Stem cell-like circulating tumor cells identified by Pep@MNP and their clinical significance in pancreatic cancer metastasis.

Objective: The circulating tumor cells (CTCs) could be captured by the peptide functionalized magnetic nanoparticles (Pep@MNP) detection system in pancreatic ductal adenocarcinoma (PDAC). CTCs and the CXCR4 expression were detected to explore their clinical significance. The CXCR4+ CTCs, this is highly metastatic-prone stem cell-like subsets of CTCs (HM-CTCs), were found to be associated with the early recurrence and metastasis of PDAC. Methods: CTCs were captured by Pep@MNP. CTCs were identified via immunofluorescence with CD45, cytokeratin antibodies, and the CXCR4 positive CTCs were assigned to be HM-CTCs. Results: The over-expression of CXCR4 could promote the migration of pancreatic cancer cell in vitro and in vivo. In peripheral blood (PB), CTCs were detected positive in 79.0% of all patients (49/62, 9 (0-71)/2mL), among which 63.3% patients (31/49, 3 (0-23)/2mL) were HM-CTCs positive. In portal vein blood (PVB), CTCs were positive in 77.5% of patients (31/40, 10 (0-40)/2mL), and 67.7% of which (21/31, 4 (0-15)/2mL) were HM-CTCs positive CTCs enumeration could be used as diagnostic biomarker of pancreatic cancer (AUC = 0.862), and the combination of CTCs positive and CA19-9 increase shows improved diagnostic accuracy (AUC = 0.963). in addition, PVB HM-CTCs were more accurate to predict the early recurrence and liver metastasis than PB HM-CTCs (AUC 0.825 vs. 0.787 and 0.827 vs. 0.809, respectively). Conclusions: The CTCs identified by Pep@MNP detection system could be used as diagnostic and prognostic biomarkers of PDAC patients. We identified and defined the CXCR4 over-expressed CTC subpopulation as highly metastatic-prone CTCs, which was proved to identify patients who were prone to suffering from early recurrence and metastasis.

Preoperative and postoperative predictive models of early recurrence for colorectal liver metastases following chemotherapy and curative-intent one-stage hepatectomy.

INTRODUCTION: Accurate prediction of patients at risk for early recurrence (ER) among patients with colorectal liver metastases (CRLM) following preoperative chemotherapy and hepatectomy remains limited. METHODS: Patients with CRLM who received chemotherapy prior to undergoing curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. Multivariable Cox regression analysis was used to assess clinicopathological factors associated with ER, and an online calculator was developed and validated. RESULTS: Among 768 patients undergoing preoperative chemotherapy and curative-intent resection, 128 (16.7 %) patients had ER. Multivariable Cox analysis demonstrated that Eastern Cooperative Oncology Group Performance status ≥1 (HR 2.09, 95%CI 1.46-2.98), rectal cancer (HR 1.95, 95%CI 1.35-2.83), lymph node metastases (HR 2.39, 95%CI 1.60-3.56), mutated Kirsten rat sarcoma oncogene status (HR 1.95, 95%CI 1.25-3.02), increase in tumor burden score during chemotherapy (HR 1.51, 95%CI 1.03-2.24), and bilateral metastases (HR 1.94, 95%CI 1.35-2.79) were independent predictors of ER in the preoperative setting. In the postoperative model, in addition to the aforementioned factors, tumor regression grade was associated with higher hazards of ER (HR 1.91, 95%CI 1.32-2.75), while receipt of adjuvant chemotherapy was associated with lower likelihood of ER (HR 0.44, 95%CI 0.30-0.63). The discriminative accuracy of the preoperative (training: c-index: 0.77, 95%CI 0.72-0.81; internal validation: c-index: 0.79, 95%CI 0.75-0.82) and postoperative (training: c-index: 0.79, 95%CI 0.75-0.83; internal validation: c-index: 0.81, 95%CI 0.77-0.84) models was favorable (https://junkawashima.shinyapps.io/CRLMfollwingchemotherapy/). CONCLUSIONS: Patient-, tumor- and treatment-related characteristics in the preoperative and postoperative setting were utilized to develop an online, easy-to-use risk calculator for ER following resection of CRLM.

Quality of lymph node dissection and early recurrence in robotic versus thoracoscopic lobectomy for stage N1-2 non-small cell lung cancer: Eleven-year real-world data from a high-volume center.

