Palliative Radiotherapy for Symptomatic Primary Tumors in Patients With Locally Advanced Breast Cancer.

Breast cancer remains a significant health concern for women, with a significant number of women facing unresectable, symptomatic, and advanced disease that severely affects their quality of life. Palliative radiotherapy (RT) is a well-established modality for managing such cases and alleviating symptoms. Recent advancements in systemic therapies and the resulting increase in long-term survival rates have not only heightened the need for retreatment in certain patients, but have also emphasized the importance of achieving durable local control. Additionally, inconsistencies in RT referral timing and variations in disease severity and extent contribute to diverse RT objectives and expected outcomes. The optimal dose fractionation for RT remains underexplored. Furthermore, a deeper understanding of breast radiobiology, along with the introduction of ultra- and moderately hypofractionated regimens and the widespread adoption of conformal techniques such as intensity-modulated RT, has diversified the approaches in RT dose and target volume. This review aimed to provides a comprehensive summary of the current evidence on the efficacy, outcomes, and toxicity profiles of palliative RT for symptomatic breast cancer. It highlights the need for more optimized regimens and further research to address the evolving treatment landscape and differing expectations of patients and physicians regarding RT.

Five-Fraction High-Conformal Ultrahypofractionated Radiotherapy for Primary Tumors in Metastatic Breast Cancer.

PURPOSE: To report on the local control and toxicity of 5-fraction, high-conformal ultrafractionated radiation therapy (RT) for primary tumors in patients with metastatic breast cancer (MBC) who did not undergo planned surgical intervention. METHODS: We retrospectively reviewed 27 patients with MBC who underwent 5-fraction high-dose ultrafractionated intensity-modulated RT for their primary tumors between 2017 and 2022 at our institution. A median dose of 66.8 Gy (range, 51.8-83.6 Gy) was prescribed to the gross tumor, calculated in 2-Gy equivalents using an α/ß ratio of 3.5, along with a simultaneous integrated boost of 81.5%. The primary endpoint of this study was local control. RESULTS: The median tumor size and volume were 5.1 cm and 112.4 cm3, respectively. Treatment was generally well tolerated, with only 15% of the patients experiencing mild acute skin toxicity, which resolved spontaneously. The best infield response rate was 82%, with the objective response observed at a median time of 10.8 months post-RT (range, 1.4-29.2), until local progression or the last follow-up. At a median follow-up of 18.3 months, the 2-year local control rate was 77%. A higher number of prior lines of systemic therapy was significantly associated with poorer 2-year local control (one-two lines, 94% vs three or more lines, 34%; p = 0.004). Post-RT, 67% of the patients transitioned to the next line of systemic therapy, and the median duration of maintaining the same systemic therapy post-RT was 16.3 months (range, 1.9-40.3). CONCLUSION: In our small dataset, 5-fraction, high-conformal ultrahypofractionated breast RT offered promising 2-year local control with minimal toxicity. Further studies are warranted to investigate the optimal dose and role in this setting.

Dosimetric Comparison of CPAP and DIBH for Left-sided Breast Cancer Radiation Therapy.

Purpose: Despite the increasing interest in using continuous positive airway pressure (CPAP) in radiation therapy (RT), direct comparisons with the more widely used deep inspiration breath-hold (DIBH) have been limited. This planning study aimed to offer comprehensive geometric and dosimetric evidence by comparing CPAP and DIBH-based RT plans. Materials and Methods: A retrospective data set of 35 patients with left-sided breast cancer with planning computed tomography scans under three breathing conditions (free breathing (FB), CPAP with 10 cmH2O pressure, and DIBH) was collected. Volumetric arc therapy plans aimed for 95% dose coverage to 95% of the planning target volume with a maximum dose below 107%. A comparative dosimetric analysis among the three plans was conducted. Additionally, geometric differences were assessed by calculating the minimum distance between the heart and the clinical target volume (CTV) in each planning computed tomography. Results: CPAP and DIBH plans demonstrated comparable mean heart doses (1.05 Gy), which were significantly lower than the FB plan (1.34 Gy). The maximum dose to the left anterior descending artery was smallest in the CPAP plan (4.44 Gy), followed by DIBH (4.73 Gy) and FB (7.33 Gy) plans. Other organ-at-risk doses for CPAP and DIBH were similar, with mean contralateral breast doses of 2.27 and 2.21 Gy, mean ipsilateral lung doses of 4.09 and 4.08 Gy, V20 at 6.11% and 6.31%, and mean contralateral lung doses of 0.94 and 0.92 Gy, respectively. No significant difference was found in the minimum heart-to-CTV distance between CPAP and DIBH. DIBH exhibited the greatest lung volume (3908 cc), followed by CPAP (3509 cc), and FB(2703 cc). Conclusions: The comparison between CPAP and DIBH shows their similarity in both geometric and dosimetric aspects, providing strong evidence for CPAP's effectiveness and feasibility in RT. This suggests its potential as an alternative to DIBH for patients with left-sided breast cancer.

