A Joint Model Based on Post-Treatment Longitudinal Prognostic Nutritional Index to Predict Survival in Nasopharyngeal Carcinoma
Abstract
Simple Summary
Abstract
1. Introduction
2. Materials and Methods
3. Results
3.1. Patient Enrollment and Demographics
3.2. Joint Modeling
3.3. Cut-Off Value Determination
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Chen, Y.P.; Chan, A.T.C.; Le, Q.T.; Blanchard, P.; Sun, Y.; Ma, J. Nasopharyngeal carcinoma. Lancet 2019, 394, 64–80. [Google Scholar] [CrossRef]
- Ferlay, J.; Ervik, M.; Lam, F. Global Cancer Observatory: Cancer Today. Available online: https://gco.iarc.fr/today (accessed on 11 March 2023).
- Luo, W. Nasopharyngeal carcinoma ecology theory: Cancer as multidimensional spatiotemporal “unity of ecology and evolution” pathological ecosystem. Theranostics 2023, 13, 1607–1631. [Google Scholar] [CrossRef] [PubMed]
- National Comprehensive Cancer Network. Head and Neck Cancers (Version 1.2023). Available online: https://www.nccn.org/professionals/physician_gls/pdf/head-and-neck.pdf (accessed on 11 March 2023).
- Chan, A.T.; Leung, S.F.; Ngan, R.K.; Teo, P.M.; Lau, W.H.; Kwan, W.H.; Hui, E.P.; Yiu, H.Y.; Yeo, W.; Cheung, F.Y.; et al. Overall survival after concurrent cisplatin-radiotherapy compared with radiotherapy alone in locoregionally advanced nasopharyngeal carcinoma. J. Natl. Cancer Inst. 2005, 97, 536–539. [Google Scholar] [CrossRef]
- Lin, J.C.; Jan, J.S.; Hsu, C.Y.; Liang, W.M.; Jiang, R.S.; Wang, W.Y. Phase III study of concurrent chemoradiotherapy versus radiotherapy alone for advanced nasopharyngeal carcinoma: Positive effect on overall and progression-free survival. J. Clin. Oncol. 2003, 21, 631–637. [Google Scholar] [CrossRef]
- Elinav, E.; Nowarski, R.; Thaiss, C.A.; Hu, B.; Jin, C.; Flavell, R.A. Inflammation-induced cancer: Crosstalk between tumours, immune cells and microorganisms. Nat. Rev. Cancer 2013, 13, 759–771. [Google Scholar] [CrossRef]
- Fearon, K.; Strasser, F.; Anker, S.D.; Bosaeus, I.; Bruera, E.; Fainsinger, R.L.; Jatoi, A.; Loprinzi, C.; MacDonald, N.; Mantovani, G.; et al. Definition and classification of cancer cachexia: An international consensus. Lancet Oncol. 2011, 12, 489–495. [Google Scholar] [CrossRef] [PubMed]
- Jiang, Y.; Xu, D.; Song, H.; Qiu, B.; Tian, D.; Li, Z.; Ji, Y.; Wang, J. Inflammation and nutrition-based biomarkers in the prognosis of oesophageal cancer: A systematic review and meta-analysis. BMJ Open 2021, 11, e048324. [Google Scholar] [CrossRef] [PubMed]
- Quail, D.F.; Joyce, J.A. Microenvironmental regulation of tumor progression and metastasis. Nat. Med. 2013, 19, 1423–1437. [Google Scholar] [CrossRef]
- Buzby, G.P.; Mullen, J.L.; Matthews, D.C.; Hobbs, C.L.; Rosato, E.F. Prognostic nutritional index in gastrointestinal surgery. Am. J. Surg. 1980, 139, 160–167. [Google Scholar] [CrossRef]