BACKGROUND: The efficacy of lymph node dissection (LND) and oncological outcomes of robot-assisted (RL) versus video-assisted thoracoscopic lobectomy (VL) for non-small cell lung cancer (NSCLC) with nodal involvement remains controversial. This study aims to compare LND quality and early recurrence (ER) rate between RL and VL for stage N1-2 NSCLC patients based on eleven-year real-world data from a high-volume center. METHODS: Pathologic stage IIB-IIIB (T1-3N1-2) NSCLC patients undergoing RL or VL in Shanghai Chest Hospital from 2010 to 2021 were retrospectively reviewed from a prospectively maintained database. Propensity-score matching (PSM, 1:4 RL versus VL) was performed to mitigate baseline differences. LND quality was evaluated by adequate (≥16) LND and nodal upstaging rates. ER was defined as recurrence occurring within 24 months post-surgery. RESULTS: Out of 1578 cases reviewed, PSM yielded 200 RL and 800 VL cases. Without compromising perioperative outcomes, RL assessed more N1 and N2 LNs and N1 stations, and led to higher incidences of adequate LND (58.5 % vs. 42.0 %, p < 0.001) and nodal upstaging (p = 0.026), compared to VL. Notably, RL improved perioperative outcomes for patients undergoing adequate LND than VL. Finally, RL notably reduced ER rate (22.0 % vs. 29.6 %, p = 0.032), especially LN ER rate (15.0 % vs. 21.5 %, p = 0.041), and prolonged disease-free survival (DFS; hazard ratio = 0.837, p = 0.040) compared with VL. Further subgroup analysis of ER and DFS within the cN1-2-stage cohort verified this survival benefit. CONCLUSIONS: RL surpasses VL in enhancing LND quality, reducing ER rates, and improving perioperative outcomes when adequate LND is performed for stage N1-2 NSCLC patients.

Validation of a novel BCM model for recurrence risk prediction after mucosectomy of colorectal lateral spreading tumors in a European cohort.

BACKGROUND AND AIM: Piecemeal endoscopic mucosal resection (pEMR) is the best approach to resect large lateral spreading tumors (LST, > 20 mm width). However, it is associated with early recurrence (ER) and late recurrence (LR). This study aims to assess the risk factors associated with ER and LR and to validate different predictive scores (SMSA, SERT, and BCM) in identifying the risk of ER and LR after LST resected by pEMR in a European cohort. METHODS: Retrospective observational cohort study, based on a prospectively collected database, of large LST submitted to pEMR. RESULTS: A total of 108 patients were included in the study and the incidence rates of ER and LR were 22 % and 8 %, respectively. The lesion's size, SERT, and BCM scores were independent predictor factors of ER (p-value < 0.05), while the lesion's site and BCM score were independent predictor factors of LR (p-value < 0.05). For the prediction of ER, the SERT score (cut-off > 1) presented the highest AUROC (0.758 vs 0.697 from BCM and 0.647 from SMSA). Regarding LR, the BCM model (cut-off > 2) presented the highest AUROC (0.817 vs 0.708 from SERT and 0.691 from SMSA). CONCLUSIONS: We present the first external validation of the three scores mentioned in an European cohort. SERT and BCM scores had an acceptable performance in predicting ER and LR. However, the BCM model was the only score that proved to be an independent predictor of both ER and LR, proving to be valuable for both applications.

Preoperative and postoperative MRI-based models versus clinical staging systems for predicting early recurrence in hepatocellular carcinoma.

BACKGROUND: To predict the early recurrence of HCC patients who received radical resection using preoperative variables based on Gd-EOB-DTPA enhanced MRI, followed by the comparison with the postoperative model and clinical staging systems. METHODS: One hundred and twenty-nine HCC patients who received radical resection were categorized into the early recurrence group (n = 48) and the early recurrence-free group (n = 81). Through COX regression analysis, statistically significant variables of laboratory, pathologic, and Gd-EOB-DTPA enhanced MRI results were identified. The preoperative and postoperative models were established to predict early recurrence, and the prognostic performances and differences were compared between the two models and clinical staging systems. RESULTS: Six variables were incorporated into the preoperative model, including alpha-fetoprotein (AFP) level, aspartate aminotransferase/platelet ratio index (APRI), rim arterial phase hyperenhancement (rim APHE), peritumoral hypointensity on hepatobiliary phase (HBP), CERHBP (tumor-to-liver SI ratio on hepatobiliary phase imaging), and ADC value. Moreover, the postoperative model was developed by adding microvascular invasion (MVI) and histological grade. The C-index of the preoperative model and postoperative model were 0.889 and 0.901 (p = 0.211) respectively. Using receiver operating characteristic curve analysis (ROC) and decision curve analysis (DCA), it was determined that the innovative models we developed had superior predictive capabilities for early recurrence in comparison to current clinical staging systems. HCC patients who received radical resection were stratified into low-, medium-, and high-risk groups on the basis of the preoperative and postoperative models. CONCLUSION: The preoperative and postoperative MRI-based models built in this study were more competent compared with clinical staging systems to predict the early recurrence in hepatocellular carcinoma.

