Image-guided Radiotherapy Research Articles

Deep learning enables MV-based real-time image guided radiation therapy for prostate cancer patients

Objective. Using MV images for real-time image guided radiation therapy (IGRT) is ideal as it does not require additional imaging equipment, adds no additional imaging dose and provides motion data in the treatment beam frame of reference. However, accurate tracking using MV images is challenging due to low contrast and modulated fields. Here, a novel real-time marker tracking system based on a convolutional neural network (CNN) classifier was developed and evaluated on retrospectively acquired patient data for MV-based IGRT for prostate cancer patients. Approach. MV images, acquired from 29 volumetric modulated arc therapy (VMAT) prostate cancer patients treated in a multi-institutional clinical trial, were used to train and evaluate a CNN-based marker tracking system. The CNN was trained using labelled MV images from 9 prostate cancer patients (35 fractions) with implanted markers. CNN performance was evaluated on an independent cohort of unseen MV images from 20 patients (78 fractions), using a Precision–Recall curve (PRC), area under the PRC plot (AUC) and sensitivity and specificity. The accuracy of the tracking system was evaluated on the same unseen dataset and quantified by calculating mean absolute (±1 SD) and [1st, 99th] percentiles of the geometric tracking error in treatment beam co-ordinates using manual identification as the ground truth. Main results. The CNN had an AUC of 0.99, sensitivity of 98.31% and specificity of 99.87%. The mean absolute geometric tracking error was 0.30 ± 0.27 and 0.35 ± 0.31 mm in the lateral and superior–inferior directions of the MV images, respectively. The [1st, 99th] percentiles of the error were [−1.03, 0.90] and [−1.12, 1.12] mm in the lateral and SI directions, respectively. Significance. The high classification performance on unseen MV images demonstrates the CNN can successfully identify implanted prostate markers. Furthermore, the sub-millimetre accuracy and precision of the marker tracking system demonstrates potential for adaptation to real-time applications.

The use of in-vivo dosimetry to identify head and neck cancer patients needing adaptive radiotherapy

Background and purposeHead and neck cancer (HNC) patients experiencing anatomical changes during their radiotherapy (RT) course may benefit from adaptive RT (ART). We investigated the sensitivity of an electronic portal imaging device (EPID)-based in-vivo dosimetry (EIVD) system to detect patients that require ART and identified its limitations. Materials and methodsA retrospective study was conducted for 182 HNC patients: laryngeal cancer without elective lymph nodes (group A), postoperative RT (group B) and primary RT including elective lymph nodes (group C). The effect of anatomical changes on the dose distribution and volumetric changes was quantified. The receiver operating characteristic curve was used to obtain the optimal cut-off value for the gamma passing rate (%GP) with a dose difference of 3% and a distance to agreement of 3 mm. ResultsFifty HNC patients receiving ART were analyzed: 1 in group A, 10 in group B and 39 in group C. Failed fractions (FFs) occurred in 1/1, 6/10 and 23/39 cases before ART in group A, B and C respectively. In the four cases in group B without FFs, only minor dosimetric changes were observed. One of the cases in group C without FFs had significant dosimetric changes (false negative). Three cases received ART because of clinical reasons that cannot be detected by EIVD. The optimal cut-off value for the %GP was 95%/95.2% for old/new generation machines respectively. ConclusionEIVD combined with 3D imaging techniques can be synergistic in the detection of anatomical changes in HNC patients who benefit from ART.

Long-term experience in quality assurance of on-rail computed tomography systems for image-guided radiotherapy using in-house multifunctional phantoms

To report the long-term quality assurance (QA) experience of an on-rail computed tomography (CT) system for image-guided radiotherapy using an in-house phantom. An on-rail CT system combining the Elekta Synergy and Canon Aquilion LB was used. The treatment couch was shared by the linear accelerators and CT, and the couch was rotated by 180° when using the on-rail-CT system to ensure that the CT direction was toward the head. All QA analyses were performed by radiation technologists on CBCT or on-rail CT images of the in-house phantom. The CBCT center accuracy from the linac laser, couch rotational accuracy (CBCT center vs. on-rail CT center), horizontal accuracy by CT gantry shift, and remote couch shift accuracy were evaluated. This study reported the QA status of the system during the period 2014-2021. The absolute mean accuracy of couch rotation was 0.4 ± 0.28mm, 0.44 ± 0.36mm, and 0.37 ± 0.27mm in the SI, RL, and AP directions, respectively. Horizontal and remote movement accuracies of the treatment couch were also within 0.5mm of the absolute mean value. A decrease in the accuracy of couch rotation was also observed due to aging deterioration of related parts caused by the frequent use of couch rotation. The three-dimensional accuracy of on-rail CT systems derived mainly from treatment couches can be maintained within 0.5mm with appropriate accuracy assurance for at least > 8years.

