Replacing Manual Planning of Whole Breast Irradiation With Knowledge-Based Automatic Optimization by Virtual Tangential-Fields Arc Therapy

Multi-centre real-world validation of automated treatment planning for breast radiotherapy

PURPOSE

To present the results of the first multi-centre real-world validation of autoplanning for whole breast irradiation after breast-sparing surgery, encompassing high complexity cases (e.g. with a boost or regional lymph nodes) and a wide range of clinical practices.

METHODS

The 24 participating centers each included 10 IMRT/VMAT/Tomotherapy patients, previously treated with a manually generated plan ('manplan'). There were no restrictions regarding case complexity, planning aims, plan evaluation parameters and criteria, fractionation, treatment planning system or treatment machine/technique. In addition to dosimetric comparisons of autoplans with manplans, blinded plan scoring/ranking was conducted by a clinician from the treating center. Autoplanning was performed using a single configuration for all patients in all centres. Deliverability was verified through measurements at delivery units.

RESULTS

Target dosimetry showed comparability, while reductions in OAR dose parameters were 21.4 % for heart Dmean, 16.7 % for ipsilateral lung Dmean, and 101.9 %, 45.5 %, and 35.7 % for contralateral breast D0.03cc, D5% and Dmean, respectively (all p < 0.001). Among the 240 patients included, the clinicians preferred the autoplan for 119 patients, with manplans preferred for 96 cases (p = 0.01). Per centre there were on average 5.0 ± 2.9 (1SD) patients with a preferred autoplan (range [0-10]), compared to 4.0 ± 2.7 with a preferred manplan ([0,9]). No differences were observed regarding deliverability.

CONCLUSION

The automation significantly reduced the hands-on planning workload compared to manual planning, while also achieving an overall superiority. However, fine-tuning of the autoplanning configuration prior to clinical implementation may be necessary in some centres to enhance clinicians' satisfaction with the generated autoplans.

Dosimetric evaluation of different planning strategies for hypofractionated whole-breast irradiation technique

Purpose.The dose hotspot areas in hypofractionated whole-breast irradiation (WBI) greatly increase the risk of acute skin toxicity because of the anatomical peculiarities of the breast. In this study, we presented several novel planning strategies that integrate multiple sub-planning target volumes (sub-PTVs), field secondary placement, and RapidPlan models for right-sided hypofractionated WBI.Methods.A total of 35 cases of WBI with a dose of 42.5 Gy for PTVs using tangential intensity-modulated radiotherapy (IMRT) were selected. Both PTVs were planned for simultaneous treatment using the original manual multiple sub-PTV plan (OMMP) and the original manual single-PTV plan (OMSP). The manual field secondary placement multiple sub-PTV plan (m-FSMP) with multiple objects on the original PTV and the manual field secondary placement single-objective plan (m-FSSP) were initially planned, which were distribution-based of V105 (volume receiving 105% of the prescription dose). In addition, two RapidPlan-based plans were developed, including the RapidPlan-based multiple sub-PTVs plan (r-FSMP) and the RapidPlan-based single-PTV plan (r-FSSP). Dosimetric parameters of the plans were compared, and V105 was evaluated using multivariate analysis to determine how it was related to the volume of PTV and the interval of lateral beam angles (ILBA).Results.The lowest mean V105 (5.64 ± 6.5%) of PTV was observed in m-FSMP compared to other manual plans. Upon validation, r-FSSP demonstrated superior dosimetric quality for OAR compared to the two other manual planning methods, except for V5(the volume of ipsilateral lung receiving 5 Gy) of the ipsilateral lung. While r-FSMP showed no significant difference (p = 0.06) compared to r-FSSP, it achieved the lowest V105 value (4.3 ± 4.5%), albeit with a slight increase in the dose to some OARs. Multivariate GEE linear regression showed that V105 is significantly correlated with target volume and ILBA.Conclusions.m-FSMP and r-FSMP can substantially enhance the homogeneity index (HI) and reduce V105, thereby minimizing the risk of acute skin toxicities, even though there may be a slight dose compromise for certain OARs.

