Temporal Structures in Positron Spectra and Charge-Sign Effects in Galactic Cosmic Rays

We present the precision measurements of 11 years of daily cosmic positron fluxes in the rigidity range from 1.00 to 41.9 GV based on 3.4×10^{6} positrons collected with the Alpha Magnetic Spectrometer (AMS) aboard the International Space Station. The positron fluxes show distinctly different time variations from the electron fluxes at short and long timescales. A hysteresis between the electron fluxes and the positron fluxes is observed with a significance greater than 5σ at rigidities below 8.5 GV. On the contrary, the positron fluxes and the proton fluxes show similar time variation. Remarkably, we found that positron fluxes are modulated more than proton fluxes with a significance greater than 5σ for rigidities below 7 GV. These continuous daily positron fluxes, together with AMS daily electron, proton, and helium fluxes over an 11-year solar cycle, provide unique input to the understanding of both the charge-sign and mass dependencies of cosmic rays in the heliosphere.

Observation of an Excess of Dicharmonium Events in the Four-Muon Final State with the ATLAS Detector

A search is made for potential ccc[over ¯]c[over ¯] tetraquarks decaying into a pair of charmonium states in the four muon final state using proton-proton collision data at sqrt[s]=13  TeV, corresponding to an integrated luminosity of 140  fb^{-1} recorded by the ATLAS experiment at LHC. Two decay channels, J/ψ+J/ψ→4μ and J/ψ+ψ(2S)→4μ, are studied. Backgrounds are estimated based on a hybrid approach involving Monte Carlo simulations and data-driven methods. Statistically significant excesses with respect to backgrounds dominated by the single parton scattering are seen in the di-J/ψ channel consistent with a narrow resonance at 6.9 GeV and a broader structure at lower mass. A statistically significant excess is also seen in the J/ψ+ψ(2S) channel. The fitted masses and decay widths of the structures are reported.

Observation of the γγ→ττ Process in Pb+Pb Collisions and Constraints on the τ-Lepton Anomalous Magnetic Moment with the ATLAS Detector

This Letter reports the observation of τ-lepton-pair production in ultraperipheral lead-lead collisions Pb+Pb→Pb(γγ→ττ)Pb and constraints on the τ-lepton anomalous magnetic moment a_{τ}. The dataset corresponds to an integrated luminosity of 1.44  nb^{-1} of LHC Pb+Pb collisions at sqrt[s_{NN}]=5.02  TeV recorded by the ATLAS experiment in 2018. Selected events contain one muon from a τ-lepton decay, an electron or charged-particle track(s) from the other τ-lepton decay, little additional central-detector activity, and no forward neutrons. The γγ→ττ process is observed in Pb+Pb collisions with a significance exceeding 5 standard deviations and a signal strength of μ_{ττ}=1.03_{-0.05}^{+0.06} assuming the standard model value for a_{τ}. To measure a_{τ}, a template fit to the muon transverse-momentum distribution from τ-lepton candidates is performed, using a dimuon (γγ→μμ) control sample to constrain systematic uncertainties. The observed 95% confidence-level interval for a_{τ} is -0.057 Collapse

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Measurement of Ultra-High-Energy Diffuse Gamma-Ray Emission of the Galactic Plane from 10 TeV to 1 PeV with LHAASO-KM2A

The diffuse Galactic γ-ray emission, mainly produced via interactions between cosmic rays and the interstellar medium and/or radiation field, is a very important probe of the distribution, propagation, and interaction of cosmic rays in the Milky Way. In this Letter, we report the measurements of diffuse γ rays from the Galactic plane between 10 TeV and 1 PeV energies, with the square kilometer array of the Large High Altitude Air Shower Observatory (LHAASO). Diffuse emissions from the inner (15°10  TeV). The energy spectrum in the inner Galaxy regions can be described by a power-law function with an index of -2.99±0.04, which is different from the curved spectrum as expected from hadronic interactions between locally measured cosmic rays and the line-of-sight integrated gas content. Furthermore, the measured flux is higher by a factor of ∼3 than the prediction. A similar spectrum with an index of -2.99±0.07 is found in the outer Galaxy region, and the absolute flux for 10≲E≲60  TeV is again higher than the prediction for hadronic cosmic ray interactions. The latitude distributions of the diffuse emission are consistent with the gas distribution, while the longitude distributions show clear deviation from the gas distribution. The LHAASO measurements imply that either additional emission sources exist or cosmic ray intensities have spatial variations.

A Comparison of Clinicopathologic Outcomes and Patterns of Lymphatic Spread across Neoadjuvant Chemotherapy, Neoadjuvant Chemoradiotherapy and Neoadjuvant Immunochemotherapy in Locally Advanced Esophageal Squamous Cell Carcinoma

PURPOSE/OBJECTIVE(S)

To evaluate the differences in pathologic complete response (pCR) rates, TRG score, pathologic T stage and the pattern of lymphatic spread among patients receiving neoadjuvant chemotherapy (NCT) or neoadjuvant chemoradiotherapy (NCRT) or neoadjuvant immunochemotherapy (NICT) prior to esophagectomy for locally advanced esophageal squamous cell carcinoma (ESCC).

MATERIALS/METHODS

A total of 702 patients with ESCC who completed transthoracic esophagectomy followed neoadjuvant therapy at three cancer centers from January 2017 to December 2022 were enrolled. Among the included patients, 382 patients were treated with NCR, 172 with NCRT, and 148 with NICT. Inverse probability of treatment weighting (IPTW) was performed to control potential confounding factors. Pathological response of primary tumor was evaluated using the Chirieac modified tumor regression grade (TRG) system. The complete regression of primary lesion and nodal metastases were considered pCR. Lymph node classification system used the 8th edition of AJCC. Specimens were assessed for pattern of lymphatic spread.

RESULTS

After adjusting for baseline characteristics, the R0 resection rate did not significantly differ between the patients receiving NCT or NCRT or NICT (99.48% vs.100% vs.98.65%, P = 0.273). Compared with the NCT group, the NCRT group and NICT group had an advantage in pathological response (P<0.05). The pCR rate was 7.07% in the NCT group, 30.23% in the NCRT group, and 22.30% in the NICT group. Compared to the other two groups, the TRG score (P<0.05) and pathologic T stage (P<0.05) in the NCT group were significantly higher. In the NCT group, 9.97% had ypT0 disease, compared with 35.76% in the NCRT group and 25.68% in the NICT group. And in the NCT group, 9.71% had TRG1 disease, compared with 32.76% in the NCRT group and 25% in the NICT group. Compared with NICT, NCRT can significantly reduce the rate of LNM in station 1R (0 vs 3.38%, P<0.05) and 2R (1.15% vs 6.76%, P<0.05). Subgroup analysis according to the tumor location distribution showed that in upper thoracic cases, there was no statistical difference in LNM rates among stations no matter whether patients received NCT or NCRT or NICT. NICT group had higher LNM rates in station 2R (9.1%) in middle thoracic cases (P<0.05) and in station 18 (7.5%) (P<0.05) in lower thoracic cases, compared with the NCRT group and NCT group.

