Identifying optimal candidates for tumor resection among borderline and locally advanced pancreatic cancer: A population-based predictive model.

BACKGROUND: Whether patients with borderline resectable and locally advanced pancreatic cancer (BR/LAPC) benefit from resection of the primary cancer is controversial. We developed a nomogram to screen who would benefit from surgery for the primary tumor. METHODS: We identified patients from the Surveillance, Epidemiology, and End Results (SEER) database and then divided them into surgical and non-surgical groups. A 1:1 propensity score matching (PSM) was used to mitigate the bias. We hypothesized that patients who underwent surgery would benefit from surgery by having a longer median overall survival (OS) than patients who did not undergo surgery. Univariate and multivariate logistic regression analyses were used to determine the variables affecting surgical outcomes, and a nomogram was created based on the multivariate logistic results. Finally, we verified the discrimination and calibration of the nomogram with receiver operator characteristic (ROC) curve and calibration plots. RESULTS: A total of 518 pairs of surgical and non-surgical pancreatic cancer patients were matched after PSM. Survival curves showed longer OS in the surgical group than in the non-surgical group, median survival times were 14 months versus 8 months. In the surgical group, 340 (65.63%) patients have a longer survival time than 8 months (beneficial group). Multifactorial logit regression results showed that including age, tumor size, degree of differentiation, and chemotherapy were significant influences on the benefit of surgery for primary tumors and were used as predictors to construct a nomogram. The area under the ROC curve (AUC) reached 0.747 and 0.706 in the training and validation sets. CONCLUSION: We developed a practical predictive model to support clinical decision-making that can be used to help clinicians determine if there is a benefit to surgical resection of the primary tumor in patients with BR/LAPC.

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.

Lymph node ratio is a superior predictor in surgically treated early-onset pancreatic cancer.

Background: The prognostic performance of four lymph node classifications, the 8th American Joint Committee on Cancer (AJCC) Tumor Node Metastasis (TNM) N stage, lymph node ratio (LNR), log odds of positive lymph nodes (LODDS), and examined lymph nodes (ELN) in early-onset pancreatic cancer (EOPC) remains unclear. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was searched for patients with EOPC from 2004 to 2016. 1048 patients were randomly divided into training (n = 733) and validation sets (n = 315). The predictive abilities of the four lymph node staging systems were compared using the Akaike information criteria (AIC), receiver operating characteristic area under the curve (AUC), and C-index. Multivariate Cox analysis was performed to identify independent risk factors. A nomogram based on lymph node classification with the strongest predictive ability was established. The nomogram's precision was verified by the C-index, calibration curves, and AUC. Kaplan-Meier analysis and log-rank tests were used to compare differences in survival at each stage of the nomogram. Results: Compared with the 8th N stage, LODDS, and ELN, LNR had the highest C-index and AUC and the lowest AIC. Multivariate analysis showed that N stage, LODDS, LNR were independent risk factors associated with cancer specific survival (CSS), but not ELN. In the training set, the AUC values for the 1-, 3-, and 5-year CSS of the nomogram were 0.663, 0.728, and 0.760, respectively and similar results were observed in the validation set. In addition, Kaplan-Meier survival analysis showed that the nomogram was also an important factor in the risk stratification of EOPC. Conclusion: We analyzed the predictive power of the four lymph node staging systems and found that LNR had the strongest predictive ability. Furthermore, the novel nomogram prognostic staging mode based on LNR was also an important factor in the risk stratification of EOPC.

Effects of Total Pancreatectomy on Survival of Patients With Pancreatic Ductal Adenocarcinoma: A Population-Based Study.

Background: Total pancreatectomy (TP) seems to be experiencing a renaissance in recent years. In this study, we aimed to determine the long-term survival of pancreatic ductal adenocarcinoma (PDAC) patients who underwent TP by comparing with pancreaticoduodenectomy (PD), and formulate a nomogram to predict overall survival (OS) for PDAC individuals following TP. Methods: Patients who were diagnosed with PDAC and received PD (n = 5,619) or TP (n = 1,248) between 2004 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. OS and cancer-specific survival (CSS) of the PD and TP groups were compared using Kaplan-Meier method and log-rank test. Furthermore, Patients receiving TP were randomly divided into the training and validation cohorts. Univariate and multivariate Cox regression were applied to identify the independent factors affecting OS to construct the nomogram. The performance of the nomogram was measured according to concordance index (C-index), calibration plots, and decision curve analysis (DCA). Results: There were no significant differences in OS and CSS between TP and PD groups. Age, differentiation, AJCC T stage, radiotherapy, chemotherapy, and lymph node ratio (LNR) were identified as independent prognostic indicators to construct the nomogram. The C-indexes were 0.67 and 0.69 in the training and validation cohorts, while 0.59 and 0.60 of the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system. The calibration curves showed good uniformity between the nomogram prediction and actual observation. DCA curves indicated the nomogram was preferable to the AJCC staging system in terms of the clinical utility. A new risk stratification system was constructed which could distinguish patients with different survival risks. Conclusions: For PDAC patients following TP, the OS and CSS are similar to those who following PD. We developed a practical nomogram to predict the prognosis of PDAC patients treated with TP, which showed superiority over the conventional AJCC staging system.

Histological composition behind CT-based thrombus density and perviousness in acute ischemic stroke.

