Clinical implementation of artificial intelligence-driven cone-beam computed tomography-guided online adaptive radiotherapy in the pelvic region

Visual analysis of image-guided radiation therapy based on bibliometrics: A review

Radiation therapy plays an important role in tumor treatment. The development of image-guided radiation therapy (IGRT) technology provides a strong guarantee for precise radiation therapy of tumors. However, bibliometric studies on IGRT research have rarely been reported. This study uses literature collected from the Web of Science during 1987 to 2021 as a sample and uses the bibliometric method to reveal the current research status, hotspots, and development trends in IGRT. Based on 6407 papers published from the Web of Science during 1987 to 2021, we utilized Microsoft Excel 2007 and cite space software to perform statistical analysis and visualization of IGRT. A total of 6407 articles were included, this area of IGRT has gone through 4 stages: budding period, growth period, outbreak period, and stationary period. The research category is mainly distributed in Radiology Nuclear Medicine Medical Imaging, which intersects with the research categories of Materials, Physics, and Mathematics. Yin FF, Tanderup K, and Sonke JJ are highly productive scholars who are active in IGRT research, while Jaffray DA, van Herk M and Guckenberger M are authors with high impact in this field. The team of scholars has close cooperation within the team and weak cooperation among teams. The League of European Research Universities, University of Texas System, University of Toronto, and Princess Margaret Cancer are the main research institutions in this field. The United States has the most research literature, followed by China and Germany. Six thousand four hundred seven articles are distributed in 712 journals, and the top 3 journals are Med Phys, Int J Radiat Oncol, and Radiather Oncol. Precise registration, intelligence, magnetic resonance guidance, and deep learning are current research hotspots. These results demonstrate that the research in this field is relatively mature and fruitful in the past 35 years, providing a solid theoretical basis and practical experience for precision radiotherapy.

Same-day adaptive palliative radiotherapy without prior CT simulation: Early outcomes in the FAST-METS study

BACKGROUND AND PURPOSE

Standard palliative radiotherapy workflows involve waiting times or multiple clinic visits. We developed and implemented a rapid palliative workflow using diagnostic imaging (dCT) for pre-planning, with subsequent on-couch target and plan adaptation based on a synthetic computed tomography (CT) obtained from cone-beam CT imaging (CBCT).

MATERIALS AND METHODS

Patients with painful bone metastases and recent diagnostic imaging were eligible for inclusion in this prospective, ethics-approved study. The workflow consisted of 1) telephone consultation with a radiation oncologist (RO); 2) pre-planning on the dCT using planning templates and mostly intensity-modulated radiotherapy; 3) RO consultation on the day of treatment; 4) CBCT scan with on-couch adaptation of the target and treatment plan; 5) delivery of either scheduled or adapted treatment plan. Primary outcomes were dosimetric data and treatment times; secondary outcome was patient satisfaction.

RESULTS

47 patients were enrolled between December 2021 and October 2022. In all treatments, adapted treatment plans were chosen due to significant improvements in target coverage (PTV/CTV V95%, p-value < 0.005) compared to the original treatment plan calculated on daily anatomy. Most patients were satisfied with the workflow. The average treatment time, including consultation and on-couch adaptive treatment, was 85 minutes. On-couch adaptation took on average 30 min. but was longer in cases where the automated deformable image registration failed to correctly propagate the targets.

CONCLUSION

A fast treatment workflow for patients referred for painful bone metastases was implemented successfully using online adaptive radiotherapy, without a dedicated CT simulation. Patients were generally satisfied with the palliative radiotherapy workflow.

Leveraging intelligent optimization for automated, cardiac-sparing accelerated partial breast treatment planning

Background

Accelerated partial breast irradiation (APBI) yields similar rates of recurrence and cosmetic outcomes as compared to whole breast radiation therapy (RT) when patients and treatment techniques are appropriately selected. APBI combined with stereotactic body radiation therapy (SBRT) is a promising technique for precisely delivering high levels of radiation while avoiding uninvolved breast tissue. Here we investigate the feasibility of automatically generating high quality APBI plans in the Ethos adaptive workspace with a specific emphasis on sparing the heart.

Methods

Nine patients (10 target volumes) were utilized to iteratively tune an Ethos APBI planning template for automatic plan generation. Twenty patients previously treated on a TrueBeam Edge accelerator were then automatically replanned using this template without manual intervention or reoptimization. The unbiased validation cohort Ethos plans were benchmarked via adherence to planning objectives, a comparison of DVH and quality indices against the clinical Edge plans, and qualitative reviews by two board-certified radiation oncologists.

Results

85% (17/20) of automated validation cohort plans met all planning objectives; three plans did not achieve the contralateral lung V1.5Gy objective, but all other objectives were achieved. Compared to the Eclipse generated plans, the proposed Ethos template generated plans with greater evaluation planning target volume (PTV_Eval) V100% coverage (p = 0.01), significantly decreased heart V1.5Gy (p< 0.001), and increased contralateral breast V5Gy, skin D0.01cc, and RTOG conformity index (p = 0.03, p = 0.03, and p = 0.01, respectively). However, only the reduction in heart dose was significant after correcting for multiple testing. Physicist-selected plans were deemed clinically acceptable without modification for 75% and 90% of plans by physicians A and B, respectively. Physicians A and B scored at least one automatically generated plan as clinically acceptable for 100% and 95% of planning intents, respectively.

Conclusions

Standard left- and right-sided planning templates automatically generated APBI plans of comparable quality to manually generated plans treated on a stereotactic linear accelerator, with a significant reduction in heart dose compared to Eclipse generated plans. The methods presented in this work elucidate an approach for generating automated, cardiac-sparing APBI treatment plans for daily adaptive RT with high efficiency.

Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)

Objective. Dynamic cone-beam CT (CBCT) imaging is highly desired in image-guided radiation therapy to provide volumetric images with high spatial and temporal resolutions to enable applications including tumor motion tracking/prediction and intra-delivery dose calculation/accumulation. However, dynamic CBCT reconstruction is a substantially challenging spatiotemporal inverse problem, due to the extremely limited projection sample available for each CBCT reconstruction (one projection for one CBCT volume).Approach. We developed a simultaneous spatial and temporal implicit neural representation (STINR) method for dynamic CBCT reconstruction. STINR mapped the unknown image and the evolution of its motion into spatial and temporal multi-layer perceptrons (MLPs), and iteratively optimized the neuron weightings of the MLPs via acquired projections to represent the dynamic CBCT series. In addition to the MLPs, we also introduced prior knowledge, in the form of principal component analysis (PCA)-based patient-specific motion models, to reduce the complexity of the temporal mapping to address the ill-conditioned dynamic CBCT reconstruction problem. We used the extended-cardiac-torso (XCAT) phantom and a patient 4D-CBCT dataset to simulate different lung motion scenarios to evaluate STINR. The scenarios contain motion variations including motion baseline shifts, motion amplitude/frequency variations, and motion non-periodicity. The XCAT scenarios also contain inter-scan anatomical variations including tumor shrinkage and tumor position change.Main results. STINR shows consistently higher image reconstruction and motion tracking accuracy than a traditional PCA-based method and a polynomial-fitting-based neural representation method. STINR tracks the lung target to an average center-of-mass error of 1-2 mm, with corresponding relative errors of reconstructed dynamic CBCTs around 10%.Significance. STINR offers a general framework allowing accurate dynamic CBCT reconstruction for image-guided radiotherapy. It is a one-shot learning method that does not rely on pre-training and is not susceptible to generalizability issues. It also allows natural super-resolution. It can be readily applied to other imaging modalities as well.