Inter-institutional variability of knowledge-based plan prediction of left whole breast irradiation

PURPOSE

Within a multi-institutional project, we aimed to assess the transferability of knowledge-based (KB) plan prediction models in the case of whole breast irradiation (WBI) for left-side breast irradiation with tangential fields (TF).

METHODS

Eight institutions set KB models, following previously shared common criteria. Plan prediction performance was tested on 16 new patients (2 pts per centre) extracting dose-volume-histogram (DVH) prediction bands of heart, ipsilateral lung, contralateral lung and breast. The inter-institutional variability was quantified by the standard deviations (SDint) of predicted DVHs and mean-dose (Dmean). The transferability of models, for the heart and the ipsilateral lung, was evaluated by the range of geometric Principal Component (PC1) applicability of a model to test patients of the other 7 institutions.

RESULTS

SDint of the DVH was 1.8 % and 1.6 % for the ipsilateral lung and the heart, respectively (20 %-80 % dose range); concerning Dmean, SDint was 0.9 Gy and 0.6 Gy for the ipsilateral lung and the heart, respectively (<0.2 Gy for contralateral organs). Mean predicted doses ranged between 4.3 and 5.9 Gy for the ipsilateral lung and 1.1-2.3 Gy for the heart. PC1 analysis suggested no relevant differences among models, except for one centre showing a systematic larger sparing of the heart, concomitant to a worse PTV coverage, due to high priority in sparing the left anterior descending coronary artery.

CONCLUSIONS

Results showed high transferability among models and low inter-institutional variability of 2% for plan prediction. These findings encourage the building of benchmark models in the case of TF-WBI.

Knowledge-based versus deep learning based treatment planning for breast radiotherapy

Background and Purpose

To improve radiotherapy (RT) planning efficiency and plan quality, knowledge-based planning (KBP) and deep learning (DL) solutions have been developed. We aimed to make a direct comparison of these models for breast cancer planning using the same training, validation, and testing sets.

Materials and Methods

Two KBP models were trained and validated with 90 RT plans for left-sided breast cancer with 15 fractions of 2.6 Gy. The versions either used the full dataset (non-clean model) or a cleaned dataset (clean model), thus eliminating geometric and dosimetric outliers. Results were compared with a DL U-net model (previously trained and validated with the same 90 RT plans) and manually produced RT plans, for the same independent dataset of 15 patients. Clinically relevant dose volume histogram parameters were evaluated according to established consensus criteria.

Results

Both KBP models underestimated the mean heart and lung dose equally 0.4 Gy (0.3-1.1 Gy) and 1.4 Gy (1.1-2.8 Gy) compared to the clinical plans 0.8 Gy (0.5-1.8 Gy) and 1.7 Gy (1.3-3.2 Gy) while in the final calculations the mean lung dose was higher 1.9-2.0 Gy (1.5-3.5 Gy) for both KPB models. The U-Net model resulted in a mean planning target volume dose of 40.7 Gy (40.4-41.3 Gy), slightly higher than the clinical plans 40.5 Gy (40.1-41.0 Gy).

Conclusions

Only small differences were observed between the estimated and final dose calculation and the clinical results for both KPB models and the DL model. With a good set of breast plans, the data cleaning module is not needed and both KPB and DL models lead to clinically acceptable results.

Fetal dose estimation for Virtual Tangential-fields Arc Therapy whole breast irradiation by optically stimulated luminescence dosimeters

Breast cancer is the most frequently diagnosed tumor in pregnant women and radiation therapy should carefully be weighted up because of the dose to the fetus. The aim of this study was to investigate fetal dose in a patient treated with Virtual Tangential-fields Arc Therapy (ViTAT), an innovative technique for whole breast irradiation. Optically stimulated luminescence detectors (OSLDs) were calibrated on a Varian TrueBeam linac, with both a 6X and 6XFFF beam quality, and used for out-of-field measurements. Fetal dose related with ViTAT technique was compared to the standard 3D conformal radiation therapy technique (3DCRT). The fetal dose delivered with a ViTAT technique planned with 6XFFF beam was also investigated. Measurements were taken on a phantom composed of Rando Alderson Phantom slices and solid water slabs. OSLDs were placed in a region identified by the height of the fundus from conception to the twentieth week using a custom made PMMA grid. Due to the higher number of monitor units, the peripheral dose of ViTAT delivered with 6X beams is higher than that of 3DCRT. However, nanoDots measurements prove that ViTAT can be used in place of 3DCRT while maintaining similar fetal dose levels if 6XFFF beams are used.

