Magnetic resonance imaging and deoxyribonucleic acid methylation-based radiogenomic models for survival risk stratification of glioblastoma.

Glioblastoma multiforme (GBM) is one of the deadliest tumours. This study aimed to construct radiogenomic prognostic models of glioblastoma overall survival (OS) based on magnetic resonance imaging (MRI) Gd-T1WI images and deoxyribonucleic acid (DNA) methylation-seq and to understand the related biological pathways. The ResNet3D-18 model was used to extract radiomic features, and Lasso-Cox regression analysis was utilized to establish the prognostic models. A nomogram was constructed by combining the radiogenomic features and clinicopathological variables. The DeLong test was performed to compare the area under the curve (AUC) of the models. We screened differentially expressed genes (DEGs) with original ribonucleic acid (RNA)-seq in risk stratification and used Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) annotations for functional enrichment analysis. For the 1-year OS models, the AUCs of the radiogenomic set, methylation set and deep learning set in the training cohort were 0.864, 0.804 and 0.787, and those in the validation cohort were 0.835, 0.768 and 0.651, respectively. The AUCs of the 0.5-, 1- and 2-year nomograms in the training cohort were 0.943, 0.861 and 0.871, and those in the validation cohort were 0.864, 0.885 and 0.805, respectively. A total of 245 DEGs were screened; functional enrichment analysis showed that these DEGs were associated with cell immunity. The survival risk-stratifying radiogenomic models for glioblastoma OS had high predictability and were associated with biological pathways related to cell immunity.

One 3D VOI-based deep learning radiomics strategy, clinical model and radiologists for predicting lymph node metastases in pancreatic ductal adenocarcinoma based on multiphasic contrast-enhanced computer tomography.

Purpose: We designed to construct one 3D VOI-based deep learning radiomics strategy for identifying lymph node metastases (LNM) in pancreatic ductal adenocarcinoma on the basis of multiphasic contrast-enhanced computer tomography and to assist clinical decision-making. Methods: This retrospective research enrolled 139 PDAC patients undergoing pre-operative arterial phase and venous phase scanning examination between 2015 and 2021. A primary group (training group and validation group) and an independent test group were divided. The DLR strategy included three sections. (1) Residual network three dimensional-18 (Resnet 3D-18) architecture was constructed for deep learning feature extraction. (2) Least absolute shrinkage and selection operator model was used for feature selection. (3) Fully connected network served as the classifier. The DLR strategy was applied for constructing different 3D CNN models using 5-fold cross-validation. Radiomics scores (Rad score) were calculated for distinguishing the statistical difference between negative and positive lymph nodes. A clinical model was constructed by combining significantly different clinical variables using univariate and multivariable logistic regression. The manifestation of two radiologists was detected for comparing with computer-developed models. Receiver operating characteristic curves, the area under the curve, accuracy, precision, recall, and F1 score were used for evaluating model performance. Results: A total of 45, 49, and 59 deep learning features were selected via LASSO model. No matter in which 3D CNN model, Rad score demonstrated the deep learning features were significantly different between non-LNM and LNM groups. The AP+VP DLR model yielded the best performance in predicting status of lymph node in PDAC with an AUC of 0.995 (95% CI:0.989-1.000) in training group; an AUC of 0.940 (95% CI:0.910-0.971) in validation group; and an AUC of 0.949 (95% CI:0.914-0.984) in test group. The clinical model enrolled the histological grade, CA19-9 level and CT-reported tumor size. The AP+VP DLR model outperformed AP DLR model, VP DLR model, clinical model, and two radiologists. Conclusions: The AP+VP DLR model based on Resnet 3D-18 demonstrated excellent ability for identifying LNM in PDAC, which could act as a non-invasive and accurate guide for clinical therapeutic strategies. This 3D CNN model combined with 3D tumor segmentation technology is labor-saving, promising, and effective.

