Prognostic value of 18F-FDG PET/CT with texture analysis in patients with rectal cancer treated by surgery



The aim of this study was to evaluate the ability of texture analysis using pretreatment 18F-FDG PET/CT to predict prognosis in patients with surgically treated rectal cancer.


We analyzed 94 patients with pathologically proven rectal cancer who underwent pretreatment 18F-FDG PET/CT and were subsequently treated with surgery. The volume of interest of the primary tumor was defined using a threshold of 40% of the maximum standardized uptake value (SUVmax), and conventional (SUVmax, metabolic tumor volume [MTV], total lesion glycolysis [TLG]) and textural PET features were extracted. Harmonization of PET features was performed with the ComBat method. The study endpoints were overall survival (OS) and progression-free survival (PFS), and the prognostic value of PET features was evaluated by Cox regression analysis.


In the follow-up period (median 41.7 [interquartile range, 30.5–60.4] months), 21 (22.3%) and 30 (31.9%) patients had cancer-related death or disease progression, respectively. Univariate analysis revealed a significant association of (1) MTV, TLG, and gray-level co-occurrence matrix (GLCM) entropy with OS; and (2) SUVmax, MTV, TLG, and GLCM entropy with PFS. In multivariate analysis including clinical characteristics, GLCM entropy (≥ 2.13) was the only relevant prognostic PET feature for poor OS (hazard ratio [HR]: 4.16, p = 0.035) and PFS (HR: 2.70, p = 0.046).


GLCM entropy, which indicates metabolic intratumoral heterogeneity, was an independent prognostic factor in patients with surgically treated rectal cancer. Compared with conventional PET features, GLCM entropy has better predictive value and shows potential to facilitate precision medicine.

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  1. 1.

    Favoriti P, Carbone G, Greco M, Pirozzi F, Pirozzi RE, Corcione F. Worldwide burden of colorectal cancer: a review. Updates Surg. 2016;68(1):7–11.

    Article  Google Scholar 

  2. 2.

    Edwards BK, Ward E, Kohler BA, Eheman C, Zauber AG, Anderson RN, et al. Annual report to the nation on the status of cancer, 1975–2006, featuring colorectal cancer trends and impact of interventions (risk factors, screening, and treatment) to reduce future rates. Cancer. 2010;116(3):544–73.

    Article  Google Scholar 

  3. 3.

    Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.

    Article  Google Scholar 

  4. 4.

    Taylor FG, Quirke P, Heald RJ, Moran BJ, Blomqvist L, Swift IR, et al. Preoperative magnetic resonance imaging assessment of circumferential resection margin predicts disease-free survival and local recurrence: 5-year follow-up results of the MERCURY study. J Clin Oncol. 2014;32(1):34–43.

    Article  Google Scholar 

  5. 5.

    Bedard PL, Hansen AR, Ratain MJ, Siu LL. Tumour heterogeneity in the clinic. Nature. 2013;501(7467):355–64.

    CAS  Article  Google Scholar 

  6. 6.

    Hatt M, Tixier F, Visvikis D, Cheze LRC. Radiomics in PET/CT: more than meets the eye? J Nucl Med. 2017;58(3):365–6.

    Article  Google Scholar 

  7. 7.

    Lovinfosse P, Janvary ZL, Coucke P, Jodogne S, Bernard C, Hatt M, et al. FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy. Eur J Nucl Med Mol Imaging. 2016;43(8):1453–60.

    CAS  Article  Google Scholar 

  8. 8.

    Molina-García D, García-Vicente AM, Pérez-Beteta J, Amo-Salas M, Martínez-González A, Tello-Galán MJ, et al. Intratumoral heterogeneity in (18)F-FDG PET/CT by textural analysis in breast cancer as a predictive and prognostic subrogate. Ann Nucl Med. 2018;32(6):379–88.

    Article  Google Scholar 

  9. 9.

    Wu WJ, Li ZY, Dong S, Liu SM, Zheng L, Huang MW, et al. Texture analysis of pretreatment [(18)F]FDG PET/CT for the prognostic prediction of locally advanced salivary gland carcinoma treated with interstitial brachytherapy. EJNMMI Res. 2019;9(1):89.

