A deep learning-based method for the prediction of temporal lobe injury in patients with nasopharyngeal carcinoma

PURPOSE

To establish a deep learning-based model to predict radiotherapy-induced temporal lobe injury (TLI).

MATERIALS AND METHODS

Spatial features of dose distribution within the temporal lobe were extracted using both the three-dimensional convolution (C3D) network and the dosiomics method. The Minimal Redundancy-Maximal-Relevance (mRMR) method was employed to rank the extracted features and select the most relevant ones. Four machine learning (ML) classifiers, including logistic regression (LR), k-nearest neighbors (kNN), support vector machines (SVM) and random forest (RF), were used to establish prediction models. Nested sampling and hyperparameter tuning methods were applied to train and validate the prediction models. For comparison, a prediction model base on the conventional D0.5cc of the temporal lobe obtained from dose volume (DV) histogram was established. The area under the receiver operating characteristic (ROC) curve (AUC) was utilized to compare the predictive performance of the different models.

RESULTS

A total of 127 nasopharyngeal carcinoma (NPC) patients were included in the study. In the model based on C3D deep learning features, the highest AUC value of 0.843 was achieved with 5 features. For the dosiomics features model, the highest AUC value of 0.715 was attained with 1 feature. Both of these models demonstrated superior performance compared to the prediction model based on DV parameters, which yielded an AUC of 0.695.

CONCLUSION

The prediction model utilizing C3D deep learning features outperformed models based on dosiomics features or traditional parameters in predicting the onset of TLI. This approach holds promise for predicting radiation-induced toxicities and guide individualized radiotherapy.

[Research progress of macrophage polarization in silicosis fibrosis]

Silicosis is a common occupational disease, and its main characteristic pathological features are the formation of silicon nodules and diffuse pulmonary fibrosis. In the process of silicosis fibrosis, macrophages can be polarized into M1 macrophages and M2 macrophages. M1 macrophages play a pro-inflammatory role in the early stage of silicosis and release a variety of inflammatory factors, which is the core of inflammatory response. M2 macrophages promote inflammation resolution and tissue repair in silicosis fibrosis stage by secreting anti-inflammatory cytokines and pro-fibrotic mediators. M1/M2 polarization balance plays an important role in the occurrence and development of silicosis, and the regulation of macrophage polarization direction may play a positive role in the prevention and treatment of silicosis fibrosis. In this review, the role of macrophage polarization in silicosis fibrosis, the related signaling pathways regulating macrophage polarization in silicosis fibrosis, and the potential therapeutic targets based on macrophage polarization in silicosis fibrosis are reviewed, with a view to further strengthening the understanding of the mechanism of macrophage polarization in the pathogenesis and treatment of silicosis fibrosis.

Molluscicide screening and identification of novel targets against Pomacea canaliculata

BACKGROUND

Owing to the nonavailability of any clear targets for molluscicides against Pomacea canaliculata, target-based screening strategy cannot be employed. In this study, the molluscicidal effects of typical pesticides on P. canaliculata were evaluated to obtain the molluscicide target. A series of arylpyrrole compounds were synthesized based on the discovered target, and their structure-activity relationships explored. A preliminary strategy for screening molluscicides based on specific targets was also developed.

RESULTS

A laboratory colony of P. canaliculata was developed, which showed no difference in sensitivity to niclosamide compared with the wild group, while exhibiting a higher stability against pesticide response. Mitochondrial adenosine triphosphate (ATP) synthase inhibitors and mitochondrial membrane potential uncouplers were identified and validated as potential targets for molluscicide screening against P. canaliculata. A series of arylpyrrole compounds were designed and synthesized. The median lethal concentration of 4-bromo-2-(4-chlorophenyl)-5-(trifluoromethyl)-1H-pyrrole-3-carbonitrile (Compound 102) was 10-fold lower than that of niclosamide.

CONCLUSION

New molluscicide targets were discovered and validated, and preliminary strategies were explored for pesticide screening based on these targets. Compound 102 exhibited a high molluscicidal activity and had a great potential value for exploring a molluscicide to control P. canaliculata. © 2024 Society of Chemical Industry.

A novel loss function to reproduce texture features for deep learning-based MRI-to-CT synthesis

BACKGROUND

Studies on computed tomography (CT) synthesis based on magnetic resonance imaging (MRI) have mainly focused on pixel-wise consistency, but the texture features of regions of interest (ROIs) have not received appropriate attention.

PURPOSE

This study aimed to propose a novel loss function to reproduce texture features of ROIs and pixel-wise consistency for deep learning-based MRI-to-CT synthesis. The method was expected to assist the multi-modality studies for radiomics.

METHODS

The study retrospectively enrolled 127 patients with nasopharyngeal carcinoma. CT and MRI images were collected for each patient, and then rigidly registered as pre-procession. We proposed a gray-level co-occurrence matrix (GLCM)-based loss function to improve the reproducibility of texture features. This novel loss function could be embedded into the present deep learning-based framework for image synthesis. In this study, a typical image synthesis model was selected as the baseline, which contained a Unet trained mean square error (MSE) loss function. We embedded the proposed loss function and designed experiments to supervise different ROIs to prove its effectiveness. The concordance correlation coefficient (CCC) of the GLCM feature was employed to evaluate the reproducibility of GLCM features, which are typical texture features. Besides, we used a publicly available dataset of brain tumors to verify our loss function.

RESULTS

Compared with the baseline, the proposed method improved the pixel-wise image quality metrics (MAE: 107.5 to 106.8 HU; SSIM: 0.9728 to 0.9730). CCC values of the GLCM features in GTVnx were significantly improved from 0.78 ± 0.12 to 0.82 ± 0.11 (p < 0.05 for paired t-test). Generally, > 90% (22/24) of the GLCM-based features were improved compared with the baseline, where the Informational Measure of Correlation feature was improved the most (CCC: 0.74 to 0.83). For the public dataset, the loss function also shows its effectiveness. With our proposed loss function added, the ability to reproduce texture features was improved in the ROIs.

CONCLUSIONS

The proposed method reproduced texture features for MRI-to-CT synthesis, which would benefit radiomics studies based on image multi-modality synthesis.

A grayscale compression method to segment bone structures for 2D-3D registration of setup images in non-coplanar radiotherapy

Purpose. To evaluate the performance of an automated 2D-3D bone registration algorithm incorporating a grayscale compression method for quantifying patient position errors in non-coplanar radiotherapy.Methods. An automated 2D-3D registration incorporating a grayscale compression method to segment bone structures was proposed. Portal images containing only bone structures (Portalbone) and digitally reconstructed radiographs containing only bone structures (DRRbone) were used for registration. First, the portal image was filtered by a high-pass finite impulse response (FIR) filter. Then the grayscale range of the filtered portal image was compressed. Thresholds were determined based on the difference in gray values of bone structures in the filtered and compressed portal image to obtainPortalbone.Another threshold was applied to generateDRRbonewhen the CT image uses the ray-casting algorithm to generate DRR images. The compression performance was assessed by registering theDRRbonewith thePortalboneobtained by compressing the portal image into various grayscale ranges. The proposed registration method was quantitatively and visually validated using (1) a CT image of an anthropomorphic head phantom and its portal images obtained in different poses and (2) CT images and pre-treatment portal images of 20 patients treated with non-coplanar radiotherapy.Results. Mean absolute registration errors for the best compression grayscale range test were 0.642 mm, 0.574 mm, and 0.643 mm, with calculation times of 50.6 min, 42.2 min, and 49.6 min for grayscale ranges of 0-127, 0-63 and 0-31, respectively. For the accuracy validation (1), the mean absolute registration errors for couch angles 0°, 45°, 90°, 270°, and 315° were 0.694 mm, 0.839 mm, 0.726 mm, 0.833 mm, and 0.873 mm, respectively. Among the six transformation parameters, the translation error in the vertical direction contributed the most to the registration errors. Visual inspection of the patient registration results revealed success in every instance.Conclusions. The implemented grayscale compression method successfully enhances and segments bone structures in portal images, allowing for accurate determination of patient setup errors in non-coplanar radiotherapy.

Genetic variation of six specific SNPs of chronic obstructive pulmonary disease among Chinese population

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a chronic bronchitis (or) emphysema with a high disability and fatality rate. This study aimed to explore the correlation between the six selected single nucleotide polymorphisms (SNPs) and the risk of COPD in the Chinese population.

METHODS

The Agena MassARRAY platform was used to select six SNPs from 629 subjects for genotyping. The correlation between SNPs and COPD risk was evaluated using calculated odds ratios (ORs) and 95% confidence intervals (CIs). Multi-factor dimensionality reduction (MDR) was performed to analyze the impact of SNP interactions on COPD risk. Multiple comparisons were performed using Bonferroni- correction.

RESULTS

Our results indicated that rs4719841 and rs7934083 variants were associated with a reduced risk of COPD. The analysis results of age, gender and non-smokers showed that rs4719841 and rs7934083 were associated with reducing the risk of COPD. In addition, the results showed that the genetic models of rs4719841, rs7934083 and rs7780562 were related to the forced vital capacity, respiratory rate per second, and respiratory rate / forced vital capacity of COPD patients, respectively. The results of the MDR analysis showed that the three-locus model (rs4719841, rs7934083, and rs78750958) is the best for COPD risk assessment.

CONCLUSION

This study shows that rs4719841 and rs7934083 are associated with the risk of COPD in the Chinese population, which provides some insights for early screening, prevention, and diagnosis of COPD in high-risk populations.

Novel multileaf collimator designs with tongues and grooves in leaf end and Monte Carlo simulations

In this study, we proposed 2 new multileaf collimator leaf designs to eliminate leaf gaps for closed leaf pairs so that radiation leakage can be avoided. In the new designs, multi tongues and grooves were added to the conventional multileaf collimators leaf ends. Thus, when a pair of leaves closed, tongues of a leaf can enter grooves of its opposing leaf. Consequently, there would be no radiation leakage through closed leaves. One design was named finger-shaped MLC, and another design with doubled leaf end thickness was named hand-shaped MLC. Monte Carlo simulations were performed to simulate dosimetric characteristics of the new MLC designs and comparison to conventional MLCs was performed. The simulations show that for the closed field, the new designs reduce leakage dramatically. And for the open field, the finger-shaped MLC has a larger penumbra width than conventional MLC, while the penumbra for the hand-shaped MLC is comparable to that of conventional MLC. With the application of new MLC designs, it is expected to eliminate leaf gaps for MLC usage and protect normal tissues better.

Pretreatment information-aided automatic segmentation for online magnetic resonance imaging-guided prostate radiotherapy

BACKGROUND

It is necessary to contour regions of interest (ROIs) for online magnetic resonance imaging (MRI)-guided adaptive radiotherapy (MRIgART). These updated contours are used for online replanning to obtain maximum dosimetric benefits. Contouring can be accomplished using deformable image registration (DIR) and deep learning (DL)-based autosegmentation methods. However, these methods may require considerable manual editing and thus prolong treatment time.

PURPOSE

The present study aimed to improve autosegmentation performance by integrating patients' pretreatment information in a DL-based segmentation algorithm. It is expected to improve the efficiency of current MRIgART process.