Prediction Model and Nomogram of Early Recurrence of Hepatocellular Carcinoma after Ultrasound-guided Microwave Ablation.

BACKGROUND: Ultrasound-guided microwave ablation (MWA) is recommended as a first-line treatment for early liver cancer due to its minimally invasive, efficient, and cost-effective nature. It utilizes microwave radiation to heat and destroy tumor cells as a local thermal therapy and offers the benefits of being minimally invasive, repeatable, and applicable to tumors of various sizes and locations. However, despite the efficacy of MWA, early recurrence after treatment remains a challenge, particularly when it occurs within a year and has a significant impact on the prognosis of the patient. OBJECTIVE: This study aimed to identify the risk factors for early recurrence after MWA in patients with hepatocellular carcinoma (HCC) and establish a predictive model. METHODS: A total of 119 patients with hepatocellular carcinoma (HCC) treated in the Department of Ultrasound at the First Affiliated Hospital of the Air Force Medical University from January, 2020 to April, 2022 were included in this study. Patients were categorized into the early recurrence group and the non-early recurrence group based on whether recurrence occurred within 1 year. We conducted univariate analysis on 29 variables. A predictive model was developed using multiple-factor logistic regression analysis, and a risk column graph was created. RESULTS: A total of 28 patients were included in the early recurrence group, with an early recurrence rate of 23%. Tumor size ≥ 3cm, multiple tumors, AST > 35 U/L, low pathological differentiation, CD34 positive, Ki67 level, quantitative parameters mean transit time (mTT), and rise time (RT) were confirmed as risk factors affecting early recurrence after ablation (P < 0.05). Furthermore, the model constructed based on these 5 predictive factors, including tumor size, tumor number, pathological differentiation, CD34, and quantitative analysis parameter mTT, demonstrated good predictive ability, with an AUC of 0.93 in the training set and 0.86 in the validation set. CONCLUSION: Our research indicates that the risk column graph can be utilized to predict the risk of early postoperative recurrence in patients after MWA. This contributes to guiding personalized clinical treatment decisions and provides important references for improving the prognosis of patients.

Novel immune classification based on machine learning of pathological images predicts early recurrence of hepatocellular carcinoma.

Introduction: Immune infiltration within the tumor microenvironment (TME) plays a significant role in the onset and progression of hepatocellular carcinoma (HCC). Machine learning applied to pathological images offers a practical means to explore the TME at the cellular level. Our former research employed a transfer learning procedure to adapt a convolutional neural network (CNN) model for cell recognition, which could recognize tumor cells, lymphocytes, and stromal cells autonomously and accurately within the images. This study introduces a novel immune classification system based on the modified CNN model. Method: Patients with HCC from both Beijing Hospital and The Cancer Genome Atlas (TCGA) database were included in this study. Additionally, least absolute shrinkage and selection operator (LASSO) analyses, along with logistic regression, were utilized to develop a prognostic model. We proposed an immune classification based on the percentage of lymphocytes, with a threshold set at the median lymphocyte percentage. Result: Patients were categorized into high or low infiltration subtypes based on whether their lymphocyte percentages were above or below the median, respectively. Patients with different immune infiltration subtypes exhibited varying clinical features and distinct TME characteristics. The low-infiltration subtype showed a higher incidence of hypertension and fatty liver, more advanced tumor stages, downregulated immune-related genes, and higher infiltration of immunosuppressive cells. A reliable prognostic model for predicting early recurrence of HCC based on clinical features and immune classification was established. The area under the curve (AUC) of the receiver operating characteristic (ROC) curves was 0.918 and 0.814 for the training and test sets, respectively. Discussion: In conclusion, we proposed a novel immune classification system based on cell information extracted from pathological slices, provides a novel tool for prognostic evaluation in HCC.

