Radiotherapy Quality Assurance in the PORTEC-3 (TROG 08.04) Trial

AIMS

Quality assurance in radiotherapy (QART) is essential to ensure the scientific integrity of a clinical trial. This paper reports the findings of the retrospective QART assessment for all centres that participated in PORTEC-3; a randomised controlled trial that compared pelvic radiotherapy with concurrent chemoradiotherapy to the pelvis followed by adjuvant chemotherapy. The trial showed an overall survival benefit for the addition of the chemotherapy in the management of women with high-risk endometrial cancer.

MATERIALS AND METHODS

Clinicians were invited to upload a randomly selected case/s treated at each of the participating sites. Panel reviewers analysed the contours to certify that the target volumes and organ at risk structures were contoured according to guidelines. The results were categorised into acceptable, minor variation, major variation or unevaluable. The radiotherapy plans were dosimetrically evaluated using the well-established Trans-Tasman Radiation Oncology Group (TROG) protocol.

RESULTS

Between August 2010 and January 2018, data from 146 patients of 686 consecutively treated patients were retrospectively reviewed. All 16 Australia and New Zealand and 71 of 77 international centres uploaded data for evaluation. In total, 3514 dosimetric and contour variables were reviewed. Of these, 3136 variables were deemed acceptable (89.2%), with 335 minor (9.6%) and 43 major variations (1.2%). Major contour variations included the clinical target volume vaginal vault, clinical target volume parametria and differential planning target volume vault expansion.

CONCLUSION

The results of the QART assessment confirmed high uniformity and low rates of both minor and major deviations in contouring and dosimetry in all sites. This supports the safe introduction of the PORTEC-3 treatment protocol into routine clinical practice.

A Systematic Review on the Role of the Perfusion Computed Tomography in Abdominal Cancer

Background and purpose

Perfusion Computed Tomography (CTp) is an imaging technique which allows quantitative and qualitative evaluation of tissue perfusion through dynamic CT acquisitions. Since CTp is still considered a research tool in the field of abdominal imaging, the aim of this work is to provide a systematic summary of the current literature on CTp in the abdominal region to clarify the role of this technique for abdominal cancer applications.

Materials and Methods

A systematic literature search of PubMed, Web of Science, and Scopus was performed to identify original articles involving the use of CTp for clinical applications in abdominal cancer since 2011. Studies were included if they reported original data on CTp and investigated the clinical applications of CTp in abdominal cancer.

Results

Fifty-seven studies were finally included in the study. Most of the included articles (33/57) dealt with CTp at the level of the liver, while a low number of studies investigated CTp for oncologic diseases involving UGI tract (8/57), pancreas (8/57), kidneys (3/57), and colon–rectum (5/57).

Conclusions

Our study revealed that CTp could be a valuable functional imaging tool in the field of abdominal oncology, particularly as a biomarker for monitoring the response to anti-tumoral treatment.

Beam modeling and commissioning for Monte Carlo photon beam on an Elekta Versa HD LINAC

PURPOSE

This study aims at analyzing beam data commissioning along with verifying Monte Carlo-based treatment planning system on the basis of the manufacturer guidelines for Elekta Versa HD Linear Accelerator. Moreover, evaluating the beam match process in terms of quality index, photon profile and multi leaf collimator (MLC) offset is aimed as well.

MATERIALS AND METHODS

The process of collecting beam data for Monaco 5.51 Treatment Planning System commissioning was done based on the instructions provided by the manufacturer as well as AAPM TG-106. Monte Carlo analysis was done for output factors in water, percent depth dose and beam profiles. A set of eight static and intensity modulated radiation therapy fields were used to verify the MLC parameters.

RESULTS

The difference between the measured and modeled penetrative quality (D₁₀) was achieved to be 0.54%. The output factors for 6 MV photon energy were measured and the difference between the measured and Monte Carlo output results was smaller than 1% for all the fields. The average percentage of passing the gamma criteria for commissioning test fields was (95+-4)%, however, the minimum agreement was 87.5% belonging to "7SEGA". Additionally, the agreement between both LINAC is 96%, however, the second LINAC reveals a positive offset in the point of approximately -4 cm on the x-axis at the crossplane.

CONCLUSION

Test commissioning was successfully verified using a homogeneous phantom for point dose measurements, post modelling MLC parameters and patient QA plans. All plan parameters pass the gamma criteria. 6 MV photon beam was successfully commissioned for Elekta VersaHD LINAC and is ready for clinical implementation.

Radiomics in surgical oncology: applications and challenges

Surgery is a curative treatment option for many patients with malignant tumors. Increased attention has focused on the combination of surgery with chemotherapy, as multimodality treatment has been associated with promising results in certain cancer types. Despite these data, there remains clinical equipoise on optimal timing and patient selection for neoadjuvant or adjuvant strategies. Radiomics, an emerging field involving the extraction of advanced features from radiographic images, has the potential to revolutionize oncologic treatment and contribute to the advance of personalized therapy by helping predict tumor behavior and response to therapy. This review analyzes and summarizes studies that use radiomics with machine learning in patients who have received neoadjuvant and/or adjuvant chemotherapy to predict prognosis, recurrence, survival, and therapeutic response for various cancer types. While studies in both neoadjuvant and adjuvant settings demonstrate above average performance on ability to predict progression-free and overall survival, there remain many challenges and limitations to widespread implementation of this technology. The lack of standardization of common practices to analyze radiomics, limited data sharing, and absence of auto-segmentation have hindered the inclusion and rapid adoption of radiomics in prospective, clinical studies.

Clinical applications of magnetic resonance imaging based functional and structural connectivity

Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.

Predicting Three-Dimensional Dose Distribution of Prostate Volumetric Modulated Arc Therapy Using Deep Learning

Background: Volumetric modulated arc therapy (VMAT) planning is a time-consuming process of radiation therapy. With a deep learning approach, 3D dose distribution can be predicted without the need for an actual dose calculation. This approach can accelerate the process by guiding and confirming the achievable dose distribution in order to reduce the replanning iterations while maintaining the plan quality. Methods: In this study, three dose distribution predictive models of VMAT for prostate cancer were developed, evaluated, and compared. Each model was designed with a different input data structure to train and test the model: (1) patient CT alone (PCT alone), (2) patient CT and generalized organ structure (PCTGOS), and (3) patient CT and specific organ structure (PCTSOS). The generative adversarial network (GAN) model was used as a core learning algorithm. The models were trained slice-by-slice using 46 VMAT plans for prostate cancer, and then used to predict and evaluate the dose distribution from 8 independent plans. Results: VMAT dose distribution was generated with a mean prediction time of approximately 3.5 s per patient, whereas the PCTSOS model was excluded due to a mean prediction time of approximately 17.5 s per patient. The highest average 3D gamma passing rate was 80.51 ± 5.94, while the lowest overall percentage difference of dose-volume histogram (DVH) parameters was 6.01 ± 5.44% for the prescription dose from the PCTGOS model. However, the PCTSOS model was the most reliable for the evaluation of multiple parameters. Conclusions: This dose prediction model could accelerate the iterative optimization process for the planning of VMAT treatment by guiding the planner with the desired dose distribution.

CT-guided versus MR-guided radiotherapy: Impact on gastrointestinal sparing in adrenal stereotactic body radiotherapy

BACKGROUND AND PURPOSE

To quantify the indication for adaptive, gated breath-hold (BH) MR-guided radiotherapy (MRgRT_BH) versus BH or free-breathing (FB) CT-based image-guided radiotherapy (CT-IGRT) for the ablative treatment of adrenal malignancies.

MATERIALS AND METHODS

Twenty adrenal patients underwent adaptive IMRT MRgRT_BH to a median dose of 50 Gy/5 fractions. Each patient was replanned for VMAT CT-IGRT_BH and CT-IGRT_FB on a c-arm linac. Only CT-IGRT_FB used an ITV, summed from GTVs of all phases of the 4DCT respiratory evaluation. All used the same 5 mm GTV/ITV to PTV expansion. Metrics evaluated included: target volume and coverage, conformality, mean ipsilateral kidney and 0.5 cc gastrointestinal organ-at-risk (OAR) doses (D_0.5cc). Adaptive dose for MRgRT_BH and predicted dose (i.e., initial plan re-calculated on anatomy of the day) was performed for CT-IGRT_BH and MRgRT_BH to assess frequency of OAR violations and coverage reductions for each fraction.

RESULTS

The more common VMAT CT-IGRT_FB, with its significantly larger target volumes, proved inferior to MRgRT_BH in mean PTV and ITV/GTV coverage, as well as small bowel D_0.5cc. Conversely, VMAT CT-IGRT_BH delivered a dosimetrically superior initial plan in terms of statistically significant (p ≤ 0.02) improvements in target coverage, conformality and D_0.5cc to the large bowel, duodenum and mean ipsilateral kidney compared to IMRT MRgRT_BH. However, non-adaptive CT-IGRT_BH had a 71.8% frequency of predicted indications for adaptation and was 2.8 times more likely to experience a coverage reduction in PTV D_95% than predicted for MRgRT_BH.

