Imaging and histopathologic characteristics of typical pancreatic hamartoma: a case report and literature review.

Background: Pancreatic hamartoma, a rare benign non-neoplastic condition, presents challenges in differentiating from other pancreatic diseases due to its atypical imaging and unreliable biopsy results. In this study, we present a case of pancreatic hamartoma and conduct a comprehensive review of relevant literature to outline its characteristic features, aiming to underscore its clinical relevance and implications. Case presentation: A 63-year-old man presented with a pancreatic mass, discovered during evaluation of abdominal pain and distension. Laboratory tests were largely unremarkable. Ultrasound revealed a hypoechoic mass in the head of the pancreas. Subsequent computed tomography and magnetic resonance imaging demonstrated an inhomogeneous mass with a clear boundary in the uncinate process of the pancreas. Furthermore, a distinct delayed enhancement pattern was noted on imaging. Histopathological examination confirmed the diagnosis of pancreatic hamartoma. Conclusions: Preoperative diagnosis of pancreatic hamartoma remains challenging. Imaging modalities can play a crucial role in facilitating accurate diagnosis and potentially avoiding unnecessary surgical intervention in patients with this condition.

Diagnostic Performance of Multiparametric MRI for the Detection of suspected Prostate Cancer in Biopsy-Naive Patients: A Systematic Review and Meta-analysis.

RATIONALE AND OBJECTIVES: This meta-analysis aimed to assess the diagnostic accuracy of multiparametric MRI (mpMRI) in detecting suspected prostate cancer (PCa) in biopsy-naive men. MATERIALS AND METHODS: PubMed, Scopus, and the Cochrane Library databases were systematically searched for studies published from January 2013 to April 2024. Sixteen studies comprising 4973 patients met the inclusion criteria. Data were extracted to construct 2×2 contingency tables for sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). A random-effects model was used for pooled estimation, and subgroup analyses were conducted. Summary receiver operating characteristic (SROC) curves were generated to summarize overall diagnostic performance. RESULTS: The overall detection rate of PCa across studies was 57.3%. For detecting any PCa, mpMRI showed pooled sensitivity of 82% (95% CI, 80-83%) and specificity of 62% (95% CI, 60-64%), with positive likelihood ratio (LR) of 1.97 (95% CI, 1.71-2.26), negative LR of 0.28 (95% CI, 0.24-0.34), and diagnostic odds ratio (DOR) of 7.34 (95% CI, 5.60-9.63), and an area under the SROC curve of 0.81. For clinically significant PCa (csPCa), mpMRI had pooled sensitivity of 88% (95% CI, 87-90%) and specificity of 64% (95% CI, 63-66%), with positive LR of 2.49 (95% CI, 2.03-3.05), negative LR of 0.20 (95% CI, 0.16-0.25), DOR of 13.83 (95% CI, 9.14-20.9), and area under the curve of 0.90. CONCLUSION: This meta-analysis suggests that mpMRI is effective in detecting PCa in biopsy-naive patients, particularly for csPCa. It can help reduce unnecessary biopsies and lower the risk of missing clinically significant cases, thereby guiding informed biopsy decisions.

An integrated radiopathomics machine learning model to predict pathological response to preoperative chemotherapy in gastric cancer.

RATIONALE AND OBJECTIVES: Accurately predicting the pathological response to chemotherapy before treatment is important for selecting the appropriate treatment groups, formulating individualized treatment plans, and improving the survival rates of patients with gastric cancer (GC). METHODS: We retrospectively enrolled 151 patients diagnosed with GC who underwent preoperative chemotherapy and surgical resection at the Affiliated Hospital of Qingdao University between January 2015 and June 2023. Both pretreatment-enhanced computer technology images and whole slide images of pathological hematoxylin and eosin-stained sections were available for each patient. The image features were extracted and used to construct an ensemble radiopathomics machine learning model. In addition, a nomogram was developed by combining the imaging features and clinical characteristics. RESULTS: In total, 962 radiomics and 999 pathomics signatures were extracted from 106 patients in the training cohort. A fusion radiopathomics model was constructed using 13 radiomics and 5 pathomics signatures. The fusion model showed favorable performance compared to single-omics models, with an area under the curve (AUC) of 0.789 in the validation cohort. Moreover, a combined radiopathomics nomogram (RPN) was developed based on radiopathomics features and the Borrmann type, which is a classification method for advanced GC according to tumor growth pattern and gross morphology. The RPN showed superior predictive performance in the training (AUC 0.880) and validation cohorts (AUC 0.797). The decision curve analysis showed that RPN could provide favorable clinical benefits to patients with GC. CONCLUSIONS: RPN was able to predict the pathological response to preoperative chemotherapy with high accuracy, and therefore provides a novel tool for personalized treatment of GC.

