Clinical and prognostic significance of combined plasma fibrinogen concentrations and the monocyte-to-lymphocyte ratio in patients with ovarian cancer

A clinicoradiological model based on clinical and CT features for preoperative prediction of histological classification in patients with epithelial ovarian cancers: a two-center study

OBJECTIVES

To develop and validate a clinicoradiological model integrating clinical and computed tomography (CT) features to preoperative predict histological classification in patients with epithelial ovarian cancers (EOCs).

METHODS

This retrospective study included 470 patients who were pathologically proven EOCs and performed by contrast enhanced CT before treatment from center I (training cohort, N = 329; internal test cohort, N = 141) and 83 EOC patients who were included as an external test cohort from center II. The univariate analysis and multivariate logistic regression analysis were used to select significant clinical and CT features. The significant clinical model was developed based on clinical characteristics. The significant radiological model was established by CT features. The significant clinical and CT features were used to construct the clinicoradiological model. Model performances were evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, the Brier score and decision curve analysis (DCA). The AUCs were compared by net reclassification index (NRI) and integrated discrimination improvement (IDI).

RESULTS

The significant clinical and CT parameters including age, transverse diameter, morphology, margin, ascites and lymphadenopathy were incorporated to build the clinicoradioligical model. The clinicoradiological model showed relatively satisfactory discrimination between type I and type II EOCs with the AUC of 0.841 (95% confidence interval [CI] 0.797-0.886), 0.874 (95% CI 0.811-0.937) and 0.826 (95% CI 0.729-0.923) in the training, internal and external test cohorts, respectively. The NRI and IDI showed the clinicoradiological model significantly performed than those of the clinical model (all P < 0.05). No statistical significance was found between radiological and clinicoradiological model. The clinicoradiological model demonstrated optimal classification accuracy and clinical application value.

CONCLUSION

The easily accessible nomogram based on the clinicoradiologic model showed favorable performance in distinguishing between type I and type II EOCs and could therefore be used to improve the clinical management of EOC patients.

Characterization of blood inflammatory markers in patients with non-small cell lung cancer

OBJECTIVE

To investigate the differences and correlation between blood inflammatory indexes such as monocytes (MONO), lymphocytes (LYM), haemoglobin (HGB), neutrophils (NEU), platelets (PLT), ultrasensitive C-reactive protein, albumin and platelet/lymphocyte ratio (PLR), NEU/LYM ratio (NLR), MONO/LYM ratio (MLR) and clinicopathologic characteristics of patients with non-small cell lung cancer (NSCLC).

METHODS

187 patients with NSCLC who were first diagnosed in 2017-2023 and 102 with healthy check-ups during the same period (control group) were retrospectively selected as study subjects to compare the differences in inflammatory indexes between the two groups and the levels of inflammatory indexes in NSCLC patients with different clinicopathologic characteristics.

RESULTS

Correlation analysis between blood inflammatory indexes and clinicopathologic features in NSCLC group showed that C-reactive protein, CAR, and PLR values were different in different pathologic types (P<0.05). The values of NEU, MONO, C-reactive protein, MLR, NLR, CAR and albumin were different among various degrees of differentiation (P<0.05). There were differences in LYM, albumin, MLR, NLR, CAR, and C-reactive protein among M stage subgroups (P<0.05). Analysis of the efficacy of early diagnosis of non-small cell lung cancer has been shown, the AUC of NLR was 0.796, sensitivity of 0.679, specificity of 0.176, 95% CI=0.743-0.849 (P<0.001). The AUC of albumin was 0.977, the sensitivity was 0.941, the specificity was 0.941, and 95% CI was 0.959-0.994 (P<0.001).

CONCLUSION

Blood inflammatory indexes are closely associated with NSCLC and vary according to pathologic features. Blood inflammatory indices can predict tumor pathologic staging and guide treatment for patients with NSCLC.

New use of preoperative fibrinogen in ovarian cancer management

Background

Ovarian cancer (OC) is often diagnosed at an advanced stage due to the absence of specific symptoms in its early stages. And the prognosis greatly depends on when the disease is diagnosed. Thus, we conducted to evaluate the value of preoperative fibrinogen (Fib) levels for the diagnosis of OC in the hope of improving its diagnostic efficiency.

Methods

A total of 126 ovarian tumor patients were retrospectively included in this study. Four candidate OC markers, including cancer antigen 125 (CA125), Fib, platelet (PLT) and homocysteine (Hcy) were employed to establish a diagnosis model for OC. The diagnostic performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC) and Youden index.

