Parameter based 4D dose calculations for proton therapy

Background and purpose Retrospective log file-based analysis provides the actual dose delivered based on the patient's breathing and the daily beam-delivery dynamics. To predict the motion sensitivity of the treatment plan on a patient-specific basis before treatment start a prospective tool is required. Such a parameter-based tool has been investigated with the aim to be used in clinical routine. Materials and Methods 4D dose calculations (4DDC) were performed for seven cancer patients with small breathing motion treated with scanned pulsed proton beams. Validation of the parameter-based 4DDC (p-4DDC) method was performed with an anthropomorphic phantom and patient data employing measurements and a log file-based 4DDC tool. The dose volume histogram parameters (Dx%) were investigated for the target and the organs at risk, compared to static and the file-based approach. Results The difference between the measured and the p-4DDC dose was within the deviation of the measurements. The maximum deviation was 0.4Gy. For the planning target volume D98% varied up to 15% compared to the static scenario, while the results from the log file and p-4DDC agreed within 2%. For the liver patients, D33%liver deviated up to 35% compared to static and 10% comparing the two 4DDC tools, while for the pancreas patients the D1%stomach varied up to 45% and 11%, respectively. Conclusion The results showed that p-4DDC could be used prospectively. The next step will be the clinical implementation of the p-4DDC tool, which can support a decision to either adapt the treatment plan or apply motion mitigation strategies.

Using density computed tomography images for photon dose calculations in radiation oncology: A patient study

Background and purpose

Conventional workflows for dose calculations require conversions between Hounsfield Units (HU) and the mass or electron density for Computed Tomography (CT) images in the Treatment Planning System (TPS). These conversions are scanner- and mostly kVp-dependent. A density representation or reconstruction at the CT level can potentially simplify the workflow. This study aimed to investigate the agreement between these two methods for patients and different calculation algorithms.

Materials and methods

Density conversions for conventional HU-density conversions were first established using two phantoms with appropriate inserts. Next, the differences in density and dose calculations between both methods were assessed using 95% Limits of Agreement (LOA) Bland-Altman analysis for 44 consecutive clinical patient cases. These cases represented a mix of indications, algorithms (collapsed cone, convolution superposition, ray tracing, finite-size pencil beam, and Monte Carlo), and scan kVp (80 to 140) in two different commercial TPS.

Results

No statistically significant bias in density or dose calculations was found between the two methods. Furthermore, 95% LOAs between both methods were ±0.05 g/cm³ and ±0.1 Gy for density and dose, respectively. Small but clinically irrelevant dose differences were found in high-density gradient regions for convolution superposition calculations or CT scans with non-delayed contrast agent injections with targets nearby vessels.

Conclusions

The in vivo density-reconstructed images at the CT level were assessed to be equivalent. Therefore, they can simplify and improve clinical workflows, allowing patient-specific acquisitions for contouring and density-reconstructed images for dose calculations.

Deep-learning magnetic resonance imaging-based automatic segmentation for organs-at-risk in the brain: Accuracy and impact on dose distribution

Background and purpose

Normal tissue sparing in radiotherapy relies on proper delineation. While manual contouring is time consuming and subject to inter-observer variability, auto-contouring could optimize workflows and harmonize practice. We assessed the accuracy of a commercial, deep-learning, MRI-based tool for brain organs-at-risk delineation.

Materials and methods

Thirty adult brain tumor patients were retrospectively manually recontoured. Two additional structure sets were obtained: AI (artificial intelligence) and AIedit (manually corrected auto-contours). For 15 selected cases, identical plans were optimized for each structure set. We used Dice Similarity Coefficient (DSC) and mean surface-distance (MSD) for geometric comparison and gamma analysis and dose-volume-histogram comparison for dose metrics evaluation. Wilcoxon signed-ranks test was used for paired data, Spearman coefficient(ρ) for correlations and Bland-Altman plots to assess level of agreement.

Results

Auto-contouring was significantly faster than manual (1.1/20 min, p < 0.01). Median DSC and MSD were 0.7/0.9 mm for AI and 0.8/0.5 mm for AIedit. DSC was significantly correlated with structure size (ρ = 0.76, p < 0.01), with higher DSC for large structures. Median gamma pass rate was 74% (71-81%) for Plan_AI and 82% (75-86%) for Plan_AIedit, with no correlation with DSC or MSD. Differences between Dmean_AI and Dmean_Ref were ≤ 0.2 Gy (p < 0.05). The dose difference was moderately correlated with DSC. Bland Altman plot showed minimal discrepancy (0.1/0) between AI and reference Dmean/Dmax.

Conclusions

The AI-model showed good accuracy for large structures, but developments are required for smaller ones. Auto-segmentation was significantly faster, with minor differences in dose distribution caused by geometric variations.

Locally advanced non-small cell lung cancer: current issues and recent trends

The focus of this paper was to review and summarise the current issues and recent trends within the framework of locally advanced (LA) non-small cell lung cancer (NSCLC). The recently proposed 8^th tumour-node-metastases (TNM) staging system exhibited significant amendments in the distribution of the T and M descriptors. Every revision to the TNM classification should contribute to clinical improvement. This is particularly necessary regarding LA NSCLC stratification, therapy and outcomes. While several studies reported the superiority of the 8^th TNM edition in comparison to the previous 7^th TNM edition, in terms of both the discrimination ability among the various T subgroups and clinical outcomes, others argued against this interpretation. Synergistic cytotoxic chemotherapy with radiotherapy is most prevalent in treating LA NSCLC. Clinical trial experience from multiple references has reported that the risk of locoregional relapse and distant metastasis was less evident for patients treated with concomitant radiochemotherapy than radiotherapy alone. Nevertheless, concern persists as to whether major incidences of toxicity may occur due to the addition of chemotherapy. Cutting-edge technologies such as four-dimensional computed tomography (4D-CT) and volumetric modulated arc therapy (VMAT) should yield therapeutic gains due to their capability to conform radiation doses to tumours. On the basis of the preceding notion, the optimum radiotherapy technique for LA NSCLC has been a controversial and much-disputed subject within the field of radiation oncology. Notably, no single-perspective research has been undertaken to determine the optimum radiotherapy modality for LA NSCLC. The landscape of immunotherapy in lung cancer is rapidly expanding. Currently, the standard of care for patients with inoperable LA NSCLC is concurrent chemoradiotherapy followed by maintenance durvalumab according to clinical outcomes from the PACIFIC trial. An estimated 42.9% of patients randomly assigned to durvalumab remained alive at five years, and free of disease progression, thereby establishing a new benchmark for the standard of care in this setting.

Instant deep sea debris detection for maneuverable underwater machines to build sustainable ocean using deep neural network

Deep sea debris is any persistent man-made material that ends up in the deep sea. The scale and rapidly increasing amount of sea debris are endangering the health of the ocean. So, many marine communities are struggling for the objective of a clean, healthy, resilient, safe, and sustainably harvested ocean. That includes deep sea debris removal with maneuverable underwater machines. Previous studies have demonstrated that deep learning methods can successfully extract features from seabed images or videos, and are capable of identifying and detecting debris to facilitate debris collection. In this paper, the lightweight neural network (termed DSDebrisNet), which can leverage the detection speed and identification performance to achieve instant detection with high accuracy, is proposed to implement compound-scaled deep sea debris detection. In DSDebrisNet, a hybrid loss function considering the illumination and detection problem was also introduced to improve performance. In addition, the DSDebris dataset is constructed by extracting images and video frames from the JAMSTEC dataset and labeled using a graphical image annotation tool. The experiments are implemented on the deep sea debris dataset, and the results indicate that the proposed methodology can achieve promising detection accuracy in real-time. The in-depth study also provides significant evidence for the successful extension branch of artificial intelligence to the deep sea research domain.

Magnetic resonance imaging in naso-oropharyngeal carcinoma: role of texture analysis in the assessment of response to radiochemotherapy, a preliminary study

OBJECTIVE

Identifying MRI texture parameters able to distinguish inflammation, fibrosis, and residual cancer in patients with naso-oropharynx carcinoma after radiochemotherapy (RT-CHT).

MATERIAL AND METHODS

In this single-centre, observational, retrospective study, texture analysis was performed on ADC maps and post-gadolinium T1 images of patients with histological diagnosis of naso-oropharyngeal carcinoma treated with RT-CHT. An initial cohort of 99 patients was selected; 57 of them were later excluded. The final cohort of 42 patients was divided into 3 groups (inflammation, fibrosis, and residual cancer) according to MRI, 18F-FDG-PET/CT performed 3-4 months after RT-CHT, and biopsy. Pre-RT-CHT lesions and the corresponding anatomic area post-RT-CHT were segmented with 3D slicer software from which 107 textural features were derived. T-Student and Wilcoxon signed-rank tests were performed, and features with p-value < 0.01 were considered statistically significant. Cut-off values-obtained by ROC curves-to discriminate post-RT-CHT non-tumoural changes from residual cancer were calculated for the parameters statistically associated to the diseased status at follow-up.

RESULTS

Two features-Energy and Grey Level Non-Uniformity-were statistically significant on T1 images in the comparison between 'positive' (residual cancer) and 'negative' patients (inflammation and fibrosis). Energy was also found to be statistically significant in both patients with fibrosis and residual cancer. Grey Level Non-Uniformity was significant in the differentiation between residual cancer and inflammation. Five features were statistically significant on ADC maps in the differentiation between 'positive' and 'negative' patients. The reduction in values of such features between pre- and post-RT-CHT was correlated with a good response to therapy.