A personal perspective on the development and future of cone-beam CT for image-guided radiotherapy.

The development of cone-beam CT guided radiotherapy has transformed radiation oncology in the 20 years since it was first released commercially. The technological pace of change has spurred a massive transformation in our daily clinical practice, forced us to evolve our approach to multi- and inter-disciplinary collaboration, and enabled new treatment paradigms. Further progress in integrating quantitative CT in these robotic platforms promises to do even more by "burying the complexity" of radiotherapy and leveraging the expanding digital fabric that uses machine learning approaches to bring semi-automated expertise to bear on the issues of expertise and quality. This is the only way we will be able to respond to the massive global shortfall in high-quality radiotherapy services across the globe.

Inter-fraction deformable image registration using unsupervised deep learning for CBCT-guided abdominal radiotherapy

Objective. CBCTs in image-guided radiotherapy provide crucial anatomy information for patient setup and plan evaluation. Longitudinal CBCT image registration could quantify the inter-fractional anatomic changes, e.g. tumor shrinkage, and daily OAR variation throughout the course of treatment. The purpose of this study is to propose an unsupervised deep learning-based CBCT-CBCT deformable image registration which enables quantitative anatomic variation analysis. Approach. The proposed deformable registration workflow consists of training and inference stages that share the same feed-forward path through a spatial transformation-based network (STN). The STN consists of a global generative adversarial network (GlobalGAN) and a local GAN (LocalGAN) to predict the coarse- and fine-scale motions, respectively. The network was trained by minimizing the image similarity loss and the deformable vector field (DVF) regularization loss without the supervision of ground truth DVFs. During the inference stage, patches of local DVF were predicted by the trained LocalGAN and fused to form a whole-image DVF. The local whole-image DVF was subsequently combined with the GlobalGAN generated DVF to obtain the final DVF. The proposed method was evaluated using 100 fractional CBCTs from 20 abdominal cancer patients in the experiments and 105 fractional CBCTs from a cohort of 21 different abdominal cancer patients in a holdout test. Main Results. Qualitatively, the registration results show good alignment between the deformed CBCT images and the target CBCT image. Quantitatively, the average target registration error calculated on the fiducial markers and manually identified landmarks was 1.91 ± 1.18 mm. The average mean absolute error, normalized cross correlation between the deformed CBCT and target CBCT were 33.42 ± 7.48 HU, 0.94 ± 0.04, respectively. Significance. In summary, an unsupervised deep learning-based CBCT-CBCT registration method is proposed and its feasibility and performance in fractionated image-guided radiotherapy is investigated. This promising registration method could provide fast and accurate longitudinal CBCT alignment to facilitate inter-fractional anatomic changes analysis and prediction.

Prostatectomy Bed Image-guided Dose-escalated Salvage Radiotherapy (SPIDER): An International Multicenter Retrospective Study