Prospective imaging comparison of anatomic delineation with rapid kV cone beam CT on a novel ring gantry radiotherapy device

INTRODUCTION

A kV imager coupled to a novel, ring-gantry radiotherapy system offers improved on-board kV-cone-beam computed tomography (CBCT) acquisition time (17-40 seconds) and image quality, which may improve CT radiotherapy image-guidance and enable online adaptive radiotherapy. We evaluated whether inter-observer contour variability over various anatomic structures was non-inferior using a novel ring gantry kV-CBCT (RG-CBCT) imager as compared to diagnostic-quality simulation CT (simCT).

MATERIALS/METHODS

Seven patients undergoing radiotherapy were imaged with the RG-CBCT system at breath hold (BH) and/or free breathing (FB) for various disease sites on a prospective imaging study. Anatomy was independently contoured by seven radiation oncologists on: 1. SimCT 2. Standard C-arm kV-CBCT (CA-CBCT), and 3. Novel RG-CBCT at FB and BH. Inter-observer contour variability was evaluated by computing simultaneous truth and performance level estimation (STAPLE) consensus contours, then computing average symmetric surface distance (ASSD) and Dice similarity coefficient (DSC) between individual raters and consensus contours for comparison across image types.

RESULTS

Across 7 patients, 18 organs-at-risk (OARs) were evaluated on 27 image sets. Both BH and FB RG-CBCT were non-inferior to simCT for inter-observer delineation variability across all OARs and patients by ASSD analysis (p < 0.001), whereas CA-CBCT was not (p = 0.923). RG-CBCT (FB and BH) also remained non-inferior for abdomen and breast subsites compared to simCT on ASSD analysis (p < 0.025). On DSC comparison, neither RG-CBCT nor CA-CBCT were non-inferior to simCT for all sites (p > 0.025).

CONCLUSIONS

Inter-observer ability to delineate OARs using novel RG-CBCT images was non-inferior to simCT by the ASSD criterion but not DSC criterion.

Do we need patient-specific QA for adaptively generated plans? Retrospective evaluation of delivered online adaptive treatment plans on Varian Ethos

BACKGROUND

The clinical introduction of dedicated treatment units for online adaptive radiation therapy (OART) has led to widespread adoption of daily adaptive radiotherapy. OART allows for rapid generation of treatment plans using daily patient anatomy, potentially leading to reduction of treatment margins and increased normal tissue sparing. However, the OART workflow does not allow for measurement of patient-specific quality assurance (PSQA) during treatment delivery sessions and instead relies on secondary dose calculations for verification of adapted plans. It remains unknown if independent dose verification is a sufficient surrogate for PSQA measurements.

PURPOSE

To evaluate the plan quality of previously treated adaptive plans through multiple standard PSQA measurements.

METHODS

This IRB-approved retrospective study included sixteen patients previously treated with OART at our institution. PSQA measurements were performed for each patient's scheduled and adaptive plans: five adaptive plans were randomly selected to perform ion chamber measurements and two adaptive plans were randomly selected for ArcCHECK measurements. The same ArcCHECK 3D dose distribution was also sent to Mobius3D to evaluate the second-check dosimetry system.

RESULTS

All (n = 96) ion chamber measurements agreed with the planned dose within 3% with a mean of 1.4% (± 0.7%). All (n = 48) plans passed ArcCHECK measurements using a 95% gamma passing threshold and 3%/2 mm criteria with a mean of 99.1% (± 0.7%). All (n = 48) plans passed Mobius3D second-check performed with 95% gamma passing threshold and 5%/3 mm criteria with a mean of 99.0% (± 0.2%).

CONCLUSION

Plan measurement for PSQA may not be necessary for every online-adaptive treatment verification. We recommend the establishment of a periodic PSQA check to better understand trends in passing rates for delivered adaptive treatments.

Effect of subject motion and gantry rotation speed on image quality and dose delivery in CT-guided radiotherapy

PURPOSE

To investigate the effects of subject motion and gantry rotation speed on computed tomography (CT) image quality over a range of image acquisition speeds for fan-beam (FB) and cone-beam (CB) CT scanners, and quantify the geometric and dosimetric errors introduced by FB and CB sampling in the context of adaptive radiotherapy.

METHODS

Images of motion phantoms were acquired using four CT scanners with gantry rotation speeds of 0.5 s/rotation (denoted FB-0.5), 1.9 s/rotation (FB-1.9), 16.6 s/rotation (CB-16.6), and 60.0 s/rotation (CB-60.0). A phantom presenting various tissue densities undergoing motion with 4-s period and ranging in amplitude from ±0.5 to ±10.0 mm was used to characterize motion artifacts (streaks), motion blur (edge-spread function, ESF), and geometric inaccuracy (excursion of insert centroids and distortion of known shape). An anthropomorphic abdomen phantom undergoing ±2.5-mm motion with 4-s period was used to simulate an adaptive radiotherapy workflow, and relative geometric and dosimetric errors were compared between scanners.

RESULTS

At ±2.5-mm motion, phantom measurements demonstrated mean ± SD ESF widths of 0.6 ± 0.0, 1.3 ± 0.4, 2.0 ± 1.1, and 2.9 ± 2.0 mm and geometric inaccuracy (excursion) of 2.7 ± 0.4, 4.1 ± 1.2, 2.6 ± 0.7, and 2.0 ± 0.5 mm for the FB-0.5, FB-1.9, CB-16.6, and CB-60.0 scanners, respectively. The results demonstrated nonmonotonic trends with scanner speed for FB and CB geometries. Geometric and dosimetric errors in adaptive radiotherapy plans were largest for the slowest (CB-60.0) scanner and similar for the three faster systems (CB-16.6, FB-1.9, and FB-0.5).

CONCLUSIONS

Clinically standard CB-60.0 demonstrates strong image quality degradation in the presence of subject motion, which is mitigated through faster CBCT or FBCT. Although motion blur is minimized for FB-0.5 and FB-1.9, such systems suffer from increased geometric distortion compared to CB-16.6. Each system reflects tradeoffs in image artifacts and geometric inaccuracies that affect treatment delivery/dosimetric error and should be considered in the design of next-generation CT-guided radiotherapy systems.