Diagnostic performance of EfficientNetV2-S method for staging liver fibrosis based on multiparametric MRI.

Problem: Previous studies had confirmed that some deep learning models had high diagnostic performance in staging liver fibrosis. However, training efficiency of models predicting liver fibrosis need to be improved to achieve rapid diagnosis and precision medicine. Aim: The deep learning framework of EfficientNetV2-S was noted because of its faster training speed and better parameter efficiency compared with other models. Our study sought to develop noninvasive predictive models based on EfficientNetV2-S framework for staging liver fibrosis. Methods: Patients with chronic liver disease who underwent multi-parametric abdominal MRI were included in the retrospective study. Data augmentation methods including horizontal flip, vertical flip, perspective transformation and edge enhancement were applied to multi-parametric MR images to solve the data imbalance between different liver fibrosis groups. The EfficientNetV2-S models were used for the prediction of liver fibrosis stages F1-2, F1-3, F3, F4 and F3-4. We evaluated the diagnostic performance of our models in training, validation, and test sets by using receiver operating characteristic curve (ROC) analysis. Results: The total training time of EfficientNetV2-S was about 6 h. For differentiating of F1-2 vs F3, the accuracy, sensitivity and specificity of EfficientNetV2-S model were 96.2 %, 96.4 % and 96.0 % in the test set. The AUC in test set was 0.559. The accuracy, sensitivity and specificity were 82.1 %, 74.5 % and 89.6 % in the test set by using EfficientNetV2-S model to differentiate F1-2 vs F3-4, and the AUC in test set were 0.763. For differentiating F1-3 vs F4, the accuracy, sensitivity and specificity of EfficientNetV2-S model were 71.5 %, 73.4 % and 69.5 % in the test set. The AUC was 0.553 in test set. For differentiating F1-2 vs F4, the accuracy, sensitivity and specificity of our model were 84.3 %, 80.2 % and 88.3 % in the test set, and the AUC was 0.715, respectively. For differentiating F3 vs F4, the accuracy, sensitivity and specificity of EfficientNetV2-S model were 92.5 %, 89.1 % and 95.6 % in the test set, and the AUC was 0.696 in the test set. Conclusions: The EfficientNetV2-S models based on multi-parametric MRI had the feasibility for staging of liver fibrosis because they showed high training speed and diagnostic performance in our study.

Deep learning for colorectal cancer detection in contrast-enhanced CT without bowel preparation: a retrospective, multicentre study.

BACKGROUND: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists. METHODS: We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists' detection performance. FINDINGS: In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p < 0.0001; 94.9% vs 85.3%, p < 0.0001), and significantly improved the accuracy of radiologists (93.4% vs 86.0%, p < 0.0001; 93.6% vs 85.3%, p < 0.0001). In the real-world test set, the DL model delivered sensitivity comparable to that of radiologists who had been informed about clinical indications for most cancer cases (94.3% vs 96.2%, p > 0.99), and it detected 2 cases that had been missed by radiologists. INTERPRETATION: The developed DL model can accurately detect colorectal cancer and improve radiologists' detection performance, showing its potential as an effective computer-aided detection tool. FUNDING: This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).

Integrating Tumor and Nodal Radiomics to Predict the Response to Neoadjuvant Chemotherapy and Recurrence Risk for Locally Advanced Gastric Cancer.