A knowledge-enhanced interpretable network for early recurrence prediction of hepatocellular carcinoma via multi-phase CT imaging.

BACKGROUND: Predicting early recurrence (ER) of hepatocellular carcinoma (HCC) accurately can guide treatment decisions and further enhance survival. Computed tomography (CT) imaging, analyzed by deep learning (DL) models combining domain knowledge, has been employed for the prediction. However, these DL models utilized late fusion, restricting the interaction between domain knowledge and images during feature extraction, thereby limiting the prediction performance and compromising decision-making interpretability. METHODS: We propose a novel Vision Transformer (ViT)-based DL network, referred to as Dual-Style ViT (DSViT), to augment the interaction between domain knowledge and images and the effective fusion among multi-phase CT images for improving both predictive performance and interpretability. We apply the DSViT to develop pre-/post-operative models for predicting ER. Within DSViT, to balance the utilization between domain knowledge and images within DSViT, we propose an adaptive self-attention mechanism. Moreover, we present an attention-guided supervised learning module for balancing the contributions of multi-phase CT images to prediction and a domain knowledge self-supervision module for enhancing the fusion between domain knowledge and images, thereby further improving predictive performance. Finally, we provide the interpretability of the DSViT decision-making. RESULTS: Experiments on our multi-phase data demonstrate that DSViTs surpass the existing models across multiple performance metrics and provide the decision-making interpretability. Additional validation on a publicly available dataset underscores the generalizability of DSViT. CONCLUSIONS: The proposed DSViT can significantly improve the performance and interpretability of ER prediction, thereby fortifying the trustworthiness of artificial intelligence tool for HCC ER prediction in clinical settings.

Establishing and Externally Validating a Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score-Based Nomogram for Predicting Early Recurrence in BCLC Stage 0/A Hepatocellular Carcinoma Patients After Radical Liver Resection: A Multi-Center Study.

Purpose: Early recurrence (ER) is associated with poor prognosis in hepatocellular carcinoma (HCC). In this study, we developed and externally validated a nomogram based on the hemoglobin, albumin, lymphocytes, and platelets (HALP) score to predict ER for patients with BCLC stage 0/A HCC who underwent radical liver resection. Patients and Methods: A total of 808 BCLC stage 0/A HCC patients from six hospitals were included in this study, and they were assigned to a training cohort (n = 500) and an external validation cohort (n = 308). We used univariate and multivariate Cox regression analysis to identify the independent risk factors for disease-free survival (DFS). We also established and externally validated a nomogram based on these risk predictors. The nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), the concordance index (C-index), the calibration curve, decision curve analysis (DCA), and Kaplan‒Meier analysis. Results: Multivariate COX regression showed that HBV DNA ≥10,000 IU/mL (P < 0.001), HALP score ≤38.20 (P < 0.001), tumor size (P = 0.003), clinically significant portal hypertension (P = 0.001), Edmondson-Steiner grade (III-IV) (P = 0.007), satellite nodules (P < 0.001), and MVI (P = 0.001) were independent risk factors for post-operative tumor recurrence. The AUC of our nomogram for predicting the 2-year and 5-year DFS was 0.756 and 0.750, respectively, in the training cohort and 0.764 and 0.705, respectively, in the external validation cohort. We divided the patients into low-, intermediate- and high-risk groups according to the risk score calculated by the nomogram. There were statistically significant differences in the DFS and overall survival (OS) among the three groups of patients (P < 0.001). Conclusion: We developed and externally validated a new nomogram, which is accurate and can predict ER in BCLC stage 0/A HCC patients after curative liver resection.

Delta-radiomics Analysis Based on Multi-phase Contrast-enhanced MRI to Predict Early Recurrence in Hepatocellular Carcinoma After Percutaneous Thermal Ablation.

