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Abstract The detection of lymph node (LN) involvement is fundamental for staging colorectal cancer (CRC) and aids in clinical decision‐making. Traditionally, determining LN status predominantly relies heavily on histological examination of LN specimens, which can occasionally lead to overtreatment. This study aims to develop a clinical prediction model using machine learning algorithms to assess the risk of mesenteric LN metastasis preoperatively, based on computed tomography images and clinicopathological data from CRC patients. Our findings demonstrate that the predictive model based on XGBoost algorithms exhibited the optimal performance, with area under the curve values consistently stable across training (0.836, 95% confidence interval [CI]: 0.750–0.902) and validation (0.831, 95% CI: 0.688–0.927) cohorts. The model was further elucidated using SHapley Additive Explanation values, which ranked predictors in the XGBoost model by their importance, providing insights into the model's decision‐making process. Additionally, the force plot visualizes the contribution of each variable to the prediction for individual samples. The as‐obtained model may have the potential to aid in clinical treatment planning, optimize the selection of surgical methods, and guide the decision‐making process for adjuvant therapy before surgery.

Abstract The High‐Dynamic enhanced Loran (eLoran) signal simulator must accurately model the motion parameters and dynamic transmission delays of the carriers in the channel. A method was proposed based on B‐Spline interpolation, capable of arbitrary ratio interpolation and multi‐channel simulation. This method can regenerate carrier‐to‐transmitter distances from available discrete motion asynchronous data, it can effectively reproduce more motion details for carrier. The algorithm can be implemented using digital circuits, allowing for real‐time simulation on a hardware platform. The circuit structure is divided into integer and fractional calculations, which can satisfy the interpolation of asynchronous data with arbitrary ratios. Compared with existing methods, the approach simulates the motion parameters with satisfactory accuracy.

Abstract A model of the LOng‐RAnge Navigation signal can provide a mathematical model for the development of related technology research. The propagation variations of the signal exhibit non‐stationary and non‐linear characteristics due to the time‐varying nature of the atmospheric dielectric constant in the channel. A Dirichlet process mixtures method is used by the authors to model the statistical properties of radio wave attenuation, and the Gibbs sampling method is used to flexibly estimate the parameters of the model. The model as a non‐parametric model can be applied to capture the dynamics of time‐varying signals with the parameters being adaptively inferred. A receiver is used as a measurement device to obtain real propagation data to check the performance of the model. Kullback–Leibler Divergence (KL‐Div) was used to evaluate different datasets. The results showed that the model's statistical properties were consistent. Its flexibility allows it to represent complex distributions without prior knowledge.

The disordered growth, invasion and metastasis of cancer are mainly attributed to bidirectional cell-cell interactions. Extracellular vesicles (EVs) secreted by cancer cells are involved in orchestrating the formation of pre-metastatic niches (PMNs). Tumor-derived EVs mediate bidirectional communication between tumor and stromal cells in local and distant microenvironments. EVs carrying mRNAs, small RNAs, microRNAs, DNA fragments, proteins and metabolites determine metastatic organotropism, enhance angiogenesis, modulate stroma cell phenotypes, restructure the extracellular matrix, induce immunosuppression and modify the metabolic environment of organs. Evidence indicates that EVs educate stromal cells in secondary sites to establish metastasis-supportive microenvironments for seeding tumor cells. In this review, we provide a comprehensive overview of PMN formation and the underlying mechanisms mediated by EVs. Potential approaches to inhibit cancer metastasis by inhibiting the formation of PMNs are also presented.

Real-time and accurate temperature monitoring during microwave hyperthermia (MH) remains a critical challenge for ensuring treatment efficacy and patient safety. This study presents a novel approach to simulate real MH and precisely determine the temperature of the target region within biological tissues using a temporal-informed neural network. We conducted MH experiments on 30 sets of phantoms and 10 sets of ex vivo pork tissues. We proposed a novel perspective: the evolving tissue responses to continuous electromagnetic radiation stimulation are a joint evolution in temporal and spatial dimensions. Our model leverages TimesNet to extract periodic features and Cloblock to capture global information relevance in two-dimensional periodic vectors from ultrasound images. By assimilating more ultrasound temporal data, our model improves temperature-estimation accuracy. In the temperature range 25–65 °C, our neural network achieved temperature-estimation root mean squared errors of approximately 0.886 °C and 0.419 °C for fresh ex vivo pork tissue and phantoms, respectively. The proposed temporal-informed neural network has a modest parameter count, rendering it suitable for deployment on ultrasound mobile devices. Furthermore, it achieves temperature accuracy close to that prescribed by clinical standards, making it effective for non-destructive temperature monitoring during MH of biological tissues.

