Adrenal Volume Quantitative Visualization Tool by Multiple Parameters and an nnU-Net Deep Learning Automatic Segmentation Model.

Abnormalities in adrenal gland size may be associated with various diseases. Monitoring the volume of adrenal gland can provide a quantitative imaging indicator for such conditions as adrenal hyperplasia, adrenal adenoma, and adrenal cortical adenocarcinoma. However, current adrenal gland segmentation models have notable limitations in sample selection and imaging parameters, particularly the need for more training on low-dose imaging parameters, which limits the generalization ability of the models, restricting their widespread application in routine clinical practice. We developed a fully automated adrenal gland volume quantification and visualization tool based on the no new U-Net (nnU-Net) for the automatic segmentation of deep learning models to address these issues. We established this tool by using a large dataset with multiple parameters, machine types, radiation doses, slice thicknesses, scanning modes, phases, and adrenal gland morphologies to achieve high accuracy and broad adaptability. The tool can meet clinical needs such as screening, monitoring, and preoperative visualization assistance for adrenal gland diseases. Experimental results demonstrate that our model achieves an overall dice coefficient of 0.88 on all images and 0.87 on low-dose CT scans. Compared to other deep learning models and nnU-Net model tools, our model exhibits higher accuracy and broader adaptability in adrenal gland segmentation.

Optimal debulking surgery in ovarian cancer patients: MRI may predict the necessity of rectosigmoid resection.

OBJECTIVES: To determine whether MRI can predict the necessity of rectosigmoid resection (RR) for optimal debulking surgery (ODS) in ovarian cancer (OC) patients and to compare the predictive accuracy of pre- and post-neoadjuvant chemotherapy (NACT) MRI. METHODS: The MRI of 82 OC were retrospectively analyzed, including six bowel signs (length, transverse axis, thickness, circumference, muscularis involvement, and submucosal edema) and four para-intestinal signs (vaginal, parametrial, ureteral, and sacro-recto-genital septum involvement). The parameters reflecting the degree of muscularis involvement were measured. Patients were divided into non-RR and RR groups based on the operation and postoperative outcomes. The independent predictors of the need for RR were identified by multivariate logistic regression analysis. RESULTS: Imaging for 82 patients was evaluated (67 without and 15 with NACT). Submucosal edema and muscularis involvement (OR 13.33 and 8.40, respectively) were independent predictors of the need for RR, with sensitivities of 83.3% and 94.4% and specificities of 93.9% and 81.6%, respectively. Among the parameters reflecting the degree of muscularis involvement, circumference ≥ 3/12 had the highest prediction accuracy, increasing the specificity from 81.6% for muscularis involvement only to 98.0%, with only a slight decrease in sensitivity (from 94.4% to 88.9%). The predictive sensitivities of pre-NACT and post-NACT MRI were 100.0% and 12.5%, respectively, and the specificities were 85.7% and 100.0%, respectively. CONCLUSIONS: MRI analysis of rectosigmoid muscularis involvement and its circumference can help predict the necessity of RR in OC patients, and pre-NACT MRI may be more suitable for evaluation. CRITICAL RELEVANCE STATEMENT: We analyzed preoperative pelvic MRI in OC patients. Our findings suggest that MRI has predictive potential for identifying patients who require RR to achieve ODS. KEY POINTS: The need for RR must be determined to optimize treatment for OC patients. Muscularis involvement circumference ≥ 3/12 could help predict RR. Pre-NACT MRI may be superior to post-NACT MRI in predicting RR.

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).

Preoperative CT-based radiomics and deep learning model for predicting risk stratification of gastric gastrointestinal stromal tumors.

