Assessment of multi-criteria optimization (MCO) for volumetric modulated arc therapy (VMAT) in hippocampal avoidance whole brain radiation therapy (HA-WBRT)

The Role of Proton Therapy for Prostate Cancer in the Setting of Hip Prosthesis

PURPOSE

Given that the current standard of proton therapy (PT) for prostate cancer is through bilateral beams, this modality is typically avoided when it comes to treatment of patients with hip prosthesis. The purpose of this study was to evaluate whether novel PT methods, i.e., anterior proton beams and proton arc therapy (PArc), could be feasible options to treat this patient subpopulation. We evaluate PT methods in the context of dosimetry and robustness and compare with standard of practice volumetric modulated arc therapy (VMAT) to explore any potential benefits.

METHODS

Two PT and one VMAT treatment plans were retrospectively created for 10 patients who participated in a clinical trial with a weekly repeat CT (rCT) imaging component. All plans were robustly optimized and featured: (1) combination anterior oblique and lateral proton beams (AoL), (2) PArc, and (3) VMAT. All patients had hydrogel spacers in place, which enabled safe application of anterior proton beams. The planned dose was 70 Gy (RBE) to the entire prostate gland and 50 Gy (RBE) to the proximal seminal vesicles in 28 fractions. Along with plan dose-volume metrics, robustness to setup and interfractional variations were evaluated using the weekly rCT images. The linear energy transfer (LET)-weighted dose was evaluated for PArc plans to ensure urethra sparing given the typical high-LET region at the end of range.

RESULTS

Both PT methods were dosimetrically feasible and provided reduction of some key OAR metrics compared to VMAT except for penile bulb, while providing equally good target coverage. Significant differences in median rectum V35 (22-25%), penile bulb Dmean (5 Gy), rectum V61 (2%), right femoral head Dmean (5 Gy), and bladder V39 (4%) were found between PT and VMAT. All plans were equally robust to variations. LET-weighted dose in urethra was equivalent to the physical dose for PArc plans and hence no added urethral toxicity was expected.

CONCLUSIONS

PT for treatment of prostate cancer patients with hip prosthesis is feasible and equivalent or potentially superior to VMAT in quality in some cases. The choice of radiotherapy regimen can be personalized based on patient characteristics to achieve the best treatment outcome.

Dosimetric comparison of hippocampal-sparing technologies in patients with low-grade glioma

Background

Radiotherapy (RT) plays an integral role in the management of low-grade gliomas (LGG). Late toxicity from RT can cause progressive neurocognitive dysfunction. Radiation-induced damage to the hippocampus (HCP) plays a considerable role in memory decline. Advancements in photon planning software have resulted in the development of multi-criteria optimization (MCO) and HyperArc technologies which may improve HCP sparing while maintaining planning target volume (PTV) target coverage.

Methods

Three planning methods for hippocampal sparing (HS) were compared, volumetric modulated arc therapy (VMAT) without HS (VMAT_noHS), VMAT with HS (VMAT_HS), MCO with HS (MCO_HS), and HyperArc with HS (HyperArc_HS).

Results

Twenty-five patients were identified. The contralateral HCP was spared in 16 patients and bilateral HCP in 9 patients with superiorly located tumors. All 3 HS planning techniques showed significant reductions in dose to the spared HCP in contralateral cases but only VMAT_HS and MCO_HS achieved this in bilateral cases (P < .008). Only MCO_HS was superior to VMAT_HS in lowering the dose to both contralateral HCP and bilateral HCP in all measured metrics (P < .008). PTV and OAR (organ at risk) dose constraints were achieved for all plans.

Conclusions

This retrospective dosimetric study demonstrated the feasibility of HS for low-grade glioma. All 3 HS planning techniques achieved significant dose reductions to the spared contralateral hippocampus, but only MCO_HS and VMAT_HS achieved this in bilateral cases. MCO was superior to other planning techniques for sparing both bilateral and contralateral hippocampi.