ObjectiveThis retrospective study aimed to evaluate the feasibility and safety of intraoperative assessment of anastomotic blood supply in patients undergoing esophagojejunostomy or esophagogastrostomy for gastric cancer using Indocyanine Green Fluorescence Imaging (IGFI).Materials and methodsFrom January 2019 to October 2021, we conducted a retrospective analysis of patients who had undergone laparoscopic gastrectomy for the treatment of gastric cancer. The patients were consecutively enrolled and categorized into two study groups: the Indocyanine Green Fluorescence Imaging (IGFI) group consisting of 86 patients, and the control group comprising 92 patients. In the IGFI group, intravenous administration of Indocyanine Green (ICG) was performed, and we utilized a fluorescence camera system to assess anastomotic blood supply both before and after the anastomosis.ResultsThe demographic characteristics of patients in both groups were found to be comparable. In the IGFI group, the mean time to observe perfusion fluorescence was 26.3 ± 12.0 seconds post-ICG injection, and six patients needed to select a more proximal resection point due to insufficient fluorescence at their initial site of choice. Notably, the IGFI group exhibited a lower incidence of postoperative anastomotic leakage, with no significant disparities observed in terms of pathological outcomes, postoperative recovery, or other postoperative complication rates when compared to the control group (p > 0.05).ConclusionThis study underscores the potential of IGFI as a dependable and pragmatic tool for the assessment of anastomotic blood supply following esophagojejunostomy or esophagogastrostomy for gastric cancer. The use of IGFI may potentially reduce the occurrence of postoperative anastomotic leakage.

PurposeTo investigate the effect of surgical resection on survival in gastrointestinal stromal tumors synchronous liver metastasis (GIST-SLM) and to develop clinically usable predictive models for overall survival (OS) and cancer-specific survival (CSS) in patients.MethodsWe identified patients in the SEER database diagnosed with GISTs from 2010 to 2019. We used propensity score matching (PSM) to balance the bias between the Surgery and No surgery groups. Kaplan-Meier(K-M) analysis was used to detect differences in OS and CSS between the two groups. The nomogram to predict 1, 3, and 5-year OS and CSS were developed and evaluated.ResultsAfter PSM, 228 patients were included in this study. There were significant differences in 1, 3, and 5-year OS and CSS between the two groups (OS: 93.5% vs. 84.4%, 73.2% vs. 55.3%, 60.9% vs. 36.9%, P=0.014; CSS: 3.5% vs.86.2%,75.3% vs.57.9%, 62.6% vs. 42.9%, P=0.02). We also found that patients who received surgery combined with targeted therapy had better OS and CSS at 1, 3, and 5 years than those who received surgery only (OS: 96.6% vs.90.9%, 74.9% vs. 56.8%, 61.7% vs. 35.5%, P=0.022; CSS: 96.6% vs. 92.1%, 77.4% vs.59.2%,63.8% vs. 42.0%, P=0.023). The area under the curve (AUC) was 0.774, 0.737, and 0.741 for 1, 3, and 5-year OS, respectively, with 0.782 and 0.742 for 1, 3, and 5-year CSS. In the model, C-index was 0.703 for OS and 0.705 for CSS and showed good consistency.ConclusionSurgical treatment can improve the OS and CSS of patients with GIST-SLM. In addition, the combination with chemotherapy may be more favorable for the long-term survival of patients. Meanwhile, we constructed the nomograms for predicting OS and CSS at 1, 3, and 5-year, and validated them internally. Our model can contribute to clinical management and treatment strategy optimization.

Abstract In this study, a real‐time high accuracy signal simulation technique for an enhanced Loran (eLoran) signal simulator is proposed, which can be used to verify the performance of the eLoran receiver. The proposed method is based on a maximumly flat design of the low‐pass filter, and high simulation accuracy can be obtained in the low‐frequency band. The simulator structure is based on the variable fractional delay Farrow structure, which can regenerate eLoran signals with high‐precision time of arrival (TOA) in real‐time based on discrete samples on a hardware platform. By comparison of existing methods and the true TOA data validation, the proposed method has high performance for low‐frequency narrow‐band eLoran signals and the proposed technology can apply to arbitrary TOA real‐time simulations.