BACKGROUND: Gastrointestinal stromal tumors (GISTs) are clinically heterogeneous with various malignant potential in different individuals. It is crucial to explore a reliable method for preoperative risk stratification of gastric GISTs noninvasively. PURPOSE: To establish and evaluate a machine learning model using the combination of computed tomography (CT) morphology, radiomics, and deep learning features to predict the risk stratification of primary gastric GISTs preoperatively. METHODS: The 193 gastric GISTs lesions were randomly divided into training set, validation set, and test set in a ratio of 6:2:2. The qualitative and quantitative CT morphological features were assessed by two radiologists. The tumors were segmented manually, and then radiomic features were extracted using PyRadiomics and the deep learning features were extracted using pre-trained Resnet50 from arterial phase and venous phase CT images, respectively. Pearson correlation analysis and recursive feature elimination were used for feature selection. Support vector machines were employed to build a classifier for predicting the risk stratification of GISTs. This study compared the performance of models using different pre-trained convolutional neural networks (CNNs) to extract deep features for classification, as well as the performance of modeling features from single-phase and dual-phase images. The arterial phase, venous phase and dual-phase machine learning models were built, respectively, and the morphological features were added to the dual-phase machine learning model to construct a combined model. Receiver operating characteristic (ROC) curves were used to evaluate the efficacy of each model. The clinical application value of the combined model was determined through the decision curve analysis (DCA) and the net reclassification index (NRI) was analyzed. RESULTS: The area under the curve (AUC) of the dual-phase machine learning model was 0.876, which was higher than that of the arterial phase model or venous phase model (0.813, 0.838, respectively). The combined model had best predictive performance than the above models with an AUC of 0.941 (95% CI: 0.887-0.974) (p = 0.012, Delong test). DCA demonstrated that the combined model had good clinical application value with an NRI of 0.575 (95% CI: 0.357-0.891). CONCLUSION: In this study, we established a combined model that incorporated dual-phase morphology, radiomics, and deep learning characteristics, which can be used to predict the preoperative risk stratification of gastric GISTs.

Measurements of peri-prostatic adipose tissue by MRI predict bone metastasis in patients with newly diagnosed prostate cancer.

Objectives: To investigate the role of MRI measurements of peri-prostatic adipose tissue (PPAT) in predicting bone metastasis (BM) in patients with newly diagnosed prostate cancer (PCa). Methods: We performed a retrospective study on 156 patients newly diagnosed with PCa by prostate biopsy between October 2010 and November 2022. Clinicopathologic characteristics were collected. Measurements including PPAT volume and prostate volume were calculated by MRI, and the normalized PPAT (PPAT volume/prostate volume) was computed. Independent predictors of BM were determined by univariate and multivariate logistic regression analysis, and a new nomogram was developed based on the predictors. Receiver operating characteristic (ROC) curves were used to estimate predictive performance. Results: PPAT and normalized PPAT were associated with BM (P<0.001). Normalized PPAT positively correlated with clinical T stage(cT), clinical N stage(cN), and Grading Groups(P<0.05). The results of ROC curves indicated that PPAT and normalized PPAT had promising predictive value for BM with the AUC of 0.684 and 0.775 respectively. Univariate and multivariate analysis revealed that high normalized PPAT, cN, and alkaline phosphatase(ALP) were independently predictors of BM. The nomogram was developed and the concordance index(C-index) was 0.856. Conclusions: Normalized PPAT is an independent predictor for BM among with cN, and ALP. Normalized PPAT may help predict BM in patients with newly diagnosed prostate cancer, thus providing adjunctive information for BM risk stratification and bone scan selection.

A computed tomography-based multitask deep learning model for predicting tumour stroma ratio and treatment outcomes in patients with colorectal cancer: a multicentre cohort study.

