A prior-information-based automatic segmentation method for the clinical target volume in adaptive radiotherapy of cervical cancer

OBJECTIVE

Adaptive planning to accommodate anatomic changes during treatment often requires repeated segmentation. In this study, prior patient-specific data was integrateda into a registration-guided multi-channel multi-path (Rg-MCMP) segmentation framework to improve the accuracy of repeated clinical target volume (CTV) segmentation.

METHODS

This study was based on CT image datasets for a total of 90 cervical cancer patients who received two courses of radiotherapy. A total of 15 patients were selected randomly as the test set. In the Rg-MCMP segmentation framework, the first-course CT images (CT1) were registered to second-course CT images (CT2) to yield aligned CT images (aCT1), and the CTV in the first course (CTV1) was propagated to yield aligned CTV contours (aCTV1). Then, aCT1, aCTV1, and CT2 were combined as the inputs for 3D U-Net consisting of a channel-based multi-path feature extraction network. The performance of the Rg-MCMP segmentation framework was evaluated and compared with the single-channel single-path model (SCSP), the standalone registration methods, and the registration-guided multi-channel single-path (Rg-MCSP) model. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average surface distance (ASD) were used as the metrics.

RESULTS

The average DSC of CTV for the deformable image DIR-MCMP model was found to be 0.892, greater than that of the standalone DIR (0.856), SCSP (0.837), and DIR-MCSP (0.877), which were improvements of 4.2%, 6.6%, and 1.7%, respectively. Similarly, the rigid body DIR-MCMP model yielded an average DSC of 0.875, which exceeded standalone RB (0.787), SCSP (0.837), and registration-guided multi-channel single-path (0.848), which were improvements of 11.2%, 4.5%, and 3.2%, respectively. These improvements in DSC were statistically significant (p < 0.05).

CONCLUSION

The proposed Rg-MCMP framework achieved excellent accuracy in CTV segmentation as part of the adaptive radiotherapy workflow.

A GPU-based fast Monte Carlo code that supports proton transport in magnetic field for radiation therapy

This paper presents the effort to extend a previously reported code ARCHER, a GPU-based Monte Carlo (MC) code for coupled photon and electron transport, into protons including the consideration of magnetic fields. The proton transport is modeled using a Class-II condensed-history algorithm with continuous slowing-down approximation. The model includes ionization, multiple scattering, energy straggling, elastic and inelastic nuclear interactions, as well as deflection due to the Lorentz force in magnetic fields. An additional direction change is added for protons at the end of each step in the presence of the magnetic field. Secondary charge particles, except for protons, are terminated depositing kinetic energies locally, whereas secondary neutral particles are ignored. Each proton is transported step by step until its energy drops to below 0.5 MeV or when the proton leaves the phantom. The code is implemented using the compute unified device architecture (CUDA) platform for optimized GPU thread-level parallelism and efficiency. The code is validated by comparing it against TOPAS. Comparisons of dose distributions between our code and TOPAS for several exposure scenarios, ranging from single square beams in water to patient plan with magnetic fields, show good agreement. The 3D-gamma pass rate with a 2 mm/2% criterion in the region with dose greater than 10% of the maximum dose is computed to be over 99% for all tested cases. Using a single NVIDIA TITAN V GPU card, the computational time of ARCHER is found to range from 0.82 to 4.54 seconds for 1 × 107 proton histories. Compared to a few hours running on TOPAS, this speed improvement is significant. This work presents, for the first time, the performance of a GPU-based MC code to simulate proton transportation magnetic fields, demonstrating the feasibility of accurate and efficient dose calculations in potential magnetic resonance imaging (MRI)-guided proton therapy.

Role of Cisplatin in Inducing Acute Kidney Injury and Pyroptosis in Mice via the Exosome miR-122/ELAVL1 Regulatory Axis

Although cisplatin is an effective chemotherapy drug for the treatment of various cancers, its clinical use is limited due to its side effects, especially nephrotoxicity. Unfortunately, acute kidney injury (AKI) caused by cisplatin remains one of the main challenges in effective cancer treatment. Evidence increasingly suggests that renal inflammation and pyroptotic inflammatory cell death of renal tubular epithelial cells (RTECs) mainly determine the progression and outcome of cisplatin-induced AKI. However, it is not clear how cisplatin regulates the pyroptosis of RTECs cells in AKI. The current study aimed to determine the regulation mechanism of AKI induced by cisplatin. We used cisplatin to induce AKI in vivo. We performed H&E staining of mouse kidney tissue sections and evaluated serological indicators of kidney injury (including blood urea nitrogen (BUN), serum creatinine, and tumor necrosis factor-alpha (TNF-alpha)). We used immunohistochemistry and western blot to detect the important substrate protein gasdermin D (GSDMD) and key target caspase-1 of pyroptosis, respectively. Cisplatin induced mouse AKI and RTECs pyroptosis. HK2 cell-derived exosomes treated with cisplatin influenced pyroptosis of the surrounding HK2 cells. Cisplatin-treated HK2 cells exosome-derived miR-122 regulated pyroptosis in the surrounding cells. Exosome-derived miR-122 affected cisplatin-induced AKI and HK2 cells pyroptosis by regulating the expression of embryonic lethal abnormal vision (ELAVL1). These results suggest that exosome miR-122 inhibited pyroptosis and AKI by targeting ELAVL1 under cisplatin treatment, and this offers a potential target for the treatment of AKI.

Investigation of total skin helical tomotherapy using a 3D-printed total skin bolus

OBJECTIVE

To investigate the effectiveness of using a 3D-printed total skin bolus in total skin helical tomotherapy for the treatment of mycosis fungoides.

MATERIALS AND METHODS

A 65-year-old female patient with a 3-year history of mycosis fungoides underwent treatment using an in-house desktop fused deposition modelling printer to create a total skin bolus made of a 5-mm-thick flexible material, which increased the skin dose through dose building. The patient's scan was segmented into upper and lower sections, with the division line placed 10 cm above the patella. The prescription was to deliver 24 Gy over 24 fractions, given 5 times per week. The plan parameters consisted of a field width of 5 cm, pitch of 0.287 and modulation factor of 3. The complete block was placed 4 cm away from the planned target region to reduce the area of the internal organs at risk, especially the bone marrow. Dose delivery accuracy was verified using point dose verification with a "Cheese" phantom (Gammex RMI, Middleton, WI), 3D plane dose verification with ArcCHECK (Model 1220, Sun Nuclear, Melbourne, FL), and multipoint film dose verification. Megavoltage computed tomography guidance was also utilized to ensure the accuracy of the setup and treatment.

RESULTS

A 5-mm-thick 3D-printed suit was used as a bolus to achieve a target volume coverage of 95% of the prescribed dose. The conformity index and homogeneity index of the lower segment were slightly better than those of the upper segment. As the distance from the skin increased, the dose to the bone marrow gradually decreased, and the dose to other organs at risk remained within clinical requirements. The point dose verification deviation was less than 1%, the 3D plane dose verification was greater than 90%, and the multipoint film dose verification was less than 3%, all of which confirmed the accuracy of the delivered dose. The total treatment time was approximately 1.5 h, which included 0.5 h of wearing the 3D-printed suit and 1 h with the beam on. Patients only experienced mild fatigue, nausea or vomiting, low-grade fever, and grade III bone marrow suppression.

