Phase angle is a predictor for postoperative complications in colorectal cancer.

Aim: The aim of this study was to develop a validated nomogram to predict the risk of postoperative complications in colorectal cancer (CRC) patients by analyzing the factors that contribute to these complications. Methods: We retrospectively collected clinical information on patients who underwent CRC surgery at a single clinical center from January 2021 to December 2021. Univariate and multivariate logistic regression analysis to identify independent risk factors for postoperative complications and to develop a predictive model. A receiver operating characteristic (ROC) curve was used to calculate the area under the curve (AUC) to assess the predicted probability. Calibration curve was drawn to compare the predicted probability of the nomogram with the actual probability, and decision curve analysis (DCA) was employed to evaluate the clinical utility of the nomogram. Results: A total of 190 CRC patients were included in this study. We retrospectively collected baseline information, clinical information, surgical information, and nutrition-related indicators for all patients. Through multivariate logistic regression analysis, preoperative albumin (p = 0.041, OR = 0.906, 95% CI = 0.824-0.996), surgical time (p = 0.009, OR = 1.006, 95% CI = 1.001-1.010), waistline (p = 0.049, OR = 1.011, 95% CI = 1.002-1.020) and phase angle (PA) (p = 0.022, OR = 0.615, 95% CI = 0.405-0.933) were identified as independent risk factors for postoperative complications in CRC, and a nomogram prediction model was established using the above four variables. The AUC of 0.706 for the ROC plot and the high agreement between predicted and actual probabilities in the calibration curves suggested that the prediction model has good predictive power. The DCA also confirmed the good clinical performance of the nomogram. Conclusion: This study developed a nomogram to predict the risk of postoperative complications in CRC patients, providing surgeons with a reliable reference to personalized patient management in the perioperative period and preoperative nutritional interventions.

The relationship between the collagen score at the anastomotic site of esophageal squamous cell carcinoma and anastomotic leakage.

Background: Anastomotic leakage (AL) has always been one of the most serious complications of esophagectomy with gastric conduit reconstruction. There are many strong risk factors for AL in clinical practice. Notably, the tension at the esophagogastric anastomosis and the blood supply to the gastric conduit directly affect the integrity of the anastomosis. However, there has been a lack of quantitative research on the tension and blood supply of the gastric conduit. Changes in extracellular matrix collagen reflect tension and blood supply, which affect the quality of the anastomosis. This study aimed to establish a quantitative collagen score to describe changes in the collagen structure in the extracellular matrix and to identify patients at high risk of postoperative AL. Methods: A retrospective study of 213 patients was conducted. Clinical and pathological data were collected at baseline. Optical imaging of the "donut" specimen at the anastomotic gastric end and collagen feature extraction were performed. Least absolute shrinkage and selection operator (LASSO) regression models were used to select the significant collagen features, compute collagen scores, and validate the predictive efficacy of the collagen scores for ALs. Results: LASSO regression analysis revealed three collagen-related parameters in the gastric donuts: histogram mean, histogram variance, and histogram energy. Based on this analysis, we established a formula to calculate the collagen score. The results of the univariate analysis revealed significant differences in the preoperative low albumin values (P=0.002) and collagen scores between the AL and non-AL groups (P=0.001), while the results of the multivariate analysis revealed significant differences in the collagen scores between the AL and non-AL groups (P=0.002). The areas under the curve (AUCs) of the experimental and validation cohorts were 0.978 [95% confidence interval (CI): 0.931-0.996] and 0.900 (95% CI: 0.824-0.951), respectively. Conclusions: The collagen score established herein was shown to be related to AL and can be used to predict AL in patients who underwent esophagectomy.

Predicting the Efficacy of Novel Synthetic Compounds in the Treatment of Osteosarcoma via Anti-Receptor Activator of Nuclear Factor-κB Ligand (RANKL)/Receptor Activator of Nuclear Factor-κB (RANK) Targets.