CONCLUSIONS

Texture analysis on post-gadolinium T1 images and ADC maps can differentiate residual cancer from fibrosis and inflammation in early follow-up of naso-oropharyngeal carcinoma treated with RT-CHT.

Impact of planning organ at risk volume margins and matching method on late gastrointestinal toxicity in moderately hypofractionated IMRT for locally advanced pancreatic ductal adenocarcinoma

BACKGROUND

This study examined the differences in late gastrointestinal (GI) toxicities in moderately hypofractionated intensity-modulated radiation therapy (IMRT) for locally advanced pancreatic ductal adenocarcinoma (LA-PDAC) by changing the planning organs at risk volume (PRV) margin and the target matching method and assessed the causes of adverse events.

METHODS

We examined 37 patients with LA-PDAC who underwent moderately hypofractionated IMRT between 2016 and 2020 at our institution; 23 patients were treated with wide PRV margins and soft tissue matching (Protocol A) and 14 with narrow PRV margins and fiducial marker matching (Protocol B). The GI toxicities, local control (LC) rate, and overall survival (OS) were assessed for each protocol. The initially planned and daily doses to the gross tumor volume (GTV), stomach, and duodenum, reproduced from cone-beam computed tomography, were evaluated.

RESULTS

The late GI toxicity rate of grades 3-4 was higher in Protocol B (42.9%) than in Protocol A (4.3%). Although the 2-year LC rates were significantly higher in Protocol B (90.0%) than in Protocol A (33.3%), no significant difference was observed in OS rates. In the initial plan, no deviations were found for the stomach and duodenum from the dose constraints in either protocol. In contrast, daily dose evaluation for the stomach to duodenal bulb revealed that the frequency of deviation of V3 Gy per session was 44.8% in Protocol B, which was significantly higher than the 24.3% in Protocol A.

CONCLUSIONS

Reducing PRV margins with fiducial marker matching increased GI toxicities in exchange for improved LC. Daily dose analysis indicated the trade-off between the GTV dose coverage and the irradiated doses to the GI. This study showed that even with strict matching methods, the PRV margin could not be reduced safely because of GI inter-fractional error, which is expected to be resolved with online adaptive radiotherapy.

Accelerated hypofractionated magnetic resonance-guided adaptive radiotherapy for oligoprogressive non-small cell lung cancer

Given the positive results from recent randomized controlled trials in patients with oligometastatic, oligoprogressive, or oligoresidual disease, the role of radiotherapy has expanded in patients with metastatic non-small cell lung cancer (NSCLC). While small metastatic lesions are commonly treated with stereotactic body radiotherapy (SBRT), treatment of the primary tumor and involved regional lymph nodes may require prolonged fractionation schedules to ensure safety especially when treating larger volumes in proximity to critical organs-at-risk (OARs). We have developed an institutional MR-guided adaptive radiotherapy (MRgRT) workflow for these patients. We present a 71-year-old patient with stage IV NSCLC with oligoprogression of the primary tumor and associated regional lymph nodes in which MR-guided, online adaptive radiotherapy was performed, prescribing 60 Gy in 15 fractions. We describe our workflow, dosimetric constraints, and daily dosimetric comparisons for the critical OARs (esophagus, trachea, and proximal bronchial tree [PBT] maximum doses [D0.03cc]), in comparison to the original treatment plan recalculated on the anatomy of the day (i.e., predicted doses). During MRgRT, few fractions met the original dosimetric objectives: 6.6% for esophagus, 6.6% for PBT, and 6.6% for trachea. Online adaptive radiotherapy reduced the cumulative doses to the structures by 11.34%, 4.2%, and 5.62% when comparing predicted plan summations to the final delivered summation. Therefore, this case study presets a workflow and treatment paradigm for accelerated hypofractionated MRgRT due to the significant variations in daily dose to the central thoracic OARs to reduce treatment-related toxicity associated with radiotherapy.

Image Guided Radiotherapy (IGRT) and Delta (Δ) Radiomics-An Urgent Alliance for the Front Line of the War against Head and Neck Cancers

The identification of a biomarker that is response predictive could offer a solution for the stratification of the treatment of head and neck cancers (HNC) in the context of high recurrence rates, especially those associated with loco-regional failure. Delta (Δ) radiomics, a concept based on the variation of parameters extracted from medical imaging using artificial intelligence (AI) algorithms, demonstrates its potential as a predictive biomarker of treatment response in HNC. The concept of image-guided radiotherapy (IGRT), including computer tomography simulation (CT) and position control imaging with cone-beam-computed tomography (CBCT), now offers new perspectives for radiomics applied in radiotherapy. The use of Δ features of texture, shape, and size, both from the primary tumor and from the tumor-involved lymph nodes, demonstrates the best predictive accuracy. If, in the case of treatment response, promising Δ radiomics results could be obtained, even after 24 h from the start of treatment, for radiation-induced xerostomia, the evaluation of Δ radiomics in the middle of treatment could be recommended. The fused models (clinical and Δ radiomics) seem to offer benefits, both in comparison to the clinical model and to the radiomic model. The selection of patients who benefit from induction chemotherapy is underestimated in Δ radiomic studies and may be an unexplored territory with major potential. The advantage offered by "in house" simulation CT and CBCT favors the rapid implementation of Δ radiomics studies in radiotherapy departments. Positron emission tomography (PET)-CT Δ radiomics could guide the new concepts of dose escalation on radio-resistant sub-volumes based on radiobiological criteria, but also guide the "next level" of HNC adaptive radiotherapy (ART).

Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.

A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers

PURPOSE

To systematically assess the quality of radiomics research in giant cell tumor of bone (GCTB) and to test the feasibility of analysis at the level of radiomics feature.

METHODS

We searched PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data to identify articles of GCTB radiomics until 31 July 2022. The studies were assessed by radiomics quality score (RQS), transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement, checklist for artificial intelligence in medical imaging (CLAIM), and modified quality assessment of diagnostic accuracy studies (QUADAS-2) tool. The radiomic features selected for model development were documented.

RESULTS

Nine articles were included. The average of the ideal percentage of RQS, the TRIPOD adherence rate and the CLAIM adherence rate were 26%, 56%, and 57%, respectively. The risk of bias and applicability concerns were mainly related to the index test. The shortness in external validation and open science were repeatedly emphasized. In GCTB radiomics models, the gray level co-occurrence matrix features (40%), first order features (28%), and gray-level run-length matrix features (18%) were most selected features out of all reported features. However, none of the individual feature has appeared repeatably in multiple studies. It is not possible to meta-analyze radiomics features at present.

CONCLUSION

The quality of GCTB radiomics studies is suboptimal. The reporting of individual radiomics feature data is encouraged. The analysis at the level of radiomics feature has potential to generate more practicable evidence for translating radiomics into clinical application.

Radiosurgery for ventricular tachycardia (RAVENTA): interim analysis of a multicenter multiplatform feasibility trial

BACKGROUND

Single-session cardiac stereotactic radiation therapy (SBRT) has demonstrated promising results for patients with refractory ventricular tachycardia (VT). However, the full safety profile of this novel treatment remains unknown and very limited data from prospective clinical multicenter trials are available.

METHODS

The prospective multicenter multiplatform RAVENTA (radiosurgery for ventricular tachycardia) study assesses high-precision image-guided cardiac SBRT with 25 Gy delivered to the VT substrate determined by high-definition endocardial and/or epicardial electrophysiological mapping in patients with refractory VT ineligible for catheter ablation and an implanted cardioverter defibrillator (ICD). Primary endpoint is the feasibility of full-dose application and procedural safety (defined as an incidence of serious [grade ≥ 3] treatment-related complications ≤ 5% within 30 days after therapy). Secondary endpoints comprise VT burden, ICD interventions, treatment-related toxicity, and quality of life. We present the results of a protocol-defined interim analysis.

RESULTS

Between 10/2019 and 12/2021, a total of five patients were included at three university medical centers. In all cases, the treatment was carried out without complications. There were no serious potentially treatment-related adverse events and no deterioration of left ventricular ejection fraction upon echocardiography. Three patients had a decrease in VT episodes during follow-up. One patient underwent subsequent catheter ablation for a new VT with different morphology. One patient with local VT recurrence died 6 weeks after treatment in cardiogenic shock.

CONCLUSION

The interim analysis of the RAVENTA trial demonstrates early initial feasibility of this new treatment without serious complications within 30 days after treatment in five patients. Recruitment will continue as planned and the study has been expanded to further university medical centers.

TRIAL REGISTRATION NUMBER

NCT03867747 (clinicaltrials.gov). Registered March 8, 2019. Study start: October 1, 2019.

Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer

Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature-MRDI A median-and the activities of the TFs STAT6 (-0.64) and TFAP2A (-0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (-0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.

ESGO/ESTRO quality indicators for radiation therapy of cervical cancer

BACKGROUND

The European Society of Gynaecological Oncology (ESGO) has previously defined and established a list of quality indicators for the surgical treatment of cervical cancer. As a continuation of this effort to improve overall quality of care for cervical cancer patients across all aspects, ESGO and the European SocieTy for Radiotherapy and Oncology (ESTRO) initiated the development of quality indicators for radiation therapy of cervical cancer.

OBJECTIVE

To develop a list of quality indicators for radiation therapy of cervical cancer that can be used to audit and improve clinical practice by giving to practitioners and administrators a quantitative basis to improve care and organizational processes, notably for recognition of the increased complexity of modern external radiotherapy and brachytherapy techniques.