CONCLUSION

The use of a 3D-printed suit for total skin helical tomotherapy can result in a uniform dose distribution, short treatment time, simple implementation process, good clinical outcomes, and low toxicity. This study presents an alternative treatment approach that can potentially yield improved clinical outcomes for mycosis fungoides.

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.

Dosimetric comparison of deformable image registration and synthetic CT generation based on CBCT images for organs at risk in cervical cancer radiotherapy

OBJECTIVE

Anatomical variations existing in cervical cancer radiotherapy treatment can be monitored by cone-beam computed tomography (CBCT) images. Deformable image registration (DIR) from planning CT (pCT) to CBCT images and synthetic CT (sCT) image generation based on CBCT are two methods for improving the quality of CBCT images. This study aims to compare the accuracy of these two approaches geometrically and dosimetrically in cervical cancer radiotherapy.

METHODS

In this study, 40 paired pCT-CBCT images were collected to evaluate the accuracy of DIR and sCT generation. The DIR method was based on a 3D multistage registration network that was trained with 150 paired pCT-CBCT images, and the sCT generation method was performed based on a 2D cycle-consistent adversarial network (CycleGAN) with 6000 paired pCT-CBCT slices for training. Then, the doses were recalculated with the CBCT, pCT, deformed pCT (dpCT) and sCT images by a GPU-based Monte Carlo dose code, ArcherQA, to obtain Dose_CBCT, Dose_pCT, Dose_dpCT and Dose_sCT. Organs at risk (OARs) included small intestine, rectum, bladder, spinal cord, femoral heads and bone marrow, CBCT and pCT contours were delineated manually, dpCT contours were propagated through deformation vector fields, sCT contours were auto-segmented and corrected manually.

RESULTS

The global gamma pass rate of Dose_sCT and Dose_dpCT was 99.66% ± 0.34%, while that of Dose_CBCT and Dose_dpCT was 85.92% ± 7.56% at the 1%/1 mm criterion and a low-dose threshold of 10%. Based on Dose_dpCT as uniform dose distribution, there were comparable errors in femoral heads and bone marrow for the dpCT and sCT contours compared with CBCT contours, while sCT contours had lower errors in small intestine, rectum, bladder and spinal cord, especially for those with large volume difference of pCT and CBCT.

CONCLUSIONS

For cervical cancer radiotherapy, the DIR method and sCT generation could produce similar precise dose distributions, but sCT contours had higher accuracy when the difference in planning CT and CBCT was large.

Substituted benzoate-anchored decanuclear titanium-oxo clusters featuring unprecedented defective double-cubane geometry

Based on DMF solvent, four titanium-oxo clusters (TOCs) [Ti10(μ3-O)12(L)6 (OiPr)10 (DMF)2] (Ti-L) (L=4-methylbenzoate(MB), 3,5-Di-tert-butylbenzoate (DTBB), 4-Methoxybenzoate (MOB) and 4-Methyl-3,5-dinitrobenzoate(MDNB) with the same coordination environments have been solvothermally synthesized with high...

APOBEC mutagenesis, kataegis, chromothripsis in EGFR-mutant osimertinib-resistant lung adenocarcinomas

BACKGROUND

Studies of targeted therapy resistance in lung cancer have primarily focused on single-gene alterations. Based on prior work implicating apolipoprotein b mRNA-editing enzyme, catalytic polypeptide-like (APOBEC) mutagenesis in histological transformation of epidermal growth factor receptor (EGFR)-mutant lung cancers, we hypothesized that mutational signature analysis may help elucidate acquired resistance to targeted therapies.

PATIENTS AND METHODS

APOBEC mutational signatures derived from an Food and Drug Administration-cleared multigene panel [Memorial Sloan Kettering Cancer Center Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT)] using the Signature Multivariate Analysis (SigMA) algorithm were validated against the gold standard of mutational signatures derived from whole-exome sequencing. Mutational signatures were decomposed in 3276 unique lung adenocarcinomas (LUADs), including 93 paired osimertinib-naïve and -resistant EGFR-mutant tumors. Associations between APOBEC and mechanisms of resistance to osimertinib were investigated. Whole-genome sequencing was carried out on available EGFR-mutant lung cancer samples (10 paired, 17 unpaired) to investigate large-scale genomic alterations potentially contributing to osimertinib resistance.

RESULTS

APOBEC mutational signatures were more frequent in receptor tyrosine kinase (RTK)-driven lung cancers (EGFR, ALK, RET, and ROS1; 25%) compared to LUADs at large (20%, P < 0.001); across all subtypes, APOBEC mutational signatures were enriched in subclonal mutations (P < 0.001). In EGFR-mutant lung cancers, osimertinib-resistant samples more frequently displayed an APOBEC-dominant mutational signature compared to osimertinib-naïve samples (28% versus 14%, P = 0.03). Specifically, mutations detected in osimertinib-resistant tumors but not in pre-treatment samples significantly more frequently displayed an APOBEC-dominant mutational signature (44% versus 23%, P < 0.001). EGFR-mutant samples with APOBEC-dominant signatures had enrichment of large-scale genomic rearrangements (P = 0.01) and kataegis (P = 0.03) in areas of APOBEC mutagenesis.

CONCLUSIONS

APOBEC mutational signatures are frequent in RTK-driven LUADs and increase under the selective pressure of osimertinib in EGFR-mutant lung cancer. APOBEC mutational signature enrichment in subclonal mutations, private mutations acquired after osimertinib treatment, and areas of large-scale genomic rearrangements highlights a potentially fundamental role for APOBEC mutagenesis in the development of resistance to targeted therapies, which may be potentially exploited to overcome such resistance.

Effects of Mobile Identity on Smartphone Symbolic Use: An Attachment Theory Perspective

Smartphones are not only multifunctional tools but also users' personal extensions and identity symbols, as they are constantly with users and highly visible to the public while in use. Due to this public property as well as the close bond between smartphones and users, they are frequently used for personal identity expression besides functional purposes. The current study conceptualizes such behavior as symbolic use and aims to understand it. Anchoring on the attachment theory, mobile identity is postulated as an important antecedent of symbolic use. Mobile identity in turn is formed by mobile symbolism and mobile design esthetics. The research model was tested by a hybrid of both online and offline survey with 271 valid responses. SEM analysis was used to test the research model and SPSS was used for descriptive statistics. The results confirmed the role of mobile identity in affecting smartphone symbolic use. Additionally, individual materialism was confirmed as a moderator using hierarchical analysis. By defining and explaining smartphone symbolic use, this study clarifies the unique characteristics of the smartphone usage context as compared to non-portable technologies, thereby enriching the mobile usage literature and the application of attachment theory. It also defines the boundary condition of attachment formation by studying the contingent role of individual characteristics.

Influencing Factors of Total Skin Irradiation With Helical Tomotherapy

Purpose

To investigate the influencing factors of total skin irradiation (TSI) with helical tomotherapy for guiding the clinical selection of the suitable parameters and optimizing the plan quality and efficiency.

