Perioperative Serum Scoring Systems Predict Early Recurrence and Poor Prognosis of Resectable Pancreatic Cancer

The development and validation of biomarkers-based scoring systems for predicting early recurrence in patients with borderline resectable pancreatic cancer undergoing resection after neoadjuvant therapy

Background

Neoadjuvant therapy (NAT) is a key component of the treatment strategy for borderline resectable pancreatic cancer (BRPC). However, early recurrence (ER) frequently occurs, leading to a poor prognosis. Effective approaches for ER risk stratification in patients with BRPC undergoing NAT have not been well established currently. This study aimed to develop biomarker-based perioperative scoring systems to predict ER in patients with BRPC who underwent resection after NAT.

Methods

Patients with BRPC who underwent radical resection following NAT at our institute between 2018 and 2023 were retrospectively enrolled. Serum biochemical marker tests and imaging examinations were performed to evaluate recurrence. Perioperative biochemical and clinicopathological parameters were analyzed. Univariate and multivariate Cox regression analyses were performed to identify independent risk factors for recurrence and to construct nomograms for ER prediction. Internal validation was conducted using the bootstrapping method. The accuracy in predicting ER was evaluated using receiver operating characteristic curve analysis. Survival analysis was performed using the Kaplan-Meier survival plots and log-rank test.

Results

A total of 194 patients were enrolled. Recurrence occurred in 69.0% of all patients, and 61.1% of all recurrences were found within 6 months postoperatively. A preoperative scoring system was developed based on preoperative carbohydrate antigen 19-9 (CA19-9) and CA125 levels to predict ER [area under the curve (AUC), 0.700; 95% confidence interval (95% CI): 0.614-0.786] with 86.4% specificity and 48.7% sensitivity (cut-off value was 0.35886). Patients with a post-NAT prognostic score (PNPS) ≥0.35886 exhibited significantly poorer recurrence-free survival (RFS) (P<0.001) and overall survival (OS) (P<0.001) than those with a PNPS <0.35886. A postoperative scoring system based on the postoperative CA19-9 response was established to predict ER (AUC, 0.785; 95% CI: 0.705-0.866) with 65.4% specificity and 80.8% sensitivity (cut-off value was 0.43949). Patients with a postoperative prognostic score (PPS) ≥0.43949 exhibited poorer RFS (P<0.001) and OS (P<0.001) than those with a PPS <0.43949. For patients with normal CA19-9 levels after NAT, PNPS ≥0.35886 or PPS ≥0.43949 indicated a poor prognosis after surgery. For patients without normal CA19-9 levels after NAT, PNPS <0.35886 or PPS <0.43949 was associated with a favorable prognosis after surgery.

Conclusions

The preoperative and postoperative scoring systems provide risk stratification for ER in patients with BRPC undergoing NAT. This may provide references to clinicians in identifying suitable candidates and optimal timing for surgery during NAT, and administering tailored adjuvant therapy (AT) after surgery.

The Significant Impact of Fibrinogen-C-Reactive Protein-Albumin Ratio on the Long-Term Outcomes After Pancreatic Resection for Pancreatic Cancer

OBJECTIVES

This study aimed to assess the prognostic significance of a novel biomarker, the fibrinogen-C-reactive protein-albumin ratio (F-CAR), in pancreatic cancer patients undergoing pancreatectomy.

METHOD

We retrospectively analyzed 163 patients undergoing pancreatectomy for pancreatic cancer. The relationship between F-CAR, calculated from preoperative serum fibrinogen, albumin, and C-reactive protein levels, and long-term outcomes following pancreatectomy was examined.

RESULTS

Multivariate analysis revealed that several factors, including age (P < 0.01), high serum carbohydrate antigen 19-9 (CA19-9) level (P < 0.01), high serum carcinoembryonic antigen level (P = 0.049), pT3 or pT4 (UICC) (P < 0.01), pN1 or pN2 (P < 0.01), and high F-CAR (hazards ratio, 1.51; 95% confidence interval, 1.03-2.22; P = 0.04), were independent and significant predictors of disease-free survival. Moreover, factors such as age (P = 0.02), high serum carcinoembryonic antigen level (P < 0.01), preoperative biliary drainage (P = 0.02), preoperative chemotherapy (P = 0.04), lymph node metastasis (P < 0.01), adjuvant chemotherapy (P = 0.046), and high F-CAR (hazards ratio, 1.55; 95% confidence interval, 1.03-2.35; P = 0.04) were found to be independent and significant predictors of overall survival.