BACKGROUND: Osteosarcoma (OS) currently demonstrates a rising incidence, ranking as the predominant primary malignant tumor in the adolescent demographic. Notwithstanding this trend, the pharmaceutical landscape lacks therapeutic agents that deliver satisfactory efficacy against OS. OBJECTIVE: This study aimed to authenticate the outcomes of prior research employing the HM and GEP algorithms, endeavoring to expedite the formulation of efficacious therapeutics for osteosarcoma. METHODS: A robust quantitative constitutive relationship model was engineered to prognosticate the IC50 values of innovative synthetic compounds, harnessing the power of gene expression programming. A total of 39 natural products underwent optimization via heuristic methodologies within the CODESSA software, resulting in the establishment of a linear model. Subsequent to this phase, a mere quintet of descriptors was curated for the generation of non-linear models through gene expression programming. RESULTS: The squared correlation coefficients and s2 values derived from the heuristics stood at 0.5516 and 0.0195, respectively. Gene expression programming yielded squared correlation coefficients and mean square errors for the training set at 0.78 and 0.0085, respectively. For the test set, these values were determined to be 0.71 and 0.0121, respectively. The s2 of the heuristics for the training set was discerned to be 0.0085. CONCLUSION: The analytic scrutiny of both algorithms underscores their commendable reliability in forecasting the efficacy of nascent compounds. A juxtaposition based on correlation coefficients elucidates that the GEP algorithm exhibits superior predictive prowess relative to the HM algorithm for novel synthetic compounds.

Chondromyxoid fibroma of the lumbar facet joint: A case report.

Chondromyxoid fibroma (CMF) is a rarely documented benign osseous neoplasm, particularly with respect to its incidence in the lumbar spinal region. CMF predominantly manifests in vertebral bodies, exhibiting atypical emergence in ancillary anatomical sites. The present report describes, to the best of our knowledge, the second documented instance of CMF originating from the lumbar facet joint. The present case provides an example of CMF in the lumbar facet joint precipitating spinal canal stenosis, thereby engendering neurological manifestations in the lower extremities due to neoplastic proliferation through the intervertebral foramen. The present therapeutic intervention entailed surgical excision of the neoplasm concomitant with facet joint arthrodesis, with the objective of achieving comprehensive neoplasm eradication, ameliorating the symptomatology and safeguarding the spinal structural integrity of the patient. The present study aimed to illustrate the clinical implications of this rare neoplasm, thereby elucidating the diagnostic quandaries and therapeutic complexities associated with CMF in the lumbar facet joint. In addition, the present study aimed to augment the existing knowledge for the diagnosis and clinical management of CMF.

Non-contrast computed tomography-based radiomics for staging of connective tissue disease-associated interstitial lung disease.

Rationale and introduction: It is of significance to assess the severity and predict the mortality of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). In this double-center retrospective study, we developed and validated a radiomics nomogram for clinical management by using the ILD-GAP (gender, age, and pulmonary physiology) index system. Materials and methods: Patients with CTD-ILD were staged using the ILD-GAP index system. A clinical factor model was built by demographics and CT features, and a radiomics signature was developed using radiomics features extracted from CT images. Combined with the radiomics signature and independent clinical factors, a radiomics nomogram was constructed and evaluated by the area under the curve (AUC) from receiver operating characteristic (ROC) analyses. The models were externally validated in dataset 2 to evaluate the model generalization ability using ROC analysis. Results: A total of 245 patients from two clinical centers (dataset 1, n = 202; dataset 2, n = 43) were screened. Pack-years of smoking, traction bronchiectasis, and nine radiomics features were used to build the radiomics nomogram, which showed favorable calibration and discrimination in the training cohort {AUC, 0.887 [95% confidence interval (CI): 0.827-0.940]}, the internal validation cohort [AUC, 0.885 (95% CI: 0.816-0.922)], and the external validation cohort [AUC, 0.85 (95% CI: 0.720-0.919)]. Decision curve analysis demonstrated that the nomogram outperformed the clinical factor model and radiomics signature in terms of clinical usefulness. Conclusion: The CT-based radiomics nomogram showed favorable efficacy in predicting individual ILD-GAP stages.

HNRNPC mediated m6A methylation of 5-methyltetrahydrofolate-homocysteine methyltransferase and involved in the occurrence of RSA.