METHODS

Forty patients with prostate cancer were enrolled retrospectively. The online adaptive MR images, patient-specific planning computed tomography (CT), and contours in CT were used for segmentation. The deformable registration of planning CT and MR images was performed first to obtain a deformable CT and corresponding contours. A novel DL network, which can integrate such patient-specific information (deformable CT and corresponding contours) into the segmentation task of MR images was designed. We performed a four-fold cross-validation for the DL models. The proposed method was compared with DIR and DL methods on segmentation of prostate cancer. The ROIs included the clinical target volume (CTV), bladder, rectum, left femur head, and right femur head. Dosimetric parameters of automatically generated ROIs were evaluated using a clinical treatment planning system.

RESULTS

The proposed method enhanced the segmentation accuracy of conventional procedures. Its mean value of the dice similarity coefficient (93.5%) over the five ROIs was higher than both DIR (87.5%) and DL (87.2%). The number of patients (n = 40) that required major editing using DIR, DL, and our method were 12, 18, and 7 (CTV); 17, 4, and 1 (bladder); 8, 11, and 5 (rectum); 2, 4, and 1 (left femur head); and 3, 7, and 1 (right femur head), respectively. The Spearman rank correlation coefficient of dosimetry parameters between the proposed method and ground truth was 0.972 ± 0.040, higher than that of DIR (0.897 ± 0.098) and DL (0.871 ± 0.134).

CONCLUSION

This study proposed a novel method that integrates patient-specific pretreatment information into DL-based segmentation algorithm. It outperformed baseline methods, thereby improving the efficiency and segmentation accuracy in adaptive radiotherapy.

Is it necessary to perform measurement-based patient-specific quality assurance for online adaptive radiotherapy with Elekta Unity MR-Linac?

This study aimed to investigate the necessity of measurement-based patient-specific quality assurance (PSQA) for online adaptive radiotherapy by analyzing measurement-based PSQA results and calculation-based 3D independent dose verification results with Elekta Unity MR-Linac. There are two workflows for Elekta Unity enabled in the treatment planning system: adapt to position (ATP) and adapt to shape (ATS). ATP plans are those which have relatively slighter shifts from reference plans by adjusting beam shapes or weights, whereas ATS plans are the new plans optimized from the beginning with probable re-contouring targets and organs-at-risk. PSQA gamma passing rates were measured using an MR-compatible ArcCHECK diode array for 78 reference plans and corresponding 208 adaptive plans (129 ATP plans and 79 ATS plans) of Elekta Unity. Subsequently, the relationships between ATP, or ATS plans and reference plans were evaluated separately. The Pearson's r correlation coefficients between ATP or ATS adaptive plans and corresponding reference plans were also characterized using regression analysis. Moreover, the Bland-Altman plot method was used to describe the agreement of PSQA results between ATP or ATS adaptive plans and reference plans. Additionally, Monte Carlo-based independent dose verification software ArcherQA was used to perform secondary dose check for adaptive plans. For ArcCHECK measurements, the average gamma passing rates (ArcCHECK vs. TPS) of PSQA (3%/2 mm criterion) were 99.51% ± 0.88% and 99.43% ± 0.54% for ATP and ATS plans, respectively, which were higher than the corresponding reference plans 99.34% ± 1.04% (p < 0.05) and 99.20% ± 0.71% (p < 0.05), respectively. The Pearson's r correlation coefficients were 0.720 between ATP and reference plans and 0.300 between ATS and reference plans with ArcCHECK, respectively. Furthermore, >95% of data points of differences between both ATP and ATS plans and reference plans were within ±2σ (standard deviation) of the mean difference between adaptive and reference plans with ArcCHECK measurements. With ArcherQA calculation, the average gamma passing rates (ArcherQA vs. TPS) were 98.23% ± 1.64% and 98.15% ± 1.07% for ATP and ATS adaptive plans, separately. It might be unnecessary to perform measurement-based PSQA for both ATP and ATS adaptive plans for Unity if the gamma passing rates of both measurements of corresponding reference plans and independent dose verification of adaptive plans have high gamma passing rates. Periodic machine QA and verification of adaptive plans were recommended to ensure treatment safety.

Determination of internal target volume with Cine-MRI sequence for prostate MRI-Guided radiotherapy

BACKGROUND

In prostate radiotherapy, the intrafractional target motion negatively affects treatment accuracy. Generating internal target volume (ITV) using four-dimensional (4D) images may resolve the issue of intrafractional target motion induced by bladder filling and bowel movement. However, no 4D imaging techniques suitable for the prostate are currently available in clinical practice.

PURPOSE

This study aimed to determine the ITV based on cine magnetic resonance imaging (MRI) sequence for intrafractional target motion management in prostate MRI-guided radiotherapy.

MATERIALS AND METHODS

A reference ITV was generated in simulation process. Then, the reference ITV was adapted with cine MRI sequence before online planning in each fraction. Finally, the reference ITV was updated with the cine MRI sequence acquired during beam delivery after each fraction. Cine MRI sequences and positioning three-dimensional (3D) MRI from 35 patients were retrospectively collected. Clinical target volume (CTV) coverage was computed according to the two-dimensional contour of CTV and ITV on cine MRI images. Relative target size was calculated as the ratio of the volume of ITV and CTV. Isotropic planning target volume (PTV; 5 mm margin) and anisotropic PTV (3 mm margin in the posterior direction and 5 mm margin in other directions) were generated for comparison.

RESULTS

The CTV coverage rate of the proposed ITV had a mean value of 98.61% ± 0.51%, whereas the CTV coverage rates of the isotropic and anisotropic PTVs were 97.43% ± 0.41% and 96.58% ± 0.73%, respectively. The proposed ITV had a relative target size of 1.79 ± 0.17, whereas the anisotropic and isotropic PTVs had relative target sizes of 1.92 ± 0.12 and 2.21 ± 0.19, respectively. For both the CTV coverage rate and target relative size, significant differences were observed between the proposed ITV and the other two PTVs (p < 0.05).

CONCLUSION

The ITV achieved higher CTV coverage with smaller size than conventional isotropic and anisotropic PTVs, indicating that it can effectively deal with the intrafractional movement of the prostate.

Deep learning framework to improve the quality of cone-beam computed tomography for radiotherapy scenarios

BACKGROUND

The application of cone-beam computed tomography (CBCT) in image-guided radiotherapy and adaptive radiotherapy remains limited due to its poor image quality.

PURPOSE

In this study, we aim to develop a deep learning framework to generate high-quality CBCT images for therapeutic applications.

METHODS

The synthetic CT (sCT) generation from the CBCT was proposed using a transformer-based network with a hybrid loss function. The network was trained and validated using the data from 176 patients to produce a general model that can be extensively applied to enhance CBCT images. After the first therapy, each patient can receive paired CBCT/planning CT (pCT) scans, and the obtained data were used to fine-tune the general model for further improvement. For subsequent treatment, a patient-specific, personalized model was made available. In total, 34 patients were examined for general model testing, and another six patients who underwent rescanned pCT scan were used for personalized model training and testing.

RESULTS

The general model decreased the mean absolute error (MAE) from 135 HU to 59 HU as compared to the CBCT. The hybrid loss function demonstrated superior performance in CT number correction and noise/artifacts reduction. The proposed transformer-based network also showed superior power in CT number correction compared to the classical convolutional neural network. The personalized model showed improvement based on the general model in some details, and the MAE was reduced from 59 HU (for the general model) to 57 HU (p < 0.05 Wilcoxon signed-rank test).

CONCLUSION

We established a deep learning framework based on transformer for clinical needs. The deep learning model demonstrated potential for continuous improvement with the help of a suggested personalized training strategy compatible with the clinical workflow.

Comprehensive evaluation of similarity between synthetic and real CT images for nasopharyngeal carcinoma

BACKGROUND

Although magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis studies based on deep learning have significantly progressed, the similarity between synthetic CT (sCT) and real CT (rCT) has only been evaluated in image quality metrics (IQMs). To evaluate the similarity between synthetic CT (sCT) and real CT (rCT) comprehensively, we comprehensively evaluated IQMs and radiomic features for the first time.

METHODS

This study enrolled 127 patients with nasopharyngeal carcinoma who underwent CT and MRI scans. Supervised-learning (Unet) and unsupervised-learning (CycleGAN) methods were applied to build MRI-to-CT synthesis models. The regions of interest (ROIs) included nasopharynx gross tumor volume (GTVnx), brainstem, parotid glands, and temporal lobes. The peak signal-to-noise ratio (PSNR), mean absolute error (MAE), root mean square error (RMSE), and structural similarity (SSIM) were used to evaluate image quality. Additionally, 837 radiomic features were extracted for each ROI, and the correlation was evaluated using the concordance correlation coefficient (CCC).

RESULTS

The MAE, RMSE, SSIM, and PSNR of the body were 91.99, 187.12, 0.97, and 51.15 for Unet and 108.30, 211.63, 0.96, and 49.84 for CycleGAN. For the metrics, Unet was superior to CycleGAN (P < 0.05). For the radiomic features, the percentage of four levels (i.e., excellent, good, moderate, and poor, respectively) were as follows: GTVnx, 8.5%, 14.6%, 26.5%, and 50.4% for Unet and 12.3%, 25%, 38.4%, and 24.4% for CycleGAN; other ROIs, 5.44% ± 3.27%, 5.56% ± 2.92%, 21.38% ± 6.91%, and 67.58% ± 8.96% for Unet and 5.16% ± 1.69%, 3.5% ± 1.52%, 12.68% ± 7.51%, and 78.62% ± 8.57% for CycleGAN.

CONCLUSIONS

Unet-sCT was superior to CycleGAN-sCT for the IQMs. However, neither exhibited absolute superiority in radiomic features, and both were far less similar to rCT. Therefore, further work is required to improve the radiomic similarity for MRI-to-CT synthesis.

TRIAL REGISTRATION

This study was a retrospective study, so it was free from registration.

Efficient segmentation using domain adaptation for MRI-guided and CBCT-guided online adaptive radiotherapy

BACKGROUND

Delineation of regions of interest (ROIs) is important for adaptive radiotherapy (ART) but it is also time consuming and labor intensive.

AIM

This study aims to develop efficient segmentation methods for magnetic resonance imaging-guided ART (MRIgART) and cone-beam computed tomography-guided ART (CBCTgART).

MATERIALS AND METHODS

MRIgART and CBCTgART studies enrolled 242 prostate cancer patients and 530 nasopharyngeal carcinoma patients, respectively. A public dataset of CBCT from 35 pancreatic cancer patients was adopted to test the framework. We designed two domain adaption methods to learn and adapt the features from planning computed tomography (pCT) to MRI or CBCT modalities. The pCT was transformed to synthetic MRI (sMRI) for MRIgART, while CBCT was transformed to synthetic CT (sCT) for CBCTgART. Generalized segmentation models were trained with large popular data in which the inputs were sMRI for MRIgART and pCT for CBCTgART. Finally, the personalized models for each patient were established by fine-tuning the generalized model with the contours on pCT of that patient. The proposed method was compared with deformable image registration (DIR), a regular deep learning (DL) model trained on the same modality (DL-regular), and a generalized model in our framework (DL-generalized).

