Objectives
Computed tomography (CT) and magnetic resonance imaging (MRI) are the most commonly selected methods for imaging gliomas. Clinically, radiotherapists always delineate the CT glioma region ...with reference to multi-modal MR image information. On this basis, we develop a deep feature fusion model (DFFM) guided by multi-sequence MRIs for postoperative glioma segmentation in CT images.
Methods
DFFM is a multi-sequence MRI–guided convolutional neural network (CNN) that iteratively learns the deep features from CT images and multi-sequence MR images simultaneously by utilizing a multi-channel CNN architecture, and then combines these two deep features together to produce the segmentation result. The whole network is optimized together via a standard back-propagation. A total of 59 CT and MRI datasets (T1/T2-weighted FLAIR, T1-weighted contrast-enhanced, T2-weighted) of postoperative gliomas as tumor grade II (
n
= 24), grade III (
n
= 18), or grade IV (
n
= 17) were included. Dice coefficient (DSC), precision, and recall were used to measure the overlap between automated segmentation results and manual segmentation. The Wilcoxon signed-rank test was used for statistical analysis.
Results
DFFM showed a significantly (
p
< 0.01) higher DSC of 0.836 than U-Net trained by single CT images and U-Net trained by stacking the CT and multi-sequence MR images, which yielded 0.713 DSC and 0.818 DSC, respectively. The precision values showed similar behavior as DSC. Moreover, DSC and precision values have no significant statistical difference (
p
> 0.01) with difference grades.
Conclusions
DFFM enables the accurate automated segmentation of CT postoperative gliomas of profit guided by multi-sequence MR images and may thus improve and facilitate radiotherapy planning.
Key Points
• A fully automated deep learning method was developed to segment postoperative gliomas on CT images guided by multi-sequence MRIs.
• CT and multi-sequence MR image integration allows for improvements in deep learning postoperative glioma segmentation method.
• This deep feature fusion model produces reliable segmentation results and could be useful in delineating GTV in postoperative glioma radiotherapy planning.
Objective
To evaluate the impact of utilizing digital breast tomosynthesis (DBT) or/and full-field digital mammography (FFDM), and different transfer learning strategies on deep convolutional neural ...network (DCNN)-based mass classification for breast cancer.
Methods
We retrospectively collected 441 patients with both DBT and FFDM on which regions of interest (ROIs) covering the malignant, benign and normal tissues were extracted for DCNN training and validation. Experiments were conducted for tasks in distinguishing malignant/benign/normal: (1) classification capabilities of DBT vs FFDM and the role of transfer learning were validated on 2D-DCNN; (2) different strategies of combining DBT and FFDM and the associated impacts on classification were explored; (3) 2D-DCNN and 3D-DCNN trained from scratch with volumetric DBT were compared.
Results
2D-DCNN with transfer learning outperformed that without for DBT in distinguishing malignant (ΔAUC = 0.059 ± 0.009,
p
< 0.001), benign (ΔAUC = 0.095 ± 0.010,
p
< 0.001) and normal tissue (ΔAUC = 0.042 ± 0.004,
p
< 0.001) (paired samples
t
test). 2D-DCNN trained on DBT (with transfer learning) achieved higher accuracy than those on FFDM (malignant: ΔAUC = 0.014 ± 0.014,
p
= 0.037; benign: ΔAUC = 0.031 ± 0.006,
p
< 0.001; normal: ΔAUC = 0.017 ± 0.004,
p
< 0.001) (independent samples
t
test). The 2D-DCNN employing both DBT and FFDM for training achieved better performances in benign (FFDM: ΔAUC = 0.010 ± 0.008,
p <
0.001; DBT: ΔAUC = 0.009 ± 0.005,
p
< 0.001) and normal (FFDM: ΔAUC = 0.005 ± 0.003,
p
< 0.001; DBT: ΔAUC = 0.002 ± 0.002,
p
< 0.001) (related samples Friedman test). The 3D-DCNN and 2D-DCNN trained from scratch with DBT only produced moderate classification.
Conclusions
Transfer learning facilitates mass classification for both DBT and FFDM, and DBT outperforms FFDM when equipped with transfer learning. Integrating DBT and FFDM in DCNN training enhances mass classification accuracy for breast cancer.
Key Points
• Transfer learning facilitates mass classification for both DBT and FFDM, and the DBT-based DCNN outperforms the FFDM-based DCNN when equipped with transfer learning.
• Integrating DBT and FFDM in DCNN training enhances breast mass classification accuracy.
• 3D-DCNN/2D-DCNN trained from scratch with volumetric DBT but without transfer learning only produce moderate mass classification result.
