Loneliness among older Chinese individuals: the status quo and relationships with activity-related factors.

BACKGROUND: With the rapid population aging, healthy aging has become a concern for society as a whole. In this study, loneliness and its relationships with activity-related individual factors were examined among older Chinese individuals from the perspective of mental health and daily leisure activities. METHODS: The data were from the fourth investigation of the Sample Survey of the Aged Population in Urban and Rural China, which had a total of 220,506 participants. Activity ability was assessed by the Barthel Activity of Daily Living Index, a self-designed activity type questionnaire was used to evaluate activity participation, and loneliness was measured with a single-item question. RESULTS: The prevalence of varying degrees of loneliness among Chinese older individuals was 36.6%. The prevalence of loneliness among the older individuals differed significantly by age gender, age, physical health status, annual household income, education level, marital status, living status, ethnic minority status, religious faith and territory of residence. There were differences in activity participation among older Chinese adults in terms of all the demographic factors mentioned above, while there were no significant differences in living status or religious faith, and significant differences in several other demographic factors in terms of activity ability. Self-care ability, as a form of activity ability, and activity participation significantly predicted loneliness among the older participants. CONCLUSION: The topic of loneliness among Chinese older individuals is complex and requires greater attention. The buffering effect of activity-related factors on loneliness suggests that old people should improve their activity ability and participate more in daily activities.

Radiomics of skeletal muscle helps to predict gastrointestinal toxicity in locally advanced rectal cancer patients receiving neoadjuvant chemoradiotherapy.

Background: The skeletal muscle index (SMI) can serve as a surrogate for a patient's nutritional status, which is associated with treatment toxicity. This study aims to investigate the potential of baseline skeletal muscle radiomics features to predict gastrointestinal toxicity of neoadjuvant chemoradiotherapy for rectal cancer. Methods: A total of 214 rectal cancer patients (115, 49 and 50 in the training, internal and external validation set, respectively) who underwent neoadjuvant pelvic radiotherapy with capecitabine and irinotecan were retrospectively identified. The skeletal muscle at the level of the third lumber vertebra was contoured, and the radiomics features were extracted from computed tomography scans. In the training set, the least absolute shrinkage and selection operator (LASSO) regression algorithm was applied to select features that were most significantly associated with grade 3-4 gastrointestinal toxicity (diarrhea, nausea, vomiting and proctitis). The predictive performance and clinical utility were estimated using the area under the receiver operator characteristic curve (AUC), F1-score and decision curve analysis (DCA). Results: Nine features, including the SMI and eight radiomics features, were associated with grade 3-4 gastrointestinal toxicity and included in the logistic regression. This combined predictive model, which incorporated the SMI and radiomics features, showed better discrimination than the SMI alone, with an AUC of 0.856 (95 % CI: 0.782-0.929) in the training cohort, 0.812 (95 % CI: 0.667-0.956) in the internal validation cohort and 0.745 (95 % CI: 0.600-0.890) in the external validation cohort. DCA further verified the clinical utility of the combined predictive model. Conclusion: Radiomics features of skeletal muscle were significantly associated with gastrointestinal toxicity. The predictive model incorporating the SMI and radiomics features exhibits favorable discrimination and may be highly informative for clinical decision-makings.

Implementation and evaluation of an iterative-based algorithm for automatic beam angle optimization in breast cancer treatment planning.

INTRODUCTION: A beam angle optimization (BAO) algorithm was developed to evaluate its clinical feasibility and investigate the impact of varying BAO constraints on breast cancer treatment plans. MATERIALS AND METHODS: A two-part study was designed. In part 1, we retrospectively selected 20 patients treated with radiotherapy after breast-conserving surgery. For each patient, BAO plans were designed using beam angles optimized by the BAO algorithm and the same optimization constraints as manual plans. Dosimetric indices were compared between BAO and manual plans. In part 2, fifteen patients with left breast cancer were included. For each patient, three distinct cardiac constraints (mean heart dose < 5 Gy, 3 Gy or 1 Gy) were established during the BAO process to obtain three optimized beam sets which were marked as BAO_H1, BAO_H3, BAO_H5, respectively. These sets of beams were then utilized under identical IMRT constraints for planning. Comparative analysis was conducted among the three groups of plans. RESULTS: For part 1, no significant differences were observed between BAO plans and manual plans in all dosimetric indices, except for ipsilateral lung V5, where BAO plans performed slightly better than manual plans (35.5% ± 5.6% vs 36.9% ± 4.3%, p = 0.034). For part 2, Stricter BAO heart constraints resulted in more perpendicular beams. However, there was no significant difference between BAO_H1, BAO_H3 and BAO_H5 with the same IMRT constraint in the heart dose. Meanwhile, the left lung dose was increased while the right breast and lung doses were decreased with stricter heart constraints in BAO. When mean heart dose < 5 Gy in IMRT constraint, the mean dose to the right lung was decreased from 0.46 Gy for BAO_H5 to 0.33 Gy for BAO_H1 (p = 0.027). CONCLUSIONS: The BAO algorithm can achieve quality plans comparable to manual plans. IMRT constraints dominate the final plan dose, while varying BAO constraints alter the trade-off among structures, providing an additional degree of freedom in planning design.

