Development of a predictive model for patients with bone metastases referred to palliative radiotherapy: Secondary analysis of a multicenter study (the PRAIS trial).
LASSO
bone metastasis
multicenter
palliative therapy
predictive model
radiotherapy
Journal
Cancer medicine
ISSN: 2045-7634
Titre abrégé: Cancer Med
Pays: United States
ID NLM: 101595310
Informations de publication
Date de publication:
Oct 2024
Oct 2024
Historique:
revised:
03
07
2024
received:
13
01
2024
accepted:
13
07
2024
medline:
11
10
2024
pubmed:
11
10
2024
entrez:
11
10
2024
Statut:
ppublish
Résumé
The decision to administer palliative radiotherapy (RT) to patients with bone metastases (BMs), as well as the selection of treatment protocols (dose, fractionation), requires an accurate assessment of survival expectancy. In this study, we aimed to develop three predictive models (PMs) to estimate short-, intermediate-, and long-term overall survival (OS) for patients in this clinical setting. This study constitutes a sub-analysis of the PRAIS trial, a longitudinal observational study collecting data from patients referred to participating centers to receive palliative RT for cancer-induced bone pain. Our analysis encompassed 567 patients from the PRAIS trial database. The primary objectives were to ascertain the correlation between clinical and laboratory parameters with the OS rates at three distinct time points (short: 3 weeks; intermediate: 24 weeks; prolonged: 52 weeks) and to construct PMs for prognosis. We employed machine learning techniques, comprising the following steps: (i) identification of reliable prognostic variables and training; (ii) validation and testing of the model using the selected variables. The selection of variables was accomplished using the LASSO method (Least Absolute Shrinkage and Selection Operator). The model performance was assessed using receiver operator characteristic curves (ROC) and the area under the curve (AUC). Our analysis demonstrated a significant impact of clinical parameters (primary tumor site, presence of non-bone metastases, steroids and opioid intake, food intake, and body mass index) and laboratory parameters (interleukin 8 [IL-8], chloride levels, C-reactive protein, white blood cell count, and lymphocyte count) on OS. Notably, different factors were associated with the different times for OS with only IL-8 included both in the PMs for short- and long-term OS. The AUC values for ROC curves for 3-week, 24-week, and 52-week OS were 0.901, 0.767, and 0.806, respectively. We successfully developed three PMs for OS based on easily accessible clinical and laboratory parameters for patients referred to palliative RT for painful BMs. While our findings are promising, it is important to recognize that this was an exploratory trial. The implementation of these tools into clinical practice warrants further investigation and confirmation through subsequent studies with separate databases.
Sections du résumé
BACKGROUND
BACKGROUND
The decision to administer palliative radiotherapy (RT) to patients with bone metastases (BMs), as well as the selection of treatment protocols (dose, fractionation), requires an accurate assessment of survival expectancy. In this study, we aimed to develop three predictive models (PMs) to estimate short-, intermediate-, and long-term overall survival (OS) for patients in this clinical setting.
MATERIALS AND METHODS
METHODS
This study constitutes a sub-analysis of the PRAIS trial, a longitudinal observational study collecting data from patients referred to participating centers to receive palliative RT for cancer-induced bone pain. Our analysis encompassed 567 patients from the PRAIS trial database. The primary objectives were to ascertain the correlation between clinical and laboratory parameters with the OS rates at three distinct time points (short: 3 weeks; intermediate: 24 weeks; prolonged: 52 weeks) and to construct PMs for prognosis. We employed machine learning techniques, comprising the following steps: (i) identification of reliable prognostic variables and training; (ii) validation and testing of the model using the selected variables. The selection of variables was accomplished using the LASSO method (Least Absolute Shrinkage and Selection Operator). The model performance was assessed using receiver operator characteristic curves (ROC) and the area under the curve (AUC).
RESULTS
RESULTS
Our analysis demonstrated a significant impact of clinical parameters (primary tumor site, presence of non-bone metastases, steroids and opioid intake, food intake, and body mass index) and laboratory parameters (interleukin 8 [IL-8], chloride levels, C-reactive protein, white blood cell count, and lymphocyte count) on OS. Notably, different factors were associated with the different times for OS with only IL-8 included both in the PMs for short- and long-term OS. The AUC values for ROC curves for 3-week, 24-week, and 52-week OS were 0.901, 0.767, and 0.806, respectively.
CONCLUSIONS
CONCLUSIONS
We successfully developed three PMs for OS based on easily accessible clinical and laboratory parameters for patients referred to palliative RT for painful BMs. While our findings are promising, it is important to recognize that this was an exploratory trial. The implementation of these tools into clinical practice warrants further investigation and confirmation through subsequent studies with separate databases.