Evaluating Pareto optimal tradeoffs for hippocampal avoidance whole brain radiotherapy with knowledge-based multicriteria optimization

The goal of this study is to investigate the Pareto optimal tradeoffs between target coverage and hippocampal sparing using knowledge-based multicriteria optimization (MCO). Ten prior clinical cases were selected that were treated with hippocampal avoidance whole brain radiotherapy (HA-WBRT) using VMAT. A new, balanced plan was generated for each case using an in-house RapidPlan model in the Eclipse V16.1 treatment planning system. The MCO decision support tool was used to create 4 Pareto optimal plans. The Pareto optimal plans were created using PTV Dmin and hippocampus Dmax as tradeoff criteria. The tradeoff plans were generated for each patient by adjusting PTV Dmin from the value achieved by the corresponding balanced plan in fixed intervals as follows: -4 Gy, -2 Gy, +2 Gy, and +4 Gy. All plans were normalized so that 95% of the PTV was covered by the prescription dose. A 1-way ANOVA, with Geisser-Greenhouse correction, was used for statistical analysis. When evaluating the achieved PTV Dmin and D98%, the results showed the dose to the hippocampus decreased as coverage lowered and in comparison, D98% was higher when the PTV coverage was increased. When comparing multiple tradeoffs, the p-value for PTV D98% was 0.0026, and the p-values for PTV D2%, PTV Dmin, Hippocampus Dmax, Dmin, and Dmean were all less than 0.0001, indicating that the tradeoff plans achieved statistically significant differences. The results also showed that Pareto optimal plans failed to reduce hippocampal dose beyond a certain point, indicating more limited achievability of the MCO-navigated plans than the interface suggested. This study presents valuable data for planning results for HA-WBRT using MCO. MCO has shown to be mostly effective in adjusting the tradeoff between PTV coverage and hippocampal dose.

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.

Volumetric modulated arc therapy (VMAT): a review of clinical outcomes—what is the clinical evidence for the most effective implementation?

Modern conformal radiation therapy using techniques such as modulation, image guidance and motion management have changed the face of radiotherapy today offering superior conformity, efficiency, and reproducibility to clinics worldwide. This review assesses the impact of these advanced radiotherapy techniques on patient toxicity and survival rates reported from January 2017 to September 2020. The main aims are to establish if dosimetric and efficiency gains correlate with improved survival and reduced toxicities and to answer the question ‘What is the clinical evidence for the most effective implementation of VMAT?’. Compared with 3DCRT, improvements have been reported with VMAT in prostate, locally advanced cervical carcinoma and various head and neck applications, leading to the shift in technology to VMAT. Other sites such as thoracic neoplasms and nasopharyngeal carcinomas have observed some improvement with VMAT although not in line with improved dosimetric measures, and the burden of toxicity and the incidence of cancer related deaths remain high, signaling the need to further mitigate toxicity and increase survival. As technological advancement continues, large randomised long-term clinical trials are required to determine the way-forward and offer site-specific recommendations. These studies are usually expensive and time consuming, therefore utilising pooled real-world data in a prospective nature can be an alternative solution to comprehensively assess the efficacy of modern radiotherapy techniques.

RapidPlan hippocampal sparing whole brain model version 2-how far can we reduce the dose?

Whole-brain radiotherapy has been the standard palliative treatment for patients with brain metastases due to its effectiveness, availability, and ease of administration. Recent clinical trials have shown that limiting radiation dose to the hippocampus is associated with decreased cognitive toxicity. In this study, we updated an existing Knowledge Based Planning model to further reduce dose to the hippocampus and improve other dosimetric plan quality characteristics. Forty-two clinical cases were contoured according to guidelines. A new dosimetric scorecard was created as an objective measure for plan quality. The new Hippocampal Sparing Whole Brain Version 2 (HSWBv2) model adopted a complex recursive training process and was validated with five additional cases. HSWBv2 treatment plans were generated on the Varian Halcyon^TM and TrueBeam^TM systems and compared against plans generated from the existing (HSWBv1) model released in 2016. On the Halcyon^TM platform, 42 cases were re-planned. Hippocampal D_100% from HSWBv2 and HSWBv1 models had an average dose of 5.75 Gy and 6.46 Gy, respectively (p < 0.001). HSWBv2 model also achieved a hippocampal D_mean of 7.49 Gy, vs 8.10 Gy in HSWBv1 model (p < 0.001). Hippocampal D_0.03CC from HSWBv2 model was 9.86 Gy, in contrast to 10.57 Gy in HSWBv1 (p < 0.001). For PTV_3000, D_98% and D_2% from HSWBv2 model were 28.27 Gy and 31.81 Gy, respectively, compared to 28.08 Gy (p = 0.020) and 32.66 Gy from HSWBv1 (p < 0.001). Among several other dosimetric quality improvements, there was a significant reduction in PTV_3000 V_105% from 35.35% (HSWBv1) to 6.44% (HSWBv2) (p < 0.001). On 5 additional validation cases, dosimetric improvements were also observed on TrueBeam^TM. In comparison to published data, the HSWBv2 model achieved higher quality hippocampal avoidance whole brain radiation therapy treatment plans through further reductions in hippocampal dose while improving target coverage and dose conformity/homogeneity. HSWBv2 model is shared publicly.