AIMS: To develop and evaluate machine learning models using tumor and nodal radiomics features for predicting the response to neoadjuvant chemotherapy (NAC) and recurrence risk in locally advanced gastric cancer (LAGC). BACKGROUND: Early and accurate response prediction is vital to stratify LAGC patients and select proper candidates for NAC. OBJECTIVE: A total of 218 patients with LAGC undergoing NAC followed by gastrectomy were enrolled in our study and were randomly divided into a training cohort (n = 153) and a validation cohort (n = 65). METHODS: We extracted 1316 radiomics features from the volume of interest of the primary lesion and maximal lymph node on venous phase CT images. We built 3 radiomics signatures for distinguishing good responders and poor responders based on tumor radiomics (TR), nodal radiomics (NR), and a combination of the two (TNR), respectively. A nomogram was then developed by integrating the radiomics signature and clinical factors. Kaplan- Meier survival curves were used to evaluate the prognostic value of the nomogram. RESULTS: The TNR signature achieved improved predictive value, with AUCs of 0.755 and 0.744 in the training and validation cohorts. Our proposed nomogram model (TNRN) showed a good performance for GR prediction in the prediction efficacy, calibration ability, and clinical benefit, with AUCs of 0.779 and 0.732 in the training and validation cohorts, superior to the clinical model. Moreover, the TNRN could accurately classify the patients into high-risk and low-risk groups in both training and validation cohorts with regard to postoperative recurrence and metastasis. CONCLUSION: The TNRN performed well in identifying good responders and provided valuable information for predicting progression-free survival time (PFS) in patients with LAGC who underwent NAC.

Neurodevelopmental outcomes of preterm with necrotizing enterocolitis: a systematic review and meta-analysis.

While neonatal necrotising enterocolitis (NEC) is associated with high mortality rates in newborns, survivors can face long-term sequelae. However, the relationship between NEC and neurodevelopmental impairment (NDI) in preterm infants remains unclear. To explore the relationship between neonatal NEC and neurodevelopmental outcomes in preterm infants, we searched PubMed, EMBASE, and the Cochrane Library from their inception to February 2024 for relevant studies. Studies included were cohort or case-control studies reporting neurodevelopmental outcomes of NEC in preterm infants. Two independent investigators extracted data regarding brain damage and neurodevelopmental outcomes in these infants at a corrected age exceeding 12 months. Odds ratios (ORs) were pooled using a random effects model. We included 15 cohort studies and 18 case-control studies, encompassing 60,346 infants. Meta-analysis of unadjusted and adjusted ORs demonstrated a significant association between NEC and increased odds of NDI (OR 2.15, 95% CI 1.9-2.44; aOR 1.89, 95% CI 1.46-2.46). Regarding brain injury, pooled crude ORs indicated an association of NEC with severe intraventricular haemorrhage (IVH) (OR 1.42, 95% CI 1.06-1.92) and periventricular leucomalacia (PVL) (OR 2.55, 95% CI 1.76-3.69). When compared with conservatively treated NEC, surgical NEC potentially carries a higher risk of NDI (OR 1.78, 95% CI 1.09-2.93) and severe IVH (OR 1.57, 95% CI 1.20-2.06). However, the risk of PVL did not show a significant difference (OR 1.60, 95% CI 0.47-5.40). CONCLUSIONS:  Our meta-analysis provides evidence suggesting an association between NEC and NDI. Additionally, the severity of intestinal lesions appears to correlate with a higher risk of NDI. Further high-quality studies with comprehensive adjustments for potential confounding factors are required to definitively establish whether the association with NDI is causal. WHAT IS KNOWN: • NEC is a serious intestinal disease in the neonatal period with a high mortality rate, and surviving children may have digestive system sequelae. • Compared with non-NEC preterm infants, the reported incidences of brain injury and neurodevelopmental disorders in NEC preterm infants are not the same. WHAT IS NEW: • The risk of neonatal brain injury and neurodevelopmental disorders in preterm infants with NEC is higher than that in non-NEC infants, and the risk of NDI in surgical NEC infants is higher than that in the conservative treatment group. • NEC may increase the risk of motor, cognitive, language development delays, and attention deficits in children.

Improved deep learning for automatic localisation and segmentation of rectal cancer on T2-weighted MRI.

