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Background Whole brain radiotherapy (WBRT) can impair patients’ cognitive function. Hippocampal avoidance during WBRT can potentially prevent this side effect. However, manually delineating the target area is time-consuming and difficult. Here, we proposed a credible approach of automatic hippocampal delineation based on convolutional neural networks. Methods Referring to the hippocampus contouring atlas proposed by RTOG 0933, we manually delineated (MD) the hippocampus on the MRI data sets (3-dimensional T1-weighted with slice thickness of 1 mm, n = 175), which were used to construct a three-dimensional convolutional neural network aiming for the hippocampus automatic delineation (AD). The performance of this AD tool was tested on three cohorts: (a) 3D T1 MRI with 1-mm slice thickness (n = 30); (b) non-3D T1-weighted MRI with 3-mm slice thickness (n = 19); (c) non-3D T1-weighted MRI with 1-mm slice thickness (n = 11). All MRIs confirmed with normal hippocampus has not been violated by any disease. Virtual radiation plans were created for AD and MD hippocampi in cohort c to evaluate the clinical feasibility of the artificial intelligence approach. Statistical analyses were performed using SPSS version 23. P Results The Dice similarity coefficient (DSC) and Average Hausdorff Distance (AVD) between the AD and MD hippocampi are 0.86 ± 0.028 and 0.18 ± 0.050 cm in cohort a, 0.76 ± 0.035 and 0.31 ± 0.064 cm in cohort b, 0.80 ± 0.015 and 0.24 ± 0.021 cm in cohort c, respectively. The DSC and AVD in cohort a were better than those in cohorts b and c (P Conclusion The AD of the hippocampus based on a deep learning algorithm showed satisfying results, which could have a positive impact on improving delineation accuracy and reducing work load.

Background Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy. Methods Independent patient cohorts from two hospitals were included for training (n = 87) and validation (n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for ‘Cox’ method was used for feature selection. The survival prediction model was constructed based on the sub-region radiomics features using the Cox proportional hazards model. The clinical and biological significance of radiomics features were assessed by correlation analysis of clinical characteristics and copy number alterations(CNAs) in the validation dataset. Findings The overall survival prediction model combining with seven sub-regional radiomics features was constructed. The C-indexes of the proposed model were 0.729 (0.656–0.801, 95% CI) and 0.705 (0.628–0.782, 95%CI) in the training and validation cohorts, respectively. The 3-year survival receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.811 (0.670–0.952, 95%CI) in training and 0.805 (0.638–0.973, 95%CI) in validation. The correlation analysis showed a significant correlation between radiomics features and CNAs. Interpretation The proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. Fund This work was supported by the Zhejiang Provincial Foundation for Natural Sciences, National Natural Science Foundation of China.

Background: Few studies to date have assessed the incidence of radiation pneumonitis (RP) in lung cancer patients who have been treated with volumetric modulated arc therapy (VMAT). This study is aimed at reporting the RP incidence rate and the risk factors associated with a symptomatic RP in patients with lung cancer treated with VMAT. Methods: A total of 77 consecutive lung cancer patients treated with VMAT from 2013 through 2015 were reviewed. RP severity was graded according to the Common Terminology Criteria for Adverse Events (CTCEA) v.4. Univariate and multivariate analyses were performed to identify the significant factors associated with RP. Results: VMAT allowed us to achieve most planning objectives on the target volumes and organs at risk, for PTV V 95% =96.8%±3.1%, for lung V 5 =41.3%±8.7%, V 10 =30.0%±7.1%, V 20 =20.9%±5.7%, for heart V 5 =43.2%±29.9%, for esophagus V 60 =8.1%±12.9%. The maximum dose of spinal cord was 34.4±9.5 Gy. The overall incidence of symptomatic RP (grade ≥2 by CTCAE) was 28.6% in the entire cohort, and the rate of grade ≥3 RP was 11.7%. Based on the multivariate analysis, factors predictive of symptomatic RP included lung volume receiving ≥10 Gy (V 10 ) (P=0.019) and C-reactive protein changing level (P=0.013). Conclusions: Our data showed that the incidence rate of RP was acceptable in lung cancer patients treated with VMAT. Additionally, we found that V10 might be an important factor for predicting the development of RP when VMAT was used; but this observation needs to be validated in future studies.