Dosimetric feasibility of hippocampal avoidance whole brain radiotherapy with an MRI-guided linear accelerator

Purpose/Objective(s)

Whole brain radiotherapy with hippocampal avoidance (HA‐WBRT) is a technique utilized to treat metastatic brain disease while preserving memory and neurocognitive function. We hypothesized that the treatment planning and delivery of HA‐WBRT plans is feasible with an MRI‐guided linear accelerator (linac) and compared plan results with clinical non‐MRI‐guided C‐Arm linac plans.

Materials/Methods

Twelve HA‐WBRT patients treated on a non‐MRI‐guided C‐Arm linac were selected for retrospective analysis. Treatment plans were developed using a 0.35T MRI‐guided linac system for comparison to clinical plans. Treatment planning goals were defined as provided in the Phase II Trial NRG CC001. MRI‐guided radiotherapy (MRgRT) treatment plans were developed by a dosimetrist and compared with clinical plans. quality assurance (QA) plans were generated and delivered on the MRI‐guided linac to a cylindrical diode detector array. Planning target volume (PTV) coverage was normalized to ∼95% to provide a control point for comparison of dose to the organs at risk.

Results

MRgRT plans were deliverable and met all clinical goals. Mean values demonstrated that the clinical plans were less heterogeneous than MRgRT plans with mean PTV V37.5 Gy of 0.00% and 0.03% (p = 0.013), respectively. Average hippocampi maximum doses were 14.19 ± 1.29 Gy and 15.00 ± 1.51 Gy, respectively. The gamma analysis comparing planned and measured doses resulted in a mean of 99.9% ± 0.12% of passing points (3%/2mm criteria). MRgRT plans had an average of 38.33 beams with average total delivery time and beam‐on time of 13.7 (11.2–17.5) min and 4.1 (3.2–5.4) min, respectively. Clinical plan delivery times ranged from 3 to 7 min depending on the number of noncoplanar arcs. Planning time between the clinical and MRgRT plans was comparable.

Conclusion

This study demonstrates that HA‐WBRT can be treated using an MRI‐guided linear accelerator with comparable treatment plan quality and delivery accuracy.

Fully automated planning and delivery of hippocampal-sparing whole brain irradiation

The goal of this study is to fully automate the treatment planning and delivery process of hippocampal-sparing whole brain irradiation (HS-WBRT) by combining a RapidPlan (RP) knowledge-based planning model and HyperArc (HA) technology. Additionally, this study compares the dosimetric performance of RapidPlan-HyperArc (RP-HA) treatment plans with RP plans and volumetric modulated arc therapy (VMAT) plans. Ten patients previously treated with HS-WBRT using conventional VMAT were re-planned using RP-HA technique and RP model for HS-WBRT. Treatment plans were generated for 30Gy in 3Gy fractions using 6MV photon beam on a TrueBeam linear accelerator (Varian Medical Systems, Palo Alto, CA) equipped with high definition multileaf collimator (HDMLC). Target coverage, homogeneity index (HI), Paddick Conformity index (CI), dose to organs-at-risk (OARs) provided by the 3 planning modalities were compared, and a paired t-test was performed. Total number of monitor units (MU), effective planning time and beam-on-time time were reported and evaluated for each plan. RP-HA plans achieved on average a 4% increase in D_98% of PTV, a 26% improvement in HI, a 2.3% increase in CI, when compared to RP plans. Furthermore, RP-HA plans provided on average 11% decrease in D_100% of hippocampi when compared to VMAT plans. All RP-HA plans were generated in less than 30 minutes while RP plans took 40 minutes and VMAT plans required on average 9 hours to complete. Regarding beam-on-time time, it was estimated that RP-HA plans take on average 5 minutes to deliver while RP and VMAT plans require 6.5 and 10 minutes, respectively. RP-HA method provides fully automated planning and delivery for HS-WBRT. The auto-generated plans together with automated treatment delivery allow standardization of plan quality, increased efficiency and ultimately improved patient care.

