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.

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.

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.

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.

Maternal insomnia during the COVID-19 pandemic: associations with depression and anxiety.

BACKGROUND: The COVID-19 outbreak has made people more prone to depression, anxiety and insomnia, and females are at a high risk of developing these conditions. As a special group, pregnant and lying-in women must pay close attention to their physical and mental health, as both have consequences for the mother and the fetus. However, knowledge regarding the status of depression, anxiety and insomnia among these women is limited. AIM: This study aimed to examine insomnia and psychological factors among pregnant and lying-in women during the COVID-19 pandemic and provide theoretical support for intervention research. METHODS: In total, 2235 pregnant and lying-in women from 12 provinces in China were surveyed; their average age was 30.25 years (SD = 3.99, range = 19-47 years). PARTICIPANTS AND SETTING: The participants completed electronic questionnaires designed to collect demographic information and assess levels of depression, anxiety and insomnia. RESULTS: The prevalence of insomnia in the sample was 18.9%. Depression and anxiety were significant predictors of insomnia. Participants in high-risk areas, those with a disease history, those with economic losses due to the outbreak, and those in the postpartum period had significantly higher insomnia scores. DISCUSSION: The incidence of insomnia among pregnant and lying-in women is not serious in the context of the epidemic, which may be related to the sociocultural background and current epidemic situation in China. CONCLUSION: Depression and anxiety are more indicative of insomnia than demographic variables.

Commissioning of and preliminary experience with a new fully integrated computed tomography linac.

PURPOSE: A new medical linear accelerator (linac) platform integrated with helical computed tomography (CT), the uRT-linac 506c, was introduced into clinical application in 2019 by United Imaging Healthcare (UIH) Co., Ltd. (Shanghai, China). It combines a Carm linac with a diagnostic-quality 16-slice CT imager, providing seamless workflow from simulation to treatment. The aim of this report is to assess the technical characteristics, commissioning results and preliminary experiences stemming from clinical usage. METHODS: The mechanical and imaging test procedures, commissioning data collection and TPS validation were summarized. CTIGRT accuracy was investigated with different loads and couch extensions. A series of end-to-end cases for different treatment sites and delivery techniques were tested preclinically to estimate the overall accuracy for the entire treatment scheme. The results of patient-specific QA and machine stability during a one-year operation are also reported. RESULTS: Gantry/couch/collimator isocentricity was measured as 0.63 mm in radius. The TPS models were in agreement with the beam commissioning data within a deviation of 2%. An overall submillimeter accuracy was demonstrated for the CT-IGRT process under all conditions. The absolute point dose difference for all the preclinical end-to-end tests was within 3%, and the gamma passing rate of the 2D dose distribution measured by EBT3 film was better than 90% (3% DD, 3 mm DTA and 10% threshold). Pretreatment QA of clinical cases resulted with better than 3% point dose difference and more than 99% gamma passing rate (3% DD/2 mm DTA/10% threshold) tested with Delta4. The output of the linac was mostly within 1% of variation in a one-year operation. CONCLUSION: The commissioning results and clinical QA results show that the uRT-linac 506c platform exhibits good and stable performance in mechanical and dosimetric accuracy. The integrated CT system provides an efficient workflow for image guidance with submillimeter localization precision, and will be a good starting point to proceed advanced adaptive radiotherapy.

Automatic T Staging Using Weakly Supervised Deep Learning for Nasopharyngeal Carcinoma on MR Images.

BACKGROUND: Recent studies have shown that deep learning can help tumor staging automatically. However, automatic nasopharyngeal carcinoma (NPC) staging is difficult due to the lack of large and slice-level annotated datasets. PURPOSE: To develop a weakly-supervised deep-learning method to predict NPC patients' T stage without additional annotations. STUDY TYPE: Retrospective. POPULATION/SUBJECTS: In all, 1138 cases with NPC from 2010 to 2012 were enrolled, including a training set (n = 712) and a validation set (n = 426). FIELD STRENGTH/SEQUENCE: 1.5T, T1 -weighted images (T1 WI), T2 -weighted images (T2 WI), contrast-enhanced T1 -weighted images (CE-T1 WI). ASSESSMENT: We used a weakly-supervised deep-learning network to achieve automated T staging of NPC. T usually refers to the size and extent of the main tumor. The training set was employed to construct the deep-learning model. The performance of the automated T staging model was evaluated in the validation set. The accuracy of the model was assessed by the receiver operating characteristic (ROC) curve. To further assess the performance of the deep-learning-based T score, the progression-free survival (PFS) and overall survival (OS) were performed. STATISTICAL TESTS: The Sklearn package in Python was applied to calculate the area under the curve (AUC) of the ROC. The survcomp package was used for calculations and comparisons between C-indexes. The software SPSS was employed to conduct survival analysis and chi-square tests. RESULTS: The accuracy of the deep-learning model was 75.59% in the validation set. The average AUC of the ROC curve of different stages was 0.943. There were no significant differences in the C-indexes of PFS and OS from the deep-learning model and those from TNM staging, with P values of 0.301 and 0.425, respectively. DATA CONCLUSION: This weakly-supervised deep-learning approach can perform fully automated T staging of NPC and achieve good prognostic performance. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;52:1074-1082.