CONCLUSION

Breath-hold VMAT radiotherapy provides superior target coverage and conformality over MRgRT_BH, but the ability of MRgRT_BH to safely provide ablative doses to adrenal lesions near mobile luminal OAR through adaptation and direct, real-time motion tracking is unmatched.

Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine

Simple Summary

Radiomics and radiogenomics offer new insight into high-grade glioma biology, as well as into glioma behavior in response to standard therapies. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the role of radiomics in providing more accurate diagnoses, prognostication, and surveillance of patients with high-grade glioma, and on the potential application of radiomics in clinical practice, with the overarching goal of advancing precision medicine for optimal patient care.

Abstract

Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.

Dosimetric Uncertainties in Dominant Intraprostatic Lesion Simultaneous Boost Using Intensity Modulated Proton Therapy

Purpose

While intensity modulated proton therapy can deliver simultaneous integrated boost (SIB) to the dominant intraprostatic lesion (DIL) with high precision, it is sensitive to anatomic changes. We investigated the dosimetric effects from these changes based on pretreatment cone-beam computed tomographic (CBCT) images and identified the most important factors using a multilayer perceptron neural network (MLPNN).

Methods and Materials

DILs were contoured based on coregistered multiparametric magnetic resonance images for 25 previously treated prostate cancer patients. SIB plans were created with (1) prostate clinical target volume − V70 Gy = 98%; (2) DIL − V98 Gy > 95%; and (3) all organs at risk (OARs)"?> within clinical constraints. SIB plans were applied to daily CBCT-based deformed planning computed tomography (CT)"?>. DIL − V98 Gy, bladder/rectum maximum dose (Dmax) and volume changes, femur shifts, and the distance from DIL to organs at riskOARs"?> in both planning computed tomogramsCT"?> and CBCT were calculated. Wilcoxon signed-ranks tests were used to compare the changes. MLPNNs were used to model the change in ΔDIL − V98 Gy > 10% and bladder/rectum Dmax > 80 Gy, and the relative importance factors for the model were provided. The performances of the models were evaluated with receiver operating characteristic curves.

Results

Comparing initial plan to the average from evaluation plans, respectively, DIL − V98 Gy was 89.3% ± 19.9% versus 86.2% ± 21.3% (P = .151); bladder Dmax 71.9 ± 0.6 Gy versus 74.5 ± 2.9 Gy (P < .001); and rectum Dmax 70.1 ± 2.4 Gy versus 74.9 ± 9.1Gy (P = .007). Bladder and rectal volumes were 99.6% ± 39.5% and 112.8% ± 27.2%, respectively, of their initial volume. The femur shift was 3.16 ± 2.52 mm. In the modeling of ΔDIL V98 Gy > 10%, DIL to rectum distance changes, DIL to bladder distance changes, and rectum volume changes ratio are the 3 most important factors. The areas under the receiver operating characteristic curves were 0.89, 1.00, and 0.99 for the modeling of ΔDIL − V98 Gy > 10%, and bladder and rectum Dmax > 80 Gy, respectively.

Conclusions

Dosimetric changes in DIL SIB with intensity modulated proton therapy can be modeled and classified based on anatomic changes on pretreatment images by an MLPNN.

Comparing the dosimeter-specific corrections for influence quantities of some parallel-plate ionization chambers in conventional electron beam dosimetry

The performance characteristics of some widely employed parallel-plate ionization chambers in dosimetry of conventional high energy electron beams were evaluated and compared in the present study following the recommendations of the IAEA TRS-398 reference dosimetry protocol. Three different types of PTW-made parallel-plate ionization chambers including Roos (TM34001), Markus (TM23343), and Advanced Markus (TM34045) were employed, and correction factors for polarity (k_pol), recombination (k_s), and quality conversion factor ( [Formula: see text] ) were determined at different nominal electron energies of 4, 6, 9, 12, 16, and 20 MeV produced by a Varian Trilogy clinical Linac. All measurements were performed inside a MP3-M automatic water phantom in the reference condition of 100 cm SSD (source to surface distance), reference measurement depth (z_ref), and 10 × 10 cm² field size at the phantom surface. The maximum and minimum range of k_pol deviations from unity were respectively found for Markus and Roos ionization chambers. The maximum k_s values also belonged to the Markus ionization chamber, while the minimum k_s values were observed for the Advanced Markus chamber. The measured k_s values through recommendations of the TRS-398 dosimetry protocol were in good accordance with those obtained by Jaffe-plot analysis for all considered ionization chambers. The type of employed ionization chamber can minimally affect the measured electron beam quality index (R₅₀), while it can have a more considerable impact on [Formula: see text] value, especially in the case of the Markus chamber. From the results, it can be concluded that the Roos and Advanced Markus ionization chambers have a superior performance in the case of electron beam dosimetry, although all considered ionization chambers fulfilled the criteria requested by relevant reference dosimetry protocols.

Robustness of three external beam treatment techniques against inter-fractional positional variations of the metal port in breast tissue expanders

INTRODUCTION

Temporary breast tissue expanders contain a metal port that varies in position throughout the course of radiation treatments. The purpose of this study was to quantify the robustness of the three most common external beam treatment techniques (tangential three-dimensional conformal radiation therapy [3DCRT], volumetric modulated arc therapy [VMAT], and helical tomotherapy) against our measured inter-fractional positional variations of the port.

METHODS

For eight breast cases, a clinical plan was created for each of the three techniques. The dosimetric effect of our previously measured inter-fractional port errors was evaluated for two classes of error: internal port errors (IPEs) and patient registration errors (PREs). For both classes of error, daily variable and systematic errors were modeled, and their cumulative effects were compared against the originally planned doses.

RESULTS

For systematic IPE, the 1%-99% range in point dose differences inside a 5-mm target abutting the implant was the highest for tangential 3DCRT, and it was within 6% and 9% when calculated with Monte Carlo and collapsed cone calculation engines, respectively. Daily variable PRE resulted in mean changes of -3.0% and -3.5% to V_100%Rx of the target for VMAT and tomotherapy, respectively. For nearby organs, daily variable PRE resulted in changes to V_20Gy of the ipsilateral lung of less than 2% in all three techniques, while V_5Gy of the heart increased by as much as 6% in VMAT and 10% in tomotherapy.

CONCLUSIONS

When IPEs were modeled, dose variability was the largest in tangential 3DCRT, leading to areas of underdosage in the shadow of the port. When PREs were modeled, the target coverage and nearby organs were affected the most in VMAT and helical tomotherapy. In reality, port positional errors result from a combination of IPE and PRE, suggesting that VMAT and tomotherapy are more robust when patient registration errors are minimized, despite the presence of IPE.

A customizable anthropomorphic phantom for dosimetric verification of 3D-printed lung, tissue, and bone density materials

PURPOSE

To design and manufacture a customized thoracic phantom slab utilizing the 3D printing process, also known as additive manufacturing, consisting of different tissue density materials. Here, we demonstrate the 3D-printed phantom's clinical feasibility for imaging and dosimetric verification of volumetric modulated arc radiotherapy (VMAT) plans for lung and spine stereotactic ablative body radiotherapy (SABR) through end-to-end dosimetric verification.

METHODS

A customizable anthropomorphic phantom slab was designed using the CT dataset of a commercial phantom (adult female ATOM dosimetry phantom, CIRS Inc.). Material extrusion 3D printing was utilized to manufacture the phantom slab consisting of acrylonitrile butadiene styrene material for the lung and the associated lesion, polylactic acid (PLA) material for soft tissue and spinal cord, and both PLA and iron-reinforced PLA materials for bone. CT images were acquired for both the commercial phantom and 3D-printed phantom for HU comparison. VMAT plans were generated for spine and lung SABR scenarios and were delivered as per departmental SABR protocols using a Varian TrueBeam STx linear accelerator. End-to-end dosimetry was implemented with radiochromic films, analyzed with gamma criteria of 5% dose difference, and a distance-to-agreement of 1 mm, at a 10% low-dose threshold by comparing with calculated dose using the Acuros algorithm of the Eclipse treatment planning system (v15.6).

RESULTS

3D-printed phantom inserts were observed to produce HU ranging from -750 to 2100. The 3D-printed phantom slab was observed to achieve a similar range of HU from the commercial phantom including a mean HU of -760 for lung tissue, a mean HU of 50 for soft tissue, and a mean HU of 220 and 630 for low- and high-density bone, respectively. Film dosimetry results show 2D-gamma passing rates for lung SABR (internal and superior) and spine SABR (inferior and superior) over 98% and 90%, respectively.

CONCLUSIONS

The end-to-end testing of VMAT plans for spine and lung SABR suggests the clinical feasibility of the 3D-printed phantom, consisting of different tissue density materials that emulate lung, soft tissue, and bone in kV imaging and megavoltage photon dosimetry. Further investigation of the proposed 3D printing techniques for manufacturability and reproducibility will enable the development of clinical 3D-printed phantoms in radiotherapy.

Adaptive proton therapy

Radiation therapy treatments are typically planned based on a single image set, assuming that the patient's anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.