BackgroundManagement of macroscopic local recurrence (MLR) after radical prostatectomy is a challenging situation with no standardized approach. ObjectiveThe objective of our study was to assess the efficacy and safety of functional image-guided salvage radiotherapy (SRT) in patients with MLR in the prostate bed. Design, setting, and participantsIn this international multicenter retrospective study across 16 European centers, eligible patients were initially treated by radical prostatectomy (RP) with or without pelvic lymph node dissection for localized or locally advanced adenocarcinoma of the prostate. Outcome measurements and statistical analysisProstate-specific antigen (PSA) measured 4 wk after RP was <0.1 ng/ml. All patients presented a biochemical relapse after RP defined by an increase in PSA level of ≥0.2 ng/ml on two successive measures. Only patients with an MLR lesion in the prostatectomy bed visualized on functional imaging (multiparametric magnetic resonance imaging, positron emission tomography/computed tomography [PET/CT] choline, or PET/CT prostate-specific membrane antigen) were eligible. Patients with lymph node, bone, or visceral dissemination at restaging imaging (CT and/or bone scintigraphy and/or magnetic resonance imaging and/or PET) were excluded. Dose escalation was defined as a dose of >66 Gy prescribed to the prostate bed or to MLR. Toxicities were classified using the Common Terminology Criteria for Adverse Events scale, version 4.03. The primary endpoint was progression-free survival (PFS). Secondary outcomes were metastasis-free survival (MPFS), biochemical progression-free survival, and overall survival. Genitourinary (GU) and gastrointestinal (GI) toxicities were analyzed. Results and limitationsBetween January 2000 and December 2019, 310 patients received at least one dose escalation on MLR and 25 patients did not receive any dose escalation. The median PSA level before SRT was 0.63 ng/ml (interquartile range [IQR], 0.27–1.7). The median follow-up was 54 mo (IQR, 50–56). Five-year PFS and MPFS were 70% (95% confidence interval [CI]: [64; 75]) and 84% (95% CI: [78; 88]), respectively. Grade ≥2 GU and GI late toxicities were observed in 43 (12%) and 11 (3%) patients, respectively. When the prescribed dose on the MLR lesion was ≥72 Gy, an improvement in 5-yr PFS was found for patients received at least one dose escalation (73% [95% CI: 65–79]) vs 60% [95% CI: 48; 70]; p = 0.03). ConclusionsIn this contemporary study integrating functional imaging data, we found potential efficacy of SRT with dose escalation ≥72 Gy for patients with MLR in the prostate bed and with an acceptable toxicity profile. Prospective data exploring this MLR dose escalation strategy are awaited. Patient summaryIn this report, we looked at the outcomes from salvage radiotherapy for prostate cancer and macroscopic relapse in a large European population. We found that outcomes varied with prostate-specific antigen at relapse, Gleason score, and dose escalation. We found potential efficacy of salvage radiotherapy with dose escalation for macroscopic relapse in the prostate bed, with an acceptable toxicity profile.

A GPU-accelerated Monte Carlo dose computation engine for small animal radiotherapy.

Accurate dose computation is critical in precision small animal radiotherapy. The Monte Carlo simulation method is the gold standard for radiation dose computation but has not been widely implemented in practice due to its low computation efficiency. This study aims to develop a GPU-accelerated radiation dose engine (GARDEN) based on the Monte Carlo simulation method for fast and accurate dose computation. In the GARDEN simulation, Compton scattering, Rayleigh scattering, and photoelectric effect were considered. The Woodcock tracking algorithm and GPU-specific acceleration techniques were used to obtain a high computational efficiency. Benchmark studies against both Geant4 simulations and experimental measurements were performed for various phantoms and beams. Finally, a conformal arc treatment plan was designed for a lung tumor to further evaluate the accuracy and efficiency in small animal radiotherapy. The engine attained a speed-up of 1232 times in a homogeneous water phantom and 935 times in a water-bone-lung heterogeneous phantom when compared with Geant4. Both the depth-dose curves and cross-sectional dose profiles for various radiation field sizes showed a great match between measurements and the GARDEN calculations. For in vivo dose validation, the differences between calculations and measurements in the mouse thorax and abdomen were 2.50%±1.50% and 1.56%±1.40%, respectively. The computation time for an arc treatment plan delivered from 36 angles was 2 s at a<1% uncertainty level using an NVIDIA GeForce RTX 2060 SUPER GPU. When compared with Geant4, the 3D gamma comparison passing rate was 98.7% at 2%/0.3mm criteria. GARDEN can perform fast and accurate dose computations in heterogeneous tissue environments and is expected to play a vital role in image-guided precision small animal radiotherapy.

Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks.