RESULTS

The proposed method achieved better or comparable performance. For MRIgART of the prostate cancer patients, the mean dice similarity coefficient (DSC) of four ROIs was 87.2%, 83.75%, 85.36%, and 92.20% for the DIR, DL-regular, DL-generalized, and proposed method, respectively. For CBCTgART of the nasopharyngeal carcinoma patients, the mean DSC of two target volumes were 90.81% and 91.18%, 75.17% and 58.30%, for the DIR, DL-regular, DL-generalized, and the proposed method, respectively. For CBCTgART of the pancreatic cancer patients, the mean DSC of two ROIs were 61.94% and 61.44%, 63.94% and 81.56%, for the DIR, DL-regular, DL-generalized, and the proposed method, respectively.

CONCLUSION

The proposed method utilizing personalized modeling improved the segmentation accuracy of ART.

A generalized fit index for evaluating treatment plans of multiple target volumes with different prescribed dose: Generalized dose distribution fit index

BACKGROUND AND PURPOSE

The differential fit index (dFI) and cumulative fit index (cFI) were defined in our previous study to evaluate the fit of isodose surfaces to the target volume. They were only applicable to plans for a single target volume. Therefore, this study aimed to generalize these indices for evaluating plans for multiple target volumes and different prescribed doses.

MATERIALS AND METHODS

dFI was redefined as the ratio of the integral dose of the volume occupied by an isodose surface to that of the union of all target volumes. cFI was defined as the integral of dFI from a certain dose level of interest to the prescribed dose to be evaluated. To evaluate the performance of the generalized fit index, brain metastasis, head and neck, lung cancer, liver cancer, and cervical cancer cases were selected. For each case, a pair of plans was designed, with one plan having a better fitting dose distribution. The dose fit of these plans was investigated using cFI, the dose gradient index (GI), and the conformity index (CI).

RESULTS

In total, 26 pairs of evaluations were performed. The correct evaluation rates for cFI, GI, and CI were 96%, 26.92%, and 92.31%, respectively, illustrating that GI was not valid for evaluating complex plans.

CONCLUSIONS

The generalized fit index proved effective for evaluating the dose fit of plans for multiple target volumes with different prescribed doses.

Prior information guided auto-segmentation of clinical target volume of tumor bed in postoperative breast cancer radiotherapy

BACKGROUND

Accurate delineation of clinical target volume of tumor bed (CTV-TB) is important but it is also challenging due to surgical effects and soft tissue contrast. Recently a few auto-segmentation methods were developed to improve the process. However, those methods had comparatively low segmentation accuracy. In this study the prior information was introduced to aid auto-segmentation of CTV-TB based on a deep-learning model.

METHODS

To aid the delineation of CTV-TB, the tumor contour on preoperative CT was transformed onto postoperative CT via deformable image registration. Both original and transformed tumor contours were used for prior information in training an auto-segmentation model. Then, the CTV-TB contour on postoperative CT was predicted by the model. 110 pairs of preoperative and postoperative CT images were used with a 5-fold cross-validation strategy. The predicted contour was compared with the clinically approved contour for accuracy evaluation using dice similarity coefficient (DSC) and Hausdorff distance.

RESULTS

The average DSC of the deep-learning model with prior information was improved than the one without prior information (0.808 vs. 0.734, P < 0.05). The average DSC of the deep-learning model with prior information was higher than that of the traditional method (0.808 vs. 0.622, P < 0.05).

CONCLUSIONS

The introduction of prior information in deep-learning model can improve segmentation accuracy of CTV-TB. The proposed method provided an effective way to automatically delineate CTV-TB in postoperative breast cancer radiotherapy.

Prediction of Anatomical Presentation during Radiotherapy of Nasopharyngeal Carcinoma Using GAN-LSTM for Plan Adaption Decision

PURPOSE/OBJECTIVE(S)

During treatment, patients frequently undergo anatomical changes that might result in dose degradation. Adaptive radiation therapy (ART) is now available to overcome this problem. However, this method is time-consuming, and the lack of criteria to trigger replanning prevents its widespread use. To overcome these obstacles, anatomical presentation models are necessary. In this study, we developed a novel deep-learning method to make a series of predictions for the remaining treatment course.

MATERIALS/METHODS

Total 230 nasopharyngeal carcinoma patients who received radiotherapy in 33 fractions were enrolled. The data included cone-beam computed tomography (CBCT) and planning CT images. CBCT image quality was improved to CT level using an in-house software. A generative adversarial network-long short-term memory network model was proposed, with the generation abilities of the former network and forecasting abilities of the latter. To predict the anatomical presentation for 3-6 weeks, we trained four models. The planning CT and CBCT acquired earlier were used as the input. Physicians segmented the gross target volume (GTVnx) and parotid glands on prediction and real CBCT (ground truth). Contours, dosimetry parameters, and plan adaptation decision were used to evaluate the models.

RESULTS

The table shows the overall performance of the test set (18 cases). The anatomical changes were predicted over the treatment course with a dice similarity coefficient (DSC) of 0.96, 0.90 and 0.92 and mean distance to agreement (MDA, in mm) of 0.37, 0.70, and 0.60 for GTVnx, left parotid, and right parotid, respectively. Bland-Altman analysis revealed that dosimetry parameters did not show significant difference between prediction and ground truth. The prescription coverage (%) of GTVnx, V30 of the left parotid, and V30 of the right parotid had mean absolute biases of 0.09, 1.09, and 0.27, respectively. At week 6, there were two cases that required plan adaptation, and the model effectively triggered replanning one week in advance.

CONCLUSION

We developed a framework that predicts the anatomical changes occurring in future fractions. Establishing such a framework provides a proactive approach to ART and saves clinical time by anticipating and preparing for treatment strategies in advance.

Tumor Treatment Response Assessed During the Concurrent Chemoradiotherapy for Nasopharyngeal Patients

PURPOSE/OBJECTIVE(S)

To evaluate intratumoral treatment response distribution with using FDG-PET/CT during the chemoradiotherapy of nasopharyngeal patients (NPC).

MATERIALS/METHODS

A total of 5 of 30 patients with stage III-IVA NPC were enrolled in the institutional protocol for induction/concurrent chemoradiotherapy with radiation dose of 70 Gy in 33 fractions. For each patient, a pre-radiation treatment FDG-PET/MRI image (SUV0) and a mid-treatment image (SUVm) at the treatment dose of 31.8 Gy were obtained. Followed by deformable PET/MRI registration between SUV0 and SUVm, the tumor voxel SUV reduction ratio was obtained to construct a tumor dose response matrix (DRM). Tumor SUVavid was also constructed by limiting tumor voxel SUVm > a given value. Spatial correlations of the tumor SUV0, SUVm, SUVavid and DRM were determined.

RESULTS

The mean and coefficient variation (CV) of the SUV0, SUVm and DRM for all tumors were 5.05 (52%), 2.72 (49%) and 0.64 (63%) (Table contains the individual data), which were smaller than those on the SUVs of head-n-neck HPV+ patients reported previously due to the induction chemotherapy, but had much larger DRM mean and CV. The inter-tumoral CVs of SUV0 and DRM were 29% and 27%, which were much lower than those of the intra-tumoral CVs 43% and 57%. Meanwhile, the intra-tumoral variations on SUV0 was smaller than the one of head-neck HPV+ patients, but the DRM intra-variation was much larger. There was a weak correlation between SUV0 and SUVm with the correlation coefficient 0.13, a medium correlation of -0.55 between SUV0 and DRM, but a strong correlation, 0.72, between SUVm and DRM. However, the spatial correlation between tumor DRM and SUVavid was getting weaker as the SUVavid value increasing and equal 0.47 with SUVavid value > 3.

CONCLUSION

The spatial dose response DRM for NPC in the concurrent chemoradiotherapy was relatively high, while had relatively low baseline tumor metabolic activity SUV0. It was most likely due to the induction chemotherapy. In addition, the tumor dose response showed vary large intra-tumoral variation. The high correlations between DRM and SUVm imply that SUVavid could be used partially to guide adaptive modification of NPC treatment with carefully selected boundary value.

Efficacy and Safety of High-Dose Vitamin C Combined with Total Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer (HCCSC R02 Study)

PURPOSE/OBJECTIVE(S)

Forpatients with locally advanced rectal cancer (LARC), the standard treatment is fluoropyrimidine (FU) -based neoadjuvant chemoradiotherapy (NCRT) combined with curative surgery. The CAO/ARO/AIO-04 trial and FORWARC trial reported that the addition of oxaliplatin to FU -based NCRT contributed to improve pathologic complete response (pCR), nevertheless, increased the acute therapeutic toxicity. Some studies showed that vitamin C (VitC) had potential benefits on anti-tumor therapy and anti-inflammatory response. Therefore, we conducted this HCCSC R02 study to explore the efficacy and safety of adding a high-dose intravenous VitC to mFOLFOX6/XELOX -based NCRT in LARC.

MATERIALS/METHODS

HCCSCR02 study was designed as a prospective, single-center phase II trial, which including pts aged 18-75 years with stage II/III rectal adenocarcinoma, distance from anus ≤12cm. The enrollment criteria included: staged with MRI as cT3/cT4 or cN1/2, or mesorectal fascia involvement (MRF+), or difficult to preserve the anus. Patients with glucose-6-phosphate dehydrogenase enzyme(G6PD) deficiency were excluded. Pelvic intensity modulated radiation therapy (IMRT) was given in 45-50.4Gy/25-28 fractions. Concurrently, two cycles of chemotherapy (mFOLFOX6 or XELOX) were administered during IMRT, as well as intravenous VitC (24g) delivered daily after the end of each radiation therapy. Additional 2-3 cycles of mFOLFOX6 / XELOX were adopted between the completion of radiotherapy and surgery. The primary endpoint was pCR rate. The secondary endpoints included radiation-related toxicities, overall survival (OS) and disease-free survival (DFS). This study is still recruiting.

RESULTS

From May 15, 2021 to Feb 8, 2023, 19 pts were recruited and finished all the scheduled NCRT, of which the proportion of cT4, cT3, cN2, cN1 were 31.6%, 63.2%, 52.6%, 36.8%, respectively. In addition, 10 pts (52.6%) were diagnosed as MRF+ initially, and 8 pts (42.1%) had a lower primary tumor(≤5cm) who were considered difficult for anal preservation before NCRT. All subjects enrolled were confirmed to be proficient mismatch repair (pMMR). As a result, 18 pts underwent a total mesorectal excision (TME) all with R0-resection, and 8 pts were evaluated as pCR (44.4%, 8/18, confidence interval: 0.246-0.663), 11 as major pathological response rate (MPR) (61.6%, 11/18), respectively. The anus preservation rate in patients with lower diseases was 87.5% (7/8). One case accepted a watch-and-wait strategy because of clinical complete response (cCR). Overall, grade 3 toxicities were observed in 4 pts, including 3 leucopenia (15.8%, 3/19), 2 neutropenia (10.5%, 2/19) and 1 diarrhea (5.3%, 1/19). No grade 4 adverse event was observed.

CONCLUSION

The addition of high-dose VitC to the mFOLFOX6/XELOX-based NCRT in LARC showed a promising pCR, well tolerance, particularly low rate of diarrhea, thus warrants further investigation.

CLINICAL TRIAL INFORMATION

NCT04801511.

