A fast and robust constraint-based online re-optimization approach for automated online adaptive intensity modulated proton therapy in head and neck cancer.

Objective. In head-and-neck cancer intensity modulated proton therapy, adaptive radiotherapy is currently restricted to offline re-planning, mitigating the effect of slow changes in patient anatomies. Daily online adaptations can potentially improve dosimetry. Here, a new, fully automated online re-optimization strategy is presented. In a retrospective study, this online re-optimization approach was compared to our trigger-based offline re-planning (offlineTBre-planning) schedule, including extensive robustness analyses.Approach. The online re-optimization method employs automated multi-criterial re-optimization, using robust optimization with 1 mm setup-robustness settings (in contrast to 3 mm for offlineTBre-planning). Hard planning constraints and spot addition are used to enforce adequate target coverage, avoid prohibitively large maximum doses and minimize organ-at-risk doses. For 67 repeat-CTs from 15 patients, fraction doses of the two strategies were compared for the CTVs and organs-at-risk. Per repeat-CT, 10.000 fractions with different setup and range robustness settings were simulated using polynomial chaos expansion for fast and accurate dose calculations.Main results. For 14/67 repeat-CTs, offlineTBre-planning resulted in <50% probability ofD98%≥ 95% of the prescribed dose (Dpres) in one or both CTVs, which never happened with online re-optimization. With offlineTBre-planning, eight repeat-CTs had zero probability of obtainingD98%≥ 95%Dpresfor CTV7000, while the minimum probability with online re-optimization was 81%. Risks of xerostomia and dysphagia grade ≥ II were reduced by 3.5 ± 1.7 and 3.9 ± 2.8 percentage point [mean ± SD] (p< 10-5for both). In online re-optimization, adjustment of spot configuration followed by spot-intensity re-optimization took 3.4 min on average.Significance. The fast online re-optimization strategy always prevented substantial losses of target coverage caused by day-to-day anatomical variations, as opposed to the clinical trigger-based offline re-planning schedule. On top of this, online re-optimization could be performed with smaller setup robustness settings, contributing to improved organs-at-risk sparing.

Time-action and patient experience analyses of locally advanced cervical cancer brachytherapy.

BACKGROUND AND PURPOSE: Although MRI-based image guided adaptive brachytherapy (IGABT) for locally advanced cervical cancer (LACC) has resulted in favorable outcomes, it can be logistically complex and time consuming compared to 2D image-based brachytherapy, and both physically and emotionally intensive for patients. This prospective study aims to perform time-action and patient experience analyses during IGABT to guide further improvements. MATERIALS AND METHODS: LACC patients treated with IGABT were included for the time-action (56 patients) and patient experience (29 patients) analyses. Times per treatment step were reported on a standardized form. For the patient experience analysis, a baseline health status was established with the EQ-5D-5L questionnaire and the perceived pain, anxiety and duration for each treatment step were assessed with the NRS-11. RESULTS: The median total procedure time from arrival until discharge was 530 (IQR: 480-565) minutes. Treatment planning (delineation, reconstruction, optimization) required the most time and took 175 (IQR: 145-195) minutes. Highest perceived pain was reported during applicator removal and treatment planning, anxiety during applicator removal, and duration during image acquisition and treatment planning. Perceived pain, anxiety and duration were correlated. Higher pre-treatment pain and anxiety scores were associated with higher perceived pain, anxiety and duration. CONCLUSION: This study highlights the complexity, duration and impact on patient experience of the current IGABT workflow. Patient reported pre-treatment pain and anxiety can help identify patients that may benefit from additional support. Research and implementation of measures aiming at shortening the overall procedure duration, which may include logistical, staffing and technological aspects, should be prioritized.

Tumor tracking with non-linear internal/external correlation models in the presence of respiratory motion baseline drifts and phase shifts.