Substances chimiques
Interleukin-8
0
Types de publication
Journal Article
Multicenter Study
Observational Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
e70050Informations de copyright
© 2024 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd.
Références
Christakis NA, Lamont EB. Extent and determinants of error in doctors' prognoses in terminally ill patients: prospective cohort study. BMJ. 2000;320(7233):469‐472. doi:10.1136/bmj.320.7233.469
Vu E, Steinmann N, Schröder C, et al. Applications of machine learning in palliative care: a systematic review. Cancer. 2023;15(5):1596. doi:10.3390/cancers15051596
Pobar I, Job M, Holt T, Hargrave C, Hickey B. Prognostic tools for survival prediction in advanced cancer patients: a systematic review. J Med Imaging Radiat Oncol. 2021;65(6):806‐816. doi:10.1111/1754-9485.13185
Mizumoto M, Harada H, Asakura H, et al. Prognostic factors and a scoring system for survival after radiotherapy for metastases to the spinal column: a review of 544 patients at Shizuoka cancer center hospital. Cancer. 2008;113(10):2816‐2822. doi:10.1002/cncr.23888
Maltoni M, Pirovano M, Scarpi E, et al. Prediction of survival of patients terminally ill with cancer. Results of an Italian prospective multicentric study. Cancer. 1995;75(10):2613‐2622.
Maltoni M, Caraceni A, Brunelli C, et al. Prognostic factors in advanced cancer patients: evidence‐based clinical recommendations—a study by the Steering Committee of the European Association for Palliative Care. J Clin Oncol. 2005;23(25):6240‐6248. doi:10.1200/JCO.2005.06.866
Maltoni M, Scarpi E, Dall'Agata M, et al. Prognostication in palliative radiotherapy—ProPaRT: accuracy of prognostic scores. Front Oncol. 2022;12:12. doi:10.3389/fonc.2022.918414
Tayjasanant S, Bruera E, Hui D. How far along the disease trajectory? An examination of the time‐related patient characteristics in the palliative oncology literature. Support Care Cancer. 2016;24(9):3997‐4004. doi:10.1007/s00520-016-3225-z
Hui D, Paiva CE, Del Fabbro EG, et al. Prognostication in advanced cancer: update and directions for future research. Support Care Cancer. 2019;27(6):1973‐1984. doi:10.1007/s00520-019-04727-y
Stone P, Buckle P, Dolan R, et al. Prognostic evaluation in patients with advanced cancer in the last months of life: ESMO clinical practice guideline. ESMO Open. 2023;8(2):101195. doi:10.1016/j.esmoop.2023.101195
Habberstad R, Frøseth TCS, Aass N, et al. Clinical predictors for analgesic response to radiotherapy in patients with painful bone metastases. J Pain Symptom Manag. 2021;62(4):681‐690. doi:10.1016/j.jpainsymman.2021.03.022
Habberstad R, Frøseth TCS, Aass N, et al. The palliative radiotherapy and inflammation study (PRAIS)—protocol for a longitudinal observational multicenter study on patients with cancer induced bone pain. BMC Palliat Care. 2018;17(1):110. doi:10.1186/s12904-018-0362-9
Baggiolini M, Imboden P, Detmers P. Neutrophil activation and the effects of interleukin‐8/neutrophil‐activating peptide 1 (IL‐8/NAP‐1). Cytokines. 1992;4:1‐17.
Mukaida N, Harada A, Matsushima K. Interleukin‐8 (IL‐8) and monocyte chemotactic and activating factor (MCAF/MCP‐1), chemokines essentially involved in inflammatory and immune reactions. Cytokine Growth Factor Rev. 1998;9(1):9‐23. doi:10.1016/s1359-6101(97)00022-1
Iguchi H, Ono M, Matsushima K, Kuwano M. Overproduction of IL‐8 results in suppression of bone metastasis by lung cancer cells in vivo. Int J Oncol. 2000;17(2):329‐333. doi:10.3892/ijo.17.2.329
Konno H, Ohta M, Baba M, Suzuki S, Nakamura S. The role of circulating IL‐8 and VEGF protein in the progression of gastric cancer. Cancer Sci. 2003;94(8):735‐740. doi:10.1111/j.1349-7006.2003.tb01511.x
Kuwada Y, Sasaki T, Morinaka K, Kitadai Y, Mukaida N, Chayama K. Potential involvement of IL‐8 and its receptors in the invasiveness of pancreatic cancer cells. Int J Oncol. 2003;22(4):765‐771.
