With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate ...prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR). We show that, as an example, we are able to accurately predict the dose of intensity-modulated radiation therapy (IMRT) for prostate cancer patients, where the average Dice similarity coefficient is 0.91 when comparing the predicted vs. true isodose volumes between 0% and 100% of the prescription dose. The average value of the absolute differences in max, mean dose is found to be under 5% of the prescription dose, specifically for each structure is 1.80%, 1.03%(PTV), 1.94%, 4.22%(Bladder), 1.80%, 0.48%(Body), 3.87%, 1.79%(L Femoral Head), 5.07%, 2.55%(R Femoral Head), and 1.26%, 1.62%(Rectum) of the prescription dose. We thus managed to map a desired radiation dose distribution from a patient's PTV and OAR contours. As an additional advantage, relatively little data was used in the techniques and models described in this paper.
Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides but not translated into proteins. LncRNAs regulate gene expressions at multiple levels, such as chromatin, transcription, ...and post-transcription. Further, lncRNAs participate in various biological processes such as cell differentiation, cell cycle regulation, and maintenance of stem cell pluripotency. We have previously reported that lncRNAs are closely related to programmed cell death (PCD), which includes apoptosis, autophagy, necroptosis, and ferroptosis. Overexpression of lncRNA can suppress the extrinsic apoptosis pathway by downregulating of membrane receptors and protect tumor cells by inhibiting the expression of necroptosis-related proteins. Some lncRNAs can also act as competitive endogenous RNA to prevent oxidation, thereby inhibiting ferroptosis, while some are known to activate autophagy. The relationship between lncRNA and PCD has promising implications in clinical research, and reports have highlighted this relationship in various cancers such as non-small cell lung cancer and gastric cancer. This review systematically summarizes the advances in the understanding of the molecular mechanisms through which lncRNAs impact PCD.
Purpose:
Image reconstruction and motion model estimation in four-dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at ...each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4D-CBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR).
Methods:
The proposed SMEIR algorithm consists of two alternating steps: (1) model-based iterative image reconstruction to obtain a motion-compensated primary CBCT (m-pCBCT) and (2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction technique (SART) coupled with total variation minimization. During the forward- and backprojection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The quality of reconstructed 4D images and the accuracy of tumor motion trajectory are assessed by comparing with those resulting from conventional sequential 4D-CBCT reconstructions (FDK and total variation minimization) and motion estimation (demons algorithm). The performance of the SMEIR algorithm is further evaluated by reconstructing a lung cancer patient 4D-CBCT.
Results:
Image quality of 4D-CBCT is greatly improved by the SMEIR algorithm in both phantom and patient studies. When all projections are used to reconstruct a 3D-CBCT by FDK, motion-blurring artifacts are present, leading to a 24.4% relative reconstruction error in the NACT phantom. View aliasing artifacts are present in 4D-CBCT reconstructed by FDK from 20 projections, with a relative error of 32.1%. When total variation minimization is used to reconstruct 4D-CBCT, the relative error is 18.9%. Image quality of 4D-CBCT is substantially improved by using the SMEIR algorithm and relative error is reduced to 7.6%. The maximum error (MaxE) of tumor motion determined from the DVF obtained by demons registration on a FDK-reconstructed 4D-CBCT is 3.0, 2.3, and 7.1 mm along left–right (L-R), anterior–posterior (A-P), and superior–inferior (S-I) directions, respectively. From the DVF obtained by demons registration on 4D-CBCT reconstructed by total variation minimization, the MaxE of tumor motion is reduced to 1.5, 0.5, and 5.5 mm along L-R, A-P, and S-I directions. From the DVF estimated by SMEIR algorithm, the MaxE of tumor motion is further reduced to 0.8, 0.4, and 1.5 mm along L-R, A-P, and S-I directions, respectively.
Conclusions:
The proposed SMEIR algorithm is able to estimate a motion model and reconstruct motion-compensated 4D-CBCT. The SMEIR algorithm improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy as compared to conventional sequential 4D-CBCT reconstruction and motion estimation.