Background: Pancreatic adenocarcinoma (PAAD) has a high degree of malignancy and a very poor prognosis, and the 5-year overall survival rate of patients is approximately 7%. To improve the prognosis of patients with PAAD, a more comprehensive and in-depth study of the pathogenesis of PAAD and the identification of new diagnostic markers and treatment targets are urgently needed. Increasing evidence supports that the small ubiquitin-like modifier (SUMO) family is closely related to the occurrence and development of a variety of cancers. However, the function of the SUMO family in PAAD is not clear, and related research is very scarce.Methods: R, Cytoscape, cBioPortal, and other software and online databases were used to comprehensively analyze the expression characteristics, prognostic value, and oncogenic mechanism of the SUMO family in PAAD.Results: SUMO family members are highly expressed in PAAD, and high expression of SUMO family members is significantly associated with poor clinicopathological features and poor prognosis in PAAD patients. In addition, SUMO family members are significantly coexpressed with M6A methylation regulators and various oncogenes and play an activating role in various oncogenic pathways, including EMT. Furthermore, it is worth noting that the close association between SUMO family members and TP53 mutation status and the negative regulatory effect of SUMO1/2 on PAAD immunity may represent the potential mechanism by which SUMO family members promote the development of PAAD. Moreover, the coexpression characteristics of SUMO family members and a variety of cancer-promoting immune checkpoint genes, as well as the positive correlation between SUMO4 expression level and the sensitivity of various targeted or chemotherapeutic drugs, including gemcitabine, paclitaxel, and doxorubicin, suggest future clinical directions of this study.Conclusion: The SUMO family is closely related to the occurrence and development of PAAD and can be used as a new biomarker and therapeutic target for patients with PAAD.

BackgroundThe risk factors for acute heart failure (AHF) vary, reducing the accuracy and convenience of AHF prediction. The most common causes of AHF are coronary heart disease (CHD). A short-term clinical predictive model is needed to predict the outcome of AHF, which can help guide early therapeutic intervention. This study aimed to develop a clinical predictive model for 1-year prognosis in CHD patients combined with AHF.Materials and methodsA retrospective analysis was performed on data of 692 patients CHD combined with AHF admitted between January 2020 and December 2020 at a single center. After systemic treatment, patients were discharged and followed up for 1-year for major adverse cardiovascular events (MACE). The clinical characteristics of all patients were collected. Patients were randomly divided into the training (n = 484) and validation cohort (n = 208). Step-wise regression using the Akaike information criterion was performed to select predictors associated with 1-year MACE prognosis. A clinical predictive model was constructed based on the selected predictors. The predictive performance and discriminative ability of the predictive model were determined using the area under the curve, calibration curve, and clinical usefulness.ResultsOn step-wise regression analysis of the training cohort, predictors for MACE of CHD patients combined with AHF were diabetes, NYHA ≥ 3, HF history, Hcy, Lp-PLA2, and NT-proBNP, which were incorporated into the predictive model. The AUC of the predictive model was 0.847 [95% confidence interval (CI): 0.811–0.882] in the training cohort and 0.839 (95% CI: 0.780–0.893) in the validation cohort. The calibration curve indicated good agreement between prediction by nomogram and actual observation. Decision curve analysis showed that the nomogram was clinically useful.ConclusionThe proposed clinical prediction model we have established is effective, which can accurately predict the occurrence of early MACE in CHD patients combined with AHF.

Background: Increasing evidence supports that the APOBEC family is associated with development of a variety of cancers. However, the function of APOBEC1/3A/3G/3H in pancreatic adenocarcinoma (PAAD) is still unclear.Methods: Comprehensive bioinformatic analysis using R (version 3.6.3), TISIDB, Metascape etc. were performed to study the clinicopathological characteristics, prognostic value, immune features and functional mechanisms of the APOBEC1/3A/3G/3H in PAAD.Results: APOBEC1/3A/3G/3H showed significantly elevated expression in PAAD than para-cancerous or normal tissues. Their high expression or amplification were significantly correlated with worse clinicopathological characteristics and prognosis in PAAD patients. In addition, the role of APOBEC1/3A/3G/3H in the immune regulation is diverse and complex, the high expression of APOBEC1 may inhibit the infiltration level of many kinds of immunoreactive tumor-infiltrating cells, which may be an important factor leading to immune escape of PAAD cells. Mechanistically, APOBEC1/3A/3G/3H played an activating role in multiple oncogenic pathways, including the EMT, RAS/MAPK and TSC/mTOR pathways. Moreover, we found that the expression level of APOBEC3G was positively correlated with the sensitivity of gemcitabine and doxorubicin.Conclusion: APOBEC1/3A/3G/3H play an oncogenic role in the development of PAAD and might serve as new biomarkers or therapeutic targets.