INTRODUCTION: The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians from heavy workloads and enhance working efficiency. This study aimed to compare the segmentation accuracy of a proposed model with the other three models and the inter-observer consistency. METHODS: A total of 65 patients with rectal cancer who underwent MRI examination were enrolled in our cohort and were randomly divided into a training cohort (n = 45) and a validation cohort (n = 20). Two experienced radiologists independently segmented rectal cancer lesions. A novel segmentation model (AttSEResUNet) was trained on T2WI based on ResUNet and attention mechanisms. The segmentation performance of the AttSEResUNet, U-Net, ResUNet and U-Net with Attention Gate (AttUNet) was compared, using Dice similarity coefficient (DSC), Hausdorff distance (HD), mean distance to agreement (MDA) and Jaccard index. The segmentation variability of automatic segmentation models and inter-observer was also evaluated. RESULTS: The AttSEResUNet with post-processing showed perfect lesion recognition rate (100%) and false recognition rate (0), and its evaluation metrics outperformed other three models for two independent readers (observer 1: DSC = 0.839 ± 0.112, HD = 9.55 ± 6.68, MDA = 0.556 ± 0.722, Jaccard index = 0.736 ± 0.150; observer 2: DSC = 0.856 ± 0.099, HD = 11.0 ± 10.1, MDA = 0.789 ± 1.07, Jaccard index = 0.673 ± 0.130). The segmentation performance of AttSEResUNet was comparable and similar to manual variability (DSC = 0.857 ± 0.115, HD = 10.0 ± 10.0, MDA = 0.704 ± 1.17, Jaccard index = 0.666 ± 0.139). CONCLUSION: Comparing with other three models, the proposed AttSEResUNet model was demonstrated as a more accurate model for contouring the rectal tumours in axial T2WI images, whose variability was similar to that of inter-observer.

Comparative metabolomics reveals complex metabolic shifts associated with nitrogen-induced color development in mature pepper fruit.

Pigments derived from red pepper fruits are widely used in food and cosmetics as natural colorants. Nitrogen (N) is a key nutrient affecting plant growth and metabolism; however, its regulation of color-related metabolites in pepper fruit has not been fully elucidated. This study analyzed the effects of N supply (0, 250, and 400 kg N ha-1) on the growth, fruit skin color, and targeted and non-target secondary metabolites of field-grown pepper fruits at the mature red stage. Overall, 16 carotenoids were detected, of which capsanthin, zeaxanthin, and capsorubin were the dominant ones. N application at 250 kg ha-1 dramatically increased contents of red pigment capsanthin, yellow-orange zeaxanthin and ß-carotene, with optimum fruit yield. A total of 290 secondary metabolites were detected and identified. The relative content of most flavonoids and phenolic acids was decreased with increasing N supply. Correlation analysis showed that color parameters were highly correlated with N application rates, carotenoids, flavonoids, phenolic acids, lignans, and coumarins. Collectively, N promoted carotenoid biosynthesis but downregulated phenylpropanoid and flavonoid biosynthesis, which together determined the spectrum of red color expression in pepper fruit. Our results provide a better understanding of the impact of N nutrition on pepper fruit color formation and related physiology, and identification of target metabolites for enhancement of nutritional quality and consumer appeal.

Staging liver fibrosis: comparison of radiomics model and fusion model based on multiparametric MRI in patients with chronic liver disease.

OBJECTIVES: To develop and compare radiomics model and fusion model based on multiple MR parameters for staging liver fibrosis in patients with chronic liver disease. MATERIALS AND METHODS: Patients with chronic liver disease who underwent multiparametric abdominal MRI were included in this retrospective study. Multiparametric MR images were imported into 3D-Slicer software for drawing bounding boxes on MR images. By using a 3D-Slicer extension of SlicerRadiomics, radiomics features were extracted from these MR images. The z-score normalization method was used for post-processing radiomics features. The least absolute shrinkage and selection operator method (LASSO) was performed for selecting significant radiomics features. The logistic regression analysis was used for building the radiomics model. A fusion model was built by integrating serum fibrosis biomarkers of aspartate transaminase-to-platelet ratio index (APRI) and the fibrosis-4 index (FIB-4) with radiomics signatures. RESULTS: In the training cohort, AUCs of radiomics and fusion model were 0.707-0.842 and 0.718-0.854 for differentiating different groups. In the testing cohort, AUCs were 0.514-0.724 and 0.609-0.728. For the training cohort, there was no significant difference of AUCs between radiomics and fusion model (p > 0.05). For the testing cohort, AUCs of fusion model were higher than those of radiomics model in differentiating F1-3 vs. F4 and F1-2 vs. F4 (p = 0.011 & 0.042). CONCLUSIONS: Radiomics model and fusion model based on multiparametric MRI exhibited the feasibility for staging liver fibrosis in patients with CLD, and APRI and FIB-4 could improve the diagnostic performance of radiomics model in differentiating F1-3 vs. F4 and F1-2 vs. F4.