Cone beam computed tomography (CBCT) is often employed on radiation therapy treatment devices (linear accelerators) used in image-guided radiation therapy (IGRT). For each treatment session, it is necessary to obtain the image of the day in order to accurately position the patient and to enable adaptive treatment capabilities including auto-segmentation and dose calculation. Reconstructed CBCT images often suffer from artifacts, in particular those induced by patient motion. Deep-learning based approaches promise ways to mitigate such artifacts. We propose a novel deep-learning based approach with the goal to reduce motion induced artifacts in CBCT images and improve image quality. It is based on supervised learning and includes neural network architectures employed as pre- and/or post-processing steps during CBCT reconstruction. Our approach is based on deep convolutional neural networks which complement the standard CBCT reconstruction, which is performed either with the analytical Feldkamp-Davis-Kress (FDK) method, or with an iterative algebraic reconstruction technique (SART-TV). The neural networks, which are based on refined U-net architectures, are trained end-to-end in a supervised learning setup. Labeled training data are obtained by means of a motion simulation, which uses the two extreme phases of 4D CT scans, their deformation vector fields, as well as time-dependent amplitude signals as input. The trained networks are validated against ground truth using quantitative metrics, as well as by using real patient CBCT scans for a qualitative evaluation by clinicalexperts. The presented novel approach is able to generalize to unseen data and yields significant reductions in motion induced artifacts as well as improvements in image quality compared with existing state-of-the-art CBCT reconstruction algorithms (up to +6.3 dB and +0.19 improvements in peak signal-to-noise ratio, PSNR, and structural similarity index measure, SSIM, respectively), as evidenced by validation with an unseen test dataset, and confirmed by a clinical evaluation on real patient scans (up to 74% preference for motion artifact reduction over standard reconstruction). For the first time, it is demonstrated, also by means of clinical evaluation, that inserting deep neural networks as pre- and post-processing plugins in the existing 3D CBCT reconstruction and trained end-to-end yield significant improvements in image quality and reduction of motionartifacts.

A Review of 3D-2D Registration Methods and Applications based on Medical Images

The registration of preoperative three-dimensional (3D) medical images with intraoperative two-dimensional (2D) data is a key technology for image-guided radiotherapy, minimally invasive surgery, and interventional procedures. In this paper, we review 3D-2D registration methods using computed tomography (CT) and magnetic resonance imaging (MRI) as preoperative 3D images and ultrasound, X-ray, and visible light images as intraoperative 2D images. The 3D-2D registration techniques are classified into intensity-based, structure-based, and gradient-based according to the different registration features. In addition, we investigated the different application scenarios of this registration technology in medical clinical treatment, which can be divided into disease diagnosis, surgical guidance and postoperative evaluation, and also investigated the evaluation method of 3D-2D registration effect.

Cone-beam computed tomography (CBCT) dose optimization technique and image quality assessment scoring.

Linear accelerator (LINAC) embedded with kV source-imager system is capable to do image-guided radiotherapy. The only disadvantage of cone-beam computed tomography image acquisition during treatment is the extra radiation dose to the patient. The aim of this study is to optimize the CBCT imaging doses likely to be received by the patient undergoing radiotherapy without affecting image quality. The imaging dose to the patient was estimated on CTDI phantoms. The effect of additional filters of different materials (copper, brass, aluminum of thickness 0.1 mm each) was evaluated to find the optimized dose imaging technique. For the pelvis, a single imaging protocol available on the machine was used, whereas for the head and neck region, two protocols, high-quality head and standard-dose head were used. The image quality was assessed on CATPHAN-504 phantom using Owl CATPHAN® QA online tool. A new term "Image Assessment score" (IAS) was introduced to evaluate the image quality. In the pelvis protocol, CBCT imaging doses with an additional 0.1-mm brass, copper, and aluminum filter were measured to be reduced by 7.1%, 4.7%, and 2.5%, respectively, whereas for high-quality head protocol, the dose reduction was 25.4% (with brass filter), 22% (with copper filter), and 3.1% (with aluminum filter). For the standard-dose head protocol, doses were reduced by 7.5%, 2.8%, and 2.1% with additional 0.1-mm brass copper and aluminum filters, respectively. Acceptable image quality was observed with all the filters. Although the reconstructed images were found somewhat noisier, they did not affect the purpose of imaging, that is, treatment position verification. It was observed that these extra filters further reduce the imaging dose without much affecting the image quality.

Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy

Unlike the high imaging radiation dose of computed tomography (CT), cone-beam CT (CBCT) has smaller radiation dose and presents less harm to patients. Therefore, CBCT is often used for target delineation, dose planning, and postoperative evaluation in the image-guided radiotherapy (IGRT) of various cancers. In the process of IGRT, CBCT images usually need to be collected multiple times in a radiotherapy stage for postoperative evaluation. The effectiveness of radiotherapy is measured by comparing and analyzing the registered CBCT and the source CT image obtained before radiotherapy. Hence, the registration of CBCT and CT is the most important step in IGRT. CBCT images usually have poor visual effects due to the small imaging dose used, which adversely affects the registration performance. In this paper, we propose a novel adaptive visual saliency feature enhancement method for CBCT in IGRT. Firstly, we denoised CBCT images using a structural similarity based low-rank approximation model (SSLRA) and then enhanced the denoised results with a visual saliency feature enhancement (VSFE)-based method. Experimental results show that the enhancement performance of the proposed method is superior to the comparison enhancement algorithms in visual objective comparison. In addition, the extended experiments prove that the proposed enhancement method can improve the registration accuracy of CBCT and CT images, demonstrating their application prospects in IGRT-based cancer treatment.