CONCLUSION

NCRT or NICT followed by surgery may result in a promising pCR rate and show a better performance in therapeutic response of primary lesion. No matter whether patients received NCT or NCRT or NICT, multiple level and skip node metastases are common, and adequate lymphadenectomy should be achieved to ensure the complete removal of metastatic lymph nodes.

Application of a Contralateral Esophageal-Sparing Technique to Reduce Radiation Esophagitis in Limited-Stage Small Cell Lung Cancer Treated with Twice-Daily Radiotherapy and Concurrent Chemotherapy

PURPOSE/OBJECTIVE(S)

Acute esophagitis (AE) is a common radiation-related toxicity after concurrent twice-daily hyperfractionated radiotherapy and chemotherapy in limited-stage small cell lung cancer (LS-SCLC) patients, which could limit dose-escalation of the target and make treatment postponed to decrease local tumor control. More esophageal protective techniques should be proposed to reduce radiation severe esophagitis of LS-SCLC patients.

MATERIALS/METHODS

We retrospectively applied a contralateral esophagus sparing technique (CEST) in 20 unresectable LS-SCLC patients, who had gross tumor within 1 cm of the esophagus and received a total dose of 45 Gy of concurrent twice-daily radiation and standard chemotherapy regimen. The contralateral esophagus (CE) was contoured as an avoidance structure, and the feasibility of CEST on promoting a steep dose falloff beyond the target volume near esophagus was analyzed. The appropriate dose constraints of CE were also investigated. The AE events were recorded according to the RTOG acute toxicity grading system.

RESULTS

We performed CEST in 20 LS-SCLC consecutive patients, among whom three patients experienced severe AE after concurrent chemoradiotherapy. Each treatment plan of eligible patients assured high radiation doses delivering, with the planning and gross tumor volume covered by 95% and 100% of the prescription dose. Among these patients, the median maximum esophagus dose declined from 47.9 Gy (range, 46.6-49.7 Gy) to 41.3 Gy (range, 35.9-48.2 Gy), as well as V30 and V36 of esophagus decreased from 9.22 Gy (range, 0.42-17.71 Gy) and 7.39 Gy (range, 0-16.19 Gy) to 2.40 Gy (range, 0-5.68 Gy) and 0.53 Gy (range, 0 -2.69 Gy) after CEST applying, respectively (all p<0.001). The CE's median maximum dose, V30, and V36 were 41.3 Gy, 2.13 cc, and 0.24 cc, respectively.

CONCLUSION

By using proposed CE dose constraints of Dmax≤42 Gy, V30 ≤3.5 cc and V36 ≤0.5 cc, we confirmed the feasibility and efficacy of CEST to avoid exposing the esophagus cross-section to high prescription doses in LS-SCLC patients receiving twice-daily hyperfractionated IMRT and concurrent chemotherapy. These findings support the clinical practice of CEST in LS-SCLC patients, while more prospective and large-scale studies are warranted.

Novel Deep Learning Segmentation Models for Accurate GTV and OAR Segmentation in MR-Guided Adaptive Radiotherapy for Pancreatic Cancer Patients

PURPOSE/OBJECTIVE(S)

MR-guided adaptive radiotherapy (MRgART) improves target coverage and organ-at-risk (OAR) sparing in pancreatic cancer radiation therapy (RT). Inter-fractional changes in patients undergoing RT require time intensive re-delineation of gross tumor volume (GTV) and OARs prior to adaptive optimization. Accurate automatic segmentation has the potential to significantly improve efficiency of the adaptive workflow. We hypothesized that state-of-the-art deep learning (DL) segmentation models could adequately segment GTV and OARs in both planning and daily fractional MR scans.

MATERIALS/METHODS

The study included 21 patients with pancreatic cancer treated with MRgART (10 Gy x 5 fractions). The planning MR as well as all daily MR images and registrations were collected (6 image sets per patient and a total of 126 image sets). The planning MR and fraction 1-4 image sets were used as the training set (N = 105), while the test set (N = 21) comprised images for fraction 5, to simulate the last step of incremental learning from planning to final fraction. Evaluated contours included the GTV, Small Bowel, Large Bowel, Duodenum, Left and Right Kidney, Liver, Spinal Cord, and Stomach. To mimic clinical conditions, contour accuracy was evaluated within the ring structure surrounding the PTV, inside of which daily adaptive re-contouring is applied (2 cm expansion in the cradio-caudal direction, 3 cm expansion otherwise). We evaluated three DL model architectures: SegResNet, SegResNet 2D, and SwinUNETR to autosegment GTV and OARs. The segmentation models were trained on the training set using 5-fold cross-validation (CV) and quantitatively analyzed by comparing against clinically used contours with DICE scores. Qualitative analysis was performed by a radiation oncologist using a scoring scale: 1 = perfect, 2 = minor discrepancy, 3 = moderate discrepancy, and 4 = rejected.

RESULTS

Overall, the DL segmentations were in acceptable agreement with clinical contours. The best performing model was the SwinUNETR model with overall training DICE = 0.88±0.06, test DICE = 0.78±0.11, and qualitative score of 1.6±0.8. The agreement between the DL model and clinical segmentation for the GTV was 0.79±0.08, with a qualitative score of 2.2±0.9. The highest and lowest OAR DICE scores were for the Left Kidney (DICE = 0.93) and Small Bowel (DICE = 0.68), respectively. The highest qualitative OAR scores were for the Kidney, Liver, and Spinal Cord (score = 1.0) and the lowest qualitative score was for the Duodenum (score = 2.3) CONCLUSION: We report here the most comprehensive work on DL segmentation for pancreatic cancer MRgART, including quantitative and clinically-pertinent qualitative evaluations of 126 image sets and 3 DL architectures. Our data show good quantitative agreement between DL and clinical contours, and acceptable clinician evaluations for the majority of GTVs and OARs. The current work has great potential to significantly reduce a major bottleneck in the MRgART workflow for pancreatic cancer patients.

Performance of Adaptive Deep Learning Models for Dose Predictions on High-Quality Cone-Beam Computed Tomography Images

PURPOSE/OBJECTIVE(S)

Online plan generation remains a patient-specific and time-consuming process that can place a significant burden on clinics strained with staffing shortages. As previous research show that dose-volume histogram (DVH) prediction plays a crucial role in automatic treatment planning, the objective of this study is to assess the capability of adaptive deep learning models in predicting dose information in volumetric modulation radiotherapy plans using the high-quality CBCT images and contour information of organs-at-risk (OARs).

MATERIALS/METHODS

The relationship between dose-volume histograms (DVHs) in radiotherapy plans and the geometric information of organs-at-risk (OAR) and planning target volume (PTV) has been well established. To evaluate the performance of the current state-of-the-art convolutional neural network (CNN) models including VIT3D and Unet3D, and intuitive machine learning methods (i.e., SVM and MLP), we implemented those models for dose prediction and conducted a comprehensive analysis with treatment plans created from images acquired from patients who consented to participate an IRB-approved imaging study designed to evaluate the imaging performance of the system. In total, 20 plans created by certified medical dosimetrists were employed in this study, with 15 used for training the machine-learning models and the remaining 5 used for performance testing. Two evaluation metrics were used: 1) root mean square error (RMSE) of the predicted dose and true dose and 2) time spent on dose prediction.

RESULTS

The results of the analysis showed that the ViT-3D (Transformer) model had the lowest RMSE of 3.682 ±0.010, followed by the Unet-3D (CNN) model with an RMSE of. 3.973 ±0.021 The MLP model had an RMSE of 8.007 ±0.019 while the SVM model had the highest RMSE of 9.156 ±0.032. For a fair comparison, we use 4-fold cross validation (each has 15 training plans and 5 testing plans), and report the mean value with standard deviation. All models are optimized with Adam optimizer of a learning rate 0.01, and the training process is stopped after 100 epochs. These findings indicate that the ViT-3D (Transformer) model performed the best in terms of predicting the dose information in volumetric modulation radiotherapy plans based on the CBCT images and contour information of OARs. For tested plan which contains 81 CT images (512 × 512 resolution), the inference time to predict dose information with a general CPU machine (6-Core Intel Core i7) is about 1.5 minutes. With GPU resources, such as NVIDIA A100, the inference process can be finished within seconds.

CONCLUSION

The study demonstrated that current state-of-the-art machine-learning models can achieve promising accuracy in dose prediction using high-quality CBCT images. A well-trained machine-learning model could offer clinicians a quick and reliable prediction of the true dose to patients in the case of significant anatomical changes or provide patient-specific optimization objectives if replanning is warranted.

SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data

PURPOSE/OBJECTIVE(S)

Limited-angle CBCT (LA-CBCT) is of great clinical interest, because the scanning time and the patient dose are proportional to the scanning range of gantry rotation angles of CBCT. However, the image reconstruction for LA-CBCT remains technically challenging, which suffers from severe wedge artifacts and image distortions. This work aims to improve LA-CBCT by developing deep learning (DL) methods for real clinical CBCT projection data, which is the first feasibility study of clinical-projection-data-based LA-CBCT, to the best of our knowledge.

MATERIALS/METHODS

Targeting at real clinical projection data, we have explored various DL methods such as image/data/hybrid-domain methods and finally developed a so-called Structure-Enhanced Attention Network (SEA-Net) method that has the best image quality from clinical projection data among the DL methods we have implemented. Specifically, the proposed SEA-Net employs a specialized structure enhancement sub-network to promote texture preservation. Based on the observation that the distribution of wedge artifacts in reconstruction images is non-uniform, the spatial attention module is utilized to emphasize the relevant regions while ignores the irrelevant ones, which leads to more accurate texture restoration.

RESULTS

SEA-Net was validated in comparison with analytic (FDK), iterative (TV), image-domain DL (DDNet and FED-INet, data-domain DL (DCAR), dual-domain DL (Sam'Net), and various unrolling DL (hdNet, CTNet, FSR-Net, CasRedSCAN) methods. Among all methods, the SEA-Net had the best image reconstruction quality as quantified by root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), for various LA-CBCT problems of 90°-180° projection data. In addition, LA-CBCT via SEA-Net provided comparable accuracy for both patient setup (quantified by image registration accuracy from planning CT (pCT) to CBCT) and dose calculation (see the table), with full-view CBCT.

CONCLUSION

We explored various DL methods and developed an image-domain-based method termed SEA-Net that provided the best image quality for clinical projection data. To the best of our knowledge, this is the first feasibility study of the real clinical-projection-data-based LA-CBCT. Moreover, LA-CBCT via SEA-Net can potentially provide comparable accuracy for patient setup and dose calculation, with full-view CBCT.

Effect of artesunate on cardiovascular complications in periodontitis in a type I diabetes rat model and related mechanisms

PURPOSE

Both cardiovascular disease and periodontitis are complications of diabetes that have a great impact on human life and health. Our previous research found that artesunate can effectively improve cardiovascular disease in diabetes and has an inhibitory effect on periodontal disease. Therefore, the present study aimed to explore the potential therapeutic possibility of artesunate in the protection against cardiovascular complications in periodontitis with type I diabetes rats and to elucidate the possible underlying mechanisms.

METHODS

Sprague‒Dawley rats were randomly divided into the healthy, diabetic, periodontitis, diabetic with periodontitis, and artesunate treatment groups (10, 30, and 60 mg/kg, i.g.). After artesunate treatment, oral swabs were collected and used to determine changes in the oral flora. Micro-CT was performed to observe changes in alveolar bone. Blood samples were processed to measure various parameters, while cardiovascular tissues were evaluated by haematoxylin-eosin, Masson, Sirius red, and TUNEL staining to observe fibrosis and apoptosis. The protein and mRNA expression levels in the alveolar bone and cardiovascular tissues were detected using immunohistochemistry and RT‒PCR.

RESULTS

Diabetic rats with periodontitis and cardiovascular complications maintained heart and body weight but exhibited reduced blood glucose levels, and they were able to regulate blood lipid indicators at normal levels after artesunate treatment. The staining assays suggested that treatment with 60 mg/kg artesunate has a significant therapeutic effect on myocardial apoptotic fibrosis. The high expression of NF-κB, TLR4, VEGF, ICAM-1, p38 MAPK, TGF-β, Smad2, and MMP9 in the alveolar bone and cardiovascular tissue in the type I diabetes and type I diabetes with periodontitis rat models was reduced after treatment with artesunate in a concentration-dependent manner. Micro-CT showed that treatment with 60 mg/kg artesunate effectively alleviated alveolar bone resorption and density reduction. The sequencing results suggested that each model group of rats had vascular and oral flora dysbiosis, but artesunate treatment could correct the dysbacteriosis.

CONCLUSIONS

Periodontitis-related pathogenic bacteria cause dysbiosis of the oral and intravascular flora in type I diabetes and aggravate cardiovascular complications. The mechanism by which periodontitis aggravates cardiovascular complications involves the NF-κB pathway, which induces myocardial apoptosis, fibrosis, and vascular inflammation.

Inter-fractional Assessment during MR-guided Online Adaptive Radiotherapy for Glioblastoma

PURPOSE/OBJECTIVE(S)

Magnetic resonance image (MRI) guided radiation therapy has the potential to improve outcomes for glioblastoma by adapting to tumor changes during radiation therapy. This study aimed to assess the feasibility and potential benefits of MR-guided online adaptive radiotherapy (MRgOART) for patients with glioblastoma.

MATERIALS/METHODS

Twenty consecutive patients with glioblastoma were treated with MRgOART of 60 Gy in 30 fractions by the 1.5 T MR-Linac. The MRgOART fractions employed daily MR scans and the contours were utilized to create each adapted plan. The gross tumor volume (GTV) and clinical target volume (CTV) were delineated on MRI of pre-treatment simulation (Fx0) and all fractions (Fx1, Fx2, Fx3 ... Fx30) to evaluate the inter-fractional changes. These changes were quantified using absolute/relative volume (∆V), Dice similarity coefficient (DSC) and Hausdorff distance (HD) metrics. The reference treatment plans were generated using step-and-shoot IMRT and utilized 7-9 beam groups on original CT. Before the treatment, a synthetic CT (sCT) quality assurance (QA) process was performed to assess the dose accuracy of bulk relative electron density (rED) assignment for online MRI based treatment plan in terms of gamma analysis, point dose comparison and dose volume histogram (DVH) parameters. Then, the online adaptative treatment plans were obtained by re-optimizing based on the contours on daily pre-treatment MRI by "adapt to shape" workflow. Non-adaptive plans for each patient were generated by recalculating the dose from the reference plans on daily online MRI by "adapt to position" workflow. GTV and CTV coverage and organ at risk (OAR) constraints were used to compare non-adaptive and adaptive plans.

RESULTS

For both criteria, the 1%/1mm (98.58%±0.15%) and 2%/2mm (99.88%±0.18%) gamma passing rate results were always clinically acceptable in sCT QA process. The differences on point dose and DVH parameters between the plans based on sCT and original CT were less than 1%. A total of 20 patients with 600 fractions were evaluated. The results showed that large inter-fractional changes for GTV limited the efficacy of radiation therapy (DSC: 0.78±0.08, HD: 20.94±3.64mm, ∆V: 2.92%±6.36%). The inter-fractional CTV changes were smaller (DSC: 0.91±0.04, HD: 15.31±3.09mm, ∆V: 1.41%±1.29%). GTV coverage of non-adaptive plans was below the prescribed coverage in 228/600 fractions (38%), with 90 (15%) failing by more than 10%. For CTV coverage of non-adaptive plans, the changes were less than 5%. Online adaptative plans improved GTV and CTV coverage significantly (p<0.001) to 99%. The adaptive plans also had lower dose to whole brain than non-adaptive plans (p<0.001).

CONCLUSION

Significant inter-fractional tumor changes could be found during radiotherapy in patients with glioblastoma treated by the 1.5 T MR-Linac. Daily MR-guided re-optimization of treatment plans corrected for day-to-day anatomical variations and resulted in adequate target coverage in all fractions.

Delta-Radiomics Guides Adaptive De-Intensification after Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma in the IMRT Era

PURPOSE/OBJECTIVE(S)

In the setting of intensity-modulated radiotherapy (IMRT) and induction chemotherapy (IC), the benefits from concurrent chemotherapy remained controversial for locoregionally advanced nasopharyngeal carcinoma (LANPC). This study aimed to construct a delta-radiomics model for benefit prediction and patient selection for omitting concurrent chemotherapy.

MATERIALS/METHODS

Between December 2009 and December 2015, a total of 718 patients with LANPC treated with IC+IMRT or IC+concurrent chemoradiotherapy (CCRT) were retrospectively enrolled and randomly assigned to a training set (n = 503) and a validation set (n = 215). Radiomic features were extracted from magnetic resonance images of pre-IC and post-IC. Interclass correlation coefficients and Pearson correlation coefficients were calculated to select robust radiomic features. After univariate Cox analysis, a delta-radiomics signature was built using the LASSO-Cox regression. A nomogram incorporating the delta-radiomics signature and clinical prognostic factors was then developed and evaluated for calibration and discrimination. Risk stratification by the nomogram was evaluated by Kaplan-Meier methods. The primary outcome was overall survival (OS).

RESULTS

The delta-radiomics signature, which comprised 19 selected features, was independently associated with prognosis. It yielded an area under the receiver operating characteristic curve (AUC) of 0.77 (95% confidence interval [CI] 0.71 to 0.82) for the training set and 0.71 (95% CI 0.61 to 0.81) for the validation set. The nomogram composed of the delta-radiomic signature, age, T category, N category, pre-treatment Epstein-Barr virus DNA, and treatment showed great calibration and discrimination performance with an AUC of 0.80 (95% CI 0.75 to 0.85) for the training set and 0.75 (95% CI 0.64 to 0.85) for the validation set. Risk stratification by the nomogram excluding the treatment variable resulted in two risk groups with distinct OS. Significant better outcomes were observed in the high-risk patients with IC+CCRT compared to those with IC+IMRT (5-year OS: 73.8% vs. 61.4% in the training set and 85.8% vs. 65.6% in the validation set; all log-rank p < 0.05), while comparable outcomes between IC+CCRT and IC+IMRT were shown for the low-risk patients (95.8% vs. 95.8% in the training set and 92.2% vs. 88.3% in the validation set; all log-rank p > 0.05).

CONCLUSION

The delta-radiomics signature was identified as an independent indicator of LANPC. Integrating clinical predictors with the delta-radiomics signature, the radiomics-based nomogram could predict individual's survival outcomes and benefits from concurrent chemotherapy after IC for LANPC. Low-risk patients with LANPC determined by the nomogram may be potential candidates for omission of concurrent chemotherapy following IC in the IMRT era.

Deep Learning-Based Esophageal Tumor Identification on CT Slice

PURPOSE/OBJECTIVE(S)

The study aims to use deep learning to improve the accuracy of identifying esophageal tumors on CT slices for radiotherapy planning. The identification of Gross Tumor Volume (GTV) can be challenging due to low contrast with surrounding tissue. Other methods like endoscopy and PET scan can provide additional information, but may not be suitable for radiotherapy due to differences in tissue density and alignment needs.

MATERIALS/METHODS

A multi-task deep learning network was developed, which perform both segmentation and slice classification, simultaneously. For segmentation, the normal esophagus and tumor were treated as a single structure due to the difficulty in distinguishing between them on CT images. The slices were divided into 3 categories, including tumor, normal esophageal and other. An Unet was used for segmentation and generate the mask to remove irrelevant areas. The masked image will be input into a Resnet to obtain the categories of slices. The performance of classification was accessed by ROC curve, AUC and confusion matrix on a new dataset and PET images.

RESULTS

The multi-task deep learning network was developed on a dataset of 315 patients' CT images and GTV segmentations, which were reviewed and verified by physicians. The model was then evaluated on an additional validation dataset of 30 patients, resulting in an accuracy of 88%. In terms of sensitivity and specificity, the model showed high performance, with a sensitivity of 97% and specificity of 95% for tumor and normal esophagus in the validation dataset. Meanwhile, the specificity was 85% and the specificity was 80% for tumor and normal esophagus in PET images dataset.

CONCLUSION

This multi-task deep learning approach effectively combines the benefits of both segmentation and classification techniques, resulting in improved accuracy and efficiency in identifying esophageal tumors on CT slices for radiotherapy planning.

HDAC-Mediated Glial Crosstalk Mediates Radiation Induced Memory Changes from Whole Brain Radiation in a Mouse Model

PURPOSE/OBJECTIVE(S)

Prior work demonstrated radiation sensitivity can be regulated via Class III HDAC-mediated intracellular DNA damage response coordinated with Wnt-beta catenin signaling. We hypothesized that radiation induced functional alterations in HDAC mediated glial crosstalk in the CNS produce intercellular signaling alterations leading to phenotypic changes in cognition following whole brain radiation exposure.

MATERIALS/METHODS

Primary human astrocytes, human astrocytoma (U251), and immortalized human microglia (HMC3) were examined in vitro in response to treatment with conditioned media isolated from irradiated glial cells. Prior to treatment with either single-dose or fractionated radiation schedules, cells were transfected with siRNA expression vector for Sirt2, a class III HDAC, versus scrambled controls. Quantitative PCR and immunoblots for neurotransmitters, metabolism, and inflammatory markers were obtained. Cells exposed to conditioned media were examined by immunofluorescence for cytoskeletal alterations and changes in junctional proteins. The findings were correlated with in vivo qPCR and immunohistochemical changes in brain tissue and functional memory changes in Sirt2 knockout versus wild type mice after whole brain radiation using novel object recognition testing at 2 weeks and 6 month post radiation timepoints. Statistical analysis was performed using paired Students T test between treated and control groups.

RESULTS

Cells treated with conditioned media produced from irradiated U251, astrocytes and microglia produced increased expression of inflammatory cytokines IL-6, IL-8, GM-CSF and VEGF. However, immunofluorescence of cells treated with conditioned media from irradiated Sirt2 knockdown microglia and primary astrocytes demonstrated qualitative disruptions in glutamate neurotransmitter metabolism and actin cytoskeletal alterations with changes in pseudopodia and lamellipodia after staining with phalloidin and neurofilament L, suggesting altered intercellular communication. Connexin 43 in gap junctions was increased >2-fold (p<0.05) after exposure to conditioned media, whereas E-cadherin in adherens junctions was not significantly affected. Novel object recognition testing of Sirt2 knockout mice demonstrated resistance to radiation induced memory decline from whole brain radiation at pre radiation day 5 versus post radiation days 5 and 185 compared to controls (p<0.05).

CONCLUSION

Glial crosstalk can be mediated via elimination of a Class III HDAC and appears to be a key mediator of radiation induced disruptions of intercellular communication in the CNS, connecting radiation to structural changes on the cell surface to synaptic activity and neurotransmitter metabolism, leading to functional disputations in recognition memory similar to what is experienced by patients receiving brain radiotherapy. These data suggest glial cross talk as a new therapeutic avenue to combat radiation induced cognitive dysfunction.

An Efficient Transformer Model for Synthesizing Dual Energy CT from Single Energy Scanner

PURPOSE/OBJECTIVE(S)

Dual-energy CT can be used to optimize radiation treatment. Recently, deep learning has been demonstrated to synthesize high-energy CT images from low-energy ones for dose reduction and lower CT system burden. As the state-of-the-art deep learning architecture, the computation burden of Transformer increases quadratically with the feature size, making the model training resource-demanding or even infeasible. Here, we introduce an efficient transformer for the balance between CT image synthesis quality and computational burden.

MATERIALS/METHODS

The model is a U-shape deep neural network with encoders and decoders built by Transformer blocks. The model input is low-energy 100kVp CT image and the output is high-energy 140kVp one. Each block has a Self Channel Correlation Unit (SCCU) and a Self Spatial Attention Unit (SSAU). Local shortcuts are applied for both units. Under-sampling operation achieved by pixel shuffling is used to obtain multi-scale feature maps, and the transformer block is applied on each feature scale. Symmetric skip connection sending features from shallow layers to deep layers, thus an additional 1 × 1 convolution is used for feature fusion in each decoder. In a SCCU, the feature is first mapped to one Query, one Key, and one Value. Then the Query and the Key tensors perform matrix multiplication to compute cross covariance of feature channels. The channel correlation score can be obtained by normalization of the covariance, and it is used to weight the Value tensor. As a result, the model complexity only increases linearly with the feature size. Besides the channel weighting, we enhance spatial information using SSAU, where the feature is mapped to two tensors. One tensor after activation is used to point-wisely calibrate another tensor. Additional Transformer blocks are cascaded to the last decoder for feature refinement. Because of the structure similarity of low- and high-energy CT images, a global shortcut is used to ease model training. Clinical iodine contrast-enhanced dual energy CT image datasets of 19 patients are used in this study. The dual-energy scanning is performed by a SOMATOM Definition Flash DECT scanner. We split the datasets into training dataset of 15 patients, validation dataset of 1 patient, and testing dataset of 3 patients. The image size is 512 × 512 with pixel size 0.5 × 0.5 mm2.

RESULTS

The U-Net model with 1.95M parameters and 44.87G FLOPS achieved the averaged PSNR value of 44.55 dB (s.t.d. 1.34) and averaged RMSE value of 0.0060 (s.t.d. 0.001). In comparison, our efficient Transformer with 1.408M parameters and 31.375G FLOPS achieved the averaged PSNR value of 44.78 dB (s.t.d. 1.37) and RMSE value of 0.0059 (s.t.d. 0.001), demonstrating our model has better performance with small model size and less computation.

CONCLUSION

The efficient Transformer model allows high-resolution CT image synthesis with small model scale and computation burden from low-energy CT image.

Deep Learning-Based Multi-Modality Segmentation of Primary Gross Tumor Volume in CT and MRI for Nasopharyngeal Carcinoma

PURPOSE/OBJECTIVE(S)

The delineation of primary gross tumor volume (GTV) of nasopharyngeal carcinoma (NPC) is an essential step for radiotherapy planning. In clinical practice, radiation oncologists manually delineate the GTV in planning CT with the help of diagnostic MRI. This is because NPC tumors are closely adjacent to many important anatomic structures, and CT and MRI provide complementary strength to accurately determine the tumor extension boundary. Manual delineation is time-consuming with the potential registration errors between MRI and CT decreasing the delineation accuracy. In this study, we propose a fully automated GTV segmentation method based on CT and MRI by first aligning MRI to CT, and then, segmenting the GTV using a multi-modality deep learning model.

MATERIALS/METHODS

We collected 104 nasopharyngeal carcinoma patients with both planning CT and diagnostic MRI scans (T1 & T2 phases). An experienced radiation oncologists manually delineated the GTV, which was further examined by another senior radiation oncologist. Then, a coarse to fine cross-modality registration from MRI to CT was conducted as follows: (1) A rigid transformation was performed on MRI to roughly align MRI to CT with similar anatomic position. (2) Then, the region of interest (RoI) on both CT and rigid-transformed MRI were cropped. (3) A leading cross-modality deformable registration algorithm, named DEEDS, was applied on the cropped MRI and CT RoIs to find an accurate local alignment. Next, using CT and registered MRI as the combined input, a multi-modality deep segmentation network based on nnUNet was trained to generate the GTV prediction. 20% patients were randomly selected as the unseen testing set to quantitatively evaluate the performance.

RESULTS

The quantitative NPC GTV segmentation performance is summarized in Table 1. The deep segmentation model using CT alone achieved reasonable high performance with 76.6% Dice score and 1.34mm average surface distance (ASD). When both CT and registered MRI were used, the segmentation model further improved the performance by 0.9% Dice score increase and 11% relative ASD error reduction, demonstrating the complementary strength of CT and MRI in determining NPC GTV. Notably, the achieved 77.5% Dice score and 1.19mm ASD by the multimodality model is among the top performing results reported in recent automatic NPC GTV segmentation using either CT or MRI modality.

CONCLUSION

We developed a fully automated multi-modal deep-learning model for NPC GTV segmentation. The developed model can segment the NPC GTV in high accuracy. With further optimization and validation, this automated model has potential to standardize the NPC GTV segmentation and significantly decrease the workload of radiation oncologists in clinical practice.

Development and Validation of a Deep Learning-Based Auto-Delineation of Target Volume and Organs at Risk in Pancreatic Cancer Radiotherapy

PURPOSE/OBJECTIVE(S)

The delineation of the clinical target volume (CTV), gross target volume (GTV) and organs at risk (OARs) is a crucial and laborious in pancreatic cancer radiotherapy. In this work, we propose and evaluate a three-dimensional (3D) novel convolutional neural network (CNN) for automatic and accurate CTV, GTV and OARs in pancreatic cancer.

MATERIALS/METHODS

A total of 120 computed tomography (CT) scans patients with pancreatic cancer were collected. A novel 3D CNN network, called ResUNet3D, was developed to achieve auto-delineation. 96 patients chosen randomly were used for training, 12 patients for validation, and 12 patients for testing. Meanwhile, the Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95%) were used to assess the performance.

RESULTS

The DSC values for the test data were 80.9±8.6%, 77.5±5.6%, 94.5±1.3%, 66.2±13.4%, 73.6±7.6%, 79.0±8.7%, 94.1±1.9%, 94.6±1.4%, 87.3±5.8% for CTV, GTV, liver, duodenum, spinal cord, bowel, kidney left, kidney right, stomach. The corresponding HD95% values were 10.7±6.9mm, 7.8±5.7mm, 11.6±5.6mm, 18.6±5.6mm, 2.7±0.7mm, 17.7±8.6mm, 3.9±1.4mm, 3.7±1.9mm, 13.4±5.7mm, respectively. The average delineation time for one patient's CT images was within 5 seconds.

CONCLUSION

The experimental results demonstrate that the CTV, GTV and OARs delineated for pancreatic cancer by ResUNet3D achieved a close agreement with the ground truth. ResUNet3D could significantly reduce the radiation oncologists' contouring time.

Evaluation of 68Ga-Fibroblast Activation Protein Inhibitor vs. 18F-FDG as a Novel Radiotracer for Biologically Guided Radiation Therapy

PURPOSE/OBJECTIVE(S)

Real-time biology guided radiation therapy (BgRT) uses real-time positron emissions from a PET tracer during treatment to guide targeted radiation to cancerous lesions. Fibroblast activation protein alpha (FAP) is highly expressed on cancer-associated fibroblasts in tumors with low expression in normal tissues. While 18F-FDG-PET requires fasting and has background in the liver and brain, 68-Gallium labeled FAP inhibitor (FAPI) does not require fasting and has less background uptake. The goal of this study was to investigate the utility of FAPI as a potential universal fiducial for BgRT. We hypothesized that 68Ga-FAPI would be a better radiotracer than 18F-FDG, as assessed by the Normalized Minimal kBq/mL and the Normal Target Signal (NTS), two parameters used to gauge the suitability of BgRT.

MATERIALS/METHODS

PET-CTs were obtained for 50 patients with pancreatic, liver, lung, head & neck, and cervical cancer using 18F-FDG and 68Ga-FAPI (n = 10 for each). Four DICOM images were obtained per patient (FDG PET + CT, FAPI PET + CT). Radiation oncologists delineated the gross tumor volume (GTV) on PET images. A separate set of auto-contours were generated from the PET using an auto-threshold of 40% maximum SUV for all tumors. A 1 cm expansion was added to the GTV to create a ring around the physician-generated contours and auto-contours. The following parameters were measured: GTV volume, SUV max of GTV, SUV mean of GTV, Normalized Minimal kBq/mL within the GTV, and NTS (= SUV max/Ring SUV mean). Values were compared using paired t-test. For the BgRT product with similar calculations, the required Normalized Minimal kBq/mL is > 5 kBq/mL; the required NTS is > 2.7 for treatment planning and > 2.0 for BgRT delivery.

RESULTS

The Normalized Minimal kBq/mL for FAPI was > 5 kBq/mL for all tumors and greater for auto-contoured GTVs compared to physician-contoured GTVs. The mean NTS for the auto-contours for all tumor sites was > 2.0. In addition, there was a statistically significant increase in the NTS for FAPI compared to FDG in pancreatic, liver and head & neck cancers. In pancreatic cancer, there was a statistically significant increase in Normalized Minimal kBq/mL for FAPI compared to FDG (26.0 vs 14.2) (p = 0.01) and the SUVmax of FAPI was almost double that of FDG (15.9 vs 8.2) (p = 0.01). FAPI had no background in the liver, but had high background in the uterus, suggesting it may have a role in liver cancer but not cervical cancer.

CONCLUSION

This is the first study demonstrating the potential superiority of 68Ga-FAPI compared to 18F-FDG as a biologic fiducial for BgRT when treating pancreatic, liver and head & neck cancers, with a similar efficacy for lung cancer. Our results indicate that auto-contoured GTVs generate a higher NTS than physician-contoured GTVs but all are > 2.0. In addition, the Normalized Minimal kBq/mL for auto-contours is > 5 kBq/mL for all tumors. As hypothesized, FAPI-based BgRT is most likely to be successful when treating tumors with significant desmoplastic stroma, such as pancreatic cancer.

Comparing 90-Day Post-Operative Mortality after Neoadjuvant Proton-Based vs. Photon-Based Chemoradiotherapy for Esophageal Cancer

PURPOSE/OBJECTIVE(S)

Standard of treatment for locally advanced esophageal cancer consists of chemoradiotherapy (CRT) followed by surgery. Evidence suggests that proton beam therapy (PBT) results in lower toxicity and fewer post-operative complications compared to photon-based radiotherapy (RT). Mortality in the first 90 days after surgery is a rare event occurring in 2-8% of patients, with higher reported rates (of up to 17%) in older patients. This 90-day mortality (90DM) rate is an important measure of post-operative (non-oncologic) mortality as a proxy of quality of care. We hypothesize that PBT could reduce the incidence of 90DM compared to photon-based RT.

MATERIALS/METHODS

From a single-center retrospectively acquired database patients with esophageal cancer treated with neoadjuvant CRT and esophagectomy in 1998-2022 were selected. Univariable logistic regression analyses were used to study the associations of RT modality and other patient- and treatment-related characteristics with 90DM. Subsequently, 3 separate methods were applied to adjust for confounding bias. These included multivariable logistic regression, 1:1 nearest-neighbor propensity score matching (PSM), and inverse probability of treatment weighting (IPTW). Finally, stratified analyses for patient groups aged ≥67 vs. <67 years were performed.

RESULTS

A total of 894 eligible patients were included (PBT, n = 202; photon-based RT, n = 692). PBT patients had a significantly higher age, better performance score, and a higher number of comorbidities. The 90DM rate was 5 (2.5%) in the PBT group and 29 (4.2%) in the photon-based RT group (p = 0.262). Significant univariable predictors of 90DM included higher age and tumor location. After multivariable adjustment, PBT vs. photon therapy was not significantly associated with 90DM (OR 0.49, 95% CI 0.18-1.31). The 90DM rates in the PSM cohort (n = 181 vs. n = 181) were 2.8% for PBT and 3.3% for photon-based RT (p = 0.379). The 90DM rates in the IPTW cohort were 2.8% for PBT and 4.1% for photon-based RT (p = 0.427). In the full cohort, stratified analysis for age groups revealed that in patients aged ≥67 years, PBT was associated with a decreased risk of 90DM compared to photon-based RT (1.3% vs. 8.8%; p = 0.046), which was not the case in patients aged <67 years. In the PSM cohort, a comparable (but non-significant) difference was observed in favor of PBT in patients aged ≥67 years (i.e., 1.5% vs. 7.5%; p = 0.099). Within-group analyses in the original cohort demonstrated that a higher age significantly increased the risk of 90DM within the photon-based RT group (8.8% vs. 2.7% for age ≥67 vs. <67 years; p = 0.001), but not within the PBT group (1.3% vs. 3.2%; p = 0.398).

CONCLUSION

Post-operative 90DM after esophagectomy for cancer was not significantly different between PBT and photon-based neoadjuvant CRT. However, among older patients we observed a signal that PBT may reduce the risk of 90DM. Higher age increased the risk of 90DM in patients who underwent photon-based RT, but not in patients who underwent PBT.

The Incidence and Prognosis Value of Perineural Invasion in Rectal Carcinoma: From Meta-Analyses and Real-World Clinical Pathological Features

AIMS

Perineural invasion (PNI) is a special type of metastasis of several cancers and has been reported as being a factor for poor prognosis in colorectal carcinoma. However, investigations of PNI in only rectal cancer and a comprehensive analysis combining meta-analyses with real-world case studies remain lacking.

MATERIALS AND METHODS

First, articles from 2000 to 2020 concerning the relationship between PNI and rectal cancer prognoses and clinical features were meta-analysed. Subsequently, we carried out a retrospective analysis of 312 rectal cancer cases that underwent radical surgery in the real world. The incidence of PNI and the relationship between PNI and prognosis, as well as clinicopathological factors, were investigated.

RESULTS

The incidence of PNI was 23.09% and 33.01% in the meta-analysis and clinical cases, respectively. PNI occurred as early as stage I (2.94%). Moreover, neoadjuvant therapy significantly reduced the PNI-positive rate (20.34% versus 26.54%). Both meta-analysis and real-world clinical case studies suggested that PNI-positive patients had poorer prognoses than PNI-negative patients. We established an effective risk model consisting of T stage, differentiation and lymphovascular invasion to predict PNI in rectal cancer.

CONCLUSION

PNI is a poor prognostic factor for rectal cancer and could occur even in stage I. Additionally, neoadjuvant therapy could sufficiently reduce the PNI-positive rate. T stage, lymphovascular invasion and differentiation grade were independent risk factors for PNI and the risk model that included these factors could predict the probability of PNI.

A prospective multicentre controlled study of Gaoweikang (Chinese multiherb extract-based tincture) used in high-risk HPV infections

OBJECTIVE

The aim of the study was to investigate the safety and antiviral efficacy of a Chinese multiherb extract-based tincture (GWK) on a population of patients with high-risk human papilloma (hrHPV) infections and hrHPV-caused cervical low-grade squamous intraepithelial lesions (LSILs).

PATIENTS AND METHODS

Patients with persistent hrHPV infection were enrolled in Group A, including A1 subjects, who received the intervention, and A2 subjects, who received the control. Patients with hrHPV infection causing cervical LSIL were enrolled in Group B, which included B1 subjects, who received the intervention, and B2 subjects, who served as the control. For Groups A1 and B1, hrHPV was tested at 3 months (M3) and 6 months (M6) after the intervention. The side effects were also analyzed.

RESULTS

At baseline (D0), a total of 99 patients were enrolled in Group A, with 50 subjects in Group A1 and 49 subjects in Group A2. A total of 91 patients were enrolled in Group B, with 45 subjects in Group B1 and 46 subjects in Group B2. There was no significant difference in the characteristics, including average age, age stratification, and HPV genotype. At M6, both Group A1 and Group B1 had a higher hrHPV clearance rate than the control group (A1/A2: 80.0% vs. 20.4%; B1/B2: 64.4% vs. 15.2%, p<0.001). At M6, the effective rates of Group A1 and Group B1 were 84% (42/50) and 68.9% (31/45), respectively. The side effect rates of Groups A1 and B1 were 11.5% (6/52) and 11.1% (5/45), respectively. Most adverse reactions involved local discomfort, including vulvar erythema, vulvar itch, increased vaginal discharge, cervical bleeding, and mild pain in the lower abdomen. Univariate logistic regression analysis showed that the intervention had an OR of 12 (95% CI 4.431-32.50) for clearing persistent HPV infection (p<0.001). For cervical LSIL, the intervention had an OR of 10.1 for clearing persistent HPV infection (95% CI 3.68-27.7) (p<0.001).

CONCLUSIONS

The results of this study suggest that the Chinese multiherb extract-based tincture GWK is safe and well tolerated. Furthermore, this preliminary study showed that this Chinese multiherb extract-based tincture is helpful for promoting HPV clearance in cases of persistent HPV and HPV-induced LSIL.

Improvement of soil acidification and ammonium nitrogen content in tea plantations by long-term use of organic fertilizer

Soil acidification is common in some Chinese tea plantations, which seriously affected growth of tea trees. Hence, it is essential to explore soil remediation in acidified tea plantations for sustainable development of the tea industry. We sought to determine how different fertilizers affect acidified soil and their N transformation in tea plantations. Different fertilizers were used on acidified tea plantation soils for 4 years (2017-2021), and changes in soil pH, indices related to soil N transformation and tea yield were analysed to construct interaction networks of these indices and find which had the largest influence on fertilization. Long-term use of sheep manure reduced soil acidification, increased soil pH, enhanced the number and intensity of N-fixing and ammonifying bacteria, urease, protease, asparaginase and N-acetamide glucose ribosidase activity and nifH gene expression. This treatment reduced the number and intensity of soil nitrifying and denitrifying bacteria, nitrate reductase and nitrite reductase activity, while the expression of amoA-AOA, nirK, nirS, narG and nosZ in turn increased ammonium N content of the soil, reduced nitrate N content, and enhanced tea yield. Topsis index weight analysis showed that ammonium N content in the soil had the largest impact among fertilization effects. Long-term use of sheep manure was beneficial in restoring the balance of the micro-ecosystem in acidified soil. This study provides an important practical basis for soil remediation and fertilizer management in acidified tea plantation soils.

Ceruloplasmin regulating fibrosis in orbital fibroblasts provides a novel therapeutic target for Graves' orbitopathy

PURPOSE

In diagnosing the pathogenesis of Graves' orbitopathy (GO), there is a growing interest in fibrosis generated by orbital fibroblasts (OFs); nevertheless, the involvement of ceruloplasmin (CP) in OFs remains unknown.

METHODS

Differentially expressed genes (DEGs) were identified through bioinformatic analysis. OFs were isolated from orbital tissue and identified with immunofluorescent staining. The levels of DEGs were validated in GO tissue samples and TGF-β-challenged OFs, and CP was selected for the following laboratory investigations. CP overexpression or knockdown was achieved, and cell viability and fibrosis-associated proteins were investigated to assess the cell phenotype and function. Signaling pathways were subsequently investigated to explore the mechanism of CP function in OFs.

RESULTS

CP and cathepsin C (CTSC) are two overlapped DEGs in GSE58331 and GSE105149. OFs were isolated and identified through fibrotic biomarkers. CP and CTSC were downregulated in GO tissue samples and TGF-β-challenged OFs. CP overexpression or knockdown was achieved in OFs by transducing a CP overexpression vector or small interfering RNA against CP (si1-CP or si2-CP) and verified using a qRT-PCR. CP overexpression inhibited cell viability and reduced the levels of α-SMA, vimentin, fibronectin, and collagen I, whereas CP knockdown exerted opposite effects on OFs. CP overexpression inhibited the phosphorylation of Smad3, Erk1/2, p38, JNK, and AKT; conversely, CP knockdown exerted opposite effects on the phosphorylation of factors mentioned above.

CONCLUSION

CP was downregulated in GO and suppressed the expression of fibrosis-associated proteins in both GO and normal OFs. CP might serve as a promising therapeutic agent in the treatment regimens for GO.

Long-Term Follow-Up Analysis of NRG Oncology RTOG 0415: A Randomized Phase III Non-Inferiority Study Comparing Two Fractionation Schedules in Patients with Favorable-Risk Prostate Cancer

PURPOSE/OBJECTIVE(S)

To assess whether the efficacy of a hypofractionated (H) schedule is no worse than a conventional (C) schedule in men with low-risk prostate cancer.

MATERIALS/METHODS

Accrual began April 2006 and ended in December 2009. 1115 men with favorable-risk prostate cancer were randomly assigned 1:1 to a conventional (C) schedule (73.8 Gy in 41 fractions over 8.2 weeks) or to a hypofractionated (H) schedule (70 Gy in 28 fractions over 5.6 weeks). The trial was designed to establish with 90% power and alpha = 0.05 that (H) results in 5-year disease-free survival (DFS) that is not lower than (C) by more than 7% (hazard ratio (HR) < 1.52). Protocol specified secondary endpoints evaluated for noninferiority include: biochemical recurrence (BR), local progression, disease-specific survival, and overall survival.

RESULTS

One thousand ninety-two protocol eligible men were analyzed: 542 to C and 550 to H. Median follow-up is 12.75 years. Baseline characteristics were not different according to treatment arm. The estimated 12-year DFS is 56.1% (95% CI 51.5, 60.5) in the C arm and 61.8% (57.2, 66.0) in the H arm. The DFS hazard ratio (H/C) is 0.85 (0.71-1.03), confirming non-inferiority (p<0.001). Twelve-year cumulative incidence of biochemical recurrence (BR) was 17.0% (CI 13.8, 20.5) in the C-RT and 9.9% (CI 7.5, 12.6) in the H-RT arm; (HR = 0.56, (0.40-0.78) suggesting improved efficacy with H. Additional pre-specified secondary endpoints were non-inferior Late Grade ≥ 3 GI toxicity is 3.2% (C) vs. 4.4% (H), Relative risk (RR) for H vs. C 1.39 (CI 0.75, 2.55) Late Grade ≥ 3 GU toxicity is 3.4% (C) vs. 4.2% (H), RR = 1.26 (CI 0.69, 2.30).

CONCLUSION

In men with favorable-risk prostate cancer, long-term disease-free survival is non-inferior with 70 Gy in 28 fractions compared to 73.8 Gy in 41 fractions. The risk of BR is reduced with moderate hypofractionation. No differences in late Grade ≥3 GI/GU toxicity were observed between the arms. (ClinicalTrials.gov identifier: NCT00331773).

2V-CBCT: Two-Orthogonal-Projection Based CBCT Reconstruction and Dose Calculation from Real CBCT Projection Data

PURPOSE/OBJECTIVE(S)

Not all radiation therapy (RT) treatments/fractions have CBCT acquired, but two orthogonal projections (i.e., KV radiography) are always available. This work demonstrates the feasibility of two-orthogonal-projection-based CBCT (2V-CBCT) reconstruction and dose calculation for RT from real CBCT projection data, which is the first 2V-CBCT feasibility study using real projection data, to the best of our knowledge.

MATERIALS/METHODS

2V-CBCT is a severely ill-posed inverse problem for which we propose a coarse-to-fine learning strategy. First, a 3D deep neural network that can extract and exploit the inter-slice and intra-slice information is adopted to predict the initial 3D volumes. Then, a 2D deep neural network is utilized to fine-tune the initial 3D volumes slice-by-slice. During the fine-tuning stage, a perceptual loss based on multi-frequency features is employed to enhance the image reconstruction. Dose calculation results from both photon and proton RT demonstrate that 2V-CBCT provides comparable accuracy with full-view CBCT based on real projection data.

RESULTS

The proposed method was evaluated on real HN data acquired from on-board CBCT scanners rather than the low-resolution simulated data or down-sampled data. Both visual assessment and quantitative analysis demonstrate that the proposed coarse-to-fine learning strategy has the potential to produce satisfactory volumetric images from two orthogonal projections. Furthermore, we assessed the utility of 2V-CBCT in RT. The results show that the dose distribution maps, dose-volume histograms, and dose parameters calculated using 2V-CBCT have comparable accuracy with the counterparts calculated using the corresponding full-view CBCT for both photon and proton RT. In the table, the methods under comparison are pCT (planning CT), FV-CBCT (CBCT reconstructed with full-view projection data), and 2V-CBCT (CBCT reconstructed with two orthogonal projections).

CONCLUSION

A new effective 2V-CBCT reconstruction method is proposed and validated using real CBCT projection data, which can potentially provide comparable dose calculation accuracy for both photon and proton RT.