More Efficient Auto-Segmentation Framework Using Patient-Specific Information for CBCT-Guided Adaptive Radiotherapy

PURPOSE/OBJECTIVE(S)

Accurate and fast delineation of regions of interest (ROIs) is critical for online adaptive radiotherapy (ART). Due to the noise, artifacts and low contrast of soft tissue on cone-beam computed tomography (CBCT), the CBCT-guided ART (CBCTgART) needs an effective tool to assist the segmentation of ROIs. This study aims to develop an efficient auto-segmentation workflow with personalized modeling for CBCTgART based on deep learning (DL) model.

MATERIALS/METHODS

Five hundred fifty-two patients with nasopharyngeal carcinoma were enrolled in this study. At the beginning, a cycle consistent adversarial network (CycleGAN) model was trained on 172 patients' CBCT/CT paired images to improve the image quality of CBCT to CT level. A generalized segmentation model was trained using the planning CT (pCT) and contour data from 530 patients. For personalized modeling, the generalized segmentation model was fine-tuned on the specific patient's pCT and contour to get the personalized model. When CBCT images were available, the trained CycleGAN model transformed the CBCT to synthetic pCT. Then the personalized auto-segmentation model generated the contour of ROIs on the synthetic pCT. We randomly selected 22 patients for model test. The proposed method (DL-personalized) was compared with other DL methods based on the same architecture of network: regular deep learning method (DL-regular), which was trained on the CBCT and corresponding contours, and generalized model in our framework (DL-generalized). So, 22 personalized, 1 generalized and 1 regular DL models were tested. The paired t-test was performed to test the significance for mean dice similarity coefficient (DSC), mean distance to agreement (MDA), and Hausdorff distance (HD) between the alternative and proposed methods.

RESULTS

Two ROIs were included: the clinical target volume (CTV) and nasopharynx gross tumor volume (GTVnx). The proposed DL-personalized model achieved better results compared with others as shown in the table. The accuracy and robustness of our proposed framework was reliable. All of p values were under 0.01, which indicated the statistically significant difference.

CONCLUSION

The proposed framework utilizing patient-specific information can improve the segmentation accuracy of ART. This method has potential to be integrated into the ART workflow to improve efficiency.

Enhanced prediction of postoperative radiotherapy-induced esophagitis in non-small cell lung cancer: Dosiomic model development in a real-world cohort and validation in the PORT-C randomized controlled trial

BACKGROUND

Radiotherapy-induced esophagitis (RE) diminishes the quality of life and interrupts treatment in patients with non-small cell lung cancer (NSCLC) undergoing postoperative radiotherapy. Dosimetric models showed limited capability in predicting RE. We aimed to develop dosiomic models to predict RE.

METHODS

Models were trained with a real-world cohort and validated with PORT-C randomized controlled trial cohort. Patients with NSCLC undergoing resection followed by postoperative radiotherapy between 2004 and 2015 were enrolled. The endpoint was grade ≥2 RE. Esophageal three-dimensional dose distribution features were extracted using handcrafted and convolutional neural network (CNN) methods, screened using an entropy-based method, and selected using minimum redundancy and maximum relevance. Prediction models were built using logistic regression. The areas under the receiver operating characteristic curve (AUC) and precision-recall curve were used to evaluate prediction model performance. A dosimetric model was built for comparison.

RESULTS

A total of 190 and 103 patients were enrolled in the training and validation sets, respectively. Using handcrafted and CNN methods, 107 and 4096 features were derived, respectively. Three handcrafted, four CNN-extracted and three dosimetric features were selected. AUCs of training and validation sets were 0.737 and 0.655 for the dosimetric features, 0.730 and 0.724 for handcrafted features, and 0.812 and 0.785 for CNN-extracted features, respectively. Precision-recall curves revealed that CNN-extracted features outperformed dosimetric and handcrafted features.

CONCLUSIONS

Prediction models may identify patients at high risk of developing RE. Dosiomic models outperformed the dosimetric-feature model in predicting RE. CNN-extracted features were more predictive but less interpretable than handcrafted features.

Effect of Segmental Abutting Esophagus-Sparing Technique to Reduce Severe Esophagitis in Limited-Stage Small-Cell Lung Cancer Patients Treated with Concurrent Hypofractionated Thoracic Radiation and Chemotherapy

PURPOSE/OBJECTIVE(S)

To evaluate the effect of segmental abutting esophagus-sparing (SAES) radiotherapy to reduce severe (G3+) acute esophagitis from 20% to 5% in patients with limited-stage small cell lung cancer (LS-SCLC) treated with concurrent chemoradiotherapy.

MATERIALS/METHODS

Patients with a clinical target volume (CTV) ≤1 cm close to the esophagus were enrolled in the experimental arm (45 Gy in 3 Gy daily fractions in 3 weeks) of an ongoing phase III randomized clinical trial (NCT02675088), which enrolled patients with histologically confirmed SCLC and clinically staged as LS or I-IIIB (AJCC 7th). This trial was designed to determine whether HYPO TRT (45 Gy in 3 Gy QD, experimental arm) has the same efficacy as CF TRT (60 Gy in 2 Gy QD, controlled arm) in patients with LS-SCLC. The whole esophagus was divided into the involved esophagus and abutting esophagus (AE) to receive different dose limitations according to the distance from the edge of the CTV. The primary endpoint was grade ≥ 3 acute esophagitis.

RESULTS

From 1 May 2021 to 30 April 2022, 30 patients were enrolled and completed four cycles of planned chemotherapy and radiotherapy. Our patient population was predominantly male (66.7% men vs. 33.3% women), with a median age of 62 years. A majority of patients presented with Stage N2-3 (90.0%) and T2-4 (76.7%), in which 4 patients had ultracentral-located primary tumors. With the SAES technique, all dosimetric parameters were significantly reduced for the whole esophagus and AE. The maximal and mean dose of the esophagus (47.4±1.9 Gy and 13.5 ± 5.8 Gy, respectively) and AE (42.9±2.3 Gy and 8.6 ± 3.6 Gy, respectively) in the SAES plan were significantly lower than those (esophagus 48.0±1.9 Gy and 14.7± 6.1 Gy, AE 45.1±2.4 Gy and 9.8± 4.2 Gy, respectively) in the non-SAES plan. After the follow-up of more than 7 months (range, 7.0-18.1 months) for all patients, only one patient (3.3%, 95% CI 0.1%-17.2%) experienced grade 3 acute esophagitis and no grade 4-5 acute esophagitis happened (Table 3). For late toxicities, one patient suffered sustained grade 1 late esophagitis and all others had no symptoms of esophagitis. The rate of radiation pneumonitis was very low, with one grade 3 event and no grade 4-5 event. Twelve (40.0%) patients had G3+ hematologic toxic events, including 2 patients with febrile neutropenia. The 1-year OS, LRFS, DMFS and PFS was 96.4%, 88.7%, 78.4% and 64.3%, respectively. No patient developed local recurrence in the abutting esophagus-sparing region.

CONCLUSION

SAES radiotherapy has significant dosimetric advantages compared with standard radiotherapy, which are successfully translated into clinical benefits for patients with LS-SCLC treated with 45 Gy in 3 Gy daily fractions. This may facilitate dose escalation for TRT in LS-SCLC patients.

Tumor Treatment Response Assessed During the Chemo-Radiotherapy for Locally Advanced NSCLC

PURPOSE/OBJECTIVE(S)

To evaluate the capability of assessing intratumoral treatment response distribution with using FDG-PET/CT during the chemoradiotherapy of locally advanced NSCLC.

MATERIALS/METHODS

Twelve of total 50 patients with stage III NSCLC were enrolled in the institutional protocol for concurrent chemoradiotherapy with treatment dose of 54-60 Gy in 27-30 fractions. For each patient, a pre-treatment FDG-PET/CT image (SUV0) and a mid-treatment image (SUVm) obtained within the treatment dose of 24 ∼ 46 Gy were obtained. Followed by deformable PET/CT registration between SUV0 and SUVm, the tumor voxel SUV reduction ratio was obtained to construct a tumor dose response matrix (DRM). Tumor SUVavid was also constructed by limiting tumor voxel SUVm > a given value. Spatial correlations of the tumor SUV0, SUVm, SUVavid and DRM were determined.

RESULTS

The mean and coefficient variation (CV) of the SUV0, SUVm and DRM for all tumors were 6.56(64%), 2.82(59%) and 0.52(70%) (Table contains the individual data), which were like those on the SUVs and the mean DRM of head-neck HPV- patients reported previously, but much larger on the DRM variation. The inter-tumoral CVs of SUV0 and DRM were 17% and 43%, which were much smaller than those of the intra-tumoral CVs 61% and 55%. Meanwhile, the intra-tumoral variations on both SUV0 and DRM were much larger than those of head-neck HPV- patients. There was a weak correlation between SUV0 and SUVm with the correlation coefficient 0.32, a medium correlation of -0.51 between SUV0 and DRM; 0.58 between SUVm and DRM. It implies that the rule of tumor dose response DRM on treatment modification decision cannot be fully replaced by either SUV0 or SUVm. The spatial correlation between tumor DRM and SUVavid was 0.23 with SUVavid value > 3, which was getting weaker when increasing SUVavid value.

CONCLUSION

Spatial dose response for NSCLC assessed using FDG-PET/CT feedback demonstrated high treatment resistant patterns, which had a large intra-tumoral variation. In addition, the medium correlations of DRM vs SUV0 and DRM vs SUVm imply that all these factors could be used to guide adaptive modification of NSCLC treatment.

Ten-Year Outcomes of Hypofractionated (45 Gy in 9 Fractions) Intensity Modulated Radiotherapy for Localized Prostate Cancer

PURPOSE/OBJECTIVE(S)

We reported 10-year outcomes of localized prostate cancers treated with hypofractionated intensity-modulated radiotherapy of 45 Gy in 9 consecutive fractions.

MATERIALS/METHODS

From October 2011 to April 2017, thirty patients with localized prostate cancer were enrolled in this prospective trial. The median age of the patients was 72.5 years. According to NCCN recurrence risk criteria, eight patients were at low-risk group, 17 at intermediate risk group, 5 at high-risk group. All patients were treated with hypofractionated intensity-modulated radiotherapy (IMRT) of 45 Gy in 9 consecutive fractions to their prostate with or without seminal vesicles. Before radiotherapy, three gold fiducials were implanted into the prostate. In order to reduce the rectal high dose irradiation volume, an inflated rectal balloon was placed in the rectum at simulation and every treatment and patients were treated with comfortable full bladder. Static Intensity-modulated radiotherapy (SIMRT) was applied in 1 patient, Volumetric Modulated Arc Therapy (VMAT) in 27 patients, and tomotherapy in 2 patients. Image guided radiotherapy (IGRT) with gold fiducial registration was adopted. Twenty-six patients also received androgen deprivation therapy (ADT). The median time of ADT was 6 months. Progression⁃free survival (PFS) and overall survival (OS) were analyzed using Kaplan-Meier analysis. All grade ≥1 genitourinary (GU) and gastrointestinal (GI) toxicities were recorded using Common Terminology Criteria for Adverse Event version 5.0 (CTCAE 5.0) and Radiation Therapy Oncology Group (RTOG) late morbidity criteria, and GU and GI toxicities were cumulatively calculated.

