Advertisement

Prognostic significance of lab data and performance comparison by validating survival prediction models for patients with spinal metastases after radiotherapy

  • Author Footnotes
    1 Hung-Kuan Yen, and Ming-Hsiao Hu contribute equally as first authors.
    Hung-Kuan Yen
    Footnotes
    1 Hung-Kuan Yen, and Ming-Hsiao Hu contribute equally as first authors.
    Affiliations
    Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan

    Department of Orthopaedic Surgery, National Taiwan University Hospital, Hsin-Chu Branch, Hsinchu, Taiwan

    Department of Medical Education, National Taiwan University Hospital, Hsin-Chu Branch, Hsinchu, Taiwan
    Search for articles by this author
  • Author Footnotes
    1 Hung-Kuan Yen, and Ming-Hsiao Hu contribute equally as first authors.
    Ming-Hsiao Hu
    Footnotes
    1 Hung-Kuan Yen, and Ming-Hsiao Hu contribute equally as first authors.
    Affiliations
    Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
    Search for articles by this author
  • Hester Zijlstra
    Affiliations
    Department of Orthopaedics, University Medical Center Utrecht, Utrecht, Netherlands

    Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, United States
    Search for articles by this author
  • Olivier Q. Groot
    Affiliations
    Department of Orthopaedics, University Medical Center Utrecht, Utrecht, Netherlands

    Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, United States
    Search for articles by this author
  • Hsiang-Chieh Hsieh
    Affiliations
    Department of Orthopaedic Surgery, National Taiwan University Hospital, Hsin-Chu Branch, Hsinchu, Taiwan
    Search for articles by this author
  • Jiun-Jen Yang
    Affiliations
    Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
    Search for articles by this author
  • Aditya V. Karhade
    Affiliations
    Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, United States
    Search for articles by this author
  • Po-Chao Chen
    Affiliations
    Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
    Search for articles by this author
  • Yu-Han Chen
    Affiliations
    Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
    Search for articles by this author
  • Po-Hao Huang
    Affiliations
    Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
    Search for articles by this author
  • Yu-Hung Chen
    Affiliations
    Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
    Search for articles by this author
  • Fu-Ren Xiao
    Affiliations
    Division of Neurosurgery, Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan
    Search for articles by this author
  • Jorrit-Jan Verlaan
    Affiliations
    Department of Orthopaedics, University Medical Center Utrecht, Utrecht, Netherlands
    Search for articles by this author
  • Joseph H. Schwab
    Affiliations
    Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, United States
    Search for articles by this author
  • Rong-Sen Yang
    Affiliations
    Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
    Search for articles by this author
  • Shu-Hua Yang
    Affiliations
    Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
    Search for articles by this author
  • Wei-Hsin Lin
    Correspondence
    Corresponding authors at: Division of Radiation Oncology, Department of Oncology, National Taiwan University Hospital, No.7 Chung-Shan South Road, Taipei, 10002, Taiwan (F.-M. Hsu). Department of Orthopaedic Surgery, National Taiwan University Hospital, No.7 Chung-Shan South Road, Taipei 10002, Taiwan (W.-H. Lin).
    Affiliations
    Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
    Search for articles by this author
  • Feng-Ming Hsu
    Correspondence
    Corresponding authors at: Division of Radiation Oncology, Department of Oncology, National Taiwan University Hospital, No.7 Chung-Shan South Road, Taipei, 10002, Taiwan (F.-M. Hsu). Department of Orthopaedic Surgery, National Taiwan University Hospital, No.7 Chung-Shan South Road, Taipei 10002, Taiwan (W.-H. Lin).
    Affiliations
    Division of Radiation Oncology, Department of Oncology, National Taiwan University Hospital, Taipei, Taiwan

    Graduate Institute of Oncology, National Taiwan University College of Medicine, Taipei, Taiwan

    Department of Radiation Oncology, National Taiwan University Cancer Center, Taipei, Taiwan
    Search for articles by this author
  • Author Footnotes
    1 Hung-Kuan Yen, and Ming-Hsiao Hu contribute equally as first authors.
Published:September 03, 2022DOI:https://doi.org/10.1016/j.radonc.2022.08.029

      Highlights

      • Laboratory data is of prognostic value in predicting survival for spinal metastasis.
      • Machine-learning-based survival predicting model outperforms regression-based model.
      • Accurate survival prediction model aids patient-centered care for spinal metastasis.

      Abstract

      Background and purpose

      Well-performing survival prediction models (SPMs) help patients and healthcare professionals to choose treatment aligning with prognosis. This retrospective study aims to investigate the prognostic impacts of laboratory data and to compare the performances of Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy (METSSS) model, New England Spinal Metastasis Score (NESMS), and Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) for spinal metastases (SM).