Purpose
The purpose of this study is to investigate the effect of different magnetic resonance (MR) sequences on the accuracy of deep learning‐based synthetic computed tomography (sCT) generation in ...the complex head and neck region.
Methods
Four sequences of MR images (T1, T2, T1C, and T1DixonC‐water) were collected from 45 patients with nasopharyngeal carcinoma. Seven conditional generative adversarial network (cGAN) models were trained with different sequences (single channel) and different combinations (multi‐channel) as inputs. To further verify the cGAN performance, we also used a U‐net network as a comparison. Mean absolute error, structural similarity index, peak signal‐to‐noise ratio, dice similarity coefficient, and dose distribution were evaluated between the actual CTs and sCTs generated from different models.
Results
The results show that the cGAN model with multi‐channel (i.e., T1 + T2 + T1C + T1DixonC‐water) as input to predict sCT achieves higher accuracy than any single MR sequence model. The T1‐weighted MR model achieves better results than T2, T1C, and T1DixonC‐water models. The comparison between cGAN and U‐net shows that the sCTs predicted by cGAN retains additional image details are less blurred and more similar to the actual CT.
Conclusions
Conditional generative adversarial network with multiple MR sequences as model input shows the best accuracy. The T1‐weighted MR images provide sufficient image information and are suitable for sCT prediction in clinical scenarios with limited acquisition sequences or limited acquisition time.
The human intestine houses an astounding number and species of microorganisms, estimated at more than 10(14) gut microbiota and composed of over a thousand species. An individual's profile of ...microbiota is continually influenced by a variety of factors including but not limited to genetics, age, sex, diet, and lifestyle. Although each person's microbial profile is distinct, the relative abundance and distribution of bacterial species is similar among healthy individuals, aiding in the maintenance of one's overall health. Consequently, the ability of gut microbiota to bidirectionally communicate with the brain, known as the gut-brain axis, in the modulation of human health is at the forefront of current research. At a basic level, the gut microbiota interacts with the human host in a mutualistic relationship - the host intestine provides the bacteria with an environment to grow and the bacterium aids in governing homeostasis within the host. Therefore, it is reasonable to think that the lack of healthy gut microbiota may also lead to a deterioration of these relationships and ultimately disease. Indeed, a dysfunction in the gut-brain axis has been elucidated by a multitude of studies linked to neuropsychological, metabolic, and gastrointestinal disorders. For instance, altered microbiota has been linked to neuropsychological disorders including depression and autism spectrum disorder, metabolic disorders such as obesity, and gastrointestinal disorders including inflammatory bowel disease and irritable bowel syndrome. Fortunately, studies have also indicated that gut microbiota may be modulated with the use of probiotics, antibiotics, and fecal microbiota transplants as a prospect for therapy in microbiota-associated diseases. This modulation of gut microbiota is currently a growing area of research as it just might hold the key to treatment.
•First study evolves RT 3D dose distribution to cancer therapy prognosis analysis.•Dosiomics improves predicting LR for HNSCC and should be recommended correlatively.•Dosiomics marker LGLE_GLDM_GTV0 ...could be a potential LR prognostic factor for HNSCC.•Dosiomics is general and suitable for other tumor site and prognosis scenarios.
To investigate whether dosiomics can benefit to IMRT treated patient’s locoregional recurrences (LR) prediction through a comparative study on prediction performance inspection between radiomics methods and that integrating dosiomics in head and neck cancer cases.
A cohort of 237 patients with head and neck cancer from four different institutions was obtained from The Cancer Imaging Archive and utilized to train and validate the radiomics-only prognostic model and integrate the dosiomics prognostic model. For radiomics, the radiomics features were initially extracted from images, including CTs and PETs, and selected on the basis of their concordance index (CI) values, then condensed via principle component analysis. Lastly, multivariate Cox proportional hazards regression models were constructed with class-imbalance adjustment as the LR prediction models by inputting those condensed features. For dosiomics integration model establishment, the initial features were similar, but with additional 3-dimensional dose distribution from radiation treatment plans. The CI and the Kaplan–Meier curves with log-rank analysis were used to assess and compare these models.
Observed from the independent validation dataset, the CI of the model for dosiomics integration (0.66) was significantly different from that for radiomics (0.59) (Wilcoxon test, p=5.9×10-31). The integrated model successfully classified the patients into high- and low-risk groups (log-rank test, p=2.5×10-02), whereas the radiomics model was not able to provide such classification (log-rank test, p=0.37).