N6-methyladenosine methylated modification has been shown to play roles in recurrent spontaneous abortion. We aimed to explore role of heterogeneous nuclear ribonucleoprotein C in the occurrence of recurrent spontaneous abortion. We collected embryonic villous tissues from 3 patients with recurrent spontaneous abortion (RSA group) and 3 normal control pregnancy patients. Methylated RNA immunoprecipitation sequencing, RNA sequencing, methylated RNA immunoprecipitation quantitative PCR were conducted to detect the differentially expressed m6A methylation modification gene and regulatory gene in patients with recurrent spontaneous abortion. Methylated RNA immunoprecipitation sequencing and RNA sequencing results showed that the mRNA expression level of heterogeneous nuclear ribonucleoprotein C significantly decreased in RSA group and mRNA expression level of 5-methyltetrahydrofolate-homocysteine methyltransferase increased. Real-time quantitative PCR confirmed the differential expression of heterogeneous nuclear ribonucleoprotein C and 5-methyltetrahydrofolate-homocysteine methyltransferase. Methylated RNA immunoprecipitation quantitative PCR result showed that mRNA m6A modification level of 5-methyltetrahydrofolate-homocysteine methyltransferase decreased in RSA group. The results of western blotting, real-time quantitative PCR, immunofluorescence, matrigel invasion and wound healing assays indicated that heterogeneous nuclear ribonucleoprotein C might regulate the expression of 5-methyltetrahydrofolate-homocysteine methyltransferase by mediating m6A modification, thereby reducing the proliferation and migration of trophoblast cell line, ultimately leading to the occurrence of recurrent spontaneous abortion.

Construction and evaluation of a novel set of 90 microhaplotypes for forensic applications using NGS technology.

Microhaplotypes (MHs), small sets of linked single nucleotide polymorphisms (SNPs), are becoming a valuable tool for paternity testing, personal identification and other different forensic purposes due to their advantages of both short tandem repeats (STRs) and SNPs. However, only a small part of MHs with small segments have been developed and reported so far. And the current population genetic data of MHs are still insufficient. MHs with small segments possess unique advantages in mixture deconvolution, degradation material identification, noninvasive prenatal paternity testing and even medical tumor diagnostic applications. In the present study, a set of 90 autosomal MHs whose PCR amplicon lengths are from 90-150 bp, of which 58 MHs are less than or equal to 100 bp are selected, and assembled into an amplification multiplex system optimized for Ion S5™ System for forensic application. Genetic diversity study of 90 MHs in the populations from different intercontinental regions shows that the polymorphism information content (PIC) values of 83 MHs are greater than 0.4 in populations from East Asia (EAS), and the average PIC value of 90 MHs is greater than 0.5. A total of EAS populations shows the highest cumulative match probability (CMP) and cumulative probability of exclusion (CPE) values in five intercontinental populations. The CMP and CPE values of 90 MHs in EAS are 1.1688 × 10-54 and 0.999999999998954. The informativeness for assignment (In) values of the 90 MHs are calculated based on data from five intercontinental populations, and the In values of 20 MHs have greater than 0.1, indicating that the 20 MHs are high effectiveness in distinguishing different intercontinental populations, which can be used as candidate ancestry informative markers. Further, we have studied the polymorphisms of the 90 MHs based on 224 unrelated individuals of Henan Han population, China, and obtained the frequency data of the 90 MHs. In the Henan Han population, the effective number of alleles (Ae) of the 90 MHs ranges from 1.7649 (MH45) to 3.9792 (MH50), and the Ae values of 10 MHs reach to 3.0; the Ae values of 80 MHs are greater than 2, and the average Ae value for these MHs is 2.422. The average expected heterozygosity, observed heterozygosity, PIC, matching probability, discrimination power and probability of exclusion values of 90 MHs in the Henan Han population are 0.5788, 0.5851, 0.5039, 0.2608, 0.7392 and 0.2806, respectively. The CMP value of 90 MHs in the study population is less than 10-54, and their CPE value reaches 0.999999999999999923. Moreover, the results of the depth of coverage, allele coverage ratio and noise level indicate that the 90 MH amplification system has well sequencing performance, and the sequencing results are reliable. The results indicate the 90 MHs show higher polymorphisms in the study population. The present panel can be well used in paternity testing and individual identification in the study population and even the populations from EAS.