BACKGROUND: Tumour-stroma interactions, as indicated by tumour-stroma ratio (TSR), offer valuable prognostic stratification information. Current histological assessment of TSR is limited by tissue accessibility and spatial heterogeneity. The authors aimed to develop a multitask deep learning (MDL) model to noninvasively predict TSR and prognosis in colorectal cancer (CRC). MATERIALS AND METHODS: In this retrospective study including 2268 patients with resected CRC recruited from four centres, the authors developed an MDL model using preoperative computed tomography (CT) images for the simultaneous prediction of TSR and overall survival. Patients in the training cohort ( n =956) and internal validation cohort (IVC, n =240) were randomly selected from centre I. Patients in the external validation cohort 1 (EVC1, n =509), EVC2 ( n =203), and EVC3 ( n =360) were recruited from other three centres. Model performance was evaluated with respect to discrimination and calibration. Furthermore, the authors evaluated whether the model could predict the benefit from adjuvant chemotherapy. RESULTS: The MDL model demonstrated strong TSR discrimination, yielding areas under the receiver operating curves (AUCs) of 0.855 (95% CI, 0.800-0.910), 0.838 (95% CI, 0.802-0.874), and 0.857 (95% CI, 0.804-0.909) in the three validation cohorts, respectively. The MDL model was also able to predict overall survival and disease-free survival across all cohorts. In multivariable Cox analysis, the MDL score (MDLS) remained an independent prognostic factor after adjusting for clinicopathological variables (all P <0.05). For stage II and stage III disease, patients with a high MDLS benefited from adjuvant chemotherapy [hazard ratio (HR) 0.391 (95% CI, 0.230-0.666), P =0.0003; HR=0.467 (95% CI, 0.331-0.659), P <0.0001, respectively], whereas those with a low MDLS did not. CONCLUSION: The multitask DL model based on preoperative CT images effectively predicted TSR status and survival in CRC patients, offering valuable guidance for personalized treatment. Prospective studies are needed to confirm its potential to select patients who might benefit from chemotherapy.

Comparative severe dermatologic toxicities of immune checkpoint inhibitors in malignant melanoma: A systematic review and network meta-analysis.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have advanced the therapeutic landscape for malignant melanoma patients. However, they can cause permanent and irreversible dermatologic immune-related adverse events (irAEs) that may lead to interruption of ICI treatment or become life-threatening. To assess the risk of severe dermatologic irAEs (grade 3 or higher) among ICIs for advanced melanoma, we conducted a network meta-analysis (NMA). METHODS: Phase II/III randomized controlled clinical trials (RCTs) involving ICIs were retrieved from various databases, including PubMed, Embase, Cochrane Library, and Web of Science. These trials were published from the inception of databases to October 15, 2022. In addition, the risk of severe dermatologic irAEs associated with ICI types and doses was evaluated and compared by NMA. RESULTS: This study included 20 Phase II/III RCTs with a total of 10 575 patients. The results indicated that ICIs carry a higher risk of severe dermatologic irAEs compared to chemotherapy. Additionally, the combinational therapy of Nivolumab + Ipilimumab was associated with a higher risk than ICI monotherapy. Comparatively, the latest treatment option involving dual ICI therapy with Relatlimab + Nivolumab showed a lower toxicity risk, but higher than Ipilimumab alone. Lastly, Nivolumab, at a dose of 3 mg/kg every 2 weeks, was observed as the lowest-risk dosing regimen for severe dermatologic irAEs in patients with advanced melanoma. CONCLUSION: The findings suggest that Nivolumab (1 mg/kg) + Ipilimumab (3 mg/kg) administered every 3 weeks should be used cautiously in patients with advanced melanoma at high risk for dermatologic irAEs. While we recommend the preferred regimen of Nivolumab (dose = 3 mg/kg, every 2 weeks).

Outcomes of different parenchymal-sparing hepatectomies in patients with colorectal liver metastases and prognostic impact of peritumoral imaging features.

