Dose-volume Histogram Analysis of Knowledge-based Volumetric-modulated Arc Therapy Planning in Postoperative Breast Cancer Irradiation

Validating knowledge-based volumetric modulated arc therapy plans with a multi-institution model (broad model) using a complete open-loop dataset for prostate cancer

This study examined the characteristics of the broad model (KBPbroad) through a complete open-loop evaluation of volumetric modulated arc therapy (VMAT) plans for prostate cancer in 30 patients at two institutions. KBPbroad, trained using 561 prostate cancer VMAT plans from five institutions with different treatment protocols, was shared with two institutions. The institutions were not involved in the creation of KBPbroad. Plan created with KBPbroad were compared with clinical plans (CPs) and plans created using a single-institution model at each institution (KBPonsite). KBPbroad maintained the target coverage of CPs while meeting dose limits across varied settings at each institution. At institution X, KBPbroad provided 40, 60, and 70 Gy (V40Gy, V60Gy, and V70Gy, respectively) to 30.8% ± 9.9%, 15.3% ± 8.5%, and 9.0% ± 6.4% of the volume at the rectal wall, respectively, which were significantly smaller than those provided by KBPonsite and CPs. At institution Y, compared with CPs, KBPbroad provided significantly greater V50Gy, V70Gy, dose to 2% of the volume (D2%) at the rectum, and D2% at the bladder but significantly lower V50Gy and V70Gy at the bladder, in addition to superior dose homogeneity and conformality at the planning target volume. Our complete open-loop evaluation of VMAT plans for prostate cancer at two institutions demonstrated the clinical effectiveness of KBPbroad at institutions producing plans with insufficient reductions in OAR doses. Thus, the quality of KBPbroad plans is likely greater than that of KBPonsite plans and CPs.

Feasibility of Coplanar VMAT for Brain Metastases Using Halcyon With Knowledge-based Planning from Non-coplanar Plan

BACKGROUND/AIM

This study aimed to clarify the feasibility of creating coplanar single-isocenter volumetric modulated arc therapy (SI-VMAT) with a Halcyon using a knowledge-based planning system based on non-coplanar plans for treating multiple brain metastases.

PATIENTS AND METHODS

A knowledge-based RapidPlan (RP) planning model was built using 32 TrueBeam SI-VMAT plans including one full arc and three non-coplanar partial arcs. To confirm its applicability across different beam geometries using the Halcyon system, the model was validated on the basis of the root-mean-square error (RSME), deviation rate, and absolute differences between estimated and actual dose-volume histograms (DVHs). Dosimetric performance of TrueBeam and Halcyon plans was then compared for 10 patients.

RESULTS

The average RSME values for the Halcyon and TrueBeam plans were 0.75±0.40 Gy and 0.46±0.28 Gy, respectively (p<0.05). Using the model for Halcyon plan creation, deviations from the estimated DVH boundaries were mainly observed in the low-dose region (<9 Gy), while the actual DVH closely aligned with the estimated DVH in the intermediate- and higher-dose regions. For comparison of dosimetric performance, statistically significant differences were found in the gradient index, conformity index, and normal brain volumes receiving ≥12 Gy, ≥18 Gy, and ≥27 Gy. However, these differences were deemed clinically negligible, with less than 1% disparity in the above doses to the normal brain.

CONCLUSION

The RP model can effectively predict doses for Halcyon, regardless of different beam configurations. Coplanar SI-VMAT with Halcyon can feasibly be used in clinical applications, such as the treatment of multiple brain metastases.

Association Between Thyroid Radiation Dose and Hypothyroidism in Breast Cancer Patients Undergoing Volumetric Modulated Arc Therapy for Regional Nodal Irradiation

BACKGROUND/AIM

To investigate the association between the thyroid dysfunction and thyroid radiation dose in regional nodal irradiation (RNI) using volumetric modulated arc therapy (VMAT) for breast cancer.

PATIENTS AND METHODS

We reviewed medical data of 67 patients with breast cancer who underwent curative surgery followed by adjuvant radiotherapy, including RNI using VMAT, between 2018 and 2021. All patients had normal thyroid functional test results, including thyroid stimulating hormone (TSH), T3, and free-T4. We defined subclinical hypothyroidism as increased TSH with or without decreased levels of free-T4 and T3 after the completion of VMAT. We calculated dose-volume histogram parameters (DVHPs), including the mean dose and relative thyroid volume receiving at least 10, 20, 30, and 40 Gy.

RESULTS

The median follow-up time was 23.2 months. The 3-year locoregional failure-free survival, progression-free survival, and overall survival rates were 96.3%, 94.7%, and 96.2%, respectively. The mean thyroid dose was 21.4 Gy (range=11.5-29.4 Gy). Subclinical hypothyroidism was noted in 14 patients (20.9%) and the median time to the event was 4.1 months. Among the DVHPs, the relative volume receiving ≥20 Gy (V20Gy) was associated with subclinical hypothyroidism. The 2-year rates of subclinical hypothyroidism were 24.8% and 59.1% in patients with V20Gy ≤46.3% and >46.3%, respectively.

