Multi-omics and Multi-VOIs to predict esophageal fistula in esophageal cancer patients treated with radiotherapy.

OBJECTIVE: This study aimed to develop a prediction model for esophageal fistula (EF) in esophageal cancer (EC) patients treated with intensity-modulated radiation therapy (IMRT), by integrating multi-omics features from multiple volumes of interest (VOIs). METHODS: We retrospectively analyzed pretreatment planning computed tomographic (CT) images, three-dimensional dose distributions, and clinical factors of 287 EC patients. Nine groups of features from different combination of omics [Radiomics (R), Dosiomics (D), and RD (the combination of R and D)], and VOIs [esophagus (ESO), gross tumor volume (GTV), and EG (the combination of ESO and GTV)] were extracted and separately selected by unsupervised (analysis of variance (ANOVA) and Pearson correlation test) and supervised (Student T test) approaches. The final model performance was evaluated using five metrics: average area under the receiver-operator-characteristics curve (AUC), accuracy, precision, recall, and F1 score. RESULTS: For multi-omics using RD features, the model performance in EG model shows: AUC, 0.817 ± 0.031; 95% CI 0.805, 0.825; p < 0.001, which is better than single VOI (ESO or GTV). CONCLUSION: Integrating multi-omics features from multi-VOIs enables better prediction of EF in EC patients treated with IMRT. The incorporation of dosiomics features can enhance the model performance of the prediction.

A novel automated planning approach for multi-anatomical sites cancer in Raystation treatment planning system.

PURPOSE: To develop an automated planning approach in Raystation and evaluate its feasibility in multiple clinical application scenarios. METHODS: An automated planning approach (Ruiplan) was developed by using the scripting platform of Raystation. Radiotherapy plans were re-generated both automatically by using Ruiplan and manually. 60 patients, including 20 patients with nasopharyngeal carcinoma (NPC), 20 patients with esophageal carcinoma (ESCA), and 20 patients with rectal cancer (RECA) were retrospectively enrolled in this study. Dosimetric and planning efficiency parameters of the automated plans (APs) and manual plans (MPs) were statistically compared. RESULTS: For target coverage, APs yielded superior dose homogeneity in NPC and RECA, while maintaining similar dose conformity for all studied anatomical sites. For OARs sparing, APs led to significant improvement in most OARs sparing. The average planning time required for APs was reduced by more than 43% compared with MPs. Despite the increased monitor units (MUs) for NPC and RECA in APs, the beam-on time of APs and MPs had no statistical difference. Both the MUs and beam-on time of APs were significantly lower than that of MPs in ESCA. CONCLUSIONS: This study developed a new automated planning approach, Ruiplan, it is feasible for multi-treatment techniques and multi-anatomical sites cancer treatment planning. The dose distributions of targets and OARs in the APs were similar or better than those in the MPs, and the planning time of APs showed a sharp reduction compared with the MPs. Thus, Ruiplan provides a promising approach for realizing automated treatment planning in the future.

Study on esophageal cancer radiotherapy dosimetry and position verification for volumetric modulated arc therapy.

BACKGROUND: This study analyzed the respective advantages and disadvantages by comparing volumetric modulated arc therapy (VMAT) and intensity modulated radiotherapy (IMRT) on the dose distribution and position verification distribution characteristics in esophageal cancer radiotherapy, in order to provide the reference for the clinical radiotherapy technology optimization of esophageal cancer. METHODS: A total of 56 cases of patients with esophageal cancer were selected and applied to the Pinnacle three-dimensional radiation treatment planning system (TPS), in order to design a VMAT plan and IMRT plan under the guidance of image-guided radiotherapy (IGRT). The dosimetry and position verification difference were compared between the two groups. RESULTS: Revealed that the target dose distribution of the VMAT plan and IMRT plan meets the requirements in clinical dosimetry for all 56 patients in this study. Under the premise of similar target coverage, the conformal index (CI) of the VMAT plan, homogeneity index (HI), target volume, BODY-PTV radiated volume and spinal cord Dmax, bilateral lung V5, V20 and mean lung dose (MLD), monitor unit (MU) and treatment time (TT), as well as position verification and others, were obviously superior to those in the IMRT plan; and the difference was statistically significant. CONCLUSION: CBCT guided VMAT is a potential effective treatment for esophageal cancer and may be more effective and safer than IMRT.

