1 INTRODUCTION

In online adaptive radiotherapy (OART), the treatment plan is adjusted to the specific anatomy on a given day to ensure the optimal trade-off between irradiating the treatment target and sparing of normal tissue. OART has the potential to result in significant clinical benefits for prostate patients.1-9 A study by Ahunbay et al.1 reported a 13% increase in minimum PTV dose and a 13% decrease in equivalent uniform dose to the rectum when using different online adaptive strategies for prostate.

The technical challenges of utilizing a full daily replan for OART are significant.10 An image of the daily anatomy needs to be acquired, the anatomy contoured, and a plan generated based on the anatomy of the day. The plan then needs to be evaluated by clinical staff and subject to a quality assurance (QA) process, before being delivered to the patient. This all needs to be performed in a sufficiently short timeframe such that the anatomy being adapted to does not change from the initial image. This timeframe depends on the anatomy being treated; but in the pelvis, significant changes in bladder size can occur within 15 min.11, 12

Until recently, the technical and logistical challenges of OART made it practically infeasible for most radiotherapy centers. The introduction of artificial intelligence (AI)13, 14 and graphics processing unit (GPU) based calculation engines15-17 have allowed for the many steps in an online adaptive workflow to be performed in the timeframe required for OART. The Varian Ethos system (Varian Medical Systems, Palo Alto, CA) was recently developed as a completely self-contained online adaptive solution, and has been reported to be capable of performing adaptive treatments within 15–20 min.18-20 The dosimetric accuracy of the Ethos treatment planning system has previously been comprehensively verified.21

The workflow used on Ethos for online adaptive prostate patients involves the use of “influencer” structures that are initially auto-contoured using AI. The influencer structures are then adjusted by the user if necessary and used to create a structure-guided deformable image registration (DIR) between the planning computed tomography (CT) scan and the acquired cone beam computed tomography (CBCT) scan. An elastic DIR is also created between the planning CT and CBCT. The gross tumor volumes (GTVs) and clinical target volumes (CTVs) are propagated from the planning CT to the CBCT using the structure-guided DIR if the GTV/CTV is considered mobile, or the elastic DIR if considered nonmobile.20, 22 The elastic DIR is also used to both propagate noninfluencer organs at risk (OARs) and generate a synthetic CT by deforming the planning CT into the CBCT geometry. This synthetic CT uses the planning CT Hounsfield units (HU) to provide the density information for dose calculations performed in the treatment geometry, and its accuracy is validated on a patient-specific basis by visually checking structure agreement with the CBCT. A plan is generated based upon a predefined “planning directive” optimized to the anatomy of the day and referred to as the adaptive plan. The original treatment plan (reference plan) has an automated match applied, then is recalculated based on the anatomy of the day and referred to as the scheduled plan. The user selects either the scheduled or adaptive plan for the treatment. The plan selected receives pretreatment calculation-based QA, and posttreatment delivery log file-based QA using Mobius (Varian Medical Systems, Palo Alto, CA), an established patient-specific QA solution.23 A verification image can be acquired after completing the adaptive process and before treatment, to account for any intrafraction motion, and the treatment is then delivered to the patient.24

Direct validation of many of the steps in the Ethos adaptive process is difficult because they are not able to be performed in isolation, nor are the structure guided and elastic DIRs able to be exported or visualized. The system is generally designed as a “black box,” where only the inputs and outputs are available for interrogation. For this reason, other early studies18, 19 have taken the form of analyzing DIR and AI outputs clinically.

Due to the novelty of the Ethos system, there is little published research investigating an optimal method or expected results for prostate OART using Ethos. Yoon et al.19 performed an initial evaluation of the Ethos system on retrospective head and neck patient data, finding 82% of contours were subjectively scored as ≥4 out of 5, where 1 represented unacceptable contours, and 5 represented perfect contouring. A recent study by Sibolt et al.18 presented preliminary data on the clinical implementation of the Ethos system across a range of pelvic sites. Eight retrospective prostate plans were included, and every fifth fraction was analyzed. They found that either no or minor edits to influencer contours were required in 76% of fractions, and the adaptive plan was selected in 88% of fractions. However, the number of fractions analyzed in this study was small, nodal information was not presented separately, and prostate bed data were excluded.