PURPOSE: In CyberKnife® respiratory tracking, tumor positions are predicted from external marker positions using correlation models. With available models, prediction accuracy may deteriorate when respiratory motion baseline drifts occur. Previous investigations have demonstrated that for linear models this can be mitigated by adding a time-dependent term. In this study, we have focused on added value of time-dependent terms for the available non-linear correlation models, and on phase shifts between internal and external motion tracks. METHODS: Treatment simulations for tracking with and without time-dependent terms were performed using computer generated respiratory motion tracks for 60.000 patients with variable baseline drifts and phase shifts. The protocol for acquisition of X-ray images was always the same. Tumor position prediction accuracies in simulated treatments were largely based on cumulative error-time histograms and quantified with R95: in 95% of time the prediction error is < R95 mm. RESULTS: For all available correlation models, prediction accuracy improved by adding a time-dependent term in case of occurring baseline drifts, with and without phase shifts present. For the most accurate model and 150 s between model updates, adding time dependency reduced R95 from 3.9 to 3.1 mm and from 5.4 to 3.3 mm for 0.25 and 0.50 mm/min drift, respectively. Tumor position prediction accuracy improvements with time-dependent models were obtained without increases in X-ray imaging. CONCLUSIONS: Using available correlation models with an added time-dependent term could largely mitigate negative impact of respiratory motion baseline drifts on tumor position prediction accuracy, also in case of large phase shifts.

Multibody dynamic modeling of the behavior of flexible instruments used in cervical cancer brachytherapy.

BACKGROUND: The steep radiation dose gradients in cervical cancer brachytherapy (BT) necessitate a thorough understanding of the behavior of afterloader source cables or needles in the curved channels of (patient-tailored) applicators. PURPOSE: The purpose of this study is to develop and validate computer models to simulate: (1) BT source positions, and (2) insertion forces of needles in curved applicator channels. The methodology presented can be used to improve the knowledge of instrument behavior in current applicators and aid the development of novel (3D-printed) BT applicators. METHODS: For the computer models, BT instruments were discretized in finite elements. Simulations were performed in SPACAR by formulating nodal contact force and motion input models and specifying the instruments' kinematic and dynamic properties. To evaluate the source cable model, simulated source paths in ring applicators were compared with manufacturer-measured source paths. The impact of discrepancies on the dosimetry was estimated for standard plans. To validate needle models, simulated needle insertion forces in curved channels with varying curvature, torsion, and clearance, were compared with force measurements in dedicated 3D-printed templates. RESULTS: Comparison of simulated with manufacturer-measured source positions showed 0.5-1.2 mm median and <2.0 mm maximum differences, in all but one applicator geometry. The resulting maximum relative dose differences at the lateral surface and at 5 mm depth were 5.5% and 4.7%, respectively. Simulated insertion forces for BT needles in curved channels accurately resembled the forces experimentally obtained by including experimental uncertainties in the simulation. CONCLUSION: The models developed can accurately predict source positions and insertion forces in BT applicators. Insights from these models can aid novel applicator design with improved motion and force transmission of BT instruments, and contribute to the estimation of overall treatment precision. The methodology presented can be extended to study other applicator geometries, flexible instruments, and afterloading systems.

Validation of Fully Automated Robust Multicriterial Treatment Planning for Head and Neck Cancer IMPT.

PURPOSE: Our purpose was to compare robust intensity modulated proton therapy (IMPT) plans, automatically generated with wish-list-based multicriterial optimization as implemented in Erasmus-iCycle, with manually created robust clinical IMPT plans for patients with head and neck cancer. METHODS AND MATERIALS: Thirty-three patients with head and neck cancer were retrospectively included. All patients were previously treated with a manually created IMPT plan with 7000 cGy dose prescription to the primary tumor (clinical target volume [CTV]7000) and 5425 cGy dose prescription to the bilateral elective volumes (CTV5425). Plans had a 4-beam field configuration and were generated with scenario-based robust optimization (21 scenarios, 3-mm setup error, and ±3% density uncertainty for the CTVs). Three clinical plans were used to configure the Erasmus-iCycle wish-list for automated generation of robust IMPT plans for the other 30 included patients, in line with clinical planning requirements. Automatically and manually generated IMPT plans were compared for (robust) target coverage, organ-at-risk (OAR) doses, and normal tissue complication probabilities (NTCP). No manual fine-tuning of automatically generated plans was performed. RESULTS: For all automatically generated plans, voxel-wise minimum D98% values for the CTVs were within clinical constraints and similar to manual plans. All investigated OAR parameters were favorable in the automatically generated plans (all P < .001). Median reductions in mean dose to OARs went up to 667 cGy for the inferior pharyngeal constrictor muscle, and median reductions in D0.03cm3 in serial OARs ranged up to 1795 cGy for the spinal cord surface. The observed lower mean dose in parallel OARs resulted in statistically significant lower NTCP for xerostomia (grade ≥2: 34.4% vs 38.0%; grade ≥3: 9.0% vs 10.2%) and dysphagia (grade ≥2: 11.8% vs 15.0%; grade ≥3: 1.8% vs 2.8%). CONCLUSIONS: Erasmus-iCycle was able to produce IMPT dose distributions fully automatically with similar (robust) target coverage and improved OAR doses and NTCPs compared with clinical manual planning, with negligible hands-on planning workload.