- Luan, C.W.; Tsai, Y.T.; Yang, H.Y.; Chen, K.Y.; Chen, P.H.; Chou, H.H. Pretreatment prognostic nutritional index as a prognostic marker in head and neck cancer: A systematic review and meta-analysis. Sci. Rep. 2021, 11, 17117. [Google Scholar] [CrossRef]
- Maejima, K.; Taniai, N.; Yoshida, H. The Prognostic Nutritional Index as a Predictor of Gastric Cancer Progression and Recurrence. J. Nippon. Med. Sch. 2022, 89, 487–493. [Google Scholar] [CrossRef] [PubMed]
- Okadome, K.; Baba, Y.; Yagi, T.; Kiyozumi, Y.; Ishimoto, T.; Iwatsuki, M.; Miyamoto, Y.; Yoshida, N.; Watanabe, M.; Baba, H. Prognostic Nutritional Index, Tumor-infiltrating Lymphocytes, and Prognosis in Patients with Esophageal Cancer. Ann. Surg. 2020, 271, 693–700. [Google Scholar] [CrossRef]
- Sun, K.; Chen, S.; Xu, J.; Li, G.; He, Y. The prognostic significance of the prognostic nutritional index in cancer: A systematic review and meta-analysis. J. Cancer Res. Clin. Oncol. 2014, 140, 1537–1549. [Google Scholar] [CrossRef] [PubMed]
- Wang, D.; Hu, X.; Xiao, L.; Long, G.; Yao, L.; Wang, Z.; Zhou, L. Prognostic Nutritional Index and Systemic Immune-Inflammation Index Predict the Prognosis of Patients with HCC. J. Gastrointest. Surg. 2021, 25, 421–427. [Google Scholar] [CrossRef]
- Wang, Z.; Wang, Y.; Zhang, X.; Zhang, T. Pretreatment prognostic nutritional index as a prognostic factor in lung cancer: Review and meta-analysis. Clin. Chim. Acta 2018, 486, 303–310. [Google Scholar] [CrossRef]
- Atasever Akkas, E.; Erdis, E.; Yucel, B. Prognostic value of the systemic immune-inflammation index, systemic inflammation response index, and prognostic nutritional index in head and neck cancer. Eur. Arch. Otorhinolaryngol. 2023, 280, 3821–3830. [Google Scholar] [CrossRef]
- Tu, X.; Ren, J.; Zhao, Y. Prognostic value of prognostic nutritional index in nasopharyngeal carcinoma: A meta-analysis containing 4511 patients. Oral Oncol. 2020, 110, 104991. [Google Scholar] [CrossRef]
- Gao, Q.L.; Shi, J.G.; Huang, Y.D. Prognostic Significance of Pretreatment Prognostic Nutritional Index (PNI) in Patients with Nasopharyngeal Carcinoma: A Meta-Analysis. Nutr. Cancer 2021, 73, 1657–1667. [Google Scholar] [CrossRef] [PubMed]
- Tang, M.; Jia, Z.; Zhang, J. The prognostic role of prognostic nutritional index in nasopharyngeal carcinoma: A systematic review and meta-analysis. Int. J. Clin. Oncol. 2021, 26, 66–77. [Google Scholar] [CrossRef] [PubMed]
- Zeng, X.; Liu, G.; Pan, Y.; Li, Y. Prognostic Value of Clinical Biochemistry-Based Indexes in Nasopharyngeal Carcinoma. Front. Oncol. 2020, 10, 146. [Google Scholar] [CrossRef] [PubMed]
- Du, X.J.; Tang, L.L.; Mao, Y.P.; Guo, R.; Sun, Y.; Lin, A.H.; Ma, J. Value of the prognostic nutritional index and weight loss in predicting metastasis and long-term mortality in nasopharyngeal carcinoma. J. Transl. Med. 2015, 13, 364. [Google Scholar] [CrossRef]
