Pseudo-siamese network combined with dosimetric and clinical factors, radiomics features, CT images and 3D dose distribution for the prediction of radiation pneumonitis: A feasibility study

Feng, Bin; Zhou, Wei; Yang, Xin; Luo, Hong; Zhang, Xin; Yang, Dan; Tao, Dan; Wu, Yong-Zhong; Jin, Fu

doi:10.1016/j.ctro.2022.11.011

Cited by 6 publications

(8 citation statements)

References 28 publications

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“…The use of deep convolutional neural networks (CNNs) in the RT domain has become increasingly common. However, previous deep-learning-based dose prediction studies have mainly focused on IMRT or VMAT (volumetric modulated arc therapy) plans [12,[29][30][31], with limited research on SRT (e.g., CyberKnife) dose prediction. This study aimed to accurately and e ciently calculate the MC dose of heterogeneous tissue tumors, such as lung cancer.…”

Section: Discussionmentioning

confidence: 99%

“…RapidPlan (Varian Medical Systems, USA) is a commercially available TPS that exempli es this approach [11]. These alternative methods for automated planning have signi cantly expedited the planning process for traditional intensity-modulated radiation therapy (IMRT), reducing the need for human intervention while ensuring the production of high-quality RT plans [12][13][14].…”

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confidence: 99%

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Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations

Miao,

Li,

et al. 2024

Preprint

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Background Accurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife is essential for precise planning. We aim to employ deep learning to directly predict the 3D dose distribution calculated by the MC algorithm, enabling rapid and accurate automatic planning. However, most current methods solely focus on conventional intensity-modulated radiation therapy and assume a consistent beam configuration across all patients. This study seeks to develop a more versatile model incorporating variable beam configurations of CyberKnife and considering the patient's anatomy. Methods This study proposed the AB (anatomy and beam) model to compare with the control Mask (only anatomy) model. These models are based on a 3D U-Net network to investigate the impact of CyberKnife beam encoding information on dose prediction. The study collected 86 lung cancer patients who received the built-in MC algorithm plans of CyberKnife using different beam configurations for training/validation (66 cases) and testing (20 cases). We compared the gamma passing rate, dose difference maps, and relevant dose-volume metrics to evaluate the model's performance. In addition, the Dice similarity coefficients (DSCs) was calculated to assess the spatial correspondence of isodose volumes. Results The AB model demonstrated superior performance compared to the Mask model, particularly in the trajectory dose of the beam. The DSCs of the AB model was 20–40% higher than that of the Mask model in some dose regions. We achieved approximately 99% for the PTV and generally more than 95% for the organs at risk (OARs) referred to the clinical planning dose in the gamma passing rates (3mm/3%). Relative to the Mask model, the AB model exhibited more than 90% improvement in small voxels (P < 0.001). The AB model matched well with the clinical plan's dose-volume histograms (DVHs) and the average dose error for all organs was 1.65 ± 0.69%. Conclusions Our proposed new model signifies a crucial advancement in predicting CyberKnife 3D dose distributions for clinical applications. It enables planners to rapidly and precisely calculate MC doses for lung cancer based on patient-specific beam configurations.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations

Miao,

Li,

et al. 2024

Preprint

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. Inspired by dropout, random erasure is somewhat similar to performing dropout on the image level.…”

Section: Methodsmentioning

confidence: 99%

“…Although random erasure and color jitter may alter the physical properties of DD images, numerous studies have demonstrated that these data augmentation methods do not adversely affect the model's performance. [27][28][29][30] Dropout prevents overfitting by discarding (both hidden and visible) units of the CNN with a probability of p. Inspired by dropout, random erasure is somewhat similar to performing dropout on the image level. 27 Random erasure involves locally obscuring parts of an image, compelling the model to learn more diverse and descriptive features, thereby preventing the model from overfitting to specific visual characteristics.…”

Section: Implementation Detailsmentioning

confidence: 99%

Error detection using a multi‐channel hybrid network with a low‐resolution detector in patient‐specific quality assurance

