An Extremely Inhomogeneous Gross Tumor Dose is Suitable for Volumetric Modulated Arc-Based Radiosurgery with a 5-mm Leaf-Width Multileaf Collimator for Single Brain Metastasis

Introduction Single or multi-fraction (mf) stereotactic radiosurgery (SRS) is an indispensable treatment option for brain metastases (BMs). The integration of volumetric modulated arc therapy (VMAT) into linac-based SRS is expected to further enhance efficacy and safety and to expand the indications for the challenging type of BMs. However, the optimal treatment design and relevant optimization method for volumetric modulated arc-based radiosurgery (VMARS) remain unestablished with substantial inter-institutional differences. Therefore, this study was conducted to determine the optimal dose distribution suitable for VMARS of BMs, especially regarding dose inhomogeneity of the gross tumor volume (GTV). The GTV boundary, not margin-added planning target volume, was regarded as a basis for planning optimization and dose prescription. Materials and methods This was a planning study for the clinical scenario of a single BM. Eight sphere-shaped objects with diameters of 5-40 mm in 5-mm increments were assumed as GTVs. The treatment system included a 5-mm leaf width multileaf collimator (MLC) Agility® (Elekta AB, Stockholm, Sweden) and a dedicated planning system Monaco® (Elekta AB). The prescribed dose (PD) was uniformly assigned to just cover 98% of the GTV (D_98%). Three VMARS plans with different dose inhomogeneities of the GTV were generated for each GTV: the % isodose surfaces (IDSs) of GTV D_98%, normalized to 100% at the maximum dose (D_max), were ≤70% (extremely inhomogeneous dose, EIH); ≈80% (inhomogeneous dose, IH); and ≈90% (rather homogeneous dose, RH). VMARS plans were optimized using simple and similar cost functions. In particular, no dose constraint to the GTV D_max was assigned to the EIH plans. Results Intended VMARS plans fulfilling the prerequisites were generated without problems for all GTVs of ≥10 mm, whereas 86.4% was the lowest IDS for the D_98% for 5-mm GTV. Therefore, additional plans for 9- and 8-mm GTVs were generated, which resulted in 68.6% and 75.1% being the lowest IDSs for the D_98% values of 9- and 8-mm GTVs, respectively. The EIH plans were the best in terms of the following: 1) dose conformity, i.e., minimum spillage of PD outside the GTV; 2) moderate, not too excessive, dose attenuation outside the GTV, i.e., appropriate marginal dose 2-mm outside the GTV boundary as a function of GTV size; and 3) lowest dose of the surrounding normal tissue outside the GTV. In contrast, the RH plans were the worst based on all of the aforementioned measures. Conclusions On the assumption of uniform dose assignment to the GTV margin, a very inhomogeneous GTV dose is basically the most suitable for SRS of BMs in terms of 1) superior dose conformity; 2) minimizing the dose of the surrounding normal tissue outside the GTV; and 3) moderate dose spillage margin outside the GTV with a tumor volume-dependent rational increase, i.e., appropriate dose of the common PTV boundary. The concentrically laminated steep dose increase inside the GTV boundary for the EIH plan may also be advantageous for achieving superior tumor response, although early and excessive GTV shrinkage caused by the EIH plan during mfSRS can lead to surrounding brain injury.

Dosimetric benefits of adaptive radiation therapy for patients with stage III non-small cell lung cancer

BACKGROUND

Daily adaptive radiation therapy (ART) of patients with non-small cell lung cancer (NSCLC) lowers organs at risk exposure while maintaining the planning target volume (PTV) coverage. Thus, ART allows an isotoxic approach with increased doses to the PTV that could improve local tumor control. Herein we evaluate daily online ART strategies regarding their impact on relevant dose-volume metrics.

METHODS

Daily cone-beam CTs (1 × n = 28, 1 × n = 29, 11 × n = 30) of 13 stage III NSCLC patients were converted into synthetic CTs (sCTs). Treatment plans (TPs) were created retrospectively on the first-fraction sCTs (sCT₁) and subsequently transferred unaltered to the sCTs of the remaining fractions of each patient (sCT_2-n) (IGRT scenario). Two additional TPs were generated on sCT_2-n: one minimizing the lung-dose while preserving the D_95%(PTV) (isoeffective scenario), the other escalating the D_95%(PTV) with a constant V_20Gy(lung_ipsilateral) (isotoxic scenario).

RESULTS

Compared to the original TPs predicted dose, the median D_95%(PTV) in the IGRT scenario decreased by 1.6 Gy ± 4.2 Gy while the V_20Gy(lung_ipsilateral) increased in median by 1.1% ± 4.4%. The isoeffective scenario preserved the PTV coverage and reduced the median V_20Gy(lung_ipsilateral) by 3.1% ± 3.6%. Furthermore, the median V_5%(heart) decreased by 2.9% ± 6.4%. With an isotoxic prescription, a median dose-escalation to the gross target volume of 10.0 Gy ± 8.1 Gy without increasing the V_20Gy(lung_ipsilateral) and V_5%(heart) was feasible.

CONCLUSIONS

We demonstrated that even without reducing safety margins, ART can reduce lung-doses, while still reaching adequate target coverage or escalate target doses without increasing ipsilateral lung exposure. Clinical benefits by means of toxicity and local control of both strategies should be evaluated in prospective clinical trials.

Recent Metal Nanotheranostics for Cancer Diagnosis and Therapy: A Review

In recent years, there has been an increasing interest in using nanoparticles in the medical sciences. Today, metal nanoparticles have many applications in medicine for tumor visualization, drug delivery, and early diagnosis, with different modalities such as X-ray imaging, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), etc., and treatment with radiation. This paper reviews recent findings of recent metal nanotheranostics in medical imaging and therapy. The study offers some critical insights into using different types of metal nanoparticles in medicine for cancer detection and treatment purposes. The data of this review study were gathered from multiple scientific citation websites such as Google Scholar, PubMed, Scopus, and Web of Science up through the end of January 2023. In the literature, many metal nanoparticles are used for medical applications. However, due to their high abundance, low price, and high performance for visualization and treatment, nanoparticles such as gold, bismuth, tungsten, tantalum, ytterbium, gadolinium, silver, iron, platinum, and lead have been investigated in this review study. This paper has highlighted the importance of gold, gadolinium, and iron-based metal nanoparticles in different forms for tumor visualization and treatment in medical applications due to their ease of functionalization, low toxicity, and superior biocompatibility.

Evaluation of real-time tumor contour prediction using LSTM networks for MR-guided radiotherapy

BACKGROUND AND PURPOSE

Magnetic resonance imaging guided radiotherapy (MRgRT) with deformable multileaf collimator (MLC) tracking would allow to tackle both rigid displacement and tumor deformation without prolonging treatment. However, the system latency must be accounted for by predicting future tumor contours in real-time. We compared the performance of three artificial intelligence (AI) algorithms based on long short-term memory (LSTM) modules for the prediction of 2D-contours 500ms into the future.

MATERIALS AND METHODS

Models were trained (52 patients, 3.1h of motion), validated (18 patients, 0.6h) and tested (18 patients, 1.1h) with cine MRs from patients treated at one institution. Additionally, we used three patients (2.9h) treated at another institution as second testing set. We implemented 1) a classical LSTM network (LSTM-shift) predicting tumor centroid positions in superior-inferior and anterior-posterior direction which are used to shift the last observed tumor contour. The LSTM-shift model was optimized both in an offline and online fashion. We also implemented 2) a convolutional LSTM model (ConvLSTM) to directly predict future tumor contours and 3) a convolutional LSTM combined with spatial transformer layers (ConvLSTM-STL) to predict displacement fields used to warp the last tumor contour.

RESULTS

The online LSTM-shift model was found to perform slightly better than the offline LSTM-shift and significantly better than the ConvLSTM and ConvLSTM-STL. It achieved a 50% Hausdorff distance of 1.2mm and 1.0mm for the two testing sets, respectively. Larger motion ranges were found to lead to more substantial performance differences across the models.

CONCLUSION

LSTM networks predicting future centroids and shifting the last tumor contour are the most suitable for tumor contour prediction. The obtained accuracy would allow to reduce residual tracking errors during MRgRT with deformable MLC-tracking.

Challenges and opportunities for implementing hypofractionated radiotherapy in Africa: lessons from the HypoAfrica clinical trial

The rising cancer incidence and mortality in sub-Saharan Africa (SSA) warrants an increased focus on adopting or developing approaches that can significantly increase access to treatment in the region. One such approach recommended by the recent Lancet Oncology Commission for sub-Saharan Africa is hypofractionated radiotherapy (HFRT), which can substantially increase access to radiotherapy by reducing the overall duration of time (in days) each person spends being treated. Here we highlight challenges in adopting such an approach identified during the implementation of the HypoAfrica clinical trial. The HypoAfrica clinical trial is a longitudinal, multicentre study exploring the feasibility of applying HFRT for prostate cancer in SSA. This study has presented an opportunity for a pragmatic assessment of potential barriers and facilitators to adopting HFRT. Our results highlight three key challenges: quality assurance, study harmonisation and machine maintenance. We describe solutions employed to resolve these challenges and opportunities for longer term solutions that can facilitate scaling-up use of HFRT in SSA in clinical care and multicentre clinical trials. This report provides a valuable reference for the utilisation of radiotherapy approaches that increase access to treatment and the conduct of high-quality large-scale/multi-centre clinical trials involving radiotherapy.

