Evaluation of Local Retinal Function in Light-Damaged Rats Using Multifocal Electroretinograms and Multifocal Visual Evoked Potentials.

Electroretinograms (ERGs) are often used to evaluate retinal function. However, assessing local retinal function can be challenging; therefore, photopic and scotopic ERGs are used to record whole-retinal function. This study evaluated focal retinal function in rats exposed to continuous light using a multifocal ERG (mfERG) system. The rats were exposed to 1000 lux of fluorescent light for 24 h to induce photoreceptor degeneration. After light exposure, the rats were reared under cyclic light conditions (12 h: 5 lux, 12 h: dark). Photopic and multifocal ERGs and single-flash and multifocal visual evoked potentials (mfVEPs) were recorded 7 days after light exposure. Fourteen days following light exposure, paraffin-embedded sections were prepared from the eyes for histological evaluation. The ERG and VEP responses dramatically decreased after 24 h of light exposure, and retinal area-dependent decreases were observed in mfERGs and mfVEPs. Histological assessment revealed severe damage to the superior retina and less damage to the inferior retina. Considering the recorded visual angles of mfERGs and mfVEPs, the degenerated area shown on the histological examinations correlates well with the responses from multifocal recordings.

Physical and biological dosimetric margin according to prescription method for stereotactic body radiation therapy.

This study aimed to expand the biological conversion factor (BCF) model, which converts the physical dosimetric margin (PDM) to the biological dosimetric margin (BDM) for point prescription with 3-dimensional conformal radiation therapy (3DCRT) and the marginal prescription method with volumetric-modulated arc radiotherapy (VMAT). The VMAT of the marginal prescription and the 3DCRT of the point prescription with lung stereotactic body radiation therapy (SBRT) by using RayStation were planned. The biological equivalent dose (BED) for a dose per fraction (DPF) of 3-20 Gy was calculated from these plans. The dose was perturbed with the calculation using a 1-mm step isocenter shift. The dose covering 95% of the target was greater than or equal to 90% of the prescribed physical dose, and the BED were defined as the PDM and BDM, respectively. The BCF was created as a function of the DPF. The PDM and BDM for all DPFs were larger with the point prescription method than with the marginal prescription method. The marginal prescription method with a 60% isodose line had a larger PDM and BDM. The BCF with the point prescription was smaller than that with the marginal prescription in the left-right (LR), anterior-posterior (AP) and cranio-caudal (CC) directions. In the marginal prescription method, the 60% isodose line had a higher BCF. In conclusion, the improved BCF method could be converted to BDM for point prescription with 3DCRT and marginal prescription method with VMAT, which is required for stereotactic radiation therapy in radiobiology-based treatment planning.

A synthesized gamma distribution-based patient-specific VMAT QA using a generative adversarial network.

BACKGROUND: Artificial intelligence (AI)-based gamma passing rate (GPR) prediction has been proposed as a time-efficient virtual patient-specific QA method for the delivery of volumetric modulation arc therapy (VMAT). However, there is a limitation that the GPR value loses the locational information of dose accuracy. PURPOSE: The objective was to predict the failing points in the gamma distribution and the GPR using a synthesized gamma distribution of VMAT QA with a deep convolutional generative adversarial network (GAN). METHODS: The fluence maps of 270 VMAT beams for prostate cancer were measured using an electronic portal imaging device and analyzed using gamma evaluation with 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances. The 270 gamma distributions were divided into two datasets: 240 training datasets for creating a model and 30 test datasets for evaluation. The image prediction network for the fluence maps calculated by the treatment planning system (TPS) to the gamma distributions was created using a GAN. The sensitivity, specificity, and accuracy of detecting failing points were evaluated using measured and synthesized gamma distributions. In addition, the difference between measured GPR (mGPR) and predicted GPR (pGPR) values calculated from the synthesized gamma distributions was evaluated. RESULTS: The root mean squared errors between mGPR and pGPR were 1.0%, 2.1%, 3.5%, and 3.6% for the 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances, respectively. The accuracies for detecting failing points were 98.9%, 96.9%, 94.7%, and 93.7% for 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances, respectively. The sensitivity and specificity were the highest for 1%/0.5-mm and 3%/2-mm tolerances, which were 82.7% and 99.6%, respectively. CONCLUSIONS: We developed a novel system using a GAN to generate a synthesized gamma distribution-based patient-specific VMAT QA. The system is promising from the point of view of quality assurance in radiotherapy because it shows high performance and can detect failing points.