Improving knowledge-based treatment planning for lung cancer radiotherapy with automatic multi-criteria optimized training plans.

BACKGROUND: Knowledge-based planning (KBP) is a method for automated radiotherapy treatment planning where appropriate optimization objectives for new patients are predicted based on a library of training plans. KBP can save time and improve organ at-risk sparing and inter-patient consistency compared to manual planning, but its performance depends on the quality of the training plans. We used another system for automated planning, which generates multi-criteria optimized (MCO) plans based on a wish list, to create training plans for the KBP model, to allow seamless integration of knowledge from a new system into clinical routine. Model performance was compared for KBP models trained with manually created and automatic MCO treatment plans. MATERIAL AND METHODS: Two RapidPlan models with the same 30 locally advanced non-small cell lung cancer patients included were created, one containing manually created clinical plans (RP_CLIN) and one containing fully automatic multi-criteria optimized plans (RP_MCO). For 15 validation patients, model performance was compared in terms of dose-volume parameters and normal tissue complication probabilities, and an oncologist performed a blind comparison of the clinical (CLIN), RP_CLIN, and RP_MCO plans. RESULTS: The heart and esophagus doses were lower for RP_MCO compared to RP_CLIN, resulting in an average reduction in the risk of 2-year mortality by 0.9 percentage points and the risk of acute esophageal toxicity by 1.6 percentage points with RP_MCO. The oncologist preferred the RP_MCO plan for 8 patients and the CLIN plan for 7 patients, while the RP_CLIN plan was not preferred for any patients. CONCLUSION: RP_MCO improved OAR sparing compared to RP_CLIN and was selected for implementation in the clinic. Training a KBP model with clinical plans may lead to suboptimal output plans, and making an extra effort to optimize the library plans in the KBP model creation phase can improve the plan quality for many future patients.

Machine Learning for Predicting Clinician Evaluation of Treatment Plans for Left-Sided Whole Breast Radiation Therapy.

Purpose: The objective of this work was to investigate the ability of machine learning models to use treatment plan dosimetry for prediction of clinician approval of treatment plans (no further planning needed) for left-sided whole breast radiation therapy with boost. Methods and Materials: Investigated plans were generated to deliver a dose of 40.05 Gy to the whole breast in 15 fractions over 3 weeks, with the tumor bed simultaneously boosted to 48 Gy. In addition to the manually generated clinical plan of each of the 120 patients from a single institution, an automatically generated plan was included for each patient to enhance the number of study plans to 240. In random order, the treating clinician retrospectively scored all 240 plans as (1) approved without further planning to seek improvement or (2) further planning needed, while being blind for type of plan generation (manual or automated). In total, 2 × 5 classifiers were trained and evaluated for ability to correctly predict the clinician's plan evaluations: random forest (RF) and constrained logistic regression (LR) classifiers, each trained for 5 different sets of dosimetric plan parameters (feature sets [FS]). Importances of included features for predictions were investigated to better understand clinicians' choices. Results: Although all 240 plans were in principle clinically acceptable for the clinician, only for 71.5% was no further planning required. For the most extensive FS, accuracy, area under the receiver operating characteristic curve, and Cohen's κ for generated RF/LR models for prediction of approval without further planning were 87.2 ± 2.0/86.7 ± 2.2, 0.80 ± 0.03/0.86 ± 0.02, and 0.63 ± 0.05/0.69 ± 0.04, respectively. In contrast to LR, RF performance was independent of the applied FS. For both RF and LR, whole breast excluding boost PTV (PTV40.05Gy) was the most important structure for predictions, with importance factors of 44.6% and 43%, respectively, dose recieved by 95% volume of PTV40.05 (D95%) as the most important parameter in most cases. Conclusions: The investigated use of machine learning to predict clinician approval of treatment plans is highly promising. Including nondosimetric parameters could further increase classifiers' performances. The tool could become useful for aiding treatment planners in generating plans with a high probability of being directly approved by the treating clinician.

Per-fraction planning to enhance optimization degrees of freedom compared to the conventional single-plan approach.