Kozłowski L, Zakrzewska I, Tokajuk P, Wojtukiewicz MZ. Concentration of interleukin‐6 (IL‐6), interleukin‐8 (IL‐8) and interleukin‐10 (IL‐10) in blood serum of breast cancer patients. Rocz Akad Med Bialymst. 1995;2003(48):82‐84.
Kogan‐Sakin I, Cohen M, Paland N, et al. Prostate stromal cells produce CXCL‐1, CXCL‐2, CXCL‐3 and IL‐8 in response to epithelia‐secreted IL‐1. Carcinogenesis. 2009;30(4):698‐705. doi:10.1093/carcin/bgp043
Matsuo Y, Ochi N, Sawai H, et al. CXCL8/IL‐8 and CXCL12/SDF‐1alpha co‐operatively promote invasiveness and angiogenesis in pancreatic cancer. Int J Cancer. 2009;124(4):853‐861. doi:10.1002/ijc.24040
Derin D, Soydinc HO, Guney N, et al. Serum IL‐8 and IL‐12 levels in breast cancer. Med Oncol Northwood Lond Engl. 2007;24(2):163‐168. doi:10.1007/BF02698035
Lurje G, Zhang W, Schultheis AM, et al. Polymorphisms in VEGF and IL‐8 predict tumor recurrence in stage III colon cancer. Ann Oncol. 2008;19(10):1734‐1741. doi:10.1093/annonc/mdn368
Tjiong MY, van der Vange N, ten Kate FJ, et al. Increased IL‐6 and IL‐8 levels in cervicovaginal secretions of patients with cervical cancer. Gynecol Oncol. 1999;73(2):285‐291. doi:10.1006/gyno.1999.5358
Zhou DH, Trauzold A, Röder C, Pan G, Zheng C, Kalthoff H. The potential molecular mechanism of overexpression of uPA, IL‐8, MMP‐7 and MMP‐9 induced by TRAIL in pancreatic cancer cell. Hepatobiliary Pancreat Dis. 2008;7(2):201‐209.
Bauer S, Adrian N, Siebenborn U, et al. Sequential cancer immunotherapy: targeted activity of dimeric TNF and IL‐8. Cancer Immun. 2009;9:2.
Vinante F, Rigo A, Vincenzi C, et al. IL‐8 mRNA expression and IL‐8 production by acute myeloid leukemia cells. Leukemia. 1993;7(10):1552‐1556.
Bazzichetto C, Milella M, Zampiva I, et al. Interleukin‐8 in colorectal cancer: a systematic review and meta‐analysis of its potential role as a prognostic biomarker. Biomedicine. 2022;10(10):2631. doi:10.3390/biomedicines10102631
Sanmamed MF, Perez‐Gracia JL, Schalper KA, et al. Changes in serum interleukin‐8 (IL‐8) levels reflect and predict response to anti‐PD‐1 treatment in melanoma and non‐small‐cell lung cancer patients. Ann Oncol. 2017;28(8):1988‐1995. doi:10.1093/annonc/mdx190
Chen Y, Shi M, Yu GZ, et al. Interleukin‐8, a promising predictor for prognosis of pancreatic cancer. World J Gastroenterol. 2012;18(10):1123‐1129. doi:10.3748/wjg.v18.i10.1123
Deng Y, Ning Z, Hu Z, Yu Q, He B, Hu G. High interleukin‐8 and/or extracellular signal‐regulated kinase 2 expression predicts poor prognosis in patients with hepatocellular carcinoma. Oncol Lett. 2019;18(5):5215‐5224. doi:10.3892/ol.2019.10907
Zhai J, Shen J, Xie G, et al. Cancer‐associated fibroblasts‐derived IL‐8 mediates resistance to cisplatin in human gastric cancer. Cancer Lett. 2019;454:37‐43. doi:10.1016/j.canlet.2019.04.002
Botticelli A, Cirillo A, Pomati G, et al. The role of opioids in cancer response to immunotherapy. J Transl Med. 2021;19(1):119. doi:10.1186/s12967-021-02784-8
Mao Z, Jia X, Jiang P, et al. Effect of concomitant use of analgesics on prognosis in patients treated with immune checkpoint inhibitors: a systematic review and meta‐analysis. Front Immunol. 2022;13:861723. doi:10.3389/fimmu.2022.861723
Boland JW, McWilliams K, Ahmedzai SH, Pockley AG. Effects of opioids on immunologic parameters that are relevant to anti‐tumour immune potential in patients with cancer: a systematic literature review. Br J Cancer. 2014;111(5):866‐873. doi:10.1038/bjc.2014.384
Boland JW. Effect of opioids on survival in patients with cancer. Cancer. 2022;14(22):5720. doi:10.3390/cancers14225720
Maltoni M, Rossi R. Risk of detrimental recommendations for cancer pain management. J Transl Med. 2021;19(1):160. doi:10.1186/s12967-021-02831-4
Gagnon B, Hanna AMR. Risk of confounding variables in multivariate analysis. J Transl Med. 2022;20(1):165. doi:10.1186/s12967-022-03348-0
