Comparison of Different Fusion Radiomics for Predicting Benign and Malignant Sacral Tumors: A Pilot Study.

Differentiating between benign and malignant sacral tumors is crucial for determining appropriate treatment options. This study aims to develop two benchmark fusion models and a deep learning radiomic nomogram (DLRN) capable of distinguishing between benign and malignant sacral tumors using multiple imaging modalities. We reviewed axial T2-weighted imaging (T2WI) and non-contrast computed tomography (NCCT) of 134 patients pathologically confirmed as sacral tumors. The two benchmark fusion models were developed using fusion deep learning (DL) features and fusion classical machine learning (CML) features from multiple imaging modalities, employing logistic regression, K-nearest neighbor classification, and extremely randomized trees. The two benchmark models exhibiting the most robust predictive performance were merged with clinical data to formulate the DLRN. Performance assessment involved computing the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV). The DL benchmark fusion model demonstrated superior performance compared to the CML fusion model. The DLRN, identified as the optimal model, exhibited the highest predictive performance, achieving an accuracy of 0.889 and an AUC of 0.961 in the test sets. Calibration curves were utilized to evaluate the predictive capability of the models, and decision curve analysis (DCA) was conducted to assess the clinical net benefit of the DLR model. The DLRN could serve as a practical predictive tool, capable of distinguishing between benign and malignant sacral tumors, offering valuable information for risk counseling, and aiding in clinical treatment decisions.

Duodenal papilla radiomics-based prediction model for post-endoscopic retrograde cholangiopancreatography pancreatitis using machine learning: a retrospective multicohort study.

BACKGROUND AND AIMS: The duodenal papillae are the primary and essential pathway for ERCP, greatly determining its complexity and outcome. We aimed to investigate the association between papilla morphology and post-ERCP pancreatitis (PEP), and to construct a robust model for PEP prediction. METHODS: We enrolled retrospectively patients underwent ERCP in 2 centers from January 2019 and June 2022. Radiomic features of papilla were extracted from endoscopic images with deep learning. Potential predictors and their importance were evaluated with three machine learning algorithms. A predictive model was developed using best subset selection by logistic regression, and its performance was evaluated in terms of discrimination, calibration, and clinical utility based on area under curve (AUC) of receiver operation characteristics (ROC), calibration and clinical decision curve, respectively. RESULTS: A total of 2038 and 334 ERCP patients from 2 centers were enrolled in this study with PEP rates of 7.9% and 9.6%, respectively. The R-score was significantly associated with PEP and showed great diagnostic value (AUC, 0.755-0.821). Six hub predictors were selected to conduct a predictive model. The radiomics-based model demonstrated excellent discrimination (AUC, 0.825-0.857) and therapeutic benefits in the training, testing, and validation cohorts. The addition of the R-score significantly improved diagnostic accuracy of the predictive model (NRI, 0.151-0.583, p<0.05; IDI, 0.097-0.235, p<0.001). CONCLUSIONS: Radiomic signature of papilla is a crucial independent predictor of PEP. The papilla-radiomics-based model performs well for the clinical prediction of PEP.

Deep learning nomogram for preoperative distinction between Xanthogranulomatous cholecystitis and gallbladder carcinoma: A novel approach for surgical decision.

The distinction between Xanthogranulomatous Cholecystitis (XGC) and Gallbladder Carcinoma (GBC) is challenging due to their similar imaging features. This study aimed to differentiate between XGC and GBC using a deep learning nomogram model built from contrast enhanced computed tomography (CT) scans. 297 patients were included with confirmed XGC (94) and GBC (203) as the training and internal validation cohort from 2017 to 2021. The deep learning model Resnet-18 with Fourier transformation named FCovResnet18, shows most impressive potential in distinguishing XGC from GBC using 3-phase merged images. The accuracy, precision and area under the curve (AUC) of the model were then calculated. An additional cohort of 74 patients consisting of 22 XGC and 52 GBC patients was enrolled from two subsidiary hospitals as the external validation cohort. The accuracy, precision and AUC achieve 0.98, 0.99, 1.00 in the internal validation cohort and 0.89, 0.92, 0.92 in external validation cohort. A nomogram model combining clinical characteristics and deep learning prediction score showed improved predicting value. Altogether, FCovResnet18 nomogram has demonstrated its ability to effectively differentiate XGC from GBC preoperatively, which significantly aid surgeons in making informed and accurate surgical decisions for XGC and GBC patients.