Results

All included markers could be used for the diagnosis of OC. The AUCs of CA125, Fib, PLT and Hcy were 0.881, 0.825, 0.676 and 0.647, respectively. The new diagnosis model combining CA125 and Fib (CA125-Fib) had a higher AUC (0.924), Youden index (0.730), and best sensitivity (SN) (74.6%) and specificity (SP) (98.41%). CA125-Fib also had a high value in the diagnosis of stage I-II OC (AUC, Youden index, SN and SP: 0.853, 0.624, 81.48% and 80.95%).

Conclusions

Fib could be used for OC diagnosis. In particular, the combination of Fib and CA125 could further improve the diagnostic efficiency. And the diagnostic value of PLT and Hcy was found to be poor.

Development and validation of a prognostic nomogram model incorporating routine laboratory biomarkers for preoperative patients with endometrial cancer

BACKGROUND

Some biomarkers collected from routine laboratory tests have shown important value in cancer prognosis. The study aimed to evaluate the prognostic significance of routine laboratory biomarkers in patients with endometrial cancer (EC) and to develop credible prognostic nomogram models for clinical application.

METHODS

A total of 727 patients were randomly divided into a training set and a validation set. Cox proportional hazards models were used to evaluate each biomarker's prognostic value, and independent prognostic factors were used to generate overall survival (OS) and progression-free survival (PFS) nomgrams. The efficacy of the nomograms were evaluated by Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves, decision curve analysis (DCA), calibration curves, X-tile analysis and Kaplan‒Meier curves.

RESULTS

Ten significant biomarkers in multivariate Cox analysis were integrated to develop OS and PFS nomograms. The C-indices of the OS- nomogram in the training and validation sets were 0.885 (95% confidence interval (CI), 0.810-0.960) and 0.850 (95% CI, 0.761-0.939), respectively; those of the PFS- nomogram in the training and validation sets were 0.903 (95% CI, 0.866-0.940) and 0.825 (95% CI, 0.711-0.939), respectively. ROC, DCA and calibration curves showed better clinical application value for the nomograms incorporating routine laboratory biomarkers. X-tile analysis and Kaplan‒Meier curves showed that the nomograms were stable and credible in evaluating patients at different risks.

CONCLUSIONS

Nomogram models incorporating routine laboratory biomarkers, including NLR, MLR, fibrinogen, albumin and AB blood type, were demonstrated to be simple, reliable and favourable in predicting the outcomes of patients with EC.

Multi-clinical index classifier combined with AI algorithm model to predict the prognosis of gallbladder cancer

Objectives

It is significant to develop effective prognostic strategies and techniques for improving the survival rate of gallbladder carcinoma (GBC). We aim to develop the prediction model from multi-clinical indicators combined artificial intelligence (AI) algorithm for the prognosis of GBC.

Methods

A total of 122 patients with GBC from January 2015 to December 2019 were collected in this study. Based on the analysis of correlation, relative risk, receiver operator characteristic curve, and importance by AI algorithm analysis between clinical factors and recurrence and survival, the two multi-index classifiers (MIC1 and MIC2) were obtained. The two classifiers combined eight AI algorithms to model the recurrence and survival. The two models with the highest area under the curve (AUC) were selected to test the performance of prognosis prediction in the testing dataset.

Results

The MIC1 has ten indicators, and the MIC2 has nine indicators. The combination of the MIC1 classifier and the "avNNet" model can predict recurrence with an AUC of 0.944. The MIC2 classifier and "glmet" model combination can predict survival with an AUC of 0.882. The Kaplan-Meier analysis shows that MIC1 and MIC2 indicators can effectively predict the median survival of DFS and OS, and there is no statistically significant difference in the prediction results of the indicators (MIC1: χ² = 6.849, P = 0.653; MIC2: χ² = 9.14, P = 0.519).

Conclusions

The MIC1 and MIC2 combined with avNNet and mda models have high sensitivity and specificity in predicting the prognosis of GBC.

[Establishment and validation of a nomogram for predicting prognosis of gastric neuroendocrine neoplasms based on data from 490 cases in a single center]

OBJECTIVE

To develop and validate a nomogram for predicting outcomes of patients with gastric neuroendocrine neoplasms (G-NENs).

METHODS

We retrospectively collected the clinical data from 490 patients with the diagnosis of G-NEN at our medical center from 2000 to 2021. Log-rank test was used to analyze the overall survival (OS) of the patients. The independent risk factors affecting the prognosis of G-NEN were identified by Cox regression analysis to construct the prognostic nomogram, whose performance was evaluated using the C-index, receiver-operating characteristic (ROC) curve, area under the ROC curve (AUC), calibration curve, DCA, and AUDC.