Shirasu H, Yokota T, Hamauchi S, et al. Risk factors for aspiration pneumonia during concurrent chemoradiotherapy or bio‐radiotherapy for head and neck cancer. BMC Cancer. 2020;20(1):182. doi:10.1186/s12885-020-6682-1
Zembower TR. Epidemiology of infections in cancer patients. Cancer Treat Res. 2014;161:43‐89. doi:10.1007/978-3-319-04220-6_2
Zylla D, Steele G, Gupta P. A systematic review of the impact of pain on overall survival in patients with cancer. Support Care Cancer. 2017;25(5):1687‐1698. doi:10.1007/s00520-017-3614-y
Woopen H, Richter R, Inci G, Alavi S, Chekerov R, Sehouli J. The prognostic and predictive role of pain before systemic chemotherapy in recurrent ovarian cancer: an individual participant data meta‐analysis of the north‐eastern German Society of Gynecological Oncology (NOGGO) of 1226 patients. Support Care Cancer. 2020;28(4):1997‐2003. doi:10.1007/s00520-019-05000-y
Page GG. The immune‐suppressive effects of pain. Adv Exp Med Biol. 2003;521:117‐125.
Chow E, Abdolell M, Panzarella T, et al. Predictive model for survival in patients with advanced cancer. J Clin Oncol. 2008;26(36):5863‐5869. doi:10.1200/JCO.2008.17.1363
Katagiri H, Okada R, Takagi T, et al. New prognostic factors and scoring system for patients with skeletal metastasis. Cancer Med. 2014;3(5):1359‐1367. doi:10.1002/cam4.292
Krishnan MS, Epstein‐Peterson Z, Chen YH, et al. Predicting life expectancy in patients with metastatic cancer receiving palliative radiotherapy: the TEACHH model. Cancer. 2014;120(1):134‐141. doi:10.1002/cncr.28408
Huang Z, Hu C, Chi C, Jiang Z, Tong Y, Zhao C. An artificial intelligence model for predicting 1‐year survival of bone metastases in non‐small‐cell lung cancer patients based on XGBoost algorithm. Biomed Res Int. 2020;2020:3462363. doi:10.1155/2020/3462363
Elledge CR, LaVigne AW, Fiksel J, et al. External validation of the bone metastases ensemble trees for survival (BMETS) machine learning model to predict survival in patients with symptomatic bone metastases. JCO Clin Cancer Inform. 2021;5:304‐314. doi:10.1200/CCI.20.00128
Cui Y, Shi X, Wang S, et al. Machine learning approaches for prediction of early death among lung cancer patients with bone metastases using routine clinical characteristics: an analysis of 19,887 patients. Front Public Health. 2022;10:1019168. doi:10.3389/fpubh.2022.1019168
Le Y, Xu W, Guo W. The construction and validation of a new predictive model for overall survival of clear cell renal cell carcinoma patients with bone metastasis based on machine learning algorithm. Technol Cancer Res Treat. 2023;22:15330338231165131. doi:10.1177/15330338231165131
Li C, Liu M, Li J, et al. Machine learning predicts the prognosis of breast cancer patients with initial bone metastases. Front Public Health. 2022;10:1003976. doi:10.3389/fpubh.2022.1003976
Long Z, Yi M, Qin Y, et al. Development and validation of an ensemble machine‐learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma. Front Oncol. 2023;13:1144039. doi:10.3389/fonc.2023.1144039
Tong Y, Xu S, Jiang L, Zhao C, Zhao D. A visualized model for identifying optimal candidates for aggressive locoregional surgical treatment in patients with bone metastases from breast cancer. Front Endocrinol (Lausanne). 2023;5(14):1266679. doi:10.3389/fendo.2023.1266679
Awodutire PO, Kattan MW, Ilori OS, Ilori OR. An accelerated failure time model to predict cause‐specific survival and prognostic factors of lung and bronchus cancer patients with at least bone or brain metastases: development and internal validation using a SEER‐based study. Cancers (Basel). 2024;16(3):668. doi:10.3390/cancers16030668
Pan YT, Lin YP, Yen HK, et al. Are current survival prediction tools useful when treating subsequent skeletal‐related events from bone metastases? Clin Orthop Relat Res. 2024. doi:10.1097/CORR.0000000000003030