The implementation of RapidPlan in predicting deep inspiration breath-hold candidates with left-sided breast cancer

Rapid Selection of Patients Suitable for Deep Inspiration Breath-Hold Using an Automatic Delineating System and RapidPlan Model in Patients With Left Breast Cancer Undergoing Adjuvant Radiation Therapy With IMRT

PURPOSE

This study aimed to determine whether radiation therapy plans created using an automatic delineating system and a RapidPlan (RP) module could rapidly and accurately predict heart doses and benefit from deep inspiratory breath-hold (DIBH) in patients with left breast cancer.

METHODS AND MATERIALS

One hundred thirty-six clinically approved free breathing (FB) plans for patients with left breast cancer were included, defined as manual delineation-manual plan (MD-MP). A total of 104 of 136 plans were selected for RP model training. A total of 32 of 136 patients were automatically delineated by software, after which the RP generated plans, defined as automatic delineation-RapidPlan (AD-RP). In addition, 40 patients who used DIBH were included to analyze differences in heart benefits from DIBH.

RESULTS

Two RP models were established for post-breast-conserving surgery (BCS) and post-modified radical mastectomy. There were no significant differences in most of the dosimetric parameters between the MD-MP and AD-RP. The heart doses of the 2 plans were strongly correlated in patients after BCS (0.80 ≤ r ≤ 0.88, P < .05) and moderately correlated in patients after postmodified radical mastectomy (0.46 ≤ r ≤ 0.58, P <.05). The RP model predicted the mean heart dose (MHD) within ± 59.67 cGy and ± 63.32 cGy for patients who underwent the 2 surgeries described above. The heart benefits from DIBH were significantly greater in patients with FB-MHD ≥ 4 Gy than in those with FB-MHD < 4 Gy.

CONCLUSIONS

The combined automatic delineation RP model allows for the rapid and accurate prediction of heart dose under FB in patients with left breast cancer. FB-MHD ≥ 4 Gy can be used as a dose threshold to select patients suitable for DIBH.

Effectiveness of multi-criteria optimization in combination with knowledge-based modeling in radiotherapy of left-sided breast including regional nodes

Multi-criteria optimization (MCO) is a method that was added to treatment planning to create high-quality treatment plans. This study aimed to investigate the effectiveness of MCO in combination with knowledge-based planning (KBP) in radiotherapy for left-sided breasts, including regional nodes. Dose/volume parameters were evaluated for manual plans (MP), KBP, and KBP + MCO. Planning target volume doses of MP had better coverage while KBP + MCO plans demonstrated the lowest organ at risk doses. KBP and KBP + MCO plans had increasing complexity as expressed in the number of monitor units.

Evaluating machine learning enhanced intelligent-optimization-engine (IOE) performance for ethos head-and-neck (HN) plan generation

PURPOSE

Varian Ethos utilizes novel intelligent-optimization-engine (IOE) designed to automate the planning. However, this introduced a black box approach to plan optimization and challenge for planners to improve plan quality. This study aims to evaluate machine-learning-guided initial reference plan generation approaches for head & neck (H&N) adaptive radiotherapy (ART).

METHODS

Twenty previously treated patients treated on C-arm/Ring-mounted were retroactively re-planned in the Ethos planning system using a fixed 18-beam intensity-modulated radiotherapy (IMRT) template. Clinical goals for IOE input were generated using (1) in-house deep-learning 3D-dose predictor (AI-Guided) (2) commercial knowledge-based planning (KBP) model with universal RTOG-based population criteria (KBP-RTOG) and (3) an RTOG-based constraint template only (RTOG) for in-depth analysis of IOE sensitivity. Similar training data was utilized for both models. Plans were optimized until their respective criteria were achieved or DVH-estimation band was satisfied. Plans were normalized such that the highest PTV dose level received 95% coverage. Target coverage, high-impact organs-at-risk (OAR) and plan deliverability was assessed in comparison to clinical (benchmark) plans. Statistical significance was evaluated using a paired two-tailed student t-test.

RESULTS

AI-guided plans were superior to both KBP-RTOG and RTOG-only plans with respect to clinical benchmark cases. Overall, OAR doses were comparable or improved with AI-guided plans versus benchmark, while they increased with KBP-RTOG and RTOG plans. However, all plans generally satisfied the RTOG criteria. Heterogeneity Index (HI) was on average <1.07 for all plans. Average modulation factor was 12.2 ± 1.9 (p = n.s), 13.1 ± 1.4 (p = <0.001), 11.5 ± 1.3 (p = n.s.) and 12.2 ± 1.9 for KBP-RTOG, AI-Guided, RTOG and benchmark plans, respectively.