Throughout the course of delivering a radiation therapy treatment, which may take several weeks, a patient's anatomy may change drastically, and adaptive radiation therapy (ART) may be needed. ...Cone-beam computed tomography (CBCT), which is often available during the treatment process, can be used for both patient positioning and ART re-planning. However, due to the prominent amount of noise, artifacts, and inaccurate Hounsfield unit (HU) values, the dose calculation based on CBCT images could be inaccurate for treatment planning. One way to solve this problem is to convert CBCT images to more accurate synthesized CT (sCT) images. In this work, we have developed a cycle-consistent generative adversarial network framework (CycleGAN) to synthesize CT images from CBCT images. This model is capable of image-to-image translation using unpaired CT and CBCT images in an unsupervised learning setting. The sCT images generated from CBCT through this CycleGAN model are visually and quantitatively similar to real CT images with decreased mean absolute error (MAE) from 69.29 HU to 29.85 HU for head-and-neck (H&N) cancer patients. The dose distributions calculated on the sCT by CycleGAN demonstrated a higher accuracy than those on CBCT in a 3D gamma index analysis with increased gamma index pass rate from 86.92% to 96.26% under 1 mm/1% criteria, when using the deformed planning CT image (dpCT) as the reference. We also compared the CycleGAN model with other unsupervised learning methods, including deep convolutional generative adversarial networks (DCGAN) and progressive growing of GANs (PGGAN), and demonstrated that CycleGAN outperformed the other two models. A phantom study has been conducted to compare sCT with dpCT, and the increase of structural similarity index from 0.91 to 0.93 shows that CycleGAN performed better than DIR in terms of preserving anatomical accuracy.
Due to a limited number of projections at each phase, severe view aliasing artifacts are present in four-dimensional cone beam computed tomography (4D-CBCT) when reconstruction is performed using ...conventional algorithms. In this work, we aim to obtain high-quality 4D-CBCT of lung cancer patients in radiation therapy by deforming the planning CT. The deformation vector fields (DVF) to deform the planning CT are estimated through matching the forward projection of the deformed prior image and measured on-treatment CBCT projection. The estimation of the DVF is formulated as an unconstrained optimization problem, where the objective function to be minimized is the sum of the squared difference between the forward projection of the deformed planning CT and the measured 4D-CBCT projection. A nonlinear conjugate gradient method is used to solve the DVF. As the number of the variables in the DVF is much greater than the number of measurements, the solution to such a highly ill-posed problem is very sensitive to the initials during the optimization process. To improve the estimation accuracy of DVF, we proposed a new strategy to obtain better initials for the optimization. In this strategy, 4D-CBCT is first reconstructed by total variation minimization. Demons deformable registration is performed to register the planning CT and the 4D-CBCT reconstructed by total variation minimization. The resulted DVF from demons registration is then used as the initial parameters in the optimization process. A 4D nonuniform rotational B-spline-based cardiac-torso (NCAT) phantom and a patient 4D-CBCT are used to evaluate the algorithm. Image quality of 4D-CBCT is substantially improved by using the proposed strategy in both NCAT phantom and patient studies. The proposed method has the potential to improve the temporal resolution of 4D-CBCT. Improved 4D-CBCT can better characterize the motion of lung tumors and will be a valuable tool for image-guided adaptive radiation therapy.
Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning ...convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Objective
Pregnant women experience enormous psychological pressure, particularly during the late trimester. Symptoms of depression in late pregnancy may persist postpartum, increasing the ...incidence of postpartum depression. This study is aimed to investigate the factors influencing depressive symptoms among pregnant women in their third trimester at a Chinese tertiary hospital and provide information for effective intervention.
Methods
Pregnant women in their third trimester who visited the Ningbo Women and Children’s Hospital between January 1, 2020 and June 30, 2022 participated in this study. A score of ≥ 13 on the Edinburgh Postnatal Depression Scale (EPDS) was considered as positive for depressive symptom. Potential influencing factors were examined by using an online questionnaire and analyzed using multivariate logistic regression.