The Effect of Bilirubin on Clinical Outcomes of Patients With Colorectal Cancer Surgery: A Ten-Year Volume Single-Center Retrospective Study.

The current study aimed to evaluate the effect of bilirubin on the outcomes of colorectal cancer (CRC) in patients who underwent radical CRC surgery. The levels of serum bilirubin, including total bilirubin (TBil), direct bilirubin (DBil) and indirect bilirubin (IBil), were divided into higher groups and lower groups according to the median. Multivariate logistic regression was performed to analyze the independent predictors for overall complications and major complications. For TBil, the hospitalization time of the higher TBil group was longer than that of the lower TBil group (p = 0.014 < 0.05). For DBil, the higher DBil group had longer operation times (p < 0.01), more intraoperative bleeding (p < 0.01), longer hospital stays (p < 0.01), and higher rates of overall complications (p < 0.01) and major complications (p = 0.021 < 0.05) than the lower DBil group. For the IBil group, blood loss during operation (p < 0.01) and hospital stays (p = 0.041 < 0.05) in the higher IBil group were lower than those in the lower IBil group. In terms of complications, we found that DBil was an independent predictor for overall complications (p < 0.01, OR = 1.036, 95% CI = 1.014-1.058) and major complications (p = 0.043, HR= 1.355, 95% CI= 1.009-1.820). Higher preoperative DBil increase the risk of complications after primary CRC surgery.

Corrigendum: Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma.

[This corrects the article DOI: 10.3389/fonc.2022.1016583.].

Development and validation of a CT-based radiomics model for differentiating pneumonia-like primary pulmonary lymphoma from infectious pneumonia: A multicenter study.

BACKGROUND: Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia. METHODS: In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter- and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared. RESULTS: A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94-0.99), 0.93 (95% CI: 0.85-0.98), and 0.94 (95% CI: 0.87-0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63-0.83), 0.72 (95% CI: 0.62-0.82), and 0.73 (95% CI: 0.62-0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model ( P <0.05). CONCLUSIONS: The CT-based radiomics model may provide an effective and non-invasive tool to differentiate pneumonia-like PPL from infectious pneumonia, which might provide assistance for clinicians in tailoring precise therapy.

The Impact of Serum Parameters Associated with Kidney Function on the Short-Term Outcomes and Prognosis of Colorectal Cancer Patients Undergoing Radical Surgery.

Purpose: The current study was designed to investigate the impact of blood urea nitrogen (BUN), serum uric acid (UA), and cystatin (CysC) on the short-term outcomes and prognosis of colorectal cancer (CRC) patients undergoing radical surgery. Methods: CRC patients who underwent radical resection were included from Jan 2011 to Jan 2020 in a single clinical centre. The short-term outcomes, overall survival (OS), and disease-free survival (DFS) were compared in different groups. A Cox regression analysis was conducted to identify independent risk factors for OS and DFS. Results: A total of 2047 CRC patients who underwent radical resection were included in the current study. Patients in the abnormal BUN group had a longer hospital stay (p=0.002) and more overall complications (p=0.001) than that of the normal BUN group. The abnormal CysC group had longer hospital stay (p < 0.01), more overall complications (p=p < 0.01), and more major complications (p=0.001) than the normal CysC group. Abnormal CysC was associated with worse OS and DFS for CRC patients in tumor stage I (p < 0.01). In Cox regression analysis, age (p < 0.01, HR = 1.041, 95% CI = 1.029-1.053), tumor stage (p < 0.01, HR = 2.134, 95% CI = 1.828-2.491), and overall complications (p=0.002, HR = 1.499, 95% CI = 1.166-1.928) were independent risk factors for OS. Similarly, age (p < 0.01, HR = 1.026, 95% CI = 1.016-1.037), tumor stage (p < 0.01, HR = 2.053, 95% CI = 1.788-2.357), and overall complications (p=0.002, HR = 1.440, 95% CI = 1.144-1.814) were independent risk factors for DFS. Conclusion: In conclusion, abnormal CysC was significantly associated with worse OS and DFS at TNM stage I, and abnormal CysC and BUN were related to more postoperative complications. However, preoperative BUN and UA in the serum might not affect OS and DFS for CRC patients who underwent radical resection.