Materials and Methods

Six patients with mycosis fungoides (MF) who received TSI were retrospectively selected. They were all dressed with 5 mm thick diving suits during the CT scan and treatment as a bolus to increase the superficial dose through buildup. The dose prescription was 24 Gy in 20 fractions and 5 times per week. During the planned pretreatment, Ring0, Ring1, Ring2, Ring3, and Ring4 of 1 cm thick away from the planning target volume (PTV) at the distances of 0, 1, 2, 3, and 4 cm and other normal tissues (NTs) were generated, respectively. The auxiliary structures were completely blocked during planning; while the field widths were 5 and 2.5 cm, the pitches were 0.287 and 0.215, the modulation factors were 4 and 3, and the other parameters remained consistent. Finally, the dose parameters of PTV and auxiliary structures, as well as the beam on time (BOT) and gantry period, were compared and analyzed.

Results

when the auxiliary structures were completely blocked with distance to PTV (d_PTV) above 3 cm were used, the mean dose (D_mean), conformity index (CI), and heterogeneity index (HI) of the PTV met the clinical requirements. As the d_PTV gradually increased, the BOT decreased while the volume of normal tissue that received excessive radiation increased correspondingly. If the d_PTV was less than 3 cm, the clinical requirements were not met. The field widths (FWs), pitches, and modulation factors (MFs) had no effect on PTV_mean and the HI. The FW of 2.5 cm was slightly better than 5 cm for the CI. The FW and MF had a significant impact on the BOT, which gradually increased with decreasing FW and increasing MF. Pitch had no effect on the BOT.

Conclusion

During planning with TSI patients, d_PTV is the key factor that has a significant influence on the plan quality. We found that the plan with the d_PTV above 3 cm can meet clinical objectives. The BOT increases as the d_PTV increases. The FWs also have an effect on the CI and BOT. Therefore, it is necessary to comprehensively balance these factors to optimize the quality and efficiency of the plan. We also found that different MFs and pitches have no obvious effect on the results.

Development of a GPU-accelerated Monte Carlo dose calculation module for nuclear medicine, ARCHER-NM: demonstration for a PET/CT imaging procedure

Objective. This paper describes the development and validation of a GPU-accelerated Monte Carlo (MC) dose computing module dedicated to organ dose calculations of individual patients undergoing nuclear medicine (NM) internal radiation exposures involving PET/CT examination. Approach. This new module extends the more-than-10-years-long ARCHER project that developed a GPU-accelerated MC dose engine by adding dedicated NM source-definition features. To validate the code, we compared dose distributions from the point ion source, including 18F, 11C, 15O, and 68Ga, calculated for a water phantom against a well-tested MC code, GATE. To demonstrate the clinical utility and advantage of ARCHER-NM, one set of 18F-FDG PET/CT data for an adult male NM patient is calculated using the new code. Radiosensitive organs in the CT dataset are segmented using a CNN-based tool called DeepViewer. The PET image intensity maps are converted to radioactivity distributions to allow for MC radiation transport dose calculations at the voxel level. The dose rate maps and corresponding statistical uncertainties were calculated at the acquisition time of PET image. Main results. The water-phantom results show excellent agreement, suggesting that the radiation physics module in the new NM code is adequate. The dose rate results of the 18F-FDG PET imaging patient show that ARCHER-NM’s results agree very well with those of the GATE within −2.45% to 2.58% (for a total of 28 organs considered in this study). Most impressively, ARCHER-NM obtains such results in 22 s while it takes GATE about 180 min for the same number of 5 × 108 simulated decay events. Significance. This is the first study presenting GPU-accelerated patient-specific MC internal radiation dose rate calculations for clinically realistic 18F-FDG PET/CT imaging case involving autosegmentation of whole-body PET/CT images. This study suggests that the proposed computing tools—ARCHER-NM— are accurate and fast enough for routine internal dosimetry in NM clinics.

A conjugate gradient-assisted multi-objective evolutionary algorithm for fluence map optimization in radiotherapy treatment

Intensity-modulated radiotherapy (IMRT) is one of the most applied techniques for cancer radiotherapy treatment. The fluence map optimization is an essential part of IMRT plan designing, which has a significant impact on the radiotherapy treatment effect. In fact, the treatment planing of IMRT is an inverse multi-objective optimization problem. Existing approaches of solving the fluence map optimization problem (FMOP) obtain a satisfied treatment plan via trying different coupling weights, the optimization process needs to be conducted many times and the coupling weight setting is completely based on the experience of a radiation physicist. For fast obtaining diverse high-quality radiotherapy plans, this paper formulates the FMOP into a three-objective optimization problem, and proposes a conjugate gradient-assisted multi-objective evolutionary algorithm (CG-MOEA) to solve it. The proposed algorithm does not need to set the coupling weights and can produce the diverse radiotherapy plans within a single run. Moreover, the convergence speed is further accelerated by an adaptive local search strategy based on the conjugate-gradient method. Compared with five state-of-the-art multi-objective evolutionary algorithms (MOEAs), the proposed CG-MOEA can obtain the best hypervolume (HV) values and dose–volume histogram (DVH) performance on five clinical cases in cancer radiotherapy. Moreover, the proposed algorithm not only obtains the more optimal solution than traditional method used to solve the FMOP, but also can find diverse Pareto solution set which can be provided to radiation physicist to select the best treatment plan. The proposed algorithm outperforms dose-volume histogram state-of-the-art multi-objective evolutionary algorithms and traditional method for FMOP on five clinical cases in cancer radiotherapy.

Gestational bisphenol A exposure impairs hepatic lipid metabolism by altering mTOR/CRTC2/SREBP1 in male rat offspring

Lipid metabolism is an important biochemical process in the body. Recent studies have found that environmental endocrine disruptors play an important role in the regulation of lipid metabolism. Bisphenol A (BPA), a common environmental endocrine disruptor, has adverse effects on lipid metabolism, but the mechanism is still unclear. This study aimed to investigate the effects of gestational BPA exposure on hepatic lipid metabolism and its possible mechanism in male offspring. The pregnant Sprague-Dawley rats were exposed to BPA (0, 0.05, 0.5, 5 mg/kg/day) from day 5 to day 19 of gestation to investigate the levels of triglyceride (TG) and total cholesterol (TC), and the expression of liver lipid metabolism-related genes in male offspring rats. The results showed that compared with the control group, the TG and TC levels in serum and liver in BPA-exposed groups was increased. And the expressions of liver fatty acid oxidation related genes, such as peroxisome proliferators-activated receptor α (PPARα) and carnitine palmitoyl transferase 1α (CPT1α), were down-regulated. However, the expressions of fatty acid synthesis related genes, such as sterol regulatory element binding proteins 1 (SREBP-1), acetyl-CoA carboxylase 1 (ACC1), fatty acid synthase (FAS) and stearoyl-CoA desaturase 1 (SCD-1), were up-regulated. The increased protein levels of mTOR and p-CRTC2 suggested that CREB-regulated transcription coactivator 2 (CRTC2) might be an important mediator in the mTOR/SREBP-1 pathway. In conclusion, these results demonstrated that mTOR/CRTC2/SREBP-1 could be affected by gestational BPA exposure, which may involve in the lipid metabolic disorders in later life.

A Comparison Study Between CNN-Based Deformed Planning CT and CycleGAN-Based Synthetic CT Methods for Improving iCBCT Image Quality

Purpose

The aim of this study is to compare two methods for improving the image quality of the Varian Halcyon cone-beam CT (iCBCT) system through the deformed planning CT (dpCT) based on the convolutional neural network (CNN) and the synthetic CT (sCT) generation based on the cycle-consistent generative adversarial network (CycleGAN).