BACKGROUND: Clot composition could impact recanalization outcomes of thrombectomy, and preoperative imaging markers may help know about the histological components of thrombus. METHODS: Consecutive patients who underwent thrombectomy from June 2017 to December 2019 were reviewed. The mean Hounsfield unit (HU) of thrombus (aHU) and contralateral artery (cHU) were recorded based on non-enhanced CT. The relative thrombus density was calculated (dHU=aHU-cHU). Hyperdense artery sign (HAS) was identified if dHU≥ 4HU. The clot perviousness was evaluated via thrombus attenuation increase (δHU) on contrast-enhanced CT compared to non-enhanced CT. Pervious clots were identified when δHU≥ 11HU. Tissue quantification for thrombus was based on Martius Scarlet Blue staining, using the Orbit Imaging Analysis Software. Spearman rank correlations was used to detect the association between imaging markers and clot composition. The differences in clinical characteristics were compared according to the presence of HAS or pervious clots. RESULTS: Fifty-three patients were included. The dHU was positively correlated with erythrocyte fractions (r = 0.337, p = 0.014), while there was no significant association between aHU and erythrocyte components (r = 0.146, p = 0.296). HAS (+) patients showed a comparable proportion of modified Thrombolysis In Cerebral Infarction (mTICI) 2b-3 (94.6% vs. 87.5%, p = 0.740) and modified Rankin Scale score (mRS) 0-2 (35.1% vs. 56.3%, p = 0.152) compared with those HAS (-). Forty-seven cases were available for the analysis of clot perviousness. Clot perviousness was negatively associated with platelet fractions (r = -0.577, p < 0.001). Patients with pervious clots also showed a comparable proportion of mTICI 2b-3 (86.2% vs. 100%, p = 0.283) and mRS 0-2 (37.9% vs. 50.0%, p = 0.416) compared with impervious clots. CONCLUSIONS: This study suggests that relative thrombus density was positively correlated with erythrocyte fractions, while clot perviousness showed a negative relationship with platelet components. Yet, the presence of HAS or pervious clots did not show significant associations with recanalization and clinical outcomes. The conclusions should be drawn with caution.

Risk of secondary embolism events during mechanical thrombectomy for acute ischemic stroke: A single-center study based on histological analysis.

OBJECTIVE: Secondary embolism (SE) is a common adverse event during mechanical thrombectomy (MT) for acute intracranial large vessel occlusion, which could lead to incomplete revascularization and increased maneuvers. However, the mechanisms behind SE are still unclear. In this study, we aimed to investigate the risk factors of SE, with a focus on clot composition. PATIENTS AND METHODS: Consecutive patients with retrieved clots were reviewed. Histologic examination for thrombus included Hematoxylin and eosin, Martius Scarlet Blue, immunohistochemistry for von Willebrand factor (VWF). Patients included were assigned to SE or no SE group. The differences in histological composition and clinical characteristics were compared, and logistic regression was conducted for predictors of SE. RESULTS: Fifty-four patients were included, of which 19 were identified as having an SE. For patients with SE, there was more history of stroke or transient cerebral ischemia (TIA) (57.9 % vs. 28.6 %, p = 0.035), more occlusion located in terminal internal carotid artery (ICA) (63.2 % vs. 25.7 %, p = 0.007), relatively more contact aspiration used as frontline strategy (68.4 % vs. 45.7 %, p = 0.110), and less eTICI2c-3 recanalization achieved (52.6 % vs. 91.4 %, p = 0.003). As for histologic composition, the clots in SE group showed a higher proportion of erythrocyte fractions (42.9 % vs. 26.8 %, p = 0.045), while the other components were comparable with the non-SE group. Multivariate analysis suggested that a history of stroke or TIA (OR 6.45, 95 %CI 1.41-29.44, p = 0.016) and ICA occlusion (OR 8.05, 95 %CI 1.80-36.10, p = 0.006) could independently predict SE. CONCLUSION: History of TIA or stroke and occlusion in the terminal ICA were found to be independent predictors for SE. Thrombus with a higher erythrocyte fractions might be more fragile. Further studies are needed.

The role of diabetes mellitus on the thrombus composition in patients with acute ischemic stroke.

PURPOSE: Diabetes mellitus indicated poor clinical prognosis for patients with acute ischemic stroke. Furthermore, diabetes mellitus could also impact the hemostatic system, while its influence on the histological composition of thrombus is unclear. METHODS: Consecutive patients with retrieved clots were included. Histologic staining for thrombus included hematoxylin and eosin, Martius Scarlet Blue, immunohistochemistry for von Willebrand factor. The differences in clot composition were compared according to diabetes mellitus history or hyperglycemia (≥7.8 mmol/L) on admission. RESULTS: A total of 52 patients were included; half of them were diagnosed as diabetes mellitus previously. Diabetic patients showed higher serum glucose on admission (8.90 vs. 7.40, p = 0.012). The baseline characteristics (expect smoking history and thrombus location), procedural, and clinical outcomes were similar between diabetic patients and nondiabetic patients. As for histologic composition, thrombus in patients with diagnosed diabetes mellitus had more fibrin (44.2% vs. 28.3%, p = 0.004) and fewer red blood cells (26.0% vs. 42.9%, p = 0.013) and equivalent content of platelets (24.0% vs. 21.5%, p = 0.694) and von Willebrand factor (0.041 vs. 0.031, p = 0.234) than patients without diabetes mellitus. However, there was no statistical difference in the content of red blood cells (41.6% vs. 27.3%, p = 0.105), fibrin (37.6% vs. 34.3%, p = 0.627), platelets (21.2% vs. 24.2%, p = 0.498), and von Willebrand factor (0.038 vs. 0.034, p = 0.284) between patients with or without hyperglycemia on admission. CONCLUSION: Clots in diabetic patients had more fibrin and fewer erythrocyte components compared with patients without diabetes mellitus, while hyperglycemia on admission did not show association with clot composition. Further studies are needed to confirm these results.