- Yang, L.; Xia, L.; Wang, Y.; Hong, S.; Chen, H.; Liang, S.; Peng, P.; Chen, Y. Low Prognostic Nutritional Index (PNI) Predicts Unfavorable Distant Metastasis-Free Survival in Nasopharyngeal Carcinoma: A Propensity Score-Matched Analysis. PLoS ONE 2016, 11, e0158853. [Google Scholar] [CrossRef]
- Zhang, C.; Zhan, Z.; Fang, Y.; Ruan, Y.; Lin, M.; Dai, Z.; Zhang, Y.; Yang, S.; Xiao, S.; Chen, B. Prognostic nutritional index and serum lactate dehydrogenase predict the prognosis of nasopharyngeal carcinoma patients who received intensity-modulated radiation therapy. J. Cancer Res. Clin. Oncol. 2023, 149, 17795–17805. [Google Scholar] [CrossRef] [PubMed]
- Duan, Y.Y.; Deng, J.; Su, D.F.; Li, W.Q.; Han, Y.; Li, Z.X.; Huan, X.Z.; Zhu, S.H.; Yang, Q.L.; Hu, W.; et al. Construction of a comprehensive nutritional index and comparison of its prognostic performance with the PNI and NRI for survival in older patients with nasopharyngeal carcinoma: A retrospective study. Support. Care Cancer 2021, 29, 5371–5381. [Google Scholar] [CrossRef] [PubMed]
- Küçükarda, A.; Erdoğan, B.; Gökyer, A.; Sayın, S.; Gökmen, İ.; Özcan, E.; Hacıoğlu, M.B.; Uzunoğlu, S.; Çiçin, İ. Prognostic nutritional index and its dynamics after curative treatment are independent prognostic factors on survival in non-metastatic nasopharyngeal carcinoma. Support. Care Cancer 2022, 30, 2131–2139. [Google Scholar] [CrossRef] [PubMed]
- Wulfsohn, M.S.; Tsiatis, A.A. A joint model for survival and longitudinal data measured with error. Biometrics 1997, 53, 330–339. [Google Scholar] [CrossRef]
- Wang, Y.; Taylor, J.M.G. Jointly Modeling Longitudinal and Event Time Data With Application to Acquired Immunodeficiency Syndrome. J. Am. Stat. Assoc. 2001, 96, 895–905. [Google Scholar] [CrossRef]
- Faucett, C.L.; Schenker, N.; Taylor, J.M. Survival analysis using auxiliary variables via multiple imputation, with application to AIDS clinical trial data. Biometrics 2002, 58, 37–47. [Google Scholar] [CrossRef]
- Rizopoulos, D. JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event Data. J. Stat. Softw. 2010, 35, 1–33. [Google Scholar] [CrossRef]
- Buta, G.; Taye, A.; Worku, H. Bayesian Joint Modelling of Disease Progression Marker and Time to Death Event of HIV/AIDS Patients under ART Follow-up. Br. J. Med. Med. Res. 2015, 5, 1034–1043. [Google Scholar] [CrossRef]
- Kirkwood, J.M.; Ibrahim, J.G.; Sondak, V.K.; Richards, J.; Flaherty, L.E.; Ernstoff, M.S.; Smith, T.J.; Rao, U.; Steele, M.; Blum, R.H. High- and low-dose interferon alfa-2b in high-risk melanoma: First analysis of intergroup trial E1690/S9111/C9190. J. Clin. Oncol. 2000, 18, 2444–2458. [Google Scholar] [CrossRef]
- Law, N.J.; Taylor, J.M.; Sandler, H. The joint modeling of a longitudinal disease progression marker and the failure time process in the presence of cure. Biostatistics 2002, 3, 547–563. [Google Scholar] [CrossRef]
