Skip to main content

Advertisement

SpringerLink
Log in

Prognosis prediction of extremity and trunk wall soft-tissue sarcomas treated with surgical resection with radiomic analysis based on random survival forest

  • Original Article
  • Published:
Updates in Surgery Aims and scope Submit manuscript

Abstract

Many researches have applied machine learning methods to find associations between radiomic features and clinical outcomes. Random survival forests (RSF), as an accurate classifier, sort all candidate variables as the rank of importance values. There was no study concerning on finding radiomic predictors in patients with extremity and trunk wall soft-tissue sarcomas using RSF. This study aimed to determine associations between radiomic features and overall survival (OS) by RSF analysis. To identify radiomic features with important values by RSF analysis, construct predictive models for OS incorporating clinical characteristics, and evaluate models’ performance with different method. We collected clinical characteristics and radiomic features extracted from plain and contrast-enhanced computed tomography (CT) from 353 patients with extremity and trunk wall soft-tissue sarcomas treated with surgical resection. All radiomic features were analyzed by Cox proportional hazard (CPH) and followed RSF analysis. The association between radiomics-predicted risks and OS was assessed by Kaplan–Meier analysis. All clinical features were screened by CPH analysis. Prognostic clinical and radiomic parameters were fitted into RSF and CPH integrative models for OS in the training cohort, respectively. The concordance indexes (C-index) and Brier scores of both two models were evaluated in both training and testing cohorts. The model with better predictive performance was interpreted with nomogram and calibration plots. Among all 86 radiomic features, there were three variables selected with high importance values. The RSF on these three features distinguished patients with high predicted risks from patients with low predicted risks for OS in the training set (P < 0.001) using Kaplan–Meier analysis. Age, lymph node involvement and grade were incorporated into the combined models for OS (P < 0.05). The C-indexes in both two integrative models fluctuated above 0.80 whose Brier scores maintained less than 15.0 in the training and testing datasets. The RSF model performed little advantages over the CPH model that the calibration curve of the RSF model showed favorable agreement between predicted and actual survival probabilities for the 3-year and 5-year survival prediction. The multimodality RSF model including clinical and radiomic characteristics conducted high capacity in prediction of OS which might assist individualized therapeutic regimens. Level III, prognostic study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Gutierrez JC, Perez EA, Franceschi D, Moffat FL Jr, Livingstone AS, Koniaris LG (2007) Outcomes for soft-tissue sarcoma in 8249 cases from a large state cancer registry. J Surg Res 141(1):105–114

    Article  Google Scholar 

  2. Crago AM, Brennan MF (2015) Principles in management of soft tissue sarcoma. Adv Surg 49(1):107–122

    Article  Google Scholar 

  3. Miwa S, Yamamoto N, Hayashi K, Takeuchi A, Igarashi K, Tsuchiya H (2019) Therapeutic targets for bone and soft-tissue sarcomas. Int J Mol Sci. 20(1):1

    Article  Google Scholar 

  4. Patel DB, Matcuk GR Jr (2018) Imaging of soft tissue sarcomas. Chin Clin Oncol 7(4):35

    Article  Google Scholar 

  5. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

    Article  CAS  Google Scholar 

  6. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577

    Article  Google Scholar 

  7. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue R, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749–762

    Article  Google Scholar 

  8. O’Sullivan F, Roy S, O’Sullivan J, Vernon C, Eary J (2005) Incorporation of tumor shape into an assessment of spatial heterogeneity for human sarcomas imaged with FDG-PET. Biostatistics 6(2):293–301

    Article  CAS  Google Scholar 

  9. Skamene SR, Rakheja R, Dahlstrom KR, Roberge D, Nahal A, Charest M, Turcotte R, Hickeson M, Freeman C (2014) Metabolic activity measured on PET/CT correlates with clinical outcomes in patients with limb and girdle sarcomas. J Surg Oncol 109(5):410–414

    Article  Google Scholar 

  10. Wolsztynski E, O’Sullivan F, Keyes E, O’Sullivan J, Eary JF (2018) Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma. J Med Imaging (Bellingham) 5(2):024502

    Google Scholar 

  11. Spraker MB, Wootton LS, Hippe DS, Ball KC, Peeken JC, Macomber MW, Chapman TR, Hoff MN, Kim EY, Pollack SM, Combs SE, Nyflot MJ (2019) MRI radiomic features are independently associated with overall survival in soft tissue sarcoma. Adv Radiat Oncol 4(2):413–421

    Article  Google Scholar 

  12. Peeken JC, Bernhofer M, Spraker MB, Pfeiffer D, Devecka M, Thamer A, Shouman MA, Ott A, Nüsslin F, Mayr NA, Rost B, Nyflot MJ, Combs SE (2019) CT-based radiomic features predict tumor grading and have prognostic value in patients with soft tissue sarcomas treated with neoadjuvant radiation therapy. Radiother Oncol 135:187–196

