitoring, with a particular consideration for wearable measurement systems. Luminescence-based electronic paramagnetic resonance and photoacoustic sensors are investigated. Optical sensors appear to be the most promising, giving fast and accurate measurements without the need for frequent calibrations and being suitable for integration into wearable measurement systems.
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Background and purpose: Many 4D particle therapy research concepts have been recently translated into clinics, however, remaining substantial differences depend on the indication and institute-related aspects. This work aims to summarise current state-of-the-art 4D particle therapy technology and outline a roadmap for future research and developments. Material and methods: This review focused on the clinical implementation of 4D approaches for imaging, treatment planning, delivery and evaluation based on the 2021 and 2022 4D Treatment Workshops for Particle Therapy as well as a review of the most recent surveys, guidelines and scientific papers dedicated to this topic. Results: Available technological capabilities for motion surveillance and compensation determined the course of each 4D particle treatment. 4D motion management, delivery techniques and strategies including imaging were diverse and depended on many factors. These included aspects of motion amplitude, tumour location, as well as accelerator technology driving the necessity of centre-specific dosimetric validation. Novel methodologies for X-ray based image processing and MRI for real-time tumour tracking and motion management were shown to have a large potential for online and offline adaptation schemes compensating for potential anatomical changes over the treatment course. The latest research developments were dominated by particle imaging, artificial intelligence methods and FLASH adding another level of complexity but also opportunities in the context of 4D treatments. Conclusion: This review showed that the rapid technological advances in radiation oncology together with the available intrafractional motion management and adaptive strategies paved the way towards clinical implementation.
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Background and purpose: Imaging of respiration-induced anatomical changes is essential to ensure high accuracy in radiotherapy of lung cancer. We expanded here on methods for retrospective reconstruction of time-resolved volumetric magnetic resonance (4DMR) of the thoracic region and benchmarked the results against 4D computed tomography (4DCT). Materials and method: MR data of six lung cancer patients were collected by interleaving cine-navigator images with 2D data frame images, acquired across the thorax. The data frame images have been stacked in volumes based on a similarity metric that considers the anatomical deformation of lungs, while addressing ambiguities in respiratory phase detection and interpolation of missing data. The resulting images were validated against cine-navigator images and compared to paired 4DCTs in terms of amplitude and period of motion, assessing differences in internal target volume (ITV) margin definition. Results: 4DMR-based motion amplitude was on average within 1.8 mm of that measured in the corresponding 2D cine-navigator images. In our dataset, the 4DCT motion and the 4DMR median amplitude were always within 3.8 mm. The median period was generally close to CT references, although deviations up to 24 % have been observed. These changes were reflected in the ITV, which was generally larger for MRI than for 4DCT (up to 39.7 %). Conclusions: The proposed algorithm for retrospective reconstruction of time-resolved volumetric MR provided quality anatomical images with high temporal resolution for motion modelling and treatment planning. The potential for imaging organ motion variability makes 4DMR a valuable complement to standard 4DCT imaging.
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MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms.