Intra-fraction motion monitoring during fast modulated radiotherapy delivery in a closed-bore gantry linac

•

Surface scanning allows for continuous intra fraction monitoring in a closed-bore gantry.

•

Patient baseline drift during fast cone-beam computed tomography imaging is non-negligible.

•

Peak-to-peak breathing amplitude is smaller than baseline drift in 69% of fractions.

Background and purpose

New closed-bore linacs allow for highly streamlined workflows and fast treatment delivery resulting in brief treatment sessions. Motion management technology has only recently been integrated inside the bore, yet is required in future online adaptive workflows. We measured patient motion during every step of the workflow: image acquisition, evaluation and treatment delivery using surface scanning.

Materials and methods

Nineteen patients treated for breast, lung or esophageal cancer were prospectively monitored from the end of setup to the end of treatment delivery in the Halcyon linac (Varian Medical Systems). Motion of the chest was tracked by way of 6 degrees-of-freedom surface tracking. Baseline drift and rate of drift were determined. The influence of fraction number, patient and fraction duration were analyzed with multi-way ANOVA.

Results

Median fraction duration was 4 min 48 s including the IGRT procedure (kV-CBCT acquisition and evaluation) (N = 221). Baseline drift at the end of the fraction was −1.8 ± 1.5 mm in the anterior-posterior, −0.0 ± 1.7 mm in the cranio-caudal direction and 0.1 ± 1.8 mm in the medio-lateral direction of which 75% occurred during the IGRT procedure. The highest rate of baseline drift was observed between 1 and 2 min after the end of patient setup (-0.62 mm/min). Baseline drift was patient and fraction duration dependent (p < 0.001), but fraction number was not significant (p = 0.33).

Conclusion

Even during short treatment sessions, patient baseline drift is not negligible. Drift is largest during the initial minutes after completion of patient setup, during verification imaging and evaluation. Patients will need to be monitored during extended contouring and re-planning procedures in online adaptive workflows.

PSA-Net: Deep learning-based physician style-aware segmentation network for postoperative prostate cancer clinical target volumes

PURPOSE

Automatic segmentation of medical images with deep learning (DL) algorithms has proven highly successful in recent times. With most of these automation networks, inter-observer variation is an acknowledged problem that leads to suboptimal results. This problem is even more significant in segmenting postoperative clinical target volumes (CTV) because they lack a macroscopic visible tumor in the image. This study, using postoperative prostate CTV segmentation as the test case, tries to determine 1) whether physician styles are consistent and learnable, 2) whether physician style affects treatment outcome and toxicity, and 3) how to explicitly deal with different physician styles in DL-assisted CTV segmentation to facilitate its clinical acceptance.

METHODS

A dataset of 373 postoperative prostate cancer patients from UT Southwestern Medical Center was used for this study. We used another 83 patients from Mayo Clinic to validate the developed model and its adaptability. To determine whether physician styles are consistent and learnable, we trained a 3D convolutional neural network classifier to identify which physician had contoured a CTV from just the contour and the corresponding CT scan. Next, we evaluated whether adapting automatic segmentation to specific physician styles would be clinically feasible based on a lack of difference between outcomes. Here, biochemical progression-free survival (BCFS) and grade 3+ genitourinary and gastrointestinal toxicity were estimated with the Kaplan-Meier method and compared between physician styles with the log rank test and subsequently with a multivariate Cox regression. When we found no statistically significant differences in outcome or toxicity between contouring styles, we proposed a concept called physician style-aware (PSA) segmentation by developing an encoder-multidecoder network with perceptual loss to model different physician styles of CTV segmentation.

RESULTS

The classification network captured the different physician styles with 87% accuracy. Subsequent outcome analysis showed no differences in BCFS and grade 3+ toxicity among physicians. With the proposed physician style-aware network (PSA-Net), Dice similarity coefficient (DSC) accuracy for all physicians was 3.4% higher on average than with a general model that does not differentiate physician styles. We show that these stylistic contouring variations also exist between institutions that follow the same segmentation guidelines, and we show the proposed method's effectiveness in adapting to new institutional styles. We observed an accuracy improvement of 5% in terms of DSC when adapting to the style of a separate institution.

CONCLUSION

The performance of the classification network established that physician styles are learnable, and the lack of difference between outcomes among physicians shows that the network can feasibly adapt to different styles in the clinic. Therefore, we developed a novel PSA-Net model that can produce contours specific to the treating physician, thus improving segmentation accuracy and avoiding the need to train multiple models to achieve different style segmentations. We successfully validated this model on data from a separate institution, thus supporting the model's generalizability to diverse datasets.

The lifetime of a linac monitor unit ion chamber

This study is the first to report the clinical lifetime of Varian Kapton sealed ion chambers as a retrospective review. The data have been analyzed using ion chamber gain values, daily quality assurance results, monthly quality assurance results, and delivered treatment field data were analyzed to comprehensively review trends. The data show the average lifetimes of the ion chambers from our institution, so other physicists can prepare for replacement. Additionally, we share our experience in performing quality assurance tests to calibrate and validate the radiation beam after ion chamber replacement.

The Effects of Ionising and Non-Ionising Electromagnetic Radiation on Extracellular Matrix Proteins

Exposure to sub-lethal doses of ionising and non-ionising electromagnetic radiation can impact human health and well-being as a consequence of, for example, the side effects of radiotherapy (therapeutic X-ray exposure) and accelerated skin ageing (chronic exposure to ultraviolet radiation: UVR). Whilst attention has focused primarily on the interaction of electromagnetic radiation with cells and cellular components, radiation-induced damage to long-lived extracellular matrix (ECM) proteins has the potential to profoundly affect tissue structure, composition and function. This review focuses on the current understanding of the biological effects of ionising and non-ionising radiation on the ECM of breast stroma and skin dermis, respectively. Although there is some experimental evidence for radiation-induced damage to ECM proteins, compared with the well-characterised impact of radiation exposure on cell biology, the structural, functional, and ultimately clinical consequences of ECM irradiation remain poorly defined.

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti-cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI-powered cancer care.

The Chinese Society of Clinical Oncology (CSCO) clinical guidelines for the diagnosis and treatment of nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is a malignant epithelial tumor originating in the nasopharynx and has a high incidence in Southeast Asia and North Africa. To develop these comprehensive guidelines for the diagnosis and management of NPC, the Chinese Society of Clinical Oncology (CSCO) arranged a multi‐disciplinary team comprising of experts from all sub‐specialties of NPC to write, discuss, and revise the guidelines. Based on the findings of evidence‐based medicine in China and abroad, domestic experts have iteratively developed these guidelines to provide proper management of NPC. Overall, the guidelines describe the screening, clinical and pathological diagnosis, staging and risk assessment, therapies, and follow‐up of NPC, which aim to improve the management of NPC.

MR SIGnature MAtching (MRSIGMA) with retrospective self-evaluation for real-time volumetric motion imaging

Objective. MR SIGnature MAtching (MRSIGMA) is a real-time volumetric MRI technique to image tumor and organs at risk motion in real-time for radiotherapy applications, where a dictionary of high-resolution 3D motion states and associated motion signatures are computed first during offline training and real-time 3D imaging is performed afterwards using fast signature-only acquisition and signature matching. However, the lack of a reference image with similar spatial resolution and temporal resolution introduces significant challenges forin vivovalidation.Approach. This work proposes a retrospective self-validation for MRSIGMA, where the same data used for real-time imaging are used to create a non-real-time reference for comparison. MRSIGMA with self-validation is tested in patients with liver tumors using quantitative metrics defined on the tumor and nearby organs-at-risk structures. The dice coefficient between contours defined on the real-time MRSIGMA and non-real-time reference was used to assess motion imaging performance.Main Results. Total latency (including signature acquisition and signature matching) was between 250 and 314 ms, which is sufficient for organs affected by respiratory motion. Mean ± standard deviation dice coefficient over time was 0.74 ± 0.03 for patients imaged without contrast agent and 0.87 ± 0.03 for patients imaged with contrast agent, which demonstrated high-performance real-time motion imaging.Signficance. MRSIGMA with self-evaluation provides a means to perform real-time volumetric MRI for organ motion tracking with quantitative performance measures.

Dosimetric evaluation of different planning techniques based on flattening filter-free beams for central and peripheral lung stereotactic body radiotherapy

This study aimed to dosimetrically compare and evaluate the flattening filter-free (FFF) photon beam-based three-dimensional conformal radiotherapy (3DCRT), intensity-modulated radiation therapy (IMRT), and volumetric modulated arc therapy (VMAT) for lung stereotactic body radiotherapy (SBRT). RANDO phantom computed tomography (CT) images were used for treatment planning. Gross tumor volumes (GTVs) were delineated in the central and peripheral lung locations. Planning target volumes (PTVs) was determined by adding a 5 mm margin to the GTV. 3DCRT, IMRT, and VMAT plans were generated using a 6-MV FFF photon beam. Dose calculations for all plans were performed using the anisotropic analytical algorithm (AAA) and Acuros XB algorithms. The accuracy of the algorithms was validated using the dose measured in a CIRS thorax phantom. The conformity index (CI), high dose volume (HDV), low dose location (D_2cm), and homogeneity index (HI) improved with FFF-VMAT compared to FFF-IMRT and FFF-3DCRT, while low dose volume (R_50%) and gradient index (GI) showed improvement with FFF-3DCRT. Compared with FFF-3DCRT, a drastic decrease in the mean treatment time (TT) value was observed with FFF-VMAT for different lung sites between 57.09% and 60.39%, while with FFF-IMRT it increased between 10.78% and 17.49%. The dose calculation with Acuros XB was found to be superior to that of AAA. Based on the comparison of dosimetric indices in this study, FFF-VMAT provides a superior treatment plan to FFF-IMRT and FFF-3DCRT in the treatment of peripheral and central lung PTVs. This study suggests that Acuros XB is a more accurate algorithm than AAA in the lung region.

Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.

Stereotactic Radiosurgery for Postoperative Spine Malignancy: A Systematic Review and International Stereotactic Radiosurgery Society (ISRS) Practice Guidelines

PURPOSE

To determine safety and efficacy of postoperative spine stereotactic body radiation therapy (SBRT) in the published literature, and to present practice recommendations on behalf of the International Stereotactic Radiosurgery Society (ISRS).

MATERIALS AND METHODS

A systematic review of the literature was performed, specific to postoperative spine SBRT, using PubMed and Embase databases. A meta-analysis for 1-year local control (LC), overall survival (OS) and vertebral compression fracture (VCF) probability was conducted.

RESULTS

The literature search revealed 251 potentially relevant articles after duplicates were removed. Of these 56 were reviewed in-depth for eligibility and 12 met all the inclusion criteria for analysis. 7 studies were retrospective, 2 prospective observational and 3 were prospective phase I/II clinical trials. Outcomes for a total of 461 patients and 499 spinal segments were reported. 10 studies used an MRI fused to CT-simulation for treatment planning, 2 investigations reported on all patients receiving a CT-myelogram at the time of planning. Meta-analysis for 1 year LC and OS was 88.9% and 57%, respectively. The crude reported VCF rate was 5.6%. One case of myelopathy was described in a patient with a previously irradiated spinal segment. One patient developed an esophageal fistula requiring surgical repair.

CONCLUSIONS

Postoperative spine SBRT delivers a high 1-year LC with acceptably low toxicity. Patients that may benefit from this include those with oligometastatic disease, radioresistant histology, paraspinal masses and/or those with a history of prior irradiation to the affected spinal segment. The ISRS recommends a minimum interval of 8 to 14 days after invasive surgery prior to simulation for SBRT, with initiation of radiotherapy within 4 weeks of surgery. An MRI fused to the planning CT, and/or the use of a CT-myelogram, are necessary for target and organ-at-risk delineation. A planning organ-at-risk volume (PRV) of 1.5 to 2mm for the spinal cord is advised.

Uncertainty in organ delineation using low-dose computed tomography images with high-strength iterative reconstruction technique in radiotherapy for prostate cancer

Introduction

This study aimed to investigate the uncertainty in organ delineation of low-dose computed tomography (CT) images using a high-strength iterative reconstruction (IR) during radiotherapy planning for the treatment of prostate cancer.

Methods

Two CT datasets were prepared with different dose levels by adjusting the reconstruction slice thickness. Two observers independently delineated the prostate, seminal vesicles, bladder and rectum on both images without referring to other modality images. The delineated organ volumes were compared between both images. Observer delineation variability was assessed using Dice similarity coefficient (DSC) and mean distance to agreement.

Results

No significant differences regarding the delineated organ volumes were observed between the low- and standard-dose images for all organs. Regarding inter-observer variability, the DSC was relatively high for both images, whereas mean distance to agreement was not significantly different between images (p > 0·05 for all). Intra-observer variability for each observer showed high DSC (>0·8 and >0·9 for seminal vesicles and other organs, respectively) but no significant differences in the mean distance to agreement (p > 0·05 for all).

Conclusions

Our results indicate that low-dose CT images with high-strength IR would be available for organ delineation in the radiotherapy treatment planning for prostate cancer.

Small-field measurement and Monte Carlo model validation of a novel image-guided radiotherapy system

PURPOSE

The RefleXion™ X1 is a novel radiotherapy system that is designed for image-guided radiotherapy, and eventually, biology-guided radiotherapy (BgRT). BgRT is a treatment paradigm that tracks tumor motion using real-time positron emission signals. This study reports the small-field measurement results and the validation of a Monte Carlo (MC) model of the first clinical RefleXion unit.

METHODS

The RefleXion linear accelerator (linac) produces a 6 MV flattening filter free (FFF) photon beam and consists of a binary multileaf collimator (MLC) system with 64 leaves and two pairs of y-jaws. The maximum clinical field size achievable is 400 × 20 mm² . The y-jaws provide either a 10 or 20 mm opening at source-to-axis distance (SAD) of 850 mm. The width of each MLC leaf at SAD is 6.25 mm. Percentage depth doses (PDDs) and relative beam profiles were acquired using an Edge diode detector in a water tank for field sizes from 12.5 × 10 to 100 × 20 mm² . Beam profiles were also measured using films. Output factors of fields ranging from 6.25 × 10 to 100 × 20 mm² were measured using W2 scintillator detector, Edge detector, and films. Output correction factors k of the Edge detector for RefleXion were calculated. An MC model of the linac including pre-MLC beam sources and detailed structures of MLC and lower y-jaws was validated against the measurements. Simulation codes BEAMnrc and GATE were utilized.

RESULTS

The diode measured PDD at 10 cm depth (PDD10) increases from 53.6% to 56.9% as the field opens from 12.5 × 10 to 100 × 20 mm² . The W2-measured output factor increases from 0.706 to 1 as the field opens from 6.25 × 10 to 100 × 20 mm² (reference field size). The output factors acquired by diode and film differ from the W2 results by 1.65% (std = 1.49%) and 2.09% (std = 1.41%) on average, respectively. The profile penumbra and full-width half-maximum (FWHM) measured by diode agree well with the film results with a deviation of 0.60 mm and 0.73% on average, respectively. The averaged beam profile consistency calculated between the diode- and film-measured profiles among different depths is within 1.72%. By taking the W2 measurements as the ground truth, the output correction factors k for Edge detector ranging from 0.958 to 1 were reported. For the MC model validation, the simulated PDD10 agreed within 0.6% to the diode measurement. The MC-simulated output factor differed from the W2 results by 2.3% on average (std = 3.7%), while the MC simulated beam penumbra differed from the diode results by 0.67 mm on average (std = 0.42 mm). The MC FWHM agreed with the diode results to within 1.40% on average. The averaged beam profile consistency calculated between the diode and MC profiles among different depths is less than 1.29%.

CONCLUSIONS

This study represents the first small-field dosimetry of a clinical RefleXion system. A complete and accurate MC model of the RefleXion linac has been validated.

Renal atrophy following gated delivery of stereotactic ablative radiotherapy to adrenal metastases

Stereotactic ablative radiotherapy (SABR) planning for adrenal metastases aims to minimize doses to the adjacent kidney. Renal dose constraints for SABR delivery are not well defined. In 20 patients who underwent MR-guided breath-hold SABR in five daily fractions of 8–10 Gy, ipsilateral renal volumes receiving ≥20 Gy best correlated with loss of renal volumes, with median renal volume reduction being 6% (range: 3%-11%, 10th-90th percentiles). Organ function did not deteriorate in 18 patients, who had post treatment renal function tests available. This suggests that the ipsilateral renal volume receiving 20 Gy can be used as partial organ dose constraint for SABR to targets in the upper abdomen.

Effect of stereotactic body radiotherapy on regional metabolic liver function investigated in patients by dynamic [18F]FDGal PET/CT

Purpose

Stereotactic body radiotherapy (SBRT) is increasingly used for treatment of liver tumors but the effect on metabolic liver function in surrounding tissue is largely unknown. Using 2-deoxy-2-[¹⁸F]fluoro-d-galactose ([¹⁸F]FDGal) positron emission tomography (PET)/computed tomography (CT), we aimed to determine a dose–response relationship between radiation dose and metabolic liver function as well as recovery.

Procedures.

One male subject with intrahepatic cholangiocarcinoma and five subjects (1 female, 4 male) with liver metastases from colorectal cancer (mCRC) underwent [¹⁸F]FDGal PET/CT before SBRT and after 1 and 3 months. The dose response was calculated using the data after 1 month and the relative recovery was evaluated after 3 months. All patients had normal liver function at time of inclusion.

Results

A linear dose–response relationship for the individual liver voxel dose was seen until approximately 30 Gy. By fitting a polynomial curve to data, a mean TD₅₀ of 18 Gy was determined with a 95% CI from 12 to 26 Gy. After 3 months, a substantial recovery was observed except in tissue receiving more than 25 Gy.

Conclusions

[¹⁸F]FDGal PET/CT makes it possible to determine a dose–response relationship between radiation dose and metabolic liver function, here with a TD₅₀ of 18 Gy (95% CI 12–26 Gy). Moreover, the method makes it possible to estimate metabolic recovery in liver tissue.

