Relationships among patient characteristics, irradiation treatment planning parameters, and treatment toxicity of acute radiation dermatitis after breast hybrid intensity modulation radiation therapy

Prediction of acute skin toxicity in tomotherapy of breast cancer using skin DVH data

Investigation and quantification of the relationship between the skin dose-volume histogram (DVH) and the risk of acute skin toxicity in breast cancer patients undergoing Tomotherapy by regression modeling. This prospective study included 52 breast cancer patients treated with Tomotherapy in the dose range of 42.5-60 Gy to the planned target volume. Grading of acute skin toxicity in patients was assessed by the maximum score recorded in weekly follow-ups during and up to three months' post-radiation therapy using the Common Terminology Criteria for Adverse Events (CTCAE) v4.0 guidelines. A superficial layer with a thickness of 2 mm was designated as the Skin Representative Layer (SRL-2) on the Tomotherapy planning, and DVH was extracted for that. Then, multivariable and univariable logistic analyses were performed to identify the most predictive variables of acute skin toxicity from SRL-2 DVH values and patients' clinical parameters. The regression analysis identified V51Gy, representing the absolute SRL-2 volume receiving 51 Gy or more in physical dose, as the most predictive dosimetric parameter for grade 2-3 acute skin toxicity. The optimal cut-off value was 4.74 cc for the physical dose, with an Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) value of 0.749, even when adjusted for clinical and treatment-related variables. The logistic model based on V51Gy demonstrated superior calibration, with a slope and R² value approaching 1, indicating better agreement between predicted and observed outcomes. The risk of acute skin toxicity during breast cancer Tomotherapy is correlated with the V51Gy parameter of skin DVH. Limiting V51Gy < 4.74 cc, or 23.7 cm2 of skin area, should keep the risk of grade 2-3 acute skin toxicity below 26%.

Utilizing radiomics and dosiomics with AI for precision prediction of radiation dermatitis in breast cancer patients

PURPOSE

This study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT).

MATERIALS AND METHODS

This study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information. Using a Treatment Planning System (TPS), we segmented CT images into Regions of Interest (ROIs) to extract radiomic and dosiomic features, focusing on intensity, shape, texture, and dose distribution characteristics. Features significantly associated with the development of RD were identified using ANOVA and LASSO regression (p-value < 0.05). These features were then employed to train and evaluate Logistic Regression (LR) and Random Forest (RF) models, using tenfold cross-validation to ensure robust assessment of model efficacy.

RESULTS

In this study, 102 out of 120 VMAT-treated breast cancer patients were included in the detailed analysis. Thirty-two percent of these patients developed Grade 2+ RD. Age and BMI were identified as significant clinical predictors. Through feature selection, we narrowed down the vast pool of radiomic and dosiomic data to 689 features, distributed across 10 feature subsets for model construction. In the LR model, the J subset, comprising DVH, Radiomics, and Dosiomics features, demonstrated the highest predictive performance with an AUC of 0.82. The RF model showed that subset I, which includes clinical, radiomic, and dosiomic features, achieved the best predictive accuracy with an AUC of 0.83. These results emphasize that integrating radiomic and dosiomic features significantly enhances the prediction of Grade 2+ RD.

CONCLUSION

Integrating clinical, radiomic, and dosiomic characteristics into AI models significantly improves the prediction of Grade 2+ RD risk in breast cancer patients post-VMAT. The RF model analysis demonstrates that a comprehensive feature set maximizes predictive efficacy, marking a promising step towards utilizing AI in radiation therapy risk assessment and enhancing patient care outcomes.

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac

BACKGROUND

Radiation induced acute skin toxicity (AST) is considered as a common side effect of breast radiation therapy. The goal of this study was to design dosiomics-based machine learning (ML) models for prediction of AST, to enable creating optimized treatment plans for high-risk individuals.

METHODS

Dosiomics features extracted using Pyradiomics tool (v3.0.1), along with treatment plan-derived dose volume histograms (DVHs), and patient-specific treatment-related (PTR) data of breast cancer patients were used for modeling. Clinical scoring was done using the Common Terminology Criteria for Adverse Events (CTCAE) V4.0 criteria for skin-specific symptoms. The 52 breast cancer patients were grouped into AST 2 + (CTCAE ≥ 2) and AST 2 - (CTCAE < 2) toxicity grades to facilitate AST modeling. They were randomly divided into training (70%) and testing (30%) cohorts. Multiple prediction models were assessed through multivariate analysis, incorporating different combinations of feature groups (dosiomics, DVH, and PTR) individually and collectively. In total, seven unique combinations, along with seven classification algorithms, were considered after feature selection. The performance of each model was evaluated on the test group using the area under the receiver operating characteristic curve (AUC) and f1-score. Accuracy, precision, and recall of each model were also studied. Statistical analysis involved features differences between AST 2 - and AST 2 + groups and cutoff value calculations.