RESULTS

After a median follow-up of 102 months (65∼131 months), the 10-year OS was 90.0% (95% confidence interval, 83.3%-96.7%), and the 10-year PFS was 86.5% (95% confidence interval, 79.1%-93.9%). According to CTCAE 5.0, grade 1 acute gastrointestinal (GI) toxicity developed in 12 patients, grade 2 in 2 patients, grade 3 in 2 patients, and grade 1 acute genitourinary (GU) toxicity developed in 12 patients, grade 2 in 2 patients, and no grade 3 or higher toxicity occurred. According to RTOG late morbidity criteria, late (≥3 months after radiotherapy) grade 1 GI toxicity developed in 4 patients (13.3%), grade 2 in 1 (3.3%), grade 3 in 1 (3.3%), and late grade 1 GU toxicity occurred in 1 patient (3.3%), grade 2 in 1 (3.3%), grade 3 in 1 (3.3%). No grade 4 or higher GI and GU toxicities developed. Only one grade 3 GI and one grade 2 GU toxicities were observed for the maximum toxicity at the last follow-up. The potency was not evaluated.

CONCLUSION

The 10-year oncologic outcomes of this shortened hypofractionated IMRT regimen for mainly low/intermediate risk prostate cancer patients is favorable with acceptable acute and late toxicities.

[Evaluation of the determination of hydrogen sulfide in the air of workplace by the detection tube method]

Objective: To evaluate the accuracy and applicability of detection tube method for quantitative detection of hydrogen sulfide in workplace air. Methods: In September 2021, the lower limit of quantification, accuracy, precision, environmental factors, interfering gases and other performance indicators of the method for determining hydrogen sulfide in the air of workplace were verified by the detection tube, and the results were compared with those of GB 11742-89 "Standard method for hygienic examination of hydrogen sulfide in air of residential areas-methylene blue spectrophotometric method" to evaluate the application effect of the detection tube method for quantitative detection of hydrogen sulfide in workplace air. Results: There was no significant difference in the results of 2.83 mg/m(3), 4.25 mg/m(3) and 17.00 mg/m(3) hydrogen sulfide concentration between the two methods (P>0.05) , but there was significant difference in the results of 8.50 mg/m(3) concentration (P<0.05) . The lower limit of quantification of hydrogen sulfide in workplace air was 2.83 mg/m(3), the accuracy was 96.0%-111.0%, and the precision was 0.70%-6.64%. Under the condition of 4 ℃, the measured results decreased by 3.39%-13.10%. When the humidity was 50%-80%, the relative error of the average measured value was -1.67%-4.44%. Interference gases that may exist in the workplace (including carbon dioxide, carbon monoxide, mercaptans, nitrogen oxides, sulfur dioxide, etc.) did not interfere with the results of the test tube. Conclusion: The accuracy and precision of the detection tube method meet the detection requirements. The method is simple, rapid and easy to be popularized, and can be used for the rapid detection of hydrogen sulfide gas concentration in the workplace.

On-board MRI image compression using video encoder for MR-guided radiotherapy

Background

Magnetic resonance imaging (MRI) is currently used for online target monitoring and plan adaptation in modern image-guided radiotherapy. However, storing a large amount of data accumulated during patient treatment becomes an issue. In this study, the feasibility to compress MRI images accumulated in MR-guided radiotherapy using video encoders was investigated.

Methods

Two sorting algorithms were employed to reorder the slices in multiple MRI sets for the input sequence of video encoder. Three cropping algorithms were used to auto-segment regions of interest for separate data storage. Four video encoders, motion-JPEG (M-JPEG), MPEG-4 (MP4), Advanced Video Coding (AVC or H.264) and High Efficiency Video Coding (HEVC or H.265) were investigated. The compression performance of video encoders was evaluated by compression ratio and time, while the restoration accuracy of video encoders was evaluated by mean square error (MSE), peak signal-to-noise ratio (PSNR), and video quality matrix (VQM). The performances of all combinations of video encoders, sorting methods, and cropping algorithms were investigated and their effects were statistically analyzed.

Results

The compression ratios of MP4, H.264 and H.265 with both sorting methods were improved by 26% and 5%, 42% and 27%, 72% and 43%, respectively, comparing to those of M-JPEG. The slice-prioritized sorting method showed a higher compression ratio than that of the location-prioritized sorting method for MP4 (P=0.00000), H.264 (P=0.00012) and H.265 (P=0.00000), respectively. The compression ratios of H.265 were improved significantly with the applications of morphology algorithm (P=0.01890 and P=0.00530), flood-fill algorithm (P=0.00510 and P=0.00020) and level-set algorithm (P=0.02800 and P=0.00830) for both sorting methods. Among the four video encoders, H.265 showed the best compression ratio and restoration accuracy.

Conclusions

The compression ratio and restoration accuracy of video encoders using inter-frame coding (MP4, H.264 and H.265) were higher than that of video encoders using intra-frame coding (M-JPEG). It is feasible to implement video encoders using inter-frame coding for high-performance MRI data storage in MR-guided radiotherapy.

A projection-domain correction method in CBCT reconstruction for head and neck radiotherapy using cycle-GAN and nonlocal means filter

BACKGROUND

In radiation treatments for head and neck tumors, cone-beam computed tomography (CBCT) is employed for patient positioning and dose calculation of adaptive radiotherapy. However, the quality of CBCT is degraded by the scatter and noise, majorly impacting the accuracy of patient positioning and dose calculation.

PURPOSE

To improve the quality of CBCT for patients with head and neck cancer, a projection-domain CBCT correction method was proposed using a cycle-consistent generative adversarial network (cycle-GAN) and a nonlocal means filter (NLMF) based on a reference digitally reconstructed radiograph (DRR).

METHODS

A cycle-GAN was initially trained to learn mapping from CBCT projections to a DRR using the data obtained from 30 patients. For each patient, 671 CBCT projections were measured for CBCT reconstruction. Moreover, 360 Digital Reconstructed Radiographs (DRR) were computed from each patient's planning computed tomography (CT), whose projection angles ranged from 0° to 359° with an interval of 1°. By applying the trained generator of the cycle-GAN to the unseen CBCT projection, a synthetic DRR with considerably less scatter was obtained. However, annular artifacts were observed in the CBCT reconstructed with synthetic DRR. To address this issue, a NLMF based on reference DRR was used to further correct the synthetic DRR, which corrected the synthetic DRR using the calculated DRR as a reference image. Finally, the CBCT with no annular artifact and little noise was reconstructed with the corrected synthetic DRR. The proposed method was tested using the data of six patients. The corrected synthetic DRR and CBCT were compared with the corresponding real DRR and CT images. The structural preservation ability of the proposed method was evaluated using the Dice coefficients of the automatically extracted nasal cavity. Moreover, the image quality of CBCT corrected with the proposed method was objectively assessed with an five-point human scoring system and compared with CT, original CBCT and CBCT corrected with other strategies.

RESULTS

The mean absolute value (MAE) of the relative error between the corrected synthetic and real DRR was <8%. The MAE between the corrected CBCT and corresponding CT was <30 HU. Moreover, the Dice coefficient of nasal cavity between the corrected CBCT image and the original image exceeded 98.8 for all the patients. Last but not least, the objective assessment of image quality showed the proposed method had an average score of 4.2 in overall image quality, which was higher than that of the original CBCT, CBCT reconstructed with synthetic DRR, and CBCT reconstructed with projections filtered with NLMF only.

CONCLUSIONS

The proposed method can considerably improve the CBCT image quality with little anatomical distortion, improving the accuracy of radiotherapy for head and neck patients.

[Development of Practical Proton Therapy System Based on Clinical Needs]

In recent years, proton therapy technology has developed rapidly, and the number of patients treated with proton therapy has gradually increased. However, the application of proton therapy technology was far from practical needs. Because of the shortage of resources and the high cost, proton therapy systems are not accessible and affordable for most patients. In order to change this situation, it is necessary to develop a new truly practical proton therapy system based on clinical needs. Conceptual design of a practical proton therapy system was proposed. Compared with the existing system, one feature of the newly designed system is to reduce the maximum energy of the proton beam to 175~200 MeV; another feature is the configuration of deluxe and economical treatment rooms, the deluxe room is equipped with a rotating gantry and a six-dimensional treatment bed, and the economical room is equipped with a horizontal fixed beam and a patient vertical rotating setup device. This design can not only reduce the cost of proton therapy system and equipment room construction, but also facilitate the hospital to choose the appropriate configuration, which will ultimately benefit more patients.

Improving accelerated 3D imaging in MRI-guided radiotherapy for prostate cancer using a deep learning method

PURPOSE

This study was to improve image quality for high-speed MR imaging using a deep learning method for online adaptive radiotherapy in prostate cancer. We then evaluated its benefits on image registration.

METHODS

Sixty pairs of 1.5 T MR images acquired with an MR-linac were enrolled. The data included low-speed, high-quality (LSHQ), and high-speed low-quality (HSLQ) MR images. We proposed a CycleGAN, which is based on the data augmentation technique, to learn the mapping between the HSLQ and LSHQ images and then generate synthetic LSHQ (synLSHQ) images from the HSLQ images. Five-fold cross-validation was employed to test the CycleGAN model. The normalized mean absolute error (nMAE), peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), and edge keeping index (EKI) were calculated to determine image quality. The Jacobian determinant value (JDV), Dice similarity coefficient (DSC), and mean distance to agreement (MDA) were used to analyze deformable registration.

RESULTS

Compared with the LSHQ, the proposed synLSHQ achieved comparable image quality and reduced imaging time by ~ 66%. Compared with the HSLQ, the synLSHQ had better image quality with improvement of 57%, 3.4%, 26.9%, and 3.6% for nMAE, SSIM, PSNR, and EKI, respectively. Furthermore, the synLSHQ enhanced registration accuracy with a superior mean JDV (6%) and preferable DSC and MDA values compared with HSLQ.

CONCLUSION

The proposed method can generate high-quality images from high-speed scanning sequences. As a result, it shows potential to shorten the scan time while ensuring the accuracy of radiotherapy.

Role of interferon alpha-inducible protein 6 in modulating the proliferation, apoptosis and senescence of oesophageal squamous cell carcinoma cells

Oesophageal cancer is one of the most malignant tumors worldwide. Dysfunction of interferon alpha-inducible protein 6 (IFI6) has been implicated in numerous human diseases, including cancer. We performed the study to investigate the function and potential molecular pathways of IFI6 in oesophageal squamous cell carcinoma (ESCC) cells. IFI6 expression was analysed using databases-derived data and paraffin-embedded tissue samples. CCK-8-based analyses and EdU staining, colony formation, β-galactosidase staining and Annexin V/PI double-staining assays were used to determine the influence of IFI6 on cell growth, senescence and apoptosis. Tumor growth in vivo was investigated in mouse xenograft models. RNA sequencing (RNA-seq) was performed to identify the transcripts and pathways affected by IFI6. The results showed that IFI6 expression was elevated in ESCC and correlated with poor clinical prognosis (P<0.05). IFI6 was overexpressed and silenced in TE-1 and TE-10 cells using lentiviruses. Upregulation of IFI6 promoted cell growth both in vitro and in vivo, whereas downregulation induced opposite effects. IFI6 overexpression inhibited cell senescence and apoptosis but did not influence cell cycle progression, while IFI6 downregulation increased cell senescence and apoptosis. RNA-seq revealed that 3 mRNAs (EPHA5, CLIP1 and GTF2F2) were consistently associated with both IFI6 overexpression and silencing. IFI6 appeared to modulate TE-1 cells via complex mechanisms. In conclusion, IFI6 plays a positive role in the proliferation of ESCC cells both in vitro and in vivo, which could be a novel therapeutic target for treating ESCC.