    Article  Google Scholar 

  13. Zhang B, He X, Ouyang F, Gu D, Dong Y, Zhang L, Mo X, Huang W, Tian J, Zhang S (2017) Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett 403:21–27

    Article  CAS  Google Scholar 

  14. Ishwaran H, Kogalur U, Blackstone E, Lauer M (2008) Random survival forests. Ann Appl Stat 2:1

    Article  Google Scholar 

  15. Ishwaran H, Kogalur UB, Xi C, Minn AJ (2011) Random survival forests for high-dimensional data. Stat Anal Data Min 4(1):115–132

    Article  Google Scholar 

  16. Leger S, Zwanenburg A, Pilz K, Lohaus F, Linge A, Stuschke M, Balermpas P, Rödel C, Ganswindt U, Belka C, Pigorsch S, Combs SE, Mönnich D, Zips D, Krause M, Baumann M, Troost EGC, Löck S, Richter C, Zöphel K, Kotzerke J, Schreiber A, Tinhofer I, Budach V, Sak A (2017) A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling. Sci Rep 7(1):13206

    Article  Google Scholar 

  17. Nardone V, Tini P, Nioche C, Mazzei MA, Carfagno T, Battaglia G, Pastina P, Grassi R, Sebaste L, Pirtoli L (2018) Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT. Radiol Med 123(6):415–423

    Article  Google Scholar 

  18. Ishwaran H, Kogalur UB, Gorodeski EZ, Minn AJ, Lauer MS (2010) High-dimensional variable selection for survival data. J Am Stat Assoc 105(489):205–217

    Article  CAS  Google Scholar 

  19. Schoop R, Beyersmann J, Schumacher M, Binder H (2011) Quantifying the predictive accuracy of time-to-event models in the presence of competing risks. Biom J 53(1):88–112

    Article  Google Scholar 

  20. Gerds TA, Andersen PK, Kattan MW (2014) Calibration plots for risk prediction models in the presence of competing risks. Stat Med 33(18):3191–3203

    Article  Google Scholar 

  21. Acem I, Verhoef C, Rueten-Budde AJ, Grünhagen DJ, van Houdt WJ, van de Sande MAJ (2020) Age-related differences of oncological outcomes in primary extremity soft tissue sarcoma: a multistate model including 6260 patients. Eur J Cancer 141:128–136

    Article  Google Scholar 

  22. Riad S, Griffin AM, Liberman B, Blackstein ME, Catton CN, Kandel RA, O’Sullivan B, White LM, Bell RS, Ferguson PC, Wunder JS (2004) Lymph node metastasis in soft tissue sarcoma in an extremity. Clin Orthop Relat Res 426:129–134

    Article  Google Scholar 

  23. Lazarides AL, Kerr DL, Nussbaum DP, Kreulen RT, Somarelli JA, Blazer DG 3rd, Brigman BE, Eward WC (2019) Soft tissue sarcoma of the extremities: what is the value of treating at high-volume centers? Clin Orthop Relat Res 477(4):718–727

    Article  Google Scholar 

  24. Coindre JM (2006) Grading of soft tissue sarcomas: review and update. Arch Pathol Lab Med 130(10):1448–1453

    Article  Google Scholar 

  25. Sanmamed N, Berlin A, Beiki-Ardakani A, Ballantyne H, Simeonov A, Chung P (2018) Magnetic resonance imaging-guided brachytherapy re-irradiation for isolated local recurrence of soft tissue sarcoma. Cureus 10(4):e2457

    PubMed  PubMed Central  Google Scholar 

  26. Park SY, Chung HW, Chae SY, Lee JS (2016) Comparison of MRI and PET-CT in detecting the loco-regional recurrence of soft tissue sarcomas during surveillance. Skeletal Radiol 45(10):1375–1384

    Article  Google Scholar 

  27. Kusunoki S, Terao Y, Ujihira T, Fujino K, Kaneda H, Kimura M, Ota T, Takeda S (2017) Efficacy of PET/CT to exclude leiomyoma in patients with lesions suspicious for uterine sarcoma on MRI. Taiwan J Obstet Gynecol 56(4):508–513

    Article  Google Scholar 

  28. Lauenstein TC, Sharma P, Hughes T, Heberlein K, Tudorascu D, Martin DR (2008) Evaluation of optimized inversion-recovery fat-suppression techniques for T2-weighted abdominal MR imaging. J Magn Reson Imaging 27(6):1448–1454

    Article  Google Scholar 

  29. Amini B, Jessop AC, Ganeshan DM, Tseng WW, Madewell JE (2015) Contemporary imaging of soft tissue sarcomas. J Surg Oncol 111(5):496–503