A high-Z inorganic scintillator-based detector for time-resolved in vivo dosimetry during brachytherapy

PURPOSE

High-dose rate (HDR) and pulsed-dose rate (PDR) brachytherapy would benefit from an independent treatment verification system to monitor treatment delivery and to detect errors in real time. This paper characterizes and provides an uncertainty budget for a detector based on a fiber-coupled high-Z inorganic scintillator capable of performing time-resolved in vivo dosimetry during HDR and PDR brachytherapy.

METHOD

The detector was composed of a detector probe and an optical reader. The detector probe consisted of either a 0.5 × 0.4 × 0.4 mm³ (HDR) or a 1.0 × 0.4 × 0.4 mm³ (PDR) cuboid ZnSe:O crystal glued onto an optical-fiber cable. The outer diameter of the detector probes was 1 mm, and fit inside standard brachytherapy catheters. The signal from the detector probe was read out at 20 Hz by a photodiode and a data acquisition device inside the optical reader. In order to construct an uncertainty budget for the detector, six characteristics were determined: (1) temperature dependence of the detector probe, (2) energy dependence as a function of the probe-to-source position in 2D (determined with 2 mm resolution using a robotic arm), (3) the signal-to-noise ratio (SNR), (4) short-term stability over 8 h, and (5) long-term stability of three optical readers and four probes used for in vivo monitoring in HDR and PDR treatments over 21 months (196 treatments and 189 detector calibrations, and (6) dose-rate dependence.

RESULTS

The total uncertainty of the detector at a 20 mm probe-to-source distance was < 5.1% and < 5.8% for the HDR and PDR versions, respectively. Regarding the above characteristics, (1) the sensitivity of the detector decreased by an average of 1.4%/°C for detector probe temperatures varying from 22 to 37°C; (2) the energy dependence of the detector was nonlinear and depended on both probe-to-source distance and the angle between the probe and the brachytherapy source; (3) the median SNRs were 187 and 34 at a 20 mm probe-to-source distance for the HDR and PDR versions, respectively (corresponding median source activities of 4.8 and 0.56 Ci, respectively); (4) the detector response varied by 0.6% in 11 identical irradiations over 8 h; (5) the sensitivity of the four detector probes decreased systematically by 0-1.2%/100 Gy of dose delivered to the probes, and random fluctuations of 4.8% in the sensitivity were observed for the three probes used in PDR and 1.9% for the probe used in HDR; and (6) the detector response was linear with dose rate.

CONCLUSION

ZnSe:O detectors can be used effectively for in vivo dosimetry and with high accuracy for HDR and PDR brachytherapy applications.

Accuracy and precision of apparent diffusion coefficient measurements on a 1.5 T MR-Linac in central nervous system tumour patients

BACKGROUND AND PURPOSE

MRI linear accelerators (MR-Linacs) may allow treatment adaptation to be guided by quantitative MRI including diffusion-weighted imaging (DWI). The aim of this study was to evaluate the accuracy and precision of apparent diffusion coefficient (ADC) measurements from DWI on a 1.5 T MR-Linac in patients with central nervous system (CNS) tumours through comparison with a diagnostic scanner.

MATERIALS AND METHODS

CNS patients were treated using a 1.5 T Elekta Unity MR-Linac. DWI was acquired during MR-Linac treatment and on a Philips Ingenia 1.5 T. The agreement between the two scanners on median ADC over the gross tumour/clinical target volumes (GTV/CTV) and in brain regions (white/grey matter, cerebrospinal fluid (CSF)) was computed. Repeated scans were used to estimate ADC repeatability. Daily changes in ADC over the GTV of high-grade gliomas were characterized from MR-Linac scans.

RESULTS

DWI from 59 patients was analyzed. MR-Linac ADC measurements showed a small bias relative to Ingenia measurements in white matter, grey matter, GTV, and CTV (bias: -0.05 ± 0.03, -0.08 ± 0.05, -0.1 ± 0.1, -0.08 ± 0.07 μm²/ms). ADC differed substantially in CSF (bias: -0.5 ± 0.3 μm²/ms). The repeatability of MR-Linac ADC over white/grey matter was similar to previous reports (coefficients of variation for median ADC: 1.4%/1.8%). MR-Linac ADC changes in the GTV were detectable.

CONCLUSIONS

It is possible to obtain ADC measurements in the brain on a 1.5 T MR-Linac that are comparable to those of diagnostic-quality scanners. This technical validation study adds to the foundation for future studies that will correlate brain tumour ADC with clinical outcomes.

Patient positioning and immobilization procedures for hybrid MR-Linac systems

Hybrid magnetic resonance (MR)-guided linear accelerators represent a new horizon in the field of radiation oncology. By harnessing the favorable combination of on-board MR-imaging with the possibility to daily recalculate the treatment plan based on real-time anatomy, the accuracy in target and organs-at-risk identification is expected to be improved, with the aim to provide the best tailored treatment. To date, two main MR-linac hybrid machines are available, Elekta Unity and Viewray MRIdian. Of note, compared to conventional linacs, these devices raise practical issues due to the positioning phase for the need to include the coil in the immobilization procedure and in order to perform the best reproducible positioning, also in light of the potentially longer treatment time. Given the relative novelty of this technology, there are few literature data regarding the procedures and the workflows for patient positioning and immobilization for MR-guided daily adaptive radiotherapy. In the present narrative review, we resume the currently available literature and provide an overview of the positioning and setup procedures for all the anatomical districts for hybrid MR-linac systems.

Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review

PURPOSE

In radiotherapy, MRI is used for target volume and organs-at-risk delineation for its superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the electron density of tissue necessary for dose calculation. Several methods of synthetic-CT (sCT) generation from MRI data have been developed for radiotherapy dose calculation. This work reviewed deep learning (DL) sCT generation methods and their associated image and dose evaluation, in the context of MRI-based dose calculation.

METHODS

We searched the PubMed and ScienceDirect electronic databases from January 2010 to March 2021. For each paper, several items were screened and compiled in figures and tables.

RESULTS

This review included 57 studies. The DL methods were either generator-only based (45% of the reviewed studies), or generative adversarial network (GAN) architecture and its variants (55% of the reviewed studies). The brain and pelvis were the most commonly investigated anatomical localizations (39% and 28% of the reviewed studies, respectively), and more rarely, the head-and-neck (H&N) (15%), abdomen (10%), liver (5%) or breast (3%). All the studies performed an image evaluation of sCTs with a diversity of metrics, with only 36 studies performing dosimetric evaluations of sCT.

CONCLUSIONS

The median mean absolute errors were around 76 HU for the brain and H&N sCTs and 40 HU for the pelvis sCTs. For the brain, the mean dose difference between the sCT and the reference CT was <2%. For the H&N and pelvis, the mean dose difference was below 1% in most of the studies. Recent GAN architectures have advantages compared to generator-only, but no superiority was found in term of image or dose sCT uncertainties. Key challenges of DL-based sCT generation methods from MRI in radiotherapy is the management of movement for abdominal and thoracic localizations, the standardization of sCT evaluation, and the investigation of multicenter impacts.

Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning in Medical Imaging

Deep learning and machine learning provide more consistent tools and powerful functions for recognition, classification, reconstruction, noise reduction, quantification and segmentation in biomedical image analysis. Some breakthroughs. Recently, some applications of deep learning and machine learning for low-dose optimization in computed tomography have been developed. Due to reconstruction and processing technology, it has become crucial to develop architectures and/or methods based on deep learning algorithms to minimize radiation during computed tomography scan inspections. This chapter is an extension work done by Alla et al. in 2020 and explain that work very well. This chapter introduces the deep learning for computed tomography scan low-dose optimization, shows examples described in the literature, briefly discusses new methods for computed tomography scan image processing, and provides conclusions. We propose a pipeline for low-dose computed tomography scan image reconstruction based on the literature. Our proposed pipeline relies on deep learning and big data technology using Spark Framework. We will discuss with the pipeline proposed in the literature to finally derive the efficiency and importance of our pipeline. A big data architecture using computed tomography images for low-dose optimization is proposed. The proposed architecture relies on deep learning and allows us to develop effective and appropriate methods to process dose optimization with computed tomography scan images. The real realization of the image denoising pipeline shows us that we can reduce the radiation dose and use the pipeline we recommend to improve the quality of the captured image.

Optimisation of CT scan parameters to increase the accuracy of gross tumour volume identification in brain radiotherapy

Aim:

This study aimed to optimise computed tomography (CT) simulation scan parameters to increase the accuracy for gross tumour volume identification in brain radiotherapy. For this purpose, high-contrast scan protocols were assessed.

Materials and methods:

A CT accreditation phantom (ACR Gammex 464) was used to optimise brain CT scan parameters on a Toshiba Alexion 16-row multislice CT scanner. Dose, tube voltage, tube current–time and CT dose index (CTDI) were varied to create five image quality enhancement (IQE) protocols. They were assessed in terms of contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and noise level and compared with a standard clinical protocol. Finally, the ability of the selected protocols to identify low-contrast objects was examined based on a subjective method.