CONCLUSIONS

F-CAR is a useful prognostic factor for disease-free survival and overall survival after pancreatectomy in pancreatic cancer patients.

Nomogram for prognosis prediction in metastatic pancreatic cancer patients undergoing intra-arterial infusion chemotherapy: incorporating immune-inflammation scores and coagulation indicators

BACKGROUND

Pancreatic cancer is one of the most malignant tumors with an inferior prognosis. This study aims to determine the prognostic significance of immune-inflammatory scores and coagulation indices in patients with metastatic pancreatic cancer(MPC) and develop a predictive nomogram.

METHODS

This study retrospectively analyzed the clinical data of 384 patients with MPC who underwent intra-arterial infusion chemotherapy (IAIC). Patients were randomly divided into training and validation cohorts. Firstly, the optimal cutoff values for continuous variables were obtained in the training cohort. Then, survival analysis was performed to evaluate the impact of immune-inflammatory scores neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and coagulation indicators prothrombin time (PT), fibrinogen (FIB), and D-dimer on the overall survival (OS) of patients. Next, univariate analysis was utilized to identify prognostic factors, and a stepwise regression method was employed for variable selection to construct a nomogram based on the Cox proportional hazards model. Additionally, the predictive performance of the nomogram was assessed by the concordance index (C-index), the area under the ROC curve (AUC), and calibration curves. Finally, patients were stratified into risk groups based on the total score of the nomogram.

RESULTS

The Kaplan-Meier survival curves indicated that immune-inflammatory scores NLR, PLR, SII, and coagulation indicators PT, FIB, and D-dimer were associated with OS. Through Cox regression analysis, a nomogram was ultimately constructed incorporating NLR, PLR, PT, alkaline phosphatase (ALP), carbohydrate antigen 125 (CA125), age, and ablation. The model demonstrated good discriminative ability, with a C-index of 0.722, and the AUC values at 6- and 12-month OS predictions were 0.828 and 0.851 in the training cohort, while in the validation cohort, the corresponding AUC values were 0.754 and 0.791, respectively. The calibration curves showed a good fit, confirming the stability of the model. A cutoff value of 353.3 was identified as optimal for risk stratification, with a statistically significant difference in OS between the high- and low-risk groups.

CONCLUSION

The nomogram based on immune-inflammatory scores, coagulation indicators, and other clinicopathological factors can effectively predict the OS of patients with MPC.

Circulating Neoplastic-Immune Hybrid Cells Are Biomarkers of Occult Metastasis and Treatment Response in Pancreatic Cancer

BACKGROUND/OBJECTIVES

Pancreatic ductal adenocarcinoma (PDAC) presents significant diagnostic and prognostic challenges, as current biomarkers frequently fail to accurately stage disease, predict rapid metastatic recurrence (rPDAC), or assess response to neoadjuvant therapy (NAT). We investigated the potential for circulating neoplastic-immune hybrid cells (CHCs) as a non-invasive, multifunctional biomarker for PDAC.

METHODS

Peripheral blood specimens were obtained from patients diagnosed with PDAC. CHCs were detected by co-expression of pan-cytokeratin and CD45, normalized to 50,000 peripheral blood mononuclear cells. rPDAC was defined as metastatic recurrence within six months of margin-negative pancreatectomy. Cyclic immunofluorescence (CyCIF) analyses compared hybrid phenotypes in blood and tumors.

RESULTS

Blood samples were collected from 42 patients with PDAC prior to resection. Those with radiographically occult metastatic disease and rPDAC had higher preoperative CHC numbers compared to patients who did not (65.0 and 74.4, vs. 11.52 CHCs; p < 0.001). Patients with complete or near-complete pathologic responses to NAT had lower preoperative CHC numbers than partial and/or non-responders (1.7 vs. 13.1 CHCs; p = 0.008). When assessed longitudinally, those with partial pathologic response saw CHC levels become undetectable while on treatment but increase in the interval between NAT completion and resection. In contrast, patients with poor responses or development of metastatic disease experienced persistent CHC detection during therapy or rising levels prior to radiographic evidence of metastases. Further, in metastatic PDAC patients, treatment-induced phenotypic changes in hybrid cells mirrored those in paired metastatic tumor samples.