Deep learning combined with radiomics for the classification of enlarged cervical lymph nodes.

PURPOSE: To investigate the application of deep learning combined with traditional radiomics methods for classifying enlarged cervical lymph nodes. METHODS: The clinical and computed tomography (CT) imaging data of 276 patients with enlarged cervical lymph nodes (150 with lymph-node metastasis, 65 with lymphoma, and 61 with benign lymphadenopathy) who were treated at the hospital from January 2015 to January 2021 were retrospectively analysed. The patients were randomly divided into a training group and a test group at a ratio of 8:2. The radiomics features were extracted using one-by-one convolution and neural network activation, filtered with the least absolute shrinkage and selection operator (LASSO) model, and used to construct a discrimination model with PyTorch. Then, the performance of the model was compared with the radiologists' diagnostic performance. The neural network model was evaluated using the area under the receiver-operator characteristic curve (AUC), and the accuracy, sensitivity, and specificity were analysed. RESULTS: A total of 102 features, comprising five traditional radiomic features and 97 deep learning features, were selected with LASSO and used to construct a discrimination model, which achieved a total accuracy of 87.50%. The AUC value, specificity, and sensitivity were, respectively, 0.92, 92.30%, and 90.00% for metastatic lymph nodes, 0.87, 95.45%, and 83.33% for benign lymphadenopathy, and 0.88, 90.47%, and 85.71% for lymphoma. The accuracies of the radiologists' diagnoses were 62.68% and 62.68%. The diagnostic performance of the model was significantly different from that of the radiologists (p < 0.05). CONCLUSION: CT-based deep learning combined with the traditional radiomics methods has a high diagnostic value for the classification of cervical enlarged lymph nodes.

The complete chloroplast genome of Calohypnum plumiforme (Wilson) (hypanceae, bryophyta).

Calohypnum plumiforme is a widely distributed moss in East Asia. In this study, Illumina sequencing data was used to assemble the complete chloroplast genome of C. plumiforme. The length of the circular genome is 124,037 bp. It contains a total of 121 genes, including 81 protein-coding, 36 tRNA, and 4 rRNA genes. The GC content of the chloroplast genome of C. plumiforme is 29.07%. The phylogenetic analysis suggested that C. plumiforme is sister to Calliergonella cuspidata. This study provides important sequence information for species identification and its phylogenetic relationship in the Bryopsida.

Seed germination ecology of Conyza sumatrensis populations stemming from different habitats and implications for management.

Conyza sumatrensis (Retz.) E. H. Walker is an obnoxious weed, emerging as an invasive species globally. Seed germination biology of four populations of the species stemming from arid, semi-arid, temperate, and humid regions was determined in this study. Seed germination was recorded under six different environmental cues (i.e., light/dark periods, constant and alternating day and night temperatures, pH, salinity, and osmotic potential levels) in separate experiment for each cue. Populations were main factor, whereas levels of each environmental cue were considered as sub-factor. The impact of seed burial depths on seedling emergence was inferred in a greenhouse pot experiment. Seed germination was recorded daily and four germination indices, i.e., seed germination percentage, mean germination time, time to reach 50% germination, and mean daily germination were computed. Tested populations and levels of different environmental cues had significant impact on various seed germination indices. Overall, seeds stemming from arid and semi-arid regions had higher seed germination potential under stressful and benign environmental conditions compared to temperate and humid populations. Seed of all populations required a definite light period for germination and 12 hours alternating light and dark period resulted in the highest seed germination. Seed germination of all populations occurred under 5-30°C constant and all tested alternate day and night temperatures. However, the highest seed germination was recorded under 20°C. Seeds of arid and semi-arid populations exhibited higher germination under increased temperature, salinity and osmotic potential levels indicating that maternal environment strongly affected germination traits of the tested populations. The highest seed germination of the tested populations was noted under neutral pH, while higher and lower pH than neutral had negative impact on seed germination. Arid and semi-arid populations exhibited higher seed germination under increased pH compared to temperate and humid populations. Seed burial depth had a significant effect on the seedling emergence of all tested populations. An initial increase was noted in seedling emergence percentage with increasing soil depth. However, a steep decline was recorded after 2 cm seed burial depth. These results indicate that maternal environment strongly mediates germination traits of different populations. Lower emergence from >4 cm seed burial depth warrants that deep burial of seeds and subsequent zero or minimum soil disturbance could aid the management of the species in agricultural habitats. However, management strategies should be developed for other habitats to halt the spread of the species.

