FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer

  • Pierre Lovinfosse
  • Marc Polus
  • Daniel Van Daele
  • Philippe Martinive
  • Frédéric Daenen
  • Mathieu Hatt
  • Dimitris