PET/SPECT/Spectral-CT/CBCT imaging in a small-animal radiation therapy platform: A Monte Carlo study-Part II: Biologically guided radiotherapy.

BACKGROUND: This study addresses the technical gap between clinical radiation therapy (RT) and preclinical small-animal RT, hindering the comprehensive validation of innovative clinical RT approaches in small-animal models of cancer and the translation of preclinical RT studies into clinical practices. PURPOSE: The main aim was to explore the feasibility of biologically guided RT implemented within a small-animal radiation therapy (SART) platform, with integrated quad-modal on-board positron emission tomography (PET), single-photon emission computed tomography, photon-counting spectral CT, and cone-beam CT (CBCT) imaging, in a Monte Carlo model as a proof-of-concept. METHODS: We developed a SART workflow employing quad-modal imaging guidance, integrating multimodal image-guided RT and emission-guided RT (EGRT). The EGRT algorithm was outlined using positron signals from a PET radiotracer, enabling near real-time adjustments to radiation treatment beams for precise targeting in the presence of a 2-mm setup error. Molecular image-guided RT, incorporating a dose escalation/de-escalation scheme, was demonstrated using a simulated phantom with a dose painting plan. The plan involved delivering a low dose to the CBCT-delineated planning target volume (PTV) and a high dose boosted to the highly active biological target volume (hBTV) identified by the 18F-PET image. Additionally, the Bayesian eigentissue decomposition method illustrated the quantitative decomposition of radiotherapy-related parameters, specifically iodine uptake fraction and virtual noncontrast (VNC) electron density, using a simulated phantom with Kidney1 and Liver2 inserts mixed with an iodine contrast agent at electron fractions of 0.01-0.02. RESULTS: EGRT simulations generated over 4,000 beamlet responses in dose slice deliveries and illustrated superior dose coverage and distribution with significantly lower doses delivered to normal tissues, even with a 2-mm setup error introduced, demonstrating the robustness of the novel EGRT scheme compared to conventional image-guided RT. In the dose-painting plan, doubling the dose to the hBTV while maintaining a low dose for the PTV resulted in an organ-at-risk (OAR) dose comparable to the low-dose treatment for the PTV alone. Furthermore, the decomposition of radiotherapy-related parameters in Kidney1 and Liver2 inserts, including iodine uptake fractions and VNC electron densities, exhibited average relative errors of less than 1.0% and 2.5%, respectively. CONCLUSIONS: The results demonstrated the successful implementation of biologically guided RT within the proposed quad-model image-guided SART platform, with potential applications in preclinical RT and adaptive RT studies.

Predicting radiation pneumonitis in lung cancer: a EUD-based machine learning approach for volumetric modulated arc therapy patients.

Purpose: This study aims to develop an optimal machine learning model that uses lung equivalent uniform dose (lung EUD to predict radiation pneumonitis (RP) occurrence in lung cancer patients treated with volumetric modulated arc therapy (VMAT). Methods: We analyzed a cohort of 77 patients diagnosed with locally advanced squamous cell lung cancer (LASCLC) receiving concurrent chemoradiotherapy with VMAT. Patients were categorized based on the onset of grade II or higher radiation pneumonitis (RP 2+). Dose volume histogram data, extracted from the treatment planning system, were used to compute the lung EUD values for both groups using a specialized numerical analysis code. We identified the parameter α, representing the most significant relative difference in lung EUD between the two groups. The predictive potential of variables for RP2+, including physical dose metrics, lung EUD, normal tissue complication probability (NTCP) from the Lyman-Kutcher-Burman (LKB) model, and lung EUD-calibrated NTCP for affected and whole lung, underwent both univariate and multivariate analyses. Relevant variables were then employed as inputs for machine learning models: multiple logistic regression (MLR), support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). Each model's performance was gauged using the area under the curve (AUC), determining the best-performing model. Results: The optimal α-value for lung EUD was 0.3, maximizing the relative lung EUD difference between the RP 2+ and non-RP 2+ groups. A strong correlation coefficient of 0.929 (P< 0.01) was observed between lung EUD (α = 0.3) and physical dose metrics. When examining predictive capabilities, lung EUD-based NTCP for the affected lung (AUC: 0.862) and whole lung (AUC: 0.815) surpassed LKB-based NTCP for the respective lungs. The decision tree (DT) model using lung EUD-based predictors emerged as the superior model, achieving an AUC of 0.98 in both training and validation datasets. Discussions: The likelihood of developing RP 2+ has shown a significant correlation with the advancements in RT technology. From traditional 3-D conformal RT, lung cancer treatment methodologies have transitioned to sophisticated techniques like static IMRT. Accurately deriving such a dose-effect relationship through NTCP modeling of RP incidence is statistically challenging due to the increased number of degrees-of-freedom. To the best of our knowledge, many studies have not clarified the rationale behind setting the α-value to 0.99 or 1, despite the closely aligned calculated lung EUD and lung mean dose MLD. Perfect independence among variables is rarely achievable in real-world scenarios. Four prominent machine learning algorithms were used to devise our prediction models. The inclusion of lung EUD-based factors substantially enhanced their predictive performance for RP 2+. Our results advocate for the decision tree model with lung EUD-based predictors as the optimal prediction tool for VMAT-treated lung cancer patients. Which could replace conventional dosimetric parameters, potentially simplifying complex neural network structures in prediction models.