Trial registration

Not available yet.

Fraction optimization for hybrid proton-photon treatment planning

BACKGROUND

Hybrid proton-photon radiotherapy (RT) can provide better plan quality than proton or photon only RT, in terms of robustness of target coverage and sparing of organs-at-risk (OAR).

PURPOSE

This work develops a hybrid treatment planning method that can optimize the number of proton and photon fractions as well as proton and photon plan variables, so that the hybrid plans can be clinically delivered day-to-day using either proton or photon machine.

METHODS

In the new hybrid treatment planning method, the total dose distribution (sum of proton dose and photon dose) is optimized for robust target coverage and optimal OAR sparing, by jointly optimizing proton spots and photon fluences, while the target dose uniformity is also enforced individually on both proton dose and photon dose, so that the hybrid plans can be separately and robustly delivered on proton or photon machine. To ensure the target dose uniformity for proton and photon plans, the number of proton and photon fractions is explicitly modeled and optimized, so that the target dose for proton and photon dose components is constrained to be a constant fraction of the total prescription dose while the plan quality based on total dose is optimized. The feasibility of hybrid planning using the proposed method is validated with representative clinical cases including abdomen, lung, head-and-neck (HN), and brain.

RESULTS

For all cases, hybrid plans provided better overall plan quality and OAR sparing than proton-only or photon-only plans, better target dose uniformity and robustness than proton-only plans, quantified by treatment planning objectives and dosimetric parameters. Moreover, for HN and brain cases, hybrid plans also had better target coverage than photon-only plans.

CONCLUSIONS

We have developed a new hybrid treatment planning method that optimizes number of proton and photon fractions as well as proton spots and photon fluences, for generating hybrid plans that can be separately and robustly delivered on proton or photon machines. Preliminary results have demonstrated that hybrid plans generated by the new method have better plan quality than proton-only or photon-only plans.

Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment

Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical "how-to" guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.

Implementation of Machine Learning Models to Ensure Radiotherapy Quality for Multicenter Clinical Trials: Report from a Phase III Lung Cancer Study

The outcome of the patient and the success of clinical trials involving RT is dependent on the quality assurance of the RT plans. Knowledge-based Planning (KBP) models using data from a library of high-quality plans have been utilized in radiotherapy to guide treatment. In this study, we report on the use of these machine learning tools to guide the quality assurance of multicenter clinical trial plans. The data from 130 patients submitted to RTOG1308 were included in this study. Fifty patient cases were used to train separate photon and proton models on a commercially available platform based on principal component analysis. Models evaluated 80 patient cases. Statistical comparisons were made between the KBP plans and the original plans submitted for quality evaluation. Both photon and proton KBP plans demonstrate a statistically significant improvement of quality in terms of organ-at-risk (OAR) sparing. Proton KBP plans, a relatively emerging technique, show more improvements compared with photon plans. The KBP proton model is a useful tool for creating proton plans that adhere to protocol requirements. The KBP tool was also shown to be a useful tool for evaluating the quality of RT plans in the multicenter clinical trial setting.

Patient specific contouring region of interest for abdominal stereotactic adaptive radiotherapy

Contouring during adaptive radiotherapy (ART) can be a time-consuming process. This study describes the generation of patient specific contouring regions of interest (CRoI) for evaluating the high dose fall-off in stereotactic abdominal ART. An empirical equation was derived to determine the radius of a cylindrical patient specific CRoIs. These CRoIs were applied to 60 patients and their adaptive fractions (301 unique treatment plans). Out of the 301 unique treatment plans, 284 (94%) treatment plans contained the high dose fall-off within the CRoI. There was an expected predicted average timesaving of 2.9-min-per case. Patient specific CRoIs improves the efficiency of ART.

Estimated dose to site of loco-regional recurrence after radiotherapy in anal cancer using point of origin methods

Background and purpose

Loco-regional recurrence (LRR) dominates the failure pattern after curative radiotherapy in anal cancer. The aim of this study was to estimate dose of LRRs in anal cancer using a point of origin-based method.

Method and materials

Of 321 patients with squamous cell carcinoma of the anus, 31 patients with LRR (29 local recurrences and 5 regional lymph node recurrences) were available for analysis. The recurrence volumes were delineated on recurrence magnetic resonance imaging (rMRI). Rigid and subsequent deformable co-registration of planning computerised tomography scans and rMRI were performed. Point of origin was estimated as the centre of mass (COM) and an observer-based point of origin (obs-PO). Doses to COM and obs-PO, as well as the full recurrence volume, were estimated and the relation to target volumes was extracted.

Results

The median minimum dose to COM was 63.8 Gy (range 32.5-65.1 Gy) and 63.7 Gy (range 35.5-65.2 Gy) to obs-PO of local recurrences. COM was included in the high dose volume (64 Gy) in 86 % of cases, and obs-PO was included in 75 % of cases. There was no difference in minimum dose to COM and obs-PO, and the median distance between the two points was 3.3 mm (range 0.6-19.8 mm). No recurrences occurred in primarily boosted lymph nodes.

Conclusion

The majority of LLRs were located within the high dose volume indicating radioresistance as the primary cause of recurrence in anal cancer. No difference between the use of COM and obs-PO was evident.

Feasibility of delivered dose reconstruction for MR-guided SBRT of pancreatic tumors with fast, real-time 3D cine MRI

BACKGROUND AND PURPOSE

In MR-guided SBRT of pancreatic cancer, intrafraction motion is typically monitored with (interleaved) 2D cine MRI. However, tumor surroundings are often not fully captured in these images, and motion might be distorted by through-plane movement. In this study, the feasibility of highly accelerated 3D cine MRI to reconstruct the delivered dose during MR-guided SBRT was assessed.

MATERIALS AND METHODS

A 3D cine MRI sequence was developed for fast, time-resolved 4D imaging, featuring a low spatial resolution that allows for rapid volumetric imaging at 430 ms. The 3D cines were acquired during the entire beam-on time of 23 fractions of online adaptive MR-guided SBRT for pancreatic tumors on a 1.5 T MR-Linac. A 3D deformation vector field (DVF) was extracted for every cine dynamic using deformable image registration. Next, these DVFs were used to warp the partial dose delivered in the time interval between consecutive cine acquisitions. The warped dose plans were summed to obtain a total delivered dose. The delivered dose was also calculated under various motion correction strategies. Key DVH parameters of the GTV, duodenum, small bowel and stomach were extracted from the delivered dose and compared to the planned dose. The uncertainty of the calculated DVFs was determined with the inverse consistency error (ICE) in the high-dose regions.

RESULTS

The mean (SD) relative (ratio delivered/planned) D_99% of the GTV was 0.94 (0.06), and the mean (SD) relative D_0.5cc of the duodenum, small bowel, and stomach were respectively 0.98 (0.04), 1.00 (0.07), and 0.98 (0.06). In the fractions with the lowest delivered tumor coverage, it was found that significant lateral drifts had occurred. The DVFs used for dose warping had a low uncertainty with a mean (SD) ICE of 0.65 (0.07) mm.

CONCLUSION

We employed a fast, real-time 3D cine MRI sequence for dose reconstruction in the upper abdomen, and demonstrated that accurate DVFs, acquired directly from these images, can be used for dose warping. The reconstructed delivered dose showed only a modest degradation of tumor coverage, mostly attainable to baseline drifts. This emphasizes the need for motion monitoring and development of intrafraction treatment adaptation solutions, such as baseline drift corrections.

Dose Escalation for Pancreas SBRT: Potential and Limitations of using Daily Online Adaptive Radiation Therapy and an Iterative Isotoxicity Automated Planning Approach

Purpose

To determine the dosimetric limitations of daily online adaptive pancreas stereotactic body radiation treatment by using an automated dose escalation approach.

Methods and Materials

We collected 108 planning and daily computed tomography (CT) scans from 18 patients (18 patients × 6 CT scans) who received 5-fraction pancreas stereotactic body radiation treatment at MD Anderson Cancer Center. Dose metrics from the original non-dose-escalated clinical plan (non-DE), the dose-escalated plan created on the original planning CT (DE-ORI), and the dose-escalated plan created on daily adaptive radiation therapy CT (DE-ART) were analyzed. We developed a dose-escalation planning algorithm within the radiation treatment planning system to automate the dose-escalation planning process for efficiency and consistency. In this algorithm, the prescription dose of the dose-escalation plan was escalated before violating any organ-at-risk (OAR) dose constraint. Dose metrics for 3 targets (gross target volume [GTV], tumor vessel interface [TVI], and dose-escalated planning target volume [DE-PTV]) and 9 OARs (duodenum, large bowel, small bowel, stomach, spinal cord, kidneys, liver, and skin) for the 3 plans were compared. Furthermore, we evaluated the effectiveness of the online adaptive dose-escalation planning process by quantifying the effect of the interfractional dose distribution variations among the DE-ART plans.