      Materials and methods

      From 2010 to 2018, patients who received radiotherapy (RT) for SM at a tertiary center were enrolled and the data were retrospectively collected. Multivariate logistic and Cox-proportional-hazard regression analyses were used to assess the association between laboratory values and survival. The area under receiver-operating characteristics curve (AUROC), calibration analysis, Brier score, and decision curve analysis were used to evaluate the performance of SPMs.

      Results

      A total of 2786 patients were included for analysis. The 90-day and 1-year survival rates after RT were 70.4% and 35.7%, respectively. Higher albumin, hemoglobin, or lymphocyte count were associated with better survival, while higher alkaline phosphatase, white blood cell count, neutrophil count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, or international normalized ratio were associated with poor prognosis. SORG-MLA has the best discrimination (AUROC 90-day, 0.78; 1-year 0.76), best calibrations, and the lowest Brier score (90-day 0.16; 1-year 0.18). The decision curve of SORG-MLA is above the other two competing models with threshold probabilities from 0.1 to 0.8.

      Conclusion

      Laboratory data are of prognostic significance in survival prediction after RT for SM. Machine learning-based model SORG-MLA outperforms statistical regression-based model METSSS model and NESMS in survival predictions.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Radiotherapy and Oncology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Sung H.
        • Ferlay J.
        • Siegel R.L.
        • et al.
        Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.
        CA Cancer J Clin. 2021; 71: 209-249
        • Torre L.A.
        • Bray F.
        • Siegel R.L.
        • Ferlay J.
        • Lortet-Tieulent J.
        • Jemal A.
        Global cancer statistics, 2012.
        CA Cancer J Clin. 2015; 65: 87-108
        • Ryan C.
        • Stoltzfus K.C.
        • Horn S.
        • et al.
        Epidemiology of bone metastases.
        Bone. 2020; 115783
        • Tseng T.E.
        • Lee C.C.
        • Yen H.K.
        • et al.
        International validation of the SORG machine-learning algorithm for predicting the survival of patients with extremity metastases undergoing surgical treatment.
        Clin Orthop Relat Res. 2022; 480: 367-378
        • Sciubba D.M.
        • Pennington Z.
        • Colman M.W.
        • et al.
        Spinal metastases 2021: a review of the current state of the art and future directions.
        The Spine Journal. 2021; 21: 1414-1429
        • Lutz S.
        • Balboni T.
        • Jones J.
        • et al.
        Palliative radiation therapy for bone metastases: update of an ASTRO evidence-based guideline.
        Pract Radiat Oncol. 2017; 7: 4-12
        • Ghori A.K.
        • Leonard D.A.
        • Schoenfeld A.J.
        • et al.
        Modeling 1-year survival after surgery on the metastatic spine.
        Spine J. 2015; 15: 2345-2350
        • Zaorsky N.G.
        • Liang M.
        • Patel R.
        • et al.
        Survival after palliative radiation therapy for cancer: the METSSS model.
        Radiother Oncol. 2021; 158: 104-111
        • 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
        • Wang M.
        • Wu Q.
        • Zhang J.
        • et al.
        Prognostic impacts of extracranial metastasis on non-small cell lung cancer with brain metastasis: a retrospective study based on surveillance, epidemiology, and end results database.
        Cancer Med. 2021; 10: 471-482
        • Eitz K.A.
        • Lo S.S.
        • Soliman H.
        • et al.
        Multi-institutional analysis of prognostic factors and outcomes after hypofractionated stereotactic radiotherapy to the resection cavity in patients with brain metastases.
        JAMA Oncol. 2020; 6: 1901-1909
        • Roussille P.
        • Auvray M.
        • Vansteene D.
        • et al.
        Prognostic factors of colorectal cancer patients with brain metastases.
        Radiother Oncol. 2021; 158: 67-73
        • Nieder C.
        • Mehta M.P.
        Prognostic indices for brain metastases–usefulness and challenges.
        Radiat Oncol. 2009; 4: 1-11
        • Lagerwaard F.
        • Levendag P.
        Prognostic factors in patients with brain metastases.
        Forum (Genova). 2001; 11: 27-46
        • Schoenfeld A.J.
        • Ferrone M.L.
        • Passias P.G.
        • et al.
        Laboratory markers as useful prognostic measures for survival in patients with spinal metastases.