CONCLUSION

A significant proportion of patients with breast cancer developed subclinical hypothyroidism after undergoing VMAT for RNI. Our findings highlight the importance of considering the thyroid as an organ at risk for VMAT planning, and suggest that V20Gy could be a useful dose-volume constraint.

Can knowledge based treatment planning of VMAT for post-mastectomy locoregional radiotherapy involving internal mammary chain and supraclavicular fossa improve performance efficiency?

Introduction

To validate and evaluate the performance of knowledge-based treatment planning for Volumetric Modulated Arc Radiotherapy for post-mastectomy loco-regional radiotherapy.

Material and methods

Two knowledge-based planning (KBP) models for different dose prescriptions were built using the Eclipse RapidPlanTM v 16.1 (Varian Medical Systems, Palo Alto, USA) utilising the plans of previously treated patients with left-sided breast cancer who had undergone irradiation of the left chest wall, internal mammary nodal (IMN) region and supra-clavicular fossa (SCF). Plans of 60 and 73 patients were used to generate the KBP models for the prescriptions of 40 Gy in 15 fractions and 26 Gy in 5 fractions, respectively. A blinded review of all the clinical plans (CLI) and KBPs was done by two experienced radiation oncology consultants. Statistical analysis of the two groups was also done using the standard two-tailed paired t-test or Wilcoxon signed rank test, and p&lt;0.05 was considered significant.

Results

A total of 20 metrics were compared. The KBPs were found to be either better (6/20) or comparable (10/20) to the CLIs for both the regimens. Dose to heart, contralateral breast,contralateral lung were either better or comparable in the KBP plans except of ipsilateral lung. Mean dose (Gy) for the ipsilateral lung are significantly (p˂0.001) higher in KBP though the values were acceptable clinically. Plans were of similar quality as per the result of the blinded review which was conducted by slice-by-slice evaluation of dose distribution for target coverage, overdose volume and dose to the OARs. However, it was also observed that treatment times in terms of monitoring units (MUs) and complexity indices are more in CLIs as compared with KBPs (p&lt;0.001).

Discussion

KBP models for left-sided post-mastectomy loco-regional radiotherapy were developed and validated for clinical use. These models improved the efficiency of treatment delivery as well as work flow for VMAT planning involving both moderately hypo fractionated and ultra-hypo fractionated radiotherapy regimens.

Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors

BACKGROUND

To analyze RapidPlan knowledge-based models for DVH estimation of organs at risk from breast cancer VMAT plans presenting arc sectors en-face to the breast with zero dose rate, feature imposed during the optimization phase (avoidance sectors AS).

METHODS

CT datasets of twenty left breast patients in deep-inspiration breath-hold were selected. Two VMAT plans, PartArc and AvoidArc, were manually generated with double arcs from ~ 300 to ~ 160°, with the second having an AS en-face to the breast to avoid contralateral breast and lung direct irradiation. Two RapidPlan models were generated from the two plan sets. The two models were evaluated in a closed loop to assess the model performance on plans where the AS were selected or not in the optimization.

RESULTS

The PartArc plans model estimated DVHs comparable with the original plans. The AvoidArc plans model estimated a DVH pattern with two steps for the contralateral structures when the plan does not contain the AS selected in the optimization phase. This feature produced mean doses of the contralateral breast, averaged over all patients, of 0.4 ± 0.1 Gy, 0.6 ± 0.2 Gy, and 1.1 ± 0.2 Gy for the AvoidArc plan, AvoidArc model estimation, RapidPlan generated plan, respectively. The same figures for the contralateral lung were 0.3 ± 0.1 Gy, 1.6 ± 0.6 Gy, and 1.2 ± 0.5 Gy. The reason was found in the possible incorrect information extracted from the model training plans due to the lack of knowledge about the AS. Conversely, in the case of plans with AS set in the optimization generated with the same AvoidArc model, the estimated and resulting DVHs were comparable. Whenever the AvoidArc model was used to generate DVH estimation for a plan with AS, while the optimization was made on the plan without the AS, the optimizer evidentiated the limitation of a minimum dose rate of 0.2 MU/°, resulting in an increased dose to the contralateral structures respect to the estimation.

CONCLUSIONS

The RapidPlan models for breast planning with VMAT can properly estimate organ at risk DVH. Attention has to be paid to the plan selection and usage for model training in the presence of avoidance sectors.

Artificial Intelligence in Radiation Therapy

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.