MRI image synthesis for fluid-attenuated inversion recovery and diffusion-weighted images with deep learning.

This study aims to synthesize fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted images (DWI) with a deep conditional adversarial network from T1- and T2-weighted magnetic resonance imaging (MRI) images. A total of 1980 images of 102 patients were split into two datasets: 1470 (68 patients) in a training set and 510 (34 patients) in a test set. The prediction framework was based on a convolutional neural network with a generator and discriminator. T1-weighted, T2-weighted, and composite images were used as inputs. The digital imaging and communications in medicine (DICOM) images were converted to 8-bit red-green-blue images. The red and blue channels of the composite images were assigned to 8-bit grayscale pixel values in T1-weighted images, and the green channel was assigned to those in T2-weighted images. The prediction FLAIR and DWI images were of the same objects as the inputs. For the results, the prediction model with composite MRI input images in the DWI image showed the smallest relative mean absolute error (rMAE) and largest mutual information (MI), and that in the FLAIR image showed the largest relative mean-square error (rMSE), relative root-mean-square error (rRMSE), and peak signal-to-noise ratio (PSNR). For the FLAIR image, the prediction model with the T2-weighted MRI input images generated more accurate synthesis results than that with the T1-weighted inputs. The proposed image synthesis framework can improve the versatility and quality of multi-contrast MRI without extra scans. The composite input MRI image contributes to synthesizing the multi-contrast MRI image efficiently.

Influence of different air CT numbers for IVDT on the dose distribution in TomoTherapy MVCT.

This study aims to evaluate the effect of different air computed tomography (CT) numbers of the image value density table (IVDT) on the retrospective dose calculation of head-and-neck (HN) radiotherapy using TomoTherapy megavoltage CT (MVCT) images. The CT numbers of the inside and outside air and each tissue-equivalent plug of the "Cheese" phantom were obtained from TomoTherapy MVCT. Two IVDTs with different air CT numbers were created and applied to MVCT images of the HN anthropomorphic phantom and recalculated by Planned Adaptive to verify dose distribution. We defined the recalculation dose with MVCT images using both inside and outside air of the IVDT as IVDT MVCT inair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{inair}}$ and IVDT MVCT outair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{outair}}$ , respectively. Treatment planning doses calculated on kVCT images were compared with those calculated on MVCT images using two different IVDT tables, namely, IVDT MVCT inair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{inair}}$ and IVDT MVCT outair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{outair}}$ . The difference between average MVCT numbers ±1 standard deviation on inside and outside air of the calibration phantom was 65 ± 36 HU. This difference in MVCT number of air exceeded the recommendation lung tolerance for dose calculation error of 2%. The dose differences between the planning target volume (PTV): D98% , D50% , D2% and the organ at risk (OAR): Dmax , Dmean recalculated by IVDT MVCT inair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{inair}}$ and IVDT MVCT outair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{outair}}$ using MVCT images were a maximum of 0.7% and 1.2%, respectively. Recalculated doses to the PTV and OAR with MVCT showed that IVDT MVCT outair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{outair}}$ was 0.5%-0.7% closer to the kVCT treatment planning dose than IVDT MVCT inair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{inair}}$ . This study showed that IVDT MVCT outair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{outair}}$ was more accurate than IVDT MVCT inair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{inair}}$ in recalculating the dose HN cases of MVCT using TomoTherapy.