- Chang, C.; Chiang, A.J.; Chen, W.A.; Chang, H.W.; Chen, J. A joint model based on longitudinal CA125 in ovarian cancer to predict recurrence. Biomark. Med. 2016, 10, 53–61. [Google Scholar] [CrossRef] [PubMed]
- Rustand, D.; Briollais, L.; Tournigand, C.; Rondeau, V. Two-part joint model for a longitudinal semicontinuous marker and a terminal event with application to metastatic colorectal cancer data. Biostatistics 2022, 23, 50–68. [Google Scholar] [CrossRef] [PubMed]
- Asar, O.; Ritchie, J.; Kalra, P.A.; Diggle, P.J. Joint modelling of repeated measurement and time-to-event data: An introductory tutorial. Int. J. Epidemiol. 2015, 44, 334–344. [Google Scholar] [CrossRef] [PubMed]
- Crowther, M.J.; Lambert, P.C.; Abrams, K.R. Adjusting for measurement error in baseline prognostic biomarkers included in a time-to-event analysis: A joint modelling approach. BMC Med. Res. Methodol. 2013, 13, 146. [Google Scholar] [CrossRef]
- Onodera, T.; Goseki, N.; Kosaki, G. Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients. Nihon Geka Gakkai Zasshi 1984, 85, 1001–1005. [Google Scholar] [PubMed]
- Tsai, M.S.; Lin, M.H.; Lee, C.P.; Yang, Y.H.; Chen, W.C.; Chang, G.H.; Tsai, Y.T.; Chen, P.C.; Tsai, Y.H. Chang Gung Research Database: A multi-institutional database consisting of original medical records. Biomed. J. 2017, 40, 263–269. [Google Scholar] [CrossRef] [PubMed]
- Shao, S.C.; Chan, Y.Y.; Kao Yang, Y.H.; Lin, S.J.; Hung, M.J.; Chien, R.N.; Lai, C.C.; Lai, E.C. The Chang Gung Research Database-A multi-institutional electronic medical records database for real-world epidemiological studies in Taiwan. Pharmacoepidemiol. Drug Saf. 2019, 28, 593–600. [Google Scholar] [CrossRef]
- OuYang, P.Y.; Zhang, L.N.; Lan, X.W.; Xie, C.; Zhang, W.W.; Wang, Q.X.; Su, Z.; Tang, J.; Xie, F.Y. The significant survival advantage of female sex in nasopharyngeal carcinoma: A propensity-matched analysis. Br. J. Cancer 2015, 112, 1554–1561. [Google Scholar] [CrossRef]
- McMillan, D.C.; Watson, W.S.; O’Gorman, P.; Preston, T.; Scott, H.R.; McArdle, C.S. Albumin concentrations are primarily determined by the body cell mass and the systemic inflammatory response in cancer patients with weight loss. Nutr. Cancer 2001, 39, 210–213. [Google Scholar] [CrossRef] [PubMed]
- Ignacio de Ulíbarri, J.; González-Madroño, A.; de Villar, N.G.; González, P.; González, B.; Mancha, A.; Rodríguez, F.; Fernández, G. CONUT: A tool for controlling nutritional status. First validation in a hospital population. Nutr. Hosp. 2005, 20, 38–45. [Google Scholar] [PubMed]
- Bossola, M. Nutritional interventions in head and neck cancer patients undergoing chemoradiotherapy: A narrative review. Nutrients 2015, 7, 265–276. [Google Scholar] [CrossRef]
- Laursen, I.; Briand, P.; Lykkesfeldt, A.E. Serum albumin as a modulator on growth of the human breast cancer cell line, MCF-7. Anticancer. Res. 1990, 10, 343–351. [Google Scholar] [PubMed]