Subject(s)
Artificial Intelligence , Radiotherapy, Image-Guided , Humans , Radiotherapy, Image-Guided/methods , Motion , Magnetic Resonance Imaging/methods , Algorithms , Radiotherapy Planning, Computer-Assisted/methodsABSTRACT
BACKGROUND: Current commercially available hybrid magnetic resonance linear accelerators (MR-Linac) use 2D+t cine MR imaging to provide intra-fractional motion monitoring. However, given the limited temporal resolution of cine MR imaging, target intra-frame motion deterioration effects, resulting in effective time latency and motion artifacts in the image domain, can be appreciable, especially in the case of fast breathing. PURPOSE: The aim of this work is to investigate intra-frame motion deterioration effects in MR-guided radiotherapy (MRgRT) by simulating the motion-corrupted image acquisition, and to explore the feasibility of deep-learning-based compensation approaches, relying on the intra-frame motion information which is spatially and temporally encoded in the raw data (k-space). METHODS: An intra-frame motion model was defined to simulate motion-corrupted MR images, with 4D anthropomorphic digital phantoms being exploited to provide ground truth 2D+t cine MR sequences. A total number of 10 digital phantoms were generated for lung cancer patients, with randomly selected eight patients for training or validation and the remaining two for testing. The simulation code served as the data generator, and a dedicated motion pattern perturbation scheme was proposed to build the intra-frame motion database, where three degrees of freedom were designed to guarantee the diversity of intra-frame motion trajectories, enabling a thorough exploration in the domain of the potential anatomical structure positions. U-Nets with three types of loss functions: L1 or L2 loss defined in image or Fourier domain, referred to as NNImgLoss-L1 , NNFloss-L1 and NNL2-Loss were trained to extract information from the motion-corrupted image and used to estimate the ground truth final-position image, corresponding to the end of the acquisition. Images before and after compensation were evaluated in terms of (i) image mean-squared error (MSE) and mean absolute error (MAE), and (ii) accuracy of gross tumor volume (GTV) contouring, based on optical-flow image registration. RESULTS: Image degradation caused by intra-frame motion was observed: for a linearly and fully acquired Cartesian readout k-space trajectory, intra-frame motion resulted in an imaging latency of approximately 50% of the acquisition time; in comparison, the motion artifacts exhibited only a negligible contribution to the overall geometric errors. All three compensation models led to a decrease in image MSE/MAE and GTV position offset compared to the motion-corrupted image. In the investigated testing dataset for GTV contouring, the average dice similarity coefficients (DSC) improved from 88% to 96%, and the 95th percentile Hausdorff distance (HD95 ) dropped from 4.8 mm to 2.1 mm. Different models showed slight performance variations across different intra-frame motion amplitude categories: NNImgLoss-L1 excelled for small/medium amplitudes, whereas NNFloss-L1 demonstrated higher DSC median values at larger amplitudes. The saliency maps of the motion-corrupted image highlighted the major contribution of the later acquired k-space data, as well as the edges of the moving anatomical structures at their final positions, during the model inference stage. CONCLUSIONS: Our results demonstrate the deep-learning-based approaches have the potential to compensate for intra-frame motion by utilizing the later acquired data to drive the convergence of the earlier acquired k-space components.
Subject(s)
Deep Learning , Lung Neoplasms , Radiotherapy, Image-Guided , Humans , Radiotherapy, Image-Guided/methods , Magnetic Resonance Imaging , Motion , Lung Neoplasms/diagnostic imaging , Lung Neoplasms/radiotherapyABSTRACT
Objective.To experimentally validate a method to create continuous time-resolved estimated synthetic 4D-computed tomography datasets (tresCTs) based on orthogonal cine MRI data for lung cancer treatments at a magnetic resonance imaging (MRI) guided linear accelerator (MR-linac).Approach.A breathing porcine lung phantom was scanned at a CT scanner and 0.35 T MR-linac. Orthogonal cine MRI series (sagittal/coronal orientation) at 7.3 Hz, intersecting tumor-mimicking gelatin nodules, were deformably registered to mid-exhale 3D-CT and 3D-MRI datasets. The time-resolved deformation vector fields were extrapolated to 3D and applied to a reference synthetic 3D-CT image (sCTref), while accounting for breathing phase-dependent lung density variations, to create 82 s long tresCTs at 3.65 Hz. Ten tresCTs were created for ten tracked nodules with different motion patterns in two lungs. For each dataset, a treatment plan was created on the mid-exhale phase of a measured ground truth (GT) respiratory-correlated 4D-CT dataset with the tracked nodule as gross tumor volume (GTV). Each plan was recalculated on the GT 4D-CT, randomly sampled tresCT, and static sCTrefimages. Dose distributions for corresponding breathing phases were compared in gamma (2%/2 mm) and dose-volume histogram (DVH) parameter analyses.Main results.The mean gamma pass rate between all tresCT and GT 4D-CT dose distributions was 98.6%. The mean absolute relative deviations of the tresCT with respect to GT DVH parameters were 1.9%, 1.0%, and 1.4% for the GTVD98%,D50%, andD2%, respectively, 1.0% for the remaining nodulesD50%, and 1.5% for the lungV20Gy. The gamma pass rate for the tresCTs was significantly larger (p< 0.01), and the GTVD50%deviations with respect to the GT were significantly smaller (p< 0.01) than for the sCTref.Significance.The results suggest that tresCTs could be valuable for time-resolved reconstruction and intrafractional accumulation of the dose to the GTV for lung cancer patients treated at MR-linacs in the future.