Differentiation of Glioma Mimicking Encephalitis and Encephalitis Using Multiparametric MR-Based Deep Learning.

BACKGROUND: Computational aid for diagnosis based on convolutional neural network (CNN) is promising to improve clinical diagnostic performance. Therefore, we applied pretrained CNN models in multiparametric magnetic resonance (MR) images to classify glioma mimicking encephalitis and encephalitis. METHODS: A data set containing 3064 MRI brain images from 164 patients with a final diagnosis of glioma (n = 56) and encephalitis (n = 108) patients and divided into training and testing sets. We applied three MRI modalities [fluid attenuated inversion recovery (FLAIR), contrast enhanced-T1 weighted imaging (CE-T1WI) and T2 weighted imaging (T2WI)] as the input data to build three pretrained deep CNN models (Alexnet, ResNet-50, and Inception-v3), and then compared their classification performance with radiologists' diagnostic performance. These models were evaluated by using the area under the receiver operator characteristic curve (AUC) of a five-fold cross-validation and the accuracy, sensitivity, specificity were analyzed. RESULTS: The three pretrained CNN models all had AUC values over 0.9 with excellent performance. The highest classification accuracy of 97.57% was achieved by the Inception-v3 model based on the T2WI data. In addition, Inception-v3 performed statistically significantly better than the Alexnet architecture (p<0.05). For Inception-v3 and ResNet-50 models, T2WI offered the highest accuracy, followed by CE-T1WI and FLAIR. The performance of Inception-v3 and ResNet-50 had a significant difference with radiologists (p<0.05), but there was no significant difference between the results of the Alexnet and those of a more experienced radiologist (p >0.05). CONCLUSIONS: The pretrained CNN models can automatically and accurately classify these two diseases and further help to improving clinical diagnostic performance.

One-slice CT image based kernelized radiomics model for the prediction of low/mid-grade and high-grade HNSCC.

An accurate grade prediction can help to appropriate treatment strategy and effective diagnosis to Head and neck squamous cell carcinoma (HNSCC). Radiomics has been studied for the prediction of carcinoma characteristics in medical images. The success of previous researches in radiomics is attributed to the availability of annotated all-slice medical images. However, it is very challenging to annotate all slices, as annotating biomedical images is not only tedious, laborious, and time consuming, but also demanding of costly, specialty-oriented skills, which are not easily accessible. To address this problem, this paper presents a model to integrate radiomics and kernelized dimension reduction into a single framework, which maps handcrafted radiomics features to a kernelized space where they are linearly separable and then reduces the dimension of features through principal component analysis. Three methods including baseline radiomics models, proposed kernelized model and convolutional neural network (CNN) model were compared in experiments. Results suggested proposed kernelized model best fit in one-slice data. We reached AUC of 95.91 % on self-made one-slice dataset, 67.33 % in predicting localregional recurrence on H&N dataset and 64.33 % on H&N1 dataset. While all other models were <76 %, <65 %, and <62 %. Though CNN model reached an incredible performance when predicting distant metastasis on H&N (AUC 0.88), model faced serious problem of overfitting in small datasets. When changing all-slice data to one-slice on both H&N and H&N1, proposed model suffered less loss on AUC (<1.3 %) than any other models (>3 %). These proved our proposed model is efficient to deal with the one-slice problem and makes using one-slice data to reduce annotation cost practical. This is attributed to the several advantages derived from the proposed kernelized radiomics model, including (1) the prior radiomics features reduced the demanding of huge amount of data and avoided overfitting; (2) the kernelized method mined the potential information contributed to predict; (3) generating principal components in kernelized features reduced redundant features.