RESULTS

Among the 490 G-NEN patients (mean age of 58.6±10.92 years, including 346 male and 144 female patients), 130 (26.5%) had NET G1, 54 (11.0%) had NET G2, 206 (42.0%) had NEC, and 100 (20.5%) had MiNEN. None of the patients had NET G3. The numbers of patients in stage Ⅰ-Ⅳ were 222 (45.3%), 75 (15.3%), 130 (26.5%), and 63 (12.9%), respectively. Univariate and multivariate analyses identified age, pathological grade, tumor location, depth of invasion, lymph node metastasis, distant metastasis, and F-NLR as independent risk factors affecting the survival of the patients (P < 0.05). The C-index of the prognostic nomogram was 0.829 (95% CI: 0.800-0.858), and its AUC for predicting 1-, 3- and 5-year OS were 0.883, 0.895 and 0.944, respectively. The calibration curve confirmed a good consistency between the model prediction results and the actual observations. For predicting 1-year, 3-year and 5-year OS, the TNM staging system and the nomogram had AUC of 0.033 vs 0.0218, 0.191 vs 0.148, and 0.248 vs 0.197, respectively, suggesting higher net benefit and better clinical utility of the nomogram.

CONCLUSION

The prognostic nomogram established in this study has good predictive performance and clinical value to facilitate prognostic evaluation of individual patients with G-NEN.

Lymphocyte-to-monocyte ratio after primary surgery is an independent prognostic factor for patients with epithelial ovarian cancer: A propensity score matching analysis

Background

The aim of this study was to elucidate the prognostic value of preoperative lymphocyte-to-monocyte ratio (LMR) after primary surgery in epithelial ovarian cancer (EOC) patients using a propensity score matching (PSM) analysis.

Methods

We retrospectively reviewed consecutive EOC patients who underwent primary surgery between January 2008 and December 2019. Patients were divided into two groups according to the optimal cutoff value of preoperative LMR. PSM (1:1) was conducted to eliminate confounding factors. A Cox proportional hazards model and the Kaplan-Meier estimator were employed to investigate the potential prognostic factors.

Results

A total of 368 EOC patients were included in this study. The optimal cutoff value of LMR was identified as 4.65. Low preoperative LMR was significantly correlated with low albumin, high CA125 level, more blood loss, a high likelihood of ascites, advanced FIGO stage, and poor differentiation (all p < 0.05). After matching, Kaplan-Meier curves showed that the group with LMR < 4.65 experienced significantly shorter OS (p = 0.015). Multivariate Cox analysis revealed that low LMR (HR = 1.49, p = 0.041), advanced FIGO stage (HR = 5.25, p < 0.001), and undefined residual disease (HR = 3.77, p = 0.002) were independent factors in predicting poor OS. A forest plot revealed that LMR had better prognostic value in younger EOC patients, patients with BMI ≥ 25 kg/m² and albumin ≥ 35 g/L, CA125 ≥ 35 U/L, patients who had undergone optimal surgery, and those who had completed chemotherapy. Additionally, low-LMR patients who had undergone incomplete chemotherapy had a shorter median OS compared with those who completed chemotherapy treatment (48.5 vs. 105.9 months, p = 0.026).

Conclusions

LMR could be used as an independent prognostic factor for EOC patients after primary surgery; a noticeable negative effect of LMR was observed among EOC patients with age < 65, good preoperative nutritional status, and more aggressive tumor biology, and among those who underwent optimal surgery. Completing adjuvant chemotherapy is essential to improve survival outcomes among EOC patients with LMR < 4.65 after surgery.

Driving Immune Responses in the Ovarian Tumor Microenvironment

Ovarian cancer is the leading cause of death among gynecological neoplasms, with an estimated 14,000 deaths in 2019. First-line treatment options center around a taxane and platinum-based chemotherapy regimen. However, many patients often have recurrence due to late stage diagnoses and acquired chemo-resistance. Recent approvals for bevacizumab and poly (ADP-ribose) polymerase inhibitors have improved treatment options but effective treatments are still limited in the recurrent setting. Immunotherapy has seen significant success in hematological and solid malignancies. However, effectiveness has been limited in ovarian cancer. This may be due to a highly immunosuppressive tumor microenvironment and a lack of tumor-specific antigens. Certain immune cell subsets, such as regulatory T cells and tumor-associated macrophages, have been implicated in ovarian cancer. Consequently, therapies augmenting the immune response, such as immune checkpoint inhibitors and dendritic cell vaccines, may be unable to properly enact their effector functions. A better understanding of the various interactions among immune cell subsets in the peritoneal microenvironment is necessary to develop efficacious therapies. This review will discuss various cell subsets in the ovarian tumor microenvironment, current immunotherapy modalities to target or augment these immune subsets, and treatment challenges.