Dosimetric Quality of Online Adapted Pancreatic Cancer Treatment Plans on an MRI-Guided Radiation Therapy System

Purpose

Stereotactic magnetic resonance image–guided adaptive radiation therapy (SMART) is an emerging technique that shows promise in the treatment of pancreatic cancer and other abdominopelvic malignancies. However, it is unknown whether the time-limited nature of on-table adaptive planning may result in dosimetrically suboptimal plans. The purpose of this study was to quantitatively address that question through systemic retrospective replanning of treated on-table adaptive pancreatic cancer cases.

Methods and Materials

Of 74 consecutive adapted fractions, 30 were retrospectively replanned based on deficiencies in planning target volume (PTV) and gross tumor volume (GTV) coverage or doses to organs-at-risk (OARs) that exceeded ideal constraints. Retrospective plans were created by adjusting dose-volume objectives in an iterative fashion until deemed optimized. The goal of replanning was to improve PTV/GTV coverage while keeping the dose to gastrointestinal OARs the same or lower or to reduce OAR doses while keeping PTV coverage the same or higher. The global maximum dose was required to be maintained within 2% of that of the treated adaptive plan to eliminate it as a confounding factor. A threshold of 5% improvement in PTV coverage or 5% decrease in OAR dose was used to define a clinically significant improvement.

Results

Of the 30 replans, 7 obtained at least 5% PTV coverage improvement. The average increase in PTV coverage for these plans was 11%. No plans were clinically significantly improved in terms of OAR sparing. Changes in beam-on time did not show any correlation. Statistical analysis via a linear mixed-effects model with a nested random effect suggested that both GTV and PTV coverage were improved over SMART process plans by 0.91 cc (P = .02) and 2.03 cc (P < .001), respectively.

Conclusions

Dosimetric plan quality of at least 10% of SMART fractions may be improved through more extensive replanning than is currently performed on-table. Further work is needed to develop an automated replanning workflow to streamline the in-depth replanning process to better fit into an on-table adaptive workflow.

Dosimetric Evaluation of Fractionated Stereotactic Radiation Therapy for Skull Base Meningiomas Using HyperArc and Multicriteria Optimization

Purpose

Treatment planning of skull based meningiomas can be difficult due to the irregular shaped target volumes and proximity to critical optic structures. This study evaluated the use of HyperArc (HA) radiosurgery optimization and delivery in conjunction with multicriteria optimization (MCO) to create conformal and efficient treatment plans for conventionally fractionated radiation therapy to difficult base-of-skull (BOS) lesions.

Methods and Materials

Twelve patients with BOS meningioma were retrospectively planned with HA-specific optimization algorithm, stereotactic normal tissue objective (SRS-NTO), and conventional automatic normal tissue objective to evaluate normal brain sparing (mean dose and V20 Gy). MCO was used on both SRS-NTO and automatic normal tissue objective plans to further decrease organ-at-risk doses and target dose maximum to within clinically acceptable constraints. Delivery efficiency was evaluated based on planned monitor units.

Results

The SRS-NTO in HA can be used to improve the mid- and low-dose spread to normal brain tissue in the irradiation of BOS meningiomas. Improvement in normal brain sparing can be seen in larger, more irregular shaped lesions and less so in smaller spherical targets. MCO can be used in conjunction with the SRS-NTO to reduce target dose maximum and dose to organ at risk without sacrificing the gain in normal brain sparing.