Results

The median D_95% dose to the GTV/TVI/DE-PTV was 33.1/36.2/32.4 Gy, 48.5/50.9/40.4 Gy, and 53.7/58.2/44.8 Gy for non-DE, DE-ORI, and DE-ART, respectively. Most OAR dose constraints were not violated for the non-DE and DE-ART plans, while OAR constraints were violated for the majority of the DE-ORI patients due to interfractional motion and lack of adaptation. The maximum difference per fraction in D_95%, due to interfractional motion, was 2.5 ± 2.7 Gy, 3.0 ± 2.9 Gy, and 2.0 ± 1.8 Gy for the TVI, GTV, and DE-PTV, respectively.

Conclusions

Most patients require daily adaptation of the radiation planning process to maximally escalate delivered dose to the pancreatic tumor without exceeding OAR constraints. Using our automated approach, patients can receive higher target dose than standard of care without violating OAR constraints.

Validation of automated post-adjustments of HDR prostate brachytherapy treatment plans by quantitative measures and oncologist observer study

PURPOSE

The aim was to evaluate a postprocessing optimization algorithm's ability to improve the spatial properties of a clinical treatment plan while preserving the target coverage and the dose to the organs at risk. The goal was to obtain a more homogenous treatment plan, minimizing the need for manual adjustments after inverse treatment planning.

MATERIALS AND METHODS

The study included 25 previously treated prostate cancer patients. The treatment plans were evaluated on dose-volume histogram parameters established clinical and quantitative measures of the high dose volumes. The volumes of the four largest hot spots were compared and complemented with a human observer study with visual grading by eight oncologists. Statistical analysis was done using ordinal logistic regression. Weighted kappa and Fleiss' kappa were used to evaluate intra- and interobserver reliability.

RESULTS

The quantitative analysis showed that there was no change in planning target volume (PTV) coverage and dose to the rectum. There were significant improvements for the adjusted treatment plan in: V150% and V200% for PTV, dose to urethra, conformal index, and dose nonhomogeneity ratio. The three largest hot spots for the adjusted treatment plan were significantly smaller compared to the clinical treatment plan. The observers preferred the adjusted treatment plan in 132 cases and the clinical in 83 cases. The observers preferred the adjusted treatment plan on homogeneity and organs at risk but preferred the clinical plan on PTV coverage.

CONCLUSIONS

Quantitative analysis showed that the postadjustment optimization tool could improve the spatial properties of the treatment plans while maintaining the target coverage.

Predictors for late genitourinary toxicity in men receiving radiotherapy for high-risk prostate cancer using planned and accumulated dose

Background and purpose

Significant deviations between bladder dose planned (D_P) and dose accumulated (D_A) have been reported in patients receiving radiotherapy for prostate cancer. This study aimed to construct multivariate analysis (MVA) models to predict the risk of late genitourinary (GU) toxicity with clinical and D_P or D_A as dose-volume (DV) variables.

Materials and methods

Bladder D_A obtained from 150 patients were compared with D_P. MVA models were built from significant clinical and DV variables (p < 0.05) at univariate analysis. Previously developed dose-based-region-of-interest (DB-ROI) metrics using expanded ring structures from the prostate were included. Goodness-of-fit test and calibration plots were generated to determine model performance. Internal validation was accomplished using Bootstrapping.

Results

Intermediate-high D_A (V_{30-65 Gy} and DB-ROI-20-50 mm) for bladder increased compared to D_P. However, at the very high dose region, D_A (D_{0.003 cc}, V_{75 Gy,} and DB-ROI-5-10 mm) were significantly lower. In MVA, single variable models were generated with odds ratio (OR) < 1. DB-ROI-50 mm was predictive of Grade ≥ 1 GU toxicity for D_A and D_P (D_A and D_P; OR: 0.96, p: 0.04) and achieved an area under the receiver operating curve (AUC) of > 0.6. Prostate volume (OR: 0.87, p: 0.01) was significant in predicting Grade 2 GU toxicity with a high AUC of 0.81.

Conclusions

Higher D_A (V_{30-65 Gy}) received by the bladder were not translated to higher late GU toxicity. DB-ROIs demonstrated higher predictive power than standard DV metrics in associating Grade ≥ 1 toxicity. Smaller prostate volumes have a minor protective effect on late Grade 2 GU toxicity.

Clinical use of positron emission tomography for radiotherapy planning - Medical physics considerations

PET/CT imaging plays an increasing role in radiotherapy treatment planning. The aim of this article was to identify the major use cases and technical as well as medical physics challenges during integration of these data into treatment planning. Dedicated aspects, such as (i) PET/CT-based radiotherapy simulation, (ii) PET-based target volume delineation, (iii) functional avoidance to optimized organ-at-risk sparing and (iv) functionally adapted individualized radiotherapy are discussed in this article. Furthermore, medical physics aspects to be taken into account are summarized and presented in form of check-lists.

Does radiomics play a role in the diagnosis, staging and re-staging of gastroesophageal junction adenocarcinoma?

Radiomics is an emerging field of investigation in medicine consisting in the extraction of quantitative features from conventional medical images and exploring their potentials in improving diagnosis, prognosis and outcome prediction after therapy. Clinical applications are still limited, mostly due to reproducibility and repeatability issues as well as to limited interpretability of predictive radiomic-based features/signatures. In the specific case of gastroesophageal junction (GEJ) adenocarcinoma, the expectancies are particularly high, mainly due to its increasing incidence and to the limited performance of conventional imaging techniques in assessing correct diagnosis and accurate pre-surgical tumor characterization. Accordingly, current literature was reviewed, emphasizing the methodological quality. In addition, papers were scored according to the Radiomic Quality Score (RQS), weighting more the clinical applicability and generalizability of the resulting models. According to the criteria of the search, only two papers were retained: the resulting technical quality was relatively high for both, while the corresponding RQS were 15 and 19 (on a scale of 31). Although the potentials of radiomics in the setting of GEJ adenocarcinoma are relevant, they remain largely unexplored, warranting an urgent need of high-quality, possibly prospective, multicenter studies.

Sub-regional analysis of the parotid glands: model development for predicting late xerostomia with radiomics features in head and neck cancer patients

BACKGROUND

The irradiation of sub-regions of the parotid has been linked to xerostomia development in patients with head and neck cancer (HNC). In this study, we compared the xerostomia classification performance of radiomics features calculated on clinically relevant and de novo sub-regions of the parotid glands of HNC patients.

MATERIAL AND METHODS

All patients (N = 117) were treated with TomoTherapy in 30-35 fractions of 2-2.167 Gy per fraction with daily mega-voltage-CT (MVCT) acquisition for image-guidance purposes. Radiomics features (N = 123) were extracted from daily MVCTs for the whole parotid gland and nine sub-regions. The changes in feature values after each complete week of treatment were considered as predictors of xerostomia (CTCAEv4.03, grade ≥ 2) at 6 and 12 months. Combinations of predictors were generated following the removal of statistically redundant information and stepwise selection. The classification performance of the logistic regression models was evaluated on train and test sets of patients using the Area Under the Curve (AUC) associated with the different sub-regions at each week of treatment and benchmarked with the performance of models solely using dose and toxicity at baseline.

RESULTS

In this study, radiomics-based models predicted xerostomia better than standard clinical predictors. Models combining dose to the parotid and xerostomia scores at baseline yielded an AUCtest of 0.63 and 0.61 for xerostomia prediction at 6 and 12 months after radiotherapy while models based on radiomics features extracted from the whole parotid yielded a maximum AUCtest of 0.67 and 0.75, respectively. Overall, across sub-regions, maximum AUCtest was 0.76 and 0.80 for xerostomia prediction at 6 and 12 months. Within the first two weeks of treatment, the cranial part of the parotid systematically yielded the highest AUCtest.

CONCLUSION

Our results indicate that variations of radiomics features calculated on sub-regions of the parotid glands can lead to earlier and improved prediction of xerostomia in HNC patients.

Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study

OBJECTIVES

To compare image quality between a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) and to assess the impact of these algorithms on radiomics robustness.

METHODS

A phantom with clinical-relevant densities was imaged on seven DECT scanners with the same voxel size using typical abdominal-pelvis examination protocols. On one DECT scanner, raw data were reconstructed using both conventional IR (adaptive statistical iterative reconstruction-V, ASIR-V) and DLIR. Nine sets of corresponding images were generated on other six DECT scanners using scanner-equipped conventional IR. Regions of interest were delineated through rigid registrations. Image quality was compared. Pyradiomics platform was used for radiomics feature extraction. Test-retest repeatability was assessed by Bland-Altman analysis for repeated scans. Inter-reconstruction algorithm reproducibility between conventional IR and DLIR was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Robust features were identified.

RESULTS

DLIR significantly improved image quality. Ninety-four radiomics features were extracted and nine features were considered as robust. 93.87% features were repeatable between repeated scans. ASIR-V images showed higher reproducibility to other conventional IR than DLIR (ICC mean, 0.603 vs 0.558, p = 0.001; CCC mean, 0.554 vs 0.510, p = 0.004). 7.45% and 26.83% features were reproducible among scanners evaluated by CV and QCD, respectively.

CONCLUSIONS

DLIR improves quality of DECT images but may alter radiomics features compared to conventional IR. Nine robust DECT radiomics features were identified.