Clinical commissioning of an adaptive radiotherapy platform: Results and recommendations

Online adaptive radiotherapy platforms present a unique challenge for commissioning as guidance is lacking and specialized adaptive equipment, such as deformable phantoms, are rare. We designed a novel adaptive commissioning process consisting of end-to-end tests using standard clinical resources. These tests were designed to simulate anatomical changes regularly observed at patient treatments. The test results will inform users of the magnitude of uncertainty from on-treatment changes during the adaptive workflow and the limitations of their systems. We implemented these tests for the cone-beam computed tomography (CT)-based Varian Ethos online adaptive platform. Many adaptive platforms perform online dose calculation on a synthetic CT (synCT). To assess the impact of the synCT generation and online dose calculation on dosimetric accuracy, we conducted end-to-end tests using commonly available equipment: a CIRS IMRT Thorax phantom, PinPoint ionization chamber, Gafchromic film, and bolus. Four clinical scenarios were evaluated: weight gain and weight loss were simulated by adding and removing bolus, internal target shifts were simulated by editing the CTV during the adaptive workflow to displace it, and changes in gas were simulated by removing and reinserting rods in varying phantom locations. The effect of overriding gas pockets during planning was also assessed. All point dose measurements agreed within 2.7% of the calculated dose, with one exception: a scenario simulating gas present in the planning CT, not overridden during planning, and dissipating at treatment. Relative film measurements passed gamma analysis (3%/3 mm criteria) for all scenarios. Our process validated the Ethos dose calculation for online adapted treatment plans. Based on our results, we made several recommendations for our clinical adaptive workflow. This commissioning process used commonly available equipment and, therefore, can be applied in other clinics for their respective online adaptive platforms.

Potential utility of cone-beam CT-guided adaptive radiotherapy under end-exhalation breath-hold conditions for pancreatic cancer

PURPOSE

The purpose of this study was to demonstrate the potential utility of cone-beam computed tomography (CBCT)-guided online adaptive radiotherapy (ART) under end-exhalation breath-hold (EE-BH) conditions for pancreatic cancer (PC).

METHODS

Eleven PC patients who underwent 15-fraction volumetric-modulated arc therapy under EE-BH conditions were included. Planning CT images and daily 165 CBCT images were imported into a dedicated treatment planning system. The prescription dose was set to 48 Gy in 15 fractions. The reference plan was automatically generated along with predefined clinical goals. After segmentation was completed on CBCT images, two different plans were generated: One was an adapted (ADP) plan in which re-optimization was performed on the anatomy of the day, and the other was a scheduled (SCH) plan, which was the same as the reference plan. The dose distributions calculated using the synthetic CT created from both planning CT and CBCT were compared between the two plans. Independent calculation-based quality assurance was also performed for the ADP plans, with a gamma passing rate of 3%/3 mm.

RESULTS

All clinical goals were successfully achieved during the reference plan generation. Of the 165 sessions, gross tumor volume D_98% and clinical target volume D_98% were higher in 100 (60.1%) and 122 (74.0%) ADP fractions. In each fraction, the V_3 Gy  < 1 cm³ of the stomach and duodenum was violated in 47 (28.5%) and 48 (29.1%), respectively, of the SCH fractions, whereas no violations were observed in the ADP fractions. There were statistically significant differences in the dose-volume indices between the SCH and ADP fractions (p < 0.05). The gamma passing rates were above 95% in all ADP fractions.

CONCLUSIONS

The CBCT-guided online ART under EE-BH conditions successfully reduced the dose to the stomach and duodenum while maintaining target coverage.

Dosimetric and feasibility evaluation of a CBCT-based daily adaptive radiotherapy protocol for locally advanced cervical cancer

PURPOSE

Evaluate a cone-beam computed tomography (CBCT)-based daily adaptive platform in cervical cancer for multiple endpoints: (1) physics contouring accuracy of daily CTVs, (2) CTV coverage with adapted plans and reduced PTV margins versus non-adapted plans with standard-of-care (SOC) margins, (3) dosimetric improvements to CTV and organs-at-risk (OARs), and (4) on-couch time.

METHODS AND MATERIALS

Using a Varian Ethos™ emulator and KV-CBCT scans, we simulated the doses 15 retrospective cervical cancer patients would have received with/without online adaptation for five fractions. We compared contours and doses from SOC plans (5-15 mm CTV-to-PTV margins) to adapted plans (3 mm margins). Auto-segmented CTVs and OARs were reviewed and edited by trained physicists. Physics-edited targets were evaluated by an oncologist. Time spent reviewing and editing auto-segmented structures was recorded. Metrics from the CTV (D99%), bowel (V45Gy, V40Gy), bladder (D50%), and rectum (D50%) were compared.

RESULTS

The physician approved the physics-edited CTVs for 55/75 fractions; 16/75 required reductions, and 4/75 required CTV expansions. CTVs were encapsulated by unadapted, SOC PTVs for 56/75 (72%) fractions-representative of current clinical practice. CTVs were completely covered by adapted 3 mm PTVs for 71/75 (94.6%) fractions. CTV D99% values for adapted plans were comparable to non-adapted SOC plans (average difference of -0.9%), while all OAR metrics improved with adaptation. Specifically, bowel V45Gy and V40Gy decreased on average by 87.6 and 109.4 cc, while bladder and rectum D50% decreased by 37.7% and 35.8%, respectively. The time required for contouring and calculating an adaptive plan for 65/75 fractions was less than 20 min (range: 1-29 min).

CONCLUSIONS

Improved dose metrics with daily adaption could translate to reduced toxicity while maintaining tumor control. Training physicists to perform contouring edits could minimize the time physicians are required at adaptive sessions improving clinical efficiency. All emulated adaptive sessions were completed within 30 min however extra time will be required for patient setup, image acquisition, and treatment delivery.

Adaptive margins for online adaptive radiotherapy

Objective. In online adaptive radiotherapy a new plan is generated every fraction based on the organ and clinical target volume (CTV) delineations of that fraction. This allows for a planning target volume margin that does not need to be constant over the whole course of treatment, as is the case in conventional radiotherapy. This work aims to introduce an approach to update the margins each fraction based on the per-patient treatment history and explore the potential benefits of such adaptive margins. Approach. We introduce a novel methodology to implement adaptive margins, isotropic and anisotropic, during a treatment course based on the accumulated dose to the CTV. We then simulate treatment histories for treatments delivered in up to 20 fractions using various choices for the standard deviations of the systematic and random errors and homogeneous and inhomogeneous dose distributions. The treatment-averaged adaptive margin was compared to standard constant margins. The change in the minimum dose delivered to the CTV was compared on a patient and a population level. All simulations were performed within the van Herk approach and its known limitations. Main results. The population mean treatment-averaged margins are down to 70% and 55% of the corresponding necessary constant margins for the isotropic and anisotropic approach. The reduction increases with longer fractionation schemes and an inhomogeneous target dose distribution. Most of the benefit can be attributed to the elimination of the effective systematic error over the course of treatment. Interpatient differences in treatment-averaged margins were largest for the isotropic margins. For the 10% of patients that would receive a lower than prescribed dose to the CTV this minimum dose to the CTV is increased using the adaptive margin approaches. Significance. Adaptive margins can allow to reduce margins in most patients without compromising patients with greater than average target motion.