CONCLUSION

AI-guided plans were the highest quality. Both KBP-enabled and RTOG-only plans are feasible approaches as clinics adopt ART workflows. Similar to constrained optimization, the IOE is sensitive to clinical input goals and we recommend comparable input to an institution's planning directive dosimetric criteria.

A Critical Overview of Predictors of Heart Sparing by Deep-Inspiration-Breath-Hold Irradiation in Left-Sided Breast Cancer Patients

Simple Summary

Adjuvant radiotherapy could damage the heart in left-sided breast cancer patients. The deep-inspiration-breath-hold technique may limit the heart exposure to radiation. As non-beneficiaries exist, there is some need to do an upfront cost-effective selection. Some easy-to-use anatomical predictors may help insiders in the treatment decision. The awareness of such findings may improve the efficiency of practitioners’ workflows.

Abstract

Radiotherapy represents an essential part of the therapeutic algorithm for breast cancer patients after conservative surgery. The treatment of left-sided tumors has been associated with a non-negligible risk of developing late-onset cardiovascular disease. The cardiac risk perception has especially increased over the last years due to the prolongation of patients’ survival owing to the advent of new drugs and an ever earlier cancer detection through screening programs. Improvements in radiation delivery techniques could reduce the treatment-related heart toxicity. The deep-inspiration-breath-hold (DIBH) irradiation is one of the most advanced treatment approaches, which requires specific technical equipment and uses inspiration to displace the heart from the tangential radiation fields. However, not all patients benefit from its use. Moreover, DIBH irradiation needs patient compliance and accurate training. Therefore, such a technique may be unjustifiably cumbersome and time-consuming as well as unnecessarily expensive from a mere healthcare cost point of view. Hence the need to early select only the true beneficiaries while tailoring more effective heart-sparing techniques for the others and streamlining the workflow, especially in high-volume radiation oncology departments. In this literature overview, we collected some possible predictors of cardiac dose sparing in DIBH irradiation for left breast treatment in an effort to provide an easy-to-consult summary of simple instruments to insiders for identifying patients actually benefitting from this technique. We critically reviewed the reliability and weaknesses of each retrieved finding, aiming to inspire new insights and discussions on this much-debated topic.

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.

Knowledge-Based Volumetric Modulated Arc Therapy Treatment Planning for Breast Cancer

Purpose:

To create and to validate knowledge-based volumetric modulated arc therapy (VMAT) models for breast cancer treatments without lymph node irradiation.

Materials and Methods:

One hundred VMAT-based breast plans (manual plans [MP]) were selected to create two knowledge-based VMAT models (breast left and breast right) using RapidPlan™. The plans were generated on Eclipse v15.5 (Varian Medical Systems, Palo Alto, CA) with 6 MV of a Novalis Tx equipped with a high-resolution multileaf collimator. The models were verified based on goodness-of-fit statistics using the coefficients of determination (R²) and Chi-square (χ²), and the goodness-of-estimation statistics through the mean square error (MSE). Geometrical and dosimetrical constraints were identified and removed from the RP models using statistical evaluation metrics and plots. For validation, 20 plans that integrate the models and 20 plans that do not were reoptimized with RP (closed and opened validation). Dosimetrical parameters of interest were used to compare MP versus RP plans for the Heart, Homolateral_Lung, Contralateral_Lung, and Contralateral_Breast. Optimization planning time and user independency were also analyzed.

Results:

The most unfavorable results of R² in both models for the organs at risk were as follows: for Contralateral_Lung 0.51 in RP right breast (RP_RB) and for Heart 0.60 in RP left breast (RP_LB). The most unfavorable results of χ² test were: for Contralateral_Breast 1.02 in RP_RB and for Heart 1.03 in RP_LB. These goodness-of-fit results show that no overfitting occurred in either of the models. There were no unfavorable results of mean square error (MSE, all < 0.05) in any of the two models. These goodness-of-estimation results show that the models have good estimation power. For closed validation, significant differences were found in RP_RB for Homolateral_Lung (all P ≤ 0.001), and in the RP_LB differences were found for the heart (all P ≤ 0.04) and for Homolateral_Lung (all P ≤ 0.022). For open validation, no statistically significant differences were obtained in either of the models. RP models had little impact on reducing optimization planning times for expert planners; nevertheless, the result showed a 30% reduction time for beginner planners. The use of RP models generates high-quality plans, without differences from the planner experience.