Results:

Among the five IQE protocols, the one with the highest tube current–time product (250 mA) and lowest tube voltage (100 kVp) showed higher CNR, while another with a tube current–time product of 150 mA and a tube voltage of 135 kVp had improved SNR and lower noise level compared to the standard protocol. In contouring low-contrast objects, the protocol with the highest milliampere and lowest peak kilovoltage exhibited the lowest error rate (1%) compared to the standard protocol (25%).

Findings:

CT image quality should be optimised using the high-dose parameters created in this study to provide better soft tissue contrast. This could lead to an accurate identification of gross tumour volume recognition in the planning of radiotherapy treatment.

Comparison of Daily Online Plan Adaptation Strategies for a Cohort of Pancreatic Cancer Patients Treated with SBRT

PURPOSE

To study the trade-offs of three online strategies to adapt treatment plans of patients with locally advanced pancreatic carcinoma (LAPC) treated using the CyberKnife with tumor tracking.

METHODS AND MATERIALS

A total of 35 planning computed tomography scans and 98 daily in-room computed tomography scans were collected from 35 patients with LAPC. Planned dose distributions, optimized with VOLO, were evaluated on manually contoured daily anatomies to collect daily doses. Three strategies were tested to adapt treatment plans: (1) unrestricted full replanning using a patient-specific plan template, (2) time-restricted replanning on organs at risk (OARs) within 3 cm from the planning target volume (PTV) structure, and (3) dose realignment optimization to stay within OAR constraints. Dose distributions resulting from each plan adaptation strategy were dosimetrically compared by means of gross tumor volume (GTV), PTV coverage, and OAR tolerances.

RESULTS

Planned doses did not result in dose-constraint violations for 28 of 98 daily anatomies. None of the suggested plan adaptation strategies improved planned doses significantly for this subset. For 70 of the 98 reported violations, the median (interquartile range) PTV coverage of the planned dose was 84% (76% to 86%). After plan adaptation, unrestricted replanning achieved clinically acceptable plans in 93% of these fractions, time-restricted replanning in 90%, and dose realignment in 74%, at median computational times of 8.5, 3, and 0.5 minutes. Over all 98 fractions, PTV coverage was reduced: -1% (-3% to 1%), -2% (-5% to 0%), and -2% (-8% to 0%) after each strategy, respectively. In 3 of 70 fractions, none of the suggested strategies achieved clinically acceptable OAR dose volumes.

CONCLUSIONS

Unrestricted replanning was the most time-consuming method but reached the highest number of successfully adapted plans. Time-restricted replanning and dose realignment resulted in a high number of plans within dose constraints. Depending on the resources available, an adaptive strategy can be selected for each patient to address the specific anatomic challenges on the treatment day. The increase in the complexity of the strategy corresponds with an increasing number of successfully adapted plans.

Precision Radiotherapy for Gliomas: Implementing Novel Imaging Biomarkers to Improve Outcomes With Patient-Specific Therapy

ABSTRACT

Gliomas are the most common primary brain cancer, yet are extraordinarily challenging to treat because they can be aggressive and infiltrative, locally recurrent, and resistant to standard treatments. Furthermore, the treatments themselves, including radiation therapy, can affect patients' neurocognitive function and quality of life. Noninvasive imaging is the standard of care for primary brain tumors, including diagnosis, treatment planning, and monitoring for treatment response. This article explores the ways in which advanced imaging has and will continue to transform radiation treatment for patients with gliomas, with a focus on cognitive preservation and novel biomarkers, as well as precision radiotherapy and treatment adaptation. Advances in novel imaging techniques continue to push the field forward, to more precisely guided treatment planning, radiation dose escalation, measurement of therapeutic response, and understanding of radiation-associated injury.

Lung Cancer and Heart Disease Risks Associated With Low-Dose Pulmonary Radiotherapy to COVID-19 Patients With Different Background Risks

PURPOSE

The respiratory disease COVID-19 reached global pandemic status in 2020. Excessive inflammation is believed to result in the most severe symptoms and death from this disease. Because treatment options for patients with severe COVID-19 related pulmonary symptoms remain limited, whole-lung low-dose radiation therapy is being evaluated as an anti-inflammatory modality. However, there is concern about the long-term risks associated with low-dose pulmonary irradiation. To help quantify the benefit-risk balance of low-dose radiation therapy for COVID-19, we estimated radiation-induced lifetime risks of both lung cancer and heart disease (major coronary events) for patients of different sexes, treated at ages 50 to 85, with and without other relevant risk factors (cigarette smoking and baseline heart disease risk).

METHODS AND MATERIALS

These estimates were generated by combining state-of-the-art radiation risk models for lung cancer and for heart disease together with background lung cancer and heart disease risks and age/sex-dependent survival probabilities for the U.S.

POPULATION

RESULTS

Estimated absolute radiation-induced risks were generally higher for lung cancer compared with major coronary events. The highest estimated lifetime radiation-induced lung cancer risks were approximately 6% for female smokers treated between ages 50 and 60. The highest estimated radiation-induced heart disease risks were approximately 3% for males or females with high heart disease risk factors and treated between ages 50 and 60.

CONCLUSIONS

The estimated summed lifetime risk of lung cancer and major coronary events reached up to 9% in patients with high baseline risk factors. Predicted lung cancer and heart disease risks were lowest in older nonsmoking patients and patients with few cardiac risk factors. These long-term risk estimates, along with consideration of possible acute reactions, should be useful in assessing the benefit-risk balance for low-dose radiation therapy to treat severe COVID-19 pulmonary symptoms, and suggest that background risk factors, particularly smoking, should be taken into account in such assessments.

Online Magnetic Resonance-Guided Radiotherapy (oMRgRT) for Gynecological Cancers

Radiation therapy (RT) is increasingly being used in gynecological cancer management. RT delivered with curative or palliative intent can be administered alone or combined with chemotherapy or surgery. Advanced treatment planning and delivery techniques such as intensity-modulated radiation therapy, including volumetric modulated arc therapy, and image-guided adaptive brachytherapy allow for highly conformal radiation dose delivery leading to improved tumor control rates and less treatment toxicity. Quality on-board imaging that provides accurate visualization of target and surrounding organs at risk is a critical feature of these advanced techniques. As soft tissue contrast resolution is superior with magnetic resonance imaging (MRI) compared to other imaging modalities, MRI has been used increasingly to delineate tumor from adjacent soft tissues and organs at risk from initial diagnosis to tumor response evaluation. Gynecological cancers often have poor contrast resolution compared to the surrounding tissues on computed tomography scan, and consequently the benefit of MRI is high. One example is in management of locally advanced cervix cancer where adaptive MRI guidance has been broadly implemented for adaptive brachytherapy. The role of MRI for external beam RT is also steadily increasing. MRI information is being used for treatment planning, predicting, and monitoring position shifts and accounting for tissue deformation and target regression during treatment. The recent clinical introduction of online MRI-guided radiation therapy (oMRgRT) could be the next step in high-precision RT. This technology provides a tool to take full advantage of MRI not only at the time of initial treatment planning but as well as for daily position verification and online plan adaptation. Cervical, endometrial, vaginal, and oligometastatic ovarian cancers are being treated on MRI linear accelerator systems throughout the world. This review summarizes the current state, early experience, ongoing trials, and future directions of oMRgRT in the management of gynecological cancers.

Deep learning based synthetic-CT generation in radiotherapy and PET: A review

Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods.

Clinical implementation of deep-learning based auto-contouring tools-Experience of three French radiotherapy centers

Deep-learning (DL)-based auto-contouring solutions have recently been proposed as a convincing alternative to decrease workload of target volumes and organs-at-risk (OAR) delineation in radiotherapy planning and improve inter-observer consistency. However, there is minimal literature of clinical implementations of such algorithms in a clinical routine. In this paper we first present an update of the state-of-the-art of DL-based solutions. We then summarize recent recommendations proposed by the European society for radiotherapy and oncology (ESTRO) to be followed before any clinical implementation of artificial intelligence-based solutions in clinic. The last section describes the methodology carried out by three French radiation oncology departments to deploy CE-marked commercial solutions. Based on the information collected, a majority of OAR are retained by the centers among those proposed by the manufacturers, validating the usefulness of DL-based models to decrease clinicians' workload. Target volumes, with the exception of lymph node areas in breast, head and neck and pelvic regions, whole breast, breast wall, prostate and seminal vesicles, are not available in the three commercial solutions at this time. No implemented workflows are currently available to continuously improve the models, but these can be adapted/retrained in some solutions during the commissioning phase to best fit local practices. In reported experiences, automatic workflows were implemented to limit human interactions and make the workflow more fluid. Recommendations published by the ESTRO group will be of importance for guiding physicists in the clinical implementation of patient specific and regular quality assurances.

Dosimetric benefits of daily treatment plan adaptation for prostate cancer stereotactic body radiotherapy

BACKGROUND

Hypofractionation is increasingly being applied in radiotherapy for prostate cancer, requiring higher accuracy of daily treatment deliveries than in conventional image-guided radiotherapy (IGRT). Different adaptive radiotherapy (ART) strategies were evaluated with regard to dosimetric benefits.