The benefits evaluation of abdominal deep inspiration breath hold based on knowledge-based radiotherapy treatment planning for left-sided breast cancer.

PURPOSE: To study the impact of abdominal deep inspiration breath hold (DIBH) technique on knowledge-based radiotherapy treatment planning for left-sided breast cancer to guide the application of DIBH technology. MATERIALS AND METHODS: Two kernel density estimation (KDE) models were developed based on 40 left-sided breast cancer patients with two CT acquisitions of free breathing (FB-CT) and DIBH (DIBH-CT). Each KDE model was used to predict dose volume histograms (DVHs) based on DIBH-CT and FB-CT for another 10 new patients similar to our training datasets. The predicted DVHs were taken as a substitute for dose constraints and objective functions in the Eclipse treatment planning system, with the same requirements for the planning target volume (PTV). The mean doses to the heart, the left anterior descending coronary artery (LADCA) and the ipsilateral lung were evaluated and compared using the T-test among clinical plans, KDE predictions, and KDE plans. RESULTS: Our study demonstrated that the KDE model can generate deliverable simulations equivalent to clinically applicable plans. The T-test was applied to test the consistency hypothesis on another ten left-sided breast cancer patients. In cases of the same breathing status, there was no statistically significant difference between the predicted and the clinical plans for all clinically relevant DVH indices (P > 0.05), and all predicted DVHs can be transferred into deliverable plans. For DIBH-CT images, significant differences were observed between FB model predictions and clinical plans (P < 0.05). DIBH model prediction cannot be optimized to a deliverable plan based on FB-CT, with a counsel of perfection. CONCLUSION: KDE models can predict DVHs well for the same breathing conditions but degrade with different breathing conditions. The benefits of DIBH for a given patient can be evaluated with a quick comparison of prediction results of the two models before treatment planning.

A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules.

OBJECTIVES: To develop and validate a clinical-radiomics nomogram for preoperative prediction of lung metastasis for colorectal cancer (CRC) patients with indeterminate pulmonary nodules (IPN). METHODS: 194 CRC patients with lung nodules were enrolled in this study (136 in the training cohort and 58 in the validation cohort). To evaluate the probability of lung metastasis, we developed three models, the clinical model with significant clinical risk factors, the radiomics model with radiomics features constructed by the least absolute shrinkage and selection operator algorithm, and the clinical-radiomics model with significant variables selected by the stepwise logistic regression. The Akaike information criterion (AIC) was used to compare the relative strength of different models, and the area under the curve (AUC) was used to quantify the predictive accuracy. The nomogram was developed based on the most appropriate model. Decision-curve analysis was applied to assess the clinical usefulness. RESULTS: The clinical-radiomics model (AIC = 98.893) with the lowest AIC value compared with that of the clinical-only model (AIC = 138.502) or the radiomics-only model (AIC = 116.146) was identified as the best model. The clinical-radiomics nomogram was also successfully developed with favourable discrimination in both training cohort (AUC = 0.929, 95% CI: 0.885-0.974) and validation cohort (AUC = 0.922, 95% CI: 0.857-0.986), and good calibration. Decision-curve analysis confirmed the clinical utility of the clinical-radiomics nomogram. CONCLUSIONS: In CRC patients with IPNs, the clinical-radiomics nomogram created by the radiomics signature and clinical risk factors exhibited favourable discriminatory ability and accuracy for a metastasis prediction. KEY POINTS: • Clinical features can predict lung metastasis of colorectal cancer patients. • Radiomics analysis outperformed clinical features in assessing the risk of pulmonary metastasis. • A clinical-radiomics nomogram can help clinicians predict lung metastasis in colorectal cancer patients.