Methods

A total of 190 paired pelvic CT and iCBCT image datasets were included in the study, out of which 150 were used for model training and the remaining 40 were used for model testing. For the registration network, we proposed a 3D multi-stage registration network (MSnet) to deform planning CT images to agree with iCBCT images, and the contours from CT images were propagated to the corresponding iCBCT images through a deformation matrix. The overlap between the deformed contours (dpCT) and the fixed contours (iCBCT) was calculated for purposes of evaluating the registration accuracy. For the sCT generation, we trained the 2D CycleGAN using the deformation-registered CT-iCBCT slicers and generated the sCT with corresponding iCBCT image data. Then, on sCT images, physicians re-delineated the contours that were compared with contours of manually delineated iCBCT images. The organs for contour comparison included the bladder, spinal cord, femoral head left, femoral head right, and bone marrow. The dice similarity coefficient (DSC) was used to evaluate the accuracy of registration and the accuracy of sCT generation.

Results

The DSC values of the registration and sCT generation were found to be 0.769 and 0.884 for the bladder (p < 0.05), 0.765 and 0.850 for the spinal cord (p < 0.05), 0.918 and 0.923 for the femoral head left (p > 0.05), 0.916 and 0.921 for the femoral head right (p > 0.05), and 0.878 and 0.916 for the bone marrow (p < 0.05), respectively. When the bladder volume difference in planning CT and iCBCT scans was more than double, the accuracy of sCT generation was significantly better than that of registration (DSC of bladder: 0.859 vs. 0.596, p < 0.05).

Conclusion

The registration and sCT generation could both improve the iCBCT image quality effectively, and the sCT generation could achieve higher accuracy when the difference in planning CT and iCBCT was large.

Feasibility evaluation of PET scan-time reduction for diagnosing amyloid-β levels in Alzheimer's disease patients using a deep-learning-based denoising algorithm

PURPOSE

To shorten positron emission tomography (PET) scanning time in diagnosing amyloid-β levels thus increasing the workflow in centers involving Alzheimer's Disease (AD) patients.

METHODS

PET datasets were collected for 25 patients injected with ¹⁸F-AV45 radiopharmaceutical. To generate necessary training data, PET images from both normal-scanning-time (20-min) as well as so-called "shortened-scanning-time" (1-min, 2-min, 5-min, and 10-min) were reconstructed for each patient. Building on our earlier work on MCDNet (Monte Carlo Denoising Net) and a new Wasserstein-GAN algorithm, we developed a new denoising model called MCDNet-2 to predict normal-scanning-time PET images from a series of shortened-scanning-time PET images. The quality of the predicted PET images was quantitatively evaluated using objective metrics including normalized-root-mean-square-error (NRMSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR). Furthermore, two radiologists performed subjective evaluations including the qualitative evaluation and a five-point grading evaluation. The denoising performance of the proposed MCDNet-2 was finally compared with those of U-Net, MCDNet, and a traditional denoising method called Gaussian Filtering.

RESULTS

The proposed MCDNet-2 can yield good denoising performance in 5-min PET images. In the comparison of denoising methods, MCDNet-2 yielded the best performance in the subjective evaluation although it is comparable with MCDNet in objective comparison (NRMSE, PSNR, and SSIM). In the qualitative evaluation of amyloid-β positive or negative results, MCDNet-2 was found to achieve a classification accuracy of 100%.

CONCLUSIONS

The proposed denoising method has been found to reduce the PET scan time from the normal level of 20 min to 5 min but still maintaining acceptable image quality in correctly diagnosing amyloid-β levels. These results suggest strongly that deep learning-based methods such as ours can be an attractive solution to the clinical needs to improve PET imaging workflow.

Clinical application and improvement of a CNN-based autosegmentation model for clinical target volumes in cervical cancer radiotherapy

OBJECTIVE

Clinical target volume (CTV) autosegmentation for cervical cancer is desirable for radiation therapy. Data heterogeneity and interobserver variability (IOV) limit the clinical adaptability of such methods. The adaptive method is proposed to improve the adaptability of CNN-based autosegmentation of CTV contours in cervical cancer.

METHODS

This study included 400 cervical cancer treatment planning cases with CTV delineated by radiation oncologists from three hospitals. The datasets were divided into five subdatasets (80 cases each). The cases in datasets 1, 2, and 3 were delineated by physicians A, B, and C, respectively. The cases in datasets 4 and 5 were delineated by multiple physicians. Dataset 1 was divided into training (50 cases), validation (10 cases), and testing (20 cases) cohorts, and they were used to construct the pretrained model. Datasets 2-5 were regarded as host datasets to evaluate the accuracy of the pretrained model. In the adaptive process, the pretrained model was fine-tuned to measure improvements by gradually adding more training cases selected from the host datasets. The accuracy of the autosegmentation model on each host dataset was evaluated using the corresponding test cases. The Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD_95) were used to evaluate the accuracy.

RESULTS

Before and after adaptive improvements, the average DSC values on the host datasets were 0.818 versus 0.882, 0.763 versus 0.810, 0.727 versus 0.772, and 0.679 versus 0.789, which are improvements of 7.82%, 6.16%, 6.19%, and 16.05%, respectively. The average HD_95 values were 11.143 mm versus 6.853 mm, 22.402 mm versus 14.076 mm, 28.145 mm versus 16.437 mm, and 33.034 mm versus 16.441 mm, which are improvements of 37.94%, 37.17%, 41.60%, and 50.23%, respectively.

CONCLUSION

The proposed method improved the adaptability of the CNN-based autosegmentation model when applied to host datasets.

Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning

Radiotherapy plays an important role in controlling the local recurrence of esophageal cancer after radical surgery. Segmentation of the clinical target volume is a key step in radiotherapy treatment planning, but it is time-consuming and operator-dependent. This paper introduces a deep dilated convolutional U-network to achieve fast and accurate clinical target volume auto-segmentation of esophageal cancer after radical surgery. The deep dilated convolutional U-network, which integrates the advantages of dilated convolution and the U-network, is an end-to-end architecture that enables rapid training and testing. A dilated convolution module for extracting multiscale context features containing the original information on fine texture and boundaries is integrated into the U-network architecture to avoid information loss due to down-sampling and improve the segmentation accuracy. In addition, batch normalization is added to the deep dilated convolutional U-network for fast and stable convergence. In the present study, the training and validation loss tended to be stable after 40 training epochs. This deep dilated convolutional U-network model was able to segment the clinical target volume with an overall mean Dice similarity coefficient of 86.7% and a respective 95% Hausdorff distance of 37.4 mm, indicating reasonable volume overlap of the auto-segmented and manual contours. The mean Cohen kappa coefficient was 0.863, indicating that the deep dilated convolutional U-network was robust. Comparisons with the U-network and attention U-network showed that the overall performance of the deep dilated convolutional U-network was best for the Dice similarity coefficient, 95% Hausdorff distance, and Cohen kappa coefficient. The test time for segmentation of the clinical target volume was approximately 25 seconds per patient. This deep dilated convolutional U-network could be applied in the clinical setting to save time in delineation and improve the consistency of contouring.