METHODS

Quality indicators were based on scientific evidence and/or expert consensus. The development process included a systematic literature search for identification of potential quality indicators and documentation of scientific evidence, consensus meetings of a group of international experts, an internal validation process, and external review by a large international panel of clinicians (n=99).

RESULTS

Using a structured format, each quality indicator has a description specifying what the indicator is measuring. Measurability specifications are detailed to define how the quality indicators will be measured in practice. Targets were also defined for specifying the level which each unit or center should be aiming to achieve. Nineteen structural, process, and outcome indicators were defined. Quality indicators 1-6 are general requirements related to pretreatment workup, time to treatment, upfront radiation therapy, and overall management, including active participation in clinical research and the decision making process within a structured multidisciplinary team. Quality indicators 7-17 are related to treatment indicators. Quality indicators 18 and 19 are related to patient outcomes.

DISCUSSION

This set of quality indicators is a major instrument to standardize the quality of radiation therapy in cervical cancer. A scoring system combining surgical and radiotherapeutic quality indicators will be developed within an envisaged future ESGO accreditation process for the overall management of cervical cancer, in an effort to support institutional and governmental quality assurance programs.

Are current clinical studies on artificial intelligence-based medical devices comprehensive enough to support a full health technology assessment? A systematic review

INTRODUCTION

Artificial Intelligence-based Medical Devices (AI-based MDs) are experiencing exponential growth in healthcare. This study aimed to investigate whether current studies assessing AI contain the information required for health technology assessment (HTA) by HTA bodies.

METHODS

We conducted a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to extract articles published between 2016 and 2021 related to the assessment of AI-based MDs. Data extraction focused on study characteristics, technology, algorithms, comparators, and results. AI quality assessment and HTA scores were calculated to evaluate whether the items present in the included studies were concordant with the HTA requirements. We performed a linear regression for the HTA and AI scores with the explanatory variables of the impact factor, publication date, and medical specialty. We conducted a univariate analysis of the HTA score and a multivariate analysis of the AI score with an alpha risk of 5 %.

RESULTS

Of 5578 retrieved records, 56 were included. The mean AI quality assessment score was 67 %; 32 % of articles had an AI quality score ≥ 70 %, 50 % had a score between 50 % and 70 %, and 18 % had a score under 50 %. The highest quality scores were observed for the study design (82 %) and optimisation (69 %) categories, whereas the scores were lowest in the clinical practice category (23 %). The mean HTA score was 52 % for all seven domains. 100 % of the studies assessed clinical effectiveness, whereas only 9 % evaluated safety, and 20 % evaluated economic issues. There was a statistically significant relationship between the impact factor and the HTA and AI scores (both p = 0.046).

DISCUSSION

Clinical studies on AI-based MDs have limitations and often lack adapted, robust, and complete evidence. High-quality datasets are also required because the output data can only be trusted if the inputs are reliable. The existing assessment frameworks are not specifically designed to assess AI-based MDs. From the perspective of regulatory authorities, we suggest that these frameworks should be adapted to assess the interpretability, explainability, cybersecurity, and safety of ongoing updates. From the perspective of HTA agencies, we highlight that transparency, professional and patient acceptance, ethical issues, and organizational changes are required for the implementation of these devices. Economic assessments of AI should rely on a robust methodology (business impact or health economic models) to provide decision-makers with more reliable evidence.

CONCLUSION

Currently, AI studies are insufficient to cover HTA prerequisites. HTA processes also need to be adapted because they do not consider the important specificities of AI-based MDs. Specific HTA workflows and accurate assessment tools should be designed to standardise evaluations, generate reliable evidence, and create confidence.

Clinical evaluation of a deep learning segmentation model including manual adjustments afterwards for locally advanced breast cancer

Introduction

Deep learning (DL) models are increasingly developed for auto-segmentation in radiotherapy. Qualitative analysis is of great importance for clinical implementation, next to quantitative. This study evaluates a DL segmentation model for left- and right-sided locally advanced breast cancer both quantitatively and qualitatively.

Methods

For each side a DL model was trained, including primary breast CTV (CTVp), lymph node levels 1-4, heart, lungs, humeral head, thyroid and esophagus. For evaluation, both automatic segmentation, including correction of contours when needed, and manual delineation was performed and both processes were timed. Quantitative scoring with dice-similarity coefficient (DSC), 95% Hausdorff Distance (95%HD) and surface DSC (sDSC) was used to compare both the automatic (not-corrected) and corrected contours with the manual contours. Qualitative scoring was performed by five radiotherapy technologists and five radiation oncologists using a 3-point Likert scale.

Results

Time reduction was achieved using auto-segmentation in 95% of the cases, including correction. The time reduction (mean ± std) was 42.4% ± 26.5% and 58.5% ± 19.1% for OARs and CTVs, respectively, corresponding to an absolute mean reduction (hh:mm:ss) of 00:08:51 and 00:25:38. Good quantitative results were achieved before correction, e.g. mean DSC for the right-sided CTVp was 0.92 ± 0.06, whereas correction statistically significantly improved this contour by only 0.02 ± 0.05, respectively. In 92% of the cases, auto-contours were scored as clinically acceptable, with or without corrections.

Conclusions

A DL segmentation model was trained and was shown to be a time-efficient way to generate clinically acceptable contours for locally advanced breast cancer.

Outcome prediction models incorporating clinical variables for Head and Neck Squamous cell Carcinoma: A systematic review of methodological conduct and risk of bias

Multiple outcome prediction models have been developed for Head and Neck Squamous Cell Carcinoma (HNSCC). This systematic review aimed to identify HNSCC outcome prediction model studies, assess their methodological quality and identify those with potential utility for clinical practice. Inclusion criteria were mucosal HNSCC prognostic prediction model studies (development or validation) incorporating clinically available variables accessible at time of treatment decision making and predicting tumour-related outcomes. Eligible publications were identified from PubMed and Embase. Methodological quality and risk of bias were assessed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies (CHARMS) and prediction model risk of bias assessment tool (PROBAST). Eligible publications were categorised by study type for reporting. 64 eligible publications were identified; 55 reported model development, 37 external validations, with 28 reporting both. CHARMS checklist items relating to participants, predictors, outcomes, handling of missing data, and some model development and evaluation procedures were generally well-reported. Less well-reported were measures accounting for model overfitting and model performance measures, especially model calibration. Full model information was poorly reported (3/55 model developments), specifically model intercept, baseline survival or full model code. Most publications (54/55 model developments, 28/37 external validations) were found to have high risk of bias, predominantly due to methodological issues in the PROBAST analysis domain. The identified methodological issues may affect prediction model accuracy in heterogeneous populations. Independent external validation studies in the local population and demonstration of clinical impact are essential for the clinical implementation of outcome prediction models.

First-in-human technique translation of oxygen-enhanced MRI to an MR Linac system in patients with head and neck cancer

BACKGROUND AND PURPOSE

Tumour hypoxia is prognostic in head and neck cancer (HNC), associated with poor loco-regional control, poor survival and treatment resistance. The advent of hybrid MRI - radiotherapy linear accelerator or 'MR Linac' systems - could permit imaging for treatment adaptation based on hypoxic status. We sought to develop oxygen-enhanced MRI (OE-MRI) in HNC and translate the technique onto an MR Linac system.

MATERIALS AND METHODS

MRI sequences were developed in phantoms and 15 healthy participants. Next, 14 HNC patients (with 21 primary or local nodal tumours) were evaluated. Baseline tissue longitudinal relaxation time (T1) was measured alongside the change in 1/T1 (termed ΔR1) between air and oxygen gas breathing phases. We compared results from 1.5 T diagnostic MR and MR Linac systems.

RESULTS

Baseline T1 had excellent repeatability in phantoms, healthy participants and patients on both systems. Cohort nasal concha oxygen-induced ΔR1 significantly increased (p < 0.0001) in healthy participants demonstrating OE-MRI feasibility. ΔR1 repeatability coefficients (RC) were 0.023-0.040 s-1 across both MR systems. The tumour ΔR1 RC was 0.013 s-1 and the within-subject coefficient of variation (wCV) was 25% on the diagnostic MR. Tumour ΔR1 RC was 0.020 s-1 and wCV was 33% on the MR Linac. ΔR1 magnitude and time-course trends were similar on both systems.

CONCLUSION

We demonstrate first-in-human translation of volumetric, dynamic OE-MRI onto an MR Linac system, yielding repeatable hypoxia biomarkers. Data were equivalent on the diagnostic MR and MR Linac systems. OE-MRI has potential to guide future clinical trials of biology guided adaptive radiotherapy.

Optimizing imaging resolution in brain MRI: understanding the impact of technical factors

Magnetic resonance imaging (MRI) exams are essential for diagnostic procedures, but their lengthy duration and associated costs limit their accessibility. Shorter scan times would reduce expenses and allow for more MRI exams, expanding the range of diagnostic procedures. This study investigated technical factors that could decrease scan time without compromising image quality, including field-of-view (FOV), phase field of view, phase oversampling, cross-talk, brain MRI imaging resolution, and scan time. Data were collected from September 2021 to June 2022. All patients underwent brain scans in the transverse plane following a standardized protocol using a 1.5-tesla Siemens Avanto MRI scanner. The protocol employed T2-weighted Turbo Spin Echo imaging. Twenty-four cases were included in this study. Initially, all participants underwent brain MRI scans using the original protocols with axial sections. The results indicated that altering the FOV phase and phase oversampling significantly affected the scan time, whereas other factors did not have a direct impact. The original protocol had a scan time of 3.47 minutes with a FOV of 230 mm, 90% FOV phase, and 0% phase oversampling. After implementing the modified protocol, the scan time was reduced to 2.18 minutes with a FOV of 217 mm and 93.98% phase oversampling of 13.96%. Statistical analysis confirmed the high significance of FOV phase and phase oversampling in reducing scan time. By optimizing these technical factors, MRI exams can be performed more efficiently, resulting in shorter scan times and potentially reducing costs. This would enhance patient comfort and enable a greater number of MRI exams, facilitating a more comprehensive range of diagnostic procedures.