Technical note: Comprehensive evaluations of gantry and couch rotation isocentricities for implementing proton stereotactic radiosurgery.

Mechanical accuracy should be verified before implementing a proton stereotactic radiosurgery (SRS) program. Linear accelerator (Linac)-based SRS systems often use electronic portal imaging devices (EPIDs) to verify beam isocentricity. Because proton therapy systems do not have EPID, beam isocentricity tests of proton SRS may still rely on films, which are not efficient. To validate that our proton SRS system meets mechanical precision requirements and to present an efficient method to evaluate the couch and gantry's rotational isocentricity for our proton SRS system. A dedicated applicator to hold brass aperture for proton SRS system was designed. The mechanical precision of the system was tested using a metal ball and film for 11 combinations of gantry and couch angles. A more efficient quality assurance (QA) procedure was developed, which used a scintillator device to replace the film. The couch rotational isocentricity tests were performed using orthogonal kV x-rays with the couch rotated isocentrically to five positions (0°, 315°, 270°, 225°, and 180°). At each couch position, the distance between the metal ball in kV images and the imaging isocenter was measured. The gantry isocentricity tests were performed using a cone-shaped scintillator and proton beams at five gantry angles (0°, 45°, 90°, 135°, and 180°), and the isocenter position and the distance of each beam path to the isocenter were obtained. Daily QA procedure was performed for 1month to test the robustness and reproducibility of the procedure. The gantry and couch rotational isocentricity exhibited sub-mm precision, with most measurements within ±0.5mm. The 1-month QA results showed that the procedure was robust and highly reproducible to within ±0.2mm. The gantry isocentricity test using the cone-shaped scintillator was accurate and sensitive to variations of ±0.2mm. The QA procedure was efficient enough to be completed within 30min. The 1-month isocentricity position variations were within 0.5mm, which demonstrating that the overall proton SRS system was stable and precise. The proton SRS Winston-Lutz QA procedure using a cone-shaped scintillator was efficient and robust. We were able to verify radiation delivery could be performed with sub-mm mechanical precision.

Radiotherapy delivery error detection with electronic portal imaging device (EPID)-based in vivo dosimetry.

Radiotherapy delivery error detection with electronic portal imaging device (EPID)-based in vivo dosimetry.

TOPAS-imaging: extensions to the TOPAS simulation toolkit for medical imaging systems

Objective. The TOol for PArticle Simulation (TOPAS) is a Geant4-based Monte Carlo software application that has been used for both research and clinical studies in medical physics. So far, most users of TOPAS have focused on radiotherapy-related studies, such as modeling radiation therapy delivery systems or patient dose calculation. Here, we present the first set of TOPAS extensions to make it easier for TOPAS users to model medical imaging systems. Approach. We used the extension system of TOPAS to implement pre-built, user-configurable geometry components such as detectors (e.g. flat-panel and multi-planar detectors) for various imaging modalities and pre-built, user-configurable scorers for medical imaging systems (e.g. digitizer chain). Main results. We developed a flexible set of extensions that can be adapted to solve research questions for a variety of imaging modalities. We then utilized these extensions to model specific examples of cone-beam CT (CBCT), positron emission tomography (PET), and prompt gamma (PG) systems. The first of these new geometry components, the FlatImager, was used to model example CBCT and PG systems. Detected signals were accumulated in each detector pixel to obtain the intensity of x-rays penetrating objects or prompt gammas from proton-nuclear interaction. The second of these new geometry components, the RingImager, was used to model an example PET system. Positron–electron annihilation signals were recorded in crystals of the RingImager and coincidences were detected. The simulated data were processed using corresponding post-processing algorithms for each modality and obtained results in good agreement with the expected true signals or experimental measurement. Significance. The newly developed extension is a first step to making it easier for TOPAS users to build and simulate medical imaging systems. Together with existing TOPAS tools, this extension can help integrate medical imaging systems with radiotherapy simulations for image-guided radiotherapy.