RATIONALE AND OBJECTIVES: It is critical to predict early recurrence (ER) after percutaneous thermal ablation (PTA) for hepatocellular carcinoma (HCC). We aimed to develop and validate a delta-radiomics nomogram based on multi-phase contrast-enhanced magnetic resonance imaging (MRI) to preoperatively predict ER of HCC after PTA. MATERIALS AND METHODS: We retrospectively enrolled 164 patients with HCC and divided them into training, temporal validation, and other-scanner validation cohorts (n = 110, 29, and 25, respectively). The volumes of interest of the intratumoral and/or peritumoral regions were delineated on preoperative multi-phase MR images. Original radiomics features were extracted from each phase, and delta-radiomics features were calculated. Logistic regression was used to train the corresponding radiomics models. The clinical and radiological characteristics were evaluated and combined to establish a clinical-radiological model. A fusion model comprising the best radiomics scores and clinical-radiological risk factors was constructed and presented as a nomogram. The performance of each model was evaluated and recurrence-free survival (RFS) was assessed. RESULTS: Child-Pugh grade B, high-risk tumor location, and an incomplete/absent tumor capsule were independent predictors of ER. The optimal radiomics model comprised 12 delta-radiomics features with areas under the curve (AUCs) of 0.834, 0.795, and 0.769 in the training, temporal validation, and other-scanner validation cohorts, respectively. The nomogram showed the best predictive performance with AUCs as 0.893, 0.854, and 0.827 in the three datasets. There was a statistically significant difference in RFS between the risk groups calculated using the delta-radiomics model and nomogram. CONCLUSIONS: The nomogram combined with the delta-radiomic score and clinical-radiological risk factors could non-invasively predict ER of HCC after PTA.

Prevalence, timing, and impact of early recurrence of atrial tachyarrhythmias after pulsed field ablation: A secondary analysis of the PULSED AF trial.

BACKGROUND: Early recurrence of atrial tachyarrhythmias (ERAT) within 3 months of thermal ablation for atrial fibrillation (AF) is common and often considered transient. Pulsed field ablation (PFA) is a nonthermal energy source in which ERAT is not well described. OBJECTIVE: The purpose of this study was to analyze ERAT in patients with AF undergoing PFA in the Pulsed Field Ablation to Irreversibly Electroporate Tissue and Treat AF (PULSED AF) trial. METHODS: This analysis included 294 patients (154 paroxysmal AF and 140 persistent AF) who had ≥10 rhythm assessments during the 90-day blanking period. ERAT was defined as any instance of ≥30 seconds of AF, atrial flutter, or atrial tachycardia on transtelephonic monitoring (weekly and symptomatic) or ≥10 seconds on electrocardiography (at 3 months), both within 90 days. Late recurrence of atrial tachyarrhythmias (LRAT) was defined as observed atrial tachyarrhythmias between 90 days and 12 months. RESULTS: The overall prevalence of ERAT was 27.1% in patients with paroxysmal AF and 31.6% in patients with persistent AF. In patients with ERAT, 73% had ERAT onset within the first month of the procedure. The presence of ERAT was associated with LRAT in patients with paroxysmal AF (hazard ratio 6.4; 95% confidence interval 3.6-11.3) and patients with persistent AF (hazard ratio 3.8; 95% confidence interval 2.2-6.6). Yet, in 29.4% of patients with paroxysmal AF and 34.3% of patients with persistent AF with ERAT, LRAT was not observed. LRAT was positively correlated with the number of ERAT observations. CONCLUSION: ERAT after PFA predicted LRAT in patients with paroxysmal and persistent AF. However, the concept of a blanking period after PFA is still valid, as approximately one-third of patients with ERAT did not continue to have LRAT during follow-up and may not need reablation.

Prognostic Factors for Early Recurrence After Resection of Pancreatic Cancer: A Systematic Review and Meta-analysis.

BACKGROUND & AIMS: More than half of pancreatic ductal adenocarcinomas (PDACs) recur within 12 months after curative-intent resection. This systematic review and meta-analysis was conducted to identify all reported prognostic factors for early recurrence in resected PDACs. METHODS: After a systematic literature search, a meta-analysis was conducted using a random effects model. Separate analyses were performed for adjusted vs unadjusted effect estimates as well as reported odds ratios (ORs) and hazard ratios (HRs). Risk of bias was assessed using the Quality in Prognostic Studies tool, and evidence was rated according to Grading of Recommendations Assessment, Development and Evaluation recommendations. RESULTS: After 2,903 abstracts were screened, 65 studies were included. Of these, 28 studies (43.1%) defined early recurrence as evidence of recurrence within 6 months, whereas 34 (52.3%) defined it as evidence of recurrence within 12 months after surgery. Other definitions were uncommon. Analysis of unadjusted ORs and HRs revealed 41 and 5 prognostic factors for early recurrence within 6 months, respectively. When exclusively considering adjusted data, we identified 25 and 10 prognostic factors based on OR and HR, respectively. Using a 12-month definition, we identified 38 (OR) and 15 (HR) prognostic factors from unadjusted data and 38 (OR) and 30 (HR) prognostic factors from adjusted data, respectively. On the basis of frequency counts of adjusted data, preoperative carbohydrate antigen 9-9, N status, nondelivery of adjuvant therapy, grading, and tumor size based on imaging were identified as key prognostic factors for early recurrence. CONCLUSIONS: Reported prognostic factors of early recurrence vary considerably. Identified key prognostic factors could aid in the development of a risk stratification framework for early recurrence. However, prospective validation is necessary.