Development and validation of multiparametric models based on computed tomography enterography to determine endoscopic activity and surgical risk in patients with Crohn's disease: A multi-center study.

Objective: To develop novel multiparametric models based on computed tomography enterography (CTE) scores to identify endoscopic activity and surgical risk in patients with Crohn's disease (CD). Methods: We analyzed 171 patients from 3 hospitals. Correlations between CTE outcomes and endoscopic scores were assessed using Spearman's rank correlation analysis. Predictive models for moderate to severe CD were developed, and receiver operating characteristic (ROC) curves were constructed to determine the area under the ROC curve (AUC). A combined nomogram based on CTE scores and clinical variables was also developed for predicting moderate to severe CD and surgery. Results: CTE scores were significantly correlated with endoscopy scores at the segment level. The global CTE score was an independent predictor of severe (HR = 1.231, 95% CI: 1.048-1.446, p = 0.012) and moderate-to-severe Simplified Endoscopic Scores for Crohn's Disease (SES-CD) (HR = 1.202, 95% CI: 1.090-1.325, p < 0.001). The nomogram integrating CTE and clinical data predicted moderate to severe SES-CD and severe SES-CD scores in the validation cohort with AUCs of 0.837 and 0.807, respectively. The CTE score (HR = 1.18; 95% CI: 1.103-1.262; p = 0.001) and SES-CD score (HR = 3.125, 95% CI: 1.542-6.33; p = 0.001) were independent prognostic factors for surgery-free survival. A prognostic nomogram incorporating CTE scores, SES-CD and C-reactive protein (CRP) accurately predicted the risk of surgery in patients with CD. Conclusion: The newly developed CTE score and multiparametric models displayed high accuracy in predicting moderate to severe CD and surgical risk for CD patients.

Pulmonary nodules segmentation based on domain adaptation.

With the development of deep learning, the methods based on transfer learning have promoted the progress of medical image segmentation. However, the domain shift and complex background information of medical images limit the further improvement of the segmentation accuracy. Domain adaptation can compensate for the sample shortage by learning important information from a similar source dataset. Therefore, a segmentation method based on adversarial domain adaptation with background mask (ADAB) is proposed in this paper. Firstly, two ADAB networks are built for the source and target data segmentation, respectively. Next, to extract the foreground features that are the input of the discriminators, the background masks are generated according to the region growth algorithm. Then, to update the parameters in the target network without being affected by the conflict between the distinguishing differences of the discriminator and the domain shift reduction of the adversarial domain adaptation, a gradient reversal layer propagation is embedded in the ADAB model for the target data. Finally, an enhanced boundaries loss is deduced to make the target network sensitive to the edge of the area to be segmented. The performance of the proposed method is evaluated in the segmentation of pulmonary nodules in computed tomography images. Experimental results show that the proposed approach has a potential prospect in medical image processing.

Temperature-Regulated Bidirectional Capillary Force Self-Assembly of Femtosecond Laser Printed Micropillars for Switchable Chiral Microstructures.

Bottom-up self-assembly is regarded as an alternative way to manufacture series of microstructures in many fields, especially chiral microstructures, which attract tremendous attention because of their optical micromanipulations and chiroptical spectroscopies. However, most of the self-assembled microstructures cannot be tuned after processing, which largely hinders their broad applications. Here, we demonstrate a promising manufacturing strategy for switchable microstructures by combining the flexibility of femtosecond laser printing induced capillary force self-assembly and the temperature-responsive characteristics of smart hydrogels. Through designing asymmetric cross-link density, the printed microarchitectures can be deformed in the opposite direction and assembled into switchable ordered microstructures driven by capillary forces under different temperatures. Finally, the assembled chiral microstructures with switchable opposite handedness are realized, which shows tunable vortical dichroism. The proposed strategy holds potential applications in the fields of chiral photonics, chiral sensing, and so on.