RESULTS

Results showed that 44% of the patients developed AST 2 + after Tomotherapy. The dosiomics (DOS) model, developed using dosiomics features, exhibited a noteworthy improvement in AUC (up to 0.78), when spatial information is preserved in the dose distribution, compared to DVH features (up to 0.71). Furthermore, a baseline ML model created using only PTR features for comparison with DOS models showed the significance of dosiomics in early AST prediction. By employing the Extra Tree (ET) classifiers, the DOS + DVH + PTR model achieved a statistically significant improved performance in terms of AUC (0.83; 95% CI 0.71-0.90), accuracy (0.70), precision (0.74) and sensitivity (0.72) compared to other models.

CONCLUSIONS

This study confirmed the benefit of dosiomics-based ML in the prediction of AST. However, the combination of dosiomics, DVH, and PTR yields significant improvement in AST prediction. The results of this study provide the opportunity for timely interventions to prevent the occurrence of radiation induced AST.

Electron stream effect in 0.35 Tesla magnetic resonance image guided radiotherapy for breast cancer

Purpose

This research aimed to analyze electron stream effect (ESE) during magnetic resonance image guided radiotherapy (MRgRT) for breast cancer patients on a MR-Linac (0.35 Tesla, 6MV), with a focus on the prevention of redundant radiation exposure.

Materials and methods

RANDO phantom was used with and without the breast attachment in order to represent the patients after breast conserving surgery (BCS) and those received modified radical mastectomy (MRM). The prescription dose is 40.05 Gy in fifteen fractions for whole breast irradiation (WBI) or 20 Gy single shot for partial breast irradiation (PBI). Thirteen different portals of intensity-modulated radiation therapy were created. And then we evaluated dose distribution in five areas (on the skin of the tip of the nose, the chin, the neck, the abdomen and the thyroid.) outside of the irradiated field with and without 0.35 Tesla. In addition, we added a piece of bolus with the thickness of 1cm on the skin in order to compare the ESE difference with and without a bolus. Lastly, we loaded two patients' images for PBI comparison.

Results

We found that 0.35 Tesla caused redundant doses to the skin of the chin and the neck as high as 9.79% and 5.59% of the prescription dose in the BCS RANDO model, respectively. For RANDO phantom without the breast accessory (simulating MRM), the maximal dose increase were 8.71% and 4.67% of the prescription dose to the skin of the chin and the neck, respectively. Furthermore, the bolus we added efficiently decrease the unnecessary dose caused by ESE up to 59.8%.

Conclusion

We report the first physical investigation on successful avoidance of superfluous doses on a 0.35T MR-Linac for breast cancer patients. Future studies of MRgRT on the individual body shape and its association with ESE influence is warranted.

Predicting acute radiation dermatitis in breast cancer: a prospective cohort study

BACKGROUND

Acute radiation dermatitis (ARD) is one of the most common acute adverse reactions in breast cancer patients during and immediately after radiotherapy. As ARD affects patient quality of life, it is important to conduct individualized risk assessments of patients in order to identify those patients most at risk of developing severe ARD.

METHODS

The data of breast cancer patients who received radiotherapy were prospectively collected and analyzed. Serum ferritin, high-sensitivity C-reactive protein (hs-CRP) levels, and percentages of lymphocyte subsets were measured before radiotherapy. ARD was graded (0-6 grade), according to the Oncology Nursing Society Skin Toxicity Scale. Univariate and multivariate logistic regression analyses were used and the odds ratio (OR) and 95% confidence interval (CI) of each factor were calculated.

RESULTS

This study included 455 breast cancer patients. After radiotherapy, 59.6% and 17.8% of patients developed at least 3 (3+) grade and at least 4 (4+) grade ARD, respectively. Multivariate logistic regression analysis found that body mass index (OR: 1.11, 95% CI: 1.01-1.22), diabetes (OR: 2.70, 95% CI: 1.11-6.60), smoking (OR: 3.04, 95% CI: 1.15-8.02), higher ferritin (OR: 3.31, 95% CI: 1.78-6.17), higher hs-CRP (OR: 1.96, 95% CI: 1.02-3.77), and higher CD3 + T cells (OR: 2.99, 95% CI: 1.10-3.58) were independent risk factors for 4 + grade ARD. Based on these findings, a nomogram model of 4 + grade ARD was further established. The nomogram AUC was 0.80 (95% CI: 0.75-0.86), making it more discriminative than any single factor.