The advanced lung cancer inflammation index is a prognostic factor for gastrointestinal cancer patients undergoing surgery: a systematic review and meta-analysis.

BACKGROUND: The advanced lung cancer inflammation index (ALI) is a comprehensive assessment indicator that can reflect inflammation and nutrition conditions. However, there are some controversies about whether ALI is an independent prognostic factor for gastrointestinal cancer patients undergoing surgical resection. Thus, we aimed to clarify its prognostic value and explore the potential mechanisms. METHODS: Four databases including PubMed, Embase, the Cochrane Library, and CNKI were used for searching eligible studies from inception to June 28, 2022. All gastrointestinal cancers, including colorectal cancer (CRC), gastric cancer (GC), esophageal cancer (EC), liver cancer, cholangiocarcinoma, and pancreatic cancer were enrolled for analysis. We focused on prognosis most in the current meta-analysis. Survival indicators, including overall survival (OS), disease-free survival (DFS), and cancer-special survival (CSS) were compared between the high ALI group and the low ALI group. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was submitted as a supplementary document. RESULTS: We finally included fourteen studies involving 5091 patients in this meta-analysis. After pooling the hazard ratios (HRs) and 95% confidence intervals (CIs), ALI was found to be an independent prognostic factor for both OS (HR = 2.09, I2 = 92%, 95% CI = 1.53 to 2.85, P < 0.01), DFS (HR = 1.48, I2 = 83%, 95% CI = 1.18 to 1.87, P < 0.01), and CSS (HR = 1.28, I2 = 1%, 95% CI = 1.02 to 1.60, P = 0.03) in gastrointestinal cancer. After subgroup analysis, we found that ALI was still closely related to OS for CRC (HR = 2.26, I2 = 93%, 95% CI = 1.53 to 3.32, P < 0.01) and GC (HR = 1.51, I2 = 40%, 95% CI = 1.13 to 2.04, P = 0.006) patients. As for DFS, ALI also has a predictive value on the prognosis of CRC (HR = 1.54, I2 = 85%, 95% CI = 1.14 to 2.07, P = 0.005) and GC (HR = 1.37, I2 = 0%, 95% CI = 1.09 to 1.73, P = 0.007) patients. CONCLUSION: ALI affected gastrointestinal cancer patients in terms of OS, DFS, and CSS. Meanwhile, ALI was a prognostic factor both for CRC and GC patients after subgroup analysis. Patients with low ALI had poorer prognoses. We recommended that surgeons should perform aggressive interventions in patients with low ALI before the operation.

MRI-based radiomics nomogram for differentiation of solitary metastasis and solitary primary tumor in the spine.

BACKGROUND: Differentiating between solitary spinal metastasis (SSM) and solitary primary spinal tumor (SPST) is essential for treatment decisions and prognosis. The aim of this study was to develop and validate an MRI-based radiomics nomogram for discriminating SSM from SPST. METHODS: One hundred and thirty-five patients with solitary spinal tumors were retrospectively studied and the data set was divided into two groups: a training set (n = 98) and a validation set (n = 37). Demographics and MRI characteristic features were evaluated to build a clinical factors model. Radiomics features were extracted from sagittal T1-weighted and fat-saturated T2-weighted images, and a radiomics signature model was constructed. A radiomics nomogram was established by combining radiomics features and significant clinical factors. The diagnostic performance of the three models was evaluated using receiver operator characteristic (ROC) curves on the training and validation sets. The Hosmer-Lemeshow test was performed to assess the calibration capability of radiomics nomogram, and we used decision curve analysis (DCA) to estimate the clinical usefulness. RESULTS: The age, signal, and boundaries were used to construct the clinical factors model. Twenty-six features from MR images were used to build the radiomics signature. The radiomics nomogram achieved good performance for differentiating SSM from SPST with an area under the curve (AUC) of 0.980 in the training set and an AUC of 0.924 in the validation set. The Hosmer-Lemeshow test and decision curve analysis demonstrated the radiomics nomogram outperformed the clinical factors model. CONCLUSIONS: A radiomics nomogram as a noninvasive diagnostic method, which combines radiomics features and clinical factors, is helpful in distinguishing between SSM and SPST.