In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from 4-D computed tomography (4DCT), which is applied to patients with lung cancer treated by stereotactic body radiation therapy (SBRT). Seventy seven patients who underwent SBRT followed by 4DCT scans were incorporated in this retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework. For the purpose of comparison, three other IGTVs based on common methods was also delineated. We compared the relative volume difference (RVI), matching index (MI), and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis was performed to assess the tumor volume and motion range as clinical influencing factors in the MI variation. The results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 breath per minute (BPM), the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential solution for an optimal combination to synthesize IGTV in all respiration amplitudes.

This study aimed to design automated volumetric-modulated arc therapy (VMAT) plans in Pinnacle auto-planning and compare it with manual plans for patients with lower thoracic esophageal cancer (EC). Thirty patients with lower thoracic EC were randomly selected for replanning VMAT plans using auto-planning in Pinnacle treatment planning system (TPS) version 9.10. Historical plans of these patients were then compared. Dose-volume histogram (DVH) statistics, dose uniformity, and dose homogeneity were analyzed to evaluate treatment plans. Auto-planning was superior in terms of conformity index (CI) and homogeneity index (HI) for planning target volume (PTV), significantly improving 8.2% (p = 0.013) and 25% (p = 0.007) compared with manual planning, respectively, and decreasing dose of heart and liver irradiated by 20 to 40 Gy and 5 to 30 Gy, respectively (p < 0.05). Meanwhile, auto-planning further reduced the maximum dose (Dmax) of spinal cord by 6.9 Gy compared with manual planning (p = 0.000). Additionally, manual planning showed the significantly lower low-dose volume (V5) for the lung (p = 0.005). For auto-planning, the V5 of the lung was significantly associated with the relative volume index (the volume ratio of PTV to the lung), and the correlation coefficient (R) and p-value were 0.994 and 0.000. Pinnacle auto-planning achieved superior target conformity and homogeneity and similar target coverage compared with historical manual planning. Most of organs at risk (OARs) sparing was significantly improved by auto-planning except for the V5 of the lung, and the low dose distribution was highly associated with PTV volume and lung volume in auto-planning.

Purpose/Objective(s) The precise radiomics analysis on thoracic 4DCT data is easily compromised by the respiratory motion and CT scan parameter setting, thus leading to the risk of overfitting and/or misinterpretation of data in AI-enabled therapeutic model building. In this study, we investigated the impact of respiratory amplitudes, frequencies and CT scan pitch settings within the thoracic 4DCT scan on robust radiomics feature selection. Materials/Methods A Three-dimensional QUSARTM lung tumor phantom was used to simulate different respiratory amplitudes and frequencies along with different CT scan pitch settings. A total of 43 tumor respiratory patterns extracted from 43 patients with non-small cell lung cancer were used to drive the QUSARTM lung tumor phantom to mimic the human tumor motion. The 4DCT images of the QUSARTM lung tumor phantom with different respiratory patterns and different CT scan pitch setups were acquired for radiomics feature extraction. A static high-quality CT images of the phantom acquired were also used as a reference for radiomics feature extraction. The range of respiratory amplitudes was mimicked at 3mm at left and right (LR) and anterior and posterior (AP) directions and 3mm - 15 mm at the superior and inferior (SI) direction with an interval of 2 mm. The respiratory frequencies were set at 10, 11, 12, 13, 14, 15 and 20 beats per minute (BPMs), respectively. The CT scan pitches were set at 0.025, 0.048, 0.071, 0.93, 0.108, 0.14, 0.16, 0.18, 0.21, 0.23, and 0.25, respectively, which was based on a procedure described in Med. Phys. 30(1):88-97. The pairwise Concordance Correlation Coefficient (CCC) was used to determine the robustness of radiomics feature extraction via comparing the agreement in feature values between 1766 radiomics features extracted from each image acquired under different combinations of respiratory amplitudes and frequencies and CT scan pitches of 4DCT and those extracted from the static CT images. Results (1) When the respiratory amplitudes were at 3, 5, 7, 9, 12 and 15mm in the SI direction, the maximum CCC index could be achieved at the reconstructed 4DCT phase images of 60%, 70%, 30%, 20%, 60%~70% and 10%, respectively. Under these six amplitudes, the maximum intensity projection (MIP) and average intensity projection (AIP) images reconstructed show mean CCC values of 0.778 and 0.609, respectively, in pairwise radiomics feature extraction comparison between 4DCT and static CT. (2) When the respiratory amplitude was set at 12 mm in the SI direction, the maximum CCC index could be consistently achieved at the reconstructed 4DCT phase of 90% for the seven respiratory frequencies of 10, 11, 12, 13, 14, 15 and 20 BPMs, respectively. Under these respiratory states, the MIP and AIP images reconstructed show mean CCC values of 0.702 and 0.562, respectively. (3) When the respiratory amplitude was set at 12 mm and the respiratory frequency was set at 13 BPM, the maximum CCC index could be obtained at the reconstructed 4DCT phase of 90% for all scan pitches used except the 0% phase which was obtained at the pitch setting of 0.048. Under these CT scan pitch settings, the MIP and AIP images reconstructed show mean CCC values of 0.558 and 0.782, respectively. (4) The total number of robust features were 50, 34 and 35 with different respiratory amplitudes and phases and CT scanning pitch used (CCC values ≥ 0.99). Conclusion In 4DCT, the respiratory amplitude, frequency and CT scan pitch are three limiting factors that greatly affect the robustness of radiomics feature extraction. The reconstructed 4DCT phases with better robustness along with suitable respiratory amplitude, frequency and CT scan pitch determined could be used to guide the breathing training for patients with lung cancer for radiation therapy to improve the robust radiomics feature extraction process.