OBJECTIVES: Parenchymal-sparing hepatectomy (PSH) is recommended in patients with colorectal liver metastases (CRLM). Based on the principle of PSH, to investigate the impact of anatomical resection (AR) and non-anatomic resection (NAR) on the outcome of CRLM and to evaluate the potential prognostic impact of three peritumoral imaging features. METHODS: Fifty-six patients who had abdominal gadoxetic acid-enhanced magnetic resonance imaging (MRI) before CRLM surgery were included in this retrospective research. Peritumoral early enhancement, peritumoral hypointensity on hepatobiliary phase (HBP), and biliary dilatation to the CRLM at MRI were evaluated. Survival estimates were calculated using the Kaplan-Meier method, and multivariate analysis was conducted to identify independent predictors of liver recurrence-free survival (LRFS), recurrence-free survival (RFS) and overall survival (OS). RESULTS: NAR had a lower 3-year LRFS compared with AR (36.6% vs. 78.6%, p = 0.012). No significant differences were found in 3-year RFS (34.1% vs. 41.7%) and OS (61.7% vs. 81.3%) (p > 0.05). In NAR group, peritumoral early enhancement was associated with poor LRFS (p = < 0.001, hazard ratio [HR] = 6.260; 95% confidence interval [CI], 2.322,16.876]) and poor RFS (p = 0.035, HR =2.516; 95% CI, 1.069,5.919). No independent predictors of CRLM were identified in the AR group. CONCLUSIONS: In patients with CRLM, peritumoral early enhancement was a predictor of LRFS and RFS after NAR according to the principle of PSH.

Paeoniflorin inhibits colorectal cancer cell stemness through the miR-3194-5p/catenin beta-interacting protein 1 axis.

Paeoniflorin (PF) is a natural plant ingredient with remarkable antitumor effects. Herein, we investigated the biological effects and mechanism of PF in colorectal cancer (CRC) cell stemness. The messenger RNA (mRNA) and protein expressions were assessed using quantitative real-time polymerase chain reaction and western blot. The viability, proliferation, and migration and invasion of CRC cells were evaluated using cell counting kit-8, clone-formation, and transwell migration and invasion assays, respectively. The sphere-formation capacity was determined using the sphere-formation assay. A dual-luciferase reporter gene assay was employed to analyze the interaction between miR-3194-5p and catenin beta-interacting protein 1 (CTNNBIP1). The viability, migration, invasion, epithelial-mesenchymal transition, and stemness of CRC cells were repressed by PF. MiR-3194-5p was upregulated in CRC tissues and cells. MiR-3194-5p knockdown suppressed CRC cell stemness, while miR-3194-5p overexpression had the opposite effect. In addition, the inhibition of CRC cell stemness caused by PF was eliminated by miR-3194-5p overexpression. CTNNBIP1 functioned as the target of miR-3194-5p, whose knockdown abrogated the repression of CRC cell stemness and Wnt/ß-catenin signaling activation by PF.PF regulated the miR-3194-5p/CTNNBIP1/Wnt/ß-catenin axis to repress CRC cell stemness.

Desmoplastic Reaction Associates with Prognosis and Adjuvant Chemotherapy Response in Colorectal Cancer: A Multicenter Retrospective Study.

Desmoplastic reaction (DR) is one of many tumor-host interactions and is associated with the overall survival (OS) of patients with colorectal cancer. However, the clinical significance of DR requires further study in large multicenter cohorts and its predictive value in adjuvant chemotherapy (ACT) response remains unclear. Here, a total of 2,225 patients with colorectal cancer from five independent institutions were divided into primary (N = 1,012 from two centers) and validation (N = 1,213 from three centers) cohorts. DR was classified as immature, middle, or mature depending on the presence of myxoid stroma and hyalinized collagen bundles at the invasive front of the primary tumor. OS among different subgroups were compared, and the correlations of DR type with tumor-infiltrating lymphocytes (TILs) within stroma, tumor stroma ratio (TSR), and Stroma AReactive Invasion Front Areas (SARIFA) were also analyzed. In the primary cohort, patients with mature DR had the highest 5-year survival rate. These findings were confirmed in validation cohort. In addition, for stage II colorectal cancer, patients classified as non-mature DR would benefit from ACT compared with surgery alone. Furthermore, immature and middle DR were more associated with high TSR, less distribution of TILs within stroma and positive SARIFA compared with mature. Taken together, these data suggest that DR is a robust-independent prognostic factor for patients with colorectal cancer. For patients with stage II colorectal cancer, non-mature DR could be a potential marker for recognizing high-risk patients who may benefit from ACT. Significance: DR has the potential to identify patients with high-risk colorectal cancer and predict the efficacy of adjuvant chemotherapy in patients with stage II colorectal cancer. Our findings support reporting DR types as additional pathologic parameters in clinical practice for more precise risk stratification.