Phosphoproteomics Reveals the Wolframin-Calcium Axis as an Important Pathogenic Signaling Node in Primary Aldosteronism.

BACKGROUND: Aldosterone-producing adenoma (APA) is a benign adrenal tumor with autonomous aldosterone production which causes hypertension and excess cardiovascular risk. Protein phosphorylation regulates aldosterone secretion from adrenal cortical cells, but how signaling networks are remodeled in APA remains unknown. METHODS: We performed an integrated proteomic and phosphoproteomic profiling of 15 APA and 10 matched nonfunctioning adrenocortical tumors (NFAT) based on the 4-dimensional label-free technique. We further validated our main findings in enlarged APA samples, mice, and adrenocortical cell line. RESULTS: The proteomic and phosphoproteomic profiling of APA and NFAT quantified 5989 proteins and 9011 phosphopeptides. We highlighted differentially expressed and phosphorylated proteins which modulated aldosterone synthesis and secretion from APA. As intracellular calcium is the central signal for aldosterone synthesis, our integrated calcium signaling network implicated wolframin in the control of calcium influx and CYP11B2 (aldosterone synthase) activation in APA (ratio of wolframin expression in APA to NFAT: 6.411, P<0.001). Among 97 APA cases for validation, a higher expression level of wolframin was associated with a higher plasma aldosterone concentration postcaptopril challenge test and a higher systolic blood pressure. In vitro, the secretion of aldosterone was enhanced by wolframin overexpression, while aldosterone secretion in response to potassium or angiotensin II was inhibited by the knockdown of wolframin. Further in vivo and in vitro data demonstrated the wolframin-calcium axis as an important regulator of CYP11B2 expression and aldosterone production. CONCLUSIONS: Wolframin is a regulatory protein in aldosterone hypersecretion. Remodeled calcium transportation and mitochondrial function are involved in wolframin-related aldosterone secretion.

Short-term outcomes after simultaneous gastrectomy plus cholecystectomy in gastric cancer: A pooling up analysis.

The purpose of this study was to evaluate the short-term outcomes after simultaneous gastrectomy plus cholecystectomy in gastric cancer patients. PUBMED, EMBASE, and the Cochrane Library were searched from inception to Apr 15, 2021. Short-term surgical outcomes were compared between the simultaneous gastrectomy plus cholecystectomy group and the gastrectomy only group. Five retrospective studies with 3,315 patients and 1 randomized controlled trial with 130 patients were included. There was no significant difference in age, sex, surgical methods, or reconstruction. In terms of short-term outcomes, no significance was found in postoperative complications (odds ratio, OR = 1.08, I 2 = 24%, 95% CI = 0.78-1.50, P = 0.65), postoperative biliary complications (OR = 0.98, I 2 = 0%, 95% CI = 0.43-2.25, P = 0.96), mortality (OR = 1.28, I 2 = 0%, 95% CI = 0.49-3.37, P = 0.61), and postoperative hospital stay (MD = -0.10, I 2 = 0%, 95% CI = -0.73-0.54, P = 0.77) between the two groups. Simultaneous gastrectomy plus cholecystectomy in gastric cancer patients is safe and does not increase the short-term outcomes.

CT-based radiomics nomogram for differentiating gastric hepatoid adenocarcinoma from gastric adenocarcinoma: a multicentre study.

BACKGROUND: To develop a CT-based radiomics nomogram for the high-precision preoperative differentiation of gastric hepatoid adenocarcinoma (GHAC) patients from gastric adenocarcinoma (GAC) patients. RESEARCH DESIGN AND METHODS: 108 patients with GHAC from 6 centers and 108 GAC patients matched by age, sex and T stage undergoing pathological examination were retrospectively reviewed. Patients from 5 centers were divided into two cohorts (training and internal validation) at a 7:3 ratio, the remaining patients were external test cohort. Venous-phase CT images were retrieved for tumor segmentation and feature extraction. A radiomics model was developed by the least absolute shrinkage and selection operator method. The nomogram was developed by clinical factors and the radiomics score. RESULTS: 1409 features were extracted and a radiomics model consisting of 19 features was developed, which showed a favorable performance in discriminating GHAC from GAC (AUCtraining cohort = 0.998, AUCinternal validation set = 0.942, AUCexternal test cohort = 0.731). The radiomics nomogram, including the radiomics score, AFP, and CA72_4, achieved good calibration and discrimination (AUCtraining cohort = 0.998, AUCinternal validation set = 0.954, AUCexternal test cohort = 0.909). CONCLUSIONS: The noninvasive CT-based nomogram, including radiomics score, AFP, and CA72_4, showed favorable predictive efficacy for differentiating GHAC from GAC and might be useful for clinical decision-making.