KEY POINTS

• DLIR improves DECT image quality in terms of signal-to-noise ratio and contrast-to-noise ratio compared with ASIR-V and showed the highest noise reduction rate and lowest peak frequency shift. • Most of radiomics features are repeatable between repeated DECT scans, while inter-reconstruction algorithm reproducibility between conventional IR and DLIR, and inter-scanner reproducibility, are low. • Although DLIR may alter radiomics features compared to IR algorithms, nine radiomics features survived repeatability and reproducibility analysis among DECT scanners and reconstruction algorithms, which allows further validation and clinical-relevant analysis.

EANM guidelines for PET-CT and PET-MR routine quality control

We present guidelines by the European Association of Nuclear Medicine (EANM) for routine quality control (QC) of PET-CT and PET-MR systems. These guidelines are partially based on the current EANM guidelines for routine quality control of Nuclear Medicine instrumentation but focus more on the inherent multimodal aspect of the current, state-of-the-art PET-CT and PET-MR scanners. We briefly discuss the regulatory context put forward by the International Electrotechnical Commission (IEC) and European Commission (EC) and consider relevant guidelines and recommendations by other societies and professional organizations. As such, a comprehensive overview of recommended quality control procedures is provided to ensure the optimal operational status of a PET system, integrated with either a CT or MR system. In doing so, we also discuss the rationale of the different tests, advice on the frequency of each test and present the relevant MR and CT tests for an integrated system. In addition, we recommend a scheme of preventive actions to avoid QC tests from drifting out of the predefined range of acceptable performance values such that an optimal performance of the PET system is maintained for routine clinical use.

Performance of automatic exposure control on dose and image quality: comparison between slot-scanning and flat-panel digital radiography systems

BACKGROUND

EOSedge™^* (EOS Imaging, Paris, France) is an X-ray imaging system using automatic exposure control (AEC) with tube current modulation, in order to optimize dose deposition in patients.

PURPOSE

This study aims at characterizing EOSedge organ dose deposition in comparison to a digital radiography (DR) system and the previous EOS system (EOS-1st generation), in relation to their respective image quality levels.

METHOD

Organ doses were measured in an anthropomorphic female adult phantom and a 5-year-old pediatric phantom using optically stimulated luminescence (OSL) dosimeters, which were carefully calibrated within the studied energy range. Organ doses were recorded on the EOSedge and the Fuji Visionary DRF (Fujifilm Medical Systems U.S.A., Inc, Lexington, MA). The resulting effective doses were compared to the EOS-1st-generation values present in the literature. Image quality assessment was carried out on end-user images. Quantitative image quality metrics were computed for all tested modalities on a quality assurance phantom. Qualitative assessment of EOSedge image quality was based on anthropomorphic phantom acquisitions against the EOS-1st-generation system, and on clinical images against the tested DR system.

RESULTS

For a full-spine exam, and on the female adult phantom (respectively, the pediatric phantom), an effective dose of 92 μSv (respectively, 32 μSv) was obtained on EOSedge, and 572 μSv (respectively, 179 μSv) on the DR system; these values were compared to effective dose values of 290 μSv (respectively, 200 μSv) from the literature on EOS-1st generation, leading to an effective dose reduction factor of 6 with respect to the DR system, and of 3-6 with respect to EOS-1st generation. EOSedge provides the best compromise between contrast-to-noise ratio (CNR) and dose, with more consistent CNR values than the other tested modalities, in a range of attenuation from 10 to 40 cm of poly(methyl methacrylate) (PMMA). Within this range, EOSedge is also comparable to DR for 10 and 20 cm of PMMA, and better than DR for 30 and 40 cm of PMMA, both in terms of spatial resolution and low-contrast detection. The anatomical landmarks of interest in the follow-up of spinal deformities can be detected in all tested modalities.

CONCLUSION

Results showed that EOSedge provides significant dose reduction factors for full spine imaging in both adults and children compared to the other tested modalities, without compromising image quality. We believe that this work could help raise awareness on the capabilities of modern X-ray systems, when equipped with appropriate AEC strategies, to perform ultra-low-dose, long-axis images.

A systematic review of radiomics in chondrosarcoma: assessment of study quality and clinical value needs handy tools

OBJECTIVE

To evaluate the study quality and clinical value of radiomics studies on chondrosarcoma.

METHODS

PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data were searched for articles on radiomics for evaluating chondrosarcoma as of January 31, 2022. The study quality was assessed according to Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, Image Biomarker Standardization Initiative (IBSI) guideline, and modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The level of evidence supporting clinical use of radiomics on chondrosarcoma differential diagnosis was determined based on meta-analyses.

RESULTS

Twelve articles were included. The median RQS was 10.5 (range, -3 to 15), with an adherence rate of 36%. The adherence rate was extremely low in domains of high-level evidence (0%), open science and data (17%), and imaging and segmentation (35%). The adherence rate of the TRIPOD checklist was 61%, and low for section of title and abstract (13%), introduction (42%), and results (56%). The reporting rate of pre-processing steps according to the IBSI guideline was 60%. The risk of bias and concern of application were mainly related to the index test. The meta-analysis on differential diagnosis of enchondromas vs. chondrosarcomas showed a diagnostic odds ratio of 43.90 (95% confidential interval, 25.33-76.10), which was rated as weak evidence.

CONCLUSIONS

The current scientific and reporting quality of radiomics studies on chondrosarcoma was insufficient. Radiomics has potential in facilitating the optimization of operation decision-making in chondrosarcoma.

KEY POINTS

• Among radiomics studies on chondrosarcoma, although differential diagnostic models showed promising performance, only pieces of weak level of evidence were reached with insufficient study quality. • Since the RQS rating, the TRIPOD checklist, and the IBSI guideline have largely overlapped with each other, it is necessary to establish one widely acceptable methodological and reporting guideline for radiomics research. • The TRIPOD model typing, the phase classification of image mining studies, and the level of evidence category are useful tools to assess the gap between academic research and clinical application, although their modifications for radiomics studies are needed.

Dosimetric feasibility of direct post-operative MR-Linac-based stereotactic radiosurgery for resection cavities of brain metastases

BACKGROUND

Post-operative radiosurgery (SRS) of brain metastases patients is typically planned on a post-recovery MRI, 2-4 weeks after resection. However, the intracranial metastasis may (re-)grow in this period. Planning SRS directly on the post-operative MRI enables shortening this time interval, anticipating the start of adjuvant systemic therapy, and so decreasing the chance of extracranial progression. The MRI-Linac (MRL) allows the simultaneous execution of the post-operative MRI and SRS treatment. The aim of this work was investigating the dosimetric feasibility of MRL-based post-operative SRS.

METHODS

MRL treatments based on the direct post-operative MRI were simulated, including thirteen patients with resectable single brain metastases. The gross tumor volume (GTV) was contoured on the direct post-operative scans and compared to the post-recovery MRI GTV. Three plans for each patient were created: a non-coplanar VMAT CT-Linac plan (ncVMAT) and a coplanar IMRT MRL plan (cIMRT) on the direct post-operative MRI, and a ncVMAT plan on the post-recovery MRI as the current clinical standard.

RESULTS

Between the direct post-operative and post-recovery MRI, 15.5 % of the cavities shrunk by > 2 cc, and 46 % expanded by ≥ 2 cc. Although the direct post-operative cIMRT plans had a higher median gradient index (3.6 vs 2.7) and median V3Gy of the skin (18.4 vs 1.1 cc) compared to ncVMAT plans, they were clinically acceptable.

CONCLUSION

Direct post-operative MRL-based SRS for resection cavities of brain metastases is dosimetrically acceptable, with the advantages of increased patient comfort and logistics. Clinical benefit of this workflow should be investigated given the dosimetric plausibility.

Dosimetry in 1.5 T MR-Linacs: Monte Carlo determination of magnetic field correction factors and investigation of the air gap effect

BACKGROUND

In Magnetic Resonance-Linac (MR-Linac) dosimetry formalisms, a new correction factor, k_B,Q , has been introduced to account for corresponding changes to detector readings under the beam quality, Q, and the presence of magnetic field, B.

PURPOSE

This study aims to develop and implement a Monte Carlo (MC)-based framework for the determination of k_B,Q correction factors for a series of ionization chambers utilized for dosimetry protocols and dosimetric quality assurance checks in clinical 1.5 T MR-Linacs. Their dependencies on irradiation setup conditions are also investigated. Moreover, to evaluate the suitability of solid phantoms for dosimetry checks and end-to-end tests, changes to the detector readings due to the presence of small asymmetrical air gaps around the detector's tip are quantified.

METHODS

Phase space files for three irradiation fields of the ELEKTA Unity 1.5 T/7 MV flattening-filter-free MR-Linac were provided by the manufacturer and used as source models throughout this study. Twelve ionization chambers (three farmer-type and nine small-cavity detectors, from three manufacturers) were modeled (including their dead volume) using the EGSnrc MC code package. k_B,Q values were calculated for the 10 × 10 cm² irradiation field and for four cardinal orientations of the detectors' axes with respect to the 1.5 T magnetic field. Potential dependencies of k_B,Q values with respect to field size, depth, and phantom material were investigated by performing additional simulations. Changes to the detectors' readings due to the presence of small asymmetrical air gaps (0.1 up to 1 mm) around the chambers' sensitive volume in an RW3 solid phantom were quantified for three small-cavity chambers and two orientations.