Conclusions

HA can be beneficial both in treatment planning by using the SRS-NTO and in delivery efficiency through the decrease in monitor units and automated delivery.

The Capabilities and Characteristics of Helical Tomotherapy and Co-Planar Dual Arcs Volumetric-Modulated arc Therapy Associated with Hippocampal Sparing During Prophylactic Cranial Irradiation

Objective: To investigate the features of helical tomotherapy and co-planar dual Arcs volumetric-modulated arc therapy during prophylactic cranial irradiation associated with bilateral hippocampal tissue sparing. Materials and methods: Helical tomotherapy and co-planar dual arcs volumetric-modulated arc therapy treatment plans were generated with a dose of 30 Gy/10 fractions in 16 patients treated with prophylactic cranial irradiation. The dose to the bilateral hippocampal tissues, organs at risk, and planning target volume were determined when the average dose of bilateral hippocampal tissues was reduced by approximately 4 Gy as an observation point. Changes in dosimetry when sparing the bilateral hippocampal tissues were determined for both modalities. Results: When bilateral hippocampal tissues were restricted to 8 Gy, D40%mean-bilateral hippocampal tissues = 7.64 ± 0.41 Gy in helical tomotherapy, while D40%mean-bilateral hippocampal tissues = 10.96 ± 0.38 Gy in co-planar dual arcs volumetric-modulated arc therapy volumetric-modulated arc therapy. Helical tomotherapy was associated with significantly lower doses to organs at risk, including Dmean-bilateral hippocampal tissues (P = .03), D98%-bilateral hippocampal tissues (P = .01), D2%-bilateral hippocampal tissues (P = .01), Dmean-inner ear (P = .02), Dmean-parotid glands (P = .02), Dmax-lens (P = .02), and Dmax-brainstem (P = .02), but not Dmax-optic nerves (P = .87). Helical tomotherapy provided better target coverage, with lower average D2%-PTV (P = .02), higher average D98%-PTV (P = .02), and better conformal index (0.87 vs 0.84, P = .02) and homogeneity index (0.15 vs 0.21, P = .05). With smaller bilateral hippocampal tissues doses, the planning target volume dose changed across 3 dosimetry regions for both modalities; the plateau region (>20.0 Gy for helical tomotherapy versus >16.0 Gy for co-planar dual arcs volumetric-modulated arc therapy), gradient region (20.0-12.0 Gy vs 16.0-11.0 Gy), and falling region (<12.0 Gy vs <11.0 Gy). The average delivery duration of helical tomotherapy was almost 7.7 times longer than that of co-planar dual arcs volumetric-modulated arc therapy. Conclusions: Helical tomotherapy was better at sparing the bilateral hippocampal tissues and organs at risk and had better target coverage but a significantly longer treatment duration than co-planar dual arcs volumetric-modulated arc therapy. Further dose decreases in the bilateral hippocampal tissues would yield worse target dose coverage.

A knowledge-based quantitative approach to characterize treatment plan quality: Application to prostate VMAT planning

PURPOSE

To characterize treatment plan (TP) quality, a quantitative quality control (QC) tool is proposed. The tool is validated using volumetric modulated arc therapy (VMAT) plans for treatment of prostate cancer by estimating the achievable organ at risk (OAR) sparing, based on the knowledge learned from prior plans.

METHODS

Prostate TP quality was investigated by evaluating the achieved OAR sparing in the rectum and bladder, based on their proximity to target surface. The knowledge base used in this work comprises 450 plans, consisting of 181 homogenous prostate plans and 269 simultaneous integrated boost (SIB) prostate plans. A knowledge-based algorithm was used to relate the absorbed doses of the OARs (rectum and bladder) and their proximity to the planning target volume (PTV). A metric (M_q,r value) was calculated to characterize the OAR sparing based on the weighted differences of the mean doses at binned distances to the PTV surface. The 90% probability ellipse of the normally distributed OARs M_q,r values was considered to define a threshold above which the treatment plan was re-optimized.