    Article  Google Scholar 

  10. 10.

    Bundschuh RA, Dinges J, Neumann L, Seyfried M, Zsótér N, Papp L, et al. Textural parameters of tumor heterogeneity in 18F-FDG PET/CT for therapy response assessment and prognosis in patients with locally advanced rectal cancer. J Nucl Med. 2014;55(6):891–7.

    CAS  Article  Google Scholar 

  11. 11.

    Bang JI, Ha S, Kang SB, Lee KW, Lee HS, Kim JS, et al. Prediction of neoadjuvant radiation chemotherapy response and survival using pretreatment [(18)F]FDG PET/CT scans in locally advanced rectal cancer. Eur J Nucl Med Mol Imaging. 2016;43(3):422–31.

    CAS  Article  Google Scholar 

  12. 12.

    Lovinfosse P, Polus M, Van Daele D, Martinive P, Daenen F, Hatt M, et al. FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer. Eur J Nucl Med Mol Imaging. 2018;45(3):365–75.

    Article  Google Scholar 

  13. 13.

    Watanabe T, Itabashi M, Shimada Y, Tanaka S, Ito Y, Ajioka Y, et al. Japanese society for cancer of the colon and rectum (JSCCR) Guidelines 2014 for treatment of colorectal cancer. Int J Clin Oncol. 2015;20(2):207–39.

    Article  Google Scholar 

  14. 14.

    Watanabe T, Muro K, Ajioka Y, Hashiguchi Y, Ito Y, Saito Y, et al. Japanese society for cancer of the colon and rectum (JSCCR) guidelines 2016 for the treatment of colorectal cancer. Int J Clin Oncol. 2018;23(1):1–34.

    Article  Google Scholar 

  15. 15.

    Hashiguchi Y, Muro K, Saito Y, Ito Y, Ajioka Y, Hamaguchi T, et al. Japanese society for cancer of the colon and rectum (JSCCR) guidelines 2019 for the treatment of colorectal cancer. Int J Clin Oncol. 2020;25(1):1–42.

    Article  Google Scholar 

  16. 16.

    Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78(16):4786–9.

    CAS  Article  Google Scholar 

  17. 17.

    Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol. 2016;61(13):R150–66.

    CAS  Article  Google Scholar 

  18. 18.

    Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018;2(1):36.

    Article  Google Scholar 

  19. 19.

    Reuzé S, Schernberg A, Orlhac F, Sun R, Chargari C, Dercle L, et al. Radiomics in nuclear medicine applied to radiation therapy: methods, pitfalls, and challenges. Int J Radiat Oncol Biol Phys. 2018;102(4):1117–42.

    Article  Google Scholar 

  20. 20.

    Orlhac F, Boughdad S, Philippe C, Stalla-Bourdillon H, Nioche C, Champion L, et al. A postreconstruction harmonization method for multicenter radiomic studies in PET. J Nucl Med. 2018;59(8):1321–8.

    CAS  Article  Google Scholar 

  21. 21.

    Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882–3.

    CAS  Article  Google Scholar 

  22. 22.

    Memon S, Lynch AC, Akhurst T, Ngan SY, Warrier SK, Michael M, et al. Systematic review of FDG-PET prediction of complete pathological response and survival in rectal cancer. Ann Surg Oncol. 2014;21(11):3598–607.

    Article  Google Scholar 

  23. 23.

    Lee SJ, Kim JG, Lee SW, Chae YS, Kang BW, Lee YJ, et al. Clinical implications of initial FDG-PET/CT in locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy. Cancer Chemother Pharmacol. 2013;71(5):1201–7.

    CAS  Article  Google Scholar 

  24. 24.

    Kim SJ, Chang S. Volumetric parameters changes of sequential 18F-FDG PET/CT for early prediction of recurrence and death in patients with locally advanced rectal cancer treated with preoperative chemoradiotherapy. Clin Nucl Med. 2015;40(12):930–5.

    Article  Google Scholar 

  25. 25.

