Independent predictive value of blood inflammatory composite markers in ovarian cancer: recent clinical evidence and perspective focusing on NLR and PLR

Zhang, Chuanlong; Jiang, Xiaochen; Li, Yi; Pan, Xue; Gao, Mengqi; Chen, Yan; Pang, Bo

doi:10.1186/s13048-023-01116-2

Cited by 25 publications

(23 citation statements)

References 94 publications

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“…However, the addition of radiomic features did not significantly improve the performance of the model possibly due to a small sample size. The predictive value of some blood inflammatory composite markers in OC has been extensively reported ( 20 ). They can be used for early detection and differential diagnosis of OC and can also predict survival, treatment response, and recurrence in the affected patients.…”

Section: Discussionmentioning

confidence: 99%

Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics

Fu,

Wang,

Lin

et al. 2024

Front. Med.

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ObjectiveHigh-grade serous ovarian cancer (HGSOC) has the highest mortality rate among female reproductive system tumors. Accurate preoperative assessment is crucial for treatment planning. This study aims to develop multitask prediction models for HGSOC using radiomics analysis based on preoperative CT images.MethodsThis study enrolled 112 patients diagnosed with HGSOC. Laboratory findings, including serum levels of CA125, HE-4, and NLR, were collected. Radiomic features were extracted from manually delineated ROI on CT images by two radiologists. Classification models were developed using selected optimal feature sets to predict R0 resection, lymph node invasion, and distant metastasis status. Model evaluation was conducted by quantifying receiver operating curves (ROC), calculating the area under the curve (AUC), De Long’s test.ResultsThe radiomics models applied to CT images demonstrated superior performance in the testing set compared to the clinical models. The area under the curve (AUC) values for the combined model in predicting R0 resection were 0.913 and 0.881 in the training and testing datasets, respectively. De Long’s test indicated significant differences between the combined and clinical models in the testing set (p = 0.003). For predicting lymph node invasion, the AUCs of the combined model were 0.868 and 0.800 in the training and testing datasets, respectively. The results also revealed significant differences between the combined and clinical models in the testing set (p = 0.002). The combined model for predicting distant metastasis achieved AUCs of 0.872 and 0.796 in the training and test datasets, respectively. The combined model displayed excellent agreement between observed and predicted results in predicting R0 resection, while the radiomics model demonstrated better calibration than both the clinical model and combined model in predicting lymph node invasion and distant metastasis. The decision curve analysis (DCA) for predicting R0 resection favored the combined model over both the clinical and radiomics models, whereas for predicting lymph node invasion and distant metastasis, DCA favored the radiomics model over both the clinical model and combined model.ConclusionThe identified radiomics signature holds potential value in preoperatively evaluating the R0, lymph node invasion and distant metastasis in patients with HGSC. The radiomics nomogram demonstrated the incremental value of clinical predictors for surgical outcome and metastasis estimation.

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Section: Discussionmentioning

confidence: 99%

Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics

Fu,

Wang,

Lin

et al. 2024

Front. Med.

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“… 31 , 32 The progression of tumors is linked to inflammation, which is initiated by a range of blood factors, including neutrophils, lymphocytes, and platelets. 33 , 34 Neutrophils have been identified as possessing tumor-promoting characteristics, as they are able to migrate to the tumor microenvironment and secrete a variety of pro-angiogenic factors, such as Bv8, VEGFA, and MMP9, thereby promoting tumor progression. 35 , 36 Additionally, neutrophils are capable of secreting pro-inflammatory factors, which can result in genetic instability and cellular DNA damage, ultimately leading to carcinogenesis.…”

Section: Discussionmentioning

confidence: 99%

Preoperative Systemic Immune-Inflammation Index is a Potential Biomarker in Adult Patients with High-Grade Gliomas Undergoing Radical Resection