The aim of this study is to report early results on the accuracy of automated contouring, plan quality, and treatment fraction timing for Ethos OART to the prostate. A range of clinical metrics for each fraction are reported for intact prostate, prostate and nodes, and prostate bed and nodes treatments from one institution. This will assist centers to gain an understanding of the dosimetric benefits possible with this treatment technique, as well as a starting point when developing their own OART workflow with this new technology.

2 METHODS 2.1 Study dataset

Eighteen patients were selected for the study dataset. This was made up of 12 patients previously treated on a Halcyon that had a simulated treatment performed on the Ethos treatment emulator, a nonclinical version of the Ethos software setup for treatment fraction simulations. This retrospective dataset was supplemented with six clinical adaptive cases treated on the Ethos system.

At our institution, prescribed doses and organ at risk (OAR) limits are primarily based on the eviQ guidelines (an Australian evidence-based and peer-reviewed guideline).25 Contouring was adjusted and plan selection carried out based on standard plan criteria. The 12 retrospective patients consisted of four intact prostate cases (prescribed 60 Gy/20 Fx), four prostate bed and node cases (prescribed 66 Gy/33 Fx), and four prostate and node cases (prescribed 78 Gy/39 Fx). Within the retrospective dataset, every second fraction was simulated in the treatment emulator, thus analyzing images over the entire treatment course. The total retrospective dataset consisted of 182 simulated fractions.

The six clinical adaptive cases included two intact prostate cases, two prostate and node cases, and two prostate bed and node cases. These patients were treated using the same online adaptive workflow tested on the retrospective patients. This dataset included every fraction, 184 in total.

Considering the extended treatment times on Ethos, the patient comfort was ensured by marginally reducing pretreatment bladder filling from 500 ml (used previously within the institution) to 400 ml. Note that the retrospective dataset used the previous 500 ml filling, while the clinical patients used the new 400 ml filling. The full test dataset is summarized in Table 1.

TABLE 1. Summary of dataset used for study Treatment site Number of retrospective patients Number of clinical patients Total fractions Number of patients with implanted fiducials Comments Intact prostate 4 2 84 3 2 with hydrogel spacer  Intact prostate and nodes 4 2 150 3 1 with hydrogel spacer  Prostate bed and nodes 4 2 132 0 Surgical clips visible in 3 cases, 1 patient with prosthetic hip 2.2 Reference plan generation

To generate a plan in Ethos, a set of clinical goals is required. The goals have a dual function of being the clinical intent of the plan, as well as the goals used in the optimization. Each goal has a minimum acceptable value and an ideal value. Generally, the clinical goal (based on eviQ25) was entered as the minimum acceptable value, and an ideal value (somewhat equivalent to an optimization goal) was entered as the ideal value. More information regarding the Intelligent Optimization Engine (IOE) is provided by Archambault et al.20 All planning directives used in the study had at least three minimum dose goals per prescribed dose level (CTV D98, PTV D98, and D95).

All plans used rectum and bladder as influencer structures, and cases with an intact prostate also used the prostate and seminal vesicles as influencer structures. CTVs were determined by the radiation oncologist based on CT images. For intact prostate patients, CTVs were created as independent structures, not derived from the prostate and seminal vesicle structures. All other structures were determined by an RT and reviewed by the radiation oncologist. As this was a first step in developing OART for prostate at our institution, standard IGRT margins based on eviQ guidelines25 were used, without any reductions.

A 7, 9, or 12 field IMRT plan was generated for each case. The study included plans from both Ethos v1.0 and v1.0 MR1. VMAT was not used as it has been observed to give inferior plan quality in the current version of Ethos.18

2.3 Treatment

All staff involved in performing treatments for this study (either retrospective or clinical) underwent vendor supplied Ethos training in addition to in-house credentialling. The in-house credentialling included graded assessments of workflow knowledge, delineation of pelvic anatomy on CT images, and Ethos adaptive treatments on the emulator.

Retrospective emulator treatments were carried out by a physicist or radiation therapist. Users were instructed to match the influencers with the anatomy seen on the CBCT, while targets and noninfluencer OARs were assessed and adjusted if changes were expected to make a clinical impact to the plan. In practice, this meant changes to targets and noninfluencer OARs smaller than 2 mm were not applied, and changes to noninfluencer OARs further than 3 cm from the PTV were not applied.