RESULTS

For both parallel (to the magnetic field) orientations, k_B,Q values were found close to unity. The maximum correction needed was 1.1%. For each detector studied, the k_B,Q values calculated for the two parallel orientations agreed within uncertainties. Larger corrections (up to 5%) were calculated when the detectors were oriented perpendicularly to the magnetic field. Results were compared with corresponding ones found in the literature, wherever available. No considerable dependence of k_B,Q with respect to field size (down to 3 × 3 cm² ), depth, or phantom material was noticed, for the detectors investigated. As compared to the perpendicular one, in the parallel to the magnetic field orientation, the air gap effect is minimized but is still considerable even for the smallest air gap considered (0.1 mm).

CONCLUSION

For the 10 × 10 cm² field, magnetic field correction factors for 12 ionization chambers and four orientations were determined. For each detector, the k_B,Q value may be also applied for dosimetry procedures under different irradiation parameters provided that the orientation is taken into account. Moreover, if solid phantoms are used, even the smallest asymmetrical air gap may still bias small-cavity chamber response. This work substantially expands the availability and applicability of k_B,Q correction factors that are detector- and orientation-specific, enabling more options in MR-Linac dosimetry checks, end-to-end tests, and quality assurance protocols.

Deformed dose restoration to account for tumor deformation and position changes for adaptive proton therapy

BACKGROUND

Online adaptation during intensity-modulated proton therapy (IMPT) can minimize the effect of inter-fractional anatomical changes, but remains challenging because of the complex workflow. One approach for fast and automated online IMPT adaptation is dose restoration, which restores the initial dose distribution on the updated anatomy. However, this method may fail in cases where tumor deformation or position changes occur.

PURPOSE

To develop a fast and robust IMPT online adaptation method named "deformed dose restoration (DDR)" that can adjust for inter-fractional tumor deformation and position changes.

METHODS

The DDR method comprises two steps: (1) calculation of the deformed dose distribution, and (2) restoration of the deformed dose distribution. First, the deformable image registration (DIR) between the initial clinical target volume (CTV) and the new CTV were performed to calculate the vector field. To ensure robustness for setup and range uncertainty and the ability to restore the deformed dose distribution, an expanded CTV-based registration to maintain the dose gradient outside the CTV was developed. The deformed dose distribution was obtained by applying the vector field to the initial dose distribution. Then, the voxel-by-voxel dose difference optimization was performed to calculate beam parameters that restore the deformed dose distribution on the updated anatomy. The optimization function was the sum of total dose differences and dose differences of each field to restore the initial dose overlap of each field. This method only requires target contouring, which eliminates the need for organs at risk (OARs) contouring. Six clinical cases wherein the tumor deformation and/or position changed on repeated CTs were selected. DDR feasibility was evaluated by comparing the results with those from three other strategies, namely, not adapted (continuing the initial plan), adapted by previous dose restoration, and fully optimized.

RESULTS

In all cases, continuing the initial plan was largely distorted on the repeated CTs and the dose-volume histogram (DVH) metrics for the target were reduced due to the tumor deformation or position changes. On the other hand, DDR improved DVH metrics for the target to the same level as the initial dose distribution. Dose increase was seen for some OARs because tumor growth had reduced the relative distance between CTVs and OARs. Robustness evaluation for setup and range uncertainty (3 mm/3.5%) showed that deviation in DVH-bandwidth for CTV D_95% from the initial plan was 0.4% ± 0.5% (Mean ± S.D.) for DDR. The calculation time was 8.1 ± 6.4 min.

CONCLUSIONS

An online adaptation algorithm was developed that improved the treatment quality for inter-fractional anatomical changes and retained robustness for intra-fractional setup and range uncertainty. The main advantage of this method is that it only requires target contouring alone and saves the time for OARs contouring. The fast and robust adaptation method for tumor deformation and position changes described here can reduce the need for offline adaptation and improve treatment efficiency.

SBRT focal dose intensification using an MR-Linac adaptive planning for intermediate-risk prostate cancer: An analysis of the dosimetric impact of intra-fractional organ changes

INTRODUCTION

Using an magnetic resonance linear accelerator (MR-Linac) may improve the precision of visible tumor boosting with ultra-hypofractionation by accounting for daily positional changes in the target and organs at risk (OAR).

PATIENTS AND METHODS

Fifteen patients with prostate cancer and an MR-detected dominant lesion were treated on the MR-Linac with stereotactic body radiation (SBRT) to 40 Gy in 5 fractions, boosting the gross tumor volume (GTV) to 45 Gy with daily adaptive planning. Imaging was acquired again after initial planning (verification scan), and immediately after treatment (post-treatment scan). Prior to beam-on, additional adjustments were made on the verification scan. Contours were retrospectively adjusted on verification and post-treatment scans, and the daily plan recalculated on these scans to estimate the true dose delivered.

RESULTS

The median prostate D95% for plan 1, 2 and 3 was 40.3 Gy, 40.5 Gy and 40.3 Gy and DIL D95% was 45.7 Gy, 45.2 Gy and 44.6 Gy, respectively. Bladder filling was associated with reduced GTV coverage (p = 0.03, plan 1 vs 2) and prostate coverage (p = 0.03, plan 2 vs 3). The D0.035 cc constraint was exceeded on verification and post-treatment plans in 24 % and 33 % of fractions for the urethra, 31 % and 45 % for the bladder, and 35 % and 25 % for the rectum, respectively.

CONCLUSION

MR-Linac guided, daily adaptive SBRT with focal boosting of the GTV yields acceptable planned and delivered dosimetry. Adaptive planning with a MR-Linac may reliably deliver the prescribed dose to the intended tumor target.

A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy

Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of consensus on how to assess and validate auto-contouring systems currently limits clinical use. This review formally quantifies the assessment metrics used in studies published during one calendar year and assesses the need for standardised practice. A PubMed literature search was undertaken for papers evaluating radiotherapy auto-contouring published during 2021. Papers were assessed for types of metric and the methodology used to generate ground-truth comparators. Our PubMed search identified 212 studies, of which 117 met the criteria for clinical review. Geometric assessment metrics were used in 116 of 117 studies (99.1%). This includes the Dice Similarity Coefficient used in 113 (96.6%) studies. Clinically relevant metrics, such as qualitative, dosimetric and time-saving metrics, were less frequently used in 22 (18.8%), 27 (23.1%) and 18 (15.4%) of 117 studies, respectively. There was heterogeneity within each category of metric. Over 90 different names for geometric measures were used. Methods for qualitative assessment were different in all but two papers. Variation existed in the methods used to generate radiotherapy plans for dosimetric assessment. Consideration of editing time was only given in 11 (9.4%) papers. A single manual contour as a ground-truth comparator was used in 65 (55.6%) studies. Only 31 (26.5%) studies compared auto-contours to usual inter- and/or intra-observer variation. In conclusion, significant variation exists in how research papers currently assess the accuracy of automatically generated contours. Geometric measures are the most popular, however their clinical utility is unknown. There is heterogeneity in the methods used to perform clinical assessment. Considering the different stages of system implementation may provide a framework to decide the most appropriate metrics. This analysis supports the need for a consensus on the clinical implementation of auto-contouring.

Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images

Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray images was used for COVID-19 prognosis prediction as a case study. Random forest, support vector machine, and XGBoost were trained (on a subset of 1103 images) after a rigorous feature selection strategy to build-up the predictive models. Next, to evaluate the models generalization capability on unseen data, a test phase was performed (on a subset of 486 images). The experimental findings showed that Bior1.5, Coif1, Haar, and Sym2 kernels guarantee better and similar performance for all three machine learning models considered. Support vector machine and random forest showed comparable performance, and they were better than XGBoost. Additionally, random forest proved to be the most stable model, ensuring an appropriate balance between sensitivity and specificity.

Remote EPID-based dosimetric auditing using DVH patient dose analysis

Objective.The aim of this work was to develop and validate a method for remote dosimetric auditing that enables dose-volume histogram parameter comparisons of measured and planned dose in the patient CT volume.Approach. The method is derived by adapting and combining a remote electronic portal imaging (EPID) based auditing method (Virtual Epid based Standard Phantom Audit-VESPA) and a method to estimate 3D in-patient dose distributions from planar dosimetric measurements. The method was tested with a series of error-induced plans including monitor unit and multileaf collimator (MLC) positioning errors. A pilot audit study was conducted with eleven radiotherapy centres. IMRT plans from two clinical trials, a post-prostatectomy (RAVES trial) plan and a head and neck (HPV trial) plan were utilized. Clinically relevant DVH parameters for the planned dose and estimated measured dose were compared.Main results. The method was found to reproduce the induced dose errors within 0.5% and was sensitive to MLC positioning errors as small as 0.5 mm. For the RAVES plan audit all DVH results except one were within 3% and for the HPV plan audit all DVH results were within 3% except three with a maximum difference of 3.2%.Significance. The results from the audit method produce clinically meaningful DVH metrics for the audited plan and could enable an improved understanding of a centre's radiotherapy quality.

Assessing the impact of adaptations to the clinical workflow in radiotherapy using transit in vivo dosimetry

Background and Purpose

Currently in-vivo dosimetry (IVD) is primarily used to identify individual patient errors in radiotherapy. This study investigated possible correlations of observed trends in transit IVD results, with adaptations to the clinical workflow, aiming to demonstrate the possibility of using the bulk data for continuous quality improvement.