Jiang¹,

Wang²,

Zhang³

2023

JIR

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Background Increasing evidence has highlighted that systemic immune-inflammation index (SII), a recently developed prognostic biomarker that utilizes peripheral platelet, lymphocyte and neutrophil counts, is associated with unfavorable prognosis in various tumors. Nevertheless, the prognostic significance of SII in high-grade gliomas patients undergoing radical resection remains unclear. Therefore, the present study aimed to assess the potential of SII as a prognostic biomarker in this patient population. Methods A total of 111 adult patients with high-grade gliomas who underwent radical resection were consecutively enrolled in this investigation. The study involved the categorization of patients into high and low SII groups using predetermined cut-off values. Subsequently, forward stepwise logistic regression was employed to identify autonomous predictors for early gliomas recurrence. To mitigate the impact of confounding factors, a propensity score matching (PSM) analysis was performed between high and low SII patients. Finally, the Kaplan-Meier approach was utilized to compare the progression-free survival (PFS) and overall survival (OS) of the two groups. Results The study involved the categorization of patients into two groups based on their SII levels, namely high SII (> 604.8) and low SII (≤ 604.8) groups. Forward stepwise logistic regression revealed that high SII (p < 0.001) and tumor size ≥ 50 mm (p < 0.001) were significantly related to early recurrence of gliomas. Furthermore, the results indicate that PFS and OS were significantly shorter in the high SII group compared to the low SII group, both before and after PSM (p < 0.05). Conclusion Preoperative biomarker SII can serve as a prognostic biomarker for early recurrence and prognosis in patients with high-grade gliomas undergoing radical resection. Furthermore, the combination of tumor size and SII demonstrates a robust predictive capacity for early recurrence and prognosis in this patient population.

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“…Platelets, neutrophils, and lymphocytes, which can aggregate in vessels and release factors, such as vascular endothelial growth factor, TGF-β, and platelet-derived growth factor, can affect the biological behavior of cancer cells. [40][41][42][43][44] Thrombopoietin and inflammatory mediators released by cancer cells may stimulate platelet growth, and in turn, tumor growth. Neutrophils, by releasing VEGF and matrix metalloproteinase, can promote angiogenesis, tumor growth, and metastasis.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic value of CA125, HE4, and systemic immune-inflammation index in the preoperative investigation of ovarian masses

Song,

Qi,

Zhao

et al. 2023

Medicine

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This study aimed to ascertain the diagnostic accuracy of CA125, HE4, systemic immune-inflammation index (SII), fibrinogen-to-albumin ratio (FAR), prognostic nutritional index (PNI), and their combination for ovarian cancer (OC) to discover an optimal combined diagnostic index for early diagnosis of OC. A thorough investigation was conducted to ascertain the correlation between these markers and the pathological characteristics of OC, thereby providing a foundation for early identification and treatment of this disorder. One hundred seventy patients with documented OC and benign ovarian tumors (BOTs) treated at Hebei General Hospital between January 2019 and December 2022 were included in this retrospective study. Data analysis was conducted using IBM SPSS Statistics version V26.0, MedCalc Statistical Software version 19.4.0, and the R Environment for Statistical Computing software (R Foundation for Statistical Computing). Isolated CA125 showed the best application value for differentiating benign ovarian tumors from OC when the defined variables were compared separately. The combination of CA125, HE4, FAR, SII, and PNI displayed a greater area under the operating characteristic curve curve than any one of them or other combinations of the 5 variables. Compared to CA125 alone, the combination of CA125, HE4, FAR, SII, and PNI showed a slight gain in sensitivity (83.91%), negative predictive value (83.91%), accuracy (85.88%), and a decrease in negative likelihood ratio (0.180%). Higher preoperative CA125, HE4, SII, and FAR levels, and lower PNI levels predicted a higher probability of advanced OC progression and lymph node metastasis. FAR has better application value than other inflammation-related markers (PNI and SII). This study suggests that preoperative serum SII, PNI, and FAR may be clinically valuable markers in patients with OC. FAR has better application value than other inflammation-related markers (PNI and SII). As we delve deeper into the inflammatory mechanisms associated with tumors, we may discover more effective combinations of tumor and inflammatory biomarkers.

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Independent predictive value of blood inflammatory composite markers in ovarian cancer: recent clinical evidence and perspective focusing on NLR and PLR

Cited by 25 publications

References 94 publications

Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics

Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics

Preoperative Systemic Immune-Inflammation Index is a Potential Biomarker in Adult Patients with High-Grade Gliomas Undergoing Radical Resection

Diagnostic value of CA125, HE4, and systemic immune-inflammation index in the preoperative investigation of ovarian masses

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