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While dual-energy computed tomography (DECT) technology introduces energy-specific information in clinical practice, single-energy CT (SECT) is predominantly used, limiting the number of people who can benefit from DECT. This study proposed a novel method to generate synthetic low-energy virtual monochromatic images at 50 keV (sVMI 50keV ) from SECT images using a transformer-based deep learning model, SwinUNETR. Data were obtained from 85 patients who underwent head and neck radiotherapy. Among these, the model was built using data from 70 patients for whom only DECT images were available. The remaining 15 patients, for whom both DECT and SECT images were available, were used to predict from the actual SECT images. We used the SwinUNETR model to generate sVMI 50keV . The image quality was evaluated, and the results were compared with those of the convolutional neural network-based model, Unet. The mean absolute errors from the true VMI 50keV were 36.5 ± 4.9 and 33.0 ± 4.4 Hounsfield units for Unet and SwinUNETR, respectively. SwinUNETR yielded smaller errors in tissue attenuation values compared with those of Unet. The contrast changes in sVMI 50keV generated by SwinUNETR from SECT were closer to those of DECT-derived VMI 50keV than the contrast changes in Unet-generated sVMI 50keV . This study demonstrated the potential of transformer-based models for generating synthetic low-energy VMIs from SECT images, thereby improving the image quality of head and neck cancer imaging. It provides a practical and feasible solution to obtain low-energy VMIs from SECT data that can benefit a large number of facilities and patients without access to DECT technology., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Purpose: Deep learning (DL) is a state-of-the-art technique for developing artificial intelligence in various domains and it improves the performance of natural language processing (NLP). Therefore, we aimed to develop a DL-based NLP model that classifies the status of bone metastasis (BM) in radiology reports to detect patients with BM., Materials and Methods: The DL-based NLP model was developed by training long short-term memory using 1,749 free-text radiology reports written in Japanese. We adopted five-fold cross-validation and used 200 reports for testing the five models. The accuracy, sensitivity, specificity, precision, and area under the receiver operating characteristics curve (AUROC) were used for the model evaluation., Results: The developed model demonstrated classification performance with mean ± standard deviation of 0.912 ± 0.012, 0.924 ± 0.029, 0.901 ± 0.014, 0.898 ± 0.012, and 0.968 ± 0.004 for accuracy, sensitivity, specificity, precision, and AUROC, respectively., Conclusion: The proposed DL-based NLP model may help in the early and efficient detection of patients with BM., (© 2023. The Author(s) under exclusive licence to Japan Radiological Society.)

Accurate clinical target volume (CTV) delineation is important for head and neck intensity-modulated radiation therapy. However, delineation is time-consuming and susceptible to interobserver variability (IOV). Based on a manual contouring process commonly used in clinical practice, we developed a deep learning (DL)-based method to delineate a low-risk CTV with computed tomography (CT) and gross tumor volume (GTV) input and compared it with a CT-only input. A total of 310 patients with oropharynx cancer were randomly divided into the training set (250) and test set (60). The low-risk CTV and primary GTV contours were used to generate label data for the input and ground truth. A 3D U-Net with a two-channel input of CT and GTV (U-Net GTV ) was proposed and its performance was compared with a U-Net with only CT input (U-Net CT ). The Dice similarity coefficient (DSC) and average Hausdorff distance (AHD) were evaluated. The time required to predict the CTV was 0.86 s per patient. U-Net GTV showed a significantly higher mean DSC value than U-Net CT (0.80 ± 0.03 and 0.76 ± 0.05) and a significantly lower mean AHD value (3.0 ± 0.5 mm vs 3.5 ± 0.7 mm). Compared to the existing DL method with only CT input, the proposed GTV-based segmentation using DL showed a more precise low-risk CTV segmentation for head and neck cancer. Our findings suggest that the proposed method could reduce the contouring time of a low-risk CTV, allowing the standardization of target delineations for head and neck cancer., (Copyright © 2022 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved.)

Purpose: Deep learning (DL)-based dose distribution prediction can potentially reduce the cost of inverse planning process. We developed and introduced a structure-focused loss (L struct ) for 3D dose prediction to improve prediction accuracy. This study investigated the influence of L struct on DL-based dose prediction for patients with prostate cancer. The proposed L struct , which is similar in concept to dose-volume histogram (DVH)-based optimization in clinical practice, has the potential to provide more interpretable and accurate DL-based optimization., Methods: This study involved 104 patients who underwent prostate radiotherapy. We used 3D U-Net-based architecture to predict dose distributions from computed tomography and contours of the planning target volume and organs-at-risk. We trained two models using different loss functions: L2 loss and L struct . Predicted doses were compared in terms of dose-volume parameters and the Dice similarity coefficient of isodose volume., Results: DVH analysis showed that the L struct model had smaller errors from the ground truth than the L2 model. The L struct model achieved more consistent dose distributions than the L2 model, with errors close to zero. The isodose Dice score of the L struct model was greater than that of the L2 model by >20% of the prescribed dose., Conclusions: We developed L struct using labels of inputted contours for DL-based dose prediction for prostate radiotherapy. L struct can be generalized to any DL architecture, thereby enhancing the dose prediction accuracy., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Purpose: To develop a deep learning-based metal artifact reduction (DL-MAR) method using unpaired data and to evaluate its dosimetric impact in head and neck intensity-modulated radiation therapy (IMRT) compared with the water density override method., Methods: The data set comprised the data of 107 patients who underwent radiotherapy. Fifteen patients with dental fillings were used as the test data set. The computed tomography (CT) images of the remaining 92 patients were divided into two domains: the metal artifact and artifact-free domains. CycleGAN was used for domain translation. The artifact index of the DL-MAR images was compared with that of the original uncorrected (UC) CT images. The dose distributions of the DL-MAR and UC plans were created by comparing the reference clinical plan with the water density override method (water plan) in each dataset. Dosimetric deviation in the oral cavity from the water plan was evaluated., Results: The artifact index of the DL-MAR images was significantly smaller than that of the UC images in all patients (13.2 ± 4.3 vs. 267.3 ± 113.7). Compared with the reference water plan, dose differences of the UC plans were greater than those of the DL-MAR plans. DL-MAR images provided dosimetric results that were more similar to those of the water plan than the UC plan., Conclusions: We developed a fast DL-MAR method using CycleGAN for head and neck IMRT. The proposed method could provide consistent dose calculation against metal artifact and improve the efficiency of the planning process by eliminating manual delineation., (Copyright © 2020 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)