Clinical treatments were carried out by a team consisting of at least two radiation therapists and a physicist under the supervision of the treating radiation oncologist. Depending on progress through the treatment course, the radiation oncologist was available in-person or online. A postadaptive pretreatment verification CBCT scan was acquired between completion of the adaptive planning process and treatment delivery, with assessment of intrafraction motion during the adaptive planning timeframe. If deemed necessary by the clinical team, translational couch shifts were applied before delivering the treatment.

2.4 Metrics assessed

The metrics assessed for each delivered fraction are outlined below, they capture the frequency and magnitude of contour edits, changes in plan quality, and time required for OART to the prostate.

2.4.1 Contour accuracy The frequency and magnitude of edits for influencers, targets, and noninfluencer OARs were recorded as an indicator of amount of manual intervention required. They are based on the method applied by Sibolt et al.18 and act as a surrogate for automated contouring accuracy. For each structure in each fraction, users were required to categorize the editing required as either:

No edits required—no changes made to the structure.

Minor edits required—less than 10% of slices requiring small edits.

Moderate edits required— > 10% of slices requiring minor edits, or major edits required to a small number (10%) of slices.

Major edits required—edits not described in above categories, up to and including deletion of structure and recontouring.

Not applicable—not relevant to the fraction or not assessed.

2.4.2 Plan quality

A range of plan quality metrics were analyzed for each fraction. To assess average plan quality for the adaptive plan as compared to the scheduled plan, the number of clinical goals met at both the minimum acceptable level and the ideal level was recorded for each fraction. The value for the scheduled plan was then subtracted from the value for the adapted plan, giving the difference in the number of goals met, where a positive value indicates that more goals were met for the adaptive plan, and a negative value indicates that more goals were met for the scheduled plan. This metric was chosen because: it is calculated from the clinical goals which are indicative of clinical outcomes, it combines all plan metrics into a single comparative value for each fraction, and it allows specific goals used to vary between treatment sites. Note that the use of this metric means that all clinical goals are considered equal, whereas in clinical practice, the oncologist will usually prioritize some metrics over others. The frequency of adaptive plan selection was also recorded for each fraction. To investigate how the goals differed between the adaptive and scheduled plans, the median PTV and OAR goals were analyzed over the treatment course for a representative patient. Statistical significance was determined using a Wilcoxon signed rank test and the pseudo-median (Hodges–Lehmann) displayed, as some DVH parameters were not normally distributed. The null hypothesis (H0) was that there was no difference between the adapted and scheduled plans, with significance set at p = 0.05.

2.4.3 Adaptive time

The time for the retrospective emulator treatments was recorded from simulated completion of image acquisition to plan acceptance. Clinical treatment time was recorded from the time of opening the patient on the Ethos treatment machine to the time of closing the patient. In a small number of clinical treatments, the patient was given additional time or taken off the couch to release rectal gas; these fractions were excluded from the timing dataset.

3 RESULTS 3.1 Influencer contouring accuracy

The frequency and magnitude of edits are shown for each influencer in Figure 1a. No edits were required in 11% of fractions overall, and minor edits were required in 81% of fractions overall.

Frequency of edits required for (a) influencer structures; (b) target structures; (c) noninfluencer OAR structures; and (d) frequency of sigmoid colon editing by treatment site

The bladder contouring of the patient with a hip prosthesis was significantly worse and accounted for all the fractions, where the user had to make major edits to the bladder contour, as well as a large number of fractions with moderate edits. The bowel influencer had previously been tested and was found to give inconsistent results. It was not used in the planning intents in this study.

3.2 Target contouring accuracy

Figure 1b shows the frequency of CTV editing required. As can be seen for a prostate target, no change is required more than 80% of the time. For cases involving nodes and prostate bed, the frequency of CTV editing increases significantly. Overall the percentage of CTVs requiring no change was 72%, and requiring no or minor changes was 91%.

3.3 OAR contouring accuracy (excluding influencers)

The frequency of editing the noninfluencer OARs is shown in Figure 1c. Noninfluencer OARs were assessed in every fraction they were available for assessment, however depending on the priority assigned to the structure in planning, in many cases they were not available for assessment. The sigmoid colon contouring required changes much more frequently than any other structure. No comparable data for noninfluencer OARs have been reported in the literature.

By separating the sigmoid colon contouring data by treatment site, treatment site-specific differences can be visualized. It was found that there were considerable differences in sigmoid colon contouring accuracy for the prostate bed cases, shown in Figure 1d.