Dosimetric Comparison Study Between Free Breathing and Breath Hold Techniques in Patients Treated by Liver-Directed Stereotactic Body Radiation Therapy

Background Breathing motion management is the key to delivering stereotactic body radiation therapy (SBRT) for liver lesions. This study aimed to compare the dosimetric parameters of liver SBRT using two different techniques: free breathing and breath hold. Method The study included 11 patients with liver metastases or hepatocellular carcinoma who underwent liver-directed SBRT. A dosimetric comparison was performed using dose-volume histogram analysis, evaluating parameters such as the maximum dose to 5 cc of bowel volume, mean liver dose (MLD), and liver V20 and V30. Statistical analyses were performed to compare results. Results The findings revealed that the breath hold technique resulted in significantly lower doses to the bowel and smaller volumes of normal liver tissue receiving 20 Gy (V20) and 30 Gy (V30) than the free breathing. Although there was no statistically significant difference in the MLD between the two techniques, the breath hold technique resulted in a lower MLD. Conclusion This dosimetric comparison study suggests that the breath hold technique is associated with lower radiation exposure to the bowel and normal liver tissues. Although this may not be feasible for all patients, it may be an appropriate procedure for selected individuals. Further research is needed to validate these findings in different patient populations and explore their impact on clinical outcomes and patient-reported quality of life.

ESGO/ESTRO quality indicators for radiation therapy of cervical cancer

BACKGROUND

The European Society of Gynaecological Oncology (ESGO) has previously defined and established a list of quality indicators for the surgical treatment of cervical cancer. As a continuation of this effort to improve overall quality of care for cervical cancer patients across all aspects, ESGO and the European SocieTy for Radiotherapy and Oncology (ESTRO) initiated the development of quality indicators for radiation therapy of cervical cancer.

OBJECTIVE

To develop a list of quality indicators for radiation therapy of cervical cancer that can be used to audit and improve clinical practice by giving to practitioners and administrators a quantitative basis to improve care and organizational processes, notably for recognition of the increased complexity of modern external radiotherapy and brachytherapy techniques.

METHODS

Quality indicators were based on scientific evidence and/or expert consensus. The development process included a systematic literature search for identification of potential quality indicators and documentation of scientific evidence, consensus meetings of a group of international experts, an internal validation process, and external review by a large international panel of clinicians (n = 99).

RESULTS

Using a structured format, each quality indicator has a description specifying what the indicator is measuring. Measurability specifications are detailed to define how the quality indicators will be measured in practice. Targets were also defined for specifying the level which each unit or center should be aiming to achieve. Nineteen structural, process, and outcome indicators were defined. Quality indicators 1-6 are general requirements related to pretreatment workup, time to treatment, upfront radiation therapy, and overall management, including active participation in clinical research and the decision making process within a structured multidisciplinary team. Quality indicators 7-17 are related to treatment indicators. Quality indicators 18 and 19 are related to patient outcomes.

DISCUSSION

This set of quality indicators is a major instrument to standardize the quality of radiation therapy in cervical cancer. A scoring system combining surgical and radiotherapeutic quality indicators will be developed within an envisaged future ESGO accreditation process for the overall management of cervical cancer, in an effort to support institutional and governmental quality assurance programs.

Reappraisal of the role of radiation therapy in lymphoma treatment

Radiation therapy (RT) for lymphomas has improved dramatically with modern imaging and treatment techniques, encompassing only the necessary volume with minimal doses to normal structures. Prescribed radiation doses are reduced, and fractionation schedules are under revision. With effective systemic treatment only initial macroscopic disease is irradiated. With no or less effective systemic treatment, possible microscopic disease is also included. Risks of long-term side effects of RT have diminished dramatically and should be weighed against risks from more systemic treatment or increased risk of relapse. Lymphoma patients are often elderly, they tolerate modern limited RT very well. Lymphomas refractory to systemic treatments often remain radioresponsive, and brief, mild RT may offer effective palliation. New roles for RT are emerging with immune therapies. RT for "bridging," keeping the lymphoma under control while waiting for immune therapy, is well established. Enhancement of the immune response to lymphomas, so-called "priming," is being intensively researched.

Image guidance and interfractional anatomical variation in paediatric abdominal radiotherapy

OBJECTIVES

To identify variables predicting interfractional anatomical variations measured with cone-beam CT (CBCT) throughout abdominal paediatric radiotherapy, and to assess the potential of surface-guided radiotherapy (SGRT) to monitor these changes.

METHODS

Metrics of variation in gastrointestinal (GI) gas volume and separation of the body contour and abdominal wall were calculated from 21 planning CTs and 77 weekly CBCTs for 21 abdominal neuroblastoma patients (median 4 years, range: 2 - 19 years). Age, sex, feeding tubes, and general anaesthesia (GA) were explored as predictive variables for anatomical variation. Furthermore, GI gas variation was correlated with changes in body and abdominal wall separation, as well as simulated SGRT metrics of translational and rotational corrections between CT/CBCT.

RESULTS

GI gas volumes varied 74 ± 54 ml across all scans, while body and abdominal wall separation varied 2.0 ± 0.7 mm and 4.1 ± 1.5 mm from planning, respectively. Patients < 3.5 years (p = 0.04) and treated under GA (p < 0.01) experienced greater GI gas variation; GA was the strongest predictor in multivariate analysis (p < 0.01). Absence of feeding tubes was linked to greater body contour variation (p = 0.03). GI gas variation correlated with body (R = 0.53) and abdominal wall (R = 0.63) changes. The strongest correlations with SGRT metrics were found for anterior-posterior translation (R = 0.65) and rotation of the left-right axis (R = -0.36).

CONCLUSIONS

Young age, GA, and absence of feeding tubes were linked to stronger interfractional anatomical variation and are likely indicative of patients benefiting from adaptive/robust planning pathways. Our data suggest a role for SGRT to inform the need for CBCT at each treatment fraction in this patient group.

ADVANCES IN KNOWLEDGE

This is the first study to suggest the potential role of SGRT for the management of internal interfractional anatomical variation in paediatric abdominal radiotherapy.

Magnetic Resonance Imaging in the Clinical Care for Uveal Melanoma Patients-A Systematic Review from an Ophthalmic Perspective

Conversely to most tumour types, magnetic resonance imaging (MRI) was rarely used for eye tumours. As recent technical advances have increased ocular MRI's diagnostic value, various clinical applications have been proposed. This systematic review provides an overview of the current status of MRI in the clinical care of uveal melanoma (UM) patients, the most common eye tumour in adults. In total, 158 articles were included. Two- and three-dimensional anatomical scans and functional scans, which assess the tumour micro-biology, can be obtained in routine clinical setting. The radiological characteristics of the most common intra-ocular masses have been described extensively, enabling MRI to contribute to diagnoses. Additionally, MRI's ability to non-invasively probe the tissue's biological properties enables early detection of therapy response and potentially differentiates between high- and low-risk UM. MRI-based tumour dimensions are generally in agreement with conventional ultrasound (median absolute difference 0.5 mm), but MRI is considered more accurate in a subgroup of anteriorly located tumours. Although multiple studies propose that MRI's 3D tumour visualisation can improve therapy planning, an evaluation of its clinical benefit is lacking. In conclusion, MRI is a complementary imaging modality for UM of which the clinical benefit has been shown by multiple studies.

MR-based follow-up after brachytherapy and proton beam therapy in uveal melanoma

PURPOSE

MRI is increasingly used in the diagnosis and therapy planning of uveal melanoma (UM). In this prospective cohort study, we assessed the radiological characteristics, in terms of anatomical and functional imaging, of UM after ruthenium-106 plaque brachytherapy or proton beam therapy (PBT) and compared them to conventional ultrasound.

METHODS

Twenty-six UM patients were evaluated before and 3, 6 and 12 months after brachytherapy (n = 13) or PBT (n = 13). Tumour prominences were compared between ultrasound and MRI. On diffusion-weighted imaging, the apparent diffusion value (ADC), and on perfusion-weighted imaging (PWI), the time-intensity curves (TIC), relative peak intensity and outflow percentages were determined. Values were compared between treatments and with baseline.

RESULTS

Pre-treatment prominences were comparable between MRI and ultrasound (mean absolute difference 0.51 mm, p = 0.46), but larger differences were observed post-treatment (e.g. 3 months: 0.9 mm (p = 0.02)). Pre-treatment PWI metrics were comparable between treatment groups. After treatment, brachytherapy patients showed favourable changes on PWI (e.g. 67% outflow reduction at 3 months, p < 0.01). After PBT, significant perfusion changes were observed at a later timepoint (e.g. 38% outflow reduction at 6 months, p = 0.01). No consistent ADC changes were observed after either treatment, e.g. a 0.11 × 10^-3mm²/s increase 12 months after treatment (p = 0.15).

CONCLUSION

MR-based follow-up is valuable for PBT-treated patients as favourable perfusion changes, including a reduction in outflow, can be detected before a reduction in size is apparent on ultrasound. For brachytherapy, a follow-up MRI is of less value as already 3 months post-treatment a significant size reduction can be measured on ultrasound.

