Prognostic and clinicopathological value of Ki-67 expression in patients with nasopharyngeal carcinoma: a meta-analysis

Shi, Zhongqi; Jiang, Weihong; Chen, Xiao; Xu, Min; Wang, Xiaocheng; Zha, Dingjun

doi:10.1177/1758835920951346

Cited by 10 publications

(6 citation statements)

References 40 publications

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“…KI-67 has been proved to be an prognostic index for multiple solid tumors, for example, high ki-67 expression is closely associated with poor OS and DFS (Disease Free Survival) in lung adenocarcinoma ( 29 ). A meta-analysis including 8 studies showed that high ki-67 expression was associated with poor OS, PFS, and DMFS (distant metastasis-free survival) in patients with nasopharyngeal carcinoma ( 8 ). In hepatocellular carcinoma, high expression of KI-67 has been associated with poor DFS, RFS, and OS ( 14 ).…”

Section: Discussionmentioning

confidence: 99%

“…Immunohistochemical (IHC) staining is usually used to detect Ki-67, and the expression level of Ki-67 is related to cell proliferation activity, disease progression and cancer recurrence (7). Some studies have shown that Ki-67 is one of the prognostic indices of multiple solid tumors, such as nasopharyngeal carcinoma (8), stage I non-small cell lung cancer (9), gastrointestinal stromal tumour (10), and gliomas (11), resected triple-negative breast cancer (12), colorectal cancer (13), hepatocellular carcinoma (14), and thyroid cancer (15). Previous studies have shown an association between Ki-67 expression and melanoma patient prognosis, but the results have been contradictory (16)(17)(18)(19)(20)(21)(22)(23)(24)(25).…”

Section: Introductionmentioning

confidence: 99%

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Prognostic and Clinicopathological Value of Ki-67 in Melanoma: A Meta-Analysis

et al. 2021

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BackgroundThe prognostic and clinicopathological value of Ki-67 in melanoma is controversial. The purpose of this meta-analysis was to determine the prognostic role of Ki-67 in melanoma patients.Materials and MethodsThe PubMed, Cochrane Library, Web of Science, and Embase databases were searched systematically up to April 9, 2021. We calculated the pooled hazard ratios (HRs) and 95% confidence intervals (CIs) to determine the relationship between Ki-67 overexpression and survival outcomes. We also calculated the combined odds ratios (ORs) and 95% CIs to determine the relationship between Ki-67 expression levels and clinicopathologic parameters. All data were statistically analyzed by Stata 11.0.ResultsA total of 10 studies involving 929 patients were included in our meta-analysis. The pooled HR showed that Ki-67 overexpression was connected with poor overall survival rates (HR=2.92, 95% CI=2.17-3.91, p<0.000). However, there was no correlation between Ki-67 overexpression and the PFS (HR=0.999, 95% CI =0.958-1.041, P =0.958; I2 = 21.80%, P =0.258) or RFS (HR=1.14, 95% CI = 0.42-3.11, P =0.993; I2 = 85.00%, P =0.01) rates. Ki-67 expression levels were associated with tumor thickness, but not sex, location, ulceration or vascular invasion.ConclusionKi-67 is a useful poor prognostic indicator for melanoma patients.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prognostic and Clinicopathological Value of Ki-67 in Melanoma: A Meta-Analysis

et al. 2021

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“…After drawing the ROC curve for blood parameters, we found that monocytes had the best sensitivity and specificity. Two retrospective studies validated age, gender, Ki-67, and smoking and drinking habits as independent prognostic factors for NPC ( 11 , 31 ). Our results showed the model integrating clinical data and the rad-score was more useful than those only using radiomics features.…”

Section: Discussionmentioning

confidence: 99%

“…Many studies reported that a large number of clinical biomarkers such as monocytes (MONO), mean corpuscular volume (MCV), and Ki-67 expression are associated with the tumor microenvironment and tumor immune escape (9)(10)(11). There are no regional differences in the expression of these markers.…”

Section: Introductionmentioning

confidence: 99%

Integration of MRI-Based Radiomics Features, Clinicopathological Characteristics, and Blood Parameters: A Nomogram Model for Predicting Clinical Outcome in Nasopharyngeal Carcinoma

Fang

Yang

et al. 2022

Front. Oncol.

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PurposeThis study aimed to develop a nomogram model based on multiparametric magnetic resonance imaging (MRI) radiomics features, clinicopathological characteristics, and blood parameters to predict the progression-free survival (PFS) of patients with nasopharyngeal carcinoma (NPC).MethodsA total of 462 patients with pathologically confirmed nonkeratinizing NPC treated at Sichuan Cancer Hospital were recruited from 2015 to 2019 and divided into training and validation cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomics feature dimension reduction and screening in the training cohort. Rad-score, age, sex, smoking and drinking habits, Ki-67, monocytes, monocyte ratio, and mean corpuscular volume were incorporated into a multivariate Cox proportional risk regression model to build a multifactorial nomogram. The concordance index (C-index) and decision curve analysis (DCA) were applied to estimate its efficacy.ResultsNine significant features associated with PFS were selected by LASSO and used to calculate the rad-score of each patient. The rad-score was verified as an independent prognostic factor for PFS in NPC. The survival analysis showed that those with lower rad-scores had longer PFS in both cohorts (p < 0.05). Compared with the tumor–node–metastasis staging system, the multifactorial nomogram had higher C-indexes (training cohorts: 0.819 vs. 0.610; validation cohorts: 0.820 vs. 0.602). Moreover, the DCA curve showed that this model could better predict progression within 50% threshold probability.ConclusionA nomogram that combined MRI-based radiomics with clinicopathological characteristics and blood parameters improved the ability to predict progression in patients with NPC.

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“…The relationship between radiomics and clinical symptoms has been widely documented, but other data types, such as genomics, transcriptomics, proteomics, and metabolomics, have been less studied in relation to radiomics. In HNC, correlation studies between imaging and genomics are now available, as important molecular markers such as PD-L1/TP53/ FAT1/KMT2D/NOTCH1/Ki-67 can be predicted by predictive models of imaging features [10,32,141]. At present, the relationship between imaging and transcriptomics has been studied in other tumors, but its combination with proteomics and metabolomics is still less studied.…”

Section: Discussionmentioning

confidence: 99%

Application of radiomics and machine learning in head and neck cancers

Peng¹,

Wang²,

Wang³

et al. 2021

Int. J. Biol. Sci.

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With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.

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Prognostic and clinicopathological value of Ki-67 expression in patients with nasopharyngeal carcinoma: a meta-analysis

Cited by 10 publications

References 40 publications

Prognostic and Clinicopathological Value of Ki-67 in Melanoma: A Meta-Analysis

Prognostic and Clinicopathological Value of Ki-67 in Melanoma: A Meta-Analysis

Integration of MRI-Based Radiomics Features, Clinicopathological Characteristics, and Blood Parameters: A Nomogram Model for Predicting Clinical Outcome in Nasopharyngeal Carcinoma

Application of radiomics and machine learning in head and neck cancers

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