PET/SPECT/spectral-CT/CBCT imaging in a small-animal radiation therapy platform: A Monte Carlo study-Part I: Quad-modal imaging.

BACKGROUND: In spite of the tremendous potential of game-changing biological image- and/or biologically guided radiation therapy (RT) and adaptive radiation therapy for cancer treatment, existing limited strategies for integrating molecular imaging and/or biological information with RT have impeded the translation of preclinical research findings to clinical applications. Additionally, there is an urgent need for a highly integrated small-animal radiation therapy (SART) platform that can seamlessly combine therapeutic and diagnostic capabilities to comprehensively enhance RT for cancer treatment. PURPOSE: We investigated a highly integrated quad-modal on-board imaging configuration combining positron emission tomography (PET), single-photon emission computed tomography (SPECT), photon-counting spectral CT, and cone-beam computed tomography (CBCT) in a SART platform using a Monte Carlo model as a proof-of-concept. METHODS: The quad-modal on-board imaging configuration of the SART platform was designed and evaluated by using the GATE Monte Carlo code. A partial-ring on-board PET imaging subsystem, utilizing advanced semiconductor thallium bromide detector technology, was designed to achieve high sensitivity and spatial resolution. On-board SPECT, photon-counting spectral-CT, and CBCT imaging were performed using a single cadmium zinc telluride flat detector panel. The absolute peak sensitivity and scatter fraction of the PET subsystem were estimated by using simulated phantoms described in the NEMA NU-4 standard. The spatial resolution of the PET image of the platform was evaluated by imaging a simulated micro-Derenzo hot-rod phantom. To evaluate the quantitative imaging capability of the system's spectral CT, the Bayesian eigentissue decomposition (ETD) method was utilized to quantitatively decompose the virtual noncontrast (VNC) electron densities and iodine contrast agent fractions in the Kidney1 inserts mixed with the iodine contrast agent within the simulated phantoms. The performance of the proposed quad-model imaging in the platform was validated by imaging a simulated phantom with multiple imaging probes, including an iodine contrast agent and radioisotopes of 18F and 99mTc. RESULTS: The PET subsystem demonstrated an absolute peak sensitivity of 18.5% at the scanner center, with an energy window of 175-560 KeV, and a scatter fraction of only 3.5% for the mouse phantom, with a default energy window of 480-540 KeV. The spatial resolution of PET on-board imaging exceeded 1.2 mm. All imaging probes were identified clearly within the phantom. The PET and SPECT images agreed well with the actual spatial distributions of the tracers within the phantom. Average relative errors on electron density and iodine contrast agent fraction in the Kidney1 inserts were less than 3%. High-quality PET images, SPECT images, spectral-CT images (including iodine contrast agent fraction images and VNC electron density images), and CBCT images of the simulated phantom demonstrated the comprehensive multimodal imaging capability of the system. CONCLUSIONS: The results demonstrated the feasibility of the proposed quad-modal imaging configuration in a SART platform. The design incorporates anatomical, molecular, and functional information about tumors, thereby facilitating successful translation of preclinical studies into clinical practices.