Comparison of different dose accumulation strategies to estimate organ doses after stereotactic magnetic resonance-guided adaptive radiotherapy

INTRODUCTION

Re-irradiation is frequently performed in the era of precision oncology, but previous doses to organs-at-risk (OAR) must be assessed to avoid cumulative overdoses. Stereotactic magnetic resonance-guided online adaptive radiotherapy (SMART) enables highly precise ablation of tumors close to OAR. However, OAR doses may change considerably during adaptive treatment, which complicates potential re-irradiation. We aimed to compare the baseline plan with different dose accumulation techniques to inform re-irradiation.

PATIENTS & METHODS

We analyzed 18 patients who received SMART to lung or liver tumors inside prospective databases. Cumulative doses were calculated inside the planning target volumes (PTV) and OAR for the adapted plans and theoretical non-adapted plans via (1) cumulative dose volume histograms (DVH sum plan) and (2) deformable image registration (DIR)-based dose accumulation to planning images (DIR sum plan). We compared cumulative dose parameters between the baseline plan, DVH sum plan and DIR sum plan using equivalent doses in 2 Gy fractions (EQD2).

RESULTS

Individual patients presented relevant increases of near-maximum doses inside the proximal bronchial tree, spinal cord, heart and gastrointestinal OAR when comparing adaptive treatment to the baseline plans. The spinal cord near-maximum doses were significantly increased in the liver patients (D2% median: baseline 6.1 Gy, DIR sum 8.1 Gy, DVH sum 8.4 Gy, p = 0.04; D0.1 cm³ median: baseline 6.1 Gy, DIR sum 8.1 Gy, DVH sum 8.5 Gy, p = 0.04). Three OAR overdoses occurred during adaptive treatment (DIR sum: 1, DVH sum: 2), and four more intense OAR overdoses would have occurred during non-adaptive treatment (DIR sum: 4, DVH sum: 3). Adaptive treatment maintained similar PTV coverages to the baseline plans, while non-adaptive treatment yielded significantly worse PTV coverages in the lung (D95% median: baseline 86.4 Gy, DIR sum 82.4 Gy, DVH sum 82.2 Gy, p = 0.006) and liver patients (D95% median: baseline 87.4 Gy, DIR sum 82.1 Gy, DVH sum 81.1 Gy, p = 0.04).

CONCLUSION

OAR doses can increase during SMART, so that re-irradiation should be planned based on dose accumulations of the adapted plans instead of the baseline plan. Cumulative dose volume histograms represent a simple and conservative dose accumulation strategy.

Clinical Applications of Magnetic Resonance-Guided Radiotherapy: A Narrative Review

Magnetic resonance-guided radiotherapy (MRgRT) represents a promising new image guidance technology for radiation treatment delivery combining an onboard MRI scanner with radiation delivery technology. By enabling real-time low-field or high-field MRI acquisition, it facilitates improved soft tissue delineation, adaptive treatment, and motion management. Now that MRgRT has been available for nearly a decade, research has shown the technology can be used to effectively shrink treatment margins to either decrease toxicity (in breast, prostate cancer, and pancreatic cancer) or facilitate dose-escalation and improved oncologic outcomes (in pancreatic and liver cancer), as well as enabling indications that require clear soft tissue delineation and gating (lung and cardiac ablation). In doing so, the use of MRgRT has the potential to significantly improve the outcomes and quality of life of the patients it treats. The present narrative review aims to describe the rationale for MRgRT, the current and forthcoming state of technology, existing studies, and future directions for the advancement of MRgRT, including associated challenges.

Development of a PTV margin for preclinical irradiation of orthotopic pancreatic tumors derived from a well-known recipe for humans

In human radiotherapy a safety margin (PTV margin) is essential for successful irradiation and is usually part of clinical treatment planning. In preclinical radiotherapy research with small animals, most uncertainties and inaccuracies are present as well, but according to the literature a margin is used only scarcely. In addition, there is only little experience about the appropriate size of the margin, which should carefully be investigated and considered, since sparing of organs at risk or normal tissue is affected. Here we estimate the needed margin for preclinical irradiation by adapting a well-known human margin recipe from van Herck et al. to the dimensions and requirements of the specimen on a small animal radiation research platform (SARRP). We adjusted the factors of the described formula to the specific challenges in an orthotopic pancreatic tumor mouse model to establish an appropriate margin concept. The SARRP was used with its image-guidance irradiation possibility for arc irradiation with a field size of 10 × 10 mm² for 5 fractions. Our goal was to irradiate the clinical target volume (CTV) of at least 90% of our mice with at least 95% of the prescribed dose. By carefully analyzing all relevant factors we gain a CTV to planning target volume (PTV) margin of 1.5 mm for our preclinical setup. The stated safety margin is strongly dependent on the exact setting of the experiment and has to be adjusted for other experimental settings. The few stated values in literature correspond well to our result. Even if using margins in the preclinical setting might be an additional challenge, we think it is crucial to use them to produce reliable results and improve the efficacy of radiotherapy.

Gaussian Processes for real-time 3D motion and uncertainty estimation during MR-guided radiotherapy

Respiratory motion during radiotherapy causes uncertainty in the tumor's location, which is typically addressed by an increased radiation area and a decreased dose. As a result, the treatments' efficacy is reduced. The recently proposed hybrid MR-linac scanner holds the promise to efficiently deal with such respiratory motion through real-time adaptive MR-guided radiotherapy (MRgRT). For MRgRT, motion-fields should be estimated from MR-data and the radiotherapy plan should be adapted in real-time according to the estimated motion-fields. All of this should be performed with a total latency of maximally 200 ms, including data acquisition and reconstruction. A measure of confidence in such estimated motion-fields is highly desirable, for instance to ensure the patient's safety in case of unexpected and undesirable motion. In this work, we propose a framework based on Gaussian Processes to infer 3D motion-fields and uncertainty maps in real-time from only three readouts of MR-data. We demonstrated an inference frame rate up to 69 Hz including data acquisition and reconstruction, thereby exploiting the limited amount of required MR-data. Additionally, we designed a rejection criterion based on the motion-field uncertainty maps to demonstrate the framework's potential for quality assurance. The framework was validated in silico and in vivo on healthy volunteer data (n=5) acquired using an MR-linac, thereby taking into account different breathing patterns and controlled bulk motion. Results indicate end-point-errors with a 75th percentile below 1 mm in silico, and a correct detection of erroneous motion estimates with the rejection criterion. Altogether, the results show the potential of the framework for application in real-time MR-guided radiotherapy with an MR-linac.

A virtual audit system for intensity-modulated radiation therapy credentialing in Japan Clinical Oncology Group clinical trials: A pilot study

PURPOSE

The Medical Physics Working Group of the Radiation Therapy Study Group at the Japan Clinical Oncology Group is currently developing a virtual audit system for intensity-modulated radiation therapy dosimetry credentialing. The target dosimeters include films and array detectors, such as ArcCHECK (Sun Nuclear Corporation, Melbourne, Florida, USA) and Delta4 (ScandiDos, Uppsala, Sweden). This pilot study investigated the feasibility of our virtual audit system using previously acquired data.

METHODS

We analyzed 46 films (32 and 14 in the axial and coronal planes, respectively) from 29 institutions. Global gamma analysis between measured and planned dose distributions used the following settings: 3%/3 mm criteria (the dose denominator was 2 Gy), 30% threshold dose, no scaling of the datasets, and 90% tolerance level. In addition, 21 datasets from nine institutions were obtained for array evaluation. Five institutions used ArcCHECK, while the others used Delta4. Global gamma analysis was performed with 3%/2 mm criteria (the dose denominator was the maximum calculated dose), 10% threshold dose, and 95% tolerance level. The film calibration and gamma analysis were conducted with in-house software developed using Python (version 3.9.2).

RESULTS

The means ± standard deviations of the gamma passing rates were 99.4 ± 1.5% (range, 92.8%-100%) and 99.2 ± 1.0% (range, 97.0%-100%) in the film and array evaluations, respectively.

CONCLUSION

This pilot study demonstrated the feasibility of virtual audits. The proposed virtual audit system will contribute to more efficient, cheaper, and more rapid trial credentialing than on-site and postal audits; however, the limitations should be considered when operating our virtual audit system.

Knowledge-based plan optimization for prostate SBRT delivered with CyberKnife according to RTOG0938 protocol

PURPOSE

To extend the knowledge-based (KB) automatic planning approach to CyberKnife in the case of Stereotactic Body Radiation Therapy (SBRT) for prostate cancer.

METHODS

Seventy-two clinical plans of patients treated according to the RTOG0938 protocol (36.25 Gy/5fr) with CyberKnife were exported from the CyberKnife system to Eclipse to train a KB-model using the Rapid Plan tool. The KB approach provided dose-volume objectives for specific OARs only and not PTV. Bladder, rectum and femoral heads were considered in the model. The KB-model was successfully trained on 51 plans and then validated on 20 new patients. A KB-based template was tuned in the Precision system for both sequential optimization (SO) and VOLO optimization algorithms. Plans of the validation group were re-optimized (KB-TP) using both algorithms without any operator intervention and compared against the original plans (TP) in terms of OARs/PTV dose-volume parameters. Paired Wilcoxon signed-rank tests were performed to assess statistically significant differences (p < 0.05).

RESULTS

Regarding SO, automatic KB-TP plans were generally better than or equivalent to TP plans. PTVs V95% was slightly worse while OARs sparing for KB-TP was significantly improved. Regarding VOLO optimization, the PTVs coverage was significantly better for KB-TP while there was a limited worsening in the rectum. A significant improvement was observed in the bladder in the range of low-intermediate doses.