Subject(s)
Lung Neoplasms , Humans , Animals , Swine , Lung Neoplasms/diagnostic imaging , Lung Neoplasms/radiotherapy , Magnetic Resonance Imaging , Lung , Four-Dimensional Computed Tomography/methods , Magnetic Resonance Imaging, Cine , Radiotherapy Planning, Computer-Assisted/methodsABSTRACT
PURPOSE: To define an optimal set of b-values for accurate derivation of diffusion MRI parameters in the brain with segmented Intravoxel Incoherent Motion (IVIM) model. METHODS: Simulations of diffusion signals were performed to define an optimal set of b-values targeting different perfusion regimes, by relying on an optimization procedure which minimizes the total relative error on estimated IVIM parameters computed with a segmented fitting procedure. Then, the optimal b-values set was acquired in vivo on healthy subjects and skull base chordoma patients to compare the optimized protocol with a clinical one. RESULTS: The total relative error on simulations decreased of about 40% when adopting the optimal set of 13 b-values (0 10 20 40 50 60 200 300 400 1200 1300 1400 1500 s/mm2 ), showing significant differences and increased precision on D and f estimates with respect to simulations with a non-optimized b-values set. Similarly, in vivo acquisitions demonstrated a dependency of IVIM parameters on the b-values array, with differences between the optimal set of b-values and a clinical non-optimized acquisition. IVIM parameters were compatible to literature values, with D (0.679/0.701 [0.022/0.008] ·10-3 mm2 /s), f (5.49/5.80 [0.70/1.14] %), and D* (8.25/7.67 [0.92/0.83] ·10-3 mm2 /s) median [interquartile range] estimates for white matter/gray matter in volunteers and D (0.709/0.715/1.06 [0.035/0.023/0.271] ·10-3 mm2 /s), f (7.08/7.84/21.54 [1.20/1.06/6.05] %), and D* (10.85/11.84/2.32 [1.38/2.32/4.94] ·10-3 mm2 /s) for white matter/gray matter/Gross Tumor Volume in patients with skull-base chordoma tumor. CONCLUSIONS: The definition of an optimal b-values set can improve the estimation of quantitative IVIM parameters. This allows setting up an optimized approach that can be adopted for IVIM studies in the brain.
Subject(s)
Chordoma , Humans , Brain/diagnostic imaging , Motion , Diffusion Magnetic Resonance Imaging/methodsABSTRACT
The generation of synthetic CT for carbon ion radiotherapy (CIRT) applications is challenging, since high accuracy is required in treatment planning and delivery, especially in an anatomical site as complex as the abdomen. Thirty-nine abdominal MRI-CT volume pairs were collected and a three-channel cGAN (accounting for air, bones, soft tissues) was used to generate sCTs. The network was tested on five held-out MRI volumes for two scenarios: (i) a CT-based segmentation of the MRI channels, to assess the quality of sCTs and (ii) an MRI manual segmentation, to simulate an MRI-only treatment scenario. The sCTs were evaluated by means of similarity metrics (e.g., mean absolute error, MAE) and geometrical criteria (e.g., dice coefficient). Recalculated CIRT plans were evaluated through dose volume histogram, gamma analysis and range shift analysis. The CT-based test set presented optimal MAE on bones (86.03 ± 10.76 HU), soft tissues (55.39 ± 3.41 HU) and air (54.42 ± 11.48 HU). Higher values were obtained from the MRI-only test set (MAEBONE = 154.87 ± 22.90 HU). The global gamma pass rate reached 94.88 ± 4.9% with 3%/3 mm, while the range shift reached a median (IQR) of 0.98 (3.64) mm. The three-channel cGAN can generate acceptable abdominal sCTs and allow for CIRT dose recalculations comparable to the clinical plans.