We aimed to determine the pattern of delay and its effect on the short-term outcomes of total gastrectomy before and during the coronavirus disease 2019 (COVID-19) pandemic. Overlaid line graphs were used to visualize the dynamic changes in the severity of the pandemic, number of gastric cancer patients, and waiting time for a total gastrectomy. We observed a slightly longer waiting time during the pandemic (median: 28.00 days, interquartile range: 22.00–34.75) than before the pandemic (median: 25.00 days, interquartile range: 18.00–34.00; p = 0.0071). Moreover, we study the effect of delayed surgery (waiting time > 30 days) on short-term outcomes using postoperative complications, extreme value of laboratory results, and postoperative stay. In patients who had longer waiting times, we did not observe worse short-term complication rates (grade II–IV: 15% vs. 19%, p = 0.27; grade III–IV: 7.3% vs. 9.2%, p = 0.51, the short waiting group vs. the prolonged waiting group) or a higher risk of a longer POD (univariable: OR 1.09, 95% CI 0.80–1.49, p = 0.59; multivariable: OR 1.10, 95% CI 0.78–1.55, p = 0.59). Patients in the short waiting group, rather than in the delayed surgery group, had an increased risk of bleeding in analyses of laboratory results (plasma prothrombin activity, hemoglobin, and hematocrit). A slightly prolonged preoperative waiting time during COVID-19 pandemic might not influence the short-term outcomes of patients who underwent total gastrectomy.

Abstract Structural variations (SVs) are the greatest source of variations in the genome and can lead to oncogenesis. However, the identification and interpretation of SVs in human cancer remain technologically challenging. Here, long‐read sequencing is first employed to depict the signatures of structural variations in carcinogenesis of human pancreatic ductal epithelium. Then widespread reprogramming of the 3D chromatin architecture is revealed by an in situ Hi‐C technique. Integrative analyses indicate that the distribution pattern of SVs among the 3D genome is highly cell‐type specific and the bulk remodeling effects of SVs in the chromatin organization partly depend on intercellular genomic heterogeneity. Meanwhile, contact domains tend to minimize these disrupting effects of SVs within local adjacent genomic regions to maintain overall stability. Notably, complex genomic rearrangements involving two key driver genes CDKN2A and SMAD4 are identified, and their influence on the expression of oncogenes MIR31HG, MYO5B, etc., are further elucidated from both a linear view and 3D perspective. Overall, this work provides a genome‐wide resource and highlights the impact, complexity, and dynamicity of the interplay between structural variations and high‐order chromatin organization, which expands the current understanding of the pathogenesis of SVs in human cancer.

BackgroundPatients with diabetes have an increased risk of developing vulnerable plaques (VPs), in which dyslipidemia and chronic inflammation play important roles. Non-high-density lipoprotein cholesterol (non-HDL-C) and neutrophil-lymphocyte ratio (NLR) have emerged as potential markers of both coronary artery VPs and cardiovascular prognosis. This study aimed to investigate the predictive value of non-HDL-C and NLR for coronary artery VPs in patients with type 2 diabetes mellitus (T2DM).MethodsWe retrospectively enrolled 204 patients with T2DM who underwent coronary computed tomography angiography between January 2018 and June 2020. Clinical data including age, sex, hypertension, smoking, total cholesterol, low-density lipoprotein cholesterol, HDL-C, triglyceride, non-HDL-C, glycated hemoglobin, neutrophil count, lymphocyte count, NLR, and platelet count were analyzed. Multivariate logistic regression was used to estimate the association between non-HDL-C, NLR, and coronary artery VPs. Receiver operating curve analysis was performed to evaluate the value of non-HDL-C, NLR, and their combination in predicting coronary artery VPs.ResultsIn our study, 67 patients (32.84%) were diagnosed with VPs, 75 (36.77%) with non-VP, and 62 (30.39%) with no plaque. Non-HDL-C and NLR were independent risk factors for coronary artery VPs in patients with T2DM. The areas under the ROC curve of non-HDL-C, NLR, and their combination were 0.748 [95% confidence interval (CI): 0.676–0.818], 0.729 (95% CI: 0.650–0.800), and 0.825 (95% CI: 0.757–0.887), respectively.ConclusionEither non-HDL-C or NLR could be used as a predictor of coronary artery VPs in patients with T2DM, but the predictive efficiency and sensitivity of their combination would be better.