RESULTS

Following re-optimization, 8/11 of the homogenous plans and 6/13 of the SIB plans outside the 90% probability ellipse could be re-optimized to gain better OAR sparing while achieving the same or better target coverage. However, 3/4 of the homogenous TPs and 1/9 of the SIB TPs between 80% and 90% were improved. M_q,r values of bladder and rectum after re-optimizing the plans in both groups of homogenous and SIB showed lower values compared to the corresponding values before re-optimization, which implies that better OARs sparing was achieved.

CONCLUSIONS

This work demonstrates an effective anatomy-specific QC tool for identifying suboptimal plans and determining the achievable OAR sparing for each individual patient anatomy.

Automated contouring and planning pipeline for hippocampal-avoidant whole-brain radiotherapy

Background

Whole-brain radiotherapy (WBRT) remains an important treatment for over 200,000 cancer patients in the United States annually. Hippocampal-avoidant WBRT (HA-WBRT) reduces neurocognitive toxicity compared to standard WBRT, but HA-WBRT contouring and planning are more complex and time-consuming than standard WBRT. We designed and evaluated a workflow using commercially available artificial intelligence tools for automated hippocampal segmentation and treatment planning to efficiently generate clinically acceptable HA-WBRT radiotherapy plans.

Methods

We retrospectively identified 100 consecutive adult patients treated for brain metastases outside the hippocampal region. Each patient’s T1 post-contrast brain MRI was processed using NeuroQuant, an FDA-approved software that provides segmentations of brain structures in less than 8 min. Automated hippocampal segmentations were reviewed for accuracy, then converted to files compatible with a commercial treatment planning system, where hippocampal avoidance regions and planning target volumes (PTV) were generated. Other organs-at-risk (OARs) were previously contoured per clinical routine. A RapidPlan knowledge-based planning routine was applied for a prescription of 30 Gy in 10 fractions using volumetric modulated arc therapy (VMAT) delivery. Plans were evaluated based on NRG CC001 dose-volume objectives (Brown et al. in J Clin Oncol, 2020).

Results

Of the 100 cases, 99 (99%) had acceptable automated hippocampi segmentations without manual intervention. Knowledge-based planning was applied to all cases; the median processing time was 9 min 59 s (range 6:53–13:31). All plans met per-protocol dose-volume objectives for PTV per the NRG CC001 protocol. For comparison, only 65.5% of plans on NRG CC001 met PTV goals per protocol, with 26.1% within acceptable variation. In this study, 43 plans (43%) met OAR constraints, and the remaining 57 (57%) were within acceptable variation, compared to 42.5% and 48.3% on NRG CC001, respectively. No plans in this study had unacceptable dose to OARs, compared to 0.8% of manually generated plans from NRG CC001. 8.4% of plans from NRG CC001 were not scored or unable to be evaluated.

Conclusions

An automated pipeline harnessing the efficiency of commercially available artificial intelligence tools can generate clinically acceptable VMAT HA-WBRT plans with minimal manual intervention. This process could improve clinical efficiency for a treatment established to improve patient outcomes over standard WBRT.

Sparing the hippocampus and the hypothalamic- pituitary region during whole brain radiotherapy: a volumetric modulated arc therapy planning study

BACKGROUND

Feasibility testing of a simultaneous sparing approach of hippocampus, hypothalamus and pituitary gland in patients undergoing whole-brain radiotherapy (WBRT) with and without a concomitant boost to metastatic sites.

INTRODUCTION

Cognitive impairment and hormonal dysfunction are common side effects of cranial radiotherapy. A reduced dose application to the patho-physiologically involved functional brain areas, i.e. hippocampus, hypothalamus and pituitary gland, could reduce these common side effects. While hippocampal sparing is already a common practice to improve cognitive outcome, technical experience of additional combined sparing of the hypothalamus/pituitary gland (HT-P) is insufficient.

METHODS

Twenty patients were included in the planning study. In 11 patients, a total dose of 36 Gy of WBRT (2 Gy per fraction) plus a simultaneous integrated boost (SIB) of 9 Gy (0.5 Gy per fraction, total dose: 45 Gy) to the brain metastases was applied. In 9 patients, prophylactic cranial irradiation (PCI) was simulated with a total dose of 30 Gy (2 Gy per fraction). In both patient cohorts, a sparing approach of the hippocampus and the HT-P area was simulated during WBRT. For all treatment plans, volumetric modulated arc therapy (VMAT) was used. Quality assurance included assessment of homogeneity, conformality and target coverage.