RIFLE: a Phase II trial of stereotactic ablative radiotherapy combined with fruquintinib and tislelizumab in metastatic colorectal cancer.

Background: Currently, the prognosis for metastatic colorectal cancer (mCRC) still remains poor. The management of mCRC has become manifold because of the varied advances in the systemic and topical treatment approaches. For patients with limited number of metastases, radical local therapy plus systemic therapy can be a good choice to achieve long-term tumor control. In this study, we aimed to explore the efficacy and safety of the combination of fruquintinib, tislelizumab, and stereotactic ablative radiotherapy (SABR) in mCRC (RIFLE study). Methods: RIFLE was designed as a single-center, single-arm, prospective Phase II clinical trial. A total of 68 mCRC patients who have failed the first-line standard treatment will be recruited in the safety run-in phase (n = 6) and the expansion phase (n = 62), respectively. Eligible patients will receive SABR followed by fruquintinib (5 mg, d1-14, once every day) and tislelizumab (200 mg, d1, once every 3 weeks) within 2 weeks from completion of radiation. The expansion phase starts when the safety of the treatment is determined (dose limiting toxicity occur in no more than one-sixth of patients in the run-in phase). The primary end point is the objective response rate. The secondary end points include the disease control rate, duration of response, 3-year progression-free survival rate, 3-year overall survival rate, and toxicity. Conclusions: The results of this trial will provide a novel insight into SABR in combination with PD-1 antibody and vascular endothelial growth factor receptor inhibitor in the systematic treatment of metastatic colorectal cancer, which is expected to provide new therapeutic strategies and improve the prognosis for mCRC patients. Trial registration: NCT04948034 (ClinicalTrials.gov).

A simple method to import CAD mesh format models in FLUKA.

BACKGROUND: Monte Carlo (MC) code FLUKA possesses widespread usage and accuracy in the simulation of particle beam radiotherapy. However, the conversion from computer-aided design (CAD) mesh format models to FLUKA readable geometries could not be implemented directly and conveniently. A simple method was required to be developed. PURPOSE: The present study proposed a simple method to voxelize CAD mesh format files by using a Python-based script and establishing geometric models in FLUKA. METHODS: Five geometric models including cube, sphere, cone, ridge filter (RGF), and 1D-Ripple Filter (1D-RiFi) were created and exported as CAD mesh format files (.stl). An open-source Python-based script was used to convert them into voxels by endowing X, Y, and Z (following the Cartesian coordinates system) of solid materials in the three-dimensional (3D) grid. A FLUKA (4-2.2, CERN) predefined routine was used to establish the voxelized geometry model (VGM), while Flair (3.2-1, CERN) was used to build the direct geometry model (DGM) in FLUKA for comparison purposes. Uniform carbon ion radiation fields 8×8 cm3 and 4×4 cm3 were generated to transport through the five pairs of models, 2D and 3D dose distributions were compared. The integral depth dose (IDD) in water of three different energy levels of carbon ion beams transported through 1D-RiFis were also simulated and compared. Moreover, the volume between CAD mesh and VGMs, as well as the computing speed between FLUKA DGMs and VGMs were simultaneously recorded. RESULTS: The volume differences between VGMs and CAD mesh models were not more than 0.6%. The maximum mean point-to-point deviation of IDD distribution was 0.7% ± 0.51% (mean ± standard deviation). The 3D dose Gamma-index passing rates were never lower than 97% with criteria of 1%-1 mm. The difference in computing CPU time was 2.89% ± 0.22 on average. CONCLUSIONS: The present study proposed and verified a Python-based method for converting CAD mesh format files into VGMs and establishing them in FLUKA simply as well as accurately.