Image synthesis of effective atomic number images using a deep convolutional neural network-based generative adversarial network.

Background: The effective atomic numbers obtained from dual-energy computed tomography (DECT) can aid in characterization of materials. In this study, an effective atomic number image reconstructed from a DECT image was synthesized using an equivalent single-energy CT image with a deep convolutional neural network (CNN)-based generative adversarial network (GAN). Materials and methods: The image synthesis framework to obtain the effective atomic number images from a single-energy CT image at 120 kVp using a CNN-based GAN was developed. The evaluation metrics were the mean absolute error (MAE), relative root mean square error (RMSE), relative mean square error (MSE), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mutual information (MI). Results: The difference between the reference and synthetic effective atomic numbers was within 9.7% in all regions of interest. The averages of MAE, RMSE, MSE, SSIM, PSNR, and MI of the reference and synthesized images in the test data were 0.09, 0.045, 0.0, 0.89, 54.97, and 1.03, respectively. Conclusions: In this study, an image synthesis framework using single-energy CT images was constructed to obtain atomic number images scanned by DECT. This image synthesis framework can aid in material decomposition without extra scans in DECT.

Improved biological dosimetric margin model for different PTV margins with stereotactic body radiation therapy in homogeneous and nonhomogeneous tumor regions.

Background: The purpose of this study was to improve the biological dosimetric margin (BDM) corresponding to different planning target volume (PTV) margins in homogeneous and nonhomogeneous tumor regions using an improved biological conversion factor (BCF) model for stereotactic body radiation therapy (SBRT). Materials and methods: The PTV margin was 5-20 mm from the clinical target volume. The biologically equivalent dose (BED) was calculated using the linear-quadratic model. The biological parameters were α/ß = 10 Gy, and the dose per fraction (DPF) was d = 3-20 Gy/fr. The isocenter was offset at intervals of 1 mm; 95% of the clinical target volume covered more than 90% of the prescribed physical dose, and BED was defined as biological and physical DMs. The BCF formula was defined as a function of the DPF. Results: The difference in the BCF caused by the DPF was within 0.05 for the homogeneous and nonhomogeneous phantoms. In the virtual nonhomogeneous phantom, the data with a PTV margin of 10-20 mm were not significantly different; thus, these were combined to fit the BCF. In the virtual homogeneous phantom, the BCF was fitted to each PTV margin. Conclusions: The current study improved a scheme to estimate the BDM considering the size of the PTV margin and homogeneous and nonhomogeneous regions. This technique is expected to enable BED-based treatment planning using treatment systems based on physical doses for SBRT.

Predictive gamma passing rate of 3D detector array-based volumetric modulated arc therapy quality assurance for prostate cancer via deep learning.

To predict the gamma passing rate (GPR) of the three-dimensional (3D) detector array-based volumetric modulated arc therapy (VMAT) quality assurance (QA) for prostate cancer using a convolutional neural network (CNN) with the 3D dose distribution. One hundred thirty-five VMAT plans for prostate cancer were selected: 110 plans were used for training and validation, and 25 plans were used for testing. Verification plans were measured using a helical 3D diode array (ArcCHECK). The dose distribution on the detector element plane of these verification plans was used as input data for the CNN model. The measured GPR (mGPR) values were used as the training data. The CNN model comprises eighteen layers and predicted GPR (pGPR) values. The mGPR and pGPR values were compared, and a cumulative frequency histogram of the prediction error was created to clarify the prediction error tendency. The correlation coefficients of pGPR and mGPR were 0.67, 0.69, 0.66, and 0.73 for 3%/3-mm, 3%/2-mm, 2%/3-mm, and 2%/2-mm gamma criteria, respectively. The respective mean±standard deviations of pGPR-mGPR were -0.87±2.18%, -0.65±2.93%, -0.44±2.53%, and -0.71±3.33%. The probabilities of false positive error cases (pGPR < mGPR) were 72%, 60%, 68%, and 56% for each gamma criterion. We developed a deep learning-based prediction model of the 3D detector array-based VMAT QA for prostate cancer, and evaluated the accuracy and tendency of prediction GPR. This model can provide a proactive estimation for the results of the patient-specific QA before the verification measurement.