CT-based radiomics nomogram for differentiation of adrenal hyperplasia from lipid-poor adenoma: an exploratory study.

BACKGROUND: To establish and verify a radiomics nomogram for differentiating isolated micronodular adrenal hyperplasia (iMAD) from lipid-poor adenoma (LPA) based on computed tomography (CT)-extracted radiomic features. METHODS: A total of 148 patients with iMAD or LPA were divided into three cohorts: a training cohort (n = 72; 37 iMAD and 35 LPA), a validation cohort (n = 36; 22 iMAD and 14 LPA), and an external validation cohort (n = 40; 20 iMAD and 20 LPA). Radiomics features were extracted from contrast-enhanced and non-contrast CT images. The least absolute shrinkage and selection operator (LASSO) method was applied to develop a triphasic radiomics model and unenhanced radiomics model using reproducible radiomics features. A clinical model was constructed using certain laboratory variables and CT findings. Radiomics nomogram was established by selected radiomics signature and clinical factors. Nomogram performance was assessed by calibration curve, the areas under receiver operating characteristic curves (AUC), and decision curve analysis (DCA). RESULTS: Eleven and eight extracted features were finally selected to construct an unenhanced radiomics model and a triphasic radiomics model, respectively. There was no significant difference in AUC between the two models in the external validation cohort (0.838 vs. 0.843, p = 0.949). The radiomics nomogram inclusive of the unenhanced model, maximum diameter, and aldosterone showed the AUC of 0.951, 0.938, and 0.893 for the training, validation, and external validation cohorts, respectively. The nomogram showed good calibration, and the DCA demonstrated the superiority of the nomogram compared with the clinical factors model alone in terms of clinical usefulness. CONCLUSIONS: A radiomics nomogram based on unenhanced CT images and clinical variables showed favorable performance for distinguishing iMAD from LPA. In addition, an efficient unenhanced model can help avoid extra contrast-enhanced scanning and radiation risk.

The Effect of Metabolic Syndrome on Colorectal Cancer Prognosis after Primary Surgery.

PURPOSE: The purpose of this study was to explore whether metabolic syndrome (MetS) affects the prognosis of colorectal cancer (CRC) patients after primary surgery and to analyze the effect of the specific components of MetS on CRC prognosis. METHODS: The PubMed, Embase and Cochrane Library databases were searched from inception to July 29, 2021. Overall survival (OS) and disease-free survival (DFS) were compared between the MetS group and the non-MetS group. RESULTS: The studies included in the meta-analysis included 4773 patients. All seven studies compared OS between the two groups, and after pooling all hazard ratios (HRs), no significant difference was found between the MetS group and the non-MetS group (HR = 1.17, 95% CI = 0.91 to 1.49, P = 0.21). Four studies compared DFS between the MetS group and the non-MetS group after pooling all the HRs, and there was no difference between the MetS group and the non-MetS group (HR = 1.05, 95% CI = 0.74 to 1.49, P = 0.21). Among the specific components of MetS, high fasting plasma glucose levels (HR = 1.25, 95% CI = 1.00 to 1.58, P = 0.05) had a marginally significant association with poor OS. CONCLUSION: MetS may not affect the prognosis of CRC after primary surgery. However, high fasting plasma glucose levels might contribute to poor OS.

Two nomograms based on radiomics models using triphasic CT for differentiation of adrenal lipid-poor benign lesions and metastases in a cancer population: an exploratory study.