Unraveling the patterns and pathways of local recurrence of nasopharyngeal carcinoma: evidence for individualized clinical target volume delineation.

BACKGROUNDS: Despite publication of international guidelines, there are notable controversial points of clinical target volume (CTV) delineation in nasopharyngeal carcinoma (NPC). Recently, scholars proposed a novel way of delineation of CTV in NPC-individualization of CTV delineation based on T classification and spread patterns, which yielded excellent long-term local control with limited late toxicities. The aim of this study was to clarify the anatomic patterns and pathways of local recurrence of NPC and provide a clinical reference for the delineation of CTV. METHODS: A total of 869 patients with non-metastatic NPC were treated with intensity-modulated radiation therapy (IMRT) at our institution between 2009 and 2010. Among the 57 cases of local/locoregional recurrence, 52 cases with traceable radiotherapy plans and magnetic resonance imaging at the time of the first diagnosis of recurrence were included. Anatomical structures and gross tumor volume of local recurrence were contoured. The incidence of relapse of each anatomic structure, route of local recurrence, and their correlation were analyzed. RESULTS: Locally advanced disease had a significantly increased risk of recurrence in the posterior nasal cavity and a trend towards higher risk of recurrence in the clivus, lateral pterygoid muscle, and hypoglossal canal. Based on the incidence of local recurrence, we constructed a high-risk map for the early and locally advanced stages. Local recurrences were classified into five routes, where anterior extension accounted for the majority (30.8%), and caudal tumor extension pathway had the lowest incidence (5.8%). There was a significant correlation between the local recurrences of neural foramina and neighboring anatomical structures along each pathway. All cases relapsed at unilateral cavernous sinus, most at the same side of primary tumor. Based on our findings, we proposed some suggestions on delineations of CTV, based on T classification and local extension pattern. CONCLUSIONS: Local recurrence of NPC varied according to T classification, followed a stepwise pattern, spread via neural foramina, and recurred at ipsilateral cavernous sinus. This provides meaningful clinical evidence for delineation of CTV, especially individualized delineation.

Geometric and dosimetric evaluation of deep learning based auto-segmentation for clinical target volume on breast cancer.

BACKGROUND: Recently, target auto-segmentation techniques based on deep learning (DL) have shown promising results. However, inaccurate target delineation will directly affect the treatment planning dose distribution and the effect of subsequent radiotherapy work. Evaluation based on geometric metrics alone may not be sufficient for target delineation accuracy assessment. The purpose of this paper is to validate the performance of automatic segmentation with dosimetric metrics and try to construct new evaluation geometric metrics to comprehensively understand the dose-response relationship from the perspective of clinical application. MATERIALS AND METHODS: A DL-based target segmentation model was developed by using 186 manual delineation modified radical mastectomy breast cancer cases. The resulting DL model were used to generate alternative target contours in a new set of 48 patients. The Auto-plan was reoptimized to ensure the same optimized parameters as the reference Manual-plan. To assess the dosimetric impact of target auto-segmentation, not only common geometric metrics but also new spatial parameters with distance and relative volume ( R V ${R}_V$ ) to target were used. Correlations were performed using Spearman's correlation between segmentation evaluation metrics and dosimetric changes. RESULTS: Only strong (|R2 | > 0.6, p < 0.01) or moderate (|R2 | > 0.4, p < 0.01) Pearson correlation was established between the traditional geometric metric and three dosimetric evaluation indices to target (conformity index, homogeneity index, and mean dose). For organs at risk (OARs), inferior or no significant relationship was found between geometric parameters and dosimetric differences. Furthermore, we found that OARs dose distribution was affected by boundary error of target segmentation instead of distance and R V ${R}_V$ to target. CONCLUSIONS: Current geometric metrics could reflect a certain degree of dose effect of target variation. To find target contour variations that do lead to OARs dosimetry changes, clinically oriented metrics that more accurately reflect how segmentation quality affects dosimetry should be constructed.

Toward real-time automatic treatment planning (RTTP) with a one-step 3D fluence map prediction method and (nonorthogonal) convolution technique.