Fusion Radiomics-Based Prediction of Response to Neoadjuvant Chemotherapy for Osteosarcoma.

RATIONALE AND OBJECTIVES: Neoadjuvant chemotherapy (NAC) is the most crucial prognostic factor for osteosarcoma (OS), it significantly prolongs progression-free survival and improves the quality of life. This study aims to develop a deep learning radiomics (DLR) model to accurately predict the response to NAC in patients diagnosed with OS using preoperative MR images. METHODS: We reviewed axial T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted (T1CE) of 106 patients pathologically confirmed as OS. First, the Auto3DSeg framework was utilized for automated OS segmentation. Second, using three feature extraction methods, nine risk classification models were constructed based on three classifiers. The area under the receiver operating curve (AUC), sensitivity, specificity, accuracy, negative predictive value and positive predictive value were calculated for performance evaluation. Additionally, we developed a deep learning radiomics nomogram with clinical indicators. RESULTS: The model for OS automatic segmentation achieved a Dice coefficient of 0.868 across datasets. To predict the response to NAC, the DLR model achieved the highest prediction performance with an accuracy of 93.8% and an AUC of 0.961 in the test sets. We used calibration curves to assess the predictive ability of the models and performed decision curve analysis to evaluate the clinical net benefit of the DLR model. CONCLUSION: The DLR model can serve as a pragmatic prediction tool, capable of identifying patients with poor response to NAC, providing information for risk counseling, and assisting in making clinical treatment decisions. Poor responders are better advised to undergo immunotherapy and receive the best supportive care.

BARN: Behavior-Aware Relation Network for multi-label behavior detection in socially housed macaques.

Quantification of behaviors in macaques provides crucial support for various scientific disciplines, including pharmacology, neuroscience, and ethology. Despite recent advancements in the analysis of macaque behavior, research on multi-label behavior detection in socially housed macaques, including consideration of interactions among them, remains scarce. Given the lack of relevant approaches and datasets, we developed the Behavior-Aware Relation Network (BARN) for multi-label behavior detection of socially housed macaques. Our approach models the relationship of behavioral similarity between macaques, guided by a behavior-aware module and novel behavior classifier, which is suitable for multi-label classification. We also constructed a behavior dataset of rhesus macaques using ordinary RGB cameras mounted outside their cages. The dataset included 65 913 labels for 19 behaviors and 60 367 proposals, including identities and locations of the macaques. Experimental results showed that BARN significantly improved the baseline SlowFast network and outperformed existing relation networks. In conclusion, we successfully achieved multi-label behavior detection of socially housed macaques with both economic efficiency and high accuracy.

Skin-Inspired Ultra-Tough Supramolecular Multifunctional Hydrogel Electronic Skin for Human-Machine Interaction.

Multifunctional supramolecular ultra-tough bionic e-skin with unique durability for human-machine interaction in complex scenarios still remains challenging. Herein, we develop a skin-inspired ultra-tough e-skin with tunable mechanical properties by a physical cross-linking salting-freezing-thawing method. The gelling agent (ß-Glycerophosphate sodium: Gp) induces the aggregation and binding of PVA molecular chains and thereby toughens them (stress up to 5.79 MPa, toughness up to 13.96 MJ m-3). Notably, due to molecular self-assembly, hydrogels can be fully recycled and reprocessed by direct heating (100 °C for a few seconds), and the tensile strength can still be maintained at about 100% after six recoveries. The hydrogel integrates transparency (> 60%), super toughness (up to 13.96 MJ m-3, bearing 1500 times of its own tensile weight), good antibacterial properties (E. coli and S. aureus), UV protection (Filtration: 80%-90%), high electrical conductivity (4.72 S m-1), anti-swelling and recyclability. The hydrogel can not only monitor daily physiological activities, but also be used for complex activities underwater and message encryption/decryption. We also used it to create a complete finger joint rehabilitation system with an interactive interface that dynamically presents the user's health status. Our multifunctional electronic skin will have a profound impact on the future of new rehabilitation medical, human-machine interaction, VR/AR and the metaverse fields.