Mac-2 binding protein glycosylation isomer as a novel predictor of early recurrence after resection for hepatocellular carcinoma.

PURPOSE: Hepatocellular carcinoma (HCC) frequently recurs after radical resection, resulting in a poor prognosis. This study assessed the prognostic value of Mac-2 binding protein glycosylation isomer (M2BPGi) for early recurrence (ER) in patients with HCC. METHODS: Patients who underwent radical resection for HCC between 2015 and 2021. HCC recurrence within one year after curative resection was defined as ER. RESULTS: The 150 patients were divided into two groups: non-ER (116, 77.3%) and ER (34, 22.7%). The ER group had a lower overall survival rate (p < 0.0001) and significantly higher levels of M2BPGi (1.06 vs. 2.74 COI, p < 0.0001) than the non-ER group. High M2BPGi levels (odds ratio [OR] 1.78, 95% confidence interval [CI] 1.31-2.41, p < 0.0001) and a large tumor size (OR 1.31, 95% CI, 1.05-1.63; p = 0.0184) were identified as independent predictors of ER. M2BPGi was the best predictor of ER according to a receiver operating characteristic (ROC) analysis (area under the ROC curve 0.82, p < 0.0001). CONCLUSIONS: M2BPGi can predict ER after surgery and is useful for risk stratification in patients with HCC.

Two Strikes: A Case Study of Consecutive St-Elevation Myocardial Infarctions (Stemi) in two Different Coronary Arteries.

An acute ST-elevation myocardial infarction (STEMI) followed by reinfarction within a short period of time is typically due to stent thrombosis. However, a STEMI caused by occlusion of one vessel followed by a repeat infarction due to occlusion of a different vessel which was seemingly innocent a few hours earlier is extremely rare. We present the case of a 61-year-old male with a past medical history of prediabetes, hyperlipidemia, tobacco use, and gastroesophageal reflux disease who presented to the emergency department with complaints of chest pain. His initial electrocardiogram (EKG) revealed ST elevation in leads II, III and aVF with reciprocal changes in leads I and aVL. He promptly underwent cardiac catheterization and had percutaneous coronary intervention with placement of two drug-eluting stents (DES) in the right coronary artery (RCA). At that time coronary angiography revealed 50% stenosis of the left anterior descending (LAD) artery and 60% stenosis of the second diagonal branch artery. Shortly after the procedure he was asymptomatic, and the post procedure EKG demonstrated resolution of the ST elevations. However, within 2 hours he developed chest pain and was found to have new ST elevations in the anterolateral leads. Repeat cardiac catheterization revealed patent RCA stents with subtotal occlusion of the LAD and another DES was placed. After the second procedure the patient remained hemodynamically stable, EKG changes resolved, and he was kept on eptifibatide infusion for 18 hours after which he was switched to dual antiplatelet therapy and ultimately discharged home. LEARNING POINTS: Physicians should promptly address the recurrence of symptoms following an initial ST-elevation myocardial infarctions (STEMI) and be proactive regarding follow-up with the appropriate investigations.Although recurrence of STEMI within a few hours is extremely rare, the first 2 weeks following an initial STEMI is a critical time and patients should be educated on symptoms that will require further evaluation.The mortality associated with early recurrent myocardial infarction is up to 50% in 5 years so these patients require strict outpatient follow-up and counseling to minimize risk factors.

Unraveling early recurrence of risk factors in Gallbladder cancer: A systematic review and meta-analysis.