Acousto-optic reconfigurable filter based on vector mode fusion in dispersion-compensating fiber.

An acousto-optic reconfigurable filter (AORF) is proposed and demonstrated based on vector mode fusion in dispersion-compensating fiber (DCF). With multiple acoustic driving frequencies, the resonance peaks of different vector modes in the same scalar mode group can be effectively fused into a single peak, which is utilized to obtain arbitrary reconfiguration of the proposed filter. In the experiment, the bandwidth of the AORF can be electrically tuned from 5 nm to 18 nm with superposition of different driving frequencies. The multi-wavelength filtering is further demonstrated by increasing the interval of the multiple driving frequencies. The bandpass/band-rejection can also be electrically reconfigured by setting the combination of driving frequencies. The proposed AORF gains the feature of reconfigurable filtering types, fast and wide tunability, and zero frequency shift, which is advantageous for high-speed optical communication networks, tunable lasers, fast optical spectrum analyzing and microwave photonics signal processing.

Preoperative contrast-enhanced CT-based radiomics nomogram for differentiating benign and malignant primary retroperitoneal tumors.

OBJECTIVES: This study evaluated the ability of a preoperative contrast-enhanced CT (CECT)-based radiomics nomogram to differentiate benign and malignant primary retroperitoneal tumors (PRT). METHODS: Images and data from 340 patients with pathologically confirmed PRT were randomly placed into training (n = 239) and validation sets (n = 101). Two radiologists independently analyzed all CT images and made measurements. Key characteristics were identified through least absolute shrinkage selection combined with four machine-learning classifiers (support vector machine, generalized linear model, random forest, and artificial neural network back propagation) to create a radiomics signature. Demographic data and CECT characteristics were analyzed to formulate a clinico-radiological model. Independent clinical variables were merged with the best-performing radiomics signature to develop a radiomics nomogram. The discrimination capacity and clinical value of three models were quantified by the area under the receiver operating characteristics (AUC), accuracy, and decision curve analysis. RESULTS: The radiomics nomogram was able to consistently differentiate between benign and malignant PRT in the training and validation datasets, with AUCs of 0.923 and 0.907, respectively. Decision curve analysis manifested that the nomogram achieved higher clinical net benefits than did separate use of the radiomics signature and clinico-radiological model. CONCLUSIONS: The preoperative nomogram is valuable for differentiating between benign and malignant PRT; it can also aid in treatment planning. KEY POINTS: • A noninvasive and accurate preoperative determination of benign and malignant PRT is crucial to identifying suitable treatments and predicting disease prognosis. • Associating the radiomics signature with clinical factors facilitates differentiation of malignant from benign PRT with improved diagnostic efficacy (AUC) and accuracy from 0.772 to 0.907 and from 0.723 to 0.842, respectively, compared with the clinico-radiological model alone. • For some PRT with anatomically special locations and when biopsy is extremely difficult and risky, a radiomics nomogram may provide a promising preoperative alternative for distinguishing benignity and malignancy.

Optical Encryption in the Photonic Orbital Angular Momentum Dimension via Direct-Laser-Writing 3D Chiral Metahelices.

Vortex beams, which intrinsically possess optical orbital angular momentum (OAM), are considered as one of the promising chiral light waves for classical optical communications and quantum information processing. For a long time, it has been an expectation to utilize artificial three-dimensional (3D) chiral metamaterials to manipulate the transmission of vortex beams for practical optical display applications. Here, we demonstrate the concept of selective transmission management of vortex beams with opposite OAM modes assisted by the designed 3D chiral metahelices. Utilizing the integrated array of the metahelices, a series of optical operations, including display, hiding, and even encryption, can be realized by the parallel processing of multiple vortex beams. The results open up an intriguing route for metamaterial-dominated optical OAM processing, which fosters the development of photonic angular momentum engineering and high-security optical encryption.

High-performance Marangoni hydrogel rotors with asymmetric porosity and drag reduction profile.