PURPOSE: To establish and evaluate a (quasi) real-time automated treatment planning (RTTP) strategy utilizing a one-step full 3D fluence map prediction model based on a nonorthogonal convolution operation for rectal cancer radiotherapy. METHODS: The RTTP approach directly extracts 3D projections from volumetric CT and anatomical data according to the beam incident direction. A 3D deep learning model with a nonorthogonal convolution operation was established that takes projections in cone beam space as input, extracts the features along and around the ray-trace path, and outputs a predicted fluence map (PFM) for each beam. The PFM is then converted to the MLC sequence with deliverable MUs to generate the final treatment plan. A total of 314 rectal adenocarcinoma patients with 2198 projection data samples were used in model training and validation. An extra 20 patients were used to test the feasibility of the RTTP method by comparing the plan quality, efficiency, deliverability performance, and physician blinded review results with the manual plans. RESULTS: Overall, the RTTP plans met the clinical dose criteria for target coverage, conformity, homogeneity, and organ-at-risk dose sparing. Compared to manual plans, the RTTP plans showed increases in PTV D1% by only 2.33% (p < 0.001) and a decrease in PTV D99% by 0.45% (p < 0.05). The RTTP plans showed a dose increase in the bladder, with a V50 of 14.01 ± 11.75% vs. 10.74 ± 8.51%, respectively, and no significant increases in the femoral head with the mean dose. The planning efficiency was improved in RTTP planning, with 39 s vs. 944 s in fluence map generation; the deliverability performance was saved by 1.91% (p < 0.001) in total MU. According to the blinded plan review by our physician, 55% of RTTP plans can be directly used in clinical radiotherapy treatment. CONCLUSION: The quasi RTTP method improves the planning efficiency and deliverability performance while maintaining a plan quality close to that of the optimized manual plans in rectal radiotherapy.

Technical note: First implementation of a one-stop solution of radiotherapy with full-workflow automation based on CT-linac combination.

BACKGROUND: Radiotherapy initiation is a laborious and time-consuming process that involves multiple steps and units. Workflow automation is in demand to improve the work efficiency and patient experience. PURPOSE: The purposes of this study are to describe the technical characteristics and clinical performance of an AI-powered one-stop radiotherapy workflow for initial treatment based on CT-linac combination, and provide insight into the behavior of full-workflow automation in radiotherapy. METHODS: Based on a CT-integrated linear accelerator and AI model implementation, the so-called "All-in-One" workflow incorporates routine procedures from simulation, autosegmentation, autoplanning, image guidance, beam delivery, and in vivo quality assurance (QA) into one scheme, while the patient is on the treatment couch. Clinical outcomes of the new workflow were evaluated for 10 enrolled patients with rectal cancer. RESULTS: For the enrolled patients, manual modifications of the autosegmented target volumes were necessary. The Dice similarity coefficient and 95% Hausdorff distance before and after the modifications were 0.892 ± 0.061 and 18.2 ± 13.0 mm, respectively. The autosegmented normal tissues and automatic plans were clinically acceptable without any modifications or reoptimization. The pretreatment IGRT corrections were within 2 mm in all directions, and the EPID-based in vivo QA showed γ passing rate of above 97% (3%/3 mm/10% threshold) at all the checkpoints, better than the results of rectal patients who followed a routine workflow. The duration of the whole process was 23.2 ± 3.5 minutes for the enrolled patients, depending mostly on the time required for manual modification and plan evaluation. CONCLUSION: The All-in-One workflow enables full-process automation of radiotherapy via seamless procedure integration. Compared to the routine workflow, the one-stop solution shortens the time scale it takes to ready the first treatment from days to minutes, significantly improving the patient experience and the workflow efficiency, and it also shows potential to facilitate clinical application of online adaptive replanning.

An attention base U-net for parotid tumor autosegmentation.

A parotid neoplasm is an uncommon condition that only accounts for less than 3% of all head and neck cancers, and they make up less than 0.3% of all new cancers diagnosed annually. Due to their nonspecific imaging features and heterogeneous nature, accurate preoperative diagnosis remains a challenge. Automatic parotid tumor segmentation may help physicians evaluate these tumors. Two hundred eighty-five patients diagnosed with benign or malignant parotid tumors were enrolled in this study. Parotid and tumor tissues were segmented by 3 radiologists on T1-weighted (T1w), T2-weighted (T2w) and T1-weighted contrast-enhanced (T1wC) MR images. These images were randomly divided into two datasets, including a training dataset (90%) and an validation dataset (10%). A 10-fold cross-validation was performed to assess the performance. An attention base U-net for parotid tumor autosegmentation was created on the MRI T1w, T2 and T1wC images. The results were evaluated in a separate dataset, and the mean Dice similarity coefficient (DICE) for both parotids was 0.88. The mean DICE for left and right tumors was 0.85 and 0.86, respectively. These results indicate that the performance of this model corresponds with the radiologist's manual segmentation. In conclusion, an attention base U-net for parotid tumor autosegmentation may assist physicians to evaluate parotid gland tumors.

Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy.

In radiotherapy for cancer patients, an indispensable process is to delineate organs-at-risk (OARs) and tumors. However, it is the most time-consuming step as manual delineation is always required from radiation oncologists. Herein, we propose a lightweight deep learning framework for radiotherapy treatment planning (RTP), named RTP-Net, to promote an automatic, rapid, and precise initialization of whole-body OARs and tumors. Briefly, the framework implements a cascade coarse-to-fine segmentation, with adaptive module for both small and large organs, and attention mechanisms for organs and boundaries. Our experiments show three merits: 1) Extensively evaluates on 67 delineation tasks on a large-scale dataset of 28,581 cases; 2) Demonstrates comparable or superior accuracy with an average Dice of 0.95; 3) Achieves near real-time delineation in most tasks with <2 s. This framework could be utilized to accelerate the contouring process in the All-in-One radiotherapy scheme, and thus greatly shorten the turnaround time of patients.

Evaluation of a hybrid automatic planning solution for rectal cancer.

BACKGROUND: Script-based planning and knowledge-based planning are two kinds of automatic planning solutions. Hybrid automatic planning may integrate the advantages of both solutions and provide a more robust automatic planning solution in the clinic. In this study, we evaluated and compared a commercially available hybrid planning solution with manual planning and script-based planning. METHODS: In total, 51 rectal cancer patients in our institution were enrolled in this study. Each patient generated 7 plans: one clinically accepted manual plan ([Formula: see text]), three script-based plans and three hybrid plans generated with the volumetric-modulated arc therapy technique and 3 different clinical goal settings: easy, moderate and hard ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text]). Planning goals included planning target volume (PTV) Dmax, bladder Dmean and femur head Dmean. The PTV prescription was the same (50.00 Gy) for the 3 goal settings. The hard setting required a lower PTV Dmax and stricter organ at risk (OAR) dose, while the easy setting was the opposite. Plans were compared using dose metrics and plan quality metric (PQM) scores, including bladder D15 and D50, left and right femur head D25 and D40, PTV D2, D98, CI (conformity index) and HI (homogeneity index). RESULTS: Compared to manual planning, hybrid planning with all settings significantly reduced the OAR dose (p < 0.05, paired t-test or Wilcoxon signed rank test) for all dose-volume indices, except D25 of the left femur head. For script-based planning, [Formula: see text] significantly increased the OAR dose for the femur head and D2 and the PTV homogeneity index (p < 0.05, paired t-test or Wilcoxon signed rank test). Meanwhile, the maximum dose of the PTV was largely increased with hard script-based planning (D2 = 56.06 ± 7.57 Gy). For all three settings, the comparison of PQM between hybrid planning and script-based planning showed significant differences, except for D25 of the left femur head and PTV D2. The total PQM showed that hybrid planning could provide a better and more robust plan quality than script-based planning. CONCLUSIONS: The hybrid planning solution was manual-planning comparable for rectal cancer. Hybrid planning can provide a better and more robust plan quality than script-based planning.

A clinically relevant online patient QA solution with daily CT scans and EPID-basedin vivodosimetry: a feasibility study on rectal cancer.

Objective.Adaptive radiation therapy (ART) could protect organs at risk (OARs) while maintain high dose coverage to targets. However, there is still a lack of efficient online patient quality assurance (QA) methods, which is an obstacle to large-scale adoption of ART. We aim to develop a clinically relevant online patient QA solution for ART using daily CT scans and EPID-basedin vivodosimetry.Approach.Ten patients with rectal cancer at our center were included. Patients' daily CT scans and portal images were collected to generate reconstructed 3D dose distributions. Contours of targets and OARs were recontoured on these daily CT scans by a clinician or an auto-segmentation algorithm, then dose-volume indices were calculated, and the percent deviation of these indices to their original plans were determined. This deviation was regarded as the metric for clinically relevant patient QA. The tolerance level was obtained using a 95% confidence interval of the QA metric distribution. These deviations could be further divided into anatomically relevant or delivery relevant indicators for error source analysis. Finally, our QA solution was validated on an additional six clinical patients.Main results.In rectal cancer, the 95% confidence intervals of the QA metric for PTV ΔD95(%) were [-3.11%, 2.35%], and for PTV ΔD2(%) were [-0.78%, 3.23%]. In validation, 68% for PTV ΔD95(%), and 79% for PTV ΔD2(%) of the 28 fractions are within tolerances of the QA metrics. one patient's dosimetric impact of anatomical variations during treatment were observed through the source of error analysis.Significance.The online patient QA solution using daily CT scans and EPID-basedin vivodosimetry is clinically feasible. Source of error analysis has the potential for distinguishing sources of error and guiding ART for future treatments.