BACKGROUND: Gallbladder cancer (GBC) is the most prevalent biliary tract tumor characterized by a high incidence of recurrence, even after curative-intent surgery. The object of this systematic review and meta-analysis was to investigate the risk factors related to early recurrence (ER). METHODS: A systematic literature review was conducted in PubMed, Embase, Cochrane Library, and Web of Science to identify published articles up to February 2024. Data on risk factors associated with ER reported by two or more studies were collected. Selection of different effect models based on data heterogeneity. RESULTS: Out of 6497 initially identified articles based on our search strategies, only 5 were eligible and included in this meta-analysis and 12 ER-related factors were collected. The overall recurrence rate was reported between 32.3% and 61.0 %, and the ER rate ranged from 19.6% to 26.5 %. Concentrations of CA19-9 (OR 3.03 95 % CI 2.20-4.17) and CEA (OR 1.85 95 % CI 1.24-2.77), tumor differentiation (OR 2.79, 95 % CI 1.86-4.20), AJCC T stage (OR 7.64, 95%CI 3.40-17.18), lymphovascular invasion (OR 2.71, 95 % CI 1.83-4.03), perineural invasion (OR 2.71, 95 % CI 1.79-4.12), liver involvement (OR 5.69, 95%CI 3.78-8.56) and adjuvant therapy (OR 2.19, 95 % CI 1.06-4.55) were identified as the risk factors of ER. CONCLUSION: This study may provide valuable insights for early identification of increased ER risk and making informed decisions regarding the comprehensive diagnosis and treatment of patients with GBC. To draw more definitive conclusions, there is a need for high-quality prospective studies involving multiple centers and diverse racial populations.

The role of adjuvant transcatheter arterial chemoembolization following repeated curative resection/ablation for hepatocellular carcinoma with early recurrence: a propensity score matching analysis.

BACKGROUND: The role of adjuvant transcatheter arterial chemoembolization (TACE) following repeated resection/ablation for recurrent hepatocellular carcinoma (HCC) remains uncertain. The aim of this study was to assess the effectiveness of adjuvant TACE following repeated resection or ablation in patients with early recurrent HCC. METHODS: Information for patients who underwent repeated surgery or radiofrequency ablation (RFA) for early recurrent HCCs (< 2 years) at our institution from January 2017 to December 2020 were collected. Patients were divided into adjuvant TACE and observation groups according to whether they received adjuvant TACE or not. The recurrence-free survival (RFS) and overall survival (OS) were compared between the two groups before and after propensity score matching (PSM). RESULTS: Of the 225 patients enrolled, the median time of HCC recurrence was 11 months (IQR, 6-16 months). After repeated surgery or radiofrequency ablation (RFA) for recurrent tumors, 45 patients (20%) received adjuvant TACE while the remaining 180 (80%) didn't. There were no significant differences in RFS (P = 0.325) and OS (P = 0.072) between adjuvant TACE and observation groups before PSM. There were also no significant differences in RFS (P = 0.897) and OS (P = 0.090) between the two groups after PSM. Multivariable analysis suggested that multiple tumors, liver cirrhosis, and RFA were independent risk factors for the re-recurrence of HCC. CONCLUSION: Adjuvant TACE after repeated resection or ablation for early recurrent HCCs was not associated with a long-term survival benefit in this single-center cohort.

Impact of anatomical liver resection for hepatocellular carcinoma in preventing early-phase local recurrence after surgery.

BACKGROUND: The superiority of anatomical liver resection (AR) for localized hepatocellular carcinoma (HCC) over nonanatomical liver resection (NR) remains controversial. This study aimed to investigate the impact of AR in preventing local and early HCC recurrence. METHODS: A total of 280 patients who underwent initial liver resection for solitary HCC ≤5 cm in diameter were categorized into the AR and NR groups and compared using propensity score matching analysis. RESULTS: Between the matched pairs (n = 87 in each group), the incidence rates of local and early (recurrence within 2 years after surgery) recurrences in the AR group were significantly lower than those in the NR group (13.8% vs. 28.7%, p = .025; 20.7% vs. 35.6%, p = .028, respectively). The overall survival in the AR group was better than that in the NR group (median: 13.4 vs. 7.6 years, p = .003). NR was among independent risk factors for early recurrence (odds ratio: 1.98, 95% CI: 1.1-3.6, p = .023) and prognostic factors for local recurrence (hazard ratio: 2.44, 95% CI: 1.4-4.4, p = .003). CONCLUSION: AR is superior in controlling local and early recurrence postoperatively for solitary HCC ≤5 cm in diameter compared with NR.