Dosiomics can benefit in predicting the LR in IMRT-treated patients and should not be neglected for related investigations.
Cutaneous lymphoid hyperplasia (pseudolymphoma) Zhou, Linghong Linda, BHSc; Mistry, Nisha, MD
CMAJ. Canadian Medical Association journal,
04/2018, Letnik:
190, Številka:
13
Journal Article
Recenzirano
Odprti dostop
Zhou and Mistry provide details on a 48-year-old otherwise healthy woman presented to the dermatology clinic with a six-week history of a red lesion on the right cheek. The lesion was generally ...asymptomatic, although the patient reported some occasional itch and tenderness. The referring primary care provider had made a presumptive diagnosis of acne cyst and prescribed treatment with doxycycline (100 mg administered orally and taken daily for 30 days), but there was no substantial change. Results from immunohistochemistry testing showed a mixed population of B and T cells. Based on these features, we diagnosed cutaneous lymphoid hyperplasia (pseudolymphoma). Pseudolymphoma is a benign inflammatory response occasionally linked to an inciting antigen that simulates cutaneous lymphomas. The clinical presentation is a skin-colored or red nodule on the face or chest, although multiple or generalized lesions can also be seen.
Purpose
Radiation therapy treatment planning is a trial‐and‐error, often time‐consuming process. An approximately optimal dose distribution corresponding to a specific patient's anatomy can be ...predicted by using pre‐trained deep learning (DL) models. However, dose distributions are often optimized based not only on patient‐specific anatomy but also on physicians’ preferred trade‐offs between planning target volume (PTV) coverage and organ at risk (OAR) sparing or among different OARs. Therefore, it is desirable to allow physicians to fine‐tune the dose distribution predicted based on patient anatomy. In this work, we developed a DL model to predict the individualized 3D dose distributions by using not only the patient's anatomy but also the desired PTV/OAR trade‐offs, as represented by a dose volume histogram (DVH), as inputs.
Methods
In this work, we developed a modified U‐Net network to predict the 3D dose distribution by using patient PTV/OAR masks and the desired DVH as inputs. The desired DVH, fine‐tuned by physicians from the initially predicted DVH, is first projected onto the Pareto surface, then converted into a vector, and then concatenated with feature maps encoded from the PTV/OAR masks. The network output for training is the dose distribution corresponding to the Pareto optimal DVH. The training/validation datasets contain 77 prostate cancer patients, and the testing dataset has 20 patients.
Results
The trained model can predict a 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the input desired DVH. We calculated the difference between the predicted dose distribution and the optimized dose distribution that has a DVH closest to the desired one for the PTV and for all OARs as a quantitative evaluation. The largest absolute error in mean dose was about 3.6% of the prescription dose, and the largest absolute error in the maximum dose was about 2.0% of the prescription dose.
Conclusions
In this feasibility study, we have developed a 3D U‐Net model with the patient's anatomy and the desired DVH curves as inputs to predict an individualized 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the desired one. The predicted dose distributions can be used as references for dosimetrists and physicians to rapidly develop a clinically acceptable treatment plan.
Deformable image registration (DIR) is a critical technic in adaptive radiotherapy (ART) for propagating contours between planning computerized tomography (CT) images and treatment CT/cone-beam CT ...(CBCT) images to account for organ deformation for treatment re-planning. To validate the ability and accuracy of DIR algorithms in organ at risk (OAR) contour mapping, ten intensity-based DIR strategies, which were classified into four categories-optical flow-based, demons-based, level-set-based and spline-based-were tested on planning CT and fractional CBCT images acquired from twenty-one head & neck (H&N) cancer patients who underwent 6~7-week intensity-modulated radiation therapy (IMRT). Three similarity metrics, i.e., the Dice similarity coefficient (DSC), the percentage error (PE) and the Hausdorff distance (HD), were employed to measure the agreement between the propagated contours and the physician-delineated ground truths of four OARs, including the vertebra (VTB), the vertebral foramen (VF), the parotid gland (PG) and the submandibular gland (SMG). It was found that the evaluated DIRs in this work did not necessarily outperform rigid registration. DIR performed better for bony structures than soft-tissue organs, and the DIR performance tended to vary for different ROIs with different degrees of deformation as the treatment proceeded. Generally, the optical flow-based DIR performed best, while the demons-based DIR usually ranked last except for a modified demons-based DISC used for CT-CBCT DIR. These experimental results suggest that the choice of a specific DIR algorithm depends on the image modality, anatomic site, magnitude of deformation and application. Therefore, careful examinations and modifications are required before accepting the auto-propagated contours, especially for automatic re-planning ART systems.
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