Intensity-Modulated Radiotherapy with Concomitant Boost After Breast Conserving Surgery: A Phase I-II Trial

Ultra-Hypofractionated Whole Breast Radiotherapy with Automated Hybrid-VMAT Technique: A Pilot Study on Safety, Skin Toxicity and Aesthetic Outcomes

Purpose

The most prevalent treatment-related side effect related to adjuvant radiotherapy (RT) for breast cancer is acute skin toxicity in the irradiated area. The purpose of this single-institution pilot study is to provide preliminary clinical results on the feasibility and safety of a breast ultra-hypofractionated radiation treatment delivered using an automated hybrid-VMAT technique. Skin damage was assessed both with clinical examination and objectively using a Cutometer equipment.

Patients and Methods

Patients received 26 Gy to the whole breast and 30 Gy to the tumoral bed in 5 fractions using an automated hybrid-VMAT approach with the option for the breath hold technique if necessary. Acute and late toxicities were clinically evaluated at baseline, 1- and 6-months after treatment using the CTC-AE v.5.0 scale. An instrumental evaluation of the skin elasticity was performed using a Cutometer® Dual MP580. Two parameters per patient, R0 (the total skin firmness) and Q1 (the elastic recovery), were registered at the different timelines.

Results

From June 2022 to January 2024, 30 patients, stage T1-T2, N0 were enrolled in the study. Four out of 30 (13.3%) patients reported G2 acute skin toxicities. At 6 months, G2 late toxicity was registered in 3 patients (10%). A total of 2160 measures of R0 and Q1 were recorded. At 1 month after treatment, no correlation was found between measured values of R0 and Q1 and clinical evaluation. At 6 months after treatment, clinical late toxicity ≥1 was strongly associated with decreased R0 and Q1 values ≥24% (p = 0.003) and ≥18% (p = 0.022), respectively.

Conclusion

Ultra-hypofractionated whole-breast radiotherapy, when supported by advanced treatment techniques, is both feasible and safe. No severe adverse effects were observed at any of the different timeframes. Acute and late skin toxicities were shown to be lower in contrast to data presented in the literature.

Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry

Purpose

Radiation-induced skin toxicity is a common and distressing side effect of breast radiation therapy (RT). We investigated the use of quantitative spectrophotometric markers as input parameters in supervised machine learning models to develop a predictive model for acute radiation toxicity.

Methods and materials

One hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (I_M) and erythema (I_E) indices, were used to quantitatively assess the skin physical changes. Measurements were performed at 4-time intervals: before RT, at the end of RT and 1 and 6 months after the end of RT. Together with clinical covariates, melanin and erythema indices were correlated with skin toxicity, evaluated using the Radiation Therapy Oncology Group (RTOG) guidelines. Binary group classes were labeled according to a RTOG cut-off score of ≥ 2. The patient's dataset was randomly split into a training and testing set used for model development/validation and testing (75%/25% split). A 5-times repeated holdout cross-validation was performed. Three supervised machine learning models, including support vector machine (SVM), classification and regression tree analysis (CART) and logistic regression (LR), were employed for modeling and skin toxicity prediction purposes.

Results

Thirty-four (26.4%) patients presented with adverse skin effects (RTOG ≥2) at the end of treatment. The two spectrophotometric variables at the beginning of RT (I_M,T0 and I_E,T0), together with the volumes of breast (PTV2) and boost surgical cavity (PTV1), the body mass index (BMI) and the dose fractionation scheme (FRAC) were found significantly associated with the RTOG score groups (p<0.05) in univariate analysis. The diagnostic performances measured by the area-under-curve (AUC) were 0.816, 0.734, 0.714, 0.691 and 0.664 for IM, IE, PTV2, PTV1 and BMI, respectively. Classification performances reported precision, recall and F1-values greater than 0.8 for all models. The SVM classifier using the RBF kernel had the best performance, with accuracy, precision, recall and F-score equal to 89.8%, 88.7%, 98.6% and 93.3%, respectively. CART analysis classified patients with I_M,T0 ≥ 99 to be associated with RTOG ≥ 2 toxicity; subsequently, PTV1 and PTV2 played a significant role in increasing the classification rate. The CART model provided a very high diagnostic performance of AUC=0.959.

Conclusions

Spectrophotometry is an objective and reliable tool able to assess radiation induced skin tissue injury. Using a machine learning approach, we were able to predict grade RTOG ≥2 skin toxicity in patients undergoing breast RT. This approach may prove useful for treatment management aiming to improve patient quality of life.

