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Chemotherapy-induced neutropenia during adjuvant treatment for cervical cancer patients: development and validation of a prediction model.

ABSTRACT: UNLABELLED:An artificial neuron network (ANN) model combining both the genetic risk factors and clinical factorsmay be effective in prediction of chemotherapy-induced adverse events. PURPOSE:To identify genetic factors and clinical factors associated with bone marrow suppression in cervical cancer patient, and to build a model for chemotherapy-induced neutropenia prediction. METHODS:We performed a genome wide association study on a cohort to identify genetic determinants. Samples were genotyped using the Axiom CHB 1.0. The primary analyses focused on the scan of 657178 single-nucleotide polymorphisms (SNPs). Artificial neural network were used to integrating clinical factors and genetic factors to predict the occurrence of neutropenia. RESULTS:32 variants associated with neutropenia in the patients after chemotherapy were found (P<1 × 10(-4)). During internal validation and external validation, artificial neural network performed well in predicting neutropenia with considerable accuracy, which is 88.9% and 81.7% respectively. ROC analysis had acceptable areas under the curve of 0.897 for the internal validation sample and 0.782 for the external validation sample. CONCLUSION:Neutropenia may be associated with both genetic factors and clinical factors. Our study found that the artificial neural networks model based on the multiple risk factors jointly, can effectively predict the occurring of neutropenia, which provides some guidance before the starting of chemotherapy.

SUBMITTER: Huang K

PROVIDER: S-EPMC4565260 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Chemotherapy-induced neutropenia during adjuvant treatment for cervical cancer patients: development and validation of a prediction model.

Huang Kecheng K Luo Aiyue A Li Xiong X Li Shuang S Wang Shixuan S

International journal of clinical and experimental medicine 20150715 7

<h4>Unlabelled</h4>An artificial neuron network (ANN) model combining both the genetic risk factors and clinical factorsmay be effective in prediction of chemotherapy-induced adverse events.<h4>Purpose</h4>To identify genetic factors and clinical factors associated with bone marrow suppression in cervical cancer patient, and to build a model for chemotherapy-induced neutropenia prediction.<h4>Methods</h4>We performed a genome wide association study on a cohort to identify genetic determinants. S ...[more]

PMID: 26379877

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Project description:BackgroundWomen who conceived with in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) are more likely to experience adverse pregnancy outcomes than women who conceived naturally. Cervical insufficiency (CI) is one of the important causes of miscarriage and premature birth, however there is no published data available focusing on the potential risk factors predicting CI occurrence in women who received IVF/ICSI treatment. This study aimed to identify the risk factors that could be integrated into a predictive model for CI, which could provide further personalized and clinically specific information related to the incidence of CI after IVF/ICSI treatment.Patients and methodsThis retrospective study included 4710 patients who conceived after IVF/ICSI treatment from Jan 2011 to Dec 2018 at a public university hospital. The patients were randomly divided into development (n = 3108) and validation (n = 1602) samples for the building and testing of the nomogram, respectively. Multivariate logistic regression was developed on the basis of pre-pregnancy clinical covariates assessed for their association with CI occurrence.ResultsA total of 109 patients (2.31%) experienced CI among all the enrolled patients. Body mass index (BMI), basal serum testosterone (T), gravidity and uterine length were associated with CI occurrence. The statistical nomogram was built based on BMI, serum T, gravidity and uterine length, with an area under the curve (AUC) of 0.84 (95% confidence interval: 0.76-0.90) for the developing cohort. The AUC for the validation cohort was 0.71 (95% confidence interval: 0.69-0.83), showing a satisfactory goodness-of-fit and discrimination ability in this nomogram.ConclusionThe user-friendly nomogram which graphically represents the risk factors and a pre-pregnancy predicted tool for the incidence of CI in patients undergoing IVF/ICSI treatment, provides a useful guide for medical staff on individualized decisions making, where preventive measures could be carried out during the IVF/ICSI procedure and subsequent pregnancy.

Project description:BackgroundThe majority of cancer patients undergoing chemotherapy show neutropenic condition which is a common side effect of myelosuppressive chemotherapy diagnosed as the reduced complete blood cell count. Such cancer patients have a higher risk of febrile neutropenia. The present study aimed to validate whether there was a risk of neutropenia in cancer patients receiving chemotherapy at Bhaktapur Cancer Hospital, Nepal.MethodsCross-sectional study was performed among 203 cancer patients of all age groups who attended Bhaktapur Cancer Hospital from May 2018 to January 2019 and who received a chemotherapy course. Patients receiving at least one cycle of chemotherapy as the first-line treatment were included. Statistical analysis was performed using SPSS 25. Loglinear analysis was used to analyze more than 2×2 categories among the grades and outcome of neutropenia. Multinomial logistic regression was applied to analyze the impact of various predictor variables such as chemotherapy cycles, grades of neutropenia, and gender on the outcome of neutropenia. Variation in the absolute neutrophil count (ANC) level at various days of chemotherapy cycles was assessed with the multivariate analysis of variance (MANOVA). The p-value <0.05 was considered significant at each condition.ResultsThe main cancer type during the study period was breast cancer (41, 20.2%). Out of 163 neutropenic patients, 149 had severe neutropenia and 14 had mild neutropenia. Most patients were continued up to the 6th cycle of chemotherapy. There was significant association between the grade of neutropenia and the outcome of the condition (p-value 0.017). There were significant relations of the grade of neutropenia and smoking habit with the recovering status (p values 0.033 and 0.001, respectively). The absolute neutrophil count (ANC) level increased and decreased inconsistently (statistically non-significantly) in between treatment period of day 1 to 52.ConclusionChemotherapy-induced neutropenia was a common occurrence. Majority (133, 66.5%) grade 4 neutropenic patients were recovering after the chemotherapy cycles. The physicians are warranted that they be ready for any unpredictable situation during chemotherapy treatment.