The incidence of multiple primary carcinomas (MPCs), which are defined as two or more malignancies detected in an individual person, is gradually increasing around the world. According to the timing of diagnosis for each constituent tumor, MPCs are classified into 2 categories: synchronous MPCs if constituent tumors emerge simultaneously or within 6 months or metachronous MPCs otherwise. In this report, we describe our recent observation and treatment of a female patient with synchronous primary esophagogastric junction adenocarcinoma, duodenal adenocarcinoma and pancreatic ductal adenocarcinoma (PDAC). To the best of our knowledge, this combination has not yet been reported in the literature. A crucial aspect is the decision regarding which tumor to treat initially and how to schedule further treatments according to individual tumor hazards. Our multidisciplinary team devised an individualized treatment regimen for this patient. The patient ultimately achieved an overall survival time of 18 months, which was much longer than the median survival time (6~11 months) of patients with locally advanced pancreatic cancer. Moreover, treating this rare combination raised a series of diagnostic, etiological and therapeutic questions, motivating us to carry out a critical review of the literature. In summary, an individualized treatment strategy with input from a dedicated multidisciplinary team and consideration of all options at different points along the disease trajectory is essential to optimize outcomes for patients with MPC.

Background: The histone lysine demethylase KDM5 family is an important epigenetic state-modifying enzyme family. Increasing evidence supports that epigenetic abnormalities in the KDM5 family are related to multiple cancers in humans. However, the role of the KDM5 family in pancreatic cancer is not clear, and related research is very scarce.Methods: R software, Kaplan–Meier Plotter, cBioPortal, TIMER, LinkedOmics, STRING, Metascape, TISIDB, and the GSCA Lite online tool were utilized for bioinformatics analysis.Results: KDM5A/B/C was significantly overexpressed in many kinds of tumor tissues, including pancreatic adenocarcinoma (PAAD), while the expression of KDM5D was significantly downregulated. The high expression of KDM5A/B/C was related to poor clinical features, such as worse treatment efficacy, higher tumor grade, and more advanced clinical stage. Patients with a family history of breast cancer and melanoma, history of drinking or history chronic pancreatitis were more likely to have KDM5A/B/C gene abnormalities, which were related to a variety of adverse clinical features. The results of gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathway analyses of the KDM5 family and its 800 co-expressed genes showed that many gene terms related to cell proliferation, migration and many carcinogenic pathways. Notably, we found that the expression level of KDM5A/B/C was positively correlated with the expression of multiple key driver genes such as KRAS, BRCA1, and BRCA2 etc. In addition, PPI network analysis showed KDM5 family proteins have strong interactions with histone deacetylase family 1 (HDAC1), which could modify the lysines of histone H3, and co-act on many pathways, including the “longevity-regulating pathway” and “Notch signaling pathway”. Moreover, the upregulation of KDM5A/B/C expression was associated with an increase in the infiltration of B cells, CD8+ T cells and other infiltrating immune lymphocytes and the expression levels of immune molecules such as NT5E and CD274. Interestingly, the overexpression of KDM5A/C was also corelated with reduced sensitivity of pancreatic cancer cells to many kinds of pancreatic cancer-targeting or chemotherapeutic drugs, including axitinib and gemcitabine.Conclusion: KDM5 family members may be prognostic markers and new therapeutic targets for patients with pancreatic cancer.