Clinical implementation and validation of an automated adaptive workflow for proton therapy

Background and purpose

Treatment quality of proton therapy can be monitored by repeat-computed tomography scans (reCTs). However, manual re-delineation of target contours can be time-consuming. To improve the workflow, we implemented an automated reCT evaluation, and assessed if automatic target contour propagation would lead to the same clinical decision for plan adaptation as the manual workflow.

Materials and methods

This study included 79 consecutive patients with a total of 250 reCTs which had been manually evaluated. To assess the feasibility of automated reCT evaluation, we propagated the clinical target volumes (CTVs) deformably from the planning-CT to the reCTs in a commercial treatment planning system. The dose-volume-histogram parameters were extracted for manually re-delineated (CTV_manual) and deformably mapped target contours (CTV_auto). It was compared if CTV_manual and CTV_auto both satisfied/failed the clinical constraints. Duration of the reCT workflows was also recorded.

Results

In 92% (N = 229) of the reCTs correct flagging was obtained. Only 4% (N = 9) of the reCTs presented with false negatives (i.e., at least one clinical constraint failed for CTV_manual, but all constraints were satisfied for CTV_auto), while 5% (N = 12) of the reCTs led to a false positive. Only for one false negative reCT a plan adaption was made in clinical practice, i.e., only one adaptation would have been missed, suggesting that automated reCT evaluation was possible. Clinical introduction hereof led to a time reduction of 49 h (from 65 to 16 h).

Conclusion

Deformable target contour propagation was clinically acceptable. A script-based automatic reCT evaluation workflow has been introduced in routine clinical practice.

Assessment of semi-automated stereotactic treatment planning for online adaptive radiotherapy in ethos

INTRODUCTION

The Ethos treatment planning system allows for the rapid generation of online adaptive treatment plans while the patient is on the treatment couch. One promising application of online adaptive radiotherapy is its use in stereotactic radiotherapy. The purpose of this study was to ensure the Ethos treatment planning system (TPS) can produce clinically acceptable stereotactic plans, that are non-inferior to those from the Eclipse TPS.

METHOD

Forty patients that received previous stereotactic radiotherapy treatment on a Halcyon, 20 of which were lung cases, and 20 that were brain cases, were replanned using the Ethos TPS. The generated IMRT and VMAT plans were compared to the clinical Eclipse VMAT plan.

RESULTS

This study found that the Ethos TPS can produce VMAT plans of equivalent quality (target coverage, conformity and OAR doses) to those from the Eclipse TPS for lung SBRT and brain SRT. The IMRT plans produced by the Ethos planning system were marginally inferior to Eclipse VMAT plans, with the differences likely primarily due to beam geometry rather than the optimization system. Ethos plans were generally more modulated than Eclipse plans. With careful selection of optimization structures and reduction in the body contour, VMAT plan generation time could be reduced by 87%.

CONCLUSION

Ethos can generate stereotactic VMAT plans that are equivalent to those from Eclipse in the timeframe required for online adaptive radiotherapy.

Artificial intelligence in radiotherapy

Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.

Robustness and reproducibility of an artificial intelligence-assisted online segmentation and adaptive planning process for online adaptive radiation therapy

Clinical implementation of online adaptive radiation therapy requires initial and ongoing performance assessment of the underlying auto‐segmentation and adaptive planning algorithms, although a straightforward and efficient process for this in phantom is lacking. The purpose of this work was to investigate robustness and repeatability of the artificial intelligence‐assisted online segmentation and adaptive planning process on the Varian Ethos adaptive platform, and to develop an end‐to‐end test strategy for online adaptive radiation therapy. Five synthetic deformations were generated and applied to a computed tomography image of an anthropomorphic pelvis phantom, and reference treatment plans were generated from each of the resulting deformed images. The undeformed phantom was repeatedly imaged, and the online adaptive process was performed including auto‐segmentation, review and manual correction of contours, and adaptive plan creation. One adaptive fractions in five different deformation scenarios were performed. The manually corrected contours had a high degree of consistency (> 93% Dice similarity coefficient and < 1.0 mm mean surface distance) across repeated fractions, with no significant variation across the synthetic deformation instance except for bowel (p = 0.026, one‐way ANOVA). Adaptive treatment plans also resulted in highly consistent dose–volume values for targets and organs at risk. A straightforward and efficient process was developed and used to quantify a set of organ specific contouring and dosimetric action levels to help establish uncertainty bounds for an end‐to‐end test on the Varian Ethos system.

Impact of Using Unedited CT-Based DIR-Propagated Autocontours on Online ART for Pancreatic SBRT

Purpose

To determine the dosimetric impact of using unedited autocontours in daily plan adaptation of patients with locally advanced pancreatic cancer (LAPC) treated with stereotactic body radiotherapy using tumor tracking.

Materials and Methods

The study included 98 daily CT scans of 35 LAPC patients. All scans were manually contoured (MAN), and included the PTV and main organs-at-risk (OAR): stomach, duodenum and bowel. Precision and MIM deformable image registration (DIR) methods followed by contour propagation were used to generate autocontour sets on the daily CT scans. Autocontours remained unedited, and were compared to MAN on the whole organs and at 3, 1 and 0.5 cm from the PTV. Manual and autocontoured OAR were used to generate daily plans using the VOLO™ optimizer, and were compared to non-adapted plans. Resulting planned doses were compared based on PTV coverage and OAR dose-constraints.

Results

Overall, both algorithms reported a high agreement between unclipped MAN and autocontours, but showed worse results when being evaluated on the clipped structures at 1 cm and 0.5 cm from the PTV. Replanning with unedited autocontours resulted in better OAR sparing than non-adapted plans for 95% and 84% plans optimized using Precision and MIM autocontours, respectively, and obeyed OAR constraints in 64% and 56% of replans.

Conclusion

For the majority of fractions, manual correction of autocontours could be avoided or be limited to the region closest to the PTV. This practice could further reduce the overall timings of adaptive radiotherapy workflows for patients with LAPC.

In Silico Trial of Computed Tomography-Guided Stereotactic Adaptive Radiotherapy (CT-STAR) for the Treatment of Abdominal Oligometastases

PURPOSE

We conducted a prospective, in silico clinical imaging study (NCTXXXX) to evaluate the feasibility of cone-beam computed tomography-guided stereotactic adaptive radiotherapy (CT-STAR) for the treatment of abdominal oligometastases. We hypothesized that CT-STAR produces improved dosimetry compared to non-adapted CT-stereotactic body radiotherapy (SBRT).