Combined radiation- and immune checkpoint-inhibitor-induced pneumonitis – The challenge to predict and detect overlapping immune-related adverse effects from evolving laboratory biomarkers and clinical imaging

The risk of overlapping pulmonary toxicity induced by thoracic radio(chemo)therapy and immune checkpoint inhibitor therapy in the treatment of patients suffering from non-small cell lung cancer (NSCLC) is one important challenge in successful radioimmunotherapy. In the present opinion we highlight factors that we find important to be considered before treatment initiation, during the treatment sequence, and after treatment completion combined or sequential application of radio(chemo)therapy and immune checkpoint inhibitor therapy. A major aim is to optimize the therapeutic index and to avoid immune related adverse effects. The goals in the future will be focused not only on identifying patients already in the pretreatment phase who could benefit from this complex treatment, but also in identifying patients, who are most likely to have higher grade toxicity. In this respect, proper assessment of clinical performance status, monitoring for the presence of certain comorbidities, evaluation of laboratory parameters such as TGF-α and IL-6 levels, human leukocyte antigens (HLA), and consideration of other potential biomarkers which will evolve in near future are essential. Likewise, the critical parameters must be monitored during the treatment phase and follow-up care to detect potential side effects in time. With the help of high-end imaging which is already used on a daily basis in image guided radiotherapy (IGRT) for intensity modulated radiotherapy (IMRT), its advanced form volumetric modulated arc therapy (VMAT), and adaptive radiation therapy (ART), clinically relevant changes in lung tissue can be detected at an early stage of disease. Concurrent radiotherapy and immunotherapy requires a special focus on adverse events, particularly of the lung, but, when properly approached and applied, it may offer new perspectives for patients with locally advanced NSCLC to be seriously considered as a curative option.

109P Setting up 4D-CT based image guided radiotherapy (IGRT) for locally advanced lung cancer: Is it safe to reduce PTV margin for dosimetric benefit?

109P Setting up 4D-CT based image guided radiotherapy (IGRT) for locally advanced lung cancer: Is it safe to reduce PTV margin for dosimetric benefit?

Dynamic Cross-Task Representation Adaptation for Clinical Targets Co-Segmentation in CT Image-Guided Post-Prostatectomy Radiotherapy.

Adjuvant and salvage radiotherapy after radical prostatectomy requires precise delineations of prostate bed (PB), i.e., the clinical target volume, and surrounding organs at risk (OARs) to optimize radiotherapy planning. Segmenting PB is particularly challenging even for clinicians, e.g., from the planning computed tomography (CT) images, as it is an invisible/virtual target after the operative removal of the cancerous prostate gland. Very recently, a few deep learning-based methods have been proposed to automatically contour non-contrast PB by leveraging its spatial reliance on adjacent OARs (i.e., the bladder and rectum) with much more clear boundaries, mimicking the clinical workflow of experienced clinicians. Although achieving state-of-the-art results from both the clinical and technical aspects, these existing methods improperly ignore the gap between the hierarchical feature representations needed for segmenting those fundamentally different clinical targets (i.e., PB and OARs), which in turn limits their delineation accuracy. This paper proposes an asymmetric multi-task network integrating dynamic cross-task representation adaptation (i.e., DyAdapt) for accurate and efficient co-segmentation of PB and OARs in one-pass from CT images. In the learning-to-learn framework, the DyAdapt modules adaptively transfer the hierarchical feature representations from the source task of OARs segmentation to match up with the target (and more challenging) task of PB segmentation, conditioned on the dynamic inter-task associations learned from the learning states of the feed-forward path. On a real-patient dataset, our method led to state-of-the-art results of PB and OARs co-segmentation. Code is available at https://github.com/ladderlab-xjtu/DyAdapt.