Objective. In conventional radiotherapy, a single treatment plan is generated pre-treatment, and delivered in daily fractions. In this study, we propose to generate different treatment plans for all fractions ('Per-fraction' planning) to reduce cumulative organs at risk (OAR) doses. Per-fraction planning was compared to the 'Conventional' single-plan approach for non-coplanar 4 × 9.5 Gy prostate stereotactic body radiation therapy (SBRT).Approach. An in-house application for fully automated, non-coplanar multi-criterial treatment planning with integrated beam angle and fluence optimization was used for plan generations. For the Conventional approach, a single 12-beam non-coplanar IMRT plan with individualized beam angles was generated for each of the 20 included patients. In Per-fraction planning, four fraction plans were generated for each patient. For each fraction, a different set of patient-specific 12-beam configurations could be automatically selected. Per-fraction plans were sequentially generated by adding dose to already generated fraction plan(s). For each fraction, the cumulative- and fraction dose were simultaneously optimized, allowing some minor constraint violations in fraction doses, but not in cumulative.Main results. In the Per-fraction approach, on average 32.9 ± 3.1 [29;39] unique beams per patient were used. PTV doses in the separate Per-fraction plans were acceptable and highly similar to those in Conventional plans, while also fulfilling all OAR hard constraints. When comparing total cumulative doses, Per-fraction planning showed improved bladder sparing for all patients with reductions in Dmean of 22.6% (p= 0.0001) and in D1cc of 2.0% (p= 0.0001), reductions in patient volumes receiving 30% and 50% of the prescribed dose of 54.7% and 6.3%, respectively, and a 3.1% lower rectum Dmean (p= 0.007). Rectum D1cc was 4.1% higher (p= 0.0001) and Urethra dose was similar.Significance. In this proof-of-concept paper, Per-fraction planning resulted in several dose improvements in healthy tissues compared to the Conventional single-plan approach, for similar PTV dose. By keeping the number of beams per fraction the same as in Conventional planning, reported dosimetric improvements could be obtained without increase in fraction durations. Further research is needed to explore the full potential of the Per-fraction planning approach.

A survey of practice patterns for adaptive particle therapy for interfractional changes.

Background and purpose: Anatomical changes may compromise the planned target coverage and organs-at-risk dose in particle therapy. This study reports on the practice patterns for adaptive particle therapy (APT) to evaluate current clinical practice and wishes and barriers to further implementation. Materials and methods: An institutional questionnaire was distributed to PT centres worldwide (7/2020-6/2021) asking which type of APT was used, details of the workflow, and what the wishes and barriers to implementation were. Seventy centres from 17 countries participated. A three-round Delphi consensus analysis (10/2022) among the authors followed to define recommendations on required actions and future vision. Results: Out of the 68 clinically operational centres, 84% were users of APT for at least one treatment site with head and neck being most common. APT was mostly performed offline with only two online APT users (plan-library). No centre used online daily re-planning. Daily 3D imaging was used for APT by 19% of users. Sixty-eight percent of users had plans to increase their use or change their technique for APT. The main barrier was "lack of integrated and efficient workflows". Automation and speed, reliable dose deformation for dose accumulation and higher quality of in-room volumetric imaging were identified as the most urgent task for clinical implementation of online daily APT. Conclusion: Offline APT was implemented by the majority of PT centres. Joint efforts between industry research and clinics are needed to translate innovations into efficient and clinically feasible workflows for broad-scale implementation of online APT.

A survey of practice patterns for real-time intrafractional motion-management in particle therapy.

Background and purpose: Organ motion compromises accurate particle therapy delivery. This study reports on the practice patterns for real-time intrafractional motion-management in particle therapy to evaluate current clinical practice and wishes and barriers to implementation. Materials and methods: An institutional questionnaire was distributed to particle therapy centres worldwide (7/2020-6/2021) asking which type(s) of real-time respiratory motion management (RRMM) methods were used, for which treatment sites, and what were the wishes and barriers to implementation. This was followed by a three-round DELPHI consensus analysis (10/2022) to define recommendations on required actions and future vision. With 70 responses from 17 countries, response rate was 100% for Europe (23/23 centres), 96% for Japan (22/23) and 53% for USA (20/38). Results: Of the 68 clinically operational centres, 85% used RRMM, with 41% using both rescanning and active methods. Sixty-four percent used active-RRMM for at least one treatment site, mostly with gating guided by an external marker. Forty-eight percent of active-RRMM users wished to expand or change their RRMM technique. The main barriers were technical limitations and limited resources. From the DELPHI analysis, optimisation of rescanning parameters, improvement of motion models, and pre-treatment 4D evaluation were unanimously considered clinically important future focus. 4D dose calculation was identified as the top requirement for future commercial treatment planning software. Conclusion:  A majority of particle therapy centres have implemented RRMM. Still, further development and clinical integration were desired by most centres. Joint industry, clinical and research efforts are needed to translate innovation into efficient workflows for broad-scale implementation.

