Validation of in-house knowledge-based planning model for advance-stage lung cancer patients treated using VMAT radiotherapy

Single isocenter versus dual isocenter treatment using flattening filter-free and jaw-tracking volumetrically modulated arc therapy for boot-shaped lung cancer: Evaluation of dosimetric and feasibility

BACKGROUND

To determine whether a dual-isocenter volumetrically modulated arc therapy (VMAT) technique results in lower normal pulmonary dosage compared to a traditional single isocenter technique for boot-shaped lung cancer.

METHODS

A cohort of 15 patients with advanced peripheral or central lung cancer who had metastases in the mediastinum and supraclavicular lymph nodes was randomly selected for this retrospective study. VMAT plans were generated for each patient using two different beam alignment techniques with the 6-MV flattening filter-free (FFF) photon beam: single-isocenter jaw-tracking VMAT based on the Varian TrueBeam linear accelerator (S-TV), and dual-isocenter VMAT based on both TrueBeam (D-TV) and Halcyon linear accelerator (D-HV). For all 45 treatment plans, planning target volume (PTV) dose coverage, conformity/homogeneity index (CI/HI), mean heart dose (MHD), mean lung dose (MLD) and the total lung tissue receiving 5, 20, 30 Gy (V5, V20, V30) were evaluated. The monitor units (MUs), delivery time, and plan quality assurance (QA) results were recorded.

RESULTS

The quality of the objectives of the three plans was comparable to each other. In comparison with S-TV, D-TV and D-HV improved the CI and HI of the PTV (p < 0.05). The MLD was 13.84 ± 1.44 Gy (mean ± SD) for D-TV, 14.22 ± 1.30 Gy and 14.16 ± 1.42 Gy for S-TV and D-HV, respectively. Lungs-V5Gy was 50.78 ± 6.24%, 52.00 ± 7.32% and 53.36 ± 8.48%, Lungs-V20Gy was 23.72 ± 2.27%, 26.18 ± 2.86% and 24.96 ± 3.09%, Lungs-V30Gy was 15.69 ± 1.76%, 17.20 ± 1.72% and 16.52 ± 2.07%. Compared to S-TV, D-TV provided statistically significant better protection for the total lung, with the exception of the lungs-V5. All plans passed QA according the gamma criteria of 3%/3 mm.

CONCLUSIONS

Taking into account the dosimetric results and published clinical data on radiation-induced pulmonary injury, dual-isocenter jaw-tracking VMAT may be the optimal choice for treating boot-shaped lung cancer.

Benchmarking Automated Machine Learning-Enhanced Planning With Ethos Against Manual and Knowledge-Based Planning for Locally Advanced Lung Cancer

Purpose

Currently, there is insufficient guidance for standard fractionation lung planning using the Varian Ethos adaptive treatment planning system and its unique intelligent optimization engine. Here, we address this gap in knowledge by developing a methodology to automatically generate high-quality Ethos treatment plans for locally advanced lung cancer.

Methods and Materials

Fifty patients previously treated with manually generated Eclipse plans for inoperable stage IIIA-IIIC non-small cell lung cancer were included in this institutional review board-approved retrospective study. Fifteen patient plans were used to iteratively optimize a planning template for the Daily Adaptive vs Non-Adaptive External Beam Radiation Therapy With Concurrent Chemotherapy for Locally Advanced Non-Small Cell Lung Cancer: A Prospective Randomized Trial of an Individualized Approach for Toxicity Reduction (ARTIA-Lung); the remaining 35 patients were automatically replanned without intervention. Ethos plan quality was benchmarked against clinical plans and reoptimized knowledge-based RapidPlan (RP) plans, then judged using standard dose-volume histogram metrics, adherence to clinical trial objectives, and qualitative review.

Results

Given equal prescription target coverage, Ethos-generated plans showed improved primary and nodal planning target volume V95% coverage (P < .001) and reduced lung gross tumor volume V5 Gy and esophagus D0.03 cc metrics (P ≤ .003) but increased mean esophagus and brachial plexus D0.03 cc metrics (P < .001) compared with RP plans. Eighty percent, 49%, and 51% of Ethos, clinical, and RP plans, respectively, were "per protocol" or met "variation acceptable" ARTIA-Lung planning metrics. Three radiation oncologists qualitatively scored Ethos plans, and 78% of plans were clinically acceptable to all reviewing physicians, with no plans receiving scores requiring major changes.

Conclusions

A standard Ethos template produced lung radiation therapy plans with similar quality to RP plans, elucidating a viable approach for automated plan generation in the Ethos adaptive workspace.

Learning curve of lung dose optimization in intensity-modulated radiotherapy for locally advanced non-small cell lung cancer

BACKGROUND

Intensity-modulated radiotherapy (IMRT) has been increasingly used for patients with locally advanced non-small cell lung cancer (LA-NSCLC). However, there are some barriers to implementing IMRT for LA-NSCLC, including the complexity of treatment plan optimization. This study aimed to evaluate the learning curve of lung dose optimization in IMRT for LA-NSCLC and identify the factors that affect the degree of achievement of lung dose optimization.

METHODS

We retrospectively evaluated 40 consecutive patients with LA-NSCLC who received concurrent chemoradiotherapy at our institution. These 40 patients were divided into two groups: 20 initially treated patients (earlier group) and 20 subsequently treated patients (later group). Patient and tumor characteristics were compared between the two groups. The dose-volume parameter ratio between the actually delivered IMRT plan and the simulated three-dimensional conformal radiotherapy plan was also compared between the two groups to determine the learning curve of lung dose optimization.

RESULTS

The dose-volume parameter ratio for lung volume to receive more than 5 Gy (lung V5) and mean lung dose (MLD) significantly decreased in later groups. The spread of the beam path and insufficient optimization of dose coverage of planning target volume (PTV) might cause poor control of lung V5, MLD.