CONCLUSIONS

CHC enumeration and phenotyping display promise as a real-time indicator of disease burden, recurrence risk, and treatment response in PDAC. CHCs have great potential as tumor-derived biomarkers to optimize therapeutic strategies and improve survival in patients with PDAC.

Recurrence scoring system predicting early recurrence for patients with pancreatic ductal adenocarcinoma undergoing pancreatectomy and portomesenteric vein resection

BACKGROUND

Pancreatectomy with concomitant portomesenteric vein resection (PVR) enables patients with portomesenteric vein (PV) involvement to achieve radical resection of pancreatic ductal adenocarcinoma, however, early recurrence (ER) is frequently observed.

AIM

To predict ER and identify patients at high risk of ER for individualized therapy.

METHODS

Totally 238 patients undergoing pancreatectomy and PVR were retrospectively enrolled and were allocated to the training or validating cohort. Univariate Cox and LASSO regression analyses were performed to construct serum recurrence score (SRS) based on 26 serum-derived parameters. Uni- and multivariate Cox regression analyses of SRS and 18 clinicopathological variables were performed to establish a Nomogram. Receiver operating characteristic curve analysis was used to evaluate the predictive accuracy. Survival analysis was performed using Kaplan-Meier method and log-rank test.

RESULTS

Independent serum-derived recurrence-relevant factors of LASSO regression model, including postoperative carbohydrate antigen 19-9, postoperative carcinoembryonic antigen, postoperative carbohydrate antigen 125, preoperative albumin (ALB), preoperative platelet to ALB ratio, and postoperative platelets to lymphocytes ratio, were used to construct SRS [area under the curve (AUC): 0.855, 95%CI: 0.786-0.924]. Independent risk factors of recurrence, including SRS [hazard ratio (HR): 1.688, 95%CI: 1.075-2.652], pain (HR: 1.653, 95%CI: 1.052-2.598), perineural invasion (HR: 2.070, 95%CI: 0.827-5.182), and PV invasion (HR: 1.603, 95%CI: 1.063-2.417), were used to establish the recurrence nomogram (AUC: 0.869, 95%CI: 0.803-0.934). Patients with either SRS > 0.53 or recurrence nomogram score > 4.23 were considered at high risk for ER, and had poor long-term outcomes.

CONCLUSION

The recurrence scoring system unique for pancreatectomy and PVR, will help clinicians in predicting recurrence efficiently and identifying patients at high risk of ER for individualized therapy.

Postoperative serum tumor markers-based nomogram predicting early recurrence for patients undergoing radical resections of pancreatic ductal adenocarcinoma

BACKGROUND

Early recurrence (ER) is associated with dismal outcomes in patients undergoing radical resection for pancreatic ductal adenocarcinoma (PDAC). Approaches for predicting ER will help clinicians in implementing individualized adjuvant therapies. Postoperative serum tumor markers (STMs) are indicators of tumor progression and may improve current systems for predicting ER.

AIM

To establish an improved nomogram based on postoperative STMs to predict ER in PDAC.

METHODS

We retrospectively enrolled 282 patients who underwent radical resection for PDAC at our institute between 2019 and 2021. Univariate and multivariate Cox regression analyses of variables with or without postoperative STMs, were performed to identify independent risk factors for ER. A nomogram was constructed based on the independent postoperative STMs. Receiver operating characteristic curve analysis was used to evaluate the area under the curve (AUC) of the nomogram. Survival analysis was performed using Kaplan-Meier survival plot and log-rank test.

RESULTS

Postoperative carbohydrate antigen 19-9 and carcinoembryonic antigen levels, preoperative carbohydrate antigen 125 levels, perineural invasion, and pTNM stage III were independent risk factors for ER in PDAC. The postoperative STMs-based nomogram (AUC: 0.774, 95%CI: 0.713-0.835) had superior accuracy in predicting ER compared with the nomogram without postoperative STMs (AUC: 0.688, 95%CI: 0.625-0.750) (P = 0.016). Patients with a recurrence nomogram score (RNS) > 1.56 were at high risk for ER, and had significantly poorer recurrence-free survival [median: 3.08 months, interquartile range (IQR): 1.80-8.15] than those with RNS ≤ 1.56 (14.00 months, IQR: 6.67-24.80), P < 0.001).

