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摘要
Aim. Survival prediction in brain cancer is critical for treatment planning and patient outcomes. This study aimed to develop a prognostic model for brain cancer survival using supervised machine learning approaches. The model integrated demographic, clinical, immunohistochemical, and genomic data.
Materials and Methods. We retrospectively analyzed 149 patients with intracranial tumors who underwent surgery. Demographic and clinical data were systematically collected. Tumor and adjacent tissues underwent histopathological and immunohistochemical analysis for GST-P, GST-T, GST-M, CYP1A1, CYP1B1, MDR, and p53 expression. Genomic DNA from tumors was analyzed for GSTM1, GSTT1, and p53 genotypes. Models were developed using decision tree, Naïve Bayes, and SVM algorithms in Python. Models were compared based on accuracy, precision, sensitivity, and F-measure metrics.
Results. The overall postoperative survival rate was 65%. Significant differences in protein expression were observed between cancerous and normal tissues for GST-P, GST-T, GST-M, CYP1A1, CYP1B1, MDR, and p53. GST-M1 null genotype was associated with brain tumor development. The decision tree model achieved the highest accuracy (84%) among models integrating demographic, clinical, immunohistochemical, and genetic data. Precision and sensitivity varied across models, with the decision tree showing acceptable performance.
Conclusion. Decision tree models are effective for predicting brain cancer survival, especially with limited datasets, using demographic, clinical, immunohistochemical, and genotypic variables.
参考
PDQ Adult Treatment Editorial Board. Adult Central Nervous System Tumors Treatment (PDQ®): Patient Version. 2023. In: PDQ Cancer Information Summaries. Bethesda (MD): National Cancer Institute (US); 2023.
Wang H., Zheng Q., Lu Z., et al. Role of the nervous system in cancers: a review. Cell Death Discov. 2021; 7: 76.-DOI: 10.1038/s41420-021-00450-y.
Louis D.N., Perry A., Wesseling P., et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol. 2021; 23: 1231-51.-DOI: 10.1093/neuonc/noab106.
Miller K.D., Ostrom Q.T., Kruchko C., et al. Brain and other central nervous system tumor statistics, 2021. CA Cancer J Clin. 2021; 71: 381-406.-DOI: 10.3322/caac.21693.
Chen L., Zou X., Wang Y., et al. Central nervous system tumors: a single center pathology review of 34,140 cases over 60 years. BMC Clin Pathol. 2013; 13: 14.-DOI: 10.1186/1472-6890-13-14.
Patel A.P., Fisher J.L., Nichols E., et al. Global, regional, and national burden of brain and other CNS cancer, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019; 18: 376-93.-DOI: 10.1016/S1474-4422(18)30468-X.
Salari N., Ghasemi H., Fatahian R., et al. The global prevalence of primary central nervous system tumors: a systematic review and meta-analysis. Eur J Med Res. 2023; 28: 39.-DOI: 10.1186/s40001-023-01011-y.
Fan Y., Zhang X., Gao C., et al. Burden and trends of brain and central nervous system cancer from 1990 to 2019 at the global, regional, and country levels. Archives of Public Health. 2022; 80: 209.-DOI: 10.1186/s13690-022-00965-5.
Jaiswal J., Shastry A.H., Ramesh A., et al. Spectrum of primary intracranial tumors at a tertiary care neurological institute: A hospital-based brain tumor registry. Neurol India. 2016; 64: 494-501.-DOI: 10.4103/0028-3886.181535.
Naser R.K.A., Hassan A.A.K., Shabana A.M., Omar N.N. Role of magnetic resonance spectroscopy in grading of primary brain tumors. The Egyptian Journal of Radiology and Nuclear Medicine. 2016; 47: 577-84.-DOI: 10.1016/j.ejrnm.2016.03.011.
Pálsson S., Cerri S., Poulsen H.S., et al. Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images. Sci Rep. 2022; 12: 19744.-DOI: 10.1038/s41598-022-19223-3.