Clinical and predictive significance of Plasma Fibrinogen Concentrations combined Monocyte-lymphocyte ratio in patients with Diabetic Retinopathy

Diabetic retinopathy (DR) is one of the most common causes of blindness and visual impairment. Therefore, early prediction of its occurrence and progression is important. This study aimed to assess the clinical and predictive significance of plasma fibrinogen concentrations combined monocyte-lymphocyte ratio (FC-MLR) in patients with DR. A total of 307 patients with type 2 diabetes (T2D) were enrolled. Plasma fibrinogen concentrations and peripheral white blood cells were measured, and MLR was calculated, and the associations of FC-MLR with DR and severity of disease were assessed. Regression analysis and receiver operating characteristic (ROC) curves were performed to evaluate the risk factors and predictive power of FC-MLR for DR and severity of disease, respectively. DR patients showed higher fibrinogen concentrations and a higher MLR than did T2D patients without complications (P<0.01); Moreover, DR patients in proliferative stage also showed higher fibrinogen concentrations and a higher MLR than did those in non-proliferative stage (P<0.01). FC-MLR was closely associated with occurrence and severity of DR (P<0.01), and was an independent risk factor for them (OR=6.123, 95%CI: 3.122-17.102; and 7.932, 95%CI: 4.315-16.671, respectively; P<0.001). The predictive sensitivity and specificity for DR and severity of disease were 0.86 and 0.68, and 0.85 and 0.73, respectively. The study suggests that FC-MLR may be used as a predictor for the risk and progression of diabetic retinopathy.

A novel diagnostic nomogram based on serological and ultrasound findings for preoperative prediction of malignancy in patients with ovarian masses

OBJECTIVE

To develop a novel diagnostic nomogram model to predict malignancy in patients with ovarian masses.

METHODS

In total, 1277 patients with ovarian masses were retrospectively analyzed. Receiver operating characteristic (ROC) analysis was performed to identify valuable predictive factors. Univariate and multivariate logistic regression analyses were used to identify risk factors for ovarian cancer. Subsequently, a predictive nomogram model was developed. The performance of the nomogram model was assessed by its calibration and discrimination in a validation cohort. Decision curve analysis (DCA) was applied to assess the clinical net benefit of the model.

RESULTS

Overall, 496 patients (38.8%) had ovarian cancer. Eighteen parameters were significantly different between the malignant and benign groups. Five parameters were identified as being most optimal for predicting malignancy, including age, carbohydrate antigen 125, fibrinogen-to-albumin ratio, monocyte-to-lymphocyte ratio, and ultrasound result. These parameters were incorporated to establish a nomogram model, and this model exhibited an area under the ROC curve (AUC) of 0.937 (95% confidence interval [CI], 0.920-0.954). The model was also well calibrated in the validation cohort and showed an AUC of 0.925 (95%CI, 0.896-0.953) at the cut-off point of 0.298. DCA confirmed that the nomogram model achieved the best clinical utility with almost the entire range of threshold probabilities. The model has demonstrated superior efficacy in predicting malignancy compared to currently available models, including the risk of ovarian malignancy algorithm, copenhagen index, and the risk of malignancy index. More importantly, the nomogram established here showed potential value in identification of early-stage ovarian cancer.

CONCLUSION

The cost-effective and easily accessible nomogram model exhibited favorable accuracy for preoperative prediction of malignancy in patients with ovarian masses, even at early stages.

Oncological impact of inflammatory biomarkers in elderly patients treated with radical cystectomy for urothelial bladder cancer

Objective

To evaluate the impact of preoperative markers of systemic inflammation on complications and oncological outcomes in patients aged ≥75 years treated with radical cystectomy (RC) for urothelial bladder cancer (UBC).

Patients and methods

The clinical data of 694 patients treated with open RC for UBC at our institution between January 2008 and December 2015 were retrospectively reviewed. Patients aged <75 years, with distant metastases, other-than-urothelial histological type, comorbidities that could affect the systemic inflammatory markers, and patients who received neoadjuvant chemotherapy were excluded. Multivariable regression models were built for the prediction of major postoperative surgical complications, disease recurrence, cancer-specific mortality (CSM), and overall mortality (OM).