        Spine J. 2020; 20: 5-13
        • Cook W.H.
        • Baker J.F.
        Retrospective evaluation of prognostic factors in metastatic spine disease: serum albumin and primary tumour type are key.
        ANZ J Surg. 2020; 90: 1070-1074
        • Karhade A.V.
        • Thio Q.
        • Ogink P.T.
        • et al.
        Predicting 90-day and 1-year mortality in spinal metastatic disease: development and internal validation.
        Neurosurgery. 2019; 85: E671-E681
        • Karhade A.V.
        • Thio Q.
        • Ogink P.T.
        • et al.
        Development of machine learning algorithms for prediction of 30-day mortality after surgery for spinal metastasis.
        Neurosurgery. 2019; 85: E83-E91
        • Thio Q.
        • Karhade A.V.
        • Bindels B.J.J.
        • et al.
        Development and internal validation of machine learning algorithms for preoperative survival prediction of extremity metastatic disease.
        Clin Orthop Relat Res. 2020; 478: 322-333
        • Tokuhashi Y.
        • Matsuzaki H.
        • Oda H.
        • Oshima M.
        • Ryu J.
        A revised scoring system for preoperative evaluation of metastatic spine tumor prognosis.
        Spine (Phila Pa 1976). 2005; 30: 2186-2191
        • Tokuhashi Y.
        • Matsuzaki H.
        • Toriyama S.
        • Kawano H.
        • Ohsaka S.
        Scoring system for the preoperative evaluation of metastatic spine tumor prognosis.
        Spine (Phila Pa 1976). 1990; 15: 1110-1113
        • Tomita K.
        • Kawahara N.
        • Kobayashi T.
        • Yoshida A.
        • Murakami H.
        • Akamaru T.
        Surgical strategy for spinal metastases.
        Spine (Phila Pa 1976). 2001; 26: 298-306
        • van der Linden Y.M.
        • Dijkstra S.P.
        • Vonk E.J.
        • Marijnen C.A.
        • Leer J.W.
        Dutch Bone Metastasis Study G. Prediction of survival in patients with metastases in the spinal column: results based on a randomized trial of radiotherapy.
        Cancer. 2005; 103: 320-328
        • Bauer H.C.
        • Wedin R.
        Survival after surgery for spinal and extremity metastases. Prognostication in 241 patients.
        Acta Orthop Scand. 1995; 66: 143-146
        • Paulino Pereira N.R.
        • Janssen S.J.
        • van Dijk E.
        • et al.
        Development of a prognostic survival algorithm for patients with metastatic spine disease.
        J Bone Joint Surg Am. 2016; 98: 1767-1776
        • Bollen L.
        • van der Linden Y.M.
        • Pondaag W.
        • et al.
        Prognostic factors associated with survival in patients with symptomatic spinal bone metastases: a retrospective cohort study of 1,043 patients.
        Neuro Oncol. 2014; 16: 991-998
        • Katagiri H.
        • Takahashi M.
        • Wakai K.
        • Sugiura H.
        • Kataoka T.
        • Nakanishi K.
        Prognostic factors and a scoring system for patients with skeletal metastasis.
        J Bone Joint Surg Br. 2005; 87: 698-703
        • Schoenfeld A.J.
        • Le H.V.
        • Marjoua Y.
        • et al.
        Assessing the utility of a clinical prediction score regarding 30-day morbidity and mortality following metastatic spinal surgery: the New England Spinal Metastasis Score (NESMS).
        Spine J. 2016; 16: 482-490
        • Groot O.Q.
        • Bindels B.J.J.
        • Ogink P.T.
        • et al.
        Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review.
        Acta Orthop. 2021; 1–9
        • Bongers M.E.R.
        • Karhade A.V.
        • Villavieja J.
        • et al.
        Does the SORG algorithm generalize to a contemporary cohort of patients with spinal metastases on external validation?.
        Spine J. 2020; 20: 1646-1652
        • Karhade A.V.
        • Ahmed A.K.
        • Pennington Z.
        • et al.
        External validation of the SORG 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease.
        Spine J. 2020; 20: 14-21
        • Shah A.A.
        • Karhade A.V.
        • Park H.Y.
        • et al.
        Updated external validation of the SORG machine learning algorithms for prediction of ninety-day and one-year mortality after surgery for spinal metastasis.
        Spine J. 2021; 21: 1679-1686
        • Yang J.J.
        • Chen C.W.
        • Fourman M.S.
        • et al.
        International external validation of the SORG machine learning algorithms for predicting 90-day and 1-year survival of patients with spine metastases using a Taiwanese Cohort.
        Spine J. 2021; 21: 1670-1678
        • Collins G.S.