GHA-DenseNet prediction and diagnosis of malignancy in femoral bone tumors using magnetic resonance imaging.

Background and objective: Due to their aggressive nature and poor prognosis, malignant femoral bone tumors present considerable hurdles. Early treatment commencement is essential for enhancing vital and practical outcomes. In this investigation, deep learning algorithms will be used to analyze magnetic resonance imaging (MRI) data to identify bone tumors that are malignant. Methodology: The study cohort included 44 patients, with ages ranging from 17 to 78 (22 women and 22 males). To categorize T1 and T2 weighted MRI data, this paper presents an improved DenseNet network model for the classification of bone tumor MRI, which is named GHA-DenseNet. Based on the original DenseNet model, the attention module is added to solve the problem that the deep convolutional model can reduce the loss of key features when capturing the location and content information of femoral bone tumor tissue due to the limitation of local receptive field. In addition, the sparse connection mode is used to prune the connection mode of the original model, so as to remove unnecessary and retain more useful fast connection mode, and alleviate the overfitting problem caused by small dataset size and image characteristics. In a clinical model designed to anticipate tumor malignancy, the utilization of T1 and T2 classifier output values, in combination with patient-specific clinical information, was a crucial component. Results: The T1 classifier's accuracy during the training phase was 92.88% whereas the T2 classifier's accuracy was 87.03%. Both classifiers demonstrated accuracy of 95.24% throughout the validation phase. During training and validation, the clinical model's accuracy was 82.17% and 81.51%, respectively. The clinical model's receiver operating characteristic (ROC) curve demonstrated its capacity to separate classes. Conclusions: The proposed method does not require manual segmentation of MRI scans because it makes use of pretrained deep learning classifiers. These algorithms have the ability to predict tumor malignancy and shorten the diagnostic and therapeutic turnaround times. Although the procedure only needs a little amount of radiologists' involvement, more testing on a larger patient cohort is required to confirm its efficacy.

A Bayesian optimization tunning integrated multi-stacking classifier framework for the prediction of radiodermatitis from 4D-CT of patients underwent breast cancer radiotherapy.

Purpose: In this study, we aimed to develop a novel Bayesian optimization based multi-stacking deep learning platform for the prediction of radiation-induced dermatitis (grade ≥ two) (RD 2+) before radiotherapy, by using multi-region dose-gradient-related radiomics features extracted from pre-treatment planning four-dimensional computed tomography (4D-CT) images, as well as clinical and dosimetric characteristics of breast cancer patients who underwent radiotherapy. Materials and methods: The study retrospectively included 214 patients with breast cancer who received radiotherapy after breast surgeries. Six regions of interest (ROIs) were delineated based on three PTV dose -gradient-related and three skin dose-gradient-related parameters (i.e., isodose). A total of 4309 radiomics features extracted from these six ROIs, as well as clinical and dosimetric characteristics, were used to train and validate the prediction model using nine mainstream deep machine learning algorithms and three stacking classifiers (i.e., meta-learners). To achieve the best prediction performance, a Bayesian optimization based multi-parameter tuning technology was adopted for the AdaBoost, random forest (RF), decision tree (DT), gradient boosting (GB) and extra tree (XTree) five machine learning models. The five parameter -tuned learners and the other four learners (i.e., logistic regression (LR), K-nearest neighbors (KNN), linear discriminant analysis (LDA), Bagging) whose parameters cannot be tuned, all as the primary week learners, were fed into the subsequent meta-learners for training and learning the final prediction model. Results: The final prediction model included 20 radiomics features and eight clinical and dosimetric characteristics. At the primary learner level, on base of Bayesian parameter tuning optimization, the RF, XGBoost, AdaBoost, GBDT, and LGBM models with the best parameter combinations achieved AUC of 0.82, 0.82, 0.77, 0.80, and 0.80 prediction performance in the verification data set, respectively. In the secondary meta-learner lever, compared with LR and MLP meta-learner, the best predictor of symptomatic RD 2+ for stacked classifiers was the GB meta-learner with an area under the curve (AUC) of 0.97 [95% CI: 0.91-1.0] and an AUC of 0.93 [95% CI: 0.87-0.97] in the training and validation datasets, respectively and the 10 top predictive characteristics were identified. Conclusion: A novel multi-region dose-gradient-based Bayesian optimization tunning integrated multi-stacking classifier framework can achieve a high-accuracy prediction of symptomatic RD 2+ in breast cancer patients than any other single deep machine learning algorithm.