Materials and methods

In total 84,100 transit IVD measurements were analyzed of all patients treated between 2018 and 2022, divided into four yearly periods. Failed measurements (FM) were divided per pathology and into four categories of causes of failure: technical, planning and positioning problems, and anatomic changes.

Results

The number of FM due to patient related problems gradually decreased from 9.5% to 6.6%, 6.1% and 5.6% over the study period. FM attributed to positioning problems decreased from 10.0% to 4.9% in boost breast cancer patients after introduction of extra imaging, from 9.1% to 3.9% in Head&Neck patients following education of radiation therapists on positioning of patients' shoulders, from 6.1% to 2.8% in breast cancer patients after introduction of ultrahypofractionated breast radiotherapy with daily online pre-treatment imaging and from 11.2% to 4.3% in extremities following introduction of immobilization with calculated couch parameters and a Surface Guided Radiation Therapy solution. FM related to anatomic changes decreased from 10.2% to 4.0% in rectum patients and from 6.7% to 3.3% in prostate patients following more patient education from dieticians.

Conclusions

Our study suggests that IVD can be a powerful tool to assess the impact of adaptations to the clinical workflow and its use for continuous quality improvement.

Clinical Application of a Customized 3D-Printed Bolus in Radiation Therapy for Distal Extremities

In radiation therapy (RT) for skin cancer, tissue-equivalent substances called boluses are widely used to ensure the delivery of an adequate dose to the skin surface and to provide a radioprotective effect for normal tissue. The aim of this study was to develop a new type of three-dimensional (3D) bolus for RT involving body parts with irregular geometries and to evaluate its clinical feasibility. Two 3D-printed boluses were designed for two patients with squamous cell carcinoma (SCC) of their distal extremities based on computed tomography (CT) images and printed with polylactic acid (PLA). The clinical feasibility of the boluses was evaluated by measuring the in vivo skin dose at the tumor site with optically stimulated luminescence detectors (OSLDs) and comparing the results with the prescribed and calculated doses from the Eclipse treatment planning system (TPS). The average measured dose distribution for the two patients was 94.75% of the prescribed dose and 98.8% of the calculated dose. In addition, the average measured dose during repeated treatments was 189.5 ± 3.7 cGy, thus demonstrating the excellent reproducibility of the proposed approach. Overall, the customized 3D-printed boluses for the RT of distal extremities accurately delivered doses to skin tumors with improved reproducibility.

MRI-LINAC: A transformative technology in radiation oncology

Advances in radiotherapy technologies have enabled more precise target guidance, improved treatment verification, and greater control and versatility in radiation delivery. Amongst the recent novel technologies, Magnetic Resonance Imaging (MRI) guided radiotherapy (MRgRT) may hold the greatest potential to improve the therapeutic gains of image-guided delivery of radiation dose. The ability of the MRI linear accelerator (LINAC) to image tumors and organs with on-table MRI, to manage organ motion and dose delivery in real-time, and to adapt the radiotherapy plan on the day of treatment while the patient is on the table are major advances relative to current conventional radiation treatments. These advanced techniques demand efficient coordination and communication between members of the treatment team. MRgRT could fundamentally transform the radiotherapy delivery process within radiation oncology centers through the reorganization of the patient and treatment team workflow process. However, the MRgRT technology currently is limited by accessibility due to the cost of capital investment and the time and personnel allocation needed for each fractional treatment and the unclear clinical benefit compared to conventional radiotherapy platforms. As the technology evolves and becomes more widely available, we present the case that MRgRT has the potential to become a widely utilized treatment platform and transform the radiation oncology treatment process just as earlier disruptive radiation therapy technologies have done.

A Bayesian network model for prediction of low or failed fertilization in assisted reproductive technology based on a large clinical real-world data

STUDY QUESTION

To construct prediction models based on the Bayesian network (BN) learning method for the probability of fertilization failure (including low fertilization rate [LRF] and total fertilization failure [TFF]) in assisted reproductive technology (ART) treatment. A BN model was developed to predict TFF/LFR. The model showed relatively high calibration in external validation, which could facilitate the identification of risk factors for fertilization disorders and improve the efficiency of in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment.

WHAT IS KNOWN ALREADY

The prediction of TFF/LFR is very complex. Although some studies attempted to construct prediction models for TFF/LRF, most of the reported models were based on limited variables and traditional regression-based models, which are unsuitable for analyzing real-world clinical data. Therefore, none of the reported models have been widely used in routine clinical practice. To date, BN modeling analysis is a prominent and increasingly popular machine learning method that is powerful in dealing with dynamic and complex real-world data.

STUDY DESIGN, SIZE, DURATION

A retrospective study was performed with 106,640 fresh embryo IVF/ICSI cycles from 2009 to 2019 in one of China's largest reproductive health centers.

PARTICIPANTS/MATERIALS, SETTING, METHODS

A total of 106, 640 cycles were included in this study, including 97,102 controls, 4,339 LFR cases, and 5,199 TFF cases. Twenty-four predictors were initially included, including 13 female-related variables, five male-related variables, and six variables related to IVF/ICSI treatment. BN modeling analysis with tenfold cross-validation was performed to construct the predictive model for TFF/LFR. The receiver operating characteristic (ROC) curves and the corresponding area under the curves (AUCs) were used to evaluate the performance of the BN model.

MAIN RESULTS AND THE ROLE OF CHANCE

All twenty-four predictors were first organized into seven hierarchical layers in a theoretical BN model, according to prior knowledge from previous literature and clinical practice. A machine-learning BN model was generated based on real-world clinical data, containing a total of eighteen predictors, of which the infertility type, ART method, and number of retrieved oocytes directly influence the probabilities of LFR/TFF. The prediction accuracy of the BN model was 91.7%. The AUC of the TFF versus control groups was 0.779 (95% CI: 0.766-0.791), with a sensitivity of 71.2% and specificity of 70.1%; the AUC of of TFF versus LFR groups was 0.807 (95% CI: 0.790-0.824), with a sensitivity of 49.0% and specificity of 99.0%.

LIMITATIONS, REASON FOR CAUTION

First, our study was based on clinical data from a single center, and the results of this study should be further verified by external data. In addition, some critical data (e.g., the detailed IVF laboratory parameters of the sperm and oocytes used for insemination) were not available in this study, which should be given full consideration when further improving the performance of the BN model.

WIDER IMPLICATIONS OF THE FINDINGS

Based on extensive clinical real-world data, we developed a BN model to predict the probabilities of fertilization failures in ART, which provides new clues for clinical decision-making support for clinicians in formulating personalized treatment plans and further improving ART treatment outcomes.

STUDY FUNDING/COMPETING INTEREST(S)

Dr. Y. Wang was supported by grants from the Beijing Municipal Science & Technology Commission (Z191100006619086). We declare that there are no conflicts of interest.

TRIAL REGISTRATION NUMBER

N/A.

Dose accumulation for MR-guided adaptive radiotherapy: From practical considerations to state-of-the-art clinical implementation

MRI-linear accelerator (MR-linac) devices have been introduced into clinical practice in recent years and have enabled MR-guided adaptive radiation therapy (MRgART). However, by accounting for anatomical changes throughout radiation therapy (RT) and delivering different treatment plans at each fraction, adaptive radiation therapy (ART) highlights several challenges in terms of calculating the total delivered dose. Dose accumulation strategies-which typically involve deformable image registration between planning images, deformable dose mapping, and voxel-wise dose summation-can be employed for ART to estimate the delivered dose. In MRgART, plan adaptation on MRI instead of CT necessitates additional considerations in the dose accumulation process because MRI pixel values do not contain the quantitative information used for dose calculation. In this review, we discuss considerations for dose accumulation specific to MRgART and in relation to current MR-linac clinical workflows. We present a general dose accumulation framework for MRgART and discuss relevant quality assurance criteria. Finally, we highlight the clinical importance of dose accumulation in the ART era as well as the possible ways in which dose accumulation can transform clinical practice and improve our ability to deliver personalized RT.

Evaluation of a complementary metal oxide semiconductor detector as a tool for stereotactic body radiotherapy plan quality assurance

Background and purpose

A sub-mm resolution Complementary Metal Oxide Semiconductor sensor has been developed for stereotactic radiotherapy quality assurance. Herein we evaluate its basic dosimetric performance and its application for linac C-arm stereotactic body radiotherapy (SBRT) plan quality assurance.

Materials and methods

The detector was integrated into its accompanying phantom or in Water Equivalent Plastic (WEP). The measurement reproducibility, stability, dose linearity and dependence on angularity, dose rate and field size were investigated. Clinical plan measurements were compared to our radiotherapy treatment planning system and radiochromic film. Sensitivity to introduced Multi Leaf Collimator (MLC) offsets was evaluated by simulating single MLC offsets in SBRT plans and comparing measurements to expected doses.

Results

Signal reproducibility was within ± 0.1 % and output calibration was stable over a 6 month period. Detector showed good linearity with dose (r² = 1). Signal decreased by 5 % when dose rate was decreased from 1300 MU/min to 300 MU/min. Output factors agreed within 0.5 % of chamber measurements for 1x1 cm field sizes or greater. Angularity measurements showed good agreement with reference. For measurement of planned clinical doses, gamma pass-rates were 98.5 % ± 2.3 % (treatment planning system reference, 2 %/2mm) and 99.2 % ± 1.0 % (film reference, 2 %,2mm). The detector also showed sensitivity to errors of 1 mm offsets in MLC positioning.