MVI-TR: A Transformer-Based Deep Learning Model with Contrast-Enhanced CT for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma.

In this study, we considered preoperative prediction of microvascular invasion (MVI) status with deep learning (DL) models for patients with early-stage hepatocellular carcinoma (HCC) (tumor size ≤ 5 cm). Two types of DL models based only on venous phase (VP) of contrast-enhanced computed tomography (CECT) were constructed and validated. From our hospital (First Affiliated Hospital of Zhejiang University, Zhejiang, P.R. China), 559 patients, who had histopathological confirmed MVI status, participated in this study. All preoperative CECT were collected, and the patients were randomly divided into training and validation cohorts at a ratio of 4:1. We proposed a novel transformer-based end-to-end DL model, named MVI-TR, which is a supervised learning method. MVI-TR can capture features automatically from radiomics and perform MVI preoperative assessments. In addition, a popular self-supervised learning method, the contrastive learning model, and the widely used residual networks (ResNets family) were constructed for fair comparisons. With an accuracy of 99.1%, a precision of 99.3%, an area under the curve (AUC) of 0.98, a recalling rate of 98.8%, and an F1-score of 99.1% in the training cohort, MVI-TR achieved superior outcomes. Additionally, the validation cohort's MVI status prediction had the best accuracy (97.2%), precision (97.3%), AUC (0.935), recalling rate (93.1%), and F1-score (95.2%). MVI-TR outperformed other models for predicting MVI status, and showed great preoperative predictive value for early-stage HCC patients.

Deep Learning With Data Enhancement for the Differentiation of Solitary and Multiple Cerebral Glioblastoma, Lymphoma, and Tumefactive Demyelinating Lesion.

OBJECTIVES: To explore the MRI-based differential diagnosis of deep learning with data enhancement for cerebral glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and tumefactive demyelinating lesion (TDL). MATERIALS AND METHODS: This retrospective study analyzed the MRI data of 261 patients with pathologically diagnosed solitary and multiple cerebral GBM (n = 97), PCNSL (n = 92), and TDL (n = 72). The 3D segmentation model was trained to capture the lesion. Different enhancement data were generated by changing the pixel ratio of the lesion and non-lesion areas. The 3D classification network was trained by using the enhancement data. The accuracy, sensitivity, specificity, and area under the curve (AUC) were used to assess the value of different enhancement data on the discrimination performance. These results were then compared with the neuroradiologists' diagnoses. RESULTS: The diagnostic performance fluctuated with the ratio of lesion to non-lesion area changed. The diagnostic performance was best when the ratio was 1.5. The AUCs of GBM, PCNSL, and TDL were 1.00 (95% confidence interval [CI]: 1.000-1.000), 0.96 (95% CI: 0.923-1.000), and 0.954 (95% CI: 0.904-1.000), respectively. CONCLUSIONS: Deep learning with data enhancement is useful for the accurate identification of GBM, PCNSL, and TDL, and its diagnostic performance is better than that of the neuroradiologists.

A novel extended Kalman filter with support vector machine based method for the automatic diagnosis and segmentation of brain tumors.