DNA strand breaks based on Monte Carlo simulation in and around the Lipiodol with flattening filter and flattening filter-free photon beams.

Background: The current study aims to investigate the DNA strand breaks based on the Monte Carlo simulation within and around the Lipiodol with flattening filter (FF) and flattening filter-free (FFF) photon beams. Materials and methods: The dose-mean lineal energy (yD) and DNA single- and double strand breaks (DSB/SSB) based on spatial patterns of inelastic interactions were calculated using the Monte Carlo code: particle and heavy ion transport system (PHITS). The ratios of dose using standard radiation (200 kVX) to the dose of test radiation (FF and FFF of 6 MV X-ray (6MVX) and 10 MVX beams) to produce the same biological effects was defined as RBEDSB. The RBEDSB within the Lipiodol and in the build-up and build-down regions was evaluated. Results: The RBEDSB values with the Lipiodol was larger than that without the Lipiodol at the depth of 4.9 cm by 4.2% and 2.5% for 6 MVX FFF and FF beams, and 3.3% and 2.5% for 10 MVX FFF and FF beams. The RBEDSB values with the Lipiodol was larger than that without the Lipiodol at the depth of 6.5 cm by 2.9% and 2.4% for 6 MVX FFF and FF beams, and 1.9% and 1.4% for 10 MVX FFF and FF beams. In the build-down region at the depth of 8.1 cm, the RBEDSB values with the Lipiodol was smaller than that without the Lipiodol by 4.2% and 2.9% for 6 MVX FFF and FF beams, and 1.4% and 0.1% for 10 MVX FFF and FF beams. Conclusions: The current study simulated the DNA strand break except for the physical dose difference. The lower and FFF beam occurred the higher biological effect.

Development of a radiomics and machine learning model for predicting occult cervical lymph node metastasis in patients with tongue cancer.

OBJECTIVE: We aimed to develop a predictive model for occult cervical lymph node metastasis in patients with tongue cancer using radiomics and machine learning from pretreatment contrast-enhanced computed tomography. STUDY DESIGN: This study included 161 patients with tongue cancer who received local treatment. Computed tomography images were transferred to a radiomics platform. The volume of interest was the total neck node level, including levels Ia, Ib, II, III, and IVa at the ipsilateral side, and each neck node level. The dimensionality of the radiomics features was reduced using least absolute shrinkage and selection operator logistic regression analysis. We compared 5 classifiers with or without the synthetic minority oversampling technique (SMOTE). RESULTS: For the analysis at the total neck node level, random forest with SMOTE was the best model, with an accuracy of 0.85 and an area under the curve score of 0.92. For the analysis at each neck node level, a support vector machine with SMOTE was the best model, with an accuracy of 0.96 and an area under the curve score of 0.98. CONCLUSIONS: Predictive models using radiomics and machine learning have potential as clinical decision support tools in the management of patients with tongue cancer for prediction of occult cervical lymph node metastasis.

Stepwise deep neural network (stepwise-net) for head and neck auto-segmentation on CT images.