Normal tissue complication probability models of hypothyroidism after radiotherapy for breast cancer.

Purpose: We aimed to develop Lyman-Kutcher-Burman (LKB) and multivariable normal tissue complication probability (NTCP) models to predict the risk of radiation-induced hypothyroidism (RIHT) in breast cancer patients. Materials and methods: A total of 1,063 breast cancer patients who underwent whole breast irradiation between 2009 and 2016 were analyzed. Individual dose-volume histograms were used to generate LKB and multivariable logistic regression models. LKB model was fit using the thyroid radiation dose-volume parameters. A multivariable model was constructed to identify potential dosimetric and clinical parameters associated with RIHT. Internal validation was conducted using bootstrapping techniques, and model performance was evaluated using the area under the curve (AUC) and Hosmer-Lemeshow (HL) goodness-of-fit test. Results: RIHT developed in 4 % of patients with a median follow-up of 77.7 months. LKB and multivariable NTCP models exhibited significant agreement between the predicted and observed results (HL P values > 0.05). The multivariable NTCP model outperformed the LKB model in predicting RIHT (AUC 0.62 vs. 0.54). In the multivariable model, systemic therapy, age, and percentage of thyroid volume receiving ≥ 10 Gy (V10) were significant prognostic factors for RIHT. The cumulative incidence of RIHT was significantly higher in patients who exceeded the cut-off values for all three risk predictors (systemic therapy, age ≥ 40 years, and thyroid V10 ≥ 26 %, P < 0.005). Conclusions: Systemic therapy, age, and V10 of the thyroid were identified as strong risk factors for the development of RIHT. Our NTCP models provide valuable insights to clinicians for predicting and preventing hypothyroidism by identifying high-risk patients.

Large institutional experience of early outcomes and dosimetric findings with postoperative stereotactic partial breast irradiation in breast cancer.

PURPOSE: To analyze the dosimetric and toxicity outcomes of patients treated with postoperative stereotactic partial breast irradiation (S-PBI). METHODS: We identified 799 women who underwent S-PBI at our institution between January 2016 and December 2022. The most commonly used dose-fraction and technique were 30 Gy in 5 fractions (91.7 %) and a robotic stereotactic radiation system with real-time tracking (83.7 %). The primary endpoints were dosimetric parameters and radiation-related toxicities. For comparison, a control group undergoing ultra-hypofractionated whole breast irradiation (UF-WBI, n = 468) at the same institution was selected. RESULTS: A total of 815 breasts from 799 patients, with a median planning target volume (PTV) volume of 89.6 cm3, were treated with S-PBI. Treatment plans showed that the mean and maximum doses received by the PTV were 96.2 % and 104.8 % of the prescription dose, respectively. The volume of the ipsilateral breast that received 50 % of the prescription dose was 32.3 ± 8.9 %. The mean doses for the ipsilateral lung and heart were 2.5 ± 0.9 Gy and 0.65 ± 0.39 Gy, respectively. Acute toxicity occurred in 175 patients (21.5 %), predominantly of grade 1. Overall rate of late toxicity was 4 % with a median follow-up of 31.6 months. Compared to the UF-WBI group, the S-PBI group had comparably low acute toxicity (21.5 % vs. 25.2 %, p = 0.12) but significantly lower dosimetric parameters for all organs-at-risks (all p < 0.05). CONCLUSION: In this large cohort, S-PBI demonstrated favorable dosimetric and toxicity profiles. Considering the reduced radiation exposure to surrounding tissues, external beam PBI with advanced techniques should at least be considered over traditional WBI-based approaches for PBI candidates.

Generating 3D images of VMAT plans for predictive models and activation maps associated with plan deliverability.

BACKGROUND: Intensity modulation with dynamic multi-leaf collimator (MLC) and monitor unit (MU) changes across control points (CPs) characterizes volumetric modulated arc therapy (VMAT). The increased uncertainty in plan deliverability required patient-specific quality assurance (PSQA), which remained inefficient upon Quality Assurance (QA) failure. To prevent waste before QA, plan complexity metrics (PCMs) and machine learning models with the metrics were generated, which were lack of providing CP-specific information upon QA failures. PURPOSE: By generating 3D images from digital imaging and comminications in medicine in radiation therapy (DICOM RT) plan, we proposed a predictive model that can estimate the deliverability of VMAT plans and visualize CP-specific regions associated with plan deliverability. METHODS: The patient cohort consisted of 259 and 190 cases for left- and right-breast VMAT treatments, which were split into 235 and 166 cases for training and 24 cases from each treatment for testing the networks. Three-channel 3D images generated from DICOM RT plans were fed into a DenseNet-based deep learning network. To reflect VMAT plan complexity as an image, the first two channels described MLC and MU variations between two consecutive CPs, while the last channel assigned the beam field size. The network output was defined as binary classified PSQA results, indicating deliverability. The predictive performance was assessed by accuracy, sensitivity, specificity, F1-score, and area under the curve (AUC). The gradient-weighted class activation map (Grad-CAM) highlighted the regions of CPs in VMAT plans associated with deliverability, compared against PCMs by Spearman correlation. RESULTS: The DenseNet-based predictive model yielded AUCs of 92.2% and 93.8%, F1-scores of 97.0% and 93.8% and accuracies of 95.8% and 91.7% for the left- and right-breast VMAT cases. Additionally, the specificity of 87.5% for both cases indicated that the predictive model accurately detected QA failing cases. The activation maps significantly differentiated QA failing-labeled from passing-labeled classes for the non-deliverable cases. The PCM with the highest correlation to the Grad-CAM varied from patient cases, implying that plan deliverability would be considered patient-specific. CONCLUSION: This work demonstrated that the deep learning-based network based on visualization of dynamic VMAT plan information successfully predicted plan deliverability, which also provided control-point specific planning parameter information associated with plan deliverability in a patient-specific manner.