CONCLUSION

BMI, diabetes, smoking history, higher ferritin, higher hs-CRP, and higher CD3 + T cells prior to radiotherapy for breast cancer are all independent risk factors for 4 + grade ARD. The results can provide evidence for clinicians to screen out high-risk patients, take precautions and carefully follow up on these patients before and during radiotherapy.

Prediction of radiation-induced acute skin toxicity in breast cancer patients using data encapsulation screening and dose-gradient-based multi-region radiomics technique: A multicenter study

Purpose

Radiation-induced dermatitis is one of the most common side effects for breast cancer patients treated with radiation therapy (RT). Acute complications can have a considerable impact on tumor control and quality of life for breast cancer patients. In this study, we aimed to develop a novel quantitative high-accuracy machine learning tool for prediction of radiation-induced dermatitis (grade ≥ 2) (RD 2+) before RT by using data encapsulation screening and multi-region dose-gradient-based radiomics techniques, based on the pre-treatment planning computed tomography (CT) images, clinical and dosimetric information of breast cancer patients.

Methods and Materials

214 patients with breast cancer who underwent RT between 2018 and 2021 were retrospectively collected from 3 cancer centers in China. The CT images, as well as the clinical and dosimetric information of patients were retrieved from the medical records. 3 PTV dose related ROIs, including irradiation volume covered by 100%, 105%, and 108% of prescribed dose, combined with 3 skin dose-related ROIs, including irradiation volume covered by 20-Gy, 30-Gy, 40-Gy isodose lines within skin, were contoured for radiomics feature extraction. A total of 4280 radiomics features were extracted from all 6 ROIs. Meanwhile, 29 clinical and dosimetric characteristics were included in the data analysis. A data encapsulation screening algorithm was applied for data cleaning. Multiple-variable logistic regression and 5-fold-cross-validation gradient boosting decision tree (GBDT) were employed for modeling training and validation, which was evaluated by using receiver operating characteristic analysis.

Results

The best predictors for symptomatic RD 2+ were the combination of 20 radiomics features, 8 clinical and dosimetric variables, achieving an area under the curve (AUC) of 0.998 [95% CI: 0.996-1.0] and an AUC of 0.911 [95% CI: 0.838-0.983] in the training and validation dataset, respectively, in the 5-fold-cross-validation GBDT model. Meanwhile, the top 12 most important characteristics as well as their corresponding importance measures for RD 2+ prediction in the GBDT machine learning process were identified and calculated.

Conclusions

A novel multi-region dose-gradient-based GBDT machine learning framework with a random forest based data encapsulation screening method integrated can achieve a high-accuracy prediction of acute RD 2+ in breast cancer patients.

Quantitative Thermal Imaging Biomarkers to Detect Acute Skin Toxicity From Breast Radiation Therapy Using Supervised Machine Learning

PURPOSE

Radiation-induced dermatitis is a common side effect of breast radiation therapy (RT). Current methods to evaluate breast skin toxicity include clinical examination, visual inspection, and patient-reported symptoms. Physiological changes associated with radiation-induced dermatitis, such as inflammation, may also increase body-surface temperature, which can be detected by thermal imaging. Quantitative thermal imaging markers were identified and used in supervised machine learning to develop a predictive model for radiation dermatitis.

METHODS AND MATERIALS

Ninety patients treated for adjuvant whole-breast RT (4250 cGy/f_x = 16) were recruited for the study. Thermal images of the treated breast were taken at 4 intervals: before RT, then weekly at f_x = 5, f_x = 10, and f_x = 15. Parametric thermograms were analyzed and yielded 26 thermal-based features that included surface temperature (°C) and texture parameters obtained from (1) gray-level co-occurrence matrix, (2) gray-level run-length matrix, and (3) neighborhood gray-tone difference matrix. Skin toxicity was evaluated at the end of RT using the Common Terminology Criteria for Adverse Events (CTCAE) guidelines (Ver.5). Binary group classes were labeled according to a CTCAE cut-off score of ≥2, and thermal features obtained at f_x = 5 were used for supervised machine learning to predict skin toxicity. The data set was partitioned for model training, independent testing, and validation. Fifteen patients (∼17% of the whole data set) were randomly selected as an unseen test data set, and 75 patients (∼83% of the whole data set) were used for training and validation of the model. A random forest classifier with leave-1-patient-out cross-validation was employed for modeling single and hybrid parameters. The model performance was reported using receiver operating characteristic analysis on patients from an independent test set.