CONCLUSIONS

An extension of the KB optimization approach to the CyberKnife system has been successfully developed and validated in the case of SBRT prostate cancer.

Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability

OBJECTIVES

To evaluate robustness of dual-energy CT (DECT) radiomics features of virtual unenhanced (VUE) image and virtual monoenergetic image (VMI) among different imaging platforms.

METHODS

A phantom with sixteen clinical-relevant densities was scanned on ten DECT platforms with comparable scan parameters. Ninety-four radiomic features were extracted via Pyradiomics from VUE images and VMIs at energy level of 70 keV (VMI_70keV). Test-retest repeatability was assessed by Bland-Altman analysis. Inter-platform reproducibility of VUE images and VMI_70keV was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD) among platforms, and by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) between platform pairs. The correlation between variability of CT number radiomics reproducibility was estimated.

RESULTS

92.02% and 92.87% of features were repeatable between scan-rescans for VUE images and VMI_70keV, respectively. Among platforms, 11.30% and 28.39% features of VUE images, and 15.16% and 28.99% features of VMI_70keV were with CV < 10% and QCD < 10%. The average percentages of radiomics features with ICC > 0.90 and CCC > 0.90 between platform pairs were 10.00% and 9.86% in VUE images and 11.23% and 11.23% in VMI_70keV. The CT number inter-platform reproducibility using CV and QCD showed negative correlations with percentage of the first-order radiomics features with CV < 10% and QCD < 10%, in both VUE images and VMI_70keV (r² 0.3870-0.6178, all p < 0.001).

CONCLUSIONS

The majority of DECT radiomics features were non-reproducible. The differences in CT number were considered as an indicator of inter-platform DECT radiomics variation. Critical relevance statement: The majority of radiomics features extracted from the VUE images and the VMI70keV were non-reproducible among platforms, while synchronizing energy levels of VMI to reduce the CT number value variability may be a potential way to mitigate radiomics instability.

Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions

Radiomics refers to the conversion of medical imaging into high-throughput, quantifiable data in order to analyse disease patterns, guide prognosis and aid decision making. Radiogenomics is an extension of radiomics that combines conventional radiomics techniques with molecular analysis in the form of genomic and transcriptomic data, serving as an alternative to costly, labour-intensive genetic testing. Data on radiomics and radiogenomics in the field of pelvic oncology remain novel concepts in the literature. We aim to perform an up-to-date analysis of current applications of radiomics and radiogenomics in the field of pelvic oncology, particularly focusing on the prediction of survival, recurrence and treatment response. Several studies have applied these concepts to colorectal, urological, gynaecological and sarcomatous diseases, with individual efficacy yet poor reproducibility. This article highlights the current applications of radiomics and radiogenomics in pelvic oncology, as well as the current limitations and future directions. Despite a rapid increase in publications investigating the use of radiomics and radiogenomics in pelvic oncology, the current evidence is limited by poor reproducibility and small datasets. In the era of personalised medicine, this novel field of research has significant potential, particularly for predicting prognosis and guiding therapeutic decisions. Future research may provide fundamental data on how we treat this cohort of patients, with the aim of reducing the exposure of high-risk patients to highly morbid procedures.

Evaluation treatment planning system for oropharyngeal cancer patient using machine learning

Oropharyngeal cancer (OPC) comprises a group of various malignant tumours that grow in the throat, larynx, mouth, sinuses, and nose. THE RESEARCH AIMS: to investigate the performance of the OPC VMAT model by comparison to clinical plans in terms of dosimetric parameters and normal tissue complication probabilities.

PURPOSE

Tune the model which at least matches the performance of clinical created photon treatment plans and analyse and find the most appropriate strategic plan scheme for OPC.

METHODS AND MATERIALS

The machine learning (ML) plans are compared to the reference plans (clinical plans) based on dose constraints and target coverage. VMAT oropharynx ML model of Raystation development 11B version (non-clinical) was used. A model was trained by using different modalities. A different strategy of machine learning and clinical plans was performed for five patients. The dose Prescribed for OPC is 70 Gy, 2 Gy per fraction (2Gy/Fx). The PTV was derived for the primary tumour and secondary tumour, PTV+7000 cGy and PTV_5425 cGy volumetric modulated arc therapy (VMAT) were used with beams performing a full 360° rotation around the single isocenter.

RESULTS

Organs at risk were observed that the volume of L-Eye in clinical plan (AF) for the case1 treatment planning could be successfully used ensuring efficiency and lower than MLVMAT and MLVMAT-org plans were 372 cGy, 697 cGy and 667 cGy respectively, while showed case2, case3, case4 and case5 are better to protect the critical organs in ML plan compare with a clinical plan. DHI for the PTV-7000 and PTV-5425 is between 1 and 1.34, While DCI for PTV-7000 and PTV-5425 is between 0.98 and 1.

Online adaptive planning methods for intensity-modulated radiotherapy

Online adaptive radiation therapy aims at adapting a patient's treatment plan to their current anatomy to account for inter-fraction variations before daily treatment delivery. As this process needs to be accomplished while the patient is immobilized on the treatment couch, it requires time-efficient adaptive planning methods to generate a quality daily treatment plan rapidly. The conventional planning methods do not meet the time requirement of online adaptive radiation therapy because they often involve excessive human intervention, significantly prolonging the planning phase. This article reviews the planning strategies employed by current commercial online adaptive radiation therapy systems, research on online adaptive planning, and artificial intelligence's potential application to online adaptive planning.

An anatomy-aware framework for automatic segmentation of parotid tumor from multimodal MRI

Magnetic Resonance Imaging (MRI) plays an important role in diagnosing the parotid tumor, where accurate segmentation of tumors is highly desired for determining appropriate treatment plans and avoiding unnecessary surgery. However, the task remains nontrivial and challenging due to ambiguous boundaries and various sizes of the tumor, as well as the presence of a large number of anatomical structures around the parotid gland that are similar to the tumor. To overcome these problems, we propose a novel anatomy-aware framework for automatic segmentation of parotid tumors from multimodal MRI. First, a Transformer-based multimodal fusion network PT-Net is proposed in this paper. The encoder of PT-Net extracts and fuses contextual information from three modalities of MRI from coarse to fine, to obtain cross-modality and multi-scale tumor information. The decoder stacks the feature maps of different modalities and calibrates the multimodal information using the channel attention mechanism. Second, considering that the segmentation model is prone to be disturbed by similar anatomical structures and make wrong predictions, we design anatomy-aware loss. By calculating the distance between the activation regions of the prediction segmentation and the ground truth, our loss function forces the model to distinguish similar anatomical structures with the tumor and make correct predictions. Extensive experiments with MRI scans of the parotid tumor showed that our PT-Net achieved higher segmentation accuracy than existing networks. The anatomy-aware loss outperformed state-of-the-art loss functions for parotid tumor segmentation. Our framework can potentially improve the quality of preoperative diagnosis and surgery planning of parotid tumors.

Comprehensive dose evaluation of a Deep Learning based synthetic Computed Tomography algorithm for pelvic Magnetic Resonance-only radiotherapy

BACKGROUND AND PURPOSE

Magnetic Resonance (MR)-only radiotherapy enables the use of MR without the uncertainty of MR-Computed Tomography (CT) registration. This requires a synthetic CT (sCT) for dose calculations, which can be facilitated by a novel Zero Echo Time (ZTE) sequence where bones are visible and images are acquired in 65 seconds. This study evaluated the dose calculation accuracy for pelvic sites of a ZTE-based Deep Learning sCT algorithm developed by GE Healthcare.

MATERIALS AND METHODS

ZTE and CT images were acquired in 56 pelvic radiotherapy patients in the radiotherapy position. A 2D U-net convolutional neural network was trained using pairs of deformably registered CT and ZTE images from 36 patients. In the remaining 20 patients the dosimetric accuracy of the sCT was assessed using cylindrical dummy Planning Target Volumes (PTVs) positioned at four different central axial locations, as well as the clinical treatment plans (for prostate (n=10), rectum (n=4) and anus (n=6) cancers). The sCT was rigidly and deformably registered, the plan recalculated and the doses compared using mean differences and gamma analysis.

RESULTS

Mean dose differences to the PTV D98% were ≤ 0.5% for all dummy PTVs and clinical plans (rigid registration). Mean gamma pass rates at 1%/1 mm were 98.0 ± 0.4% (rigid) and 100.0 ± 0.0% (deformable), 96.5 ± 0.8% and 99.8 ± 0.1%, and 95.4 ± 0.6% and 99.4 ± 0.4% for the clinical prostate, rectum and anus plans respectively.

CONCLUSIONS

A ZTE-based sCT algorithm with high dose accuracy throughout the pelvis has been developed. This suggests the algorithm is sufficiently accurate for MR-only radiotherapy for all pelvic sites.