A subregion-based prediction model for local-regional recurrence risk in head and neck squamous cell carcinoma

BACKGROUND AND PURPOSE

Given that the intratumoral heterogeneity of head and neck squamous cell carcinoma may be related to the local control rate of radiotherapy, the aim of this study was to construct a subregion-based model that can predict the risk of local-regional recurrence, and to quantitatively assess the relative contribution of subregions.

MATERIALS AND METHODS

The CT images, PET images, dose images and GTVs of 228 patients with head and neck squamous cell carcinoma from four different institutions of the The Cancer Imaging Archive(TCIA) were included in the study. Using a supervoxel segmentation algorithm called maskSLIC to generate individual-level subregions. After extracting 1781 radiomics and 1767 dosiomics features from subregions, an attention-based multiple instance risk prediction model (MIR) was established. The GTV model was developed based on the whole tumour area and was used to compare the prediction performance with the MIR model. Furthermore, the MIR-Clinical model was constructed by integrating the MIR model with clinical factors. Subregional analysis was carried out through the Wilcoxon test to find the differential radiomic features between the highest and lowest weighted subregions.

RESULTS

Compared with the GTV model, the C-index of MIR model was significantly increased from 0.624 to 0.721(Wilcoxon test, p value< 0.0001). When MIR model was combined with clinical factors, the C-index was further increased to 0.766. Subregional analysis showed that for LR patients, the top three differential radiomic features between the highest and lowest weighted subregions were GLRLM_ShortRunHighGrayLevelEmphasis, GRLM_HghGrayLevelRunEmphasis and GLRLM_LongRunHighGrayLevelEmphasis.

CONCLUSION

This study developed a subregion-based model that can predict the risk of local-regional recurrence and quantitatively assess relevant subregions, which may provide technical support for the precision radiotherapy in head and neck squamous cell carcinoma.

Determining the quality control frequency of an MR-linac using risk matrix (RM) analysis

PURPOSE

Quality control (QC) is performed routinely through professional guidelines. However, the recommended QC frequency may not be optimal among different institutional settings. Here we propose a novel method for determining the optimal QC frequency using risk matrix (RM) analysis.

METHODS AND MATERIALS

A newly installed Magnetic Resonance linac (MR-linac) was chosen as the testing platform and six routine QC items were investigated. Failures of these QC items can adversely affect treatment outcome for the patient. Accordingly, each QC item with its assigned frequency forms a unique failure mode (FM). Using FM-effect analysis (FMEA), the severity (S), occurrence (O), and detection (D) of each FM was obtained. Next, S and D based on RM was used to determine the appropriate QC frequency. Finally, the performance of new frequency for each QC item was evaluated using the metric E = O/D.

RESULTS

One new QC frequency was the same as the old frequency, two new QC frequencies were less than the old ones, and three new QC frequencies were higher than the old ones. For six QC items, E values at the new frequencies were not less than their values at the old frequencies. This indicates that the risk of machine failure is reduced at the new QC frequencies.

CONCLUSIONS

The application of RM analysis provides a useful tool for determining the optimal frequencies for routine linac QC. This study demonstrated that linac QC can be performed in a way that maintains high performance of the treatment machine in a radiotherapy clinic.

A method for adjusting MLC leaf positions outside a reference region for VMAT plans in a commercial treatment planning system

Multileaf collimators (MLC) leaf positions in a volumetric modulated arc therapy (VMAT) plan is determined from inverse treatment planning process. On the isocenter plane, leaf projections should not be set too far out of projected PTV boundary. In this study we developed an automatic method of adjusting leaf positions outside a reference region of interest (ROI) for VMAT plans generated in Pinnacle treatment planning system (TPS). The proposed method consisted of a Pinnacle script and a Python program. It checked each MLC leaf position for all control points in a VMAT arc relative to a reference ROI created by adding a small margin to PTV. For leaves opened outside the reference ROI, the method adjusted their positions to reduce dose to normal tissue while maintaining PTV coverage and satisfying leaf position constraints. The deliverability and dose accuracy of the method was verified by applying it to 15 VMAT plans which were delivered in five different linacs in our department. Dosimetric improvement of the proposed method was analyzed for another group of 16 randomly selected VMAT plans. The average gamma passing rate using a 3%/3 mm criteria for the verification group of VMAT plans was 98.3% and all passing rates were above our internal passing threshold. Dosimetric improvement was observed for the evaluation group of VMAT plans. The method can improve normal tissue protection for VMAT plans. It can be safely applied in routine clinic work.

Combining autoencoder with clustering analysis for anomaly detection in radiotherapy plans

Background

To develop an unsupervised anomaly detection method to identify suspicious error-prone treatment plans in radiotherapy.

Methods

A total of 577 treatment plans of breast cancer patients were used in this study. They were labeled as either normal or abnormal plans by experienced clinicians. Multiple features of each plan were extracted and selected by the learning algorithms. The training set consisted of feature samples from 400 normal plans and the testing set consisted of feature samples from 158 normal plans and 19 abnormal plans. Using the k-means clustering algorithm in the training stage, 4 normal plan clusters were formed. The distance between the samples in the testing set and the cluster centers were then determined. To evaluate the effect of dimensionality reduction (DR) on detection accuracy, principal component analysis (PCA) and autoencoder (AE) methods were compared.

Results

The sensitivity of the anomaly detection model based on PCA and AE methods were 84.2% (16/19) and 94.7% (18/19), respectively. The specificity of the anomaly detection model based on PCA and AE methods were 64.6% (102/158) and 69.0% (109/158), respectively. The areas under the receiver operating characteristic (ROC) curve (AUCs) based on PCA and AE methods were 0.81 and 0.90, respectively.

Conclusions

The unsupervised learning method was effective for detecting anomalies from the feature samples. Accuracy could be improved with the introduction of AE-based DR technique. The combination of AE and k-means clustering methods provides an automated way to identify abnormal plans among clinical treatment plans in radiotherapy.

Anomaly detection in radiotherapy plans using deep autoencoder networks

Purpose

Treatment plans are used for patients under radiotherapy in clinics. Before execution, these plans are checked for safety and quality by human experts. A few of them were identified with flaws and needed further improvement. To automate this checking process, an unsupervised learning method based on an autoencoder was proposed.

Methods

First, features were extracted from the treatment plan by human experts. Then, these features were assembled and used for model learning. After network optimization, a reconstruction error between the predicted and target signals was obtained. Finally, the questionable plans were identified based on the value of the reconstruction error. A large value of the reconstruction error indicates a longer distance from the standard distribution of normal plans. A total of 576 treatment plans for breast cancer patients were used for the test. Among them, 19 were questionable plans identified by human experts. To evaluate the performance of the autoencoder, it was compared with four baseline detection algorithms, namely, local outlier factor (LOF), hierarchical density-based spatial clustering of applications with noise (HDBSCAN), one-class support vector machine (OC-SVM), and principal component analysis (PCA).

Results

The results showed that the autoencoder achieved the best performance than the other four baseline algorithms. The AUC value of the autoencoder was 0.9985, while the second one was 0.9535 (LOF). While maintaining 100% recall, the average accuracy and precision of the results by the autoencoder were 0.9658 and 0.5143, respectively. While maintaining 100% recall, the average accuracy and precision of the results by LOF were 0.8090 and 0.1472, respectively.

Conclusion

The autoencoder can effectively identify questionable plans from a large group of normal plans. There is no need to label the data and prepare the training data for model learning. The autoencoder provides an effective way to carry out an automatic plan checking in radiotherapy.

Development and clinical application of a GPU-based Monte Carlo dose verification module and software for 1.5 T MR-LINAC

BACKGROUND

Adaptive radiotherapy (ART) has made significant advances owing to magnetic resonance linear accelerator (MR-LINAC), which provides superior soft-tissue contrast, fast speed and rich functional magnetic resonance imaging (MRI) to guide radiotherapy. Independent dose verification plays a critical role in discovering errors, while several challenges remain in MR-LINAC.

PURPOSE

A Monte Carlo-based GPU-accelerated dose verification module for Unity is proposed and integrated into the commercial software ArcherQA to achieve fast and accurate quality assurance (QA) for online ART.

METHODS

Electron or positron motion in a magnetic field was implemented, and a material-dependent step-length limit method was used to trade off speed and accuracy. Transport was verified by dose comparison with EGSnrc in three A-B-A phantoms. Then, an accurate Monte Carlo-based Unity machine model was built in ArcherQA, including an MR-LINAC head, cryostat, coils, and treatment couch. In particular, a mixed model combining measured attenuation and homogeneous geometry was adopted for the cryostat. Several parameters in the LINAC model were tuned to commission it in the water tank. An alternating open-closed MLC plan on solid water measured with EBT-XD film was used to verify the LINAC model. Finally, the ArcherQA dose was compared with ArcCHECK measurements and GPUMCD in 30 clinical cases through the gamma test.

RESULTS

ArcherQA and EGSnrc were well matched in three A-B-A phantom tests, and the relative dose difference (RDD) was less than 1.6% in the homogenous region. A Unity model was commissioned in the water tank, and the RDD in the homogenous region was less than 2%. In the alternating open-closed MLC plan, the gamma result (3%/3 mm) between ArcherQA and Film was 96.55%, better than the gamma result between GPUMCD and Film (92.13%). In 30 clinical cases, the mean three-dimensional (3D) gamma result (3%/2 mm) was 99.36% ± 1.28% between ArcherQA and ArcCHECK for the QA plans and 99.27% ± 1.04% between ArcherQA and GPUMCD for the clinical patient plans. The average dose calculation time was 106 s in all clinical patient plans.

CONCLUSIONS

A GPU-accelerated Monte Carlo-based dose verification module was developed and built for the Unity MR-LINAC. The fast speed and high accuracy were proven by comparison with EGSnrc, commission data, the ArcCHECK measurement dose, and the GPUMCD dose. This module can achieve fast and accurate independent dose verification for Unity.

Technical note: A method to synthesize magnetic resonance images in different patient rotation angles with deep learning for gantry-free radiotherapy

BACKGROUND

Recently, patient rotating devices for gantry-free radiotherapy, a new approach to implement external beam radiotherapy, have been introduced. When a patient is rotated in the horizontal position, gravity causes anatomic deformation. For treatment planning, one feasible method is to acquire simulation images at different horizontal rotation angles.

PURPOSE

This study aimed to investigate the feasibility of synthesizing magnetic resonance (MR) images at patient rotation angles of 180° (prone position) and 90° (lateral position) from those at a rotation angle of 0° (supine position) using deep learning.

METHODS

This study included 23 healthy male volunteers. They underwent MR imaging (MRI) in the supine position and then in the prone (23 volunteers) and lateral (16 volunteers) positions. T1-weighted fast spin echo was performed for all positions with the same parameters. Two two-dimensional deep learning networks, pix2pix generative adversarial network (pix2pix GAN) and CycleGAN, were developed for synthesizing MR images in the prone and lateral positions from those in the supine position, respectively. For the evaluation of the models, leave-one-out cross-validation was performed. The mean absolute error (MAE), Dice similarity coefficient (DSC), and Hausdorff distance (HD) were used to determine the agreement between the prediction and ground truth for the entire body and four specific organs.

