Development of nomogram models of inflammatory markers based on clinical database to predict prognosis for hepatocellular carcinoma after surgical resection

Mao, Shuqi; Yu, Xi; Sun, Jihan; Yang, Yong; Shan, Yuying; Sun, Jiannan; Mugaanyi, Joseph; Fan, Rui; Wu, Shengping; Lu, Caide

doi:10.1186/s12885-022-09345-2

Cited by 20 publications

(16 citation statements)

References 56 publications

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“…They also revealed that the INPS-based prognostic nomogram showed a good prognostic stratification. Mao et al ( 45 ) developed and verified a nomogram in hepatocellular carcinoma patients after surgical resection. They indicated that nomogram model may guide clinical prediction, personalized treatment approach and prognosis.…”

Section: Discussionmentioning

confidence: 99%

Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

Feng

Wang²,

Yang³

et al. 2022

Front. Nutr.

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BackgroundThis study aimed to establish and validate a novel predictive model named integrative inflammatory and nutritional score (IINS) for prognostic prediction in esophageal squamous cell carcinoma (ESCC).Materials and methodsWe retrospectively recruited 494 pathologically confirmed ESCC patients with surgery and randomized them into training (n = 346) or validation group (n = 148). The least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) regression analysis was initially used to construct a novel predictive model of IINS. The clinical features and prognostic factors with hazard ratio (HRs) and 95% confidence intervals (CIs) grouped by IINS were analyzed. Nomogram was also established to verify the prognostic value of IINS.ResultsAccording to the LASSO Cox PH regression analysis, a novel score of IINS was initially constructed based on 10 inflammatory and nutritional indicators with the optimal cut-off level of 2.35. The areas under the curve (AUCs) of IINS regarding prognostic ability in 1-year, 3-years, and 5-years prediction were 0.814 (95% CI: 0.769–0.854), 0.748 (95% CI: 0.698–0.793), and 0.792 (95% CI: 0.745–0.833) in the training cohort and 0.802 (95% CI: 0.733–0.866), 0.702 (95% CI: 0.621–0.774), and 0.748 (95% CI: 0.670–0.816) in the validation cohort, respectively. IINS had the largest AUCs in the two cohorts compared with other prognostic indicators, indicating a higher predictive ability. A better 5-years cancer-specific survival (CSS) was found in patients with IINS ≤ 2.35 compared with those with IINS > 2.35 in both training cohort (54.3% vs. 11.1%, P < 0.001) and validation cohort (53.7% vs. 18.2%, P < 0.001). The IINS was then confirmed as a useful independent factor (training cohort: HR: 3.000, 95% CI: 2.254–3.992, P < 0.001; validation cohort: HR: 2.609, 95% CI: 1.693–4.020, P < 0.001). Finally, an IINS-based predictive nomogram model was established and validated the CSS prediction (training set: C-index = 0.71 and validation set: C-index = 0.69, respectively).ConclusionPreoperative IINS is an independent predictor of CSS in ESCC. The nomogram based on IINS may be used as a potential risk stratification to predict individual CSS and guide treatment in ESCC with radical resection.

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Section: Discussionmentioning

confidence: 99%

Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

Feng

Wang²,

Yang³

et al. 2022

Front. Nutr.

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“…The nomogram model quantifies and visualizes the Cox risk regression results of disease prognosis and is currently widely used in liver, breast, and kidney cancer (19)(20)(21). Based on the multifactorial Cox risk regression analysis results, a nomogram model was constructed to predict appendiceal cancer 1-, 3-, and 5-year survival rates, and appendiceal cancer patients were classified into high-and low-risk groups with optimal cutoff values.…”

Section: Discussionmentioning

confidence: 99%

Establishment and validation of the survival prediction risk model for appendiceal cancer

Liu

Junli²,

Wang³

et al. 2022

Front. Med.

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ObjectiveEstablishing a risk model of the survival situation of appendix cancer for accurately identifying high-risk patients and developing individualized treatment plans.MethodsA total of 4,691 patients who were diagnosed with primary appendix cancer from 2010 to 2016 were extracted using Surveillance, Epidemiology, and End Results (SEER) * Stat software. The total sample size was divided into 3,283 cases in the modeling set and 1,408 cases in the validation set at a ratio of 7:3. A nomogram model based on independent risk factors that affect the prognosis of appendix cancer was established. Single-factor Cox risk regression, Lasso regression, and multifactor Cox risk regression were used for analyzing the risk factors that affect overall survival (OS) in appendectomy patients. A nomogram model was established based on the independent risk factors that affect appendix cancer prognosis, and the receiver operating characteristic curve (ROC) curve and calibration curve were used for evaluating the model. Survival differences between the high- and low-risk groups were analyzed through Kaplan–Meier survival analysis and the log-rank test. Single-factor Cox risk regression analysis found age, ethnicity, pathological type, pathological stage, surgery, radiotherapy, chemotherapy, number of lymph nodes removed, T stage, N stage, M stage, tumor size, and CEA all to be risk factors for appendiceal OS. At the same time, multifactor Cox risk regression analysis found age, tumor stage, surgery, lymph node removal, T stage, N stage, M stage, and CEA to be independent risk factors for appendiceal OS. A nomogram model was established for the multifactor statistically significant indicators. Further stratified with corresponding probability values based on multifactorial Cox risk regression, Kaplan–Meier survival analysis found the low-risk group of the modeling and validation sets to have a significantly better prognosis than the high-risk group (p < 0.001).ConclusionThe established appendix cancer survival model can be used for the prediction of 1-, 3-, and 5-year OS and for the development of personalized treatment options through the identification of high-risk patients.

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“…Laboratory examinations included blood biochemistry, complete blood count and pathological examination. The albuminbilirubin (ALBI) score and inflammatory markers were calculated according to our previous study [19,20]. The cut-off value of biomarkers was set according to the Health Industry Standard of the People's Republic of China published by the National Health Commission of the People's Republic of China.…”

Section: Laboratory Examination and Follow-upmentioning

confidence: 99%

A new prognostic model predicting hepatocellular carcinoma early recurrence in patients with microvascular invasion who received postoperative adjuvant transcatheter arterial chemoembolization

Mao¹,

Shan²,

Yu³

et al. 2023

European Journal of Surgical Oncology

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Development of nomogram models of inflammatory markers based on clinical database to predict prognosis for hepatocellular carcinoma after surgical resection

Cited by 20 publications

References 56 publications

Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

Establishment and validation of the survival prediction risk model for appendiceal cancer

A new prognostic model predicting hepatocellular carcinoma early recurrence in patients with microvascular invasion who received postoperative adjuvant transcatheter arterial chemoembolization

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