Rationale Suvorexant is a first-in-class orexin receptor antagonist for treating insomnia. There is a general concern that hypnotics may impair next-morning driving ability. Objective The objective of this study was to evaluate next-morning driving performance in older adults after single and repeated doses of suvorexant. Methods Double-blind, randomized, placebo-controlled, 4-period crossover study in 24 healthy volunteers (10 females), aged 65–80 years. Subjects were treated with suvorexant (15 and 30 mg) for eight consecutive nights, zopiclone 7.5 mg nightly on days 1 and 8, and placebo. Driving performance was assessed on days 2 and 9 (9 h after dosing) using a 1-h standardized highway driving test in normal traffic, measuring standard deviation of lateral position (SDLP). Drug-placebo differences in SDLP >2.4 cm were considered to reflect clinically meaningful driving impairment. Results Driving performance as measured by SDLP was not impaired following suvorexant. Mean drug–placebo differences in SDLP following suvorexant 15 and 30 mg on day 2 and 9 were 0.6 cm or less. Their 90 % CIs were all below the threshold of 2.4 cm for clinical relevance and included zero, indicating effects were not clinically meaningful or statistically significant. Symmetry analysis showed no significant differences between the number of participants who had SDLP differences >2.4 cm and those who had SDLP differences

IntroductionInflammation is a major risk factor for systemic bone loss. Proinflammatory cytokines like tumour necrosis factor (TNF) affect bone homeostasis and induce bone loss. It was hypothesised that impaired bone formation is a key component in inflammatory bone loss and that Dkk-1, a Wnt antagonist, is a strong inhibitor of osteoblast-mediated bone formation.MethodsTNF transgenic (hTNFtg) mice were treated with neutralising antibodies against TNF, Dkk-1 or a combination of both agents. Systemic bone architecture was analysed by bone histomorphometry. The expression of β-catenin, osteoprotegerin and osteocalcin was analysed. In vitro, primary osteoblasts were stimulated with TNF and analysed for their metabolic activity and expression of Dkk-1 and sclerostin. Sclerostin expression and osteocyte death upon Dkk-1 blockade were analysed in vivo.ResultsNeutralisation of Dkk-1 completely protected hTNFtg mice from inflammatory bone loss by preventing TNF-mediated impaired osteoblast function and enhanced osteoclast activity. These findings were accompanied by enhanced skeletal expression of β-catenin, osteocalcin and osteoprotegerin. In vitro, TNF rapidly increased Dkk-1 expression in primary osteoblasts and effectively blocked osteoblast differentiation. Moreover, blockade of Dkk-1 not only rescued impaired osteoblastogenesis but also neutralised TNF-mediated sclerostin expression in fully differentiated osteoblasts in vitro and in vivo.ConclusionsThese findings indicate that low bone formation and expression of Dkk-1 trigger inflammatory bone loss. Dkk-1 blocks osteoblast differentiation, induces sclerostin expression and leads to osteocyte death. Inhibition of Dkk-1 may thus be considered as a potent strategy to protect bone from inflammatory damage.