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Electrical impedance tomography (EIT) is a medical imaging technique based on the injection of a current or voltage pattern through electrodes on the skin of the patient, and on the reconstruction of the internal conductivity distribution from the voltages collected by the electrodes. Compared to other imaging techniques, EIT shows significant advantages: it does not use ionizing radiation, is non-invasive and is characterized by high temporal resolution. Moreover, its low cost and high portability make it suitable for real-time, bedside monitoring. However, EIT is also characterized by some technical limitations that cause poor spatial resolution. The possibility to design wearable devices based on EIT has recently given a boost to this technology. In this paper we reviewed EIT physical principles, hardware design and major clinical applications, from the classical to a wearable setup. A wireless and wearable EIT system seems a promising frontier of this technology, as it can both facilitate making clinical measurements and open novel scenarios to EIT systems, such as home monitoring.
Subject(s)
Tomography , Wearable Electronic Devices , Humans , Tomography/methods , Electric Impedance , Tomography, X-Ray Computed , Electric Conductivity , ElectrodesABSTRACT
BACKGROUND: MRI-guidance techniques that dynamically adapt radiation beams to follow tumor motion in real time will lead to more accurate cancer treatments and reduced collateral healthy tissue damage. The gold-standard for reconstruction of undersampled MR data is compressed sensing (CS) which is computationally slow and limits the rate that images can be available for real-time adaptation. PURPOSE: Once trained, neural networks can be used to accurately reconstruct raw MRI data with minimal latency. Here, we test the suitability of deep-learning-based image reconstruction for real-time tracking applications on MRI-Linacs. METHODS: We use automated transform by manifold approximation (AUTOMAP), a generalized framework that maps raw MR signal to the target image domain, to rapidly reconstruct images from undersampled radial k-space data. The AUTOMAP neural network was trained to reconstruct images from a golden-angle radial acquisition, a benchmark for motion-sensitive imaging, on lung cancer patient data and generic images from ImageNet. Model training was subsequently augmented with motion-encoded k-space data derived from videos in the YouTube-8M dataset to encourage motion robust reconstruction. RESULTS: AUTOMAP models fine-tuned on retrospectively acquired lung cancer patient data reconstructed radial k-space with equivalent accuracy to CS but with much shorter processing times. Validation of motion-trained models with a virtual dynamic lung tumor phantom showed that the generalized motion properties learned from YouTube lead to improved target tracking accuracy. CONCLUSION: AUTOMAP can achieve real-time, accurate reconstruction of radial data. These findings imply that neural-network-based reconstruction is potentially superior to alternative approaches for real-time image guidance applications.
Subject(s)
Lung Neoplasms , Magnetic Resonance Imaging , Humans , Retrospective Studies , Magnetic Resonance Imaging/methods , Neural Networks, Computer , Lung Neoplasms/diagnostic imaging , Lung Neoplasms/radiotherapy , Lung Neoplasms/pathology , Motion , Image Processing, Computer-Assisted/methodsABSTRACT
BACKGROUND: Quantitative imaging such as Diffusion-Weighted MRI (DW-MRI) can be exploited to non-invasively derive patient-specific tumor microstructure information for tumor characterization and local recurrence risk prediction in radiotherapy. PURPOSE: To characterize tumor microstructure according to proliferative capacity and predict local recurrence through microstructural markers derived from pre-treatment conventional DW-MRI, in skull-base chordoma (SBC) patients treated with proton (PT) and carbon ion (CIRT) radiotherapy. METHODS: Forty-eight patients affected by SBC, who underwent conventional DW-MRI before treatment and were enrolled for CIRT (n = 25) or PT (n = 23), were retrospectively selected. Clinically verified local recurrence information (LR) and histological information (Ki-67, proliferation index) were collected. Apparent diffusion coefficient (ADC) maps were calculated from pre-treatment DW-MRI and, from these, a set of microstructural parameters (cellular radius R, volume fraction vf, diffusion D) were derived by applying a fine-tuning procedure to a framework employing Monte Carlo simulations on synthetic cell substrates. In addition, apparent cellularity (ρapp ) was estimated from vf and R for an easier clinical interpretation. Histogram-based metrics (mean, median, variance, entropy) from estimated parameters were considered to investigate differences (Mann-Whitney U-test, α = 0.05) in estimated tumor microstructure in SBCs characterized by low or high cell proliferation (Ki-67). Recurrence-free survival analyses were also performed to assess the ability of the microstructural parameters to stratify patients according to the risk of local recurrence (Kaplan-Meier curves, log-rank test α = 0.05). RESULTS: Refined microstructural markers revealed optimal capabilities in discriminating patients according to cell proliferation, achieving best results with mean values (p-values were 0.0383, 0.0284, 0.0284, 0.0468, and 0.0088 for ADC, R, vf, D, and ρapp, respectively). Recurrence-free survival analyses showed significant differences between populations at high and low risk of local recurrence as stratified by entropy values of estimated microstructural parameters (p = 0.0110). CONCLUSION: Patient-specific microstructural information was non-invasively derived providing potentially useful tools for SBC treatment personalization and optimization in particle therapy.