Prediction of radiation-induced acute skin toxicity in breast cancer patients using data encapsulation screening and dose-gradient-based multi-region radiomics technique: A multicenter study.

Purpose: Radiation-induced dermatitis is one of the most common side effects for breast cancer patients treated with radiation therapy (RT). Acute complications can have a considerable impact on tumor control and quality of life for breast cancer patients. In this study, we aimed to develop a novel quantitative high-accuracy machine learning tool for prediction of radiation-induced dermatitis (grade ≥ 2) (RD 2+) before RT by using data encapsulation screening and multi-region dose-gradient-based radiomics techniques, based on the pre-treatment planning computed tomography (CT) images, clinical and dosimetric information of breast cancer patients. Methods and Materials: 214 patients with breast cancer who underwent RT between 2018 and 2021 were retrospectively collected from 3 cancer centers in China. The CT images, as well as the clinical and dosimetric information of patients were retrieved from the medical records. 3 PTV dose related ROIs, including irradiation volume covered by 100%, 105%, and 108% of prescribed dose, combined with 3 skin dose-related ROIs, including irradiation volume covered by 20-Gy, 30-Gy, 40-Gy isodose lines within skin, were contoured for radiomics feature extraction. A total of 4280 radiomics features were extracted from all 6 ROIs. Meanwhile, 29 clinical and dosimetric characteristics were included in the data analysis. A data encapsulation screening algorithm was applied for data cleaning. Multiple-variable logistic regression and 5-fold-cross-validation gradient boosting decision tree (GBDT) were employed for modeling training and validation, which was evaluated by using receiver operating characteristic analysis. Results: The best predictors for symptomatic RD 2+ were the combination of 20 radiomics features, 8 clinical and dosimetric variables, achieving an area under the curve (AUC) of 0.998 [95% CI: 0.996-1.0] and an AUC of 0.911 [95% CI: 0.838-0.983] in the training and validation dataset, respectively, in the 5-fold-cross-validation GBDT model. Meanwhile, the top 12 most important characteristics as well as their corresponding importance measures for RD 2+ prediction in the GBDT machine learning process were identified and calculated. Conclusions: A novel multi-region dose-gradient-based GBDT machine learning framework with a random forest based data encapsulation screening method integrated can achieve a high-accuracy prediction of acute RD 2+ in breast cancer patients.

Computer Added Simulation of the Spread of Multidrug-Resistant Bacteria in an Radiation Therapy Shelter Based on Computational Fluid Dynamics.

Purpose: Due to the poor ventilation and air stagnation in the radiation therapy ward, it is easy to cause respiratory disease transmission, which brings about the public health safety problem of infection. In order to alleviate this problem, we propose a research method based on computational fluid dynamics (CFD). Method: A three-dimensional model of a radiation therapy ward is established, and the CFD software framework is used to numerically simulate the air flow field in the constrained radiation therapy ward environment. We computed the influence of the spray speed, particle size, and inlet content of respiratory droplets on the flow and spread of multidrug-resistant bacteria. Results: In the range of the horizontal transmission line X from 0 to 3 meters, when the transmission speed (V) is 35 m/s, the multidrug-resistant bacteria concentration reaches the highest value. In the range of the vertical transmission line Y from 0 to 3 meters, when V is 35 m/s, the multidrug-resistant bacteria concentration reaches the highest value. Conclusion: A large amount of data shows that there is a positive correlation between the respiratory droplet spray velocity, inlet content, and the multidrug-resistant bacteria flow propagation speed and concentration distribution. The respiratory droplet size mainly affects the peak concentration of the multidrug-resistant bacteria flow propagation.

A COVID-19 risk score combining chest CT radiomics and clinical characteristics to differentiate COVID-19 pneumonia from other viral pneumonias.