CONCLUSIONS

A learning curve for lung dose optimization was observed with the accumulation of experience. Appropriate techniques, such as restricting the beam path and ensuring dose coverage of PTV during the optimization process, are essential to control lung dose in IMRT for LA-NSCLC.

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.

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.

Implementation of a Knowledge-Based Treatment Planning Model for Cardiac-Sparing Lung Radiation Therapy

PURPOSE

High radiation doses to the heart have been correlated with poor overall survival in patients receiving radiation therapy for stage III non-small cell lung cancer (NSCLC). We built a knowledge-based planning (KBP) tool to limit the dose to the heart during creation of volumetric modulated arc therapy (VMAT) treatment plans for patients being treated to 60 Gy in 30 fractions for stage III NSCLC.

METHODS AND MATERIALS

A previous study at our institution retrospectively delineated intracardiac volumes and optimized VMAT treatment plans to reduce dose to these substructures and to the whole heart. Two RapidPlan (RP) KBP models were built from this cohort, 1 model using the clinical plans and a separate model using the cardiac-optimized plans. Using target volumes and 6 organs at risk (OARs), models were trained to generate treatment plans in a semiautomated process. The cardiac-sparing KBP model was tested in the same cohort used for training, and both models were tested on an external validation cohort of 30 patients.

RESULTS

Both RP models produced clinically acceptable plans in terms of target coverage, dose uniformity, and dose to OARs. Compared with the previously created cardiac-optimized plans, cardiac-sparing RPs showed significant reductions in the mean dose to the esophagus and lungs while performing similarly or better in all evaluated heart dose metrics. When comparing the 2 models, the cardiac-sparing RP showed reduced (P < .05) heart mean and maximum doses as well as volumes receiving 60 Gy, 50 Gy, and 30 Gy.

CONCLUSIONS

By using a set of cardiac-optimized treatment plans for training, the proposed KBP model provided a means to reduce the dose to the heart and its substructures without the need to explicitly delineate cardiac substructures. This tool may offer reduced planning time and improved plan quality and might be used to improve patient outcomes.

Machine learning for dose-volume histogram based clinical decision-making support system in radiation therapy plans for brain tumors

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Extraction, analysis, and interpretation of historical treatment planning data is valuable but very time-consuming.

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Proposed machine learning model classifies radiotherapy plans based on their treatment planning objectives and trade-offs.

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Application of double nested cross-validation enabled to build a robust model that achieved 94% accuracy on a testing data.

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Model reasoning investigated with SHAP values showed consistency with clinical observations.

Purpose

To create and investigate a novel, clinical decision-support system using machine learning (ML).

Methods and Materials

The ML model was developed based on 79 radiotherapy plans of brain tumor patients that were prescribed a total dose of 60 Gy delivered with volumetric-modulated arc therapy (VMAT). Structures considered for analysis included planning target volume (PTV), brainstem, cochleae, and optic chiasm. The model aimed to classify the target variable that included class-0 corresponding to plans for which the PTV treatment planning objective was met and class-1 that was associated with plans for which the PTV objective was not met due to the priority trade-off to meet one or more organs-at-risk constraints. Several models were evaluated using double-nested cross-validation and an area-under-the-curve (AUC) metric, with the highest performing one selected for further investigation. The model predictions were explained with Shapely additive explanation (SHAP) interaction values.

Results

The highest-performing model was Logistic Regression achieving an accuracy of 93.8 ± 4.1% and AUC of 0.98 ± 0.02 on the testing data. The SHAP analysis indicated that the ΔD_99% metric for PTV had the greatest influence on the model predictions. The least important feature was ΔD_MAX for the left and right cochleae.

Conclusions

The trained model achieved satisfactory accuracy and can be used by medical physicists in a data-driven quality assurance program as well as by radiation oncologists to support their decision-making process in terms of treatment plan approval and potential plan modifications. Model explanation analysis showed that the model relies on clinically valid logic when making predictions.

Predicting personalised optimal arc parameter using knowledge-based planning model for inoperable locally advanced lung cancer patients to reduce organ at risk doses

Objectives. Volumetric modulated arc therapy (VMAT) allows for reduction of organs at risk (OAR) volumes receiving higher doses, but increases OAR volumes receiving lower radiation doses and can subsequently increasing associated toxicity. Therefore, reduction of this low-dose-bath is crucial. This study investigates personalizing the optimization of VMAT arc parameters (gantry start and stop angles) to decrease OAR doses.Materials and Methods. Twenty previously treated locally advanced non-small cell lung cancer (NSCLC) patients treated with half-arcs were randomly selected from our database. These plans were re-optimized with seven different arcs parameters; optimization objectives were kept constant for all plans. All resulting plans were reviewed by two clinicians and the optimal plan (lowest OAR doses and adequate target coverage) was selected. Furthermore, knowledge-based planning (KBP) model was developed using these plans as 'training data' to predict optimal arc parameters for individual patients based on their anatomy. Treatment plan complexity scores and deliverability measurements were performed for both optimal and original clinical plans.Results.The results show that different arc geometries resulted in different dose distributions to the OAR but target coverage was mostly similar. Different arc geometries were required for different patients to minimize OAR doses. Comparison of the personalized against the standard (2 half-arcs) plans showed a significant reduction in lung V₅(lung volume receiving 5 Gy), mean lung dose and mean heart doses. Reduction in lung V₂₀and heart V₃₀were statistically insignificant. Plan complexity and deliverability measurements show the test plans can be delivered as planned.Conclusions.Our study demonstrated that personalizing arc parameters based on an individual patient's anatomy significantly reduces both lung and heart doses. Dose reduction is expected to reduce toxicity and improve the quality of life for these patients.