An updating approach for knowledge-based planning models to improve plan quality and variability in volumetric-modulated arc therapy for prostate cancer

PURPOSE

The purpose of this study was to compare the dose-volume parameters and regression scatter plots of the iteratively improved RapidPlan (RP) models, specific knowledge-based planning (KBP) models, in volumetric-modulated arc therapy (VMAT) for prostate cancer over three periods.

METHODS

A RP1 model was created from 47 clinical intensity-modulated radiation therapy (IMRT)/VMAT plans. A RP2 model was created to exceed dosimetric goals which set as the mean values +1SD of the dose-volume parameters of RP1 (50 consecutive new clinical VMAT plans). A RP3 model was created with more strict dose constraints for organs at risks (OARs) than RP1 and RP2 models (50 consecutive anew clinical VMAT plans). Each RP model was validated against 30 validation plans (RP1, RP2, and RP3) that were not used for model configuration, and the dose-volume parameters were compared. The Cook's distances of regression scatterplots of each model were also evaluated.

RESULTS

Significant differences (p < 0.05) between RP1 and RP2 were found in D_mean (101.5% vs. 101.9%), homogeneity index (3.90 vs. 4.44), 95% isodose conformity index (1.22 vs. 1.20) for the target, V_40Gy (47.3% vs. 45.7%), V_60Gy (27.9% vs. 27.1%), V_70Gy (16.4% vs. 15.2%), and V_78Gy (0.4% vs. 0.2%) for the rectal wall, and V_40Gy (43.8% vs. 41.8%) and V_70Gy (21.3% vs. 20.5%) for the bladder wall, whereas only V_70Gy (15.2% vs. 15.8%) of the rectal wall differed significantly between RP2 and RP3. The proportions of cases with a Cook's distance of <1.0 (RP1, RP2, and RP3 models) were 55%, 78%, and 84% for the rectal wall, and 77%, 68%, and 76% for the bladder wall, respectively.

CONCLUSIONS

The iteratively improved RP models, reflecting the clear dosimetric goals based on the RP feedback (dose-volume parameters) and more strict dose constraints for the OARs, generated superior dose-volume parameters and the regression scatterplots in the model converged. This approach could be used to standardize the inverse planning strategies.

[Impact of Aperture Shape Controller on Knowledge-based VMAT Planning of Prostate Cancer]

PURPOSE

Knowledge-based planning (KBP) has disadvantages of high monitor unit (MU) and complex multi-leaf collimator (MLC) motion. We investigated the optimal aperture shape controller (ASC) level for the KBP to reduce these factors in volumetric modulated arc therapy (VMAT) for prostate cancer.

METHODS

The KBP model was created based on 51 clinical plans (CPs) of patients who underwent the VMAT for prostate cancer. Another 10 CPs were selected randomly, and the KBPs with/without ASC, changed stepwise from very low (KBP-VL) to very high (KBP-VH), were performed with a single auto-optimization. The parameters of dose-volume histograms (DVHs) and MLC performance metrics were evaluated. We obtained the modulation complexity score for VMAT (MCSv), closed leaf score (CLS), small aperture score (SAS), leaf travel (LT), and total MU.

RESULTS

The ASC did not affect the DVH parameters negatively. The following comparisons of MLC performance were obtained (KBP vs. KBP-VL vs. KBP-VH, respectively): 0.25 vs. 0.27 vs. 0.30 (MCSv), 0.19 vs. 0.18 vs. 0.16 (CLS), 0.50 vs. 0.45 vs. 0.40 (SAS_{10 mm}), 0.73 vs. 0.68 vs. 0.63 (SAS_{20 mm}), 768.35 mm vs. 671.50 mm vs. 551.32 mm (LT), and 672.87 vs. 642.36 vs. 607.59 (MU). There were significant differences between KBP and KBP-VH for MCSv and LT (p<0.05).

CONCLUSIONS

The KBP using an ASC set to the very high level could reduce the complexity of MLC motion significantly more without deterioration of the DVH parameters compared with the KBP in VMAT for prostate cancer.

Dosimetric Evaluation of Simplified Knowledge-Based Plan with an Extensive Stepping Validation Approach in Volumetric-Modulated Arc Therapy-Stereotactic Body Radiotherapy for Lung Cancer

PURPOSE

We investigated the performance of the simplified knowledge-based plans (KBPs) in stereotactic body radiotherapy (SBRT) with volumetric-modulated arc therapy (VMAT) for lung cancer.

MATERIALS AND METHODS

For 50 cases who underwent SBRT, only three structures were registered into knowledge-based model: total lung, spinal cord, and planning target volume. We performed single auto-optimization on VMAT plans in two steps: 19 cases used for the model training (closed-loop validation) and 16 new cases outside of training set (open-loop validation) for TrueBeam (TB) and Halcyon (Hal) linacs. The dosimetric parameters were compared between clinical plans (CLPs) and KBPs: CLPclosed, KBPclosed-TB and KBPclosed-Hal in closed-loop validation, CLPopen, KBPopen-TB and KBPopen-Hal in open-loop validation.