Implement a knowledge-based automated dose volume histogram prediction module in Pinnacle3 treatment planning system for plan quality assurance and guidance.

PURPOSE: This work aims to develop a knowledge-based automated dose volume histogram (DVH) prediction module that serves as a plan quality evaluation tool and treatment planning guidance in commercial Pinnacle3 treatment planning system (Philips Radiation Oncology Systems, Fitchburg, WI, USA). METHODS: The knowledge-based automated DVH prediction module was developed with kernel density estimation (KDE) method and applied for Pinnacle3 treatment planning system. Treatment plan data from 20 esophageal cancer cases were used for creating a module to predict DVHs. Twenty additional esophageal clinical plans were evaluated on the developed module. Predicted DVHs were compared with manual ones. Differences between the predicted and achieved DVHs were analyzed. RESULTS: The plan evaluation module was successfully implemented in Pinnacle3 treatment planning system. Strong linear correlations were found between predicted and achieved DVH for organs at risk. Suboptimal treatment plan quality could be improved according to the predicted DVHs by the module. CONCLUSION: The knowledge-based automated DVH prediction module implemented in Pinnacle3 could be used to efficiently evaluate the treatment plan quality and as guidance for further plan optimization.

Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings.

BACKGROUND: Recent studies have shown that magnetic resonance (MR) radiomic analysis is feasible and has some value in identifying tumor characteristics, but there are few data regarding the role of MR-based radiomic features in rectal cancer. PURPOSE: The aim of this study was to determine whether radiomic features extracted from T2 -weighted imaging (T2 WI) can identify pathological features in rectal cancer. STUDY TYPE: Retrospective study. POPULATION/SUBJECTS: A cohort comprising 119 rectal cancer patients who underwent surgery between January 2015 and November 2016. FIELD STRENGTH/SEQUENCE: 3.0T, axial high-resolution T2 -weighted turbo spin echo (TSE) sequence. ASSESSMENT: Patients were classified according to pathological features such as T stage, N stage, perineural invasion, histological grade, lymph-vascular invasion, tumor deposits, and circumferential resection margin (CRM). The whole tumor volume (WTV) was distinguished, and segments were quantified on axial high-resolution T2 WI by a radiologist. A total of 256 radiomic features were extracted. STATISTICAL TESTS: To achieve reliable results, cluster analysis and least absolute shrinkage and selection operator (LASSO) were implemented. In the cluster analysis, the patients were divided into two groups, and chi-square tests were performed to investigate the relationship between the pathological features and the radiomic-based clusters. The area under the curve (AUC) was calculated to evaluate the predictability of the model in the LASSO analysis. RESULTS: The cluster results revealed that patients could be stratified into two groups, and the chi-square test results indicated that the pT stage was correlated with the radiomic feature cluster results (P = 0.002). The prediction model AUC for the diagnostic T stage was 0.852 (95% confidence interval: 0.677-1; sensitivity: 79.0%, specificity: 82.0%). DATA CONCLUSION: The use of MRI-derived radiomic features to identify the T stage is feasible in rectal cancer. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Non-iterative phase-only Fourier hologram generation with high image quality.

We report a novel and non-iterative method for the generation of phase-only Fourier hologram for image projection. Briefly, target image is first added with a special quadratic phase and then padded with zeros. A complex Fourier hologram is generated via the simple fast Fourier transform. Subsequently, the error diffusion algorithm is applied to convert the complex hologram into a phase-only hologram. The numerical, as well as the optical reconstructed images with the proposed method are of higher visual quality and contain less speckle noise compared to the original random phase method, which add the random phase to the target image and then preserve the phase component of the complex hologram. The influences of quadratic phase and zero-padding on the image quality are also discussed in detail.

Vector optical field generation based on birefringent phase plate.

Vector optical field has recently gained interest in a variety of application fields due to its novel characteristics. Conventional approaches of generating vector optical fields have difficulties in forming highly continuous polarization and suffer from the issue of high energy utilization rates. In order to address these issues, in this study a single optical path was proposed to generate vector optical fields where the birefringent phase plate modulated a linear polarized light into a vector optical field, which was then demodulated to a non-uniform linear polarization distribution of the vector optical field by the polarization demodulation module. Both a theoretical model and numerical simulations of the vector optical field generator were developed, illustrating the relationship between the polarization distribution of the target vector optical field and the depth distribution of the birefringent phase plate. Furthermore, the birefringent phase plate with predefined surface distributions was fabricated by grayscale exposure and ion etching. The generated vector optical field was experimentally characterized, capable of producing continuous polarization with high light energy utilization ratio, consistent with simulations. This new approach may have the potential of being widely used in future studies of generating well-controlled vector optical fields.