Subject(s)
Chordoma , Head and Neck Neoplasms , Skull Base Neoplasms , Humans , Diffusion Magnetic Resonance Imaging/methods , Chordoma/diagnostic imaging , Chordoma/radiotherapy , Chordoma/pathology , Retrospective Studies , Ki-67 Antigen , SkullABSTRACT
PURPOSE: To investigate the static magnetic field generated by a proton pencil beam as a candidate for range verification by means of Monte Carlo simulations, thereby improving upon existing analytical calculations. We focus on the impact of statistical current fluctuations and secondary protons and electrons. METHODS: We considered a pulsed beam (10 µ ${\umu}$ s pulse duration) during the duty cycle with a peak beam current of 0.2 µ $\umu$ A and an initial energy of 100 MeV. We ran Geant4-DNA Monte Carlo simulations of a proton pencil beam in water and extracted independent particle phase spaces. We calculated longitudinal and radial current density of protons and electrons, serving as an input for a magnetic field estimation based on a finite element analysis in a cylindrical geometry. We made sure to allow for non-solenoidal current densities as is the case of a stopping proton beam. RESULTS: The rising proton charge density toward the range is not perturbed by energy straggling and only lowered through nuclear reactions by up to 15%, leading to an approximately constant longitudinal current. Their relative low density however (at most 0.37 protons/mm3 for the 0.2 µ ${\umu}$ A current and a beam cross-section of 2.5 mm), gives rise to considerable current density fluctuations. The radial proton current resulting from lateral scattering and being two orders of magnitude weaker than the longitudinal current is subject to even stronger fluctuations. Secondary electrons with energies above 10 eV, that far outnumber the primary protons, reduce the primary proton current by only 10% due to their largely isotropic flow. A small fraction of electrons (<1%), undergoing head-on collisions, constitutes the relevant electron current. In the far-field, both contributions to the magnetic field strength (longitudinal and lateral) are independent of the beam spot size. We also find that the nuclear reaction-related losses cause a shift of 1.3 mm to the magnetic field profile relative to the actual range, which is further enlarged to 2.4 mm by the electron current (at a distance of ρ = 50 $\rho =50$ mm away from the central beam axis). For ρ > 45 $\rho >45$ mm, the shift increases linearly. While the current density variations cause significant magnetic field uncertainty close to the central beam axis with a relative standard deviation (RSD) close to 100%, they average out at a distance of 10 cm, where the RSD of the total magnetic field drops below 2%. CONCLUSIONS: With the small influence of the secondary electrons together with the low RSD, our analysis encourages an experimental detection of the magnetic field through sensitive instrumentation, such as optical magnetometry or SQUIDs.