Transarterial Chemoembolization With Drug-Eluting Beads Versus Stereotactic Body Radiation Therapy for Hepatocellular Carcinoma: Outcomes From a Multicenter, Randomized, Phase 2 Trial (the TRENDY Trial).

PURPOSE: To compare transarterial chemoembolization delivered with drug eluting beads (TACE-DEB) with stereotactioc body radiation therapy (SBRT) in patients with hepatocellular carcinoma (HCC) in a multicenter randomized trial. METHODS AND MATERIALS: Patients were included if they were eligible for TACE. They could also be recruited if they required treatment prior to liver transplantation. A maximum of four TACE-DEB procedures and ablation after incomplete TACE-DEB were both allowed. SBRT was delivered in six fractions of 8-9Gy. Primary end point was time to progression (TTP). Secondary endpoints were local control (LC), overall survival (OS), response rate (RR), toxicity, and quality of life (QoL). The calculated sample size was 100 patients. RESULTS: Between May 2015 and April 2020, 30 patients were randomized to the study. Due to slow accrual the trial was closed prematurely. Two patients in the SBRT arm were considered ineligible leaving 16 patients in the TACE-DEB arm and 12 in the SBRT arm. Median follow-up was 28.1 months. Median TTP was 12 months for TACEDEB and 19 months for SBRT (p=0.15). Median LC was 12 months for TACE-DEB and >40 months (not reached) for SBRT (p=0.075). Median OS was 36.8 months for TACEDEB and 44.1 months for SBRT (p=0.36). A post-hoc analysis showed 100% for SBRT 1- and 2-year LC, and 54.4% and 43.6% for TACE-DEB (p=0.019). Both treatments resulted in RR>80%. Three episodes of possibly related toxicity grade ≥3 were observed after TACE-DEB. No episodes were observed after SBRT. QoL remained stable after both treatment arms. CONCLUSIONS: In this trial, TTP after TACE-DEB was not significantly improved by SBRT, while SBRT showed higher local antitumoral activity than TACE-DEB, without detrimental effects on OS, toxicity and QoL. To overcome poor accrual in randomized trials that include SBRT, and to generate evidence for including SBRT in treatment guidelines, international cooperation is needed.

Dosimetric impact of adaptive proton therapy in head and neck cancer - A review.

Background: Intensity Modulated Proton Therapy (IMPT) in head and neck cancer (HNC) is susceptible to anatomical changes and patient set-up inaccuracies during the radiotherapy course, which can cause discrepancies between planned and delivered dose. The discrepancies can be counteracted by adaptive replanning strategies. This article reviews the observed dosimetric impact of adaptive proton therapy (APT) and the timing to perform a plan adaptation in IMPT in HNC. Methods: A literature search of articles published in PubMed/MEDLINE, EMBASE and Web of Science from January 2010 to March 2022 was performed. Among a total of 59 records assessed for possible eligibility, ten articles were included in this review. Results: Included studies reported on target coverage deterioration in IMPT plans during the RT course, which was recovered with the application of an APT approach. All APT plans showed an average improved target coverage for the high- and low-dose targets as compared to the accumulated dose on the planned plans. Dose improvements up to 2.5 Gy (3.5 %) and up to 4.0 Gy (7.1 %) in the D98 of the high- and low dose targets were observed with APT. Doses to the organs at risk (OARs) remained equal or decreased slightly after APT was applied. In the included studies, APT was largely performed once, which resulted in the largest target coverage improvement, but eventual additional APT improved the target coverage further. There is no data showing what is the most appropriate timing for APT. Conclusion: APT during IMPT for HNC patients improves target coverage. The largest improvement in target coverage was found with a single adaptive intervention, and an eventual second or more frequent APT application improved the target coverage further. Doses to the OARs remained equal or decreased slightly after applying APT. The most optimal timing for APT is yet to be determined.

Inter- and intrafraction dose variations in robotic stereotactic body radiation therapy (SBRT) for perihilar cholangiocarcinoma in the prospective phase I STRONG trial.