Conclusion

The detector performed well when used for pre-treatment SBRT plan quality assurance, offering a good alternative to radiochromic film.

Current Role of Delta Radiomics in Head and Neck Oncology

The latest developments in the management of head and neck cancer show an increasing trend in the implementation of novel approaches using artificial intelligence for better patient stratification and treatment-related risk evaluation. Radiomics, or the extraction of data from various imaging modalities, is a tool often used to evaluate specific features related to the tumour or normal tissue that are not identifiable by the naked eye and which can add value to existing clinical data. Furthermore, the assessment of feature variations from one time point to another based on subsequent images, known as delta radiomics, was shown to have even higher value for treatment-outcome prediction or patient stratification into risk categories. The information gathered from delta radiomics can, further, be used for decision making regarding treatment adaptation or other interventions found to be beneficial to the patient. The aim of this work is to collate the existing studies on delta radiomics in head and neck cancer and evaluate its role in tumour response and normal-tissue toxicity predictions alike. Moreover, this work also highlights the role of holomics, which brings under the same umbrella clinical and radiomic features, for a more complex patient characterization and treatment optimisation.

Semi-automated 18F-FDG PET segmentation methods for tumor volume determination in Non-Hodgkin lymphoma patients: a literature review, implementation and multi-threshold evaluation

In the treatment of Non-Hodgkin lymphoma (NHL), multiple therapeutic options are available. Improving outcome predictions are essential to optimize treatment. The metabolic active tumor volume (MATV) has shown to be a prognostic factor in NHL. It is usually retrieved using semi-automated thresholding methods based on standardized uptake values (SUV), calculated from ¹⁸F-Fluorodeoxyglucose Positron Emission Tomography (¹⁸F-FDG PET) images. However, there is currently no consensus method for NHL. The aim of this study was to review literature on different segmentation methods used, and to evaluate selected methods by using an in house created software tool. A software tool, MUltiple SUV Threshold (MUST)-segmenter was developed where tumor locations are identified by placing seed-points on the PET images, followed by subsequent region growing. Based on a literature review, 9 SUV thresholding methods were selected and MATVs were extracted. The MUST-segmenter was utilized in a cohort of 68 patients with NHL. Differences in MATVs were assessed with paired t-tests, and correlations and distributions figures. High variability and significant differences between the MATVs based on different segmentation methods (p < 0.05) were observed in the NHL patients. Median MATVs ranged from 35 to 211 cc. No consensus for determining MATV is available based on the literature. Using the MUST-segmenter with 9 selected SUV thresholding methods, we demonstrated a large and significant variation in MATVs. Identifying the most optimal segmentation method for patients with NHL is essential to further improve predictions of toxicity, response, and treatment outcomes, which can be facilitated by the MUST-segmenter.

Adaptive fractionation at the MR-linac

Objective. Fractionated radiotherapy typically delivers the same dose in each fraction. Adaptive fractionation (AF) is an approach to exploit inter-fraction motion by increasing the dose on days when the distance of tumor and dose-limiting organs at risk (OAR) is large and decreasing the dose on unfavorable days. We develop an AF algorithm and evaluate the concept for patients with abdominal tumors previously treated at the MR-linac in 5 fractions.Approach. Given daily adapted treatment plans, inter-fractional changes are quantified by sparing factorsδ_tdefined as the OAR-to-tumor dose ratio. The key problem of AF is to decide on the dose to deliver in fractiont, givenδ_tand the dose delivered in previous fractions, but not knowing futureδ_ts. Optimal doses that maximize the expected biologically effective dose in the tumor (BED₁₀) while staying below a maximum OAR BED₃constraint are computed using dynamic programming, assuming a normal distribution overδwith mean and variance estimated from previously observed patient-specificδ_ts. The algorithm is evaluated for 16 MR-linac patients in whom tumor dose was compromised due to proximity of bowel, stomach, or duodenum.Main Results. In 14 out of the 16 patients, AF increased the tumor BED₁₀compared to the reference treatment that delivers the same OAR dose in each fraction. However, in 11 of these 14 patients, the increase in BED₁₀was below 1 Gy. Two patients with large sparing factor variation had a benefit of more than 10 Gy BED₁₀increase. For one patient, AF led to a 5 Gy BED₁₀decrease due to an unfavorable order of sparing factors.Significance. On average, AF provided only a small increase in tumor BED. However, AF may yield substantial benefits for individual patients with large variations in the geometry.

Assessment of delivered dose in prostate cancer patients treated with ultra-hypofractionated radiotherapy on 1.5-Tesla MR-Linac

Objective

To quantitatively characterize the dosimetric effects of long on-couch time in prostate cancer patients treated with adaptive ultra-hypofractionated radiotherapy (UHF-RT) on 1.5-Tesla magnetic resonance (MR)-linac.

Materials and methods

Seventeen patients consecutively treated with UHF-RT on a 1.5-T MR-linac were recruited. A 36.25 Gy dose in five fractions was delivered every other day with a boost of 40 Gy to the whole prostate. We collected data for the following stages: pre-MR, position verification-MR (PV-MR) in the Adapt-To-Shape (ATS) workflow, and 3D-MR during the beam-on phase (Bn-MR) and at the end of RT (post-MR). The target and organ-at-risk contours in the PV-MR, Bn-MR, and post-MR stages were projected from the pre-MR data by deformable image registration and manually adapted by the physician, followed by dose recalculation for the ATS plan.

Results

Overall, 290 MR scans were collected (85 pre-MR, 85 PV-MR, 49 Bn-MR and 71 post-MR scans). With a median on-couch time of 49 minutes, the mean planning target volume (PTV)-V_95% of all scans was 97.83 ± 0.13%. The corresponding mean clinical target volume (CTV)-V_100% was 99.93 ± 0.30%, 99.32 ± 1.20%, 98.59 ± 1.84%, and 98.69 ± 1.85%. With excellent prostate-V_100% dose coverage, the main reason for lower CTV-V_100% was slight underdosing of seminal vesicles (SVs). The median V_{29 Gy} change in the rectal wall was -1% (-20%-17%). The V_{29 Gy} of the rectal wall increased by >15% was observed in one scan. A slight increase in the high dose of bladder wall was noted due to gradual bladder growth during the workflow.

Conclusions

This 3D-MR-based dosimetry analysis demonstrated clinically acceptable estimated dose coverage of target volumes during the beam-on period with adaptive ATS workflow on 1.5-T MR-linac, albeit with a relatively long on-couch time. The 3-mm CTV-PTV margin was adequate for prostate irradiation but occasionally insufficient for SVs. More attention should be paid to restricting high-dose RT to the rectal wall when optimizing the ATS plan.

Clinical Validation of Siemens' Syngo.via Automatic Contouring System

Purpose

The manual delineation of organs at risk is a process that requires a great deal of time both for the technician and for the physician. Availability of validated software tools assisted by artificial intelligence would be of great benefit, as it would significantly improve the radiation therapy workflow, reducing the time required for segmentation. The purpose of this article is to validate the deep learning-based autocontouring solution integrated in syngo.via RT Image Suite VB40 (Siemens Healthineers, Forchheim, Germany).

Methods and Materials

For this purpose, we have used our own specific qualitative classification system, RANK, to evaluate more than 600 contours corresponding to 18 different automatically delineated organs at risk. Computed tomography data sets of 95 different patients were included: 30 patients with lung, 30 patients with breast, and 35 male patients with pelvic cancer. The automatically generated structures were reviewed in the Eclipse Contouring module independently by 3 observers: an expert physician, an expert technician, and a junior physician.

Results

There is a statistically significant difference between the Dice coefficient associated with RANK 4 compared with the coefficient associated with RANKs 2 and 3 (P < .001). In total, 64% of the evaluated structures received the maximum score, 4. Only 1% of the structures were classified with the lowest score, 1. The time savings for breast, thorax, and pelvis were 87.6%, 93.5%, and 82.2%, respectively.

Conclusions

Siemens' syngo.via RT Image Suite offers good autocontouring results and significant time savings.

Radiomics Approaches for the Prediction of Pathological Complete Response after Neoadjuvant Treatment in Locally Advanced Rectal Cancer: Ready for Prime Time?

In recent years, neoadjuvant therapy of locally advanced rectal cancer has seen tremendous modifications. Adding neoadjuvant chemotherapy before or after chemoradiotherapy significantly increases loco-regional disease-free survival, negative surgical margin rates, and complete response rates. The higher complete rate is particularly clinically meaningful given the possibility of organ preservation in this specific sub-population, without compromising overall survival. However, all locally advanced rectal cancer most likely does not benefit from total neoadjuvant therapy (TNT), but experiences higher toxicity rates. Diagnosis of complete response after neoadjuvant therapy is a real challenge, with a risk of false negatives and possible under-treatment. These new therapeutic approaches thus raise the need for better selection tools, enabling a personalized therapeutic approach for each patient. These tools mostly focus on the prediction of the pathological complete response given the clinical impact. In this article, we review the place of different biomarkers (clinical, biological, genomics, transcriptomics, proteomics, and radiomics) as well as their clinical implementation and discuss the most recent trends for future steps in prediction modeling in patients with locally advanced rectal cancer.