Gaur L., Bhandari M., Razdan T., et al. Explanation-driven deep learning model for prediction of brain tumour status using MRI image data. Front Genet. 2022; 13.-DOI: 10.3389/fgene.2022.822666.
di Noia C., Grist J.T., Riemer F., et al. Predicting survival in patients with brain tumors: current state-of-the-art of ai methods applied to MRI. Diagnostics. 2022; 12: 2125.-DOI: 10.3390/diagnostics12092125.
Piña-Sánchez P., Hernández-Hernández D.M., Taja-Chayeb L., et al. Polymorphism in exon 4 of TP53 gene associated to HPV 16 and 18 in Mexican women with cervical cancer. Medical Oncology. 2011; 28: 1507-13.-DOI: 10.1007/s12032-010-9599-8.
Huszno J., Grzybowska E. TP53 mutations and SNPs as prognostic and predictive factors in patients with breast cancer (Review). Oncol Lett. 2018.-DOI: 10.3892/ol.2018.8627.
Girault I., Lidereau R., Bièche I. Trimodal GSTT1 and GSTM1 genotyping assay by real-time PCR. Int J Biol Markers. 2005; 20: 81-6.
Song L.-F., Deng Z.-H., Gong Z.-Y., et al. Large-scale de novo oligonucleotide synthesis for whole-genome synthesis and data storage: challenges and opportunities. Front Bioeng Biotechnol. 2021; 9.-DOI: 10.3389/fbioe.2021.689797.
Słomiński B., Skrzypkowska M., Ryba-Stanisławowska M., et al. Associations of TP53 codon 72 polymorphism with complications and comorbidities in patients with type 1 diabetes. J Mol Med. 2021; 99: 675-83.-DOI: 10.1007/s00109-020-02035-1.
Charlton C.E., Poon M.T.C., Brennan P.M., Fleuriot J.D. Development of prediction models for one-year brain tumour survival using machine learning: a comparison of accuracy and interpretability. Comput Methods Programs Biomed. 2023; 233: 107482.-DOI: 10.1016/j.cmpb.2023.107482.
Cruz J.A., Wishart D.S. Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2007; 2: 59-77.
Zhang B., Shi H., Wang H. Machine learning and ai in cancer prognosis, prediction, and treatment selection: a critical approach. J Multidiscip Healthc. 2023; 16: 1779-91.-DOI: 10.2147/JMDH.S410301.
Ghadiri F., Husseini A.A., Öztaş O. A machine-learning approach for nonalcoholic steatohepatitis susceptibility estimation. Indian Journal of Gastroenterology. 2022; 41: 475-82.-DOI: 10.1007/s12664-022-01263-2.
Payabvash S., Aboian M., Tihan T., Cha S. Machine learning decision tree models for differentiation of posterior fossa tumors using diffusion histogram analysis and structural MRI findings. Front Oncol. 2020; 10: 71.-DOI: 10.3389/fonc.2020.00071.
Nematollahi M., Jajroudi M., Arbabi F., et al. The Benefits of decision tree to predict survival in patients with glioblastoma multiforme with the use of clinical and imaging features. Asian J Neurosurg. 2018; 13: 697-702.-DOI:-DOI: 10.4103/ajns.AJNS_336_16.
Shaikh F.J., Rao D.S. Prediction of cancer disease using machine learning approach. Mater Today Proc. 2022; 50: 40-7.-DOI: 10.1016/j.matpr.2021.03.625.
Cruz J.A., Wishart D.S. Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2006; 2: 117693510600200.-DOI: 10.1177/117693510600200030.
Wu Y., Guo Y., Ma J., et al. Research progress of gliomas in machine learning. Cells. 2021; 10: 3169.-DOI: 10.3390/cells10113169.
Minghui M., Chuanfeng Z. Application of support vector machines to a small-sample prediction. Advances in Petroleum Exploration and Development. 2015; 10: 72-5.-DOI: 10.3968/7830.
Chen H., Hu S., Hua R., Zhao X. Improved naive Bayes classification algorithm for traffic risk management. EURASIP J Adv Signal Process. 2021; 2021: 30.-DOI: 10.1186/s13634-021-00742-6.
Morgan J., Dougherty R., Hilchie A., Carey B. Sample Size and Modeling Accuracy with Decision Tree Based Data Mining Tools. Acad Inf Manag Sci J. 2003; 6.
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