OBJECTIVE: The current study aims to propose the auto-segmentation model on CT images of head and neck cancer using a stepwise deep neural network (stepwise-net). MATERIAL AND METHODS: Six normal tissue structures in the head and neck region of 3D CT images: Brainstem, optic nerve, parotid glands (left and right), and submandibular glands (left and right) were segmented with deep learning. In addition to a conventional convolutional neural network (CNN) on U-net, a stepwise neural network (stepwise-network) was developed. The stepwise-network was based on 3D FCN. We designed two networks in the stepwise-network. One is identifying the target region for the segmentation with the low-resolution images. Then, the target region is cropped, which used for the input image for the prediction of the segmentation. These were compared with a clinical used atlas-based segmentation. RESULTS: The DSCs of the stepwise-net was significantly higher than the atlas-based method for all organ at risk structures. Similarly, the JSCs of the stepwise-net was significantly higher than the atlas-based methods for all organ at risk structures. The Hausdorff distance (HD) was significantly smaller than the atlas-based method for all organ at-risk structures. For the comparison of the stepwise-net and U-net, the stepwise-net had a higher DSC and JSC and a smaller HD than the conventional U-net. CONCLUSIONS: We found that the stepwise-network plays a role is superior to conventional U-net-based and atlas-based segmentation. Our proposed model that is a potentially valuable method for improving the efficiency of head and neck radiotherapy treatment planning.

Baseline drift vector of multiple points on body surface using a near-infrared camera.

The purpose of this study was to extract the three-dimensional (3D) vector of the baseline drift baseline drift vector (BDV) of the specific points on the body surface and to demonstrate the importance of the 3D tracking of the body surface. Our system consisted of a near-infrared camera (NIC: Kinect V2) and software that recognized and tracked blue stickers as markers. We acquired 3D coordinates of 30 markers stuck on the body surface for 30 min for eight healthy volunteers and developed a simple technique to extract the BDV. The BDV on the sternum, rib, and abdomen was extracted from the measured data. BDV per min. was analyzed to estimate the frequency to exceed a given tolerance. Also, the correlation among BDVs for multiple body sites was analyzed. The longitudinal baseline drift was observed in the BDV of healthy volunteers. Among the eight volunteers, the maximum probability that the BDV per min. exceeded the tolerance of 1 mm and 2 mm was 30% and 15%, respectively. The correlation among BDVs of multiple body sites suggested a potential feasibility to distinguish the translational movement of the whole area and the respiratory movement. In conclusion, we constructed the 3D tracking system of multiple points on the body surface using a noninvasive NIC at a low cost and established the method to extract the BDV. The existence of the longitudinal baseline drift showed the importance of the 3D tracking in the body surface.

Predicting the Local Response of Esophageal Squamous Cell Carcinoma to Neoadjuvant Chemoradiotherapy by Radiomics with a Machine Learning Method Using 18F-FDG PET Images.

BACKGROUND: This study aimed to propose a machine learning model to predict the local response of resectable locally advanced esophageal squamous cell carcinoma (LA-ESCC) treated by neoadjuvant chemoradiotherapy (NCRT) using pretreatment 18-fluorodeoxyglucose positron emission tomography (FDG PET) images. METHODS: The local responses of 98 patients were categorized into two groups (complete response and noncomplete response). We performed a radiomics analysis using five segmentations created on FDG PET images, resulting in 4250 features per patient. To construct a machine learning model, we used the least absolute shrinkage and selection operator (LASSO) regression to extract radiomics features optimal for the prediction. Then, a prediction model was constructed by using a neural network classifier. The training model was evaluated with 5-fold cross-validation. RESULTS: By the LASSO analysis of the training data, 22 radiomics features were extracted. In the testing data, the average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve score of the five prediction models were 89.6%, 92.7%, 89.5%, and 0.95, respectively. CONCLUSIONS: The proposed machine learning model using radiomics showed promising predictive accuracy of the local response of LA-ESCC treated by NCRT.

The formation process of button ulcers in pigs experimentally infected with a subgenotype 2.1 isolate of classical swine fever virus.

We evaluated the role of classical swine fever virus (CSFV) in the formation of button ulcers in the mucosa of the gastrointestinal tract. Histopathological and immunohistochemical analyses of pigs experimentally infected with a subgenotype 2.1 isolate of CSFV, which was isolated in Japan in 2019, revealed follicular necrosis in the submucosal mucosa-associated lymphoid tissue and herniation of crypts as factors that contribute to the development of button ulcers during CSFV infection. These findings indicate that CSFV induces follicular necrosis and is one of the causative agents of button ulcers in pigs.