Deep learning based synthetic CT from cone beam CT generation for abdominal paediatric radiotherapy

Objective. Adaptive radiotherapy workflows require images with the quality of computed tomography (CT) for re-calculation and re-optimisation of radiation doses. In this work we aim to improve the quality of on-board cone beam CT (CBCT) images for dose calculation using deep learning.Approach. We propose a novel framework for CBCT-to-CT synthesis using cycle-consistent Generative Adversarial Networks (cycleGANs). The framework was tailored for paediatric abdominal patients, a challenging application due to the inter-fractional variability in bowel filling and small patient numbers. We introduced to the networks the concept of global residuals only learning and modified the cycleGAN loss function to explicitly promote structural consistency between source and synthetic images. Finally, to compensate for the anatomical variability and address the difficulties in collecting large datasets in the paediatric population, we applied a smart 2D slice selection based on the common field-of-view (abdomen) to our imaging dataset. This acted as a weakly paired data approach that allowed us to take advantage of scans from patients treated for a variety of malignancies (thoracic-abdominal-pelvic) for training purposes. We first optimised the proposed framework and benchmarked its performance on a development dataset. Later, a comprehensive quantitative evaluation was performed on an unseen dataset, which included calculating global image similarity metrics, segmentation-based measures and proton therapy-specific metrics.Main results. We found improved performance for our proposed method, compared to a baseline cycleGAN implementation, on image-similarity metrics such as Mean Absolute Error calculated for a matched virtual CT (55.0 ± 16.6 HU proposed versus 58.9 ± 16.8 HU baseline). There was also a higher level of structural agreement for gastrointestinal gas between source and synthetic images measured using the dice similarity coefficient (0.872 ± 0.053 proposed versus 0.846 ± 0.052 baseline). Differences found in water-equivalent thickness metrics were also smaller for our method (3.3 ± 2.4% proposed versus 3.7 ± 2.8% baseline).Significance. Our findings indicate that our innovations to the cycleGAN framework improved the quality and structure consistency of the synthetic CTs generated.

CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII

Even though radiomics can hold great potential for supporting clinical decision-making, its current use is mostly limited to academic research, without applications in routine clinical practice. The workflow of radiomics is complex due to several methodological steps and nuances, which often leads to inadequate reporting and evaluation, and poor reproducibility. Available reporting guidelines and checklists for artificial intelligence and predictive modeling include relevant good practices, but they are not tailored to radiomic research. There is a clear need for a complete radiomics checklist for study planning, manuscript writing, and evaluation during the review process to facilitate the repeatability and reproducibility of studies. We here present a documentation standard for radiomic research that can guide authors and reviewers. Our motivation is to improve the quality and reliability and, in turn, the reproducibility of radiomic research. We name the checklist CLEAR (CheckList for EvaluAtion of Radiomics research), to convey the idea of being more transparent. With its 58 items, the CLEAR checklist should be considered a standardization tool providing the minimum requirements for presenting clinical radiomics research. In addition to a dynamic online version of the checklist, a public repository has also been set up to allow the radiomics community to comment on the checklist items and adapt the checklist for future versions. Prepared and revised by an international group of experts using a modified Delphi method, we hope the CLEAR checklist will serve well as a single and complete scientific documentation tool for authors and reviewers to improve the radiomics literature.

Application of three-dimensional multi-imaging combination in brachytherapy of cervical cancer

BACKGROUND

Three-dimensional (3D) imaging has an important role in brachytherapy and the treatment of cervical cancer. The main imaging methods used in the cervical cancer brachytherapy include magnetic resonance imaging (MRI), computer tomography (CT), ultrasound (US), and positron emission tomography (PET). However, single-imaging methods have certain limitations compared to multi-imaging. The application of multi-imaging can make up for the shortcomings and provide a more suitable imaging selection for brachytherapy.

PURPOSE

This review details the situation and scope of existing multi-imaging combination methods in cervical cancer brachytherapy and provides a reference for medical institutions.

MATERIALS AND METHODS

Searched the literature related to application of three-dimensional multi-imaging combination in brachytherapy of cervical cancer in PubMed/Medline and Web of Science electronic databases. Summarized the existing combined imaging methods and the application of each method in cervical cancer brachytherapy.

CONCLUSION

The current imaging combination methods mainly include MRI/CT, US/CT, MRI/US, and MRI/PET. The combination of two imaging tools can be used for applicator implantation guidance, applicator reconstruction, target and organs at risk (OAR) contouring, dose optimization, prognosis evaluation, etc., which provides a more suitable imaging choice for brachytherapy.

Implementing cone-beam computed tomography-guided online adaptive radiotherapy in cervical cancer

Background and purpose

Adaptive radiotherapy (ART) in locally advanced cervical cancer (LACC) has shown promising outcomes. This study investigated the feasibility of cone-beam computed tomography (CBCT)-guided online ART (oART) for the treatment of LACC.

Material and methods

The quality of the automated radiotherapy treatment plans and artificial intelligence (AI)-driven contour delineation for LACC on a novel CBCT-guided oART system were assessed. Dosimetric analysis of 200 simulated oART sessions were compared with standard treatment. Feasibility of oART was assessed from the delivery of 132 oART fractions for the first five clinical LACC patients. The simulated and live oART sessions compared a fixed planning target volume (PTV) margin of 1.5 cm around the uterus-cervix clinical target volume (CTV) with an internal target volume-based approach. Workflow timing measurements were recorded.

Results

The automatically-generated 12-field intensity-modulated radiotherapy plans were comparable to manually generated plans. The AI-driven organ-at-risk (OAR) contouring was acceptable requiring, on average, 12.3 min to edit, with the bowel performing least well and rated as unacceptable in 16 % of cases. The treated patients demonstrated a mean PTV D98% (+/-SD) of 96.7 (+/- 0.2)% for the adapted plans and 94.9 (+/- 3.7)% for the non-adapted scheduled plans (p<10^-5). The D2cc (+/-SD) for the bowel, bladder and rectum were reduced by 0.07 (+/- 0.03)Gy, 0.04 (+/-0.05)Gy and 0.04 (+/-0.03)Gy per fraction respectively with the adapted plan (p <10^-5). In the live.setting, the mean oART session (+/-SD) from CBCT acquisition to beam-on was 29 +/- 5 (range 21-44) minutes.

Conclusion

CBCT-guided oART was shown to be feasible with dosimetric benefits for patients with LACC. Further work to analyse potential reductions in PTV margins is ongoing.

3D Capsule Networks for Brain Image Segmentation

BACKGROUND AND PURPOSE

Current autosegmentation models such as UNets and nnUNets have limitations, including the inability to segment images that are not represented during training and lack of computational efficiency. 3D capsule networks have the potential to address these limitations.

MATERIALS AND METHODS

We used 3430 brain MRIs, acquired in a multi-institutional study, to train and validate our models. We compared our capsule network with standard alternatives, UNets and nnUNets, on the basis of segmentation efficacy (Dice scores), segmentation performance when the image is not well-represented in the training data, performance when the training data are limited, and computational efficiency including required memory and computational speed.

RESULTS

The capsule network segmented the third ventricle, thalamus, and hippocampus with Dice scores of 95%, 94%, and 92%, respectively, which were within 1% of the Dice scores of UNets and nnUNets. The capsule network significantly outperformed UNets in segmenting images that were not well-represented in the training data, with Dice scores 30% higher. The computational memory required for the capsule network is less than one-tenth of the memory required for UNets or nnUNets. The capsule network is also >25% faster to train compared with UNet and nnUNet.

CONCLUSIONS

We developed and validated a capsule network that is effective in segmenting brain images, can segment images that are not well-represented in the training data, and is computationally efficient compared with alternatives.

A model for gastrointestinal tract motility in a 4D imaging phantom of human anatomy

BACKGROUND

Gastrointestinal (GI) tract motility is one of the main sources for intra/inter-fraction variability and uncertainty in radiation therapy for abdominal targets. Models for GI motility can improve the assessment of delivered dose and contribute to the development, testing, and validation of deformable image registration (DIR) and dose-accumulation algorithms.

PURPOSE

To implement GI tract motion in the 4D extended cardiac-torso (XCAT) digital phantom of human anatomy.

MATERIALS AND METHODS

Motility modes that exhibit large amplitude changes in the diameter of the GI tract and may persist over timescales comparable to online adaptive planning and radiotherapy delivery were identified based on literature research. Search criteria included amplitude changes larger than planning risk volume expansions and durations of the order of tens of minutes. The following modes were identified: peristalsis, rhythmic segmentation, high amplitude propagating contractions (HAPCs), and tonic contractions. Peristalsis and rhythmic segmentations were modeled by traveling and standing sinusoidal waves. HAPCs and tonic contractions were modeled by traveling and stationary Gaussian waves. Wave dispersion in the temporal and spatial domain was implemented by linear, exponential, and inverse power law functions. Modeling functions were applied to the control points of the nonuniform rational B-spline surfaces defined in the reference XCAT library. GI motility was combined with the cardiac and respiratory motions available in the standard 4D-XCAT phantom. Default model parameters were estimated based on the analysis of cine MRI acquisitions in 10 patients treated in a 1.5T MR-linac.

RESULTS

We demonstrate the ability to generate realistic 4D multimodal images that simulate GI motility combined with respiratory and cardiac motion. All modes of motility, except tonic contractions, were observed in the analysis of our cine MRI acquisitions. Peristalsis was the most common. Default parameters estimated from cine MRI were used as initial values for simulation experiments. It is shown that in patients undergoing stereotactic body radiotherapy for abdominal targets, the effects of GI motility can be comparable or larger than the effects of respiratory motion.

CONCLUSION

The digital phantom provides realistic models to aid in medical imaging and radiation therapy research. The addition of GI motility will further contribute to the development, testing, and validation of DIR and dose accumulation algorithms for MR-guided radiotherapy.