    Article  Google Scholar 

  30. Fisher SM, Joodi R, Madhuranthakam AJ, Öz OK, Sharma R, Chhabra A (2016) Current utilities of imaging in grading musculoskeletal soft tissue sarcomas. Eur J Radiol 85(7):1336–1344

    Article  Google Scholar 

  31. Gronchi A, Ferrari S, Quagliuolo V, Broto JM, Pousa AL, Grignani G, Basso U, Blay JY, Tendero O, Beveridge RD, Ferraresi V, Lugowska I, Merlo DF, Fontana V, Marchesi E, Donati DM, Palassini E, Palmerini E, De Sanctis R, Morosi C, Stacchiotti S, Bagué S, Coindre JM, Dei Tos AP, Picci P, Bruzzi P, Casali PG (2017) Histotype-tailored neoadjuvant chemotherapy versus standard chemotherapy in patients with high-risk soft-tissue sarcomas (ISG-STS 1001): an international, open-label, randomised, controlled, phase 3, multicentre trial. Lancet Oncol 18(6):812–822

    Article  CAS  Google Scholar 

  32. Wang H, Zhou L (2017) Random survival forest with space extensions for censored data. Artif Intell Med 79:52–61

    Article  Google Scholar 

  33. Oberije C, De Ruysscher D, Houben R, van de Heuvel M, Uyterlinde W, Deasy JO, Belderbos J, Dingemans AM, Rimner A, Din S, Lambin P (2015) A validated prediction model for overall survival from stage III non-small cell lung cancer: toward survival prediction for individual patients. Int J Radiat Oncol Biol Phys 92(4):935–944

    Article  Google Scholar 

  34. Tang XR, Li YQ, Liang SB, Jiang W, Liu F, Ge WX, Tang LL, Mao YP, He QM, Yang XJ, Zhang Y, Wen X, Zhang J, Wang YQ, Zhang PP, Sun Y, Yun JP, Zeng J, Li L, Liu LZ, Liu N, Ma J (2018) Development and validation of a gene expression-based signature to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma: a retrospective, multicentre, cohort study. Lancet Oncol 19(3):382–393

    Article  CAS  Google Scholar 

  35. Akai H, Yasaka K, Kunimatsu A, Nojima M, Kokudo T, Kokudo N, Hasegawa K, Abe O, Ohtomo K, Kiryu S (2018) Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest. Diagn Interv Imaging 99(10):643–651

    Article  CAS  Google Scholar 

  36. Wang L, Dong T, Xin B, Xu C, Guo M, Zhang H, Feng D, Wang X, Yu J (2019) Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer. Eur Radiol 29(6):2958–2967

    Article  Google Scholar 

  37. Gittleman H, Lim D, Kattan MW, Chakravarti A, Gilbert MR, Lassman AB, Lo SS, Machtay M, Sloan AE, Sulman EP, Tian D, Vogelbaum MA, Wang TJC, Penas-Prado M, Youssef E, Blumenthal DT, Zhang P, Mehta MP, Barnholtz-Sloan JS (2017) An independently validated nomogram for individualized estimation of survival among patients with newly diagnosed glioblastoma: NRG oncology RTOG 0525 and 0825. Neuro-Oncol 19(5):669–677

    PubMed  Google Scholar 

  38. Sica GT (2006) Bias in research studies. Radiology 238(3):780–789

    Article  Google Scholar 

  39. Yasaka K, Akai H, Mackin D, Court L, Moros E, Ohtomo K, Kiryu S (2017) Precision of quantitative computed tomography texture analysis using image filtering: A phantom study for scanner variability. Medicine (Baltimore) 96(21):e6993

    Article  Google Scholar 

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. West China School of Medicine, Sichuan University, No.17 People’s South Road, Chengdu, 610041, Sichuan, China

    Yuhan Yang, Yixi Wang & Xinyan Ding

  2. State Key Laboratory of Biotherapy, Department of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Guoxue Road, Chengdu, 610041, China

    Xuelei Ma

Authors
  1. Yuhan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuelei Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yixi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xinyan Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XM, YY, YW and XD made substantial contributions to conception and design, and revised the manuscript critically for important intellectual content. XM revised the manuscript and gave final approval of the version to be published. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xuelei Ma.

Ethics declarations

Conflict of interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Research involving human participants and/or animals

The procedures followed for the present Original Research were in accordance with the declaration of Helsinki.

Informed consent

Each author certified that his or her institution approved the human protocol for this investigation and that all investigations were consistent with ethical principals of research. This work performed by West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Ma, X., Wang, Y. et al. Prognosis prediction of extremity and trunk wall soft-tissue sarcomas treated with surgical resection with radiomic analysis based on random survival forest. Updates Surg 74, 355–365 (2022). https://doi.org/10.1007/s13304-021-01074-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13304-021-01074-8

Keywords

Search

Navigation