METHODS/MATERIALS

Eight patients receiving SBRT for abdominal oligometastatic disease received five additional kV cone beam CTs (CBCTs) on the ETHOS system. These additional CBCTs were used for imaging during an emulator treatment session. Initial plans were created based on their simulation (P_I) and emulated adaptive plans (P_A) were based on anatomy-of-the-day. The prescription was 50 Gy/5 fractions. Organ-at-risk (OAR) constraints were prioritized over planning target volume coverage under a strict isotoxicity approach. The P_I was applied to the patient's anatomy-of-the-day and compared to the re-optimized P_A using dose volume histogram metrics, with selection of the superior plan. Feasibility was defined as completion of the adaptive workflow and compliance with strict OAR constraints in ≥80% of fractions. Fractions were performed under time pressures by a physician and physicist to mimic the adaptive process.

RESULTS

CT-STAR was feasible, with successful workflow completion in 38/40 (95%) fractions. P_I application to daily anatomy created OAR constraint violations in 30/40 (75%) fractions. There were 8 stomach, 18 duodenum, 16 small bowel, and 11 large bowel P_I OAR constraint violations. In contrast, OAR violations occurred in 2/40 (5%) P_A (both small bowel violations, both improved from the P_I). CT-STAR also improved GTV V100 and D95 coverage in 25/40 (63%) and 20/40 (50%) fractions, respectively. 0/40 (0%) fractions were deemed non-feasible due to poor image quality and/or inability to delineate structures. Adaptation time per fraction was a median of 22.59 minutes (10.97-47.23).

CONCLUSIONS

CT-STAR resolved OAR hard constraint violations and/or improved target coverage in silico when compared to non-adapted CT-guided SBRT for the ablation of abdominal oligometastatic disease. While limitations of this study include its small sample size and in silico design, the consistently high quality CBCT images captured and comparable timing metrics to prior adaptive studies suggest that CT- STAR is a viable treatment paradigm for the ablation of abdominal oligometastatic disease. Clinical trials are in development to further evaluate CT-STAR in the clinic.

Evaluation of an automated template-based treatment planning system for radiotherapy of anal, rectal and prostate cancer

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Automated treatment planning system compared to manual planning.

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Equivalent plan quality between VMAT manually generated- and IMRT automatically generated plans.

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Evaluation of anal, prostate and rectum treatment plans.

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Generation of highly consistent IMRT automated plan within 2 to 3.5 min.

Background and purpose

The Ethos system has enabled online adaptive radiotherapy (oART) by implementing an automated treatment planning system (aTPS) for both intensity-modulated radiotherapy (IMRT) and volumetric modulated arc radiotherapy (VMAT) plan creation. The purpose of this study is to evaluate the quality of aTPS plans in the pelvic region.

Material and Methods

Sixty patients with anal (n = 20), rectal (n = 20) or prostate (n = 20) cancer were retrospectively re-planned with the aTPS. Three IMRT (7-, 9- and 12-field) and two VMAT (2 and 3 arc) automatically generated plans (APs) were created per patient. The duration of the automated plan generation was registered. The best IMRT-AP and VMAT-AP for each patient were selected based on target coverage and dose to organs at risk (OARs). The AP quality was analyzed and compared to corresponding clinically accepted and manually generated VMAT plans (MPs) using several clinically relevant dose metrics. Calculation-based pre-treatment plan quality assurance (QA) was performed for all plans.

Results

The median total duration to generate the five APs with the aTPS was 55 min, 39 min and 35 min for anal, prostate and rectal plans, respectively. The target coverage and the OAR sparing were equivalent for IMRT-APs and VMAT-MPs, while VMAT-Aps.

demonstrated lower target dose homogeneity and higher dose to some OARs. Both conformity and homogeneity index were equivalent (rectal) or better (anal and prostate) for IMRT-APs compared to VMAT-MPs. All plans passed the patient-specific QA tolerance limit.

Conclusions

The aTPS generates plans comparable to MPs within a short time-frame which is highly relevant for oART treatments.

Cone-beam computed tomography-guided online adaptive radiotherapy is feasible for prostate cancer patients

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Online adaptive radiotherapy (oART) is achievable within twenty minutes.

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Cone-beam computed tomography-guided oART is feasible in daily clinical practice.

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The adapted plan was always preferred over the scheduled plan.

Background and purpose

Studies have shown the potential of cone-beam computed tomography (CBCT)-guided online adaptive radiotherapy (oART) for prostate cancer patients in a simulation environment. The aim of this study was to evaluate the feasibility of the clinical implementation of CBCT-guided oART for prostate cancer patients.

Materials and methods

Between February and July 2020, eleven prostate cancer patients were treated with CBCT-guided oART using a fractionation scheme of 20 × 3 Gy to the prostate and 20 × 2.7/3.0 Gy to the seminal vesicles for more advanced stages. The on-couch adaptive workflow consisted of influencer (prostate, seminal vesicles, rectum, bladder) review, target review, scheduled (re-calculated) and adapted (re-optimized) plan generation, an independent QA procedure and treatment delivery. Treatment time, proportion of adapted fractions and reasons for plan adaptation were evaluated.

Results

Mean total treatment time (±SD) from CBCT acquisition to end of treatment delivery was 17.5 ± 3.2 min (range: 10.8–28.8 min). In all 220 fractions, the PTV coverage was increased for the adapted plan compared to the scheduled plan. The V60Gy of bladder and rectum were below the constraints (<5% and <3%) for both scheduled and adapted plans in 171 out of 220 fractions and for the adapted plan only in 30 out of 220 fractions. In 19 out of 220 fractions, the V60Gy of the bladder and/or rectum was above the constraint for the adapted plan.

Conclusions

The clinical implementation of CBCT-guided oART is feasible for prostate cancer patients. The adaptive workflow is possible within twenty minutes on average with a dedicated team.

Evaluation of the clinical value of automatic online dose restoration for adaptive proton therapy of head and neck cancer

INTRODUCTION

Intensity modulated proton therapy (IMPT) is highly sensitive to anatomical variations which can cause inadequate target coverage during treatment. This study compares not-adapted (NA) robust plans to two adaptive IMPT methods - a fully-offline adaptive (FOA) and a simplified automatic online adaptive strategy (dose restoration (DR)) to determine the benefit of DR, in head and neck cancer (HNC).

MATERIAL/METHODS

Robustly optimized clinical IMPT doses in planning-CTs (pCTs) were available for a cohort of 10 HNC patients. During robust re-optimization, DR used isodose contours, generated from the clinical dose on pCTs, and patient specific objectives to reproduce the clinical dose in every repeated-CT(rCT). For each rCT(n=50), NA, DR and FOA plans were robustly evaluated.

RESULTS

An improvement in DVH-metrics and robustness was seen for DR and FOA plans compared to NA plans. For NA plans, 74%(37/50) of rCTs did not fulfill the CTV coverage criteria (D98%>95%Dprescription). DR improved target coverage, target homogeneity and variability on critical risk organs such as the spinal cord. After DR, 52%(26/50) of rCTs met all clinical goals. Because of large anatomical changes and/or inaccurate patient repositioning, 48%(24/50) of rCTs still needed full offline adaptation to ensure an optimal treatment since dose restoration was not able to re-establish the initial plan quality.