Evaluation of short-term gastrointestinal motion and its impact on dosimetric parameters in stereotactic body radiation therapy for pancreatic cancer

Background

The aim of this study is to quantify the short-term motion of the gastrointestinal tract (GI-tract) and its impact on dosimetric parameters in stereotactic body radiation therapy (SBRT) for pancreatic cancer.

Methods

The analyzed patients were eleven pancreatic cancer patients treated with SBRT or proton beam therapy. To ensure a fair analysis, the simulation SBRT plan was generated on the planning CT in all patients with the dose prescription of 40 Gy in 5 fractions. The GI-tract motion (stomach, duodenum, small and large intestine) was evaluated using three CT images scanned at spontaneous expiration. After fiducial-based rigid image registration, the contours in each CT image were generated and transferred to the planning CT, then the organ motion was evaluated. Planning at risk volumes (PRV) of each GI-tract were generated by adding 5 mm margins, and the volume receiving at least 33 Gy (V₃₃) < 0.5 cm³ was evaluated as the dose constraint.

Results

The median interval between the first and last CT scans was 736 s (interquartile range, IQR:624-986). To compensate for the GI-tract motion based on the planning CT, the necessary median margin was 8.0 mm (IQR: 8.0-10.0) for the duodenum and 14.0 mm (12.0-16.0) for the small intestine. Compared to the planned V₃₃ with the worst case, the median V₃₃ in the PRV of the duodenum significantly increased from 0.20 cm³ (IQR: 0.02-0.26) to 0.33 cm³ (0.10-0.59) at Wilcoxon signed-rank test (p = 0.031).

Conclusion

The short-term motions of the GI-tract lead to high dose differences.

Artificial intelligence assists precision medicine in cancer treatment

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.

How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved Deep-Learning tool using multiple expert contours delineated on planning CT images

Background

A CE- and FDA-approved cloud-based Deep learning (DL)-tool for automatic organs at risk (OARs) and clinical target volumes segmentation on computer tomography images is available. Before its implementation in the clinical practice, an independent external validation was conducted.

Methods

At least a senior and two in training Radiation Oncologists (ROs) manually contoured the volumes of interest (VOIs) for 6 tumoral sites. The auto-segmented contours were retrieved from the DL-tool and, if needed, manually corrected by ROs. The level of ROs satisfaction and the duration of contouring were registered. Relative volume differences, similarity indices, satisfactory grades, and time saved were analyzed using a semi-automatic tool.

Results

Seven thousand seven hundred sixty-five VOIs were delineated on the CT images of 111 representative patients. The median (range) time for manual VOIs delineation, DL-based segmentation, and subsequent manual corrections were 25.0 (8.0-115.0), 2.3 (1.2-8) and 10.0 minutes (0.3-46.3), respectively. The overall time for VOIs retrieving and modification was statistically significantly lower than for manual contouring (p<0.001). The DL-tool was generally appreciated by ROs, with 44% of vote 4 (well done) and 43% of vote 5 (very well done), correlated with the saved time (p<0.001). The relative volume differences and similarity indexes suggested a better inter-agreement of manually adjusted DL-based VOIs than manually segmented ones.

Conclusions

The application of the DL-tool resulted satisfactory, especially in complex delineation cases, improving the ROs inter-agreement of delineated VOIs and saving time.

Effect of internal port on dose distribution in post-mastectomy radiotherapy for breast cancer patients after expander breast reconstruction

Background

In patients with expander-based reconstruction a few dosimetric analyses detected radiation therapy dose perturbation due to the internal port of an expander, potentially leading to toxicity or loss of local control. This study aimed at adding data on this field.

Materials and methods

A dosimetric analysis was conducted in 30 chest wall treatment planning without and with correction for port artifact. In plans with artifact correction density was overwritten as 1 g/cm³. Medium, minimum and maximum chest wall doses were compared in the two plans. Both plans, with and without correction, were compared on an anthropomorphic phantom with a tissue expander on the chest covered by a bolus simulating the skin. Ex vivo dosimetry was carried out on the phantom and in vivo dosimetry in three patients by using film strips during one treatment fraction. Estimated doses and measured film doses were compared.

Results

No significant differences emerged in the minimum, medium and maximum doses in the two plans, without and with correction for port artifacts. Ex vivo and in vivo analyses showed a good correspondence between detected and calculated doses without and with correction.

Conclusions

The port did not significantly affect dose distribution in patients who will receive post-mastectomy radiation therapy.

MRI radiomics and potential applications to glioblastoma

MRI plays an important role in the evaluation of glioblastoma, both at initial diagnosis and follow up after treatment. Quantitative analysis via radiomics can augment the interpretation of MRI in terms of providing insights regarding the differential diagnosis, genotype, treatment response, and prognosis. The various MRI radiomic features of glioblastoma are reviewed in this article.

A method for beam's eye view breath-hold monitoring during breast volumetric modulated arc therapy

Background and purpose

Deep inspiration breath-hold (DIBH) is a technique that is widely utilised to spare the heart and lungs during breast radiotherapy. In this study, a method was developed to validate directly the intrafraction accuracy of DIBH during breast volumetric modulated arc therapy (VMAT) via internal chest wall (CW) monitoring.

Materials and methods

In-house software was developed to automatically extract and compare the treatment position of the CW in cine-mode electronic portal image device (EPID) images with the planned CW position in digitally reconstructed radiographs (DRR) for breast VMAT treatments. Feasibility of this method was established by evaluating the percentage of total dose delivered to the target volume when the CW was sufficiently visible for monitoring. Geometric accuracy of the approach was quantified by applying known displacements to an anthropomorphic thorax phantom. The software was used to evaluate (offline) the geometric treatment accuracy for ten patients treated using real-time position management (RPM)-guided DIBH.

Results

The CW could be monitored within the tangential sub-arcs which delivered a median 89% (range 73% to 97%) of the dose to target volume. The phantom measurements showed a geometric accuracy within 1 mm, with visual inspection showing good agreement between the software-derived and user-determined CW positions. For the RPM-guided DIBH treatments, the CW was found to be within ±5 mm of the planned position in 97% of EPID frames in which the CW was visible.

Conclusion

An intrafraction monitoring method with sub-millimetre accuracy was successfully developed to validate target positioning during breast VMAT DIBH.

Rapid distortion correction enables accurate magnetic resonance imaging-guided real-time adaptive radiotherapy

Background and purpose

Magnetic resonance imaging (MRI)-Linac systems combine simultaneous MRI with radiation delivery, allowing treatments to be guided by anatomically detailed, real-time images. However, MRI can be degraded by geometric distortions that cause uncertainty between imaged and actual anatomy. In this work, we develop and integrate a real-time distortion correction method that enables accurate real-time adaptive radiotherapy.

Materials and methods

The method was based on the pre-treatment calculation of distortion and the rapid correction of intrafraction images. A motion phantom was set up in an MRI-Linac at isocentre (P0 ), the edge (P 1) and just outside (P 2) the imaging volume. The target was irradiated and tracked during real-time adaptive radiotherapy with and without the distortion correction. The geometric tracking error and latency were derived from the measurements of the beam and target positions in the EPID images.

Results

Without distortion correction, the mean geometric tracking error was 1.3 mm at P 1 and 3.1 mm at P 2. When distortion correction was applied, the error was reduced to 1.0 mm at P 1 and 1.1 mm at P 2. The corrected error was similar to an error of 0.9 mm at P0 where the target was unaffected by distortion indicating that this method has accurately accounted for distortion during tracking. The latency was 319 ± 12 ms without distortion correction and 335 ± 34 ms with distortion correction.

Conclusions

We have demonstrated a real-time distortion correction method that maintains accurate radiation delivery to the target, even at treatment locations with large distortion.

Effectiveness of 6D couch with daily cone beam computed tomography in reducing PTV margins for glioblastoma multiforme

Objectives

Image-guided radiotherapy maximizes therapeutic index of brain irradiation by reducing setup errors during treatment. The aim of study was to analyze setup errors in the radiation treatment of glioblastoma multiforme and if decrease in planning target volume (PTV), margin is feasible using daily cone beam CT (CBCT) and 6D couch correction.

Materials and Methods

Twenty-one patients (630 fractions of radiotherapy) were studied in which corrections were made in 6° of freedom. We determined setup errors, impact of setup errors of initial three fractions CBCT versus rest of the treatment with daily CBCT, and mean difference in setup errors with or without application of 6D couch and volumetric benefit of reduction of PTV margin from 0.5 cm to 0.3 cm.

Results

The mean shift in the conventional directions, namely, vertical, longitudinal, and lateral was 0.17 cm, 0.19 cm, and 0.11 cm. There was significant change in vertical shift when first three fractions were compared with rest of the treatment with daily CBCT. When the effect of 6D couch was nullified, all directions showed increased error with longitudinal shift being significant. The number of setup errors of magnitude >0.3 cm was more significant when only conventional shifts were applied as compared with 6D couch. There was significant decrease in volume of brain parenchyma irradiated when margin of PTV was reduced from 0.5 cm to 0.3 cm.

Conclusion

Daily CBCT along with 6D couch correction can reduce setup error which allows reduction in PTV margin during radiotherapy planning in turn improving the therapeutic index.