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

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.