CT-Based Radiomics Signature for the Preoperative Discrimination Between Head and Neck Squamous Cell Carcinoma Grades.

Background: Radiomics has been widely used to non-invasively mine quantitative information from medical images and could potentially predict tumor phenotypes. Pathologic grade is considered a predictive prognostic factor for head and neck squamous cell carcinoma (HNSCC) patients. A preoperative histological assessment can be important in the clinical management of patients. We applied radiomics analysis to devise non-invasive biomarkers and accurately differentiate between well-differentiated (WD) and moderately differentiated (MD) and poorly differentiated (PD) HNSCC. Methods: This study involved 206 consecutive HNSCC patients (training cohort: n = 137; testing cohort: n = 69). In total, we extracted 670 radiomics features from contrast-enhanced computed tomography (CT) images. Radiomics signatures were constructed with a kernel principal component analysis (KPCA), random forest classifier and a variance-threshold (VT) selection. The associations between the radiomics signatures and HNSCC histological grades were investigated. A clinical model and combined model were also constructed. Areas under the receiver operating characteristic curves (AUCs) were applied to evaluate the performances of the three models. Results: In total, 670 features were selected by the KPCA and random forest methods from the CT images. The radiomics signatures had a good performance in discriminating between the two cohorts of HNSCC grades, with an AUC of 0.96 and an accuracy of 0.92. The specificity, accuracy, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) of the abovementioned method with a VT selection for determining HNSCC grades were 0.83, 0.92, 0.96, 0.94, and 0.91, respectively; without VT, the corresponding results were 0.70, 0.83, 0.88, 0.80, and 0.84. The differences in accuracy, sensitivity and NPV were significant between these approaches (p < 0.05). The AUCs with VT and without VT were 0.96 and 0.89, respectively (p < 0.05). Compared to the combined model and the radiomics signatures, The clinical model had a worse performance, and the differences were significant (p < 0.05). The combined model had the best performance, but the difference between the combined model and the radiomics signature weren't significant (p > 0.05). Conclusions: The CT-based radiomics signature could discriminate between WD and MD and PD HNSCC and might serve as a biomarker for preoperative grading.

Multiphase contrast-enhanced computed tomography imaging features of salivary duct carcinoma: differentiation from other salivary gland malignancies.

OBJECTIVE: The aim of this study was to investigate the imaging features of salivary duct carcinoma (SDC) with multiphase contrast-enhanced computed tomography (CECT) and to compare them with those of mucoepidermoid carcinoma (MEC), adenoid cystic carcinoma (ACC), and acinic cell carcinoma. STUDY DESIGN: A total of 63 patients with histologically diagnosed salivary gland malignancies underwent preoperative multiphase CECT. Clinical information, location, size, mass pattern, enhancement pattern, borders, invasion of adjacent tissues, and lymph node metastasis were evaluated. Computed tomography (CT) number attenuation patterns were calculated. RESULTS: SDCs were significantly more common in males and in the parotid gland (P ≤ .018). They were more likely to invade into adjacent tissues and metastasize to lymph nodes (P ≤ .032). Six SDCs (66.7%) had comedonecrosis, as detected on histopathologic examination, and 3 lesions presented cribriform necrosis on CECT. CT numbers during delayed-phase scanning were significantly higher in SDC than in ACC (P = .031). Significant differences were discovered between MEC and ACC for CT numbers during arterial-phase scanning (P = .047) and in the ratio of CT numbers (P = .018). CONCLUSIONS: SDC exhibits some specific CT features, and multiphase CECT imaging is useful in the differential diagnosis of salivary gland malignancies.