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European Archives of Oto-Rhino-Laryngology

Erschienen in:

03.08.2023 | Head and Neck

Development and validation of a model for the prediction of disease-specific survival in patients with oral squamous cell carcinoma: based on random survival forest analysis

verfasst von: Na Wang, Yulan Lin, Haoyuan Song, Weihai Huang, Jingyao Huang, Liling Shen, Fa Chen, Fengqiong Liu, Jing Wang, Yu Qiu, Bin Shi, Lisong Lin, Baochang He

Erschienen in: European Archives of Oto-Rhino-Laryngology | Ausgabe 11/2023

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Abstract

Objective

To establish a model for predicting the disease-specific survival (DSS) of patients with oral squamous cell carcinoma (OSCC).

Methods

Patients diagnosed with OSCC from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled and randomly divided into development (n = 14,495) and internal validation cohort (n = 9625). Additionally, a cohort from a hospital located in Southeastern China was utilized for external validation (n = 582).

Results

TNM stage, adjuvant treatment, surgery, tumor sites, age, grade, and gender were used for RSF model construction based on the development cohort. The effectiveness of the model was confirmed through time-dependent ROC curves in different cohorts. The risk score exhibited an almost exponential increase in the hazard ratio of death due to OSCC. In development, internal, and external validation cohorts, the prognosis was significantly worse for patients in groups with higher risk scores (all log-rank P < 0.05).

Conclusion

Based on RSF, a high-performance prediction model for OSCC prognosis was created and verified in this study.

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Goldoni R, Scolaro A, Boccalari E et al (2021) Malignancies and biosensors: a focus on oral cancer detection through salivary biomarkers. Biosensors 11(10):396CrossRefPubMedPubMedCentral

Vaidya M, Dmello C, Mogre S (2022) Utility of keratins as biomarkers for human oral precancer and cancer. Life 12(3):343CrossRefPubMedPubMedCentral

Ramos DM, But M, Regezi J et al (2002) Expression of integrin β6 enhances invasive behavior in oral squamous cell carcinoma. Matrix Biol 21(3):297–307CrossRefPubMed

Brandizzi D, Gandolfo M, Velazco ML, Cabrini RL, Lanfranchi H (2008) Clinical features and evolution of oral cancer: a study of 274 cases in Buenos Aires, Argentina

Feller L, Lemmer J (2012) Oral squamous cell carcinoma: epidemiology, clinical presentation and treatment

Russo D, Mariani P, Caponio VCA et al (2021) Development and validation of prognostic models for oral squamous cell carcinoma: a systematic review and appraisal of the literature. Cancers 13(22):5755CrossRefPubMedPubMedCentral

Amin MB, Greene FL, Edge SB et al (2017) The eighth edition AJCC cancer staging manual: continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging. CA Cancer J Clin 67(2):93–99CrossRefPubMed

Oliveira LRD, Ribeiro-Silva A (2011) Prognostic significance of immunohistochemical biomarkers in oral squamous cell carcinoma. Int J Oral Maxillofac Surg 40(3):298–307CrossRefPubMed

Massano J, Regateiro FS, Januário G, Ferreira A (2006) Oral squamous cell carcinoma: review of prognostic and predictive factors. Oral Surg Oral Med Oral Pathol Oral Radiol Endodontol 102(1):67–76CrossRef

10.

Oh LJ, Asher R, Veness M et al (2021) Effect of age and gender in non-smokers with oral squamous cell carcinoma: multi-institutional study. Oral Oncol 116:105210CrossRefPubMed

11.

Yasin MM, Abbas Z, Hafeez A (2022) Correlation of histopathological patterns of OSCC patients with tumor site and habits. BMC Oral Health 22(1):1–7CrossRef

12.

Rai HC, Ahmed J (2016) Clinicopathological correlation study of oral squamous cell carcinoma in a local Indian population. Asian Pac J Cancer Prev 17(3):1251–1254CrossRefPubMed

13.

Dolens EDS, Dourado MR, Almangush A et al (2021) The impact of histopathological features on the prognosis of oral squamous cell carcinoma: a comprehensive review and meta-analysis. Front Oncol 11:784924CrossRefPubMedPubMedCentral

14.

Zhang L, Huang T, Xu F et al (2022) Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest). BMC Emerg Med 22(1):1–10CrossRef

15.

Frederick L, Page DL, Fleming ID et al (2002) AJCC cancer staging manual. Springer Science & Business Media, Berlin

16.

Kwak MS, Eun YG, Lee JW, Lee YC (2021) Development of a machine learning model for the prediction of nodal metastasis in early T classification oral squamous cell carcinoma: SEER-based population study. Head Neck 43(8):2316–2324CrossRefPubMed

17.

Wang P, Guo K, Zhang A et al (2021) Survival analysis of age-related oral squamous cell carcinoma: a population study based on SEER

18.

Chen X, Ishwaran H (2012) Random forests for genomic data analysis. Genomics 99(6):323–329CrossRefPubMed

19.

Kreppel M, Drebber U, Rothamel D et al (2011) Prognostic impact of different TNM-based stage groupings for oral squamous cell carcinoma. Head Neck 33(10):1467–1475CrossRefPubMed

20.