Results
A total of 1196 participants were recruited. The mean EPDS score was 7.12 ± 4.22. The positive screening rate for depressive symptom was 9.9%. Univariate analysis showed that living with partner, annual family income, planned pregnancy, sleep quality, and partner’s drinking habits were related to positive screening for depression(
P
< 0.05). Furthermore, multivariate logistic regression analysis showed that living away from the partner (odds ratio OR: 2.054, 95% confidence interval CI: 1.094–3.696,
P
= 0.02), annual family income < 150,000 Chinese Yuan (CNY; OR: 1.762, 95% CI: 1.170–2.678,
P
= 0.007), poor sleep quality (OR: 4.123, 95% CI: 2.764–6.163,
P
< 0.001), and partner’s frequent drinking habit (OR: 2.227, 95% CI: 1.129–4.323,
P
= 0.019) were independent influencing factors for positive depression screening (
P
< 0.05).
Conclusion
Family’s economic condition, sleep quality, living with partner, and partner's drinking habits were related to positive depression screening in late pregnancy. Pregnant women with these risk factors should be given more attention and supported to avoid developing depression.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
To evaluate the performance of a 4-dimensional (4-D) cone-beam computed tomographic (CBCT) reconstruction scheme based on simultaneous motion estimation and image reconstruction (SMEIR) through ...patient studies.
The SMEIR algorithm contains 2 alternating steps: (1) motion-compensated CBCT reconstruction using projections from all phases to reconstruct a reference phase 4D-CBCT by explicitly considering the motion models between each different phase and (2) estimation of motion models directly from projections by matching the measured projections to the forward projection of the deformed reference phase 4D-CBCT. Four lung cancer patients were scanned for 4 to 6 minutes to obtain approximately 2000 projections for each patient. To evaluate the performance of the SMEIR algorithm on a conventional 1-minute CBCT scan, the number of projections at each phase was reduced by a factor of 5, 8, or 10 for each patient. Then, 4D-CBCTs were reconstructed from the down-sampled projections using Feldkamp-Davis-Kress, total variation (TV) minimization, prior image constrained compressive sensing (PICCS), and SMEIR. Using the 4D-CBCT reconstructed from the fully sampled projections as a reference, the relative error (RE) of reconstructed images, root mean square error (RMSE), and maximum error (MaxE) of estimated tumor positions were analyzed to quantify the performance of the SMEIR algorithm.
The SMEIR algorithm can achieve results consistent with the reference 4D-CBCT reconstructed with many more projections per phase. With an average of 30 to 40 projections per phase, the MaxE in tumor position detection is less than 1 mm in SMEIR for all 4 patients.
The results from a limited number of patients show that SMEIR is a promising tool for high-quality 4D-CBCT reconstruction and tumor motion modeling.
•Swallow motion may compromise the successful delivery of glottic larynx SBRT.•We developed a motion management workflow using surface image guidance.•It was found that swallow motion was common ...during treatment.•Our method was feasible and straightforward to implement in clinic.
Involuntary motion due to swallowing cause inaccurate dose delivery during larynx radiotherapy, a deviation that may be particularly problematic during stereotactic body radiation therapy (SBRT). The goal of this study was to develop a motion management solution for larynx SBRT using surface imaging.
Ten patients were recently treated on a phase II study of larynx SBRT on a LINAC equipped with a surface guidance system. A small region of the immobilization mask was manually cut open to allow surface tracking. Pre-treatment and intra-fractional CBCTs were acquired to verify internal anatomy. Patients were verbally instructed not to swallow during treatment. During treatment delivery, beam hold was initiated by the Motion Management Interface if surface motion exceeded a patient-specific threshold. Patient motion was recorded in log files and analyzed. We also performed phantom studies to assess the theoretical impact of gating on dose delivery.
The frequency (6.5 ± 5.2 times per fraction) and duration (3.9 ± 2.5 seconds per swallow) of swallowing varied both between patients and fractions. The magnitude of each swallow showed mean peak amplitude at 5.8 ± 3.8 mm above baseline, mostly in the longitudinal direction. Beam duty cycle was 95.0% ± 7.0% (absolute range: 76–100%), with inefficiency most prominent in the early fractions. The 95th percentile residual motion was reduced from 3.4 mm to 2.3 mm with both verbal instruction and gating. Phantom studies confirmed dose delivery accuracy represented by gamma pass rate was improved by 5% using this approach.
Laryngeal motion management using surface imaging is feasible and efficacious. Uncontrolled movement of the larynx was not uncommon during treatment, with gating reducing potential for unplanned dose deviations. Additional research is needed to determine the clinical benefit with this system.
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