CONCLUSION

Robust optimization together with fully-automatized DR avoided offline adaptation in 52% of the cases. Implementation of dose restoration in clinical routine could ensure treatment plan optimality while saving valuable human and material resources to radiotherapy departments.

Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond

In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.

Quality management in radiotherapy treatment delivery

Radiation Oncology continues to rely on accurate delivery of radiation, in particular where patients can benefit from more modulated and hypofractioned treatments that can deliver higher dose to the target while optimising dose to normal structures. These deliveries are more complex, and the treatment units are more computerised, leading to a re-evaluation of quality assurance (QA) to test a larger range of options with more stringent criteria without becoming too time and resource consuming. This review explores how modern approaches of risk management and automation can be used to develop and maintain an effective and efficient QA programme. It considers various tools to control and guide radiation delivery including image guidance and motion management. Links with typical maintenance and repair activities are discussed, as well as patient-specific quality control activities. It is demonstrated that a quality management programme applied to treatment delivery can have an impact on individual patients but also on the quality of treatment techniques and future planning. Developing and customising a QA programme for treatment delivery is an important part of radiotherapy. Using modern multidisciplinary approaches can make this also a useful tool for department management.

Plan quality in radiotherapy treatment planning - Review of the factors and challenges

A high-quality treatment plan aims to best achieve the clinical prescription, balancing high target dose to maximise tumour control against sufficiently low organ-at-risk dose for acceptably low toxicity. Treatment planning (TP) includes multiple steps from simulation/imaging and segmentation to technical plan production and reporting. Consistent quality across this process requires close collaboration and communication between clinical and technical experts, to clearly understand clinical requirements and priorities and also practical uncertainties, limitations and compromises. TP quality depends on many aspects, starting from commissioning and quality management of the treatment planning system (TPS), including its measured input data and detailed understanding of TPS models and limitations. It requires rigorous quality assurance of the whole planning process and it links to plan deliverability, assessable by measurement-based verification. This review highlights some factors influencing plan quality, for consideration for optimal plan construction and hence optimal outcomes for each patient. It also indicates some challenges, sources of difference and current developments. The topics considered include: the evolution of TP techniques; dose prescription issues; tools and methods to evaluate plan quality; and some aspects of practical TP. The understanding of what constitutes a high-quality treatment plan continues to evolve with new techniques, delivery methods and related evidence-based science. This review summarises the current position, noting developments in the concept and the need for further robust tools to help achieve it.

Assessment of efficacy in automated plan generation for Varian Ethos intelligent optimization engine

Varian Ethos, a new treatment platform, is capable of automatically generating treatment plans for initial planning and for online, adaptive planning, using an intelligent optimization engine (IOE). The primary purpose of this study is to assess the efficacy of Varian Ethos IOE for auto‐planning and intercompare different treatment modalities within the Ethos treatment planning system (TPS). A total of 36 retrospective prostate and proximal seminal vesicles cases were selected for this study. The prescription dose was 50.4 Gy in 28 fractions to the proximal seminal vesicles, with a simultaneous integrated boost of 70 Gy to the prostate gland. Based on RT intent, three treatment plans were auto‐generated in the Ethos TPS and were exported to the Eclipse TPS for intercomparison with the Eclipse treatment plan. When normalized for the same planning target volume (PTV) coverage, Ethos plans D_max% were 108.1 ± 1.2%, 108.4 ± 1.6%, and 109.6 ± 2.0%, for the 9‐field IMRT, 12‐field IMRT, and 2‐full arc VMAT plans, respectively. This compared well with Eclipse plan D_max% values, which was 108.8 ± 1.4%. OAR indices were also evaluated for Ethos plans using Radiation Therapy Oncology Group report 0415 as a guide and were found to be comparable to each other and the Eclipse plans. While all Ethos plans were comparable, we found that, in general, the Ethos 12‐field IMRT plans met most of the dosimetric goals for treatment. Also, Ethos IOE consistently generated dosimetrically hotter VMAT plans versus IMRT plans. On average, Ethos TPS took 13 min to generate 2‐full arc VMAT plans, compared to 5 min for 12‐field IMRT plans. Varian Ethos TPS can generate multiple treatment plans in an efficient time frame and the quality of the plans could be deemed clinically acceptable when compared to manually generated treatment plans.

Pathway for radiation therapists online advanced adapter training and credentialing

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Online Adaptive Radiation Therapy (oART) provides a solution to account for daily patient variations, but wide spread implementation is hindered by human resources and training.

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Physicians can mentor Radiation Therapists (RTTs) through traditional tasks such as contouring and plan approval.

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With evidence-based credentialing activities, decision support aids and ‘on-call’ caveats, RTTs can lead the oART workflow and a ‘Clinician-Lite’ approach.

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Compliance with legislative, regulatory and medico-legal governing bodies can be addressed through post-graduate study, advanced practice pathways, exemptions and delegation of task.

Online adaptive radiotherapy (oART) is an emerging advanced treatment option for cancer patients worldwide. Current oART practices using magnetic resonance (MR) and cone beam computed tomography (CBCT) based imaging are resource intensive and require physician presence, which is a barrier to widespread implementation. Global evidence demonstrates Radiation Therapists (RTTs) can lead the oART workflow with decision support tools and on ‘on-call’ caveats in a ‘clinician-lite’ approach without significantly compromising on treatment accuracy, speed or patient outcomes. With careful consideration of jurisdictional regulations and guidance from the multi-disciplinary team, RTTs can elevate beyond traditional scopes of practice. By implementing robust and evidence-based credentialing activities, they enable service sustainability and expand the real-world gains of adaptive radiotherapy to a greater number of cancer patients worldwide. This work summarises the evidence for RTT-led oART treatments and proposes a pathway for training and credentialing.

Online adaptive radiotherapy potentially reduces toxicity for high-risk prostate cancer treatment

BACKGROUND AND PURPOSE

With daily, MR-guided online adapted radiotherapy (MRgART) it may be possible to reduce the PTV in pelvic RT. This study investigated the potential reduction in normal tissue complication probability (NTCP) of MRgART compared to standard radiotherapy for high-risk prostate cancer.

MATERIALS AND METHODS

Twenty patients treated with 78 Gy to the prostate and 56 Gy to elective pelvic lymph nodes were included. VMAT plans were generated with standard clinical PTV margins. Additionally to the planning MR, patients had three MRI scans during treatment to simulate an MRgART. A reference plan with PTV margins determined for MRgART was created per patient and adapted to each of the following MRs. Adapted plans were warped to the planning MR for dose accumulation. The standard plan was rigidly registered to each adaptation MR before it was warped to the planning MR for dose accumulation. Dosimetric impact was compared by DVH analysis and potential clinical effects were assessed by NTCP modeling.