Unsupervised computed tomography and cone-beam computed tomography image registration using a dual attention network.

Background: The registration of computed tomography (CT) and cone-beam computed tomography (CBCT) plays a key role in image-guided radiotherapy (IGRT). However, the large intensity variation between CT and CBCT images limits the registration performance and its clinical application in IGRT. In this study, a learning-based unsupervised approach was developed to address this issue and accurately register CT and CBCT images by predicting the deformation field. Methods: A dual attention module was used to handle the large intensity variation between CT and CBCT images. Specifically, a scale-aware position attention block (SP-BLOCK) and a scale-aware channel attention block (SC-BLOCK) were employed to integrate contextual information from the image space and channel dimensions. The SP-BLOCK enhances the correlation of similar features by weighting and aggregating multi-scale features at different positions, while the SC-BLOCK handles the multiple features of all channels to selectively emphasize dependencies between channel maps. Results: The proposed method was compared with existing mainstream methods on the 4D-LUNG data set. Compared to other mainstream methods, it achieved the highest structural similarity (SSIM) and dice similarity coefficient (DICE) scores of 86.34% and 89.74%, respectively, and the lowest target registration error (TRE) of 2.07 mm. Conclusions: The proposed method can register CT and CBCT images with high accuracy without the needs of manual labeling. It provides an effective way for high-accuracy patient positioning and target localization in IGRT.

A Transfer Learning Framework for Deep Learning-Based CT-to-Perfusion Mapping on Lung Cancer Patients.

Purpose: Deep learning model has shown the feasibility of providing spatial lung perfusion information based on CT images. However, the performance of this method on lung cancer patients is yet to be investigated. This study aims to develop a transfer learning framework to evaluate the deep learning based CT-to-perfusion mapping method specifically on lung cancer patients. Methods: SPECT/CT perfusion scans of 33 lung cancer patients and 137 non-cancer patients were retrospectively collected from two hospitals. To adapt the deep learning model on lung cancer patients, a transfer learning framework was developed to utilize the features learned from the non-cancer patients. These images were processed to extract features from three-dimensional CT images and synthesize the corresponding CT-based perfusion images. A pre-trained model was first developed using a dataset of patients with lung diseases other than lung cancer, and subsequently fine-tuned specifically on lung cancer patients under three-fold cross-validation. A multi-level evaluation was performed between the CT-based perfusion images and ground-truth SPECT perfusion images in aspects of voxel-wise correlation using Spearman's correlation coefficient (R), function-wise similarity using Dice Similarity Coefficient (DSC), and lobe-wise agreement using mean perfusion value for each lobe of the lungs. Results: The fine-tuned model yielded a high voxel-wise correlation (0.8142 ± 0.0669) and outperformed the pre-trained model by approximately 8%. Evaluation of function-wise similarity indicated an average DSC value of 0.8112 ± 0.0484 (range: 0.6460-0.8984) for high-functional lungs and 0.8137 ± 0.0414 (range: 0.6743-0.8902) for low-functional lungs. Among the 33 lung cancer patients, high DSC values of greater than 0.7 were achieved for high functional volumes in 32 patients and low functional volumes in all patients. The correlations of the mean perfusion value on the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe were 0.7314, 0.7134, 0.5108, 0.4765, and 0.7618, respectively. Conclusion: For lung cancer patients, the CT-based perfusion images synthesized by the transfer learning framework indicated a strong voxel-wise correlation and function-wise similarity with the SPECT perfusion images. This suggests the great potential of the deep learning method in providing regional-based functional information for functional lung avoidance radiation therapy.

CBCT-based synthetic CT generation using generative adversarial networks with disentangled representation.