Results

The median (interquartile range [IQR]) age at surgery was 79 (75–83) years. Major postoperative surgical complications were registered in 41.9% of the patients. The 5-year overall survival, cancer-specific survival and recurrence-free survival rates were 42.4% (95% confidence interval [CI] 34.7–49.9%), 70.3% (95% CI 62.3–76.9%), and 59.8% (95% CI 52.4–66.5), respectively. At multivariable analysis, higher levels of fibrinogen and a modified Glasgow Prognostic Score (mGPS) of 1 and 2 at baseline were independently associated with higher risk of major postoperative complications and of CSM. The inclusion of mGPS and fibrinogen to a standard multivariable model for recurrence and for CSM increased discrimination from 69.4% to 73.0% and from 71.3% to 73.9%, respectively. Preoperative neutrophil-to-lymphocyte ratio of >3 was independently associated with OM (hazard ratio 1.38, 95% CI 1.01–1.77; P = 0.01).

Conclusions

In a cohort of elderly patients with UBC treated with RC, fibrinogen and mGPS appeared to be the most relevant prognostic measurements and increased the accuracy of clinicopathological preoperative models to predict major postoperative complications, disease recurrence and mortality.

Abbreviations

ASA: American Society of Anesthesiologists; CCI: Charlson Comorbidity Index; CIS: carcinoma in situ; CRP: C-reactive protein; CSM: cancer-specific mortality; CSS: cancer-specific survival; ECOG PS: Eastern Cooperative Oncology Group Performance Status; HDL: high-density lipoprotein; (S)HR: (subdistribution) hazard ratio; LND: lymphadenectomy; LVI: lymphovascular invasion; mGPS: modified Glasgow Prognostic Score; NLR: neutrophil-to-lymphocyte ratio; NOC: non-organ-confined; OM: overall mortality; OR: odds ratio; OS: overall survival; RC: radical cystectomy; RNU: radical nephroureterectomy; UBC: urothelial bladder cancer; UTUC: upper urinary tract urothelial carcinoma

Prognostic significance of preoperative circulating FAR and FCI scores in patients with ovarian cancer

BACKGROUND

Ovarian malignancy is among the most common lethal cancers in gynaecology. It has been considered that nutrition status and inflammation are two essential factors associated with the poor survival rates of patients suffering from multiple tumours.

METHODS

This research included 174 subjects who suffered from FIGO stage I-IV OC and underwent surgeries between May 2008 and March 2013.

RESULTS

The results showed that FAR, T3-T4 depth, high CA125, stage III-IV and metastasis were highly associated with low overall survival rates. Moreover, higher FCI was markedly correlated with decreased survival in patients with OC.

CONCLUSIONS

The combination of FAR and CA125 could be a new non-invasive marker in blood and probably assist physicians in evaluating the prognosis of patients.

The Prognostic Value of Neutrophil-to-lymphocyte Ratio and Monocyte-to-lymphocyte Ratio in Metastatic Gastric Cancer Treated with Systemic Chemotherapy

Background: The prognostic value of neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR) in metastatic gastric cancer (mGC) treated with systemic chemotherapy is largely unknown, especially second-line chemotherapy. We retrospectively investigated the prognostic value of baseline NLR and MLR in the progression of mGC with systemic chemotherapy.

Methods: Patients with mGC diagnosed by pathology from January 2010 to December 2018 were identified. Baseline NLR and MLR were collected before treatment. The time to progression during or after first-line therapy from diagnosis (PFS1), and during or after second-line chemotherapy (PFS2) were primary endpoint. Overall survival (OS) was calculated from diagnosis to the date of death or final follow-up.

Results: 537 patients with first-line chemotherapy were included in the retrospective study. The cutoff values of NLR and MLR were 2.610 and 0.285, respectively. Pretreatment NLR and MLR were significantly independent prognostic factors for PFS1 (hazard ratio [HR]=1.597, 95% CI 1.261-2.022, P<0.001 and HR=1.574, 95% CI 1.239-1.999, P<0.001) and OS (HR=1.448, 95% CI 1.030-2.034, P=0.033 and HR=1.622, 95% CI 1.148-2.291, P=0.006). For 172 patients treated with second-line chemotherapy, the cutoff value of MLR was 0.355 and MLR maintained a significant association with PFS2 (HR=1.589, 95% CI 1.073-2.354, P=0.021) in multivariate analysis.

Conclusions: Elevated NLR and MLR were markedly related to the worse PFS1 and OS in mGC performed with first-line chemotherapy. In patients with second-line therapy, MLR was more closely connected to prognosis and was a significantly independent prognostic factor for PFS2.