RESULTS

Thirty-seven patients presented with adverse skin effects, denoted by a CTCAE score ≥2, and had significantly higher local increases in skin temperature, reaching 36.06°C at f_x = 10 (P = .029). However, machine-learning models demonstrated early thermal signals associated with skin toxicity after the fifth RT fraction. The cross-validated model showed high prediction accuracy on the independent test data (test accuracy = 0.87) at f_x = 5 for predicting skin toxicity at the end of RT.

CONCLUSIONS

Early thermal markers after 5 fractions of RT are predictive of radiation-induced skin toxicity in breast RT.

Incorporation of Dosimetric Gradients and Parotid Gland Migration Into Xerostomia Prediction

Purpose: Due to the sharp gradients of intensity-modulated radiotherapy (IMRT) dose distributions, treatment uncertainties may induce substantial deviations from the planned dose during irradiation. Here, we investigate if the planned mean dose to parotid glands in combination with the dose gradient and information about anatomical changes during the treatment improves xerostomia prediction in head and neck cancer patients.

Materials and methods: Eighty eight patients were retrospectively analyzed. Three features of the contralateral parotid gland were studied in terms of their association with the outcome, i.e., grade ≥ 2 (G2) xerostomia between 6 months and 2 years after radiotherapy (RT): planned mean dose (MD), average lateral dose gradient (GRADX), and parotid gland migration toward medial (PGM). PGM was estimated using daily megavoltage computed tomography (MVCT) images. Three logistic regression models where analyzed: based on (1) MD only, (2) MD and GRADX, and (3) MD, GRADX, and PGM. Additionally, the cohort was stratified based on the median value of GRADX, and a univariate analysis was performed to study the association of the MD with the outcome for patients in low- and high-GRADX domains.

Results: The planned MD failed to recognize G2 xerostomia patients (AUC = 0.57). By adding the information of GRADX (second model), the model performance increased to AUC = 0.72. The addition of PGM (third model) led to further improvement in the recognition of the outcome (AUC = 0.79). Remarkably, xerostomia patients in the low-GRADX domain were successfully identified (AUC = 0.88) by the MD alone.

Conclusions: Our results indicate that GRADX and PGM, which together serve as a proxy of dosimetric changes, provide valuable information for xerostomia prediction.

Daily Fractionation of External Beam Accelerated Partial Breast Irradiation to 40 Gy Is Well Tolerated and Locally Effective

PURPOSE

Most studies examining accelerated partial breast irradiation (APBI) have used twice-daily fractionation. Cosmesis with this approach has produced mixed results, and the optimal fractionation scheme remains unknown. We sought to evaluate the safety and efficacy of APBI with a total dose of 40 Gy in 10 daily fractions.

METHODS AND MATERIALS

Between 2010 and 2014, we prospectively enrolled 106 patients to receive APBI after lumpectomy for invasive or in situ node-negative breast cancer. Radiation was administered via 3-dimensional conformal techniques.

RESULTS

The median age was 62 years (range, 39-85), and all patients underwent APBI per protocol. With a median follow-up of 58 months, we evaluated patient-reported local toxicity and recurrence outcomes. Of 106 patients, 16 (15%) experienced grade ≥2 skin toxicity. The most common significant toxicities were acute cutaneous changes at 4 to 9 weeks after radiation therapy, including grade 2 erythema in 2 patients (1.8%) and skin color changes in 4 patients (3.8%). Only 2 instances of grade 3 toxicity were reported, including 1 patient with acute moist desquamation after radiation therapy and another with fibrosis at 2 years. Planning target volume and breast V₂₀ were significantly predictive of skin/subcutaneous toxicity, with evidence that limiting breast V₂₀ to <45% may improve tolerability. Overall, 3 breast cancer recurrences arose: 1 local recurrence in the original quadrant (3 years after APBI), 1 in a different ipsilateral quadrant (5 years after APBI), and 1 with distant disease 2 years after APBI.

CONCLUSIONS

In an appropriately selected group of patients with early stage breast cancer, APBI to a dose of 40 Gy in 10 daily fractions was well tolerated, with most patients (99%) reporting excellent/good cosmesis. Planning target volume and breast V₂₀ should be carefully constrained to limit local morbidity. Longer follow-up will be needed to establish efficacy and subsequent local recurrence rates.