A GP130-Targeting Small Molecule, LMT-28, Reduces LPS-Induced Bone Resorption around Implants in Diabetic Models by Inhibiting IL-6/GP130/JAK2/STAT3 Signaling.

In this study, we examined the effect of the GP130-targeting molecule, LMT-28, on lipopolysaccharide- (LPS-) induced bone resorption around implants in diabetic models using in vitro and rat animal experiments. First, LMT-28 was added to osteoblasts stimulated by LPS and advanced glycation end products (AGEs), and nuclear factor-κB receptor-activating factor ligand (RANKL) and associated pathways were evaluated. Then, LMT-28 was administered by gavage at 0.23 mg/kg once every 5 days for 2 weeks to type 2 diabetic rats with peri-implantitis induced by LPS injection and silk ligature. The expression of IL-6 and RANKL was evaluated by immunohistochemistry, and the bone resorption around implants was evaluated by microcomputed tomography. The results showed that LMT-28 downregulated the expression of RANKL through the JAK2/STAT3 signaling pathway in osteoblasts stimulated by LPS and AGEs, reduced bone resorption around implants with peri-implantitis, decreased the expression of IL-6 and RANKL, and decreased osteoclast activity in type 2 diabetic rats. This study confirmed the ability of LMT-28 to reduce LPS-induced bone resorption around implants in diabetic rats.

Non-invasive prediction of microsatellite instability in colorectal cancer by a genetic algorithm-enhanced artificial neural network-based CT radiomics signature.

OBJECTIVE: The stratification of microsatellite instability (MSI) status assists clinicians in making treatment decisions for colorectal cancer (CRC) patients. This study aimed to establish a CT-based radiomics signature to predict MSI status in patients with CRC. METHODS: A total of 837 CRC patients who underwent preoperative enhanced CT and had available MSI status data were recruited from two hospitals. Radiomics features were extracted from segmented tumours, and a series of data balancing and feature selection strategies were used to select MSI-related features. Finally, an MSI-related radiomics signature was constructed using a genetic algorithm-enhanced artificial neural network model. Combined and clinical models were constructed using multivariate logistic regression analyses by integrating the clinical factors with or without the signature. A Kaplan-Meier survival analysis was conducted to explore the prognostic information of the signature in patients with CRC. RESULTS: Ten features were selected to construct a signature which showed robust performance in both the internal and external validation cohorts, with areas under the curves (AUC) of 0.788 and 0.775, respectively. The performance of the signature was comparable to that of the combined model (AUCs of 0.777 and 0.767, respectively) and it outperformed the clinical model constituting age and tumour location (AUCs of 0.768 and 0.623, respectively). Survival analysis demonstrated that the signature could stratify patients with stage II CRC according to prognosis (HR: 0.402, p = 0.029). CONCLUSIONS: This study built a robust radiomics signature for identifying the MSI status of CRC patients, which may assist individualised treatment decisions. KEY POINTS: • Our well-designed modelling strategies helped overcome the problem of data imbalance caused by the low incidence of MSI. • Genetic algorithm-enhanced artificial neural network-based CT radiomics signature can effectively distinguish the MSI status of CRC patients. • Kaplan-Meier survival analysis demonstrated that our signature could significantly stratify stage II CRC patients into high- and low-risk groups.

Prevalence and Characteristics of Adrenal Tumors in an Unselected Screening Population : A Cross-Sectional Study.