Multimodal image translation via deep learning inference model trained in video domain.

BACKGROUND: Current medical image translation is implemented in the image domain. Considering the medical image acquisition is essentially a temporally continuous process, we attempt to develop a novel image translation framework via deep learning trained in video domain for generating synthesized computed tomography (CT) images from cone-beam computed tomography (CBCT) images. METHODS: For a proof-of-concept demonstration, CBCT and CT images from 100 patients were collected to demonstrate the feasibility and reliability of the proposed framework. The CBCT and CT images were further registered as paired samples and used as the input data for the supervised model training. A vid2vid framework based on the conditional GAN network, with carefully-designed generators, discriminators and a new spatio-temporal learning objective, was applied to realize the CBCT-CT image translation in the video domain. Four evaluation metrics, including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and structural similarity (SSIM), were calculated on all the real and synthetic CT images from 10 new testing patients to illustrate the model performance. RESULTS: The average values for four evaluation metrics, including MAE, PSNR, NCC, and SSIM, are 23.27 ± 5.53, 32.67 ± 1.98, 0.99 ± 0.0059, and 0.97 ± 0.028, respectively. Most of the pixel-wise hounsfield units value differences between real and synthetic CT images are within 50. The synthetic CT images have great agreement with the real CT images and the image quality is improved with lower noise and artifacts compared with CBCT images. CONCLUSIONS: We developed a deep-learning-based approach to perform the medical image translation problem in the video domain. Although the feasibility and reliability of the proposed framework were demonstrated by CBCT-CT image translation, it can be easily extended to other types of medical images. The current results illustrate that it is a very promising method that may pave a new path for medical image translation research.

Automatic Tumor Grading on Colorectal Cancer Whole-Slide Images: Semi-Quantitative Gland Formation Percentage and New Indicator Exploration.

Tumor grading is an essential factor for cancer staging and survival prognostication. The widely used the WHO grading system defines the histological grade of CRC adenocarcinoma based on the density of glandular formation on whole-slide images (WSIs). We developed a fully automated approach for stratifying colorectal cancer (CRC) patients' risk of mortality directly from histology WSI relating to gland formation. A tissue classifier was trained to categorize regions on WSI as glands, stroma, immune cells, background, and other tissues. A gland formation classifier was trained on expert annotations to categorize regions as different degrees of tumor gland formation versus normal tissues. The glandular formation density can thus be estimated using the aforementioned tissue categorization and gland formation information. This estimation was called a semi-quantitative gland formation ratio (SGFR), which was used as a prognostic factor in survival analysis. We evaluated gland formation percentage and validated it by comparing it against the WHO cutoff point. Survival data and gland formation maps were then used to train a spatial pyramid pooling survival network (SPPSN) as a deep survival model. We compared the survival prediction performance of estimated gland formation percentage and the SPPSN deep survival grade and found that the deep survival grade had improved discrimination. A univariable Cox model for survival yielded moderate discrimination with SGFR (c-index 0.62) and deep survival grade (c-index 0.64) in an independent institutional test set. Deep survival grade also showed better discrimination performance in multivariable Cox regression. The deep survival grade significantly increased the c-index of the baseline Cox model in both validation set and external test set, but the inclusion of SGFR can only improve the Cox model less in external test and is unable to improve the Cox model in the validation set.

Application of radiomics feature captured from MRI for prediction of recurrence for glioma patients.