BACKGROUND: Cone-beam computed tomography (CBCT) plays a key role in image-guided radiotherapy (IGRT), however its poor image quality limited its clinical application. In this study, we developed a deep-learning based approach to translate CBCT image to synthetic CT (sCT) image that preserves both CT image quality and CBCT anatomical structures. METHODS: A novel synthetic CT generative adversarial network (sCTGAN) was proposed for CBCT-to-CT translation via disentangled representation. The approach of disentangled representation was employed to extract the anatomical information shared by CBCT and CT image domains. Both on-board CBCT and planning CT of 40 patients were used for network learning and those of another 12 patients were used for testing. Accuracy of our network was quantitatively evaluated using a series of statistical metrics, including the peak signal-to-noise ratio (PSNR), mean structural similarity index (SSIM), mean absolute error (MAE), and root-mean-square error (RMSE). Effectiveness of our network was compared against three state-of-the-art CycleGAN-based methods. RESULTS: The PSNR, SSIM, MAE, and RMSE between sCT generated by sCTGAN and deformed planning CT (dpCT) were 34.12 dB, 0.86, 32.70 HU, and 60.53 HU, while the corresponding values between original CBCT and dpCT were 28.67 dB, 0.64, 70.56 HU, and 112.13 HU. The RMSE (60.53±14.38 HU) of sCT generated by sCTGAN was less than that of sCT generated by all the three comparing methods (72.40±16.03 HU by CycleGAN, 71.60±15.09 HU by CycleGAN-Unet512, 64.93±14.33 HU by CycleGAN-AG). CONCLUSIONS: The sCT generated by our sCTGAN network was closer to the ground truth (dpCT), in comparison to all the three comparing CycleGAN-based methods. It provides an effective way to generate high-quality sCT which has a wide application in IGRT and adaptive radiotherapy.

Improvement Using Planomics Features on Prediction and Classification of Patient-Specific Quality Assurance Using Head and Neck Volumetric Modulated Arc Therapy Plan.

Purpose: This study aimed to evaluate the utility of a new plan feature (planomics feature) for predicting the results of patient-specific quality assurance using the head and neck (H&N) volumetric modulated arc therapy (VMAT) plan. Methods: One hundred and thirty-one H&N VMAT plans in our institution from 2019 to 2021 were retrospectively collected. Dosimetric verification for all plans was carried out using the portal dosimetry system integrated into the Eclipse treatment planning system based on the electronic portal imaging devices. Gamma passing rates (GPR) were analyzed using three gamma indices of 3%/3 mm, 3%/2 mm, and 2%/2 mm with a 10% dose threshold. Forty-eight conventional features affecting the dose delivery accuracy were used in the study, and 2,476 planomics features were extracted based on the radiotherapy plan file. Three prediction and classification models using conventional features (CF), planomics features (PF), and hybrid features (HF) combining two sets of features were constructed by the gradient boosting regressor (GBR) and Ridge classifier for each GPR of 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively. The absolute prediction error (APE) and the area under the curve (AUC) were adopted for assessing the performance of prediction and classification models. Results: In the GPR prediction, the average APE of the models using CF, PF, and HF was 1.3 ± 1.2%/3.6 ± 3.0%, 1.7 ± 1.5%/3.8 ± 3.5%, and 1.1 ± 1.0%/4.1 ± 3.1% for 2%/2 mm; 0.7 ± 0.6%/2.0 ± 2.0%, 1.0±1.1%/2.2 ± 1.8%, and 0.6 ± 0.6%/2.2 ± 1.9% for 3%/2 mm; and 0.4 ± 0.3%/1.2 ± 1.2%, 0.4±0.5%/1.3 ± 1.0%, and 0.3±0.3%/1.2 ± 1.1% for 3%/3 mm, respectively. In the regression prediction, three models give a similar modeling performance for predicting the GPR. The classification results were 0.67 ± 0.03/0.66 ± 0.07, 0.77 ± 0.03/0.73 ± 0.06, and 0.78 ± 0.02/0.75 ± 0.04 for 3%/3 mm, respectively. For 3%/2 mm, the AUCs of the training and testing cohorts were 0.64 ± 0.03/0.62 ± 0.07, 0.70 ± 0.03/0.67 ± 0.06, and 0.75 ± 0.03/0.71 ± 0.07, respectively, and for 2%/2 mm, the average AUCs of the training and testing cohorts were 0.72 ± 0.03/0.72 ± 0.06, 0.78 ± 0.04/0.73 ± 0.07, and 0.81 ± 0.03/0.75 ± 0.06, respectively. In the classification, the PF model has a better classification performance than the CF model. Moreover, the HF model provides the best result among the three classifications models. Conclusions: The planomics features can be used for predicting and classifying the GPR results and for improving the model performance after combining the conventional features for the GPR classification.

Multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI): Development and initial evaluation in liver tumor patients.

PURPOSE: To develop a novel multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI) technique that expands single image contrast 4D-MRI to a spectrum of native and synthetic image contrasts and to evaluate its feasibility in liver tumor patients. METHODS AND MATERIALS: The MC-4D-MRI technique integrates multi-parametric MRI fusion, 4D-MRI, and deformable image registration (DIR) techniques. The fusion technique consists of native MRI as input, image pre-processing, fusion algorithm, adaptation, and fused multi-contrast MRI as output. Four-dimensional deformation vector fields (4D-DVF) were generated from an original T2/T1-w 4D-MRI by deforming end-of-inhalation (EOI) to nine other phase volumes via DIR. The 4D-DVF were applied to multi-contrast MRI to generate a spectrum of 4D-MRI in different image contrasts. The MC-4D-MRI technique was evaluated in five liver tumor patients on tumor contrast-to-noise ratio (CNR), internal target volume (ITV) contouring consistency, diaphragm motion range, and tumor motion trajectory; and in digital anthropomorphic phantoms on 4D-DIR introduced errors in tumor motion range, centroid location, extent, and volume. RESULTS: MC-4D-MRI consisting of 4D-MRIs in native image contrasts (T1-w, T2-w, and T2/T1-w) and synthetic image contrasts, such as tumor-enhanced contrast (TEC) were generated in five liver tumor patients. Patient tumor CNR increased from 2.6 ± 1.8 in the T2/T1-w MRI, to -4.4 ± 2.4, 6.6 ± 3.0, and 9.6 ± 3.9 in the T1-w, T2-w, and TEC MRI, respectively. Patient ITV inter-observer mean Dice similarity coefficient (mDSC) increased from 0.65 ± 0.10 in the original T2/T1-w 4D-MRI, to 0.76 ± 0.14, 0.77 ± 0.12, and 0.86 ± 0.05 in the T1-w, T2-w, and TEC 4D-MRI, respectively. Patient diaphragm motion range absolute differences between the three new 4D-MRIs and original T2/T1-w 4D-MRI were 1.2 ± 1.3, 0.3 ± 0.7, and 0.5 ± 0.5 mm, respectively. Patient tumor displacement phase-averaged absolute differences between the three 4D-MRIs and the original 4D-MRI were 0.72 ± 0.33, 0.62 ± 0.54, and 0.74 ± 0.43 mm in the superior-inferior (SI) direction, and 0.59 ± 0.36, 0.51 ± 0.30, and 0.50 ± 0.24 mm in the anterior-posterior (AP) direction, respectively. In the digital phantoms, phase-averaged absolute tumor centroid shift caused by the 4D-DIR were at or below 0.5 mm in SI, AP, and left-right (LR) directions. CONCLUSION: We developed an MC-4D-MRI technique capable of expanding single image contrast 4D-MRI along a new dimension of image contrast. Initial evaluations in liver tumor patients showed enhancements in image contrast variety, tumor contrast, and ITV contouring consistencies using MC-4D-MRI. The technique might offer new perspectives on the image contrast of MRI and 4D-MRI in MR-guided radiotherapy.

Convolutional neural network-based dosimetry evaluation of esophageal radiation treatment planning.

