&lt;p&gt;Prognostic Significance of the Preoperative Lymphocyte to Monocyte Ratio in Patients with Gallbladder Carcinoma&lt;/p&gt;

Xu, Weiyu; Wu, Xiaoqian; Wang, Xuezhu; Yu, Si; Xu, Gang; Xiong, Jianping; Zhang, Junwei; Sang, Xinting; Zheng, Yongchang; Liu, Wei

doi:10.2147/cmar.s243326

Cited by 6 publications

(7 citation statements)

References 40 publications

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“…But what is interesting is that the predictive value of MLR in patients with GBC after the operation is related to age. There is a significant difference in the cut-off value of MLR between ≤ 60 years old and > 60 years old in patients with GBC ( 16 ). In our study, ALP (RR=1.495, 95% CI: 1.145-1.952) and GGT (RR=1.472, 95%CI:1.126-1.925) were risk factors for the recurrence of GBC.…”

Section: Discussionmentioning

confidence: 99%

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

Zhou

Chen

et al. 2023

Front. Oncol.

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ObjectivesIt 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.MethodsA 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.ResultsThe 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).ConclusionsThe MIC1 and MIC2 combined with avNNet and mda models have high sensitivity and specificity in predicting the prognosis of GBC.

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Section: Discussionmentioning

confidence: 99%

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

Zhou

Chen

et al. 2023

Front. Oncol.

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“…Lymphocyte is also considered as valuable markers and effective indexes for survival prognosis of cancers (18,19). The patients with lower lymphocyte count are more likely to have poor survival outcome (20,21). LMR and monocyte are signi cantly associated with the differentiation of benign and malignant gallbladder diseases in univariate analysis and had prediction ability, but they are not included in the prediction model by stepwise multivariate regression analysis.…”

Section: Discussionmentioning

confidence: 99%

Predictive nomogram for preoperative differential diagnosis of benign and malignant gallbladder lesions. Nomogram for diagnosis of gallbladder cancer

Wu,

Zhao,

ran

et al. 2023

Preprint

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Background Gallbladder cancer is a rare and fatal malignant tumor, and difficult to be found in time due to the atypical symptoms. Early detection and treatment of gallbladder cancer is essential. Methods By evaluating the relationship between clinical features and contrast-enhanced ultrasound nature of 237 cases of gallbladder lesions, the preoperative predictors with differential diagnosis value for malignancy and benignity were identified and integrated into the nomogram by stepwise multivariate logistic regression analysis. The predictive performance of the nomogram was assessed by receiver operating characteristic curve analysis, calibration curve analysis and decision curve analysis, and compared with the prediction model combining neutrophil-to-lymphocyte ratio and CA19-9. Bootstrap analysis was used for the interval validation. Results The nomogram predicting benign and malignant gallbladder lesions was constructed by the predictors with preoperative diagnostic value (L, DBil, gallbladder wall thickness and features of gallbladder lesions by CEUS). The C-statistic of the nomogram is 0.981 and superior than the C-statistic of the combination of neutrophil-to-lymphocyte ratio and CA19-9. The prediction accuracy, reliability and clinical utility were considerable in the performance evaluation. Internal validation of the nomogram was performed using Bootstrap with 1000 resamplings, yielding a Kappa value of 0.800 and an Accuracy of 0.911. Conclusions The predictive nomogram is conducive to the differentiation of benign and malignant gallbladder lesions and play an important guiding role in the clinical treatment decision-making process.

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“…However, these prognostic factors are far from optimal in offering accurate information, since survival substantially varies among patients with the same TNM stage. Previous studies have usually focused on a single or a few parameters in blood to identify prognostic predictors for cancer (9,17,34). We previously reported that high counts of circulating P, N, and low P were significantly associated with reduced survival in patients with OSCC (13).…”

Section: Discussionmentioning

confidence: 99%

“…From the clinical perspective, immune, inflammatory, and nutritional status has also been identified as source for novel biomarkers in patient diagnosis and prognostic prediction. For example, lymphocyte counts (L), lymphocyte-monocyte ratio (LMR), neutrophil-lymphocyte ratio (NLR), plateletlymphocyte ratio (PLR), systemic immune-inflammation index (SII), prognostic nutritional index (PNI), and C-reactive protein (CRP) have been developed and verified with prognostic values in a broad spectrum of cancers (8)(9)(10)(11)(12). In our previous studies, high SII and plateletneutrophil-lymphocyte score (PNL), low PNI, high counts of platelets and neutrophils, and low lymphocyte counts were significantly associated with reduced survival in patients with resectable OSCC (12)(13)(14).…”

Section: Introductionmentioning

confidence: 99%

Identification of diagnostic and prognostic signatures derived from preoperative blood parameters for oral squamous cell carcinoma

Wu¹,

Yao²,

Dai³

et al. 2021

Ann Transl Med

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Background: We aimed to develop novel diagnostic and prognostic signatures based on preoperative inflammatory, immunological, and nutritional parameters in blood (PIINPBs) by machine learning algorithms for patients with oral squamous cell carcinoma (OSCC). Methods: A total of 486 OSCC patients and 200 age and gender-matched non-OSCC patients who were diagnosed and treated at our institution for noninfectious, nontumor diseases were retrospectively enrolled and divided into training and validation cohorts. Based on PIINPB, 6 machine learning classifiers including random forest, support vector machine, extreme gradient boosting, naive Bayes, neural network, and logistic regression were used to derive diagnostic models, while least absolute shrinkage and selection operator (LASSO) analyses were employed to construct prognostic signatures. A novel prognostic nomogram integrating a PIINPB-derived prognostic signature and selected clinicopathological parameters was further developed. Performances of these signatures were assessed by receiver operating characteristic (ROC) curves, calibrating curves, and decision tree. Results: Diagnostic models developed by machine learning algorithms from 13 PIINPBs, which included counts of white blood cells (WBC), neutrophils (N), monocytes (M), lymphocytes (L), platelets (P), albumin (ALB), and hemoglobin (Hb), along with albumin-globulin ratio (A/G), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI), displayed satisfactory discriminating capabilities in patients with or without OSCC, and among OSCC patients with diverse pathological grades and clinical stages. A prognostic signature based on 6 survival-associated PIINPBs (L, P, PNI, LMR, SII, A/G) served as an independent factor to predict patient survival. Moreover, a novel nomogram integrating prognostic signature and tumor size, pathological grade, cervical node metastasis, and clinical stage significantly enhanced prognostic power [3-year area under the curve (AUC) =0.825; 5-year AUC =0.845]. Conclusions: Our results generated novel and robust diagnostic and prognostic signatures derived from PIINPBs by machine learning for OSCC. Performance of these signatures suggest the potential for PIINPBs to supplement current regimens and provide better patient stratification and prognostic prediction.

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<p>Prognostic Significance of the Preoperative Lymphocyte to Monocyte Ratio in Patients with Gallbladder Carcinoma</p>

Cited by 6 publications

References 40 publications

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

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

Predictive nomogram for preoperative differential diagnosis of benign and malignant gallbladder lesions. Nomogram for diagnosis of gallbladder cancer

Identification of diagnostic and prognostic signatures derived from preoperative blood parameters for oral squamous cell carcinoma

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