METHODS

Treatments plans for 32 patients were retrospectively generated and analyzed according to the PACE-C trial treatment scheme (40 Gy in 5 fractions). Using a previously trained cycle-generative adversarial network algorithm, synthetic CT (sCT) were generated out of five daily cone-beam CT. Dose calculation on sCT was performed for four different adaptation approaches: IGRT without adaptation, adaptation via segment aperture morphing (SAM) and segment weight optimization (ART1) or additional shape optimization (ART2) as well as a full re-optimization (ART3). Dose distributions were evaluated regarding dose-volume parameters and a penalty score.

RESULTS

Compared to the IGRT approach, the ART1, ART2 and ART3 approaches substantially reduced the V37Gy(bladder) and V36Gy(rectum) from a mean of 7.4cm3 and 2.0cm3 to (5.9cm3, 6.1cm3, 5.2cm3) as well as to (1.4cm3, 1.4cm3, 1.0cm3), respectively. Plan adaptation required on average 2.6 min for the ART1 approach and yielded doses to the rectum being insignificantly different from the ART2 approach. Based on an accumulation over the total patient collective, a penalty score revealed dosimetric violations reduced by 79.2%, 75.7% and 93.2% through adaptation.

CONCLUSION

Treatment plan adaptation was demonstrated to adequately restore relevant dose criteria on a daily basis. While for SAM adaptation approaches dosimetric benefits were realized through ensuring sufficient target coverage, a full re-optimization mainly improved OAR sparing which helps to guide the decision of when to apply which adaptation strategy.

Personalising treatment plan quality review with knowledge-based planning in the TROG 15.03 trial for stereotactic ablative body radiotherapy in primary kidney cancer

INTRODUCTION

Quality assurance (QA) of treatment plans in clinical trials improves protocol compliance and patient outcomes. Retrospective use of knowledge-based-planning (KBP) in clinical trials has demonstrated improved treatment plan quality and consistency. We report the results of prospective use of KBP for real-time QA of treatment plan quality in the TROG 15.03 FASTRACK II trial, which evaluates efficacy of stereotactic ablative body radiotherapy (SABR) for kidney cancer.

METHODS

A KBP model was generated based on single institution data. For each patient in the KBP phase (open to the last 31 patients in the trial), the treating centre submitted treatment plans 7 days prior to treatment. A treatment plan was created by using the KBP model, which was compared with the submitted plan for each organ-at-risk (OAR) dose constraint. A report comparing each plan for each OAR constraint was provided to the submitting centre within 24 h of receiving the plan. The centre could then modify the plan based on the KBP report, or continue with the existing plan.

RESULTS

Real-time feedback using KBP was provided in 24/31 cases. Consistent plan quality was in general achieved between KBP and the submitted plan. KBP review resulted in replan and improvement of OAR dosimetry in two patients. All centres indicated that the feedback was a useful QA check of their treatment plan.

CONCLUSION

KBP for real-time treatment plan review was feasible for 24/31 cases, and demonstrated ability to improve treatment plan quality in two cases. Challenges include integration of KBP feedback into clinical timelines, interpretation of KBP results with respect to clinical trade-offs, and determination of appropriate plan quality improvement criteria.

Toward PET/MRI as one-stop shop for radiotherapy planning in cervical cancer patients

BACKGROUND

Radiotherapy (RT) planning for cervical cancer patients entails the acquisition of both Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Further, molecular imaging by Positron Emission Tomography (PET) could contribute to target volume delineation as well as treatment response monitoring. The objective of this study was to investigate the feasibility of a PET/MRI-only RT planning workflow of patients with cervical cancer. This includes attenuation correction (AC) of MRI hardware and dedicated positioning equipment as well as evaluating MRI-derived synthetic CT (sCT) of the pelvic region for positioning verification and dose calculation to enable a PET/MRI-only setup.

MATERIAL AND METHODS

16 patients underwent PET/MRI using a dedicated RT setup after the routine CT (or PET/CT), including eight pilot patients and eight cervical cancer patients who were subsequently referred for RT. Data from 18 patients with gynecological cancer were added for training a deep convolutional neural network to generate sCT from Dixon MRI. The mean absolute difference between the dose distributions calculated on sCT and a reference CT was measured in the RT target volume and organs at risk. PET AC by sCT and a reference CT were compared in the tumor volume.

RESULTS

All patients completed the examination. sCT was inferred for each patient in less than 5 s. The dosimetric analysis of the sCT-based dose planning showed a mean absolute error (MAE) of 0.17 ± 0.12 Gy inside the planning target volumes (PTV). PET images reconstructed with sCT and CT had no significant difference in quantification for all patients.

CONCLUSIONS

These results suggest that multiparametric PET/MRI can be successfully integrated as a one-stop-shop in the RT workflow of patients with cervical cancer.

Deep learning for segmentation in radiation therapy planning: a review

Segmentation of organs and structures, as either targets or organs-at-risk, has a significant influence on the success of radiation therapy. Manual segmentation is a tedious and time-consuming task for clinicians, and inter-observer variability can affect the outcomes of radiation therapy. The recent hype over deep neural networks has added many powerful auto-segmentation methods as variations of convolutional neural networks (CNN). This paper presents a descriptive review of the literature on deep learning techniques for segmentation in radiation therapy planning. The most common CNN architecture across the four clinical sub sites considered was U-net, with the majority of deep learning segmentation articles focussed on head and neck normal tissue structures. The most common data sets were CT images from an inhouse source, along with some public data sets. N-fold cross-validation was commonly employed; however, not all work separated training, test and validation data sets. This area of research is expanding rapidly. To facilitate comparisons of proposed methods and benchmarking, consistent use of appropriate metrics and independent validation should be carefully considered.

On-line daily plan optimization combined with a virtual couch shift procedure to address intrafraction motion in prostate magnetic resonance guided radiotherapy

Background and purpose

In daily adaptive magnetic resonance (MR)-guided radiotherapy, plans are adapted based on the patient's daily anatomy. During this adaptation phase, prostate intrafraction motion (IM) can occur. The aim of this study was to investigate the efficacy of always applying a subsequent virtual couch shift (VCS) to counter IM that occurred during the daily contour and plan adaption (CPa) procedure.

Material and Methods

One hundred fifty patients with low and intermediate risk prostate cancer were treated with 5x7.25 Gy fractions on a 1.5 T MR-Linac. In each fraction, contour adaptation and dose re-optimization was performed using the session’s first MR-scan. IM that occurred here was countered using two methods. One patient group had selective VCS (sVCS) applied if the CTV reached outside the PTV on a second MR acquired during plan optimization. The other group had always VCS (aVCS) applied for any prostate shift greater than 1 mm. Remaining IM during beam delivery was determined using 3D cine-MR.

Results

Percentage of fractions where a VCS was applied was 28% (sVCS) vs 78% (aVCS). Always applying VCS significantly reduced influences of systematic prostate IM. Population random and systematic median values in all translations directions were lower for the aVCS than sVCS group, but not for the population random cranial-caudal direction.

Conclusion

Applying VCS after daily CPa reduced impact of systematic prostate drift in especially the posterior and caudal translation direction. However, due to the continuous and stochastical nature of prostate IM, margin reduction below 4 mm requires fast intrafraction plan adaption methods.

[68Ga]Ga-PSMA-11: The First FDA-Approved 68Ga-Radiopharmaceutical for PET Imaging of Prostate Cancer

For the positron emission tomography (PET) imaging of prostate cancer, radiotracers targeting the prostate-specific membrane antigen (PSMA) are nowadays used in clinical practice. Almost 10 years after its discovery, [⁶⁸Ga]Ga-PSMA-11 has been approved in the United States by the Food and Drug Administration (FDA) as the first ⁶⁸Ga-radiopharmaceutical for the PET imaging of PSMA-positive prostate cancer in 2020. This radiopharmaceutical combines the peptidomimetic Glu-NH-CO-NH-Lys(Ahx)-HBED-CC with the radionuclide ⁶⁸Ga, enabling specific imaging of tumor cells expressing PSMA. Such a targeting approach may also be used for therapy planning as well as potentially for the evaluation of treatment response.

Calculation algorithms and penumbra: Underestimation of dose in organs at risk in dosimetry audits

PURPOSE

The aim of this study is to investigate overdose to organs at risk (OARs) observed in dosimetry audits in Monte Carlo (MC) algorithms and Linear Boltzmann Transport Equation (LBTE) algorithms. The impact of penumbra modeling on OAR dose was assessed with the adjustment of MC modeling parameters and the clinical relevance of the audit cases was explored with a planning study of spine and head and neck (H&N) patient cases.