RESULTS

MRgART yielded statistically significant lower doses for the bladder wall, rectum and peritoneal cavity, compared to the standard RT, which translated into reduced median risks of urine incontinence (ΔNTCP 2.8%), urine voiding pain (ΔNTCP 2.8%) and acute gastrointestinal toxicity (ΔNTCP 17.4%). Mean population accumulated doses were as good or better for all investigated OAR when planned for MRgART as standard RT.

CONCLUSION

Online adapted radiotherapy may reduce the dose to organs at risk in high-risk prostate cancer patients, due to reduced PTV margins. This potentially translates to significant reductions in the risks of acute and late adverse effects.

Comparison of Library of Plans with two daily adaptive strategies for whole bladder radiotherapy

Background and purpose

Whole bladder radiotherapy is challenging due to inter- and intrafraction size and shape changes. To account for these changes, currently a Library of Plans (LoP) technique is often applied, but daily adaptive radiotherapy is also increasingly becoming available. The aim of this study was to compare LoP with two magnetic resonance imaging guided radiotherapy (MRgRT) strategies by comparing target coverage and volume of healthy tissue inside the planning target volume (PTV) for whole bladder treatments.

Methods and materials

Data from 25 MRgRT lymph node oligometastases treatments (125 fractions) were used, with three MRI scans acquired at each fraction at 0, 15 and 30 min. Bladders were delineated and used to evaluate three strategies: 1) LoP with two plans for a 15 min fraction, 2) MRgRT_15min for a 15 min fraction and 3) MRgRT_30min for a 30 min fraction. The volumes of healthy tissue inside and bladder outside the PTV were analyzed on the simulated post-treatment images.

Results

MRgRT_30min had 120% and 121% more healthy tissue inside the PTV than LoP and MRgRT_15min. For LoP slightly more target outside the PTV was found than for MRgRT_30min and MRgRT_15min, with median 0% (range 0-23%) compared to 0% (0-20%) and 0% (0-10%), respectively.

Conclusions

Taking into account both target coverage and volume of healthy tissue inside the PTV, MRgRT_15min performed better than LoP and MRgRT_30min for whole bladder treatments. A 15 min daily adaptive radiotherapy workflow is needed to potentially benefit from replanning compared to LoP.

Varian ethos online adaptive radiotherapy for prostate cancer: Early results of contouring accuracy, treatment plan quality, and treatment time

The Varian Ethos system allows for online adaptive treatments through the utilization of artificial intelligence (AI) and deformable image registration which automates large parts of the anatomical contouring and plan optimization process. In this study, treatments of intact prostate and prostate bed, with and without nodes, were simulated for 182 online adaptive fractions, and then a further 184 clinical fractions were delivered on the Ethos system. Frequency and magnitude of contour edits were recorded, as well as a range of plan quality metrics. From the fractions analyzed, 11% of AI generated contours, known as influencer contours, required no change, and 81% required minor edits in any given fraction. The frequency of target and noninfluencer organs at risk (OAR) contour editing varied substantially between different targets and noninfluencer OARs, although across all targets 72% of cases required no edits. The adaptive plan was the preference in 95% of fractions. The adaptive plan met more goals than the scheduled plan in 78% of fractions, while in 15% of fractions the number of goals met was the same. The online adaptive recontouring and replanning process was carried out in 19 min on average. Significant improvements in dosimetry are possible with the Ethos online adaptive system in prostate radiotherapy.

Phase I Trial of Stereotactic MRI-Guided Online Adaptive Radiation Therapy (SMART) for the Treatment of Oligometastatic Ovarian Cancer

PURPOSE

Stereotactic body radiation therapy is increasingly used to treat a variety of oligometastatic histologies, but few data exist for ovarian cancer. Ablative stereotactic body radiation therapy dosing is challenging in sites like the abdomen, pelvis, and central thorax due to proximity and motion of organs at risk. A novel radiation delivery method, stereotactic magnetic-resonance-guided online-adaptive radiation therapy (SMART), may improve the therapeutic index of stereotactic body radiation therapy through enhanced soft-tissue visualization, real-time nonionizing imaging, and ability to adapt to the anatomy-of-the-day, with the goal of producing systemic-therapy-free intervals. This phase I trial assessed feasibility, safety, and dosimetric advantage of SMART to treat ovarian oligometastases.

METHODS AND MATERIALS

Ten patients with recurrent oligometastatic ovarian cancer underwent SMART for oligometastasis ablation. Initial plans prescribed 35 Gy/5 fractions with goal 95% planning target volume coverage by 95% of prescription, with dose escalation permitted, subject to strict organ-at-risk dose constraints. Daily adaptive planning was used to protect organs-at-risk and/or increase target dose. Feasibility (successful delivery of >80% of fractions in the first on-table attempt) and safety of this approach was evaluated, in addition to efficacy, survival metrics, quality-of-life, prospective timing and dosimetric outcomes.

RESULTS

Ten women with seventeen ovarian oligometastases were treated with SMART, and 100% of treatment fractions were successfully delivered. Online adaptive plans were selected at time of treatment for 58% of fractions, due to initial plan violation of organs-at-risk constraints (84% of adapted fractions) or observed opportunity for planning target volume dose escalation (16% of adapted fractions), with a median on-table time of 64 minutes. A single Grade ≥3 acute (within 6 months of SMART) treatment-related toxicity (duodenal ulcer) was observed. Local control at 3 months was 94%; median progression-free survival was 10.9 months. Median Kaplan-Meier estimated systemic-therapy-free survival after radiation completion was 11.5 months, with concomitant quality-of-life improvements.

CONCLUSIONS

SMART is feasible and safe for high-dose radiation therapy ablation of ovarian oligometastases of the abdomen, pelvis, and central thorax with minimal toxicity, high rates of local control, and prolonged systemic-therapy-free survival translating into improved quality-of-life.

Prospects for daily online adaptive radiotherapy via ethos for prostate cancer patients without nodal involvement using unedited CBCT auto-segmentation

Purpose

 Implementing new online adaptive radiation therapy technologies is challenging because extra clinical resources are required particularly expert contour review. Here, we provide the first assessment of Varian's Ethos™ adaptive platform for prostate cancer using no manual edits after auto‐segmentation to minimize this impact on clinical efficiency.

Methods

Twenty‐five prostate patients previously treated at our clinic were re‐planned using an Ethos™ emulator. Clinical target volumes (CTV) included intact prostate and proximal seminal vesicles. The following clinical margins were used: 3 mm posterior, 5 mm left/right/anterior, and 7 mm superior/inferior. Adapted plans were calculated for 10 fractions per patient using Ethos's auto‐segmentation and auto‐planning workflow without manual contouring edits. Doses and auto‐segmented structures were exported to our clinical treatment planning system where contours were modified as needed for all 250 CTVs and organs‐at‐risk. Dose metrics from adapted plans were compared to unadapted plans to evaluate CTV and OAR dose changes.