        • Reitsma J.B.
        • Altman D.G.
        • Moons K.G.
        Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.
        Ann Intern Med. 2015; 162: 55-63
        • Groot O.Q.
        • Ogink P.T.
        • Lans A.
        • et al.
        Machine learning prediction models in orthopedic surgery: a systematic review in transparent reporting.
        J Orthop Res. 2022; 40: 475-483
        • Stekhoven D.J.
        • Buhlmann P.
        MissForest–non-parametric missing value imputation for mixed-type data.
        Bioinformatics. 2012; 28: 112-118
        • Mandrekar J.N.
        Receiver operating characteristic curve in diagnostic test assessment.
        J Thorac Oncol. 2010; 5: 1315-1316
        • Van Calster B.
        • McLernon D.J.
        • van Smeden M.
        • et al.
        Calibration: the Achilles heel of predictive analytics.
        BMC Med. 2019; 17: 230
        • Debray T.P.
        • Damen J.A.
        • Snell K.I.
        • et al.
        A guide to systematic review and meta-analysis of prediction model performance.
        BMJ. 2017; 356: i6460
        • Paul P.
        • Pennell M.L.
        • Lemeshow S.
        Standardizing the power of the Hosmer-Lemeshow goodness of fit test in large data sets.
        Stat Med. 2013; 32: 67-80
        • Siegert S.
        Variance estimation for Brier Score decomposition.
        Q J R Meteorolog Soc. 2014; 140: 1771-1777
        • Vickers A.J.
        • Elkin E.B.
        Decision curve analysis: a novel method for evaluating prediction models.
        Med Decis Making. 2006; 26: 565-574
        • Demler O.V.
        • Pencina M.J.
        • D'Agostino Sr., R.B.
        Misuse of DeLong test to compare AUCs for nested models.
        Stat Med. 2012; 31: 2577-2587
        • Singh N.
        • Baby D.
        • Rajguru J.P.
        • Patil P.B.
        • Thakkannavar S.S.
        • Pujari V.B.
        Inflammation and cancer.
        Ann Afr Med. 2019; 18: 121-126
        • Baracos V.E.
        • Martin L.
        • Korc M.
        • Guttridge D.C.
        • Fearon K.C.
        Cancer-associated cachexia.
        Nat Rev Dis Primers. 2018; 4: 17105
        • Hoff C.M.
        • Hansen H.S.
        • Overgaard M.
        • et al.
        The importance of haemoglobin level and effect of transfusion in HNSCC patients treated with radiotherapy–results from the randomized DAHANCA 5 study.
        Radiother Oncol. 2011; 98: 28-33
        • Debus J.
        • Drings P.
        • Baurecht W.
        • Angermund R.
        Prospective, randomized, controlled, and open study in primarily inoperable, stage III non-small cell lung cancer (NSCLC) patients given sequential radiochemotherapy with or without epoetin alfa.
        Radiother Oncol. 2014; 112: 23-29
        • Blohmer J.U.
        • Paepke S.
        • Sehouli J.
        • et al.
        Randomized phase III trial of sequential adjuvant chemoradiotherapy with or without erythropoietin Alfa in patients with high-risk cervical cancer: results of the NOGGO-AGO intergroup study.
        J Clin Oncol. 2011; 29: 3791-3797
        • Chen J.H.
        • Alagappan M.
        • Goldstein M.K.
        • Asch S.M.
        • Altman R.B.
        Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets.
        Int J Med Inform. 2017; 102: 71-79
        • Mohri M.
        • Rostamizadeh A.
        • Talwalkar A.
        Foundations of machine learning.
        MIT Press, 2018
        • Park D.J.
        • Park M.W.
        • Lee H.
        • Kim Y.-J.
        • Kim Y.
        • Park Y.H.
        Development of machine learning model for diagnostic disease prediction based on laboratory tests.
        Sci Rep. 2021; 11: 1-11
        • Zhang C.
        • Mao M.
        • Guo X.
        • et al.
        Nomogram based on homogeneous and heterogeneous associated factors for predicting bone metastases in patients with different histological types of lung cancer.
        BMC Cancer. 2019; 19: 1-12
        • Song Q.
        • Shang J.
        • Zhang C.
        • Zhang L.
        • Wu X.
        Impact of the homogeneous and heterogeneous risk factors on the incidence and survival outcome of bone metastasis in NSCLC patients.
        J Cancer Res Clin Oncol. 2019; 145: 737-746
        • Paulino Pereira N.R.
        • Groot O.Q.
        • Verlaan J.J.
        • et al.
        Quality of life changes after surgery for metastatic spinal disease: a systematic review and meta-analysis.
        Clin Spine Surg. 2022; 35: 38-48
        • Epstein E.S.
        Stochastic dynamic prediction.
        Tellus. 1969; 21: 739-759