Reduction of margin to compensate the respiratory tumor motion by the analysis of dosimetric internal target volume in lung SBRT with nonuniform volume prescription method.

PURPOSE: To develop a dosimetric internal target volume (ITV) margin (DIM) for respiratory motion in lung stereotactic body radiotherapy (SBRT) and to evaluate DIM with a nonuniform volume prescription (NVP) and the point prescription (PP). METHODS: Volumetric modulated arc therapy (VMAT) treatment plans with PP and NVP were created on a heterogeneous programmable respiratory motion phantom, with a tumor (30-mm diameter) inside a cylindrical lung insert. The tumor was defined as the gross tumor volume (GTV), equal to the clinical target volume (CTV). Five-millimeter and 0-mm margins were used for the ITV and setup margins, respectively. The phantom was moved in cranio-caudal direction with a biquadratic sinusoidal waveform with a 4-s cycle and an amplitude of ±5-10 mm. The interplay effect was evaluated by measuring the dose profile with a film in the sagittal plane for different respiratory periods and different initial respiratory phases. DIM was based on the respiratory motion amplitude that satisfied 100% and 95% coverage of the prescribed dose by the minimum dose of the CTV. Moreover, the absolute dose was measured with and without respiratory motion for NVP by a pinpoint chamber. RESULTS: The dose difference in the tumor region due to the interplay effect was within 1.0%. The gamma passing rate was over 95.1% for different respiratory periods and 98.6% for different initial respiratory phases. DIM with PP was almost equivalent to the margin of the respiratory motion. However, DIM with NVP was 2.0 and 1.8 times larger than the margin of the respiratory motion for the 100% and 95% coverage of the prescribed doses, respectively. CONCLUSION: The interplay effects experienced between the MLC sequence and tumor motion were negligible for NVP. The DIM analysis revealed that the margin to compensate the respiratory tumor motion could be reduced by more than 44-50% for NVP in SBRT.

Formulation of objective indices to quantify machine failure risk analysis for interruptions in radiotherapy.

OBJECTIVES: To evaluate the effect of interruption in radiotherapy due to machine failure in patients and medical institutions using machine failure risk analysis (MFRA). MATERIAL AND METHODS: The risk of machine failure during treatment is assigned to three scores (biological effect, B; occurrence, O; and cost of labor and repair parts, C) for each type of machine failure. The biological patient risk (BPR) and the economic institution risk (EIR) are calculated as the product of B and O ( B × O ) and C and O ( C × O ), respectively. The MFRA is performed in two linear accelerators (linacs). RESULT: The multileaf collimator (MLC) fault has the highest BPR and second highest EIR. In particular, TrueBeam has a higher BPR and EIR for MLC failures. The total EIR in TrueBeam was significantly higher than that in Clinac iX. The minor interlock had the second highest BPR, whereas a smaller EIR. Meanwhile, the EIR for the LaserGuard fault was the highest, and that for the monitor chamber fault was the second highest. These machine failures occurred in TrueBeam. The BPR and EIR should be evaluated for each linac. Further, the sensitivity of the BPR, it decreased with higher T 1 / 2 and α/ß values. No relative difference is observed in the BPR for each machine failure when T 1 / 2 and α/ß were varied. CONCLUSION: The risk faced by patients and institutions in machine failure may be reduced using MFRA. ADVANCES IN KNOWLEDGE: For clinical radiotherapy, interruption can occur from unscheduled downtime with machine failures. Interruption causes sublethal damage repair. The current study evaluated the effect of interruption in radiotherapy owing to machine failure on patients and medical institutions using a new method, that is, machine failure risk analysis.

Image synthesis with deep convolutional generative adversarial networks for material decomposition in dual-energy CT from a kilovoltage CT.