Preoperative prediction power of radiomics and non-radiomics methods based on MRI for early recurrence in hepatocellular carcinoma: a systemic review and meta-analysis.

OBJECTIVE: To compare radiomics and non-radiomics in predicting early recurrence (ER) in patients with hepatocellular carcinoma (HCC) after curative surgery. METHODS: We systematically searched PubMed and Embase databases. Studies with clear reference criteria were selected. Data were extracted and assessed for quality using the quality in prognosis studies tool (QUIPS) by two independent authors. All included radiomics studies underwent radiomics quality score (RQS) assessment. We calculated sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) using random or fixed models with a 95%CI. Forest maps visualized the data, and summary receiver operating characteristic (sROC) curves with the area under the curve (AUC) were generated. Meta-regression and subgroup analyses explored sources of heterogeneity. We compared sensitivity, specificity, PLR, and NLR using the z-test and compared AUC values using the Delong test. RESULTS: Our meta-analysis included 10 studies comprising 1857 patients. For radiomics, the pooled sensitivity, specificity, AUC of sROC, PLR and NLR were 0.84(95%CI: 0.78-0.89), 0.80(95%CI: 0.75-0.85), 0.89(95%CI: 0.86-0.91), 4.28(95%CI: 3.48-5.27) and 0.20(95%CI: 0.14-0.27), respectively, but with significant heterogeneity (I2 = 60.78% for sensitivity, I2 = 55.79% for specificity) and potential publication bias (P = 0.04). The pooled sensitivity, specificity, AUC of sROC, PLR, NLR for non-radiomics were 0.75(95%CI:0.68-0.81), 0.78(95%CI:0.72-0.83), 0.83(95%CI: 0.80-0.86), 3.45(95%CI: 2.68-4.44) and 0.32(95%CI: 0.24-0.41), respectively. There was no significant heterogeneity in this group (I2 = 0% for sensitivity, I2 = 17.27% for specificity). Radiomics showed higher diagnostic accuracy (AUC: 0.89 vs. 0.83, P = 0.0456), higher sensitivity (0.84 vs. 0.75, P = 0.0385) and lower NLR (0.20 vs. 0.32, P = 0.0287). CONCLUSION: The radiomics from preoperative MRI effectively predicts ER of HCC and has higher diagnostic accuracy than non-radiomics. Due to potential publication bias and suboptimal RQS scores in radiomics, these results should be interpreted cautiously.

Deep Learning Model for Predicting Proliferative Hepatocellular Carcinoma Using Dynamic Contrast-Enhanced MRI: Implications for Early Recurrence Prediction Following Radical Resection.

RATIONALE AND OBJECTIVES: The proliferative nature of hepatocellular carcinoma (HCC) is closely related to early recurrence following radical resection. This study develops and validates a deep learning (DL) prediction model to distinguish between proliferative and non-proliferative HCCs using dynamic contrast-enhanced MRI (DCE-MRI), aiming to refine preoperative assessments and optimize treatment strategies by assessing early recurrence risk. MATERIALS AND METHODS: In this retrospective study, 355 HCC patients from two Chinese medical centers (April 2018-February 2023) who underwent radical resection were included. Patient data were collected from medical records, imaging databases, and pathology reports. The cohort was divided into a training set (n = 251), an internal test set (n = 62), and external test sets (n = 42). A DL model was developed using DCE-MRI images of primary tumors. Clinical and radiological models were generated from their respective features, and fusion strategies were employed for combined model development. The discriminative abilities of the clinical, radiological, DL, and combined models were extensively analyzed. The performances of these models were evaluated against pathological diagnoses, with independent and fusion DL-based models validated for clinical utility in predicting early recurrence. RESULTS: The DL model, using DCE-MRI, outperformed clinical and radiological feature-based models in predicting proliferative HCC. The area under the curve (AUC) for the DL model was 0.98, 0.89, and 0.83 in the training, internal validation, and external validation sets, respectively. The AUCs for the combined DL and clinical feature models were 0.99, 0.86, and 0.83 in these sets, while the AUCs for the combined DL, clinical, and radiological model were 0.99, 0.87, and 0.8, respectively. Among models predicting early recurrence, the DL plus clinical features model showed superior performance. CONCLUSION: The DL-based DCE-MRI model demonstrated robust performance in predicting proliferative HCC and stratifying patient risk for early postoperative recurrence. As a non-invasive tool, it shows promise in enhancing decision-making for individualized HCC management strategies.