Experience of Implementing Deep Learning-Based Automatic Contouring in Breast Radiation Therapy Planning: Insights From Over 2000 Cases.

PURPOSE: This study evaluated the impact and clinical utility of an auto-contouring system for radiation therapy treatments. METHODS AND MATERIALS: The auto-contouring system was implemented in 2019. We evaluated data from 2428 patients who underwent adjuvant breast radiation therapy before and after the system's introduction. We collected the treatment's finalized contours, which were reviewed and revised by a multidisciplinary team. After implementation, the treatment contours underwent a finalization process that involved manual review and adjustment of the initial auto-contours. For the preimplementation group (n = 369), auto-contours were generated retrospectively. We compared the auto-contours and final contours using the Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD95). RESULTS: We analyzed 22,215 structures from final and corresponding auto-contours. The final contours were generally larger, encompassing more slices in the superior or inferior directions. Among organs at risk (OAR), the heart, esophagus, spinal cord, and contralateral breast demonstrated significantly increased DSC and decreased HD95 postimplementation (all P < .05), except for the lungs, which presented inaccurate segmentation. Among target volumes, CTVn_L2, L3, L4, and the internal mammary node showed increased DSC and decreased HD95 postimplementation (all P < .05), although the increase was less pronounced than the OAR outcomes. The analysis also covered factors contributing to significant differences, pattern identification, and outlier detection. CONCLUSIONS: In our study, the adoption of an auto-contouring system was associated with an increased reliance on automated settings, underscoring its utility and the potential risk of automation bias. Given these findings, we underscore the importance of considering the integration of stringent risk assessments and quality management strategies as a precautionary measure for the optimal use of such systems.

Upfront Versus Delayed Systemic Therapy in Patients With Oligometastatic Cancer Treated With SABR in the Phase 2 SABR-5 Trial.

PURPOSE: The optimal sequencing of local and systemic therapy for oligometastatic cancer has not been established. This study retrospectively compared progression-free survival (PFS), overall survival (OS), and SABR-related toxicity between upfront versus delay of systemic treatment until progression in patients in the SABR-5 trial. METHODS AND MATERIALS: The single-arm phase 2 SABR-5 trial accrued patients with up to 5 oligometastases across SABR-5 between November 2016 and July 2020. Patients received SABR to all lesions. Two cohorts were retrospectively identified: those receiving upfront systemic treatment along with SABR and those for whom systemic treatment was delayed until disease progression. Patients treated for oligoprogression were excluded. Propensity score analysis with overlap weighting balanced baseline characteristics of cohorts. Bootstrap sampling and Cox regression models estimated the association of delayed systemic treatment with PFS, OS, and grade ≥2 toxicity. RESULTS: A total of 319 patients with oligometastases underwent treatment on SABR-5, including 121 (38%) and 198 (62%) who received upfront and delayed systemic treatment, respectively. In the weighted sample, prostate cancer was the most common primary tumor histology (48%) followed by colorectal (18%), breast (13%), and lung (4%). Most patients (93%) were treated for 1 to 2 metastases. The median follow-up time was 34 months (IQR, 24-45). Delayed systemic treatment was associated with shorter PFS (hazard ratio [HR], 1.56; 95% CI, 1.15-2.13; P = .005) but similar OS (HR, 0.90; 95% CI, 0.51-1.59; P = .65) compared with upfront systemic treatment. Risk of grade 2 or higher SABR-related toxicity was reduced with delayed systemic treatment (odds ratio, 0.35; 95% CI, 0.15-0.70; P < .001). CONCLUSIONS: Delayed systemic treatment is associated with shorter PFS without reduction in OS and with reduced SABR-related toxicity and may be a favorable option for select patients seeking to avoid initial systemic treatment. Efforts should continue to accrue patients to histology-specific trials examining a delayed systemic treatment approach.