PURPOSE: A dosimetry evaluation model for treatment planning of esophageal radiation therapy is developed using a deep learning model. The model predicts dose volume histogram (DVH) from distance to target histogram (DTH) based on stacked de-noise auto-encoder (SDAE) and one-dimensional convolutional network (1D-CN). METHOD: First, SDAE is used to extract the features from the curves of DTH and DVH. Then 1D-CN model is employed to learn the relationship between the features of DTH and DVH, and later used to predict the features of DVH from the features of DTH. Finally, the curve of DVH is restored from the features of DVH based on SDAE. Two hundred and seventy treatment plans are used for training 1D-CN and another sixty-three treatment plans are used for evaluating this model. This method is also compared with another two popular prediction methods based on support vector machine (SVM) and U-net. RESULTS: Based on the experimental result, the proposed model achieves the lowest dose endpoint error comparing to the other models. The average prediction error on planned target volume, left lung, right lung, heart, and spinal cord is 2.94% for the proposed model, while the average prediction errors are 6.79% and 3.41% for SVM and U-net, respectively. CONCLUSIONS: A dosimetry evaluation method based on SDAE and 1D-CN is developed in characterizing the correlation relationship between DTH and DVH of treatment plans. The results show that the model could be trained more efficiently in this framework and the DVH could be predicted with higher accuracy comparing to those existing methods. It provides a useful tool in supporting automated treatment planning of esophageal intensity-modulated radiotherapy.

Predicting voxel-level dose distributions for esophageal radiotherapy using densely connected network with dilated convolutions.

This work aims to develop a voxel-level dose prediction framework by integrating distance information between PTV and OARs, as well as image information, into a densely-connected network (DCNN). Firstly, a four-channel feature map, consisting of a PTV image, an OAR image, a CT image, and a distance image, is constructed. A densely connected neural network is then built and trained for voxel-level dose prediction. Considering that the shape and size of OARs are highly inconsistent, a dilated convolution is employed to capture features from multiple scales. Finally, the proposed network is evaluated with five-fold cross-validation, based on ninety-eight clinically approved treatment plans. The voxel-level mean absolute error(MAE V ) of DCNN was 2.1% for PTV, 4.6% for left lung, 4.0% for right lung, 5.1% for heart, 6.0% for spinal cord, and 3.4% for body, which outperforms conventional U-Net, Resnet-antiResnet, U-Resnet-D by 0.1-0.8%. This result shows that with the introduction of a distance image and DCNN model, the accuracy of predicted dose distribution could be significantly improved. This approach offers a new dose prediction tool to support quality assurance and the automation of treatment planning in esophageal radiotherapy.

Targeting valosin-containing protein enhances the efficacy of radiation therapy in esophageal squamous cell carcinoma.

Overcoming resistance to radiation is a great challenge in cancer therapy. Here, we highlight that targeting valosin-containing protein (VCP) improves radiation sensitivity in esophageal squamous cell carcinoma (ESCC) cell lines and show the potential of using VCP as a prognosis marker in locally advanced ESCC treated with radiation therapy. Esophageal squamous cell carcinoma cell lines with high VCP expression were treated with VCP inhibitor combined with radiotherapy. Cell proliferation, colony formation, cell death, and endoplasmic reticulum (ER) stress signaling were evaluated. Moreover, patients with newly diagnosed locally advanced ESCC who were treated with radiotherapy were analyzed. Immunohistochemistry was used to detect the expression of VCP. The correlation between overall survival and VCP was investigated. Esophageal squamous cell carcinoma cells treated with VCP inhibitor and radiotherapy showed attenuated cell proliferation and colony formation and enhanced apoptosis. Further investigation showed this combined strategy activated the ER stress signaling involved in unfolded protein response, and inhibited the ER-associated degradation (ERAD) pathway. Clinical analysis revealed a significant survival benefit in the low VCP expression group. Targeting VCP resulted in antitumor activity and enhanced the efficacy of radiation therapy in ESCC cells in vitro. Valosin-containing protein is a promising and novel target. In patients with locally advanced ESCC who received radiotherapy, VCP can be considered as a useful prognostic indicator of overall survival. Valosin-containing protein inhibitors could be developed for use as effective cancer therapies, in combination with radiation therapy.

Should stereotactic body radiotherapy doses be adjusted according to tumor size in early-stage non-small-cell lung cancer? A systematic review and meta-analysis.