Project description:BackgroundIn the absence of practical and reliable predictors for whether the endocervical curettage (ECC) procedure should be performed, decisions regarding patient selection are usually based on the colposcopists' clinical judgment instead of evidence. We aimed to develop and validate a practical prediction model that uses available information to reliably estimate the need to perform ECC in patients suspected of having cervical lesions.MethodsIn this retrospective study, 2088 patients who underwent colposcopy, colposcopically directed biopsy (CDB) and ECC procedures between September 2019 and September 2020 at the Second Hospital of Shanxi Medical University were included. The data were analyzed with univariate and multivariable logistic regression. Least absolute shrinkage and selection operator (LASSO) was used to select predictors for ECC positivity. The ECC prediction model was presented as a nomogram and evaluated in terms of discrimination and calibration. Furthermore, this model was validated internally with cross-validation and bootstrapping.ResultsSignificant trends were found for ECC positivity with increasing age (P = 0.001), menopause (P = 0.003), Human papillomavirus (HPV) status (P < 0.001), severity of ThinPrep Cytological Test (TCT) (P < 0.001), original squamous epithelium ectopia (P = 0.037) and colposcopy impression (P < 0.001) by multivariable logistic regression analysis. The ECC prediction model was developed based on the following predictors: age, menopause, symptom of contact bleeding, severity of TCT, HPV status, cervix visibility, original squamous epithelium ectopia, acetowhite changes and colposcopic impression. This model had satisfactory calibration and good discrimination, with an area under the receiver operator characteristic curve (AUC) of 0.869 (95% confidence interval 0.849 to 0.889).ConclusionsA readily applicable clinical prediction model was constructed to reliably estimate the probability of ECC positivity in patients suspicious of having cervical lesions, which may help clinicians make decisions regarding the ECC procedure and possibly prevent adverse effects.

Project description:BackgroundCervical cancer (CC) is a common cancer in female, which is associated with problems like poor prognosis. Circular RNA (circRNA) is a kind of competing endogenous RNA (ceRNA) that has an important role in regulating microRNA (miRNA) in many cancers. The regulatory mechanisms of CC immune microenvironment and the transcriptome level remain to be fully explored.MethodsIn this study, we constructed the ceRNA network through the interaction data and expression matrix of circRNA, miRNA and mRNA. Meanwhile, based on the gene expression matrix, CIBERSORT algorithm was used to reveal contents of tumor-infiltrating immune cells (TIICs). Then, we screened prognostic markers based on ceRNA network and immune infiltration and constructed two nomograms. In order to find immunological differences between the high- and low-risk CC samples, we examined multiple immune checkpoints and predicted the effect of PD-L1 ICI immunotherapy. In addition, the sensitive therapeutics for high-risk patients were screened, and the potential agents with anti-CC activity were predicted by Connective Map (CMap).ResultsWe mapped a ceRNA network including 5 circRNAs, 17 miRNAs and 129 mRNAs. From the mRNA nodes of the network six genes and two kind of cells were identified as prognostic makers for CC. Among them, there was a significant positive correlation between CD8+ T cells and SNX10 gene. The results of TIDE and single sample GSEA (ssGSEA) showed that T cells CD8 do play a key role in inhibiting tumor progression. Further, our study screened 24 drugs that were more sensitive to high-risk CC patients and several potential therapeutic agents for reference.ConclusionsOur study identified several circRNA-miRNA-mRNA regulatory axes and six prognostic genes based on the ceRNA network. In addition, through TIIC, survival analysis and a series of immunological analyses, T cells were proved to be good prognostic markers, besides play an important role in the immune process. Finally, we screened 24 potentially more effective drugs and multiple potential drug compounds for high- and low-risk patients.

Dataset Information

Chemotherapy-induced neutropenia during adjuvant treatment for cervical cancer patients: development and validation of a prediction model.

Publications

Chemotherapy-induced neutropenia during adjuvant treatment for cervical cancer patients: development and validation of a prediction model.

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