RESULTS

All organs at risk were comparable between CLPs and KBPs except for contralateral lung: V5 of KBPs was approximately 3%-7% higher than that of CLPs. V20 of total lung for KBPs showed comparable to CLPs; CLPclosed vs. KBPclosed-TB and CLPclosed vs. KBPclosed-Hal: 4.36% ± 2.87% vs. 3.54% ± 1.95% and 4.36 ± 2.87% vs. 3.54% ± 1.94% (P = 0.54 and 0.54); CLPopen vs. KBPopen-TB and CLPopen vs. KBPopen-Hal: 4.18% ± 1.57% vs. 3.55% ± 1.27% and 4.18% ± 1.57% vs. 3.67% ± 1.26% (P = 0.19 and 0.27). CI95 of KBPs with both linacs was superior to that of the CLP in closed-loop validation: CLPclosed vs. KBPclosed-TB vs. KBPclosed-Hal: 1.32% ± 0.12% vs. 1.18% ± 0.09% vs. 1.17% ± 0.06% (P < 0.01); and open-loop validation: CLPopen vs. KBPopen-TB vs. KBPopen-Hal: 1.22% ± 0.09% vs. 1.14% ± 0.04% vs. 1.16% ± 0.05% (P ≤ 0.01).

CONCLUSIONS

The simplified KBPs with limited number of structures and without planner intervention were clinically acceptable in the dosimetric parameters for lung VMAT-SBRT planning.

Dosimetric evaluation with knowledge-based planning created at different periods in volumetric-modulated arc therapy for prostate cancer: a multi-institution study

Dosimetric evaluation and variation assessment were performed with two knowledge-based planning (KBP) models created at different periods for volumetric-modulated arc therapy (VMAT) for prostate cancer at five institutes. The first and second models (F- and S-models) for KBP were created before April 2017 and April 2019, respectively. The S-model was created using feedback plans from the F-model. Dose evaluation was compared between the two models using the same two computed tomography (CT) datasets and structures. The evaluation metrics were the dose received by 95.0% and 2.0% of the planning target volume (PTV); dose-volume parameters to the rectum and bladder as V90, V80, and V50; and monitor unit (MU). Dosimetric variation was compared by exporting estimated dose-volume histograms for each model to the Model Analytics website and assessing the organ at risk volume. There were no dosimetric differences between the two models for PTV. The V50 of the rectum in the S-model had improved compared to that of the F-model (case I: 49.3 ± 15.6 and 43.5 ± 15.2 [p = 0.08]; case II: 42.5 ± 16.9 and 36.0 ± 15.6 [p = 0.138]). The differences in other parameters were within ± 1.8% between the rectum and the bladder. The MU was slightly higher in the S-model than in the F-model, and dosimetric variation was reduced to the rectum and bladder among all the institutes. The polished S-model for KBP could be used for standardization of the plan quality and sharing of KBP models in VMAT for prostate cancer.

Influence of Cleaned-up Commercial Knowledge-Based Treatment Planning on Volumetric-Modulated Arc Therapy of Prostate Cancer

PURPOSE

This study aimed to investigate the influence of cleaned-up knowledge-based treatment planning (KBP) models on the plan quality for volumetric-modulated arc therapy (VMAT) of prostate cancer.

MATERIALS AND METHODS

Thirty prostate cancer VMAT plans were enrolled and evaluated according to four KBP modeling methods as follows: (1) model not cleaned - trained by fifty other clinical plans (KBPORIG); (2) cases cleaned by removing plans that did not meet all clinical goals of the dosimetric parameters, derived from dose-volume histogram (DVH) (KBPC-DVH); (3) cases cleaned outside the range of ±1 standard deviation through the principal component analysis regression plots (KBPC-REG); and (4) cases cleaned using both methods (2) and (3) (KBPC-ALL). Rectal and bladder structures in the training models numbered 34 and 48 for KBPC-DVH, 37 and 33 for KBPC-REG, and 26 and 33 for KBPC-ALL, respectively. The dosimetric parameters for each model with one-time auto-optimization were compared.

RESULTS

All KBP models improved target dose coverage and conformity and provided comparable sparing of organs at risks (rectal and bladder walls). There were no significant differences in plan quality among the KBP models. Nevertheless, only the KBPC-ALL model generated no cases of >1% V78 Gy (prescribed dose) to the rectal wall, whereas the KBPORIG, KBPC-DVH, and KBPC-REG models included two, four, and three cases, respectively, which were difficult to overcome with KBP because the planning target volume (PTV) and rectum regions overlapped.

CONCLUSIONS

The cleaned-up KBP model based on DVH and regression plots improved plan quality in the PTV-rectum overlap region.