Using fiducial-marker-based robotic respiratory tumor tracking, we treated perihilar cholangiocarcinoma patients in the STRONG trial with 15 daily fractions of 4 Gy. For each of the included patients, in-room diagnostic-quality repeat CTs (rCT) were acquired pre- and post-dose delivery in 6 treatment fractions to analyze inter- and intrafraction dose variations. Planning CTs (pCTs) and rCTs were acquired in expiration breath-hold. Analogous to treatment, spine and fiducials were used to register rCTs with pCTs. In each rCT, all OARs were contoured, and the target was rigidly copied from the pCT based on grey values. The rCTs acquired were used to calculate the doses to be delivered through the treatment-unit settings. On average, target doses in rCTs and pCTs were similar. However, due to target displacements relative to the fiducials in rCTs, 10% of the rCTs showed PTV coverage losses of >10%. Although target coverages had been planned below desired values in order to protect OARs, many pre-rCTs contained OAR constraint violations: 44.4% for the 6 major constraints. Most OAR dose differences between pre- and post-rCTs were not statistically significant. The dose deviations observed in repeat CTs represent opportunities for more advanced adaptive approaches to enhancing SBRT treatment quality.

Patient anatomy-specific trade-offs between sub-clinical disease coverage and normal tissue dose reduction in head-and-neck cancer.

PURPOSE: Risk of subclinical disease decreases with increasing distance from the GTV in head- and-neck squamous cell carcinoma (HNSCC). Depending on individual patient anatomy, OAR sparing could be improved by reducing target coverage in regions with low risk of subclinical spread. Using automated multi-criteria optimization, we investigate patient-specific optimal trade-offs between target periphery coverage and OAR sparing. METHODS: VMAT plans for 39 HNSCC patients were retrospectively created following our clinical three-target-level protocol: high-risk (PTV1), intermediate-risk (PTV2, 5 mm expansion from PTV1), and elective (PTV3). A baseline plan fulfilling clinical constraints (D 99 % ≥95 % for all PTVs) was compared to three plans with reduced PTV2 coverage (goals: PTV2 D 99 % ≥90 % or 85 %, or no PTV2) at the outer edge of PTV2. Plans were compared on PTV D 99 %, OAR D mean, and NTCP (xerostomia/dysphagia). RESULTS: Trade-offs between PTV2 coverage and OAR doses varied considerably between patients. For plans with PTV2 D 99 % -goal 90 %, median PTV2 D 99 % was 91.5 % resulting in xerostomia (≥grade 4) and dysphagia (≥grade 2) NTCP decrease of median [maximum] 1.9 % [5.3 %] and 1.1 % [4.1 %], respectively, compared to nominal PTV2 D 99 % -goal 95 %. For PTV2 D 99 % -goal 85 % median PTV D 99 % was 87 % with NTCP improvements of 4.6 % [9.9 %] and 1.5 % [5.4 %]. For no-margin plans, PTV2 D 99 % decreased to 83.3 % with NTCP reductions of 5.1 % [10.2 %] and 1.4 % [6.1 %]. CONCLUSION: Clinically relevant, patient-specific reductions in OARs and NTCP were observed at limited cost in target under-coverage at the outermost PTV edge. Given the observed inter-patient variations, individual evaluation is warranted to determine whether trade- offs would benefit a specific patient.

A novel external/internal tumor tracking approach to compensate for respiratory motion baseline drifts.

Objective.Real-time respiratory tumor tracking as implemented in a robotic treatment unit is based on continuous optical measurement of the position of external markers and a correlation model between them and internal target positions, which are established with X-ray imaging of the tumor, or fiducials placed in or around the tumor. Correlation models are created with fifteen simultaneously measured external/internal marker position pairs divided over the respiratory cycle. Every 45-150 s, the correlation model is updated by replacing the three first acquired data pairs with three new pairs. Tracking simulations for >120.000 computer-generated respiratory tracks demonstrated that this tracking approach resulted in relevant inaccuracies in internal target position predictions, especially in case of presence of respiratory motion baseline drifts.Approach.To better cope with drifts, we introduced a novel correlation model with an explicit time dependence, and we proposed to replace the currently applied linear-motion tracking (LMT) by mixed-model tracking (MMT). In MMT, the linear correlation model is extended with an explicit time dependence in case of a detected baseline drift. MMT prediction accuracies were then established for the same >120.000 computer-generated patients as used for LMT.Main results.For 150 s update intervals, MMT outperformed LMT in internal target position prediction accuracy for 93.7 ∣ 97.2% of patients with 0.25 ∣ 0.5 mm min-1linear respiratory motion baseline drifts with similar numbers of X-ray images and similar treatment times. For the upper 25% of patients, mean 3D internal target position prediction errors reduced by 0.7 ∣ 1.8 mm, while near maximum reductions (upper 10% of patients) were 0.9 ∣ 2.0 mm.Significance.For equal numbers of acquired X-ray images, MMT greatly improved tracking accuracy compared to LMT, especially in the presence of baseline drifts. Even with almost 50% less acquired X-ray images, MMT still outperformed LMT in internal target position prediction accuracy.