With the continued transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) throughout the world, identification of highly suspected COVID-19 patients remains an urgent priority. In this study, we developed and validated COVID-19 risk scores to identify patients with COVID-19. In this study, for patient-wise analysis, three signatures, including the risk score using radiomic features only, the risk score using clinical factors only, and the risk score combining radiomic features and clinical variables, show an excellent performance in differentiating COVID-19 from other viral-induced pneumonias in the validation set. For lesion-wise analysis, the risk score using three radiomic features only also achieved an excellent AUC value. In contrast, the performance of 130 radiologists based on the chest CT images alone without the clinical characteristics included was moderate as compared to the risk scores developed. The risk scores depicting the correlation of CT radiomics and clinical factors with COVID-19 could be used to accurately identify patients with COVID-19, which would have clinically translatable diagnostic and therapeutic implications from a precision medicine perspective.

Deep learning-based automatic delineation of the hippocampus by MRI: geometric and dosimetric evaluation.

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 < 0.05 was considered significant. 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 < 0.01). There is no significant difference between the radiotherapy plans generated using the AD and MD hippocampi. 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.

The impact of respiratory motion and CT pitch on the robustness of radiomics feature extraction in 4DCT lung imaging.

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.

[Research Advances and Obstacles of CT-based Radiomics in Diagnosis and Treatment of Lung Cancer].

Radiomics, a technology based on multimodal medical image processing and analysis, is able to extract automatically and analyze massive data from computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography/computed tomography (PET/CT) via high-performance computer algorithm in order to pursue early diagnosis of disease, benign and malignant tumor discrimination, dynamic evaluation of disease treatment, and individualized precision therapy. To date, many studies demonstrate that radiomics not only has great potential in early diagnosis of lung cancer and prediction of genotype, treatment efficacy, as well as prognosis but also is based on imaging methods that are noninvasive, inexpensive, and repeatable. It does demonstrate precious values in guiding the clinical diagnosis and treatment of lung cancer, especially in the personalized and precise treatments and researches of lung cancer. However, the consistency and reproducibility of radiomics and the selection of robust characteristics still warrant further researches.
.

Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy.

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.

A phase II study of concurrent nab-paclitaxel/carboplatin combined with thoracic radiotherapy in locally advanced squamous cell lung cancer.

BACKGROUND: The development of chemoradiotherapy is urgently needed for locally advanced squamous cell lung cancer due to its poor prognosis and significant toxicity. Carboplatin combined with nab-paclitaxel is a useful choice as first-line therapy in advanced squamous cell lung cancer. This prospective phase II study aimed to explore the efficacy and toxicity of concurrent chemoradiotherapy with nab-paclitaxel, carboplatin, and thoracic radiotherapy in unresectable locally advanced squamous cell lung cancer. METHODS: Patients with unresectable stage III squamous cell lung cancer were eligible. All patients received nab-paclitaxel weekly at a dose of 60 mg/m2, in combination with carboplatin [area under the plasma concentration time curve (AUC) 2] weekly during concurrent chemoradiotherapy. Thoracic radiation was administered at a dose of 66 Gy/33 fractions. The consolidation chemotherapy consisted of nab-paclitaxel (260 mg/m2 on day 1) and carboplatin (AUC 6 on day 1) every 21 days was administered in two cycles after the concurrent chemoradiotherapy. The primary endpoint was objective response rate (ORR). Secondary endpoints included progression-free survival (PFS), overall survival (OS), and safety. RESULTS: Initially, enrollment of 21 patients was planned; however, the trial was prematurely closed due to slow recruitment. Finally, a total of 8 patients were enrolled between January 2012 and July 2015 from one institute. All patients completed concurrent chemoradiotherapy, and 6 patients (75.0%) received consolidation chemoradiotherapy. The ORR was 75%, with complete response (CR) 1 (12.5%), partial remission 6 (62.5%), stable disease 1 (12.5%), progressive disease 1 (12.5%), respectively. After a median follow-up of 15.2 (range, 2.3-51.5) months, 7 patients were dead, and 1 was alive. The median PFS and OS were 12.1 and 15.2 months, respectively. According to Common Terminology Criteria for Adverse Events version 4.0, 6 patients (75.0%) experienced acute radiation esophagitis, 4 (50.0%) were grade 2 (G2), and 2 (25.0%) were G3; 4 patients (50%) experienced acute radiation pneumonitis, 3 (37.5%) were G2, and 1 (12.5%) was G3. No late radiation-induced esophageal and pulmonary toxicity was observed after 1-year follow-up. CONCLUSIONS: Concurrent nab-paclitaxel, carboplatin, and thoracic radiotherapy was shown to be an effective regimen for patients with unresectable locally advanced squamous cell lung cancer; however, further study should exercise caution due to the severe radiation esophagitis.