RESULTS

For pix2pix GAN, the synthesized images were visually bad, and no quantitative evaluation was performed. The quantitative evaluation metrics of the body outlines calculated for the synthesized prone and lateral images using CycleGAN were as follows: MAE, 35.63 ± 3.98 and 40.45 ± 5.83, respectively; DSC, 0.97 ± 0.01 and 0.94 ± 0.01, respectively; and HD (in pixels), 16.74 ± 3.55 and 31.69 ± 12.03, respectively. The quantitative metrics of the bladder and prostate performed were also promising for both the prone and lateral images, with mean values >0.90 in DSC (p > 0.05). The mean DSC and HD values of the bilateral femur for the prone images were 0.96 and 3.63 (in pixels), respectively, and 0.78 and 12.65 (in pixels) for the lateral images, respectively (p < 0.05).

CONCLUSIONS

The CycleGAN could synthesize the MRI at lateral and prone positions using images at supine position, and it could benefit gantry-free radiation therapy.

Optimization of isocenter position for multiple targets with nonuniform-margin expansion

PURPOSE

The single isocenter for multiple-target (SIMT) technique has become a popular treatment technique for multiple brain metastases. We have implemented a method to obtain a nonuniform margin for SIMT technique. In this study, we further propose a method to determine the isocenter position so that the total expanded margin volume is minimal.

MATERIALS AND METHOD

Based on a statistical model, the relationship between nonuniform margin and the distance d (from isocenter to target point), setup uncertainties, and significance level was established. Due to the existence of rotational error, there is a nonlinear relationship between the margin volume and the isocenter position. Using numerical simulation, we study the relationship between optimal isocenter position and translational error, rotational error, and target size. In order to find the optimal isocenter position quickly, adaptive simulated annealing (ASA) algorithm was used. This method was implemented in the Pinnacle3 treatment planning system and compared with isocenter at center-of-geometric (COG), center-of-volume (COV), and center-of-surface (COS). Ten patients with multiple brain metastasis targets treated with the SIMT technique was selected for evaluation.

RESULTS

When the size of tumors is equal, the optimal isocenter obtained by ASA and numerical simulation coincides with COG, COV, and COS. When the size of tumors is different, the optimal isocenter is close to the large tumor. The position of COS point is closer to the optimal point than the COV point for nearly all cases. Moreover, in some cases the COS point can be approximately selected as the optimal point. The ASA algorithm can reduce the calculating time from several hours to tens of seconds for three or more tumors. Using multiple brain metastases targets, a series of volume difference and calculating time were obtained for various tumor number, tumor size, and separation distances. Compared with the margin volume with isocenter at COG, the margin volume for optimal point can be reduced by up to 27.7%.

CONCLUSION

Optimal treatment isocenter selection of multiple targets with large differences could reduce the total margin volume. ASA algorithm can significantly improve the speed of finding the optimal isocenter. This method can be used for clinical isocenter selection and is useful for the protection of normal tissue nearby.

A cloud-based consultation and collaboration system for radiotherapy: Remote decision support services for community radiotherapy centers

PURPOSE

This study aimed to establish a cloud-based radiotherapy consultation and collaboration system, then investigated the practicability of remote decision support for community radiotherapy centers using the system.

METHODS AND MATERIALS

A cloud-based consultation and collaboration system for radiotherapy, OncoEvidance®, was developed to provide remote services of LINAC modeling, simulation CT data import/export, target volume and organ-at-risk delineation, prescription, and treatment planning. The system was deployed on a hybrid cloud. A federate of public nodes, each corresponding to a medical institution, are managed by a central node where a group of consultants have registered. Users can access the system through network using computing devices. The system has been tested at three community radiotherapy centers. One accelerator was modeled. 12 consultants participated the remote radiotherapy decision support and 77 radiation treatment plans had been evaluated remotely.

RESULTS

All the passing rates of per-beam dose verification are > 94% and all the passing rates of composite beam dose verification are > 99%. The average downloading time for one set of simulation CT data for one patient from Internet was within 1 min under the cloud download bandwidth of 8 Mbps and local network bandwidth of 100 Mbps. The average response time for one consultant to contour target volumes and make prescription was about 24 h. And that for one consultant to design and optimize a IMRT treatment plan was about 36 h. 100% of the remote plans passed the dosimetric criteria and could be imported into the local TPS for further verification.

CONCLUSION

The cloud-based consultation and collaboration system saved the travel time for consultants and provided high quality radiotherapy to patients in community centers. The under-staffed community radiotherapy centers could benefit from the remote system with lower cost and better treatment quality control.

Combining distance and anatomical information for deep-learning based dose distribution predictions for nasopharyngeal cancer radiotherapy planning

Purpose

Deep-learning effectively predicts dose distributions in knowledge-based radiotherapy planning. Using anatomical information that includes a structure map and computed tomography (CT) data as input has been proven to work well. The minimum distance from each voxel in normal structures to planning target volume (DPTV) closely affects each voxel's dose. In this study, we combined DPTV and anatomical information as input for a deep-learning-based dose-prediction network to improve performance.

Materials and methods

One hundred patients who underwent volumetric-modulated arc therapy for nasopharyngeal cancer were selected in this study. The prediction model based on a residual network had DPTV maps, structure maps, and CT as inputs and the corresponding dose distribution maps as outputs. The performances of the combined distance and anatomical information (COM) model and the traditional anatomical (ANAT) model with two-channel inputs (structure maps and CT) were compared. A 10-fold cross validation was performed to separately train and test the COM and ANAT models. The voxel-based mean error (ME), mean absolute error (MAE), dosimetric parameters, and dice similarity coefficient (DSC) of isodose volumes were used for modeling evaluation.

Results

The mean MAE of the body volume of the COM model were 4.89 ± 1.35%, highly significantly lower than those for the ANAT model of 5.07 ± 1.37% (p<0.001). The ME values of the body for the 2-type models were similar (p >0.05). The mean DSC values of the isodose volumes in the range of 60 Gy were all better in the COM model (p<0.05), and there were highly significant differences between 10 Gy and 55 Gy (p<0.001). For most organs at risk, the ME, MAE, and dosimetric parameters predicted by both models were concurrent with the ground truth values except the MAE values of the pituitary and optic chiasm in the ANAT model and the average mean dose of the right parotid in the ANAT model.

Conclusions

The COM model outperformed the ANAT model and could improve automated planning with statistically highly significant differences.

Machine Log File and Calibration Errors-based Patient-specific Quality Assurance (QA) for Volumetric Modulated Arc Therapy (VMAT)

INTRODUCTION

Dose reconstructed based on linear accelerator (linac) log-files is one of the widely used solutions to perform patient-specific quality assurance (QA). However, it has a drawback that the accuracy of log-file is highly dependent on the linac calibration. The objective of the current study is to represent a new practical approach for a patient-specific QA during Volumetric modulated arc therapy (VMAT) using both log-file and calibration errors of linac.

METHODS

A total of six cases, including two head and neck neoplasms, two lung cancers, and two rectal carcinomas, were selected. The VMAT-based delivery was optimized by the TPS of Pinnacle^3 subsequently, using Elekta Synergy VMAT linac (Elekta Oncology Systems, Crawley, UK), which was equipped with 80 Multi-leaf collimators (MLCs) and the energy of the ray selected at 6 MV. Clinical mode log-file of this linac was used in this study. A series of test fields validate the accuracy of log-file. Then, six plans of test cases were delivered and log-file of each was obtained. The log-file errors were added to the corresponding plans through the house script and the first reconstructed plan was obtained. Later, a series of tests were performed to evaluate the major calibration errors of the linac (dose-rate, gantry angle, MLC leaf position) and the errors were added to the first reconstruction plan to generate the second reconstruction plan. At last, all plans were imported to Pinnacle and recalculated dose distribution on patient CT and ArcCheck phantom (SUN Nuclear). For the former, both target and OAR dose differences between them were compared. For the latter, γ was evaluated by ArcCheck, and subsequently, the surface dose differences between them were performed.

RESULTS

Accuracy of log-file was validated. If error recordings in the log file were only considered, there were four arcs whose proportion of control points with gantry angle errors more than ± 1°larger than 35%. Errors of leaves within ± 0.5 mm were 95% for all arcs. The distinctness of a single control point MU was bigger, but the distinctness of cumulative MU was smaller. The maximum, minimum, and mean doses for all targets were distributed between -6.79E-02-0.42%, -0.38-0.4%, 2.69E-02-8.54E-02% respectively, whereas for all OAR, the maximum and mean dose were distributed between -1.16-2.51%, -1.21-3.12% respectively. For the second reconstructed dose: the maximum, minimum, and mean dose for all targets was distributed between 0.0995~5.7145%, 0.6892~4.4727%, 0.5829~1.8931% separately. Due to OAR, maximum and mean dose distribution was observed between -3.1462~6.8920%, -6.9899~1.9316%, respectively.

CONCLUSION

Patient-specific QA based on the log-file could reflect the accuracy of the linac execution plan, which usually has a small influence on dose delivery. When the linac calibration errors were considered, the reconstructed dose was closer to the actual delivery and the developed method was accurate and practical.

Noncoplanar Volumetric Modulated Arc Therapy for Hepatocellular Carcinoma Based on a Cage-Like Radiotherapy System: A Simulation Study

BACKGROUND

The incorporation of noncoplanar beam arrangements has been proposed in liver radiotherapy modalities, which can reduce the dose in normal tissues compared to coplanar techniques. Noncoplanar radiotherapy techniques for hepatocellular carcinoma treatment based on the Linac design have a limited effective arc angle to avoid collisions.

PURPOSE

To propose a novel noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system and investigate its performance in hepatocellular carcinoma patients.

METHODS

The computed tomography was deflected 90° to meet the structure of a cage-like radiotherapy system and design the noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system plan in the Pinnacle3 planning system. An noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system plan was customized for each of 10 included hepatocellular carcinoma patients, with 6 dual arcs ranging from -30° to 30°. Six couch angles were set with an interval of 36° and distributed along with the longest diameter of planning target volume. The dosimetric parameters of noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system plan were compared with the noncoplanar volumetric modulated arc therapy and volumetric modulated arc therapy plan.

RESULTS

The 3 radiotherapy techniques regarding planning target volume were statistically different for D98%, D2%, conformity index, and homogeneity index with χ² = 9.692, 14.600, 8.600, and 12.600, and P = .008, .001, .014, and .002, respectively. Further multiple comparisons revealed that noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system significantly reduced the mean dose (P = .005) and V5 (P = .005) of the normal liver, the mean dose (P = .005) of the stomach, and V30 (P = .028) of the lung compared to noncoplanar volumetric modulated arc therapy. Noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system significantly reduced the mean dose (P = .005) and V5 (P = .005) of the normal liver, the mean dose (P = .017) of the spinal cord, V50 (P = .043) of the duodenum, the maximum dose (P = .007) of the esophagus, and V30 (P = .047) of the whole lung compared to volumetric modulated arc therapy. The results indicate that noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system protects the normal liver, stomach, and lung better than noncoplanar volumetric modulated arc therapy and protects the normal liver, spinal cord, duodenum, esophagus, and lung better than volumetric modulated arc therapy.