Evaluation of deep learning-based auto-segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients

Objective

To evaluate the accuracy of a deep learning‐based auto‐segmentation mode to that of manual contouring by one medical resident, where both entities tried to mimic the delineation "habits" of the same clinical senior physician.

Methods

This study included 125 cervical cancer patients whose clinical target volumes (CTVs) and organs at risk (OARs) were delineated by the same senior physician. Of these 125 cases, 100 were used for model training and the remaining 25 for model testing. In addition, the medical resident instructed by the senior physician for approximately 8 months delineated the CTVs and OARs for the testing cases. The dice similarity coefficient (DSC) and the Hausdorff Distance (HD) were used to evaluate the delineation accuracy for CTV, bladder, rectum, small intestine, femoral‐head‐left, and femoral‐head‐right.

Results

The DSC values of the auto‐segmentation model and manual contouring by the resident were, respectively, 0.86 and 0.83 for the CTV (P < 0.05), 0.91 and 0.91 for the bladder (P > 0.05), 0.88 and 0.84 for the femoral‐head‐right (P < 0.05), 0.88 and 0.84 for the femoral‐head‐left (P < 0.05), 0.86 and 0.81 for the small intestine (P < 0.05), and 0.81 and 0.84 for the rectum (P > 0.05). The HD (mm) values were, respectively, 14.84 and 18.37 for the CTV (P < 0.05), 7.82 and 7.63 for the bladder (P > 0.05), 6.18 and 6.75 for the femoral‐head‐right (P > 0.05), 6.17 and 6.31 for the femoral‐head‐left (P > 0.05), 22.21 and 26.70 for the small intestine (P > 0.05), and 7.04 and 6.13 for the rectum (P > 0.05). The auto‐segmentation model took approximately 2 min to delineate the CTV and OARs while the resident took approximately 90 min to complete the same task.

Conclusion

The auto‐segmentation model was as accurate as the medical resident but with much better efficiency in this study. Furthermore, the auto‐segmentation approach offers additional perceivable advantages of being consistent and ever improving when compared with manual approaches.

Hedging crash risk in optimal portfolio selection

When almost all underlying assets suddenly lose a certain part of their nominal value in a market crash, the diversification effect of portfolios in a normal market condition no longer works. We integrate the crash risk into portfolio management and investigate performance measures, hedging and optimization of portfolio selection involving derivatives. A suitable convex conic programming framework based on parametric approximation method is proposed to make the problem a tractable one. Simulation analysis and empirical study are performed to test the proposed approach.

CONCEPTUAL DESIGN AND PRELIMINARY RESULTS OF A VR-BASED RADIATION SAFETY TRAINING SYSTEM FOR INTERVENTIONAL RADIOLOGISTS

To help minimise occupational radiation exposure in interventional radiology, we conceptualised a virtual reality-based radiation safety training system to help operators understand complex radiation fields and to avoid high radiation areas through game-like interactive simulations. The preliminary development of the system has yielded results suggesting that the training system can calculate and report the radiation exposure after each training session based on a database precalculated from computational phantoms and Monte Carlo simulations and the position information provided by the Microsoft HoloLens headset. In addition, real-time dose rate and cumulative dose will be displayed to the trainee to help them adjust their practice. This paper presents the conceptual design of the overall hardware and software design, as well as preliminary results to combine HoloLens headset and complex 3D X-ray field spatial distribution data to create a mixed reality environment for safety training purpose in interventional radiology.

End-to-end unsupervised cycle-consistent fully convolutional network for 3D pelvic CT-MR deformable registration

Objective

To improve the efficiency of computed tomography (CT)‐magnetic resonance (MR) deformable image registration while ensuring the registration accuracy.

Methods

Two fully convolutional networks (FCNs) for generating spatial deformable grids were proposed using the Cycle‐Consistent method to ensure the deformed image consistency with the reference image data. In all, 74 pelvic cases consisting of both MR and CT images were studied, among which 64 cases were used as training data and 10 cases as the testing data. All training data were standardized and normalized, following simple image preparation to remove the redundant air. Dice coefficients and average surface distance (ASD) were calculated for regions of interest (ROI) of CT‐MR image pairs, before and after the registration. The performance of the proposed method (FCN with Cycle‐Consistent) was compared with that of Elastix software, MIM software, and FCN without cycle‐consistent.

Results

The results show that the proposed method achieved the best performance among the four registration methods tested in terms of registration accuracy and the method was more stable than others in general. In terms of average registration time, Elastix took 64 s, MIM software took 28 s, and the proposed method was found to be significantly faster, taking <0.1 s.

Conclusion

The proposed method not only ensures the accuracy of deformable image registration but also greatly reduces the time required for image registration and improves the efficiency of the registration process. In addition, compared with other deep learning methods, the proposed method is completely unsupervised and end‐to‐end.

A method of using deep learning to predict three-dimensional dose distributions for intensity-modulated radiotherapy of rectal cancer

Purpose

To develop and test a three‐dimensional (3D) deep learning model for predicting 3D voxel‐wise dose distributions for intensity‐modulated radiotherapy (IMRT).

Methods

A total of 122 postoperative rectal cancer cases treated by IMRT were considered in the study, of which 100 cases were randomly selected as the training–validating set and the remaining as the testing set. A 3D deep learning model named 3D U‐Res‐Net_B was constructed to predict 3D dose distributions. Eight types of 3D matrices from CT images, contoured structures, and beam configurations were fed into the independent input channel, respectively, and the 3D matrix of dose distributions was taken as the output to train the 3D model. The obtained 3D model was used to predict new 3D dose distributions. The predicted accuracy was evaluated in two aspects: (a) The dice similarity coefficients (DSCs) of different isodose volumes, the average dose difference of all voxels within the body, and 3%/5 mm global gamma passing rates of organs at risks (OARs) and planned target volume (PTV) were used to address the spatial correspondence between predicted and clinical delivered 3D dose distributions; (b) The dosimetric index (DI) including homogeneity index, conformity index, V₅₀, V₄₅ for PTV and OARs between predicted and clinical truth were statistically analyzed with the paired‐samples t test. The model was also compared with 3D U‐Net and the same architecture model without beam configurations input (named as 3D U‐Res‐Net_O).

Results

The 3D U‐Res‐Net_B model predicted 3D dose distributions accurately. For the 22 testing cases, the average prediction bias ranged from −1.94% to 1.58%, and the overall mean absolute errors (MAEs) was 3.92 ± 4.16%; there was no statistically significant difference for nearly all DIs. The model had a DSCs value above 0.9 for most isodose volumes, and global 3D gamma passing rates varying from 0.81 to 0.90 for PTV and OARs, clearly outperforming 3D U‐Res‐Net_O and being slightly superior to 3D U‐Net.

Conclusions

This study developed a more general deep learning model by considering beam configurations input and achieved an accurate 3D voxel‐wise dose prediction for rectal cancer treated by IMRT, a potentially easier clinical implementation for more comprehensive automatic planning.

A method of rapid quantification of patient-specific organ doses for CT using deep-learning-based multi-organ segmentation and GPU-accelerated Monte Carlo dose computing

PURPOSE

One technical barrier to patient-specific computed tomography (CT) dosimetry has been the lack of computational tools for the automatic patient-specific multi-organ segmentation of CT images and rapid organ dose quantification. When previous CT images are available for the same body region of the patient, the ability to obtain patient-specific organ doses for CT - in a similar manner as radiation therapy treatment planning - will open the door to personalized and prospective CT scan protocols. This study aims to demonstrate the feasibility of combining deep-learning algorithms for automatic segmentation of multiple radiosensitive organs from CT images with the GPU-based Monte Carlo rapid organ dose calculation.