BACKGROUND: Brain tumors are life-threatening, and their early detection is crucial for improving survival rates. Conventionally, brain tumors are detected by radiologists based on their clinical experience. However, this process is inefficient. This paper proposes a machine learning-based method to 1) determine the presence of a tumor, 2) automatically segment the tumor, and 3) classify it as benign or malignant. METHODS: We implemented an Extended Kalman Filter with Support Vector Machine (EKF-SVM), an image analysis platform based on an SVM for automated brain tumor detection. A development dataset of 120 patients which supported by Tiantan Hospital was used for algorithm training. Our machine learning algorithm has 5 components as follows. Firstly, image standardization is applied to all the images. This is followed by noise removal with a non-local means filter, and contrast enhancement with improved dynamic histogram equalization. Secondly, a gray-level co-occurrence matrix is utilized for feature extraction to get the image features. Thirdly, the extracted features are fed into a SVM for classify the MRI initially, and an EKF is used to classify brain tumors in the brain MRIs. Fourthly, cross-validation is used to verify the accuracy of the classifier. Finally, an automatic segmentation method based on the combination of k-means clustering and region growth is used for detecting brain tumors. RESULTS: With regard to the diagnostic performance, the EKF-SVM had a 96.05% accuracy for automatically classifying brain tumors. Segmentation based on k-means clustering was capable of identifying the tumor boundaries and extracting the whole tumor. CONCLUSION: The proposed EKF-SVM based method has better classification performance for positive brain tumor images, which was mainly due to the dearth of negative examples in our dataset. Therefore, future work should obtain more negative examples and investigate the performance of deep learning algorithms such as the convolutional neural networks for automatic diagnosis and segmentation of brain tumors.

Mitochondrial DNA typing of laser-captured single sperm cells to differentiate individuals in a mixed semen stain.

The identification of individuals in a mixture of two semen samples usually involves an analysis of autosomal and Y chromosomal short tandem repeats (STR) which can exclude unrelated individuals but cannot achieve the purpose of individual identification. In sperm cells, there are multiple copies of mitochondrial DNAs (mtDNA) which exhibit genetic polymorphisms in different matrilineal-related individuals. Single-cell capture technology can be applied to obtain some single sperm cells in a mixed semen sample, then polymerase chain reaction can be employed to amplify the mtDNA hypervariable region I (HVR I) from each cell. By pooling the cells with the same HVR I sequence, we can obtain the sufficient nuclear DNA for STR typing.

[Expression and characterization of Coprinus cinereus peroxidase].

OBJECTIVE: The aim of our study is to express Coprinus cinereus peroxidase (CIP) in Pichia Pastori efficiently. METHODS: We synthesized CIP gene with P. pastori codon bias by our Gene Synthesis and site-specific mutagenesis platform, using DNAWorks 3.1 program to design and optimize primers. Then, we sequenced the PCR products, inserted the correct gene into expression vector pPICZαA and transformed the linearized pPICZαA-Cip DNA into P. pastori GS115. We integrated CIP gene into the genome of P. pastori, using the α-mating factor from Sacchoramyces cerevisiae as signal peptide to direct the secretion of the recombinant protein. To obtain transformants with high CIP activity, we checked transformants by nested PCR and stained 82 positive ones on YPD agar plate with 1000 mg/L Zeocin. Then, we got 6 transforments with high resistance to Zeocin and expressed them in small scale; the one exhibiting the highest activity was chosen as engineered strain and named CIP/Gs115. RESULTS: We purified CIP from culture medium after induction with ethanol, the maximum activity reached 487.5 U/mL on the 4th day. The purified CIP exhibited maximal activity at pH 5.0 and 25 degrees C with ABTS as substrate. The enzyme had 61.5% of the maximal activity at 45 degrees C and was stable below 40 degrees C. However, the stability was drastically reduced above 45 degrees C. The recombinant CIP remained stable between pH 4.5 and 6.5. We studied the substrate specificity on different substrates with the purified enzyme, and the optimal substrates were in the order of ABTS > 2, 6-Dimethoxyphenol > guaiacol > 2, 4-Dichlorophenol > phenol. CONCLUSION: The highly secretory expression of CIP and high special activity lay the good foundation for it' s industrial applications in waste water treatment, decolouration of dyestuffs.