Aim: Treatment schedules of stereotactic body radiotherapy (SBRT) for patients with early-stage non-small-cell lung cancer (NSCLC) are varied. The aim of this study was to clarify the optimal biologically effective dose (BED) for the treatment of stage I NSCLC. Methods: Research findings published after 1990 detailing the effects of SBRT on early-stage NSCLC patients were compiled from the Medline, Embase, Web of Science and Cochrane Library. For comparative analyses, two groups were divided into moderate BED (100-150 Gy) and high BED (BED ≥150 Gy). Results: Two moderate BED studies and four high BED studies were selected for analysis. The results from the analysis of four moderate and high groups suggest that the 2-year local control rate was significantly lower in moderate BED group than that of high BED group (p = 0.04). Subgroup analysis by tumor size was also conducted. For patients with Stage IA disease, no difference in overall survival (OS) was found. No statistically significant difference was achieved in the instance of Stage IB tumor; however, the 2-year OS showed a trend in favor of high BED (p = 0.08). The remaining two studies, comparing 106 Gy (Stage IA) to 120-132 Gy (Stage IB) treatment, indicated a significantly higher 3-year OS in the 106 Gy group than that of 120-132 Gy group (p = 0.009). Conclusion: In patients with early-stage NSCLC treated with SBRT, our analyses suggested that a moderate BED, especially 106 Gy, is sufficient for the treatment of Stage IA tumor; although a high BED conferred no significant benefit to OS for the treatment of Stage IB tumor, a higher local control rate was achieved. Further detailed studies should be performed to explore the optimal BED for the treatment of Stage IB tumor.

Deep Learning Based Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning.

Rapid esophageal radiation treatment planning is often obstructed by manually adjusting optimization parameters. The adjustment process is commonly guided by the dose-volume histogram (DVH), which evaluates dosimetry at planning target volume (PTV) and organs at risk (OARs). DVH is highly correlated with the geometrical relationship between PTV and OARs, which motivates us to explore deep learning techniques to model such correlation and predict DVHs of different OARs. Distance to target histogram (DTH) is chosen to measure the geometrical relationship between PTV and OARs. DTH and DVH features are then undergone dimension reduction by autoencoder. The reduced feature vectors are finally imported into deep belief network to model the correlation between DTH and DVH. This correlation can be used to predict DVH of the corresponding OAR for new patients. Validation results revealed that the relative dose difference of the predicted and clinical DVHs on four different OARs were less than 3%. These promising results suggested that the predicted DVH could provide near-optimal parameters to significantly reduce the planning time.

Dosimetric Analysis of Microscopic Disease in SBRT for Lung Cancers.

OBJECTIVE: The objective of this study is to theoretically and experimentally evaluate the dosimetry in the microscopic disease regions surrounding the tumor under stereotactic body radiation therapy of lung cancer. METHODS: For simplicity, the tumor was considered moving along 1 dimension with a periodic function. The probability distribution function of the tumor position was generated according to the motion pattern and was used to estimate the delivered dose in the microscopic disease region. An experimental measurement was conducted to validate both the estimated dose with a probability function and the calculated dose from 4-dimensional computed tomography data using a dynamic thorax phantom. Four tumor motion patterns were simulated with cos4(x) and sin(x), each with 2 different amplitudes: 10 mm and 5 mm. A 7-field conformal plan was created for treatment delivery. Both films (EBT2) and optically stimulated luminescence detectors were inserted in and around the target of the phantom to measure the delivered doses. Dose differences were evaluated using gamma analysis with 3%/3 mm. RESULTS: The average gamma index between measured doses using film and calculated doses using average intensity projection simulation computed tomography was 80.8% ± 0.9%. In contrast, between measured doses using film and calculated doses accumulated from 10 sets of 4-dimensional computed tomography data, it was 98.7% ± 0.6%. The measured doses using optically stimulated luminescence detectors matched very well (within 5% of the measurement uncertainty) with the theoretically calculated doses using probability distribution function at the corresponding position. Respiratory movement caused inadvertent irradiation exposure, with 70% to 80% of the dose line wrapped around the 10 mm region outside the target. CONCLUSION: The use of static dose calculation in the treatment planning system could substantially underestimate the actual delivered dose in the microscopic disease region for a moving target. The margin for microscopic disease may be substantially reduced or even eliminated for lung stereotactic body radiation therapy.