Purpose: This study aimed to develop and validate a recurrence prediction of glioma patients through a radiomics feature training and validation model. Patients and methods: In this study, the prediction model was developed in a training cohort that consisted of 88 patients from January 2014 to July 2017 with pathologically confirmed gliomas. Their pre-radiotherapy and recurrence brain magnetic resonance imaging (MRI) images were collected, and the radiomics features were extracted. Clinical factors including age, gender, WHO grade, Isocitrate dehydrogenases (IDH) mutation status and treatment after surgery were collected. The least absolute shrinkage and selection operator (LASSO) regression model was conducted for data dimension reduction, feature selection, and radiomics feature analysis. Internal validation was assessed. An independent validation cohort contained 41 consecutive patients from August 2017 to December 2018. Furthermore, multivariable logistic regression analysis was used to develop the predicting model by combining the radiomics signature and independent clinical factors. Results: In total, 129 patients were included, among which 40 patients had recurrence. The median follow-up time was 27.4 (range, 2.6-79.2) months. We compared the tumor regions radiomics difference between the recurrence and non-recurrence patients. The radiomics signature was associated with the event of recurrence (P < 0.001 for both training and validation cohorts, respectively). The training model showed good discrimination with a C-index of 0.7578 (95%CI: 0.6549-9.8608) through internal validation on T1 contrast-enhanced magnetic resonance imaging, and a consistent trend in calibration. In the validation cohort, the model also showed good discrimination (C-index, 0.6925, 95%CI: 0.5145-0.8705) and good calibration. In the other two sequences of MRI (T1WI, T2WI), the validation model also showed positive results. Meanwhile, radiomics feature and clinical factors were significantly prognostic for recurrence (P value <0.05, respectively). Conclusion: We identified the radiomics feature derived from brain MRI that presented potential in predicting recurrence in glioma patients. This could be beneficial to risk stratification for patients. Further investigation is necessary to include expanded sample size investigation and external multicenter validation.

A hybrid optimization strategy for deliverable intensity-modulated radiotherapy plan generation using deep learning-based dose prediction.

PURPOSE: To propose a clinically feasible automatic planning solution for external beam intensity-modulated radiotherapy, including dose prediction via a deep learning and voxel-based optimization strategy. MATERIALS AND METHODS: The dose distribution of patients was predicted using a U-Net-based deep learning network based on the patient's anatomy information. One hundred seventeen patients with nasopharyngeal cancer (NPC) and 200 patients with rectal cancer were enrolled in this study. For NPC cases, 94 cases were included in the training dataset, 13 in the validation dataset, and 10 in the testing dataset. For rectal cancer cases, 172 cases were included in the training set, 18 in the validation set, and 10 in the testing set. A voxel-based optimization strategy, "Voxel," was proposed to achieve treatment planning optimization by dividing body voxels into two parts: inside planning target volumes (PTVs) and outside PTVs. Fixed dose-volume objectives were attached to the total objective function to realize individualized planning intended as the "hybrid" optimizing strategy. Automatically generated plans were compared with clinically approved plans to evaluate clinical gains, according to dosimetric indices and dose-volume histograms (DVHs). RESULTS: Similarities were found between the DVH of the predicted dose and clinical plan, although significant differences were found in some organs at risk. Better organ sparing and suboptimal PTV coverage were shown using the voxel strategy; however, the deviations in homogeneity indices (HIs) and conformity indices (CIs) of the PTV between automatically generated plans and manual plans were reduced by the hybrid strategy ([manual plans]/[voxel plans[/[hybrid plans]: HI of PTV70 [1.06/1.12/1.02] and CI of PTV70 [0.79/0.58/0.76]). The optimization time for each patient was within 1 min and included fluence map optimization, leaf sequencing, and control point optimization. All the generated plans (voxel and hybrid strategy) could be delivered on uRT-linac 506c (United Imaging Healthcare, Shanghai, China). CONCLUSION: Deliverable plans can be generated by incorporating a voxel-based optimization strategy into a commercial treatment planning system (TPS). The hybrid optimization method shows the benefit and clinical feasibility in generating clinically acceptable plans.

Joint Developmental Trajectories of Bullying and Victimization from Childhood to Adolescence: A Parallel-Process Latent Class Growth Analysis.