- Al-Shaiba, R.; McMillan, D.C.; Angerson, W.J.; Leen, E.; McArdle, C.S.; Horgan, P. The relationship between hypoalbuminaemia, tumour volume and the systemic inflammatory response in patients with colorectal liver metastases. Br. J. Cancer 2004, 91, 205–207. [Google Scholar] [CrossRef]
- Ligthart, S.; Marzi, C.; Aslibekyan, S.; Mendelson, M.M.; Conneely, K.N.; Tanaka, T.; Colicino, E.; Waite, L.L.; Joehanes, R.; Guan, W.; et al. DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases. Genome Biol. 2016, 17, 255. [Google Scholar] [CrossRef]
- Chojkier, M. Inhibition of albumin synthesis in chronic diseases: Molecular mechanisms. J. Clin. Gastroenterol. 2005, 39, S143–S146. [Google Scholar] [CrossRef]
- Esper, D.H.; Harb, W.A. The cancer cachexia syndrome: A review of metabolic and clinical manifestations. Nutr. Clin. Pract. 2005, 20, 369–376. [Google Scholar] [CrossRef]
- Ostroumov, D.; Fekete-Drimusz, N.; Saborowski, M.; Kühnel, F.; Woller, N. CD4 and CD8 T lymphocyte interplay in controlling tumor growth. Cell. Mol. Life Sci. 2018, 75, 689–713. [Google Scholar] [CrossRef]
- Cézé, N.; Thibault, G.; Goujon, G.; Viguier, J.; Watier, H.; Dorval, E.; Lecomte, T. Pre-treatment lymphopenia as a prognostic biomarker in colorectal cancer patients receiving chemotherapy. Cancer Chemother. Pharmacol. 2011, 68, 1305–1313. [Google Scholar] [CrossRef]
- Cupp, M.A.; Cariolou, M.; Tzoulaki, I.; Aune, D.; Evangelou, E.; Berlanga-Taylor, A.J. Neutrophil to lymphocyte ratio and cancer prognosis: An umbrella review of systematic reviews and meta-analyses of observational studies. BMC Med. 2020, 18, 360. [Google Scholar] [CrossRef] [PubMed]
- He, J.R.; Shen, G.P.; Ren, Z.F.; Qin, H.; Cui, C.; Zhang, Y.; Zeng, Y.X.; Jia, W.H. Pretreatment levels of peripheral neutrophils and lymphocytes as independent prognostic factors in patients with nasopharyngeal carcinoma. Head Neck 2012, 34, 1769–1776. [Google Scholar] [CrossRef] [PubMed]
- Fujiwara, D.; Tsubaki, M.; Takeda, T.; Miura, M.; Nishida, S.; Sakaguchi, K. Objective evaluation of nutritional status using the prognostic nutritional index during and after chemoradiotherapy in Japanese patients with head and neck cancer: A retrospective study. Eur. J. Hosp. Pharm. 2021, 28, 266–270. [Google Scholar] [CrossRef]
- Iwasa, Y.I.; Shimizu, M.; Matsuura, K.; Hori, K.; Hiramatsu, K.; Sugiyama, K.; Yokota, Y.; Kitano, T.; Kitoh, R.; Takumi, Y. Prognostic significance of pre- and post-treatment hematological biomarkers in patients with head and neck cancer treated with chemoradiotherapy. Sci. Rep. 2023, 13, 3869. [Google Scholar] [CrossRef]
- Long, J.D.; Mills, J.A. Joint modeling of multivariate longitudinal data and survival data in several observational studies of Huntington’s disease. BMC Med. Res. Methodol. 2018, 18, 138. [Google Scholar] [CrossRef]