OBJECTIVES: To investigate the effectiveness of CT-based radiomics nomograms in differentiating adrenal lipid-poor benign lesions and metastases in a cancer population. METHODS: This retrospective study enrolled 178 patients with cancer history from three medical centres categorised as those with adrenal lipid-poor benign lesions or metastases. Patients were divided into training, validation, and external testing cohorts. Radiomics features were extracted from triphasic CT images (unenhanced, arterial, and venous) to establish three single-phase models and one triphasic radiomics model using logistic regression. Unenhanced and triphasic nomograms were established by incorporating significant clinico-radiological factors and radscores. The models were evaluated by the receiver operating characteristic curve, Delong's test, calibration curve, and decision curve. RESULTS: Lesion side, diameter, and enhancement ratio resulted as independent factors and were selected into nomograms. The areas under the curves (AUCs) of unenhanced and triphasic radiomics models in validation (0.878, 0.914, p = 0.381) and external testing cohorts (0.900, 0.893, p = 0.882) were similar and higher than arterial and venous models (validation: 0.842, 0.765; testing: 0.814, 0.806). Unenhanced and triphasic nomograms yielded similar AUCs in validation (0.903, 0.906, p = 0.955) and testing cohorts (0.928, 0.946, p = 0.528). The calibration curves showed good agreement and decision curves indicated satisfactory clinical benefits. CONCLUSION: Unenhanced and triphasic CT-based radiomics nomograms resulted as a useful tool to differentiate adrenal lipid-poor benign lesions from metastases in a cancer population. They exhibited similar predictive efficacies, indicating that enhanced examinations could be avoided in special populations. KEY POINTS: • All four radiomics models and two nomograms using triphasic CT images exhibited favourable performances in three cohorts to characterise the cancer population's adrenal benign lesions and metastases. • Unenhanced and triphasic radiomics models had similar predictive performances, outperforming arterial and venous models. • Unenhanced and triphasic nomograms also exhibited similar efficacies and good clinical benefits, indicating that contrast-enhanced examinations could be avoided when identifying adrenal benign lesions and metastases.

Nomogram Using CT Radiomics Features for Differentiation of Pneumonia-Type Invasive Mucinous Adenocarcinoma and Pneumonia: Multicenter Development and External Validation Study.

BACKGROUND. Pneumonia-type invasive mucinous adenocarcinoma (IMA) and pneumonia show overlapping chest CT features as well as overlapping clinical characteristics. OBJECTIVE. The purpose of our study was to develop and validate a nomogram combining clinical and CT-based radiomics features to differentiate pneumonia-type IMA and pneumonia. METHODS. This retrospective study included 314 patients (172 men, 142 women; mean age, 60.3 ± 14.5 [SD] years) from six hospitals who underwent noncontrast chest CT showing consolidation and were diagnosed with pneumonia-type IMA (n = 106) or pneumonia (n = 208). Patients from three hospitals formed a training set (n = 195) and a validation set (n = 50), and patients from the other three hospitals formed the external test set (n = 69). A model for predicting pneumonia-type IMA was built using clinical characteristics that were significant independent predictors of this diagnosis. Radiomics features were extracted from CT images by placing ROIs on areas of consolidation, and a radiomics signature of pneumonia-type IMA was constructed. A nomogram for predicting pneumonia-type IMA was constructed that combined features in the clinical model and the radiomics signature. Two cardiothoracic radiologists independently reviewed CT images in the external test set to diagnose pneumonia-type IMA. Diagnostic performance was compared among models and radiologists. Decision curve analysis (DCA) was performed. RESULTS. The clinical model included fever and family history of lung cancer. The radiomics signature included 15 radiomics features. DCA showed higher overall net benefit from the nomogram than from the clinical model. In the external test set, AUC was higher for the nomogram (0.85) than for the clinical model (0.71, p = .01), radiologist 1 (0.70, p = .04), and radiologist 2 (0.67, p = .01). In the external test set, the nomogram had sensitivity of 46.9%, specificity of 94.6%, and accuracy of 72.5%. CONCLUSION. The nomogram combining clinical variables and CT-based radiomics features outperformed the clinical model and two cardiothoracic radiologists in differentiating pneumonia-type IMA from pneumonia. CLINICAL IMPACT. The findings support potential clinical use of the nomogram for diagnosing pneumonia-type IMA in patients with consolidation on chest CT.