Radiation pneumonitis in lung cancer treated with volumetric modulated arc therapy.

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 V95% =96.8%±3.1%, for lung V5 =41.3%±8.7%, V10 =30.0%±7.1%, V20 =20.9%±5.7%, for heart V5 =43.2%±29.9%, for esophagus V60 =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 (V10) (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.

Dosimetric benefits of automation in the treatment of lower thoracic esophageal cancer: Is manual planning still an alternative option?

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.

On-the-road driving performance the morning after bedtime use of suvorexant 15 and 30 mg in healthy elderly.

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 <-2.4 cm following suvorexant. CONCLUSIONS: There was no clinically meaningful residual effect of suvorexant 15 and 30 mg on next-morning driving (9 h after bedtime dosing) in healthy older adults, as assessed by mean changes in SDLP and by the number of participants on drug versus placebo that exceeded a predetermined threshold for clinically meaningful impairment.

Use of a correlation analysis model in the optimization of intensity-modulated radiotherapy of prostate cancer.

The aim of the present study was to develop a statistical model-based method for the optimization of intensity-modulated radiotherapy (IMRT). A prostate cancer IMRT plan was redesigned while retaining the same beam orientation and prescribed dose as the regular plan. A series of dosimetric parameters were generated, and a 4-step protocol was performed to analyze the data: i) The tumor control probability of the target was ensured by setting a number of strict constraint parameters so that much of the target was covered by the 95% isodose line; ii) the parameters for optimization [weight ratio, equivalent characteristic parameter a and maximum equivalent uniform dose of the organ at risk (OAR)] were adjusted; iii) the overall optimization space (OOS) was determined via analysis of the dose-parameter tables based on the correlation factor (CF) and optimization efficiency factor analysis; iv) the OOS in the Pinnacle V7.6 treatment planning system with IMRT function was transposed. A selected optimization phenomenon existed when different optimization methods were used to optimize dose distribution to the targets and OARs, which demonstrates a wide variation in the CFs between the percentage of planning target volume receiving 95% of the prescribed dose and the maximum dose of the bladder, rectum and femur. The OOS used to optimize the randomly selected plan exhibited relatively high efficiency, with benefits for the optimization of IMRT plans. For patients with prostate cancer who require complex IMRT plan optimization, the obtained OOS from the two core analysis techniques is likely to have relatively high efficiency in achieving an optimized plan. These results suggest that the correlation analysis model is a novel method for the optimization of IMRT for prostate cancer.

On-the-Road Driving Performance the Morning after Bedtime Use of Suvorexant 20 and 40 mg: A Study in Non-Elderly Healthy Volunteers.

STUDY OBJECTIVE: To evaluate next-morning driving performance in adults younger than 65 years, after single and repeated doses of suvorexant 20 and 40 mg. DESIGN: Double-blind, placebo-controlled, 4-period crossover study. SETTING: Maastricht University, The Netherlands. PARTICIPANTS: 28 healthy volunteers (15 females), aged 23 to 64 years. INTERVENTIONS: Suvorexant (20 and 40 mg) for 8 consecutive nights; zopiclone 7.5 mg nightly on day 1 and 8; placebo. MEASUREMENTS: Performance on day 2 and 9 (9 h after dosing) using a one-hour standardized highway driving test in normal traffic, measuring standard deviation of lateral position (SDLP). Drug-placebo changes in SDLP > 2.4 cm were considered to reflect meaningful driving impairment. RESULTS: Mean drug-placebo changes in SDLP following suvorexant 20 and 40 mg were 1.01 and 1.66 cm on day 2, and 0.48 and 1.31 cm on Day 9, respectively. The 90% CIs of these changes were all below 2.4 cm. Symmetry analysis showed that more subjects had SDLP changes > 2.4 cm than < -2.4 cm following suvorexant 20 and 40 mg on day 2, and following suvorexant 40 mg on day 9. Four female subjects requested that a total of 5 driving tests--all following suvorexant--stop prematurely due to self-reported somnolence. CONCLUSIONS: As assessed by mean changes in standard deviation of lateral position (SDLP), there was no clinically meaningful residual effect of suvorexant in doses of 20 and 40 mg on next-morning driving (9 h after bedtime dosing) in healthy subjects < 65 years old. There may be some individuals who experience next-day effects, as suggested by individual changes in SDLP and prematurely stopped tests. CLINICAL TRIAL REGISTRATION: clinicaltrials.gov NCT01311882.

