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.

Unsupervised pseudo CT generation using heterogenous multicentric CT/MR images and CycleGAN: Dosimetric assessment for 3D conformal radiotherapy.

PURPOSE: Absorbed dose calculation in magnetic resonance-guided radiation therapy (MRgRT) is commonly based on pseudo CT (pCT) images. This study investigated the feasibility of unsupervised pCT generation from MRI using a cycle generative adversarial network (CycleGAN) and a heterogenous multicentric dataset. A dosimetric analysis in three-dimensional conformal radiotherapy (3DCRT) planning was also performed. MATERIAL AND METHODS: Overall, 87 T1-weighted and 102 T2-weighted MR images alongside with their corresponding computed tomography (CT) images of brain cancer patients from multiple centers were used. Initially, images underwent a number of preprocessing steps, including rigid registration, novel CT Masker, N4 bias field correction, resampling, resizing, and rescaling. To overcome the gradient vanishing problem, residual blocks and mean squared error (MSE) loss function were utilized in the generator and in both networks (generator and discriminator), respectively. The CycleGAN was trained and validated using 70 T1 and 80 T2 randomly selected patients in an unsupervised manner. The remaining patients were used as a holdout test set to report final evaluation metrics. The generated pCTs were validated in the context of 3DCRT. RESULTS: The CycleGAN model using masked T2 images achieved better performance with a mean absolute error (MAE) of 61.87 ± 22.58 HU, peak signal to noise ratio (PSNR) of 27.05 ± 2.25 (dB), and structural similarity index metric (SSIM) of 0.84 ± 0.05 on the test dataset. T1-weighted MR images used for dosimetric assessment revealed a gamma index of 3%, 3 mm, 2%, 2 mm and 1%, 1 mm with acceptance criteria of 98.96% ± 1.1%, 95% ± 3.68%, 90.1% ± 6.05%, respectively. The DVH differences between CTs and pCTs were within 2%. CONCLUSIONS: A promising pCT generation model capable of handling heterogenous multicenteric datasets was proposed. All MR sequences performed competitively with no significant difference in pCT generation. The proposed CT Masker proved promising in improving the model accuracy and robustness. There was no significant difference between using T1-weighted and T2-weighted MR images for pCT generation.

Comparison of dosimetric characteristics of physical wedge and enhanced dynamic wedge in inhomogeneous medium using Monte Carlo simulations.

BACKGROUND: Widely used physical wedges in clinical radiotherapy lead to beam intensity attenuation as well as the beam hardening effect, which must be considered. Dynamic wedges devised to overcome the physical wedges (PWs) problems result in dosimetry complications due to jaw movement while the beam is on. This study was aimed to investigate the usability of physical wedge data instead of enhanced dynamic wedge due to the enhanced dynamic wedge (EDW) dosimetry measurement hardships of Varian 2100CD in inhomogeneous phantom by Monte Carlo code as a reliable method in radiation dosimetry. MATERIALS AND METHODS: A PW and EDW-equipped-linac head was simulated using BEAMnrc code. DOSXYZnrc was used for three-dimensional dosimetry calculation in the CIRS phantom. RESULTS: Based on the isodose curves, EDW generated a less scattered as well as lower penumbra width compared to the PW. The depth dose variations of PWs and EDWs were more in soft tissue than the lung tissue. Beam profiles of PW and EDW indicated good coincidence in all points, except for the heel area. CONCLUSION: Results demonstrated that it is possible to apply PW data instead of EDW due to the dosimetry and commissioning hardships caused by EDW in inhomogeneous media.

Dosimetric characteristic of physical wedge versus enhanced dynamic wedge based on Monte Carlo simulations.

AIM OF STUDY: Physical wedges (PWs) are widely used in radiotherapy to obtain tilted isodose curves, but they alter beam quality. Dynamic wedges (DWs) using moving collimator overcome this problem, but measuring their beam data is not simple. The main aim of this study is to obtain all dosimetric parameters of DWs produced by Varian 2100CD with Monte Carlo simulation and compare them to those from PWs. SUBJECTS AND METHODS: To simulate 6 MV photon beams equipped with PW and DW, BEAMnrc code was used. All dosimetric data were obtained with EDR2 films and two-dimensional diode array detector. RESULTS: Good agreement between simulated and measured dosimetric data for PW and DW fields was obtained. Our results showed that percentage depth dose and beam profiles at nonwedged direction for DWs are the same as open fields and can be used to each other. CONCLUSION: From Monte Carlo simulations, it can be concluded that DWs in spite of PW do not have effect on beam quality and are good options for treatment planning system which cannot consider hardening effect produced by PWs. Furthermore, BEAMnrc is a powerful code to acquire all date required by DWs.