Results

Overall 96% of fractions required auto‐segmentation edits, although corrections were generally minor (<10% of the volume for 70% of CTVs, 88% of bladders, and 90% of rectums). However, for one patient the auto‐segmented CTV failed to include the superior portion of prostate that extended into the bladder at all 10 fractions resulting in under‐contouring of the CTV by 31.3% ± 6.7%. For the 24 patients with minor auto‐segmentation corrections, adaptation improved CTV D98% by 2.9% ± 5.3%. For non‐adapted fractions where bladder or rectum V90% exceeded clinical thresholds, adaptation reduced them by 13.1% ± 1.0% and 6.5% ± 7.3%, respectively.

Conclusion

 For most patients, Ethos's online adaptive radiation therapy workflow improved CTV D98% and reduced normal tissue dose when structures would otherwise exceed clinical thresholds, even without time‐consuming manual edits. However, for one in 25 patients, large contour edits were required and thus scrutiny of the daily auto‐segmentation is necessary and not all patients will be good candidates for adaptation.

Harnessing benefit of highly conformal RT techniques for lymphoma patients

This status article describes current state-of-the-art radiotherapy for lymphomas and new emerging techniques. Current state-of-the-art radiotherapy is sophisticated, individualised, CT-based, intensity-modulated treatment, using PET/CT to define the target. The concept of involved site radiotherapy should be used, delineating the target using the exact same principles as for solid tumours. The optimal treatment delivery includes motion management and online treatment verification systems, which reduce intra- and interfractional anatomical variation. Emerging radiotherapy techniques in lymphomas include adaptive radiotherapy in MR- and CT-based treatment systems and proton therapy. The next generation linear accelerators have the capability to deliver adaptive treatment and allow relatively quick online adaptation to the daily variations of the anatomy. The computer systems use machine leaning to facilitate rapid automatic contouring of the target and organs-at-risk. Moreover, emerging MR-based planning and treatment facilities allow target definition directly from MR scans and allow intra-fractional tracking of structures recognisable on MR. Proton facilities are now being widely implemented. The benefits of proton therapy are due to the physical properties of protons, which in many cases allow sparing of normal tissue. The variety of techniques in modern radiotherapy means that the radiation oncologist must be able to choose the right technique for each patient. The choice is mainly based on experience and standard protocols, but new systems calculating risks for the patients with a specific treatment plan and also systems integrating clinical factors and risk factors into the planning process itself are emerging.

Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT

BACKGROUND

This study aimed to (1) develop a fully residual deep convolutional neural network (CNN)-based segmentation software for computed tomography image segmentation of the male pelvic region and (2) demonstrate its efficiency in the male pelvic region.

METHODS

A total of 470 prostate cancer patients who had undergone intensity-modulated radiotherapy or volumetric-modulated arc therapy were enrolled. Our model was based on FusionNet, a fully residual deep CNN developed to semantically segment biological images. To develop the CNN-based segmentation software, 450 patients were randomly selected and separated into the training, validation and testing groups (270, 90, and 90 patients, respectively). In Experiment 1, to determine the optimal model, we first assessed the segmentation accuracy according to the size of the training dataset (90, 180, and 270 patients). In Experiment 2, the effect of varying the number of training labels on segmentation accuracy was evaluated. After determining the optimal model, in Experiment 3, the developed software was used on the remaining 20 datasets to assess the segmentation accuracy. The volumetric dice similarity coefficient (DSC) and the 95th-percentile Hausdorff distance (95%HD) were calculated to evaluate the segmentation accuracy for each organ in Experiment 3.

RESULTS

In Experiment 1, the median DSC for the prostate were 0.61 for dataset 1 (90 patients), 0.86 for dataset 2 (180 patients), and 0.86 for dataset 3 (270 patients), respectively. The median DSCs for all the organs increased significantly when the number of training cases increased from 90 to 180 but did not improve upon further increase from 180 to 270. The number of labels applied during training had a little effect on the DSCs in Experiment 2. The optimal model was built by 270 patients and four organs. In Experiment 3, the median of the DSC and the 95%HD values were 0.82 and 3.23 mm for prostate; 0.71 and 3.82 mm for seminal vesicles; 0.89 and 2.65 mm for the rectum; 0.95 and 4.18 mm for the bladder, respectively.

CONCLUSIONS

We have developed a CNN-based segmentation software for the male pelvic region and demonstrated that the CNN-based segmentation software is efficient for the male pelvic region.

Adaptive Radiotherapy in the Management of Cervical Cancer: Review of Strategies and Clinical Implementation

The complex and varied motion of the cervix-uterus target during external beam radiotherapy (EBRT) underscores the clinical benefits afforded by adaptive radiotherapy (ART) techniques. These gains have already been realised in the implementation of image-guided adaptive brachytherapy, where adapting to anatomy at each fraction has seen improvements in clinical outcomes and a reduction in treatment toxicity. With regards to EBRT, multiple adaptive strategies have been implemented, including a personalised internal target volume, offline replanning and a plan of the day approach. With technological advances, there is now the ability for real-time online ART using both magnetic resonance imaging and computed tomography-guided imaging. However, multiple challenges remain in the widespread dissemination of ART. This review investigates the ART strategies and their clinical implementation in EBRT delivery for cervical cancer.

An in-silico assessment of the dosimetric benefits of MR-guided radiotherapy for esophageal cancer patients

PURPOSE

To assess the dosimetric benefits of online MR-guided radiotherapy (MRgRT) for esophageal cancer patients and to assess how these benefits could be translated into a local boosting strategy to improve future outcomes.

METHODS

Twenty-nine patients were in-silico treated with both a MRgRT regimen and a conventional image guided radiotherapy (IGRT) regimen using dose warping techniques. Here, the inter and intrafractional changes that occur over the course of treatment (as derived from 5 MRI scans that were acquired weekly during treatment) were incorporated to assess the total accumulated dose for each regimen.

RESULTS

A significant reduction in dose to the organs-at-risk (OARs) was observed for all dose-volume-histogram (DVH) parameters for the MRgRT regimen without concessions to target coverage compared to the IGRT regimen. The mean lung dose was reduced by 28%, from 7.9 to 5.7 Gy respectively and V20Gy of the lungs was reduced by 55% (6.3-2.8%). A reduction of 24% was seen in mean heart dose (14.8-11.2 Gy), while the V25Gy of the heart was decreased by 53% (14.3-6.7%) and the V40Gy of the heart was decreased by 69% (3.9-1.2%). In addition, MRgRT dose escalation regimens with a boost up to 66% of the prescription dose to the primary tumor yielded approximately the same dose levels to the OARs as from the conventional IGRT regimen.

CONCLUSION

This study revealed that MRgRT for esophageal cancer has the potential to significantly reduce the dose to heart and lungs. In addition, online high precision targeting of the primary tumor opens new perspectives for local boosting strategies to improve outcome of the local management of this disease.