METHODS

Dosimetric audits performed by the Australian Clinical Dosimetry Service (ACDS) of 43 anthropomorphic spine plans and 1318 C-shaped target plans compared the planned dose to doses measured with ion chamber, microdiamond, film, and ion chamber array. An MC EGSnrc model was created to simulate the C-shape target case. The electron cut-off energy E_cut(kinetic) was set at 500, 200, and 10 keV, and differences between 1 and 3 mm voxel were calculated. A planning study with 10 patient stereotactic body radiotherapy (SBRT) spine plans and 10 patient H&N plans was calculated in both Acuros XB (AXB) v15.6.06 and Anisotropic Analytical Algorithm (AAA) v15.6.06. The patient contour was overridden to water as only the penumbral differences between the two different algorithms were under investigation.

RESULTS

The dosimetry audit results show that for the SBRT spine case, plans calculated in AXB are colder than what is measured in the spinal cord by 5%-10%. This was also observed for other audit cases where a C-shape target is wrapped around an OAR where the plans were colder by 3%-10%. Plans calculated with Monaco MC were colder than measurements by approximately 7% with the OAR surround by a C-shape target, but these differences were not noted in the SBRT spine case. Results from the clinical patient plans showed that the AXB was on average 7.4% colder than AAA when comparing the minimum dose in the spinal cord OAR. This average difference between AXB and AAA reduced to 4.5% when using the more clinically relevant metric of maximum dose in the spinal cord. For the H&N plans, AXB was cooler on average than AAA in the spinal cord OAR (1.1%), left parotid (1.7%), and right parotid (2.3%). The EGSnrc investigation also noted similar, but smaller differences. The beam penumbra modeled by E_cut(kinetic)  = 500 keV was steeper than the beam penumbra modeled by E_cut(kinetic)  = 10 keV as the full scatter is not accounted for, which resulted in less dose being calculated in a central OAR region where the penumbra contributes much of the dose. The dose difference when using 2.5 mm voxels of the center of the OAR between 500 and 10 keV was 3%, reducing to 1% between 200 and 10 keV.

CONCLUSIONS

Lack of full penumbral modeling due to approximations in the algorithms in MC based or LBTE algorithms are a contributing factor as to why these algorithms under-predict the dose to OAR when the treatment volume is wrapped around the OAR. The penumbra modeling approximations also contribute to AXB plans predicting colder doses than AAA in areas that are in the vicinity of beam penumbra. This effect is magnified in regions where there are many beam penumbras, for example in the spinal cord for spine SBRT cases.

Dosimetric comparison of MR-linac-based IMRT and conventional VMAT treatment plans for prostate cancer

Background

The aim of this study was to evaluate and compare the performance of intensity modulated radiation therapy (IMRT) plans, planned for low-field strength magnetic resonance (MR) guided linear accelerator (linac) delivery (labelled IMRT MRL plans), and clinical conventional volumetric modulated arc therapy (VMAT) plans, for the treatment of prostate cancer (PCa). Both plans used the original planning target volume (PTV) margins. Additionally, the potential dosimetric benefits of MR-guidance were estimated, by creating IMRT MRL plans using smaller PTV margins.

Materials and methods

20 PCa patients previously treated with conventional VMAT were considered. For each patient, two different IMRT MRL plans using the low-field MR-linac treatment planning system were created: one with original (orig.) PTV margins and the other with reduced (red.) PTV margins. Dose indices related to target coverage, as well as dose-volume histogram (DVH) parameters for the target and organs at risk (OAR) were compared. Additionally, the estimated treatment delivery times and the number of monitor units (MU) of each plan were evaluated.

Results

The dose distribution in the high dose region and the target volume DVH parameters (D_98%, D_50%, D_2% and V_95%) were similar for all three types of treatment plans, with deviations below 1% in most cases. Both IMRT MRL plans (orig. and red. PTV margins) showed similar homogeneity indices (HI), however worse values for the conformity index (CI) were also found when compared to VMAT. The IMRT MRL plans showed similar OAR sparing when the orig. PTV margins were used but a significantly better sparing was feasible when red. PTV margins were applied. Higher number of MU and longer predicted treatment delivery times were seen for both IMRT MRL plans.

Conclusions

A comparable plan quality between VMAT and IMRT MRL plans was achieved, when applying the same PTV margin. However, online MR-guided adaptive radiotherapy allows for a reduction of PTV margins. With a red. PTV margin, better sparing of the surrounding tissues can be achieved, while maintaining adequate target coverage. Nonetheless, longer treatment delivery times, characteristic for the IMRT technique, have to be expected.

Clinical experience and cost evaluation of magnetic resonance imaging -only workflow in radiation therapy planning of prostate cancer

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Using MRI-only for prostate cancer radiation therapy planning (RTP) can reduce costs.

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An MRI-only workflow is particularly suitable for medium-sized and large departments.

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Omitting CT in the RTP workflow saves scanner, staff, and patient time.

Background and purpose

In radiation therapy (RT), significant improvements have been made recently particularly in the practices of planning imaging. This study aimed to conduct a cost evaluation between magnetic resonance imaging (MRI) -only and combined computed tomography (CT) and MRI workflows.

Materials and methods

The time-driven activity-based costing (TDABC) model was used to conduct a cost evaluation between the two workflows in those steps, where cost differences were expected. Costs were divided into capital costs and operational costs. The former consisted of fixed, one-time expenses, e.g. the purchase of a scanner, whereas the latter were partially based on the amount of activity consumed i.e. time required for image acquisition, image registration and structure contouring.

Results

In a review over a period of 10 years for 300 annual prostate cancer patients, the total cost of the workflow steps included in the study for an individual patient applying the MRI-only workflow was 903 € (100%), comprised of 537 € (59%) capital costs and 366 € (41%) operational costs. The corresponding total cost for an individual patient applying the CT + MRI workflow was 922 € (100%), comprised of 197 € (21%) capital costs and 726 € (79%) operational costs. In 10 years for 3000 patients, a total saving of 58,544 € (2%) was achieved with the MRI-only workflow compared with the dual imaging workflow.

Conclusions

MRI-only workflow is a feasible and economic way to perform clinical RT for localized prostate cancer, in particular for medium- and large-sized departments treating a sufficient number of patients.

Mitigation on bowel loops daily variations by 1.5-T MR-guided daily-adaptive SBRT for abdomino-pelvic lymph-nodal oligometastases

PURPOSE

We report preliminary dosimetric data concerning the use of 1.5-T MR-guided daily-adaptive radiotherapy for abdomino-pelvic lymph-nodal oligometastases. We aimed to assess the impact of this technology on mitigating daily variations for both target coverage and organs-at-risk (OARs) sparing.

METHODS

A total of 150 sessions for 30 oligometastases in 23 patients were analyzed. All patients were treated with MR-guided stereotactic body radiotherapy (SBRT) for a total dose of 35 Gy in five fractions. For each fraction, a quantitative analysis was performed for PTV volume, V35Gy and D_mean. Similarly, for OARs, we assessed daily variations of volume, D_mean, D_max. Any potential statistically significant change between baseline planning and daily-adaptive sessions was assessed using the Wilcoxon signed-rank test, assuming a p value < 0.05 as significant.

RESULTS

Average baseline PTV, bowel, bladder, and single intestinal loop volumes were respectively 8.9 cc (range 0.7-41.2 cc), 1176 cc (119-3654 cc), 95 cc (39.7-202.9 cc), 18.3 cc (9.1-37.7 cc). No significant volume variations were detected for PTV (p = 0.21) bowel (p = 0.36), bladder (p = 0.47), except for single intestinal loops, which resulted smaller (p = 0.026). Average baseline V35Gy and D_mean for PTV were respectively 85.6% (72-98.8%) and 35.6 Gy (34.6-36.1 Gy). We recorded a slightly positive trend in favor of daily-adaptive strategy vs baseline planning for improved target coverage, although not reaching statistical significance (p = 0.11 and p = 0.18 for PTV-V35Gy and PTV-D_mean). Concerning OARs, a significant difference was observed in favor of daily-adapted treatments in terms of single intestinal loop D_max [23.05 Gy (13.2-26.9 Gy) at baseline vs 20.5 Gy (12.1-24 Gy); p value = 0.0377] and D_mean [14.4 Gy (6.5-18 Gy) at baseline vs 13.0 Gy (6.7-17.6 Gy); p value = 0.0003]. Specifically for bladder, the average D_max was 18.6 Gy (0.4-34.3 Gy) at baseline vs 18.3 Gy (0.7-34.3 Gy) for a p value = 0.28; the average D_mean was 7.0 Gy (0.2-16.6 Gy) at baseline vs 6.98 Gy (0.2-16.4 Gy) for a p value = 0.66. Concerning the bowel, no differences in terms of D_mean [4.78 Gy (1.3-10.9 Gy) vs 5.6 Gy (1.4-10.5 Gy); p value = 0.23] were observed between after daily-adapted sessions. A statistically significant difference was observed for bowel D_max [26.4 Gy (7.7-34 Gy) vs 25.8 Gy (7.8-33.1 Gy); p value = 0.0086].

CONCLUSIONS

Daily-adaptive MR-guided SBRT reported a significantly improved single intestinal loop sparing for lymph-nodal oligometastases. Also, bowel D_max was significantly reduced with daily-adaptive strategy. A minor advantage was also reported in terms of PTV coverage, although not statistically significant.