Subject(s)
Proton Therapy , Protons , Proton Therapy/methods , Finite Element Analysis , Magnetic Fields , Monte Carlo Method , DNA , Radiotherapy DosageABSTRACT
PURPOSE /OBJECTIVE: To quantify benefits of robust optimization on multiple 4DCT acquisitions combined with off-line treatment adaptation for neoadjuvant carbon ion therapy (CIRT) of pancreatic cancer. MATERIAL/METHODS: For 10 previously treated patients, 4DCTs were acquired around -15 (CTPlan), -5 (RE1), -1 (RE2) and +6 (RE3) days from RT start. Treatment plans were newly optimized to a dose prescription of 38.4 Gy(RBE) (8 fractions) with a constraint of 38 Gy(RBE) to 1% of the gastrointestinal organs at risk volume (D1%). Three strategies were tested: (A) robust optimization on CTPlan maximum exhale (0Ex) with 3 mm set-up, 3% range uncertainty, including 30%-inhale; (B) addition of the RE1-0Ex scenario; (C) plan recalculation at each REi and adaptation (RPi) according to deviation thresholds from clinical goals. The cumulative variation of target coverage and GI-OARs doses was evaluated. Duodenum contours of all 4DCTs of each patient were registered on CTPlan-0Ex. The capacity of pre-RT acquisitions to predict duodenum position was investigated by computing the intersection of contours at CTplan, RE1, or their union, with respect to subsequent 4DCTs and the CTV, coupled with increasing margin. RESULTS: (A) No recalculation exceeded the D1% constraint. (B) The inclusion of RE1-0Ex in the optimization problem improved inter-fraction robustness on a patient-specific basis, but was non-significant on average. (C) Half of the plans would be re-optimized to recover target coverage and/or minimize duodenum dose, at least once. A significant difference was observed between pre-RT duodenum contours when intersecting subsequent contours, either with a margin expansion. CONCLUSION: Anatomical variations highlighted at multiple REi proved that a fast and efficient online adaptation is essential to optimize treatment quality of CIRT for pancreatic cancer.
Subject(s)
Heavy Ion Radiotherapy , Pancreatic Neoplasms , Proton Therapy , Radiotherapy, Intensity-Modulated , Humans , Radiotherapy Planning, Computer-Assisted/methods , Radiotherapy Dosage , Organs at Risk , Pancreatic Neoplasms/diagnostic imaging , Pancreatic Neoplasms/radiotherapy , Radiotherapy, Intensity-Modulated/methods , Proton Therapy/methods , Pancreatic NeoplasmsSubject(s)
Proton Therapy , Radiation Oncology , Humans , Biometry , Medical Oncology , Magnetic Resonance ImagingABSTRACT
INTRODUCTION: Ocular biometry in Ocular Proton Therapy (OPT) currently relies on a generic geometrical eye model built by referencing surgically implanted markers. An alternative approach based on image fusion of volumetric Magnetic Resonance Imaging (MRI) and panoramic fundus photography was investigated. MATERIALS AND METHODS: Eighteen non-consecutive uveal melanoma (UM) patients, who consented for an MRI and had their tumour base visible on panoramic fundus photography, were included in this comparative analysis. Through generating digitally-reconstructed projections from MRI images using the Lambert azimuthal equal-area projection, 2D-3D image fusion between fundus photography and an eye model delineated on MRI scans was achieved and allowed for a novel definition of the target base (MRI + FCTV). MRI + FCTV was compared with MRI-only delineation (MRIGTV) and the conventional (EyePlan) target definition (EPCTV). RESULTS: The combined use of fundus photography and MRI to define tumour volumes reduced the average discrepancies by almost 65% with respect to the MRI only tumour definitions when comparing with the conventionally planned EPCTV. With the proposed method, shallow sub-retinal tumour infiltration, otherwise invisible on MRI, can be included in the target volume definition. Moreover, a novel definition of the fovea location improves the accuracy and personalisation of the 3D eye model. CONCLUSION: MRI and fundus image fusion overcomes some of the limitations of ophthalmological MRI for tumour volume definition in OPT. This novel eye tumour modelling method might improve treatment planning personalisation, allowing to better anticipate which patients could benefit from prophylactic treatment protocols for radiation induced maculopathy.