Using Artificial Intelligence for Optimization of the Processes and Resource Utilization in Radiotherapy.

The radiotherapy (RT) process from planning to treatment delivery is a multistep, complex operation involving numerous levels of human-machine interaction and requiring high precision. These steps are labor-intensive and time-consuming and require meticulous coordination between professionals with diverse expertise. We reviewed and summarized the current status and prospects of artificial intelligence and machine learning relevant to the various steps in RT treatment planning and delivery workflow specifically in low- and middle-income countries (LMICs). We also searched the PubMed database using the search terms (Artificial Intelligence OR Machine Learning OR Deep Learning OR Automation OR knowledge-based planning AND Radiotherapy) AND (list of Low- and Middle-Income Countries as defined by the World Bank at the time of writing this review). The search yielded a total of 90 results, of which results with first authors from the LMICs were chosen. The reference lists of retrieved articles were also reviewed to search for more studies. No language restrictions were imposed. A total of 20 research items with unique study objectives conducted with the aim of enhancing RT processes were examined in detail. Artificial intelligence and machine learning can improve the overall efficiency of RT processes by reducing human intervention, aiding decision making, and efficiently executing lengthy, repetitive tasks. This improvement could permit the radiation oncologist to redistribute resources and focus on responsibilities such as patient counseling, education, and research, especially in resource-constrained LMICs.

Postgraduate education in radiation oncology during the COVID-19 pandemic - What did we learn?

Introduction: During the COVID-19 pandemic the ESTRO School who provides international non-profit postgraduate education in Radiation Oncology and related disciplines, including Medical Physics and Radiation Technology, had to close down all live educational activities and turn online, although having only limited experience. The paper describes the experience, discusses the limitations and benefits of online education and suggests directions for the future. Materials and methods: Data about format and feedback from attendees and faculty members from the course activities held in 2019, 2020 and 2021 were made available from the ESTRO School. Results: In 2020, all but two out of thirty live courses that happened before the lockdown were canceled. Among the 18 courses scheduled in the second half of the year, seven went online with a short notice. Each course planned their activities quite differently, from compressed courses with consecutive full days online program to courses over several weeks with a few hours online a week. Both numbers of participants and different nationalities were higher than live courses in 2019 for the seven courses happening online, and courses were well evaluated by participants and faculties. Roughly-one-third of participants would prefer online courses in the future. Discussion: Although online education was well received by the majority, pros and cons exist and especially the personal discussions and networking were missed. Online education and live education are not comparable but can complement each other. Careful balancing these activities in the future is important and strategies for online andragogy are needed.

Patient selection, inter-fraction plan robustness and reduction of toxicity risk with deep inspiration breath hold in intensity-modulated radiotherapy of locally advanced non-small cell lung cancer.

Background: State-of-the-art radiotherapy of locally advanced non-small cell lung cancer (LA-NSCLC) is performed with intensity-modulation during free breathing (FB). Previous studies have found encouraging geometric reproducibility and patient compliance of deep inspiration breath hold (DIBH) radiotherapy for LA-NSCLC patients. However, dosimetric comparisons of DIBH with FB are sparse, and DIBH is not routinely used for this patient group. The objective of this simulation study was therefore to compare DIBH and FB in a prospective cohort of LA-NSCLC patients treated with intensity-modulated radiotherapy (IMRT). Methods: For 38 LA-NSCLC patients, 4DCTs and DIBH CTs were acquired for treatment planning and during the first and third week of radiotherapy treatment. Using automated planning, one FB and one DIBH IMRT plan were generated for each patient. FB and DIBH was compared in terms of dosimetric parameters and NTCP. The treatment plans were recalculated on the repeat CTs to evaluate robustness. Correlations between ΔNTCPs and patient characteristics that could potentially predict the benefit of DIBH were explored. Results: DIBH reduced the median Dmean to the lungs and heart by 1.4 Gy and 1.1 Gy, respectively. This translated into reductions in NTCP for radiation pneumonitis grade ≥2 from 20.3% to 18.3%, and for 2-year mortality from 51.4% to 50.3%. The organ at risk sparing with DIBH remained significant in week 1 and week 3 of treatment, and the robustness of the target coverage was similar for FB and DIBH. While the risk of radiation pneumonitis was consistently reduced with DIBH regardless of patient characteristics, the ability to reduce the risk of 2-year mortality was evident among patients with upper and left lower lobe tumors but not right lower lobe tumors. Conclusion: Compared to FB, DIBH allowed for smaller target volumes and similar target coverage. DIBH reduced the lung and heart dose, as well as the risk of radiation pneumonitis and 2-year mortality, for 92% and 74% of LA-NSCLC patients, respectively. However, the advantages varied considerably between patients, and the ability to reduce the risk of 2-year mortality was dependent on tumor location. Evaluation of repeat CTs showed similar robustness of the dose distributions with each technique.