BACKGROUND: With the widespread use of advanced imaging technology, adrenal tumors are increasingly being identified. OBJECTIVE: To investigate the prevalence and characteristics of adrenal tumors in an unselected screening population in China. DESIGN: Cross-sectional study. (ClinicalTrials.gov: NCT04682938). SETTING: A health examination center in China. PATIENTS: Adults having an annual checkup were invited to be screened for adrenal tumors by adrenal computed tomography. MEASUREMENTS: The participants with adrenal tumors had further evaluation for malignancy risk and adrenal function. RESULTS: A total of 25 356 participants were screened, 351 of whom were found to have adrenal tumors, for a prevalence of 1.4%. The prevalence increased with age, from 0.2% in participants aged 18 to 25 years to 3.2% in those older than 65 years. Among 351 participants with adrenal tumors, 337 were diagnosed with an adrenocortical adenoma, 14 with another benign nodule, and none with a malignant mass. In 212 participants with an adenoma who completed endocrine testing, 69.3% were diagnosed with a nonfunctioning adenoma, 18.9% with cortisol autonomy, 11.8% with primary aldosteronism, and none with pheochromocytoma. Proportions of nonfunctioning adenomas were similarly high in various age groups (72.2%, 67.8%, and 72.2% in those aged <46, 46 to 65, and ≥66 years, respectively). LIMITATION: Only 212 of 337 participants with an adrenocortical adenoma had endocrine testing. CONCLUSION: The prevalence of adrenal tumors in the general adult screening population is 1.4%, and most of these tumors are nonfunctioning regardless of patient age. Cortisol and aldosterone secretion are the main causes of functional adenomas. PRIMARY FUNDING SOURCE: National Key Research and Development Program of China and National Natural Science Foundation of China.

Preoperative serum CA19-9 should be routinely measured in the colorectal patients with preoperative normal serum CEA: a multicenter retrospective cohort study.

OBJECTIVE: Whether preoperative serum carbohydrate antigen 19-9 (CA19-9) is an independent prognostic factor and there are interactions of serum CA19-9 with carcinoembryonic antigen (CEA) on the risk of recurrence in colorectal cancer (CRC) patients are still not clarified. METHODS: Consecutive patients with CRC who underwent curative resection for stage II-III colorectal adenocarcinoma at five hospitals were collected. Based on Cox models, associations of preoperative CA19-9 with recurrence-free survival (RFS) and overall survival (OS) were evaluated in patients with or without elevated CEA, and interactions between CEA and CA19-9 were also calculated. Restricted cubic spline (RCS) curves were used to evaluate the associations between preoperative CA19-9 and CRC outcomes on a continuous scale. RESULTS: A total of 5048 patients (3029 [60.0%] men; median [interquartile range, IQR] age, 61.0 [51.0, 68.0] years; median [IQR] follow-up duration 46.8 [36.5-62.4] months) were included. The risk of recurrence increased with the elevated level of preoperative CA19-9, with the slope steeper in patients with normal CEA than those with elevated CEA. Worse RFS was observed for elevated preoperative CA19-9 (> 37 U/mL) (n = 738) versus normal preoperative CA19-9 (≤ 37 U/mL) (n = 4310) (3-year RFS rate: 59.4% versus 78.0%; unadjusted hazard ratio [HR]: 2.02; 95% confidence interval [CI]:1.79 to 2.28), and significant interaction was found between CA19-9 and CEA (P for interaction = 0.001). Increased risk and interaction with CEA were also observed for OS. In the Cox multivariable analysis, elevated CA19-9 was associated with shorter RFS and OS regardless of preoperative CEA level, even after adjustment for other prognostic factors (HR: 2.08, 95% CI:1.75 to 2.47; HR: 2.25, 95% CI:1.80 to 2.81). Subgroup analyses and sensitivity analyses yielded largely similar results. These associations were maintained in patients with stage II disease (n = 2724). CONCLUSIONS: Preoperative CA19-9 is an independent prognostic factor in CRC patients. Preoperative CA19-9 can be clinically used as a routine biomarker for CRC patients, especially with preoperative normal serum CEA.

Risperidone-induced neuroleptic malignant syndrome: a case report.