3.4 Differences in number of clinical goals met

A histogram of the differences in the number of goals met over all fractions analyzed is shown in Figure 2. The distribution is strongly positively skewed, indicating that in the majority (78%) of fractions the number of goals met by the adaptive plan is greater than that met by the scheduled plan. Fifteen percent of fractions have no difference in number of goals met by the adaptive and scheduled plans, and 7% have more goals met for the scheduled plan compared to the adaptive plan.

Histogram of differences in number of planning clinical goals met per fraction

3.5 Frequency of adaptive plan selection

The frequency that the adaptive plan was selected for each treatment site is shown in Table 2. Overall the adapted plan was selected in 95% of fractions, with it being selected less frequently for prostate bed and node treatments.

TABLE 2. Percentage of fractions that the adaptive plan was selected for treatment Treatment site Percentage that adapted plan was selected Intact prostate 98.8% Intact prostate and nodes 98.7% Prostate bed and nodes 89.4% All sites 95.3% 3.6 Clinical goals per fraction

Figure 2 shows that, for the majority of fractions, the adaptive plan meets a greater number of clinical goals than the scheduled plan. However, it does not display how the goals themselves change for a given case, or over the course of the treatment. Figure 3 shows a graph of selected CTV, PTV, and OAR clinical goals over each fraction of a treatment course for a representative clinical prostate patient. As can be seen in Figure 3, there is no long-term trend in the goals over the treatment course, rather they vary day-to-day primarily due to bladder and bowel filling differences.

Selected plan parameters shown for each fraction for a representative prostate patient. Note the reference plan values (far left) match the values achieved by the adaptive plan more closely than the scheduled plan. CTVp and PTV_60 coverage is generally higher and rectum dose is generally lower for the adaptive plan for each fraction

For the same patient, Table 3 displays which goals differ significantly between the scheduled and the adaptive plan. For most of the goals, the adaptive plan was shown to be superior, and in most of those cases the null hypothesis was rejected.