Postoperative radiotherapy following mastectomy for patients with left-sided breast cancer: A comparative dosimetric study.

The purposes of this article were to compare the biophysical dosimetry for postmastectomy left-sided breast cancer using 4 different radiotherapy (RT) techniques. In total, 30 patients with left-sided breast cancer were randomly selected for this treatment planning study. They were planned using 4 RT techniques, including the following: (1) 3-dimensional conventional tangential fields (TFs), (2) tangential intensity-modulated therapy (T-IMRT), (3) 4 fields IMRT (4F-IMRT), and (4) single arc volumetric-modulated arc therapy (S-VMAT). The planning target volume (PTV) dose was prescribed 50Gy, the comparison of target dose distribution, conformity index, homogeneity index, dose to organs at risk (OARs), tumor control probability (TCP), normal tissue complication probability (NTCP), and number of monitor units (MUs) between 4 plans were investigated for their biophysical dosimetric difference. The target conformity and homogeneity of S-VMAT were better than the other 3 kinds of plans, but increased the volume of OARs receiving low dose (V5). TCP of PTV and NTCP of the left lung showed no statistically significant difference in 4 plans. 4F-IMRT plan was superior in terms of target coverage and protection of OARs and demonstrated significant advantages in decreasing the NTCP of heart by 0.07, 0.03, and 0.05 compared with TFs, T-IMRT, and S-VMAT plan. Compared with other 3 plans, TFs reduced the average number of MUs. Of the 4 techniques studied, this analysis supports 4F-IMRT as the most appropriate balance of target coverage and normal tissue sparing.

Effect of Hyperthermic Intraperitoneal Perfusion Chemotherapy in Combination with Intravenous Chemotherapy as Postoperative Adjuvant Therapy for Advanced Gastric Cancer.

BACKGROUND/AIMS: The aim is to evaluate the preliminary efficacy and side effects of paclitaxel, 5-fluorouracil, and leucovorin intravenous chemotherapy in combination with cisplatin hyperthermic intraperitoneal perfusion chemotherapy (HIPEC) as postoperative adjuvant therapy for patients of locally advanced gastric cancer (GC) at high risk for recurrence after curative resection. METHODOLOGY: Four GC patients who underwent radical gastrectomy with D2 lymphadenectomy were enrolled. All patients received paclitaxel 135 mg/m2 on day 1, 5-FU 500 mg/m2 on days 1-5, LV 200 mg/m2 on days 1-5 intravenous chemotherapy, cisplatin 75 mg/m2 on day 5, and HIPEC one month after surgery. It was repeated at 3 weeks intervals and at least two cycles administered. RESULTS: A total of 181 cycles of chemotherapy were administered (median, 4 cycles). The median disease free survival time of patients was 40.8 months. The median overall survival time was 48.0 months. The one-, two-, and three-year recurrence rates were 14.6%, 26.8%, and 46.3%, respectively. The main relapse patterns were remnant GC and metastases of retroperitoneal lymph nodes. The morbidity of grade 3 and 4 toxicities of myelosuppression, nausea/ vomiting were less than 10%. The side effects of grade 1 and 2 of hematologic toxicity, nausea and vomiting, abnormal function of liver, kidney or cardiac, fatigue and neurotoxicity were well tolerated. CONCLUSIONS: Cisplatin HIPEC combined with paclitaxel, 5-fluorouracil, and leucovorin intravenous chemotherapy regimen could improve the survival rate and decrease the postoperative recurrence of locally advanced GC.