Subject(s)
Proton Therapy , Uveal Neoplasms , Biometry , Humans , Imaging, Three-Dimensional/methods , Magnetic Resonance Imaging/methods , Uveal Neoplasms/diagnostic imaging , Uveal Neoplasms/radiotherapyABSTRACT
MRI can help to categorize tissues as malignant or non-malignant both anatomically and functionally, with a high level of spatial and temporal resolution. This non-invasive imaging modality has been integrated with radiotherapy in devices that can differentially target the most aggressive and resistant regions of tumours. The past decade has seen the clinical deployment of treatment devices that combine imaging with targeted irradiation, making the aspiration of integrated MRI-guided radiotherapy (MRIgRT) a reality. The two main clinical drivers for the adoption of MRIgRT are the ability to image anatomical changes that occur before and during treatment in order to adapt the treatment approach, and to image and target the biological features of each tumour. Using motion management and biological targeting, the radiation dose delivered to the tumour can be adjusted during treatment to improve the probability of tumour control, while simultaneously reducing the radiation delivered to non-malignant tissues, thereby reducing the risk of treatment-related toxicities. The benefits of this approach are expected to increase survival and quality of life. In this Review, we describe the current state of MRIgRT, and the opportunities and challenges of this new radiotherapy approach.
Subject(s)
Neoplasms , Radiotherapy, Image-Guided , Humans , Magnetic Resonance Imaging/methods , Neoplasms/diagnostic imaging , Neoplasms/radiotherapy , Quality of Life , Radiotherapy Planning, Computer-Assisted/methods , Radiotherapy, Image-Guided/methodsABSTRACT
Integrating the information coming from biological samples with digital data, such as medical images, has gained prominence with the advent of precision medicine. Research in this field faces an ever-increasing amount of data to manage and, as a consequence, the need to structure these data in a functional and standardized fashion to promote and facilitate cooperation among institutions. Inspired by the Minimum Information About BIobank data Sharing (MIABIS), we propose an extended data model which aims to standardize data collections where both biological and digital samples are involved. In the proposed model, strong emphasis is given to the cause-effect relationships among factors as these are frequently encountered in clinical workflows. To test the data model in a realistic context, we consider the Continuous Observation of SMOking Subjects (COSMOS) dataset as case study, consisting of 10 consecutive years of lung cancer screening and follow-up on more than 5000 subjects. The structure of the COSMOS database, implemented to facilitate the process of data retrieval, is therefore presented along with a description of data that we hope to share in a public repository for lung cancer screening research.
Subject(s)
Early Detection of Cancer , Lung Neoplasms , Databases, Factual , Humans , Information Storage and Retrieval , Lung Neoplasms/diagnostic imaging , SmokingABSTRACT
PURPOSE: In this study, we investigate the use of magnetic resonance imaging (MRI) for the clinical evaluation of gating treatment robustness in carbon-ion radiotherapy (CIRT) of pancreatic cancer. Indeed, MRI allows radiation-free repeated scans and fast dynamic sequences for time-resolved (TR) imaging (cine-MRI), providing information on inter- and intra-fraction cycle-to-cycle variations of respiratory motion. MRI can therefore support treatment planning and verification, overcoming the limitations of the current clinical standard, that is, four-dimensional computed tomography (4DCT), which describes an "average" breathing cycle neglecting breathing motion variability. METHODS: We integrated a technique to generate a virtual CT (vCT) from 3D MRI with a method for 3D reconstruction from 2D cine-MRI, to produce TR vCTs for dose recalculations. For eight patients, the method allowed evaluating inter-fraction variations at end-exhale and intra-fraction cycle-to-cycle variability within the gating window in terms of tumor displacement and dose to the target and organs at risk. RESULTS: The median inter-fraction tumor motion was in the range 3.33-12.16 mm, but the target coverage was robust (-0.4% median D95% variation). Concerning cycle-to-cycle variations, the gating technique was effective in limiting tumor displacement (1.35 mm median gating motion) and corresponding dose variations (-3.9% median D95% variation). The larger exposure of organs at risk (duodenum and stomach) was caused by inter-fraction motion, whereas intra-fraction cycle-to-cycle dose variations were limited. CONCLUSIONS: This study proposed a method for the generation of TR vCTs from MRI, which enabled an off-line evaluation of gating treatment robustness and suggested its feasibility to support treatment planning of pancreatic tumors in CIRT.