CONCLUSIONS

The noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system technique with the arrangement of noncoplanar arcs provided optimal dosimetric gains compared with noncoplanar volumetric modulated arc therapy and volumetric modulated arc therapy, except for the heart. Noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system should be considered in more clinically challenging cases.

Hydrogen sulfide protects against ischemic heart failure by inhibiting RIP1/RIP3/MLKL-mediated necroptosis

The aim of the present study was to explore whether hydrogen sulfide (H2S) protects against ischemic heart failure (HF) by inhibiting the necroptosis pathway. Mice were randomized into Sham, myocardial infarction (MI), MI + propargylglycine (PAG) and MI + sodium hydrosulfide (NaHS) group, respectively. The MI model was induced by ligating the left anterior descending coronary artery. PAG was intraperitoneally administered at a dose of 50 mg/kg/day for 4 weeks, and NaHS at a dose of 4mg/kg/day for the same period. At 4 weeks after MI, the following were observed: A significant decrease in the cardiac function, as evidenced by a decline in ejection fraction (EF) and fractional shortening (FS); an increase in plasma myocardial injury markers, such as creatine kinase-MB (CK-MB) and cardiac troponin I (cTNI); an increase in myocardial collagen content in the heart tissues; and a decrease of H2S level in plasma and heart tissues. Furthermore, the expression levels of necroptosis-related markers such as receptor interacting protein kinase 1 (RIP1), RIP3 and mixed lineage kinase domain-like protein (MLKL) were upregulated after MI. NaHS treatment increased H2S levels in plasma and heart tissues, preserving the cardiac function by increasing EF and FS, decreasing plasma CK-MB and cTNI and reducing collagen content. Additionally, NaHS treatment significantly downregulated the RIP1/RIP3/MLKL pathway. While, PAG treatment aggravated cardiac function by activated the RIP1/RIP3/MLKL pathway. Overall, the present study concluded that H2S protected against ischemic HF by inhibiting RIP1/RIP3/MLKL-mediated necroptosis which could be a potential target treatment for ischemic HF.

Real-time 3D MRI reconstruction from cine-MRI using unsupervised network in MRI-guided radiotherapy for liver cancer

PURPOSE

Respiration has a major impact on the accuracy of radiation treatment for thorax and abdominal tumours. Instantaneous volumetric imaging could provide precise knowledge of tumour and normal organs' three-dimensional (3D) movement, which is the key to reducing the negative effect of breathing motion. Therefore, this study proposed a real-time 3D MRI reconstruction method from cine-MRI using an unsupervised network.

METHODS AND MATERIALS

Cine-MRI and setup 3D-MRI from eight patients with liver cancer were utilized to establish and validate the deep learning network for 3D-MRI reconstruction. Unlike previous methods that required 4D-MRI for network training, the proposed method utilized a reference 3D-MRI and cine-MRI to generate the training data. Then, a network was trained in an unsupervised manner to estimate the relationship between the cine-MRI acquired on coronal plane and deformation vector field (DVF) that describes the patient's breathing motion. After the training process, the coronal cine-MRI were inputted into the network, and the corresponding DVF was obtained. By wrapping the reference 3D-MRI with the generated DVF, the 3D-MRI could be reconstructed.

RESULTS

The reconstructed 3D-MRI slices were compared with the corresponding phase-sorted cine-MRI using dice similarity coefficients (DSCs) of liver contours and blood vessel localization error. In all patients, the liver DSC had mean value >96.1% and standard deviation < 1.3%; the blood vessel localization error had mean value <2.6 mm, and standard deviation was <2.0 mm. Moreover, the time for 3D-MRI reconstruction was approximately 100 ms. These results indicated that the proposed method could accurately reconstruct the 3D-MRI in real time.

CONCLUSIONS

The proposed method could accurately reconstruct the 3D-MRI from cine-MRI in real time. This method has great potential in improving the accuracy of radiotherapy for moving tumours.

Delivery of intensity-modulated electron therapy by mechanical scanning: An algorithm study

Purpose

In principle, intensity-modulated electron therapy (IMET) can be delivered through mechanical scanning, with a robotic arm mounting a linac.

Materials and methods

Here is a scanning algorithm to identify the back-and-forth, top-to-bottom (zigzag) pattern scan sequence. The algorithm includes generating beam positions with a uniform resolution according to the applicator size; adopting discrete energies to achieve the depth of 90% dose by compositing energies; selecting energy by locating the target's distal edge; and employing the energy-by-energy scan strategy for step-and-shoot discrete scanning. After a zigzag scan sequence is obtained, the delivery order of the scan spots is optimized by fast simulated annealing (FSA) to minimize the path length. For algorithm evaluation, scan sequences were generated using the computed tomography data of 10 patients with pancreatic cancer undergoing intraoperative radiotherapy, and the results were compared between the zigzag path and an optimized path. A simple calculation of the treatment delivery time, which comprises the irradiation time, the total robotic arm moving time, the time for energy switch, and the time to stop and restart the beam, was also made.

Results

In these clinical cases, FSA optimization shortened the path lengths by 12%-43%. Assuming the prescribed dose was 15 Gy, machine dose rate was 15 Gy/s, energy switch time was 2 s, stop and restart beam time was 20 ms, and robotic arm move speed was 50 mm/s, the average delivery time was 124±38 s. The largest reduction in path length yielded an approximately 10% reduction in the delivery time, which can be further reduced by increasing the machine dose rate and the robotic arm speed, decreasing the time for energy switch, and/or developing more efficient algorithms.

Conclusion

Mechanically scanning IMET is potentially feasible and worthy of further exploration.

Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations

PURPOSE

This study proposes a method to identify plans that failed patient-specific quality assurance (QA) and attempts to establish a criterion to prescreen treatment plans for patient-specific QA measurements with independent Monte Carlo dose calculations.

MATERIALS AND METHODS

Patient-specific QA results measured with an ArcCHECK diode array of 207 patients (head and neck: 25; thorax: 61; abdomen: 121) were retrospectively analyzed. All patients were treated with the volumetric modulated arc therapy (VMAT) technique and plans were optimized with a Pinnacle v16.2 treatment planning system using an analytical algorithm-based dose engine. Afterwards, phantom verification plans were designed and recalculated by an independent GPU-accelerated Monte Carlo (MC) dose engine, ArcherQA. Moreover, sensitivity and specificity analyzes of gamma passing rates between measurements and MC calculations were carried out to show the ability of MC to monitor failing plans (ArcCHECK 3%/3 mm,<90%), and attempt to determine the appropriate threshold and gamma passing rate criterion utilized by ArcherQA to prescreen treatment plans for ArcCHECK measurements. The receiver operator characteristic (ROC) curve was also utilized to characterize the performance of different gamma passing rate criterion used by ArcherQA.

RESULTS

The thresholds for 100% sensitivity to detect plans that failed patient-specific QA by independent calculation were 97.0%, 95.4%, and 91.0% for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively, which corresponded to specificities of 0.720, 0.528, and 0.585, respectively. It was shown that the 3%/3 mm criterion with 97% threshold for ArcherQA demonstrated perfect sensitivity and the highest specificity compared with other criteria, which may be suitable for prescreening treatment plans treated with the investigated machine to implement measurement-based patient-specific QA of patient plans. In addition, the area under the curve (AUC) calculated from ROC analysis for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm used by ArcherQA were 0.948, 0.924, and 0.929, respectively.

CONCLUSIONS

Independent dose calculation with the MC-based program ArcherQA has potential as a prescreen treatment for measurement-based patient-specific QA. AUC values (>0.9) showed excellent classification accuracy for monitoring failing plans with independent MC calculations.

The application of multiple metrics in deformable image registration for target volume delineation of breast tumor bed

BACKGROUND AND PURPOSE

For postoperative breast cancer patients, deformable image registration (DIR) is challenged due to the large deformations and non-correspondence caused by tumor resection and clip insertion. To deal with it, three metrics (fiducial-, region-, and intensity-based) were jointly used in DIR algorithm for improved accuracy.

MATERIALS AND METHODS

Three types of metrics were combined to form a single-objective function in DIR algorithm. Fiducial-based metric was used to minimize the distance between the corresponding point sets of two images. Region-based metric was used to improve the overlap between the corresponding areas of two images. Intensity-based metric was used to maximize the correlation between the corresponding voxel intensities of two images. The two CT images, one before surgery and the other after surgery, were acquired from the same patient in the same radiotherapy treatment position. Twenty patients who underwent breast-conserving surgery and postoperative radiotherapy were enrolled in this study.

RESULTS

For target registration error, the difference between the proposed and the conventional registration methods was statistically significant for soft tissue (2.06 vs. 7.82, p = 0.00024 < 0.05) and body boundary (3.70 vs. 6.93, p = 0.021 < 0.05). For visual assessment, the proposed method achieved better matching result for soft tissue and body boundary.

CONCLUSIONS

Comparing to the conventional method, the registration accuracy of the proposed method was significantly improved. This method provided a feasible way for target volume delineation of tumor bed in postoperative radiotherapy of breast cancer patients.

A more effective CT synthesizer using transformers for cone-beam CT-guided adaptive radiotherapy

Purpose

The challenge of cone-beam computed tomography (CBCT) is its low image quality, which limits its application for adaptive radiotherapy (ART). Despite recent substantial improvement in CBCT imaging using the deep learning method, the image quality still needs to be improved for effective ART application. Spurred by the advantages of transformers, which employs multi-head attention mechanisms to capture long-range contextual relations between image pixels, we proposed a novel transformer-based network (called TransCBCT) to generate synthetic CT (sCT) from CBCT. This study aimed to further improve the accuracy and efficiency of ART.

Materials and methods

In this study, 91 patients diagnosed with prostate cancer were enrolled. We constructed a transformer-based hierarchical encoder–decoder structure with skip connection, called TransCBCT. The network also employed several convolutional layers to capture local context. The proposed TransCBCT was trained and validated on 6,144 paired CBCT/deformed CT images from 76 patients and tested on 1,026 paired images from 15 patients. The performance of the proposed TransCBCT was compared with a widely recognized style transferring deep learning method, the cycle-consistent adversarial network (CycleGAN). We evaluated the image quality and clinical value (application in auto-segmentation and dose calculation) for ART need.

Results

TransCBCT had superior performance in generating sCT from CBCT. The mean absolute error of TransCBCT was 28.8 ± 16.7 HU, compared to 66.5 ± 13.2 for raw CBCT, and 34.3 ± 17.3 for CycleGAN. It can preserve the structure of raw CBCT and reduce artifacts. When applied in auto-segmentation, the Dice similarity coefficients of bladder and rectum between auto-segmentation and oncologist manual contours were 0.92 and 0.84 for TransCBCT, respectively, compared to 0.90 and 0.83 for CycleGAN. When applied in dose calculation, the gamma passing rate (1%/1 mm criterion) was 97.5% ± 1.1% for TransCBCT, compared to 96.9% ± 1.8% for CycleGAN.

Conclusions

The proposed TransCBCT can effectively generate sCT for CBCT. It has the potential to improve radiotherapy accuracy.