Generative Adversarial Networks (GANs) have been widely used and it is expected to use for the clinical examination and image. The objective of the current study was to synthesize material decomposition images of bone-water (bone(water)) and fat-water (fat(water)) reconstructed from dual-energy computed tomography (DECT) using an equivalent kilovoltage-CT (kV-CT) image and a deep conditional GAN. The effective atomic number images were reconstructed using DECT. We used 18,084 images of 28 patients divided into two datasets: the training data for the model included 16,146 images (20 patients) and the test data for evaluation included 1938 images (8 patients). Image prediction frameworks of the equivalent single energy CT images at 120 kVp to the effective atomic number images were created. The image-synthesis framework was based on a CNN with a generator and discriminator. The mean absolute error (MAE), relative mean square error (MSE), relative root mean square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information (MI) were evaluated. The Hounsfield unit (HU) difference between the synthesized and reference material decomposition images of bone(water) and fat(water) were within 5.3 HU and 20.3 HU, respectively. The average MAE, MSE, RMSE, SSIM, and MI of the synthesized and reference material decomposition of the bone(water) images were 0.8, 1.3, 0.9, 0.9, 55.3, and 0.8, respectively. The average MAE, MSE, RMSE, SSIM, and MI of the synthesized and reference material decomposition of the fat(water) images were 0.0, 0.0, 0.1, 0.9, 72.1, and 1.4, respectively. The proposed model can act as a suitable alternative to the existing methods for the reconstruction of material decomposition images of bone(water) and fat(water) reconstructed via DECT from kV-CT.

Evaluation of optimization workflow using custom-made planning through predicted dose distribution for head and neck tumor treatment.

PURPOSE: Lack of a reference dose distribution is one of the challenges in the treatment planning used in volumetric modulated arc therapy because numerous manual processes result from variations in the location and size of a tumor in different cases. In this study, a predicted dose distribution was generated using two independent methods. Treatment planning using the predicted distribution was compared with the clinical value, and its efficacy was evaluated. METHODS: Computed tomography scans of 81 patients with oropharynx or hypopharynx tumors were acquired retrospectively. The predicted dose distributions were determined using a modified filtered back projection (mFBP) and a hierarchically densely connected U-net (HD-Unet). Optimization parameters were extracted from the predicted distribution, and the optimized dose distribution was obtained using a commercial treatment planning system. RESULTS: In the test data from ten patients, significant differences between the mFBP and clinical plan were observed for the maximum dose of the brain stem, spinal cord, and mean dose of the larynx. A significant difference between the dose distributions from the HD-Unet dose and the clinical plan was observed for the mean dose of the left parotid gland. In both cases, the equivalent coverage and flatness of the clinical plan were observed for the tumor target. CONCLUSIONS: The predicted dose distribution was generated using two approaches. In the case of the mFBP approach, no prior learning, such as deep learning, is required; therefore, the accuracy and efficiency of treatment planning will be improved even for sites where sufficient training data are unavailable.

A Single Institution's Experience of Definitive Radiotherapy Using Volumetric-modulated Arc Therapy for Hypopharyngeal Cancers.

BACKGROUND/AIM: The present study aimed to analyze the treatment outcome after definitive radiotherapy (dRT) using volumetric-modulated arc therapy (VMAT) in patients with hypopharyngeal cancer (HPC), including an examination of late toxicities. PATIENTS AND METHODS: A total of 62 patients with HPC, who underwent dRT using VMAT, were analyzed. Overall survival (OS), progression-free survival (PFS), laryngoesophageal dysfunction-free survival (LEDFS), and locoregional control (LRC) were calculated. RESULTS: The median follow-up period was 49 months. The 3- and 5-year OS, PFS, LEDFS, and LRC rates were 77% and 60%, 61% and 56%, 66% and 53%, and both 79%, respectively. Regarding late toxicities, 11 (17.7%) patients developed grade ≥2 late toxicity. Grade 3 dysphagia was observed in 4 (6.5%) patients, and grade 2 xerostomia in 6 (9.7%). CONCLUSION: VMAT was an effective treatment for HPC, with a low incidence of late toxicities.