Neuroleptic malignant syndrome (NMS) is a rare illness that results from reactions to antipsychotics. However, the diagnosis of NMS is challenging due to its atypical clinical presentation and unclear pathogenesis. We report the case of a patient with NMS induced by irregular use of antipsychotics, especially risperidone (RSP). He had typical hyperthermia, muscle rigidity and rhabdomyolysis, which led to renal impairment. We carefully analysed the mechanism by which NMS occurred in this patient. An interesting aspect of the case is the synergistic involvement of risperidone, antidepressants, opioids and stress. Because of these complex predisposing factors, it is difficult to completely rule out the diagnosis of malignant hyperthermia (MH). In addition, the rare phenomenon of elevated lipase and amylase was observed in this patient.

Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage I-III colorectal cancer.

Objective: This study aimed to establish a method to predict the overall survival (OS) of patients with stage I-III colorectal cancer (CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification. Methods: We retrospectively identified 161 consecutive patients with stage I-III CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS prediction. Results: The EcoRad signature was significantly associated with OS in the training cohort [hazard ratio (HR)=6.670; 95% confidence interval (95% CI): 3.433-12.956; P<0.001), external validation cohort 1 (HR=2.866; 95% CI: 1.646-4.990; P<0.001) and external validation cohort 2 (HR=3.342; 95% CI: 1.289-8.663; P=0.002). Incorporating the EcoRad signature into the prediction model presented a higher prediction ability (P<0.001) with respect to the C-index (0.813, 95% CI: 0.804-0.822 in the training cohort; 0.758, 95% CI: 0.751-0.765 in the external validation cohort 1; and 0.746, 95% CI: 0.722-0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis (TNM) system, as well as a better calibration, improved reclassification and superior clinical usefulness. Conclusions: This study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively predict OS and could be used as a supplement for risk stratification among stage I-III CRC patients.

Rectal Signet Ring Cell Carcinoma: Post-Chemoradiotherapy Evaluation by MRI and Corresponding to Pathology.

Background: Signet ring cell carcinoma (SRCC) is recognized as an uncommon subtype of colorectal carcinoma (CRC). It showed characteristic magnetic resonance imaging (MRI) manifestations. However, the MRI features post-chemoradiotherapy (CRT) were not reported, and it is unknown whether the current tumor regression grade (TRG) system by MRI (mrTRG) is applicable to SRCC. Purpose: To summarize the image features of rectal SRCC on post-CRT images corresponding to the pathology, and to determine the predicting value of mrTRG compared with TRG by pathology (pTRG). Methods: We retrospectively enrolled seven patients (male: female = 3:4; mean age, 45.1 years) with biopsy-pathology proved SRCC, who underwent pre- and post-CRT MR imaging followed by surgery. An experienced gastrointestinal radiologist accessed mrTRG using a 5-point grading system by mandard standard on T2 weighted image (T2WI) and then added diffusion weighted image (DWI) in a 1-month interval. Additionally, MRI features were recorded on pre- and post-CRT images as follows: pattern (target sign) and main signal intensity of T2WI, characterized manifestation of DWI, and mean Apparent Diffusion Coefficient (ADC)values. The mrTRG and all MR image features were compared to the post-operative pathology. Results: At post-CRT histology, five patients got a good response (TRG 1, n = 4; TRG 2, n = 1), one patient got a partial response, and one patient got a poor response. The accuracy of MRI predicted the pathology response by mandard standard was 14% and increased to 71.4% when added DWI. After CRT, different degrees of homogeneous high SI without enhancement representing acellular mucin were observed in all patients, and the thick-ring high SI turned into a thin-target sign in most good responders. Moreover, the tumor volume decreased or slightly increased in good responders, while it markedly increased in the partial and poor responder by 57% and 73.8%, respectively. Conclusion: Homogeneous high SI on T2WI and thin target sigh on DWI were the main MRI changes of RSRCC, which was corresponding to the mucinous regression and represents for good response post-CRT. The mrTRG and tumor volume was not a reliable indicator to the pathology response. We considered that DWI should be added to T2WI to evaluate RSRCC response to CRT.