CONCLUSION

The postoperative STMs-based nomogram improves the predictive accuracy of ER in PDAC, stratifies the risk of ER, and identifies patients at high risk of ER for tailored adjuvant therapies.

Predicting early gastric cancer risk using machine learning: A population-based retrospective study

Background

Early detection and treatment are crucial for reducing gastrointestinal tumour-related mortality. The diagnostic efficiency of the most commonly used diagnostic markers for gastric cancer (GC) is not very high. A single laboratory test cannot meet the requirements of early screening, and machine learning methods are needed to aid the early diagnosis of GC by combining multiple indicators.

Methods

Based on the XGBoost algorithm, a new model was developed to distinguish between GC and precancerous lesions in newly admitted patients between 2018 and 2023 using multiple laboratory tests. We evaluated the ability of the prediction score derived from this model to predict early GC. In addition, we investigated the efficacy of the model in correctly screening for GC given negative protein tumour marker results.

Results

The XHGC20 model constructed using the XGBoost algorithm could distinguish GC from precancerous disease well (area under the receiver operating characteristic curve [AUC] = 0.901), with a sensitivity, specificity and cut-off value of 0.830, 0.806 and 0.265, respectively. The prediction score was very effective in the diagnosis of early GC. When the cut-off value was 0.27, and the AUC was 0.888, the sensitivity and specificity were 0.797 and 0.807, respectively. The model was also effective at evaluating GC given negative conventional markers (AUC = 0.970), with the sensitivity and specificity of 0.941 and 0.906, respectively, which helped to reduce the rate of missed diagnoses.

Conclusions

The XHGC20 model established by the XGBoost algorithm integrates information from 20 clinical laboratory tests and can aid in the early screening of GC, providing a useful new method for auxiliary laboratory diagnosis.

A novel nomogram and risk stratification for early metastasis in cervical cancer after radical radiotherapy

OBJECT

This study aimed to establish an effective risk nomogram to predict the early distant metastasis (EDM) probability of cervical cancer (CC) patients treated with radical radiotherapy to aid individualized clinical decision-making.

METHODS

A total of 489 patients with biopsy-confirmed CC between December 2018 and January 2021 were enrolled. Logistic regression with the stepwise backward method was used to identify independent risk factors. The nomogram efficacy was evaluated by using the area under the receiver operating characteristic curve (AUC), C-index by 1000 bootstrap replications, etc. Finally, patients were divided into high- and low-risk groups of EDM based on the cut-off value of nomogram points.

RESULTS

36 (7.36%) CC patients had EDM, and 20 (55.6%) EDM had more than one metastatic site involved. Age below 51 (OR = 2.298, p < 0.001), tumor size larger than 4.5 cm (OR = 3.817, p < 0.001) and radiotherapy (OR = 3.319, p < 0.001) were independent risk factors of EDM. For the nomogram model, C-index was 0.701 (95% CI = 0.604-0.798), and 0.675 (95% CI = 0.578-0.760) after 1000 bootstrap resampling validations. The Hosmer-Lemeshow test demonstrated no overfitting (p = 0.924). According to the Kaplan-Meier curve of risk score, patients with high risk were more prone to get EDM (p < 0.001).

CONCLUSION

This is the first research to focus on EDM in CC patients. We have developed a robust scoring system to predict the risk of EDM in CC patients to screen out appropriate cases for consolidation therapy and more intensive follow-up.

Nomogram predicts risk of perineural invasion based on serum biomarkers for pancreatic cancer

BACKGROUND

Pancreatic cancer is a fatal tumor, and the status of perineural invasion (PNI) of pancreatic cancer was positively related to poor prognosis including overall survival and recurrence-free survival. This study aims to develop and validate a predictive model based on serum biomarkers to accurately predict the perineural invasion.

MATERIALS AND METHODS

The patients from No.924 Hospital of PLA Joint Logistic Support Force were included. The predictive model was developed in the training cohort using logistic regression analysis, and then tested in the validation cohort. The area under curve (AUC), calibration curves and decision curve analysis were used to validate the predictive accuracy and clinical benefits of nomogram.