RESULTS

The mean dose to the hippocampus and HT-P region was limited to less than 50% of the prescribed dose to the planning target volume (PTV) in all treatment plans. Dose homogeneity (HI) of the target volume was satisfying (median HI = 0.16 for WBRT+SIB and 0.1 for PCI) and target coverage (conformation number, CN) was not compromised (median CN = 0.82 for SIB and 0.86 for PCI).

CONCLUSION

Simultaneous dose reduction to the hippocampus and the HT-P area did not compromise the PTV coverage in patients undergoing WBRT+SIB or PCI using VMAT. While the feasibility of the presented approach is promising, prospective neurologic, endocrine outcome and safety studies are required.

Early experience with hippocampal avoidance whole brain radiation therapy and simultaneous integrated boost for brain metastases

PURPOSE

Cranial irradiation results in cognitive decline, which is hypothesized to be partially attributable to hippocampal injury and stem cell loss. Recent advances allow for targeted reduction of radiation dose to the hippocampi while maintaining adequate dose coverage to the brain parenchyma and additional increasing dose to brain metastases, a approach called hippocampal avoidance whole brain radiation therapy with a simultaneous integrated boost (HA-WBRT + SIB.) We review our early clinical experience with HA-WBRT + SIB.

MATERIALS AND METHODS

We evaluated treatments and clinical outcomes for patients treated with HA-WBRT + SIB between 2014 and 2018.

RESULTS

A total of 32 patients (median age, 63.5 years, range 45.3-78.8 years) completed HA-WBRT + SIB. Median follow-up for patients alive at the time of analysis was 11.3 months. The most common histology was non-small cell lung cancer (n = 22). Most patients (n = 25) were prescribed with WBRT dose of 30 Gy with SIB to 37.5 Gy in 15 fractions. Volumetric modulated arc therapy reduced treatment time (p < 0.0001). Median freedom from intracranial progression and overall survival from completion of treatment were 11.4 months and 19.6 months, respectively. Karnofsky Performance Status was associated with improved survival (p = 0.008). The most common toxicities were alopecia, fatigue, and nausea. Five patients developed cognitive impairment, including grade 1 (n = 3), grade 2 (n = 1), and grade 3 (n = 1).

CONCLUSION

HA-WBRT + SIB demonstrated durable intracranial disease control with modest side effects and merits further investigation as a means of WBRT toxicity reduction while improving long-term locoregional control in the brain.

Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique

PURPOSE

To develop an automated treatment planning strategy for external beam intensity-modulated radiation therapy (IMRT), including a deep learning-based three-dimensional (3D) dose prediction and a dose distribution-based plan generation algorithm.

METHODS AND MATERIALS

A residual neural network-based deep learning model is trained to predict a dose distribution based on patient-specific geometry and prescription dose. A total of 270 head-and-neck cancer cases were enrolled in this study, including 195 cases in the training dataset, 25 cases in the validation dataset, and 50 cases in the testing dataset. All patients were treated with IMRT with a variety of different prescription patterns. The model input consists of CT images and contours delineating the organs at risk (OARs) and planning target volumes (PTVs). The algorithm output is trained to predict the dose distribution on the CT image slices. The obtained prediction model is used to predict dose distributions for new patients. Then, an optimization objective function based on these predicted dose distributions is created for automatic plan generation.

RESULTS

Our results demonstrate that the deep learning method can predict clinically acceptable dose distributions. There is no statistically significant difference between prediction and real clinical plan for all clinically relevant dose-volume histogram (DVH) indices, except brainstem, right and left lens. However, the predicted plans were still clinically acceptable. The results of plan generation show no statistically significant differences between the automatic generated plan and the predicted plan except PTV_70.4 , but the difference is only 0.5% which is still clinically acceptable.

CONCLUSION

This study developed a new automated radiotherapy treatment planning system based on 3D dose prediction and 3D dose distribution-based optimization. It is a promising approach for realizing automated treatment planning in the future.