Miniaturized rotors based on Marangoni effect have attracted great attentions due to their promising applications in propulsion and power generation. Despite intensive studies, the development of Marangoni rotors with high rotation output and fuel economy remains challenging. To address this challenge, we introduce an asymmetric porosity strategy to fabricate Marangoni rotor composed of thermoresponsive hydrogel and low surface tension anesthetic metabolite. Combining enhanced Marangoni propulsion of asymmetric porosity with drag reduction of well-designed profile, our rotor precedes previous studies in rotation output (~15 times) and fuel economy (~34% higher). Utilizing thermoresponsive hydrogel, the rotor realizes rapid refueling within 33 s. Moreover, iron-powder dopant further imparts the rotors with individual-specific locomotion in group under magnetic stimuli. Significantly, diverse functionalities including kinetic energy transmission, mini-generator and environmental remediation are demonstrated, which open new perspectives for designing miniaturized rotating machineries and inspire researchers in robotics, energy, and environment.

Robust Helical Dichroism on Microadditively Manufactured Copper Helices via Photonic Orbital Angular Momentum.

Three-dimensional chiral metallic metamaterials have already attracted extensive attention in the wide research fields of chiroptical responses. These artificial chiral micronanostructures, possessing strong chiroptical signals, show huge significance in next-generation photonic devices and chiroptical spectroscopy techniques. However, most of the existing chiral metallic metamaterials are designed for generating chiroptical signals dependent on photonic spin angular momentum (SAM). The chiral metallic metamaterials for generating strong chiroptical responses by photonic orbital angular momentum (OAM) remain unseen. In this work, we fabricate copper microhelices with opposite handedness by additively manufacturing and further examine their OAM-dominated chiroptical response: helical dichroism (HD). The chiral copper microhelices exhibit differential reflection to the opposite OAM states, resulting in a significant HD signal (∼50%). The origin of the HD can be theoretically explained by the difference in photocurrent distribution inside copper microhelices under opposite OAM states. Moreover, the additively manufactured copper microhelices possess an excellent microstructural stability under varying annealing temperatures for robust HD responses. Lower material cost and noble-metal-similar optical properties, accompanied with well thermal stability, render the copper microhelices promising metamaterials in advanced chiroptical spectroscopy and photonic OAM engineering.

An MRI-Based Radiomics Nomogram to Assess Recurrence Risk in Sinonasal Malignant Tumors.

BACKGROUND: Sinonasal malignant tumors (SNMTs) have a high recurrence risk, which is responsible for the poor prognosis of patients. Assessing recurrence risk in SNMT patients is a current problem. PURPOSE: To establish an MRI-based radiomics nomogram for assessing relapse risk in patients with SNMT. STUDY TYPE: Retrospective. POPULATION: A total of 143 patients with 68.5% females (development/validation set, 98/45 patients). FIELD STRENGTH/SEQUENCE: A 1.5-T and 3-T, fat-suppressed fast spin echo (FSE) T2-weighted imaging (FS-T2WI), FSE T1-weighted imaging (T1WI), and FSE contrast-enhanced T1WI (T1WI + C). ASSESSMENT: Three MRI sequences were used to manually delineate the region of interest. Three radiomics signatures (T1WI and FS-T2WI sequences, T1WI + C sequence, and three sequences combined) were built through dimensional reduction of high-dimensional features. The clinical model was built based on clinical and MRI features. The Ki-67-based and tumor-node-metastasis (TNM) model were established for comparison. The radiomics nomogram was built by combining the clinical model and best radiomics signature. The relapse-free survival analysis was used among 143 patients. STATISTICAL TESTS: The intraclass/interclass correlation coefficients, univariate/multivariate Cox regression analysis, least absolute shrinkage and selection operator Cox regression algorithm, concordance index (C index), area under the curve (AUC), integrated Brier score (IBS), DeLong test, Kaplan-Meier curve, log-rank test, optimal cutoff values. A P value < 0.05 was considered statistically significant. RESULTS: The T1 + C-based radiomics signature had best prognostic ability than the other two signatures (T1WI and FS-T2WI sequences, and three sequences combined). The radiomics nomogram had better prognostic ability and less error than the clinical model, Ki-67-based model, and TNM model (C index, 0.732; AUC, 0.765; IBS, 0.185 in the validation set). The cutoff values were 0.2 and 0.7 and then the cumulative risk rates were calculated. DATA CONCLUSION: A radiomics nomogram for assessing relapse risk in patients with SNMT may provide better prognostic ability than the clinical model, Ki-67-based model, and TNM model. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 5.