Applicability and usage of dose mapping/accumulation in radiotherapy

Dose mapping/accumulation (DMA) is a topic in radiotherapy (RT) for years, but has not yet found its widespread way into clinical RT routine. During the ESTRO Physics workshop 2021 on "commissioning and quality assurance of deformable image registration (DIR) for current and future RT applications", we built a working group on DMA from which we present the results of our discussions in this article. Our aim in this manuscript is to shed light on the current situation of DMA in RT and to highlight the issues that hinder consciously integrating it into clinical RT routine. As a first outcome of our discussions, we present a scheme where representative RT use cases are positioned, considering expected anatomical variations and the impact of dose mapping uncertainties on patient safety, which we have named the DMA landscape (DMAL). This tool is useful for future reference when DMA applications get closer to clinical day-to-day use. Secondly, we discussed current challenges, lightly touching on first-order effects (related to the impact of DIR uncertainties in dose mapping), and focusing in detail on second-order effects often dismissed in the current literature (as resampling and interpolation, quality assurance considerations, and radiobiological issues). Finally, we developed recommendations, and guidelines for vendors and users. Our main point include: Strive for context-driven DIR (by considering their impact on clinical decisions/judgements) rather than perfect DIR; be conscious of the limitations of the implemented DIR algorithm; and consider when dose mapping (with properly quantified uncertainties) is a better alternative than no mapping.

Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation

BACKGROUND

Contouring error is one of the top failure modes in radiation treatment. Multiple efforts have been made to develop tools to automatically detect segmentation errors. Deep learning-based auto-segmentation (DLAS) has been used as a baseline for flagging manual segmentation errors, but those efforts are limited to using only one or two contour comparison metrics.

PURPOSE

The purpose of this research is to develop an improved contouring quality assurance system to identify and flag manual contouring errors.

METHODS AND MATERIALS

DLAS contours were used as a reference to compare with manually segmented contours. A total of 27 geometric agreement metrics were determined from the comparisons between the two segmentation approaches. Feature selection was performed to optimize the training of a machine learning classification model to identify potential contouring errors. A public dataset with 339 cases was used to train and test the classifier. Four independent classifiers were trained using five-fold cross validation, and the predictions from each classifier were ensembled using soft voting. The trained model was validated on a held-out testing dataset. An additional independent clinical dataset with 60 cases was used to test the generalizability of the model. Model predictions were reviewed by an expert to confirm or reject the findings.

RESULTS

The proposed machine learning multiple features (ML-MF) approach outperformed traditional nonmachine-learning-based approaches that are based on only one or two geometric agreement metrics. The machine learning model achieved recall (precision) values of 0.842 (0.899), 0.762 (0.762), 0.727 (0.842), and 0.773 (0.773) for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively compared to 0.526 (0.909), 0.619 (0.765), 0.682 (0.882), 0.773 (0.568) for an approach based solely on Dice similarity coefficient values. In the external validation dataset, 66.7, 93.3, 94.1, and 58.8% of flagged cases were confirmed to have contouring errors by an expert for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively.

CONCLUSIONS

The proposed ML-MF approach, which includes multiple geometric agreement metrics to flag manual contouring errors, demonstrated superior performance in comparison to traditional methods. This method is easy to implement in clinical practice and can help to reduce the significant time and labor costs associated with manual segmentation and review.

Practical and technical key challenges in head and neck adaptive radiotherapy: The GORTEC point of view

Anatomical variations occur during head and neck (H&N) radiotherapy (RT) treatment. These variations may result in underdosage to the target volume or overdosage to the organ at risk. Replanning during the treatment course can be triggered to overcome this issue. Due to technological, methodological and clinical evolutions, tools for adaptive RT (ART) are becoming increasingly sophisticated. The aim of this paper is to give an overview of the key steps of an H&N ART workflow and tools from the point of view of a group of French-speaking medical physicists and physicians (from GORTEC). Focuses are made on image registration, segmentation, estimation of the delivered dose of the day, workflow and quality assurance for an implementation of H&N offline and online ART. Practical recommendations are given to assist physicians and medical physicists in a clinical workflow.

Patient Breathing Motion and Delivery Specifics Influencing the Robustness of a Proton Pancreas Irradiation

Motion compensation strategies in particle therapy depend on the anatomy, motion amplitude and underlying beam delivery technology. This retrospective study on pancreas patients with small moving tumours analysed existing treatment concepts and serves as a basis for future treatment strategies for patients with larger motion amplitudes as well as the transition towards carbon ion treatments. The dose distributions of 17 hypofractionated proton treatment plans were analysed using 4D dose tracking (4DDT). The recalculation of clinical treatment plans employing robust optimisation for mitigating different organ fillings was performed on phased-based 4D computed tomography (4DCT) data considering the accelerator (pulsed scanned pencil beams delivered by a synchrotron) and the breathing-time structure. The analysis confirmed the robustness of the included treatment plans concerning the interplay of beam and organ motion. The median deterioration of D_50% (ΔD_50%) for the clinical target volume (CTV) and the planning target volume (PTV) was below 2%, while the only outlier was observed for ΔD_98% with -35.1%. The average gamma pass rate over all treatment plans (2%/ 2 mm) was 88.8% ± 8.3, while treatment plans for motion amplitudes larger than 1 mm performed worse. For organs at risk (OARs), the median ΔD_2% was below 3%, but for single patients, essential changes, e.g., up to 160% for the stomach were observed. The hypofractionated proton treatment for pancreas patients based on robust treatment plan optimisation and 2 to 4 horizontal and vertical beams showed to be robust against intra-fractional movements up to 3.7 mm. It could be demonstrated that the patient's orientation did not influence the motion sensitivity. The identified outliers showed the need for continuous 4DDT calculations in clinical practice to identify patient cases with more significant deviations.

Robust Beam Selection Based on Water Equivalent Thickness Analysis in Passive Scattering Carbon-Ion Radiotherapy for Pancreatic Cancer

Carbon-ion radiotherapy (CIRT) is one of the most effective radiotherapeutic modalities. This study aimed to select robust-beam configurations (BC) by water equivalent thickness (WET) analysis in passive CIRT for pancreatic cancer. The study analyzed 110 computed tomography (CT) images and 600 dose distributions of eight patients with pancreatic cancer. The robustness in the beam range was evaluated using both planning and daily CT images, and two robust BCs for the rotating gantry and fixed port were selected. The planned, daily, and accumulated doses were calculated and compared after bone matching (BM) and tumor matching (TM). The dose-volume parameters for the target and organs at risk (OARs) were evaluated. Posterior oblique beams (120-240°) in the supine position and anteroposterior beams (0° and 180°) in the prone position were the most robust to WET changes. The mean CTV V95% reductions with TM were -3.8% and -5.2% with the BC for gantry and the BC for fixed ports, respectively. Despite ensuring robustness, the dose to the OARs increased slightly with WET-based BCs but remained below the dose constraint. The robustness of dose distribution can be improved by BCs that are robust to ΔWET. Robust BC with TM improves the accuracy of passive CIRT for pancreatic cancer.

Proton Beam Therapy in the Oligometastatic/Oligorecurrent Setting: Is There a Role? A Literature Review

BACKGROUND

Stereotactic ablative radiotherapy (SABR) and stereotactic radiosurgery (SRS) with conventional photon radiotherapy (XRT) are well-established treatment options for selected patients with oligometastatic/oligorecurrent disease. The use of PBT for SABR-SRS is attractive given the property of a lack of exit dose. The aim of this review is to evaluate the role and current utilisation of PBT in the oligometastatic/oligorecurrent setting.

METHODS

Using Medline and Embase, a comprehensive literature review was conducted following the PICO (Patients, Intervention, Comparison, and Outcomes) criteria, which returned 83 records. After screening, 16 records were deemed to be relevant and included in the review.

RESULTS

Six of the sixteen records analysed originated in Japan, six in the USA, and four in Europe. The focus was oligometastatic disease in 12, oligorecurrence in 3, and both in 1. Most of the studies analysed (12/16) were retrospective cohorts or case reports, two were phase II clinical trials, one was a literature review, and one study discussed the pros and cons of PBT in these settings. The studies presented in this review included a total of 925 patients. The metastatic sites analysed in these articles were the liver (4/16), lungs (3/16), thoracic lymph nodes (2/16), bone (2/16), brain (1/16), pelvis (1/16), and various sites in 2/16.

CONCLUSIONS

PBT could represent an option for the treatment of oligometastatic/oligorecurrent disease in patients with a low metastatic burden. Nevertheless, due to its limited availability, PBT has traditionally been funded for selected tumour indications that are defined as curable. The availability of new systemic therapies has widened this definition. This, together with the exponential growth of PBT capacity worldwide, will potentially redefine its commissioning to include selected patients with oligometastatic/oligorecurrent disease. To date, PBT has been used with encouraging results for the treatment of liver metastases. However, PBT could be an option in those cases in which the reduced radiation exposure to normal tissues leads to a clinically significant reduction in treatment-related toxicities.

Using machine learning for healthcare treatment planning

We present a methodology for using machine learning for planning treatments. As a case study, we apply the proposed methodology to Breast Cancer. Most of the application of Machine Learning to breast cancer has been on diagnosis and early detection. By contrast, our paper focuses on applying Machine Learning to suggest treatment plans for patients with different disease severity. While the need for surgery and even its type is often obvious to a patient, the need for chemotherapy and radiation therapy is not as obvious to the patient. With this in mind, the following treatment plans were considered in this study: chemotherapy, radiation, chemotherapy with radiation, and none of these options (only surgery). We use real data from more than 10,000 patients over 6 years that includes detailed cancer information, treatment plans, and survival statistics. Using this data set, we construct Machine Learning classifiers to suggest treatment plans. Our emphasis in this effort is not only on suggesting the treatment plan but on explaining and defending a particular treatment choice to the patient.