High-accuracy method for holographic image projection with suppressed speckle noise.

Iterative Fourier transform algorithms are widely used for creating holograms in holographic image projection. However, the reconstructed image always suffers from the speckle noise severely due to the uncontrolled phase distribution of the image. In this paper, a new iterative method is proposed to eliminate the speckle noise. In the iteration, the amplitude and phase in the signal window in the output plane are constrained to the desired distribution and a special object-dependent quadratic phase distribution, respectively. Since the phase of the reconstructed image is assigned artificially, the speckle noise came from the destructive interference between the sampling points with random and erratic phase distribution can be eliminated. To verify the method, simulations and experiments are performed. And the result shows that high quality, low noise images can be achieved.

Are simple IMRT beams more robust against MLC error? Exploring the impact of MLC errors on planar quality assurance and plan quality for different complexity beams.

This study investigated the impact of beam complexities on planar quality assur-ance and plan quality robustness by introducing MLC errors in intensity-modulate radiation therapy. Forty patients' planar quality assurance (QA) plans were enrolled in this study, including 20 dynamic MLC (DMLC) IMRT plans and 20 static MLC (SMLC) IMRT plans. The total beam numbers were 150 and 160 for DMLC and SMLC, respectively. Six different magnitudes of MLC errors were introduced to these beams. Gamma pass rates were calculated by comparing error-free fluence and error-induced fluence. The plan quality variation was acquired by comparing PTV coverage. Eight complexity scores were calculated based on the beam flu-ence and the MLC sequence. The complexity scores include fractal dimension, monitor unit, modulation index, fluence map complexity, weighted average of field area, weighted average of field perimeter, and small aperture ratio (< 5 cm2 and < 50cm2). The Spearman's rank correlation coefficient was calculated to analyze the correlation between these scores and gamma pass rate and plan quality varia-tion. For planar QA, the most significant complexity index was fractal dimension for DMLC (p = -0.40) and weighted segment area for SMLC (p = 0.27) at low magnitude MLC error. For plan quality, the most significant complexity index was weighted segment perimeter for DMLC (p = 0.56) and weighted segment area for SMLC (p= 0.497) at low magnitude MLC error. The sensitivity of planar QA was weakly associated with the field complexity with low magnitude MLC error, but the plan quality robustness was associated with beam complexity. Plans with simple beams were more robust to MLC error.

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.

Radiomic score for lung nodules as a prognostic biomarker in locally advanced rectal cancer patients: A bi-institutional study.

BACKGROUND: Undetermined lung nodules are common in locally advanced rectal cancer (LARC) and lack precise risk stratification. This study aimed to develop a radiomic-based score (Rad-score) to distinguish metastasis and predict overall survival (OS) in patients with LARC and lung nodules. METHODS: Retrospective data from two institutions (July 10, 2006-September 24, 2015) was used to develop and validate the Rad-score for distinguishing lung nodule malignancy. The prognostic value of the Rad-score was investigated in LARC cohorts, leading to the construction and validation of a clinical and radiomic score (Cli-Rad-score) that incorporates both clinical and radiomic information for the purpose of improving personalized clinical prognosis prediction. Descriptive statistics, survival analysis, and model comparison were performed to assess the results. RESULTS: The Rad-score demonstrated great performance in distinguishing malignancy, with C-index values of 0.793 [95% CI: 0.729-0.856] in the training set and 0.730 [95% CI: 0.666-0.874] in the validation set. In independent LARC cohorts, Rad-score validation achieved C-index values of 0.794 [95% CI: 0.737-0.851] and 0.747 [95% CI: 0.615-0.879]. Regarding prognostic prediction, Rad-score effectively stratified patients. Cli-Rad-score outperformed the clinicopathological information alone in risk stratification, as evidenced by significantly higher C-index values (0.735 vs. 0.695 in the internal set and 0.618 vs. 0.595 in the external set). CONCLUSIONS: CT-based radiomics could serve as a reliable and powerful tool for lung nodule malignancy distinction and prognostic prediction in LARC patients. Rad-score predicts prognosis independently. Incorporation of Cli-Rad-score significantly enhances the persionalized clinical prognostic capacity in LARC patients with lung nodules.

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.

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.