- Dupuy, J.F.; Mesbah, M. Joint modeling of event time and nonignorable missing longitudinal data. Lifetime Data Anal. 2002, 8, 99–115. [Google Scholar] [CrossRef]
- Arisido, M.W.; Antolini, L.; Bernasconi, D.P.; Valsecchi, M.G.; Rebora, P. Joint model robustness compared with the time-varying covariate Cox model to evaluate the association between a longitudinal marker and a time-to-event endpoint. BMC Med. Res. Methodol. 2019, 19, 222. [Google Scholar] [CrossRef] [PubMed]
Patients, No. (%) (N = 2332) | |||
---|---|---|---|
Variable | Death (n = 638) | Alive (n = 1694) | p-Value a |
Age, mean ± SD (years) | 54.4 ± 13.2 | 48.1 ± 11.3 | <0.001 |
Sex | 0.001 | ||
Male | 510 (79.9%) | 1245 (73.5%) | |
Female | 128 (20.1%) | 449 (26.5%) | |
T classification | <0.001 | ||
1 | 121 (19.0%) | 631 (37.2%) | |
2 | 108 (16.9%) | 332 (19.6%) | |
3 | 146 (22.9%) | 377 (22.3%) | |
4 | 263 (41.2%) | 354 (20.9%) | |
N classification | <0.001 | ||
0 | 65 (10.2%) | 249 (14.7%) | |
1 | 194 (30.4%) | 714 (42.1%) | |
2 | 210 (32.9%) | 453 (26.7%) | |
3 | 169 (26.5%) | 278 (16.4%) | |
AJCC Stage | <0.001 | ||
1 | 9 (1.4%) | 114 (6.7%) | |
2 | 72 (11.3%) | 460 (27.2%) | |
3 | 178 (27.9%) | 552 (32.6%) | |
4 | 379 (59.4%) | 568 (33.5%) | |
DM | 44 (6.9%) | 64 (3.8%) | 0.001 |
HTN | 75 (11.8%) | 122 (7.2%) | <0.001 |
BMI (kg/m2) | 24.5 ± 4.2 | 25.1 ± 4.0 | 0.001 |
Treatment protocol | 0.519 | ||
IMRT | 73 (11.4%) | 169 (10.0%) | |
CCRT | 502 (78.7%) | 1343 (79.3%) | |
Induction C/T + CCRT | 63 (9.9%) | 182 (10.7%) |
Variable | Estimate (95% CI) | SE | p-Value |
---|---|---|---|
Intercept | 48.592 (46.934, 50.251) | 0.846 | <0.001 |
Follow-up (years) | −1.568 (−1.784, −1.352) | 0.110 | <0.001 |
Age (years) | −0.117 (−0.132, −0.101) | 0.008 | <0.001 |
Male | 0.734 (0.311, 1.158) | 0.216 | <0.001 |
DM | −1.818 (−2.752, −0.885) | 0.476 | 0.003 |
HTN | −0.274 (−0.994, 0.447) | 0.367 | 0.170 |
BMI (kg/m2) | 0.086 (0.040, 0.131) | 0.023 | <0.001 |
AJCC stage | |||
1 | Ref. | ||
2 | −2.137 (−3.248, −1.025) | 0.567 | 0.001 |
3 | −3.050 (−4.150, −1.950) | 0.561 | <0.001 |
4 | −4.138 (−5.225, −3.050) | 0.555 | <0.001 |
Treatment protocol | |||
IMRT | Ref. | ||
CCRT | 1.606 (0.843, 2.369) | 0.389 | <0.001 |
Induction C/T + CCRT | 1.492 (0.597, 2.387) | 0.457 | <0.001 |
Variable | Hazard Ratio (95% CI) | SE | p-Value |
---|---|---|---|
PNI | 0.813 (0.805, 0.821) | 0.005 | <0.001 |
Age (years) | 1.011 (1.005, 1.018) | 0.003 | 0.001 |
Male | 1.445 (1.188, 1.759) | 0.100 | <0.001 |
DM | 1.078 (0.764, 1.522) | 0.176 | 0.669 |
HTN | 1.269 (0.960, 1.678) | 0.143 | 0.094 |
BMI (kg/m2) | 0.985 (0.965, 1.006) | 0.011 | 0.163 |
AJCC stage | |||
1 | Ref. | ||
2 | 1.532 (0.741, 3.167) | 0.371 | 0.250 |
3 | 2.549 (1.253, 5.185) | 0.362 | 0.010 |
4 | 3.783 (1.873, 7.641) | 0.359 | <0.001 |
Treatment protocol | |||