Huang SH, Osullivan B (2017) Overview of the 8th edition TNM classification for head and neck cancer. Curr Treat Options Oncol 18(7):1–13CrossRef

21.

Yoon AJ, Wang S, Kutler DI et al (2020) MicroRNA-based risk scoring system to identify early-stage oral squamous cell carcinoma patients at high-risk for cancer-specific mortality. Head Neck 42(8):1699–1712CrossRefPubMedPubMedCentral

22.

Choi K-K, Kim M-J, Yun P-Y et al (2006) Independent prognostic factors of 861 cases of oral squamous cell carcinoma in Korean adults. Oral Oncol 42(2):208–217CrossRefPubMed

23.

Xu Q, Wang C, Li B et al (2019) The impact of age on oral squamous cell carcinoma: a longitudinal cohort study of 2782 patients. Oral Dis 25(3):730–741CrossRefPubMed

24.

Honorato J, Rebelo M, Dias F et al (2015) Gender differences in prognostic factors for oral cancer. Int J Oral Maxillofac Surg 44(10):1205–1211CrossRefPubMed

25.

Xu Q, Wang C, Li B et al (2018) Prognostic value of pathologic grade for patients with oral squamous cell carcinoma. Oral Dis 24(3):335–346CrossRefPubMed

26.

Sklenicka S, Gardiner S, Dierks EJ, Potter BE, Bell RB (2010) Survival analysis and risk factors for recurrence in oral squamous cell carcinoma: does surgical salvage affect outcome? J Oral Maxil Surg 68(6):1270–1275CrossRef

27.

Rogers SN, Brown JS, Woolgar JA et al (2009) Survival following primary surgery for oral cancer. Oral Oncol 45(3):201–211CrossRefPubMed

28.

Weckx A, Grochau KJ, Grandoch A, Backhaus T, Zöller JE, Kreppel M (2020) Survival outcomes after surgical treatment of oral squamous cell carcinoma. Oral Dis 26(7):1432–1439CrossRefPubMed

29.

Weckx A, Riekert M, Grandoch A, Schick V, Zöller JE, Kreppel M (2019) Time to recurrence and patient survival in recurrent oral squamous cell carcinoma. Oral Oncol 94:8–13CrossRefPubMed

30.

Beltrami C, Desinan L, Rubini C (1992) Prognostic factors in squamous cell carcinoma of the oral cavity: a retrospective study of 80 cases. Pathol-Res Pract 188(4–5):510–516CrossRefPubMed

31.

Zhang Z-Q, He G, Luo Z-W et al (2021) Individual mortality risk predictive system of patients with acute-on-chronic liver failure based on a random survival forest model. Chin Med J 134(14):1701–1708CrossRefPubMedPubMedCentral

32.

Miao F, Cai Y-P, Zhang Y-X, Li Y, Zhang Y-T (2015) Risk prediction of one-year mortality in patients with cardiac arrhythmias using random survival forest. Comput Math Methods Med 2015

33.

Mohammed M, Mboya IB, Mwambi H, Elbashir MK, Omolo B (2021) Predictors of colorectal cancer survival using cox regression and random survival forests models based on gene expression data. PLoS ONE 16(12):e0261625CrossRefPubMedCentral

34.

Bohannan ZS, Coffman F, Mitrofanova A (2022) Random survival forest model identifies novel biomarkers of event-free survival in high-risk pediatric acute lymphoblastic leukemia. Comput Struct Biotechnol J 20:583–597CrossRefPubMedPubMedCentral

35.

Kattan MW, Hess KR, Beck JR (1998) Experiments to determine whether recursive partitioning (CART) or an artificial neural network overcomes theoretical limitations of Cox proportional hazards regression. Comput Biomed Res 31(5):363–373CrossRefPubMed

36.

Xiao J, Mo M, Wang Z et al (2022) The application and comparison of machine learning models for the prediction of breast cancer prognosis: retrospective cohort study. JMIR Med Inform 10(2):e33440CrossRefPubMedPubMedCentral

37.

Lin J, Yin M, Liu L et al (2022) The development of a prediction model based on random survival forest for the postoperative prognosis of pancreatic cancer: a SEER-based study. Cancers 14(19):4667CrossRefPubMedPubMedCentral

Titel: Development and validation of a model for the prediction of disease-specific survival in patients with oral squamous cell carcinoma: based on random survival forest analysis
verfasst von: Na Wang
Yulan Lin
Haoyuan Song
Weihai Huang
Jingyao Huang
Liling Shen
Fa Chen
Fengqiong Liu
Jing Wang
Yu Qiu
Bin Shi
Lisong Lin
Baochang He
Publikationsdatum: 03.08.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: European Archives of Oto-Rhino-Laryngology / Ausgabe 11/2023
Print ISSN: 0937-4477
Elektronische ISSN: 1434-4726
DOI: https://doi.org/10.1007/s00405-023-08087-6

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Development and validation of a model for the prediction of disease-specific survival in patients with oral squamous cell carcinoma: based on random survival forest analysis