An online adaptive plan library approach for intensity modulated proton therapy for head and neck cancer.

BACKGROUND AND PURPOSE: In intensity modulated proton therapy (IMPT), the impact of setup errors and anatomical changes is commonly mitigated by robust optimization with population-based setup robustness (SR) settings and offline replanning. In this study we propose and evaluate an alternative approach based on daily plan selection from patient-specific pre-treatment established plan libraries (PLs). Clinical implementation of the PL strategy would be rather straightforward compared to daily online re-planning. MATERIALS AND METHODS: For 15 head-and-neck cancer patients, the planning CT was used to generate a PL with 5 plans, robustly optimized for increasing SR: 0, 1, 2, 3, 5 mm, and 3% range robustness. Repeat CTs (rCTs) and realistic setup and range uncertainty distributions were used for simulation of treatment courses for the PL approach, treatments with fixed SR (fSR3) and a trigger-based offline adaptive schedule for 3 mm SR (fSR3OfA). Daily plan selection in the PL approach was based only on recomputed dose to the CTV on the rCT. RESULTS: Compared to using fSR3 and fSR3OfA, the risk of xerostomia grade ≥ II & III and dysphagia ≥ grade III were significantly reduced with the PL. For 6/15 patients the risk of xerostomia and/or dysphagia ≥ grade II could be reduced by > 2% by using PL. For the other patients, adherence to target coverage constraints was often improved. fSR3OfA resulted in significantly improved coverage compared to PL for selected patients. CONCLUSION: The proposed PL approach resulted in overall reduced NTCPs compared to fSR3 and fSR3OfA at limited cost in target coverage.

Automated multi-criterial planning with beam angle optimization to establish non-coplanar VMAT class solutions for nasopharyngeal carcinoma.

PURPOSE: Complexity in selecting optimal non-coplanar beam setups and prolonged delivery times may hamper the use of non-coplanar treatments for nasopharyngeal carcinoma (NPC). Automated multi-criterial planning with integrated beam angle optimization was used to define non-coplanar VMAT class solutions (CSs), each consisting of a coplanar arc and additional 1 or 2 fixed, non-coplanar partial arcs. METHODS: Automated planning was used to generate a coplanar VMAT plan with 5 complementary computer-optimized non-coplanar IMRT beams (VMAT+5) for each of the 20 included patients. Subsequently, the frequency distribution of the 100 patient-specific non-coplanar IMRT beam directions was used to select non-coplanar arcs for supplementing coplanar VMAT. A second investigated CS with only one non-coplanar arc consisted of coplanar VMAT plus a partial arc at 90° couch angle (VMATCS90). Plans generated with the two VMATCSs were compared to coplanar VMAT. RESULTS: VMAT+5 analysis resulted in VMATCS60: two partial non-coplanar arcs at couch angles 60° and -60° to complement coplanar VMAT. Compared to coplanar VMAT, the non-coplanar VMATCS60 and VMATCS90 yielded substantial average dose reductions in OARs associated with xerostomia and dysphagia, i.e., parotids, submandibular glands, oral cavity and swallowing muscles (p < 0.05) for the same PTV coverage and without violating hard constraints. Impact of non-coplanar treatment and superiority of either VMACS60 and VMATCS90 was highly patient dependent. CONCLUSIONS: Compared to coplanar VMAT, dose to OARs was substantially reduced with a CS with one or two non-coplanar arcs. Preferences for coplanar or one or two additional arcs are highly patient-specific, balancing plan quality and treatment time.