TABLE 3. Plan parameters achieved for scheduled and adapted plans for a representative prostate patient Scheduled plan Adapted plan Structure Goal Variation Reference plan Median (Hodges-Lehmann) 95% CI Median (Hodges-Lehmann) 95% CI Superior plan Statistical significance CTVp D 98.0% ≥ 98.0% P: 1 D 98.0% ≥ 95.0% 97.5% 97.7% (97.3%–98.2%) 97.9% (97.6%–98.8%) Adapted Fail to reject H0 CTVp Dmean ≥ 100.0% P: 3 Dmean ≥ 99.0% 101.2% 101.3% (100.9%–101.8%) 101.5% (101.3%–101.8%) Adapted Reject H0 CTV_SV Prox D 98.0% ≥ 98.0% P: 1 D 98.0% ≥ 95.0% 99.4% 98.8% (97.9%–99.5%) 99.5% (98.5%–100.6%) Adapted Reject H0 CTV_SV Distal D 98.0% ≥ 98.0% P: 1 D 98.0% ≥ 95.0% 118.9% 118.9% (117.6%–119.6%) 117.5% (115.6%–119.3%) Scheduled Reject H0 PTV_60 D 98.0% ≥ 95.0% P: 1 D 98.0% ≥ 90.0% 95.8% 86.1% (82.8%–89%) 95.8% (94.7%–96.3%) Adapted Reject H0 PTV_60 D 95.0% ≥ 98.0% P: 1 D 95.0% ≥ 95.0% 97.0% 91.9% (89.6%–93.9%) 97.0% (96.5%–97.6%) Adapted Reject H0 PTV_60 Dmin 0.30 cm3 > 100.0% P: 3 Dmin 0.30 cm3 ≥ 60.0% 89.9% 74.0% (61.3%–78.4%) 87.8% (84.7%–89.7%) Adapted Reject H0 PTV_60 D 1.0% ≤ 105.0% P: 4 D 1.0% ≤ 107.0% 103.0% 103.4% (103%–104%) 103.1% (102.9%–103.3%) Adapted Reject H0 PTV_60 Dmax 0.30 cm3 ≤ 107.0% P: R Dmax 0.30 cm3 ≤ 110.0% 103.5% 104.1% (103.6%–104.7%) 103.7% (103.5%–103.9%) Adapted Reject H0 PTV_50 D 98.0% ≥ 95.0% P: 2 D 98.0% ≥ 90.0% 114.1% 102.9% (99.3%–106.4%) 111.9% (109.4%–113.7%) Adapted Reject H0 PTV_50 D 95.0% ≥ 100.0% P: 3 D 95.0% ≥ 95.0% 116.2% 109.9% (107.3%–112.2%) 115.8% (114.8%–116.5%) Adapted Reject H0 Rectum V 57.00 Gy < 15.0% P: 2 V 57.00 Gy ≤ 16.0% 1.3% 0.6% (0.1%–2.2%) 1.0% (0.4%–2%) Scheduled Reject H0 Rectum V 60.00 Gy < 3.0% P: 2 V 60.00 Gy ≤ 4.0% 0.0% 0.0% (0%–0.4%) 0.0% (0%–0%) Adapted   Rectum V 28.00 Gy ≤ 58.0% P: 2 V 28.00 Gy ≤ 60.0% 31.9% 33.3% (28.6%–37.3%) 32.6% (29.1%–34.5%) Adapted Fail to reject H0 Rectum V 18.00 Gy ≤ 60.0% P: 2 V 18.00 Gy ≤ 65.0% 56.3% 62.5% (57.3%–66.3%) 55.9% (53.1%–57.1%) Adapted Reject H0 Rectum V 52.80 Gy < 30.0% P: 3 V 52.80 Gy ≤ 30.0% 4.3% 2.9% (0.9%–5.1%) 4.4% (2.3%–5.7%) Scheduled Reject H0 Rectum V 48.60 Gy < 50.0% P: 3 V 48.60 Gy ≤ 50.0% 7.3% 5.3% (2.3%–8.1%) 7.7% (5.2%–9.4%) Scheduled Reject H0 Rectum V 40.80 Gy < 55.0% P: 3 V 40.80 Gy ≤ 60.0% 13.0% 11.0% (6.3%–14.3%) 13.7% (10.2%–15.8%) Scheduled Reject H0 Bladder D 0.30 cm3 ≤ 60.00 Gy (3.00 Gy/Fx) P: 1 D 0.30 cm3 ≤ 61.00 Gy (3.05 Gy/Fx) 2.94 2.96 (2.94–3) 2.94 (2.94–2.98) Adapted Reject H0 Bladder V 60.00 Gy ≤ 5.0% P: 3 V 60.00 Gy ≤ 7.0% 0.0% 0.0% (0%–0.1%) 0.0% (0%–0.1%) Adapted   Bladder V 48.60 Gy ≤ 25.0% P: 3 V 48.60 Gy ≤ 30.0% 12.6% 10.3% (7.5%–13%) 12.6% (8.4%–17.6%) Scheduled Reject H0 Bladder V 40.80 Gy ≤ 50.0% P: 3 V 40.80 Gy ≤ 60.0% 18.7% 16.3% (11.5%–21.5%) 17.9% (11.8%–24.4%) Scheduled Reject H0 Penile bulb D 0.30 cm3 < 60.00 Gy (3.00 Gy/Fx) P: 1 D 0.30 cm3 ≤ 61.00 Gy (3.05 Gy/Fx) 2.95 2.95 (2.93–2.98) 2.93 (2.91–2.94) Adapted Reject H0 Penile bulb Dmean ≤ 48.00 Gy (2.40 Gy/Fx) P: 3 Dmean ≤ 52.50 Gy (2.63 Gy/Fx) 2.20 1.77 (1.49–1.99) 1.88 (1.55–2.22) Scheduled Fail to reject H0 Femur head right D 0.30 cm3 < 50.00 Gy (2.50 Gy/Fx) P: 3 D 0.30 cm3 ≤ 52.00 Gy (2.60 Gy/Fx) 1.23 1.33 (1.28–1.37) 1.43 (1.32–1.52) Scheduled Reject H0 Femur head left D 0.30 cm3 < 50.00 Gy (2.50 Gy/Fx) P: 3 D 0.30 cm3 ≤ 52.00 Gy (2.60 Gy/Fx) 1.65 1.56 (1.53–1.6) 1.50 (1.39–1.56) Adapted Reject H0 Sigmoid colon D 0.30 cm3 < 59.00 Gy (2.95 Gy/Fx) P: 1 D 0.30 cm3 ≤ 60.00 Gy (3.00 Gy/Fx) 1.36 1.93 (1.5–2.33) 1.49 (1.25–2.12) Adapt