Previous studies have shown that bullying and victimization can be experienced simultaneously by an individual and can change over time. Understanding the joint longitudinal development of the two is of great significance. We conducted a 4-year longitudinal study to examine the joint developmental trajectories of bullying and victimization, gender and grade differences in trajectory group membership, and changes in specific forms of bullying and victimization (verbal, relational, and physical bullying /victimization) in each trajectory group. A total of 775 children from China participated in our study. The average age of participants at the first wave was 10.90 years (SD = 1.12), and boys accounted for 69.5% of the sample. Based on mean scores, four distinct joint developmental trajectories of bullying and victimization were found: the involvement group (both bullying and victimization increased from low to high over time, accounting for 7.6% of the total), the desisted group (both bullying and victimization decreased from high to low over time, 6.1%), the victimization group (victimization remained at a high level, whereas bullying remained at a low level for 3 years, 13.2%), and the noninvolved group (bullying and victimization remained at a stable low level, 73.1%). Boys were more likely than girls to belong to the involvement group, desisted group, and victimization group, whereas girls were more likely than boys to belong to the noninvolved group. There was no significant grade difference in the trajectory group. All forms of bullying/victimization were consistent with the overall trend and showed similar levels. These results have important implications for the prevention of and interventions for school bullying.

Evaluation of Daily CT for EPID-Based Transit In Vivo Dosimetry.

PURPOSE: The difference in anatomical structure and positioning between planning and treatment may lead to bias in electronic portal image device (EPID)-based in vivo dosimetry calculations. The purpose of this study was to use daily CT instead of planning CT as a reference for EPID-based in vivo dosimetry calculations and to analyze the necessity of using daily CT for EPID-based in vivo dosimetry calculations in terms of patient quality assurance. MATERIALS AND METHODS: Twenty patients were enrolled in this study. The study design included eight different sites (the cervical, nasopharyngeal, and oral cavities, rectum, prostate, bladder, lung, and esophagus). All treatments were delivered with a CT-linac 506c (UIH, Shanghai) using 6 MV photon beams. This machine is equipped with diagnosis-level fan-beam CT and an amorphous silicon EPID XRD1642 (Varex Imaging Corporation, UT, USA). A Monte Carlo algorithm was developed to calculate the transmit EPID image. A pretreatment measurement was performed to assess system accuracy by delivering based on a homogeneous phantom (RW3 slab, PTW, Freiburg). During treatment, each patient underwent CT scanning before delivery either once or twice for a total of 268 fractions obtained daily CT images. Patients may have had a position correction that followed our image-guided radiation therapy (IGRT) procedure. Meanwhile, transmit EPID images were acquired for each field during delivery. After treatment, all patient CTs were reviewed to ensure that there was no large anatomical change between planning and treatment. The reference of transmit EPID images was calculated based on both planning and daily CTs, and the IGRT correction was corrected for the EPID calculation. The gamma passing rate (3 mm 3%, 2 mm 3%, and 2 mm 2%) was calculated and compared between the planning CT and daily CT. Mechanical errors [ ± 1 mm, ± 2 mm, ± 5 mm multileaf collimator (MLC) systematic shift and 3%, 5% monitor unit (MU) scaling] were also introduced in this study for comparing detectability between both types of CT. RESULT: The average (standard deviation) gamma passing rate (3 mm 3%, 2 mm 3%, and 2 mm 2%) in the RW3 slab phantom was 99.6% ± 1.0%, 98.9% ± 2.1%, and 97.2% ± 3.9%. For patient measurement, the average (standard deviation) gamma passing rates were 87.8% ± 14.0%, 82.2% ± 16.9%, and 74.2% ± 18.9% for using planning CTs as reference and 93.6% ± 8.2%, 89.7% ± 11.0%, and 82.8% ± 14.7% for using daily CTs as reference. There were significant differences between the planning CT and daily CT results. All p-values (Mann-Whitney test) were less than 0.001. In terms of error simulation, nonparametric test shows that there were significant differences between practical daily results and error simulation results (p < 0.001). The receiver operating characteristic (ROC) analysis indicated that the detectability of mechanical delivery error using daily CT was better than that of planning CT. AUCDaily CT = 0.63-0.96 and AUCPlanning CT = 0.49-0.93 in MLC systematic shift and AUCDaily CT = 0.56-0.82 and AUCPlanning CT = 0.45-0.73 in MU scaling. CONCLUSION: This study shows the feasibility and effectiveness of using two-dimensional (2D) EPID portal image and daily CT-based in vivo dosimetry for intensity-modulated radiation therapy (IMRT) verification during treatment. The daily CT-based in vivo dosimetry has better sensitivity and specificity to identify the variation of IMRT in MLC-related and dose-related errors than planning CT-based.