Conclusion:

Two RP models for breast cancer treatment using VMAT were successfully implemented. The use of RP models for breast cancer reduces the optimization planning time and improves the efficiency of the treatment planning process while ensuring high-quality plans.

Deep-inspirational breath-hold (DIBH) technique in left-sided breast cancer: various aspects of clinical utility

Background

Studying the clinical utility of deep-inspirational breath-hold (DIBH) in left breast cancer radiotherapy (RT) was aimed at focusing on dosimetry and feasibility aspects.

Methods

In this prospective trial all enrolled patients went through planning CT in supine position under both DIBH and free breathing (FB); in whole breast irradiation (WBI) cases prone CT was also taken. In 3-dimensional conformal radiotherapy (3DCRT) plans heart, left anterior descending coronary artery (LAD), ipsilateral lung and contralateral breast doses were analyzed. The acceptance of DIBH technique as reported by the patients and the staff was analyzed; post-RT side-effects including radiation lung changes (visual scores and lung density measurements) were collected.

Results

Among 130 enrolled patients 26 were not suitable for the technique while in 16, heart or LAD dose constraints were not met in the DIBH plans. Among 54 and 34 patients receiving WBI and postmastectomy/nodal RT, respectively with DIBH, mean heart dose (MHD) was reduced to < 50%, the heart V_{25 Gy} to < 20%, the LAD mean dose to < 40% and the LAD maximum dose to about 50% as compared to that under FB; the magnitude of benefit was related to the relative increase of the ipsilateral lung volume at DIBH. Nevertheless, heart and LAD dose differences (DIBH vs. FB) individually varied. Among the WBI cases at least one heart/LAD dose parameter was more favorable in the prone or in the supine FB plan in 15 and 4 cases, respectively; differences were numerically small. All DIBH patients completed the RT, inter-fraction repositioning accuracy and radiation side-effects were similar to that of other breast RT techniques. Both the patients and radiographers were satisfied with the technique.

Conclusions

DIBH is an excellent heart sparing technique in breast RT, but about one-third of the patients do not benefit from that otherwise laborious procedure or benefit less than from an alternative method.

Trial registration: retrospectively registered under ISRCTN14360721 (February 12, 2021)

Supplementary information

The online version contains supplementary material available at 10.1186/s13014-021-01816-3.

Can the Student Outperform the Master? A Plan Comparison Between Pinnacle Auto-Planning and Eclipse knowledge-Based RapidPlan Following a Prostate-Bed Plan Competition

Purpose:

Pinnacle Auto-Planning and Eclipse RapidPlan are 2 major commercial automated planning engines that are fundamentally different: Auto-Planning mimics real planners in the iterative optimization, while RapidPlan generates static dose objectives from estimations predicted based on a prior knowledge base. This study objectively compared their performances on intensity-modulated radiotherapy planning for prostate fossa and lymphatics adopting the plan quality metric used in the 2011 American Association of Medical Dosimetrists Plan Challenge.

Methods:

All plans used an identical intensity-modulated radiotherapy beam setup and a simultaneous integrated boost prescription (68 Gy/56 Gy to prostate fossa/lymphatics). Auto-Planning was used to retrospectively plan on 20 patients, which were subsequently employed as the library to build an RapidPlan model. To compare the 2 engines’ performances, a test set including 10 patients and the Plan Challenge patient was planned by both Auto-Planning (master) and RapidPlan (student) without manual intervention except for a common dose normalization and evaluated using the plan quality metric that included 14 quantitative submetrics ranging over target coverage, spillage, and organ at risk doses. Plan quality metric scores were compared between the Auto-Planning and RapidPlan plans using the Mann-Whitney U test.

Results:

There was no significant difference between the overall performance of the 2 engines on the 11 test cases (P = .509). Among the 14 submetrics, Auto-Planning and RapidPlan showed no significant difference on most submetrics except for 2. On the Plan Challenge case, Auto-Planning scored 129.9 and RapidPlan scored 130.3 out of 150, as compared with the average score of 116.9 ± 16.4 (range: 58.2-142.5) among the 125 Plan Challenge participants.

Conclusion:

Using an innovative study design, an objective comparison has been conducted between 2 major commercial automated inverse planning engines. The 2 engines performed comparably with each other and both yielded plans at par with average human planners. Using a constant-performing planner (Auto-Planning) to train and to compare, RapidPlan was found to yield plans no better than but as good as its library plans.