Geometric and Dosimetric Evaluation of Deep Learning-Based Automatic Delineation on CBCT-Synthesized CT and Planning CT for Breast Cancer Adaptive Radiotherapy: A Multi-Institutional Study

Purpose

We developed a deep learning model to achieve automatic multitarget delineation on planning CT (pCT) and synthetic CT (sCT) images generated from cone-beam CT (CBCT) images. The geometric and dosimetric impact of the model was evaluated for breast cancer adaptive radiation therapy.

Methods

We retrospectively analyzed 1,127 patients treated with radiotherapy after breast-conserving surgery from two medical institutions. The CBCT images for patient setup acquired utilizing breath-hold guided by optical surface monitoring system were used to generate sCT with a generative adversarial network. Organs at risk (OARs), clinical target volume (CTV), and tumor bed (TB) were delineated automatically with a 3D U-Net model on pCT and sCT images. The geometric accuracy of the model was evaluated with metrics, including Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95). Dosimetric evaluation was performed by quick dose recalculation on sCT images relying on gamma analysis and dose-volume histogram (DVH) parameters. The relationship between ΔD95, ΔV95 and DSC-CTV was assessed to quantify the clinical impact of the geometric changes of CTV.

Results

The ranges of DSC and HD95 were 0.73–0.97 and 2.22–9.36 mm for pCT, 0.63–0.95 and 2.30–19.57 mm for sCT from institution A, 0.70–0.97 and 2.10–11.43 mm for pCT from institution B, respectively. The quality of sCT was excellent with an average mean absolute error (MAE) of 71.58 ± 8.78 HU. The mean gamma pass rate (3%/3 mm criterion) was 91.46 ± 4.63%. DSC-CTV down to 0.65 accounted for a variation of more than 6% of V95 and 3 Gy of D95. DSC-CTV up to 0.80 accounted for a variation of less than 4% of V95 and 2 Gy of D95. The mean ΔD90/ΔD95 of CTV and TB were less than 2Gy/4Gy, 4Gy/5Gy for all the patients. The cardiac dose difference in left breast cancer cases was larger than that in right breast cancer cases.

Conclusions

The accurate multitarget delineation is achievable on pCT and sCT via deep learning. The results show that dose distribution needs to be considered to evaluate the clinical impact of geometric variations during breast cancer radiotherapy.

Automated hybrid volumetric modulated arc therapy (HVMAT) for whole-breast irradiation with simultaneous integrated boost to lumpectomy area : A treatment planning study

PURPOSE

To develop an automated treatment planning approach for whole breast irradiation with simultaneous integrated boost using an automated hybrid VMAT class solution (HVMAT).

MATERIALS AND METHODS

Twenty-five consecutive patients with left breast cancer received 50 Gy (2 Gy/fraction) to the whole breast and an additional simultaneous 10 Gy (2.4 Gy/fraction) to the tumor cavity. Ipsilateral lung, heart, and contralateral breast were contoured as main organs-at-risk. HVMAT plans were inversely optimized by combining two open fields with a VMAT semi-arc beam. Open fields were setup to include the whole breast with a 2 cm flash region and to carry 80% of beams weight. HVMAT plans were compared with three tangential techniques: conventional wedged-field tangential plans (SWF), field-in-field forward planned tangential plans (FiF), and hybrid-IMRT plans (HMRT). Dosimetric differences among the plans were evaluated using Kruskal-Wallis one-way analysis of variance. Dose accuracy was validated using the PTW Octavius-4D phantom together with the 1500 2D-array.

RESULTS

No significant differences were found among the four techniques for both targets coverage. HVMAT plans showed consistently better PTVs dose contrast, conformity, and homogeneity (p < 0.001 for all metrics) and statistically significant reduction of high-dose breast irradiation. V55 and V60 decreased by 30.4, 26.1, and 20.8% (p < 0.05) and 12.3, 9.9, and 6.0% (p < 0.05) for SWF, FIF, and HMRT, respectively. Pretreatment dose verification reported a gamma pass-rate greater than the acceptance threshold of 95% for all HVMAT plans. In addition, HVMAT reduced the time for full planning optimization to about 20 min.

CONCLUSIONS

HVMAT plans resulted in superior target dose conformity and homogeneity compared to other tangential techniques. Due to fast planning time HVMAT can be applied for all patients, minimizing the impact on human or departmental resources.