IMRT | Ref. | ||
CCRT | 0.813 (0.623, 1.060) | 0.135 | 0.126 |
Induction C/T + CCRT | 0.993 (0.694, 1.422) | 0.183 | 0.971 |
Variable | Estimate (95% CI) | SE | p-Value |
---|---|---|---|
Longitudinal sub-model | |||
Intercept | 48.592 (47.251, 49.934) | 0.684 | <0.001 |
Follow-up (years) | −1.568 (−1.613, −1.523) | 0.023 | <0.001 |
Age (years) | −0.117 (−0.129, −0.104) | 0.006 | <0.001 |
Male | 0.734 (0.389, 1.080) | 0.176 | <0.001 |
DM | −1.818 (−2.516, −1.120) | 0.356 | <0.001 |
HTN | −0.274 (−0.832, 0.285) | 0.285 | 0.337 |
BMI (kg/m2) | 0.086 (0.053, 0.118) | 0.017 | <0.001 |
AJCC stage | |||
1 | Ref. | ||
2 | −2.137 (−3.126, −1.147) | 0.505 | <0.001 |
3 | −3.050 (−4.036, −2.064) | 0.503 | <0.001 |
4 | −4.138 (−5.107, −3.169) | 0.495 | <0.001 |
Treatment protocol | |||
IMRT | Ref. | ||
CCRT | 1.606 (0.960, 2.252) | 0.330 | <0.001 |
Induction C/T + CCRT | 1.492 (0.747, 2.236) | 0.380 | <0.001 |
Variable | Hazard Ratio (95% CI) | SE | p-Value |
Survival sub-model | |||
PNI | 0.864 (0.850, 0.879) | 0.009 | <0.001 |
Age (years) | 1.019 (1.012, 1.027) | 0.004 | <0.001 |
Male | 1.390 (1.135, 1.702) | 0.103 | 0.001 |
DM | 1.398 (0.980, 1.993) | 0.181 | 0.064 |
HTN | 1.119 (0.834, 1.503) | 0.150 | 0.453 |
BMI (kg/m2) | 0.957 (0.936, 0.979) | 0.012 | <0.001 |
AJCC stage | |||
1 | Ref. | ||
2 | 1.817 (0.909, 3.631) | 0.353 | 0.091 |
3 | 3.525 (1.793, 6.929) | 0.345 | <0.001 |
4 | 6.286 (3.223, 12.262) | 0.341 | <0.001 |
Treatment protocol | |||
IMRT | Ref. | ||
CCRT | 0.704 (0.531, 0.933) | 0.144 | 0.015 |
Induction C/T + CCRT | 0.660 (0.455, 0.959) | 0.191 | 0.029 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hsiao, P.-W.; Wang, Y.-M.; Wu, S.-C.; Chen, W.-C.; Wu, C.-N.; Chiu, T.-J.; Yang, Y.-H.; Luo, S.-D. A Joint Model Based on Post-Treatment Longitudinal Prognostic Nutritional Index to Predict Survival in Nasopharyngeal Carcinoma. Cancers 2024, 16, 1037. https://doi.org/10.3390/cancers16051037
Hsiao P-W, Wang Y-M, Wu S-C, Chen W-C, Wu C-N, Chiu T-J, Yang Y-H, Luo S-D. A Joint Model Based on Post-Treatment Longitudinal Prognostic Nutritional Index to Predict Survival in Nasopharyngeal Carcinoma. Cancers. 2024; 16(5):1037. https://doi.org/10.3390/cancers16051037
Chicago/Turabian StyleHsiao, Po-Wen, Yu-Ming Wang, Shao-Chun Wu, Wei-Chih Chen, Ching-Nung Wu, Tai-Jan Chiu, Yao-Hsu Yang, and Sheng-Dean Luo. 2024. "A Joint Model Based on Post-Treatment Longitudinal Prognostic Nutritional Index to Predict Survival in Nasopharyngeal Carcinoma" Cancers 16, no. 5: 1037. https://doi.org/10.3390/cancers16051037
APA StyleHsiao, P.-W., Wang, Y.-M., Wu, S.-C., Chen, W.-C., Wu, C.-N., Chiu, T.-J., Yang, Y.-H., & Luo, S.-D. (2024). A Joint Model Based on Post-Treatment Longitudinal Prognostic Nutritional Index to Predict Survival in Nasopharyngeal Carcinoma. Cancers, 16(5), 1037. https://doi.org/10.3390/cancers16051037