Analysis of the effect of NEKs on the prognosis of patients with non-small-cell lung carcinoma based on bioinformatics.

NEKs are proteins that are involved in various cell processes and play important roles in the formation and development of cancer. However, few studies have examined the role of NEKs in the development of non-small-cell lung carcinoma (NSCLC). To address this problem, the Oncomine, UALCAN, and the Human Protein Atlas databases were used to analyze differential NEK expression and its clinicopathological parameters, while the Kaplan-Meier, cBioPortal, GEPIA, and DAVID databases were used to analyze survival, gene mutations, similar genes, and biological enrichments. The rate of NEK family gene mutation was high (> 50%) in patients with NSCLC, in which NEK2/4/6/8/ was overexpressed and significantly correlated with tumor stage and nodal metastasis status. In addition, the high expression of NEK2/3mRNA was significantly associated with poor prognosis in patients with NSCLC, while high expression of NEK1/4/6/7/8/9/10/11mRNA was associated with good prognosis. In summary, these results suggest that NEK2/4/6/8 may be a potential prognostic biomarker for the survival of patients with NSCLC.

Airway epithelial cell-specific deletion of HMGB1 exaggerates inflammatory responses in mice with muco-obstructive airway disease.

High mobility group box 1 (HMGB1), a ubiquitous chromatin-binding protein required for gene transcription regulation, is released into the extracellular microenvironment by various structural and immune cells, where it is known to act as an alarmin. Here, we investigated the role of airway epithelium-specific HMGB1 in the pathogenesis of muco-obstructive lung disease in Scnn1b-transgenic (Tg+) mouse, a model of human cystic fibrosis (CF)-like lung disease. We hypothesized that airway epithelium-derived HMGB1 modulates muco-inflammatory lung responses in the Tg+ mice. The airway epithelium-specific HMGB1-deficient mice were generated and the effects of HMGB1 deletion on immune cell recruitment, airway epithelial cell composition, mucous cell metaplasia, and bacterial clearance were determined. The airway epithelium-specific deletion of HMGB1 in wild-type (WT) mice did not result in any morphological alterations in the airway epithelium. The deficiency of HMGB1 in airway epithelial cells in the Tg+ mice, however, resulted in significantly increased infiltration of macrophages, neutrophils, and eosinophils which was associated with significantly higher levels of inflammatory mediators, including G-CSF, KC, MIP-2, MCP-1, MIP-1α, MIP-1ß, IP-10, and TNF-α in the airspaces. Furthermore, as compared to the HMGB1-sufficient Tg+ mice, the airway epithelial cell-specific HMGB1-deficient Tg+ mice exhibited poor resolution of spontaneous bacterial infection. The HMGB1 deficiency in the airway epithelial cells of Tg+ mice did not alter airway epithelial cell-specific responses including epithelial cell proliferation, mucous cell metaplasia, and mucus obstruction. Collectively, our findings provide novel insights into the role of airway epithelial cell-derived HMGB1 in the pathogenesis of CF-like lung disease in Tg+ mice.

Continuous-Variable Quantum Key Distribution Based on Heralded Hybrid Linear Amplifier with a Local Local Oscillator.

An improved continuous variable quantum key distribution (CVQKD) approach based on a heralded hybrid linear amplifier (HLA) is proposed in this study, which includes an ideal deterministic linear amplifier and a probabilistic noiseless linear amplifier. The CVQKD, which is based on an amplifier, enhances the signal-to-noise ratio and provides for fine control between high gain and strong noise reduction. We focus on the impact of two types of optical amplifiers on system performance: phase sensitive amplifiers (PSA) and phase insensitive amplifiers (PIA). The results indicate that employing amplifiers, local local oscillation-based CVQKD systems can enhance key rates and communication distances. In addition, the PIA-based CVQKD system has a broader application than the PSA-based system.