Hepatocellular carcinoma (HCC) has poor prognosis and is usually diagnosed only at an advanced stage. Identification of novel biomarkers is critical to early diagnosis and better prognosis for HCC patients. N6-methyladenosine (m6A) RNA methylation regulators play important roles in the development of many tumors. However, the m6A writer complex, a key executor of m6A methylation modification, has not been independently investigated, and its specific bioinformatics analysis has not yet been performed in HCC. In this study, we used multiple public databases to evaluate the diagnostic, therapeutic, and prognostic value of the m6A writers in HCC. The results showed that expression levels of METTL3, VIRMA and CBLL1 were significantly increased, while expression levels of METTL14 and ZC3H13 were significantly decreased in HCC, which was closely related to clinicopathological factors, such as tumor stage and prognosis. Bioinformatics further explored the possible underlying mechanisms by which the m6A writer complex are involved in activation of tumor-promoting pathways and/or inhibition of tumor-suppressing pathways, including apoptosis, cell cycle, DNA damage response and EMT. Furthermore, we showed that the m6A writer complex is correlated with immune cell infiltration and immunoregulator expression in HCC. In conclusion, the m6A writer complex may represent a promising biomarker and target that can guide targeted therapy or immunotherapy for HCC patients.

Background: The triglyceride–glucose (TyG) index had been proposed as a reliable surrogate marker of insulin resistance. We aimed to evaluate the association between TyG index and myocardial fibrosis, which was quantified by extracellular volume (ECV) fraction using cardiovascular magnetic resonance (CMR) examination, and their prognostic value in patients with heart failure (HF).Methods: In this retrospective cohort study, 103 hospitalized HF patients were included. ECV fraction was calculated using CMR measurements and T1 mapping. TyG index was calculated using fasting triglyceride and blood glucose. The primary outcome events were defined as all-cause mortality and HF hospitalization during follow-up.Results: During the median follow-up of 12.3 months, 39 patients (37.9%) experienced primary outcome events and had higher levels of TyG index, N-terminal pro–B-type natriuretic peptide (NT-proBNP), and ECV fraction compared with those without events. Multivariate linear regression analysis showed that the TyG index was the significant factor determined for ECV fraction (rpartial = 0.36, P = 0.01). In multivariate Cox regression analysis, presence of diabetes [hazard ratio (HR) = 1.28, 95% confidence interval (CI) = 1.01–1.62], higher TyG index (HR = 2.01, 95% CI = 1.03–4.01), ECV fraction (HR = 1.73, 95% CI = 1.04–2.88), and NT-proBNP (HR = 2.13, 95% CI = 1.08–4.20) were independent risk factors for the primary outcome events.Conclusions: TyG index is a novel biomarker of myocardial fibrosis in HF patients and can be considered as a useful risk stratification metric in the management of HF.

Background: Existing prediction models for clinically relevant postoperative pancreatic fistula (POPF) after pancreatoduodenectomy (PD) lack discriminatory power or are too complex. This study aimed to develop a simple nomogram that could accurately predict clinically relevant POPF after PD. Methods: A high-volume, multicenter cohort of patients who underwent PD from the American College of Surgeons-National Surgical Quality Improvement Program database in the United States during 2014-2017 was used as the model training cohort (n =3609), and patients who underwent PD from the Pancreatic Center of the National Cancer Center Hospital in China during 2014-2019 were used as the external validation cohort (n= 1347). The study used lasso penalized regression to screen large-scale variables, then logistic regression was performed to screen the variables and build a model. Finally, a prediction nomogram for clinically relevant POPF was established based on the logistic model, and polynomial equations were extracted. The performance of the nomogram was evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis. Results: In the training and validation cohorts, there were 16.7% (601/3609) and 16.6% (224/1347) of patients who developed clinically relevant POPF, respectively. After screening using lasso and logistic regression, only six predictors were independently associated with clinically relevant POPF, including two preoperative indicators (weight and pancreatic duct size), one intraoperative indicator (pancreatic texture), and three postoperative indicators (deep surgical site infection, delayed gastric emptying, and pathology). The prediction of the new nomogram was accurate, with an area under the curve of 0.855 (95% CI: 0.702-0.853) in the external validation cohort, and the predictive performance was superior to three previously proposed POPF risk score models (all P <0.001, likelihood ratio test). Conclusions: A reliable lasso-logistic method was applied to establish a novel nomogram based on six readily available indicators, achieving a sustained, dynamic, and precise POPF prediction for PD patients. With a limited number of variables and easy clinical application, this new model will enable surgeons to proactively predict, identify, and manage pancreatic fistulas to obtain better outcomes from this daunting postoperative complication. [ABSTRACT FROM AUTHOR]