    Ruby JA, Leibold T, Akhurst TJ, Shia J, Saltz LB, Mazumdar M, et al. FDG-PET assessment of rectal cancer response to neoadjuvant chemoradiotherapy is not associated with long-term prognosis: a prospective evaluation. Dis Colon Rectum. 2012;55(4):378–86.

    Article  Google Scholar 

  26. 26.

    Tixier F, Hatt M, Le Rest CC, Le Pogam A, Corcos L, Visvikis D. Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med. 2012;53(5):693–700.

    Article  Google Scholar 

  27. 27.

    Desseroit MC, Tixier F, Weber WA, Siegel BA, Cheze Le Rest C, Visvikis D, et al. Reliability of PET/CT shape and heterogeneity features in functional and morphologic components of non-small cell lung cancer tumors: a repeatability analysis in a prospective multicenter cohort. J Nucl Med. 2017;58(3):406–11.

    CAS  Article  Google Scholar 

  28. 28.

    van Velden FH, Kramer GM, Frings V, Nissen IA, Mulder ER, de Langen AJ, et al. Repeatability of radiomic features in non-small-cell lung cancer [(18)F]FDG-PET/CT studies: impact of reconstruction and delineation. Mol Imaging Biol. 2016;18(5):788–95.

    Article  Google Scholar 

  29. 29.

    Larobina M, Megna R, Solla R. Comparison of three freeware software packages for (18)F-FDG PET texture feature calculation. Jpn J Radiol. 2021. (Online ahead of print)

  30. 30.

    NCCN Clinical practice guidelines in oncology (NCCN guidelines)—Rectal Cancer version 1,2021. Accessed 29 Mar 2021.

  31. 31.

    Altazi BA, Zhang GG, Fernandez DC, Montejo ME, Hunt D, Werner J, et al. Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms. J Appl Clin Med Phys. 2017;18(6):32–48.

    Article  Google Scholar 

  32. 32.

    Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, et al. The Applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics. 2019;9(5):1303–22.

    Article  Google Scholar 

  33. 33.

    Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;6:610–21.

    Article  Google Scholar 

  34. 34.

    Ha S, Choi H, Paeng JC, Cheon GJ. Radiomics in oncological PET/CT: a methodological overview. Nucl Med Mol Imaging. 2019;53(1):14–29.

    Article  Google Scholar 

  35. 35.

    Cortes-Rodicio J, Sanchez-Merino G, Garcia-Fidalgo MA, Tobalina-Larrea I. Identification of low variability textural features for heterogeneity quantification of (18)F-FDG PET/CT imaging. Rev Esp Med Nucl Imagen Mol. 2016;35(6):379–84.

    CAS  PubMed  Google Scholar 

  36. 36.

    Bailly C, Bodet-Milin C, Couespel S, Necib H, Kraeber-Bodéré F, Ansquer C, et al. Revisiting the robustness of PET-based textural features in the context of multi-centric trials. PLoS ONE. 2016;11(7):e0159984.

    Article  Google Scholar 

  37. 37.

    Hatt M, Majdoub M, Vallières M, Tixier F, Le Rest CC, Groheux D, et al. 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. J Nucl Med. 2015;56(1):38–44.

    CAS  Article  Google Scholar 

  38. 38.

    Yue Y, Osipov A, Fraass B, Sandler H, Zhang X, Nissen N, et al. Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients. J Gastrointest Oncol. 2017;8(1):127–38.

    Article  Google Scholar 

  39. 39.

    Da-Ano R, Visvikis D, Hatt M. Harmonization strategies for multicenter radiomics investigations. Phys Med Biol. 2020;65(24):24TR02.

    CAS  Article  Google Scholar 

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Correspondence to Masatoshi Hotta.

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Hotta, M., Minamimoto, R., Gohda, Y. et al. Prognostic value of 18F-FDG PET/CT with texture analysis in patients with rectal cancer treated by surgery. Ann Nucl Med 35, 843–852 (2021).

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  • Texture analysis
  • Rectal cancer
  • 18F-FDG PET/CT
  • Prognosis
  • Radiomics