Yan,

Shi,

Xue

et al. 2024

J Applied Clin Med Phys

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PurposeThis study aimed to develop a hybrid multi‐channel network to detect multileaf collimator (MLC) positional errors using dose difference (DD) maps and gamma maps generated from low‐resolution detectors in patient‐specific quality assurance (QA) for Intensity Modulated Radiation Therapy (IMRT).MethodsA total of 68 plans with 358 beams of IMRT were included in this study. The MLC leaf positions of all control points in the original IMRT plans were modified to simulate four types of errors: shift error, opening error, closing error, and random error. These modified plans were imported into the treatment planning system (TPS) to calculate the predicted dose, while the PTW seven29 phantom was utilized to obtain the measured dose distributions. Based on the measured and predicted dose, DD maps and gamma maps, both with and without errors, were generated, resulting in a dataset with 3222 samples. The network's performance was evaluated using various metrics, including accuracy, sensitivity, specificity, precision, F1‐score, ROC curves, and normalized confusion matrix. Besides, other baseline methods, such as single‐channel hybrid network, ResNet‐18, and Swin‐Transformer, were also evaluated as a comparison.ResultsThe experimental results showed that the multi‐channel hybrid network outperformed other methods, demonstrating higher average precision, accuracy, sensitivity, specificity, and F1‐scores, with values of 0.87, 0.89, 0.85, 0.97, and 0.85, respectively. The multi‐channel hybrid network also achieved higher AUC values in the random errors (0.964) and the error‐free (0.946) categories. Although the average accuracy of the multi‐channel hybrid network was only marginally better than that of ResNet‐18 and Swin Transformer, it significantly outperformed them regarding precision in the error‐free category.ConclusionThe proposed multi‐channel hybrid network exhibits a high level of accuracy in identifying MLC errors using low‐resolution detectors. The method offers an effective and reliable solution for promoting quality and safety of IMRT QA.

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“…This correlation enables the creation of a classification model capable of identifying at-risk patients [16]. Indeed, multiple studies have highlighted the utility of radiomics analysis in quantifying radiation therapy-induced damage in various organs, including the bladder, rectum, parotid, and lung [6,[17][18][19][20][21][22].…”

Section: Predictive Modeling For Radiation-induced Damagementioning

confidence: 99%

Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study

Abbaspour,

Barahman,

Abdollahi

et al. 2023

Biomed. Phys. Eng. Express

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Purpose. This study aims to predict radiotherapy-induced rectal and bladder toxicity using computed tomography (CT) and magnetic resonance imaging (MRI) radiomics features in combination with clinical and dosimetric features in rectal cancer patients. Methods. A total of sixty-three patients with locally advanced rectal cancer who underwent three-dimensional conformal radiation therapy (3D-CRT) were included in this study. Radiomics features were extracted from the rectum and bladder walls in pretreatment CT and MR-T2W-weighted images. Feature selection was performed using various methods, including Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-square (Chi2), Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and SelectPercentile. Predictive modeling was carried out using machine learning algorithms, such as K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Gradient Boosting (XGB), and Linear Discriminant Analysis (LDA). The impact of the Laplacian of Gaussian (LoG) filter was investigated with sigma values ranging from 0.5 to 2. Model performance was evaluated in terms of the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, and specificity. Results. A total of 479 radiomics features were extracted, and 59 features were selected. The pre-MRI T2W model exhibited the highest predictive performance with an AUC: 91.0/96.57%, accuracy: 90.38/96.92%, precision: 90.0/97.14%, sensitivity: 93.33/96.50%, and specificity: 88.09/97.14%. These results were achieved with both original image and LoG filter (sigma = 0.5–1.5) based on LDA/DT-RF classifiers for proctitis and cystitis, respectively. Furthermore, for the CT data, AUC: 90.71/96.0%, accuracy: 90.0/96.92%, precision: 88.14/97.14%, sensitivity: 93.0/96.0%, and specificity: 88.09/97.14% were acquired. The highest values were achieved using XGB/DT-XGB classifiers for proctitis and cystitis with LoG filter (sigma = 2)/LoG filter (sigma = 0.5–2), respectively. MRMR/RFE-Chi2 feature selection methods demonstrated the best performance for proctitis and cystitis in the pre-MRI T2W model. MRMR/MRMR-Lasso yielded the highest model performance for CT. Conclusion. Radiomics features extracted from pretreatment CT and MR images can effectively predict radiation-induced proctitis and cystitis. The study found that LDA, DT, RF, and XGB classifiers, combined with MRMR, RFE, Chi2, and Lasso feature selection algorithms, along with the LoG filter, offer strong predictive performance. With the inclusion of a larger training dataset, these models can be valuable tools for personalized radiotherapy decision-making.

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Pseudo-siamese network combined with dosimetric and clinical factors, radiomics features, CT images and 3D dose distribution for the prediction of radiation pneumonitis: A feasibility study

Cited by 6 publications

References 28 publications

Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations

Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations

Error detection using a multi‐channel hybrid network with a low‐resolution detector in patient‐specific quality assurance

Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study

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