RESULTS

A nomogram was developed using preoperative total bilirubin, preoperative blood glucose, preoperative CA19-9. It achieved good AUC values of 0.753 and 0.737 in predicting PNI in training and validation cohorts, respectively. Calibration curves showed nomogram had good uniformity of the practical probability of PNI. Decision curve analyses revealed that the nomogram provided higher diagnostic accuracy and superior net benefit compared to single indicators.

CONCLUSION

The present study constructed and validate a novel nomogram predicted the PNI of resectable PHAC patients with high stability and accuracy. Besides, it could better screen high-risk probability of PNI in these patients, and optimize treatment decision-making.

Prognostic analysis of the plasma fibrinogen combined with neutrophil-to-lymphocyte ratio in patients with non-small cell lung cancer after radical resection

BACKGROUND

To investigate the correlation between the fibrinogen combined with neutrophil-to-lymphocyte ratio (F-NLR) and the clinicopathologic features of non-small cell lung cancer (NSCLC) patients who underwent radical resection.

METHODS

This study reviewed the medical records of 289 patients with NSCLC who underwent radical resection. The patients were stratified into three groups based on F-NLR as follows: patients with low NLR and fibrinogen were group A, patients with high NLR or fibrinogen were group B, and patients with high NLR and fibrinogen were group C. Receiver operating characteristic curve and Youden index were used to determine the cutoff value of the NLR and fibrinogen. Survival curves were described by Kaplan-Meier method and compared by log-rank test. The univariate and multivariate analyses were performed with the Cox proportional hazard model to identify the prognostic factors.

RESULTS

A value of 3.19 was taken as the optimal cutoff value of NLR in this study. A value of 309 was used as the optimal cutoff value of fibrinogen. Cox multivariate analysis showed that tumor, nodes, metastasis (TNM) stage and F-NLR were independent prognostic factors affecting the survival rate of patients. The first-, third-, and fifth-year survival rates in group A were 99.2%, 96.6%, and 95.0%, respectively. The first-, third-, and fifth-year survival rates in group B were 98.4%, 76.6%, and 63.2%, respectively. The first-, third-, and fifth-year survival rates in group C were 91.3%, 41.1%, and 22.8%, respectively. F-NLR was significantly correlated with overall survival in patients with NSCLC (p < 0.001).

CONCLUSIONS

The F-NLR level is markedly related to the prognosis of patients with NSCLC undergoing radical surgery. Therefore, closer attention should be given to patients with NSCLC with a high F-NLR before surgery to provide postoperative adjuvant therapy.

Levels of tumor markers CEA/CA 19-9 in serum and peritoneal lavage predict postoperative recurrence in patients with pancreatic cancer

Aim

This study aimed to clarify the usefulness of tumor markers from peritoneal lavage in selecting patients with a high risk of recurrence and predicting site-specific recurrence in patients with pancreatic cancer.

Methods

The levels of serum carcinoembryonic antigen (CEA) and carbohydrate antigen (CA) 19-9 (sCEA/sCA 19-9) and paired peritoneal lavage CEA and CA 19-9 (pCEA/pCA 19-9) were measured in 90 patients with pancreatic cancer who underwent surgery. Using the cutoff values determined by maximally selected rank statistics for disease-free survival (DFS), the risk of recurrence and its patterns were evaluated in combination with different markers and different test specimens.

Results

In univariate and multivariate analysis, an elevated pCA 19-9 level (>1.3 U/mL) was an independent prognostic marker for both DFS (hazard ratio [HR], 2.391; P = .018) and overall survival (HR, 3.194; P = .033). Combination analyses contributed to further stratification of a very high risk of recurrence. Of the 58 patients with resectable pancreatic cancer who underwent curative resection, elevated pCA19-9 was also associated with inferior DFS and overall survival (OS). Patients with elevated pCA 19-9 levels were more likely to have an earlier onset of peritoneal recurrence than those with normal pCA 19-9 levels (P = .048, Gehan-Breslow-Wilcoxon test).

Conclusion

pCA 19-9 is a reliable marker for predicting postoperative recurrence in patients with pancreatic cancer after surgery. Further risk stratification can be achieved by using combination assays. The combination of pCA 19-9 and sCA19-9 also serves as a predictor of recurrence site-specific recurrence.