Knowledge-based multi-institution plan prediction of whole breast irradiation with tangential fields

PURPOSE

To quantify inter-institute variability of Knowledge-Based (KB) models for right breast cancer patients treated with tangential fields whole breast irradiation (WBI).

MATERIALS AND METHODS

Ten institutions set KB models by using RapidPlan (Varian Inc.), following previously shared methodologies. Models were tested on 20 new patients from the same institutes, exporting DVH predictions of heart, ipsilateral lung, contralateral lung, and contralateral breast. Inter-institute variability was quantified by the inter-institute SD_int of predicted DVHs/Dmean. Association between lung sparing vs PTV coverage strategy was also investigated. The transferability of models was evaluated by the overlap of each model's geometric Principal Component (PC1) when applied to the test patients of the other 9 institutes.

RESULTS

The overall inter-institute variability of DVH/Dmean ipsilateral lung dose prediction, was less than 2% (20%-80% dose range) and 0.55 Gy respectively (1SD) for a 40 Gy in 15 fraction schedule; it was < 0.2 Gy for other OARs. Institute 6 showed the lowest mean dose prediction value and no overlap between PTV and ipsilateral lung. Once excluded, the predicted ipsilateral lung Dmean was correlated with median PTV D99% (R² = 0.78). PC1 values were always within the range of applicability (90th percentile) for 7 models: for 2 models they were outside in 1/18 cases. For the model of institute 6, it failed in 7/18 cases. The impact of inter-institute variability of dose calculation was tested and found to be almost negligible.

CONCLUSIONS

Results show limited inter-institute variability of plan prediction models translating in high inter-institute interchangeability, except for one of ten institutes. These results encourage future investigations in generating benchmarks for plan prediction incorporating inter-institute variability.

Knowledge-based automatic plan optimization for left-sided whole breast tomotherapy

Background/Purpose

Tomotherapy may deliver high-quality whole breast irradiation at static angles. The aim of this study was to implement Knowledge-Based (KB) automatic planning for left-sided whole breast using this modality.

Materials/Methods

Virtual volumetric plans were associated to the dose distributions of 69 Tomotherapy (TT) clinical plans of previously treated patients, aiming to train a KB-model using a commercial tool completely implemented in our treatment planning system. An individually optimized template based on the resulting KB-model was generated for automatic plan optimization. Thirty patients of the training set and ten new patients were considered for internal/external validation. Fully-automatic plans (KB-TT) were generated and compared using the same geometry/number of fields of the corresponding clinical plans.

Results

KB-TT plans were successfully generated in 26/30 and 10/10 patients of the internal/external validation sets; for 4 patients whose original plans used only two fields, the manual insertion of one/two fields before running the automatic template was sufficient to obtain acceptable plans. Concerning internal validation, planning target volume V_95%/D_1%/dose distribution standard deviation improved by 0.9%/0.4Gy/0.2Gy (p < 0.05) against clinical plans; Organs at risk mean doses were also slightly improved (p < 0.05) by 0.07/0.4/0.2/0.01 Gy for left lung/heart/right breast/right lung respectively. Similarly satisfactory results were replicated in the external validation set. The resulting treatment duration was 8 ± 1 min, consistent with our clinical experience. The active planner time per patient was 5–10 minutes.

Conclusion

Automatic TT left-sided breast KB-plans are comparable to or slightly better than clinical plans and can be obtained with limited planner time. The approach is currently under clinical implementation.