Infection after prostatic transrectal fiducial marker implantation for image guided radiation therapy

PurposeThe aim of this retrospective study is to assess the risk of infection after transrectal ultrasound-guided fiducial marker insertion for image-guided radiotherapy of prostate cancer. Material and methodsBetween January 2016 and December 2020, 829 patients scheduled for intensity-modulated radiotherapy for prostate cancer had an intraprostatic fiducial marker transrectal implantation under ultrasound guidance by radiation-oncologists specialized in brachytherapy. Patients received standard oral prophylactic antibiotic with quinolone. If Gram negative bacteria resistant to quinolone were detected at the time of the prostate cancer biopsies, the antibioprophylaxis regimen was modified accordingly. The resistance to quinolone screening test was not repeated before fiducial marker insertion. Infectious complications were assessed with questionnaires at the time of CT-planning and medical record reviewed. Toxicity was evaluated according to CTCAE v5.0. ResultsThe median time between fiducial marker implantation and evaluation was 10 days (range: 0–165 days). Four patients (0.48%) developed urinary tract infection related to the procedure, mostly with Gram-negative bacteria resistant to quinolone (75%). Three had a grade 2 infection, and one patient experienced a grade 3 urosepsis. The quinolone-resistance status was known for two patients (one positive and one negative) and was unknown for the other two patients prior to fiducial marker implantation. ConclusionIntraprostatic transrectal fiducial marker implantation for image-guided radiotherapy is well tolerated with a low rate of infection. With such a low rate of infection, there is no need to repeat the search of Gram-negative bacteria resistant to quinolone before fiducial marker implantation if it was done at the time of prostate biopsies. Optimal antibioprophylaxis should be adapted to the known status of Gram-negative bacteria resistant to quinolone.

Evaluation of fan-beam kilovoltage computed tomography image quality on a novel biological-guided radiotherapy platform.

A recently developed biology-guided radiotherapy platform, equipped with positron emission tomography (PET) and computed tomography (CT), provides both anatomical and functional image guidance for radiotherapy. This study aimed to characterize performance of the kilovoltage CT (kVCT) system on this platform using standard quality metrics measured on phantom and patient images, using CT simulator images as reference. Image quality metrics, including spatial resolution/modular transfer function (MTF), slice sensitivity profile (SSP), noise performance and image uniformity, contrast-noise ratio (CNR) and low-contrast resolution, geometric accuracy, and CT number (HU) accuracy, were evaluated on phantom images. Patient images were evaluated mainly qualitatively. On phantom images the MTF10% is about 0.68 lp/mm for kVCT in PET/CT Linac. The SSP agreed with nominal slice thickness within 0.7mm. The diameter of the smallest visible target (1% contrast) is about 5mm using medium dose mode. The image uniformity is within 2.0 HU. The geometric accuracy tests passed within 0.5mm. Relative to CT simulator images, the noise is generally higher and the CNR is lower in PET/CT Linac kVCT images. The CT number accuracy is comparable between the two systems with maximum deviation from the phantom manufacturer range within 25 HU. On patient images, higher spatial resolution and image noise are observed on PET/CT Linac kVCT images. Major image quality metrics of the PET/CT Linac kVCT were within vendor-recommended tolerances. Better spatial resolution but higher noise and better/comparable low contrast visibility were observed as compared to a CT simulator when images were acquired with clinical protocols.

Comparison of two pre-treatment verification methods for arc – IMRT plans

In radiotherapy, the application of the IMRT arc technique, a special technique of external beam irradiation, while providing greater ballistic accuracy and personalization of treatment, as well as better speed of execution, requires special attention to the development of the treatment plan and its verification.Therefore, tools and methods of physical-health analysis that allow verification of the accuracy of the delivery of the radiant beam in the stages prior to treatment are essential. At the unit of Medical Physics at Dimiccoli Hospital Barletta, a prospective experimental study was conducted based on the comparison of two different methods of pre-treatment verification in patients treated for prostate cancer with the aim of increasing the performance of the treatment workflow.A comparative evaluation of gamma analysis results on images acquired with a 2D flat panel detector EPID (Electronic Portal Imaging Device) on the one hand and a 2D ionizing chambers detector on the other hand was conducted. The study sample included 38 patients undergoing radiotherapy with IMRT between December 2021 and October 2022, and each subject was studied and verified with both methods.The results of gamma analysis conducted with the two methods of pre-treatment verification of patients with prostate cancer are superimposable. In addition, with EPID we have a significant saving of machine time and thus allows an increase in the number of patients undergoing daily radiotherapy.In light of internationally accepted dosimetric and radiation protection accuracy standards according to Decree 101/2020, pre-treatment verification of dose distributions, using a 2D flat panel detection system (EPID), was found to be in line with waiting list reduction goals.