METHODS

A deep convolutional neural network (CNN) based on the U-Net for organ segmentation is developed and trained to automatically delineate multiple radiosensitive organs from CT images. Two databases are used: The lung CT segmentation challenge 2017 (LCTSC) dataset that contains 60 thoracic CT scan patients, each consisting of five segmented organs, and the Pancreas-CT (PCT) dataset, which contains 43 abdominal CT scan patients each consisting of eight segmented organs. A fivefold cross-validation method is performed on both sets of data. Dice similarity coefficients (DSCs) are used to evaluate the segmentation performance against the ground truth. A GPU-based Monte Carlo dose code, ARCHER, is used to calculate patient-specific CT organ doses. The proposed method is evaluated in terms of relative dose errors (RDEs). To demonstrate the potential improvement of the new method, organ dose results are compared against those obtained for population-average patient phantoms used in an off-line dose reporting software, VirtualDose, at Massachusetts General Hospital.

RESULTS

The median DSCs are found to be 0.97 (right lung), 0.96 (left lung), 0.92 (heart), 0.86 (spinal cord), 0.76 (esophagus) for the LCTSC dataset, along with 0.96 (spleen), 0.96 (liver), 0.95 (left kidney), 0.90 (stomach), 0.87 (gall bladder), 0.80 (pancreas), 0.75 (esophagus), and 0.61 (duodenum) for the PCT dataset. Comparing with organ dose results from population-averaged phantoms, the new patient-specific method achieved smaller absolute RDEs (mean ± standard deviation) for all organs: 1.8% ± 1.4% (vs 16.0% ± 11.8%) for the lung, 0.8% ± 0.7% (vs 34.0% ± 31.1%) for the heart, 1.6% ± 1.7% (vs 45.7% ± 29.3%) for the esophagus, 0.6% ± 1.2% (vs 15.8% ± 12.7%) for the spleen, 1.2% ± 1.0% (vs 18.1% ± 15.7%) for the pancreas, 0.9% ± 0.6% (vs 20.0% ± 15.2%) for the left kidney, 1.7% ± 3.1% (vs 19.1% ± 9.8%) for the gallbladder, 0.3% ± 0.3% (vs 24.2% ± 18.7%) for the liver, and 1.6% ± 1.7% (vs 19.3% ± 13.6%) for the stomach. The trained automatic segmentation tool takes <5 s per patient for all 103 patients in the dataset. The Monte Carlo radiation dose calculations performed in parallel to the segmentation process using the GPU-accelerated ARCHER code take <4 s per patient to achieve <0.5% statistical uncertainty in all organ doses for all 103 patients in the database.

CONCLUSION

This work shows the feasibility to perform combined automatic patient-specific multi-organ segmentation of CT images and rapid GPU-based Monte Carlo dose quantification with clinically acceptable accuracy and efficiency.

A fast robust optimizer for intensity modulated proton therapy using GPU

Robust optimization has been shown to be effective for stabilizing treatment planning in intensity modulated proton therapy (IMPT), but existing algorithms for the optimization process is time‐consuming. This paper describes a fast robust optimization tool that takes advantage of the GPU parallel computing technologies. The new robust optimization model is based on nine boundary dose distributions — two for ±range uncertainties, six for ±set‐up uncertainties along anteroposterior (A‐P), lateral (R‐L) and superior‐inferior (S‐I) directions, and one for nominal situation. The nine boundary influence matrices were calculated using an in‐house finite size pencil beam dose engine, while the conjugate gradient method was applied to minimize the objective function. The proton dose calculation algorithm and the conjugate gradient method were tuned for heterogeneous platforms involving the CPU host and GPU device. Three clinical cases — one head and neck cancer case, one lung cancer case, and one prostate cancer case — were investigated to demonstrate the clinical feasibility of the proposed robust optimizer. Compared with results from Varian Eclipse (version 13.3), the proposed method is found to be conducive to robust treatment planning that is less sensitive to range and setup uncertainties. The three tested cases show that targets can achieve high dose uniformity while organs at risks (OARs) are in better protection against setup and range errors. Based on the CPU + GPU heterogeneous platform, the execution times of the head and neck cancer case and the prostate cancer case are much less than half of Eclipse, while the run time of the lung cancer case is similar to that of Eclipse. The fast robust optimizer developed in this study can improve the reliability of traditional proton treatment planning in a much faster speed, thus making it possible for clinical utility.

Body mass index cut-off points for predicting chronic non-communicable disease should differ by gender and age group

OBJECTIVES

The objective of this study to determine whether body mass index (BMI) in different genders and age groups need different thresholds when predicting chronic non-communicable diseases (CNCDs).

STUDY DESIGN

This is a cross-sectional study.

METHODS

Data were obtained from the China Health and Nutrition Survey conducted in 2009. Sequential sample cluster analysis was used to group age according to BMI. Propensity score matching was used to eliminate the influence of age. Receiver operating characteristic curve based on gender and age group was used to evaluate the cut-off values and efficiency of BMI in each group.

RESULTS

A total of 8469 individuals were enrolled in this study. Results of sequential sample cluster analyses showed age was divided into three groups: 18-39, 40-59 and 60-99 years. There were significant differences in the distribution of BMI among the three groups for both males and females (P < 0.001). Statistical differences were observed in the distribution of BMI between genders in the 18-39- and 60-99-year-old age groups (P < 0.001). For men, the cut-off values of BMI were ≥25 kg/m², ≥24 kg/m² and ≥23 kg/m² in the 18-39, 40-59 and 60-99 years old groups, respectively; for women, the corresponding cut-off points were ≥25 kg/m², ≥23 kg/m² and ≥25 kg/m² in groups.

CONCLUSIONS

The thresholds for BMI might be different between gender and age group. In addition, it might not be suitable to determine cut-off values of BMI to predict CNCDs for people aged ≥60 years.

TOPAS Monte Carlo simulation for double scattering proton therapy and dosimetric evaluation

PURPOSE

To construct and commission a double scattering (DS) proton beam model in TOPAS Monte Carlo (MC) code. Dose comparisons of MC calculations to the measured and treatment planning system (TPS) calculated dose were performed.

METHODS

The TOPAS nozzle model was based on the manufacturer blueprints. Nozzle set-up and beam current modulations were calculated using room-specific calibration data. This model was implemented to reproduce pristine peaks, spread-out Bragg peaks (SOBP) and lateral profiles. A stair-shaped target plan in water phantom was calculated and compared to measured data to verify range compensator (RC) modeling.

RESULTS

TOPAS calculated pristine peaks agreed well with measurements, with accuracies of 0.03 cm for range R₉₀ and 0.05 cm for distal dose fall-off (DDF). The calculated SOBP range, modulation width and DDF differences between MC calculations and measurements were within 0.05 cm, 0.5 cm and 0.03 cm respectively. MC calculated lateral penumbra agreed well with measured data, with difference less than 0.05 cm. For RC calculation, TPS underestimated the additional depth dose tail due to the nuclear halo effect. Lateral doses by TPS were 10% lower than measurement outside the target, while maximum difference of MC calculation was within 2%. At deeper depths inside the target volume, TPS overestimated doses by up to 25% while TOPAS predicted the dose to within 5% of measurements.

CONCLUSION

We have successfully developed and commissioned a MC based DS nozzle model. The performance of dose accuracy by TOPAS was superior to TPS, especially for highly inhomogeneous compensator.

Low frequency, weak MCP-1 secretion and exhausted immune status of peripheral monocytes were associated with progression of severe enterovirus A71-infected hand, foot and mouth disease

A minority of hand, foot and mouth disease (HFMD) caused by enterovirus A71 (EV-A71) results in severe neural complications. However, whether monocyte-mediated immunity is involved in the disease progression of HFMD remains unknown. One hundred and twenty mild and 103 severe HFMD patients were recruited and enzyme-linked immunosorbent assay (ELISA), flow cytometry and Transwell culture were performed in the study. Peripheral monocyte counts were lower in both absolute counts and frequencies in severe cases compared to mild cases. After screening 10 monocyte-related cytokines by ELISA, only monocyte chemoattractant protein-1 (MCP-1) was found at higher levels in sera of mild cases compared to those with severe symptoms. Monocytes purified from mild cases produced more MCP-1 than the cells from severe patients when stimulated in vitro. We observed that immune exhaustion markers programmed death 1 (PD-1) and programmed death ligand 1 (PD-L1) were highly regulated on the surface of monocytes from severe cases compared to mild cases. PD-L1 blockade induced a higher production of MCP-1 in the supernatant of a Transwell system. The production of MCP-1 also increased following PD-L1 blockade of purified monocytes activated by granulocyte-macrophage colony-stimulating factor (GM-CSF) combined with R848 or EV-A71 virus. Our results indicate that absolute count, frequency and levels of MCP-1 secretion of peripheral monocytes, together with their immune status, probably contribute to differential disease prognosis in EV-A71-associated HFMD.

Abstract P4-04-01: The landscape of somatic genetic alterations in breast cancers from CHEK2 germline mutation carriers

Introduction: Checkpoint kinase 2 (CHEK2) is a tumor suppressor gene, which regulates cell cycle in response to DNA damage response. Selected CHEK2 germline mutations have been shown to confer an increased risk of breast cancer development. Multiple founder mutations in CHEK2 have been identified, and meta analyses have shown that CHEK2 truncating variants confer a higher breast cancer risk than missense variants. Here, we assessed the phenotype and repertoire of genetic alterations of breast cancers from 33 patients with CHEK2 pathogenic germline variants.

Materials and methods: We performed targeted capture massively parallel sequencing (≥410 genes) of tumor and normal samples from 13 patients with CHEK2 pathogenic germline variants, and retrieved whole exome sequencing (WES) data (BAM files) of tumor and normal samples from 20 patients with CHEK2 germline pathogenic variants included in the TCGA breast cancer study. In addition, we retrieved WES data of BRCA1, BRCA2 and ATM associated breast cancers from TCGA and Weigelt et al. (JNCI 2018). Somatic mutations, copy number alterations, mutational signatures and large-scale transitions (LSTs) were defined using state-of-the-art bioinformatics algorithms.

Results: Of the 33 CHEK2-associated breast cancers included in this study, 21 had missense and 12 had loss-of-function (LoF) germline mutations, and 81% were ER-positive and 12% HER2-positive. CHEK2-associated breast cancers statistically significantly less frequently displayed an ER-negative/HER2-negative phenotype (0%) than BRCA1- (80%) or BRCA2-associated (33%) breast cancers (BRCA1, p&lt;0.0001 for both comparisons), but were similar to ATM-associated breast cancers. Biallelic inactivation of CHEK2 through loss of heterozygosity (LOH) of the wild-type allele was present in 17 of 33 samples (52%). LOH of the CHEK2 wild-type allele was significantly more frequent in tumors with LOF mutations than in those with missense mutations (78% vs 36%, respectively; p=0.0394). PIK3CA (36%) and GATA3 (33%) were the two most recurrently mutated genes in these samples. TP53 somatic mutations were detected in five cases, four of which harbored missense CHEK2 germline mutations. Unlike BRCA1- and BRCA2-associated breast cancers, but akin to ATM-associated breast cancers, CHEK2-associated breast cancers lacked the mutational signature associated with homologous recombination (HR) DNA repair defects (i.e. signature 3) and only five cases displayed high LST scores.

Conclusion:CHEK2-associated breast cancers are phenotypically and genetically distinct from BRCA1- and BRCA2-associated breast cancers, but similar to ATM-associated breast cancers. Akin to ATM-associated breast cancers, CHEK2-associated breast cancers are preferentially ER-positive, lack genomics features consistent with defective HR, and have a repertoire of somatic genetic alterations similar to those of non-BRCA1/2 ER-positive breast cancers. Our results suggest that either CHEK2 germline mutations contribute to an increased risk of breast cancer independently of the HR DNA repair defects or that the mutational signatures caused by CHEK2 pathogenic germline mutations differ from those caused by pathogenic germline mutations affecting bona fide HR-related genes (e.g. BRCA1, BRCA2 and PALB2).

Citation Format: Kumar R, Pei X, Selenica P, Wen HY, Powell S, Robson M, Riaz N, Reis-Filho JS, Weigelt B, Mandelker D. The landscape of somatic genetic alterations in breast cancers from CHEK2 germline mutation carriers [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-04-01.

Combination of apatinib and continuous nutritional support for a gastric cancer patient with brain metastasis prolongs survival

WHAT IS KNOWN AND OBJECTIVES

Gastric cancer is the most common gastrointestinal malignant tumour in China, which rarely metastasizes into the central nervous system. However, brain metastasis leads to increased risk of death.

CASE SUMMARY

Here, we report a case of brain metastasis from gastric cancer, which was treated with apatinib and continual nutritional support, with a survival time of 2 years.

WHAT IS NEW AND CONCLUSION

The combination of apatinib and continual nutritional support may be an option for the treatment of brain metastasis from gastric cancer. A prospective study should be performed to confirm this.

Biomechanics of Immediate Postextraction Implant Osseointegration

The aim of this study was to gain insights into the biology and mechanics of immediate postextraction implant osseointegration. To mimic clinical practice, murine first molar extraction was followed by osteotomy site preparation, specifically in the palatal root socket. The osteotomy was positioned such that it removed periodontal ligament (PDL) only on the palatal aspect of the socket, leaving the buccal aspect undisturbed. This strategy created 2 distinct peri-implant environments: on the palatal aspect, the implant was in direct contact with bone, while on the buccal aspect, a PDL-filled gap existed between the implant and bone. Finite element modeling showed high strains on the palatal aspect, where bone was compressed by the implant. Osteocyte death and bone resorption predominated on the palatal aspect, leading to the loss of peri-implant bone. On the buccal aspect, where finite element modeling revealed low strains, there was minimal osteocyte death and robust peri-implant bone formation. Initially, the buccal aspect was filled with PDL remnants, which we found directly provided Wnt-responsive cells that were responsible for new bone formation and osseointegration. On the palatal aspect, which was devoid of PDL and Wnt-responsive cells, adding exogenous liposomal WNT3A created an osteogenic environment for rapid peri-implant bone formation. Thus, we conclude that low strain and high Wnt signaling favor osseointegration of immediate postextraction implants. The PDL harbors Wnt-responsive cells that are inherently osteogenic, and if the PDL tissue is healthy, it is reasonable to preserve this tissue during immediate implant placement.

A Comparative Assessment of Implant Site Viability in Humans and Rats

Our long-term objective is to devise methods to improve osteotomy site preparation and, in doing so, facilitate implant osseointegration. As a first step in this process, we developed a standardized oral osteotomy model in ovariectomized rats. There were 2 unique features to this model: first, the rats exhibited an osteopenic phenotype, reminiscent of the bone health that has been reported for the average dental implant patient population. Second, osteotomies were produced in healed tooth extraction sites and therefore represented the placement of most implants in patients. Commercially available drills were then used to produce osteotomies in a patient cohort and in the rat model. Molecular, cellular, and histologic analyses demonstrated a close alignment between the responses of human and rodent alveolar bone to osteotomy site preparation. Most notably in both patients and rats, all drilling tools created a zone of dead and dying osteocytes around the osteotomy. In rat tissues, which could be collected at multiple time points after osteotomy, the fate of the dead alveolar bone was followed. Over the course of a week, osteoclast activity was responsible for resorbing the necrotic bone, which in turn stimulated the deposition of a new bone matrix by osteoblasts. Collectively, these analyses support the use of an ovariectomy surgery rat model to gain insights into the response of human bone to osteotomy site preparation. The data also suggest that reducing the zone of osteocyte death will improve osteotomy site viability, leading to faster new bone formation around implants.

The applied value of medical history, physical examination, colour-Doppler ultrasonography and testis scintigraphy in the differential diagnosis of acute scrotum

Acute scrotum, especially testicular torsion, is a common surgical emergency. A delay in diagnosis or management may lead to permanent testicular ischaemic damage. Thus, it is particularly important to differentiate testicular torsion from other acute scrotum conditions as soon as possible. Our study has retrospectively investigated 358 patients with acute scrotum admitted to our hospital from the year 2007 to 2016. We have collected a thorough history and clinical data and drew the conclusion by comparing clinical features of different acute scrotum cases, medical history, imaging and surgical findings. Therefore, we propose an innovative "Testicular Torsion (TT) Green Channel" concept. Through the combination of a comprehensive medical history, physical examination and auxiliary colour-Doppler ultrasonography, the diagnosis of testicular torsion is definite in most circumstances. Testis scintigraphy is a novel and complementary diagnostic modality that can reduce the negative exploration rate in ambiguous and certain cases. The TT Green Channel is a new concept in the management of testicular torsion.

A Wnt-Responsive PDL Population Effectuates Extraction Socket Healing

Stem cells residing in the periodontal ligament (PDL) support the homeostasis of the periodontium, but their in vivo identity, source(s), and function(s) remain poorly understood. Here, using a lineage-tracing mouse strain, we identified a quiescent Wnt-responsive population in the PDL that became activated in response to tooth extraction. The Wnt-responsive population expanded by proliferation, then migrated from the PDL remnants that remained attached to bundle bone, into the socket. Once there, the Wnt-responsive progeny upregulated osteogenic protein expression, differentiated into osteoblasts, and generated the new bone that healed the socket. Using a liposomal WNT3A protein therapeutic, we showed that a single application at the time of extraction was sufficient to accelerate extraction socket healing 2-fold. Collectively, these data identify a new stem cell population in the intact periodontium that is directly responsible for alveolar bone healing after tooth removal.

Role of Autophagy in Ovarian Cryopreservation by Vitrification

BACKGROUND

Ovarian cryopreservation by vitrification and transplantation are useful methods to recover female fertility after radiotherapy and chemotherapy. As type II programmed cell death, autophagy plays important roles in ovarian follicle development, ovarian follicle atresia and anti-stress injury.

OBJECTIVE

The potential role of autophagy in ovarian vitrification was investigated.

MATERIALS AND METHODS

Mouse ovaries were cryopreserved by vitrification, and autophagy was treated, after which the ovarian histology was checked, and ovarian follicles were counted. The apoptotic rate was detected by TUNEL, and apoptotic molecular marker cleaved caspase-3 was checked by immunofluorescence and western blot analysis.

RESULTS

Our results suggested that autophagy was increased in the process of vitrification compared with the fresh ovaries (p<0.05). The number of primordial follicles was decreased through inhibiting or over-activating the autophagy by autophagy inhibitor or activator (p<0.05). However, the number of primary follicles, antral follicles and atretic follicles was not significantly different compared with vitrified/warmed groups. The apoptotic rate was significantly increased in the vitrified/warmed, autophagy-inhibiting and over-activating groups compared with the fresh group (p<0.05), and this result was further confirmed by western blot analysis.

CONCLUSIONS

Taken together, autophagy was activated in the ovarian cryopreservation by vitrification and plays a role in a natural adaptive response to cold stress in ovarian cryopreservation by vitrification.

Levothyroxine monotherapy versus levothyroxine and selenium combination therapy in chronic lymphocytic thyroiditis

PURPOSE

New strategies are needed for prevention and treatment of chronic lymphocytic thyroiditis (CLT). This study aimed to assess whether combination of levothyroxine treatment and selenium (Se) supplementation results in improved therapeutic effects in CLT compared with levothyroxine monotherapy.

METHODS

An open-label, randomized controlled study was performed in 60 CLT patients assigned to two groups. Levothyroxine group (LT) patients (n = 24) received levothyroxine alone for 3 months; meanwhile, the combination (LTSS) group (n = 36) was administered levothyroxine with selenium yeast capsule. Blood selenium concentrations, anti-thyroid peroxidase (TPO) and anti-thyroglobulin (Tg) antibody levels, and inflammatory cytokine amounts were compared between both groups before and after treatment.

RESULTS

At baseline, similar values were obtained in both groups for all the parameters assessed (p > 0.05). After treatment, significantly increased blood selenium levels (µg/L) [90.05 (80.69, 107.76) vs. 39.64 (29.42, 51.10), p < 0.001] and decreased anti-TPO antibody (23.63 ± 9.31 vs. 32.00 ± 10.41%, p = 0.002), anti-Tg antibody (35.84 ± 15.21 vs. 45.47 ± 14.24%, p = 0.015) and IL-2 amounts (pg/mL) [159.29 (124.54, 189.70) vs. 226.48 (190.74, 266.56), p < 0.001] were observed in the LTSS group compared with the LT group post-treatment; meanwhile, similar IL-10 concentrations [23.14 (21.65, 28.56) pg/mL vs. 24.68 (21.71, 29.67) pg/mL] were obtained in both groups. Subgroup analysis of patients with hypothyroidism showed the same trend observed in the whole population; in patients with normal thyroid function, only Se and IL-2 amounts differed between the two treatment groups. Correlation analysis of of the indexes: in HT patients, the basal serum selenium concentration was positively correlated with TT₄ (r = 0.294, p < 0.05), significantly negatively correlated with TSH (r = -0.343, p < 0.01), and had no significant correlation with TT3 (p > 0.05).

CONCLUSIONS

These findings indicated that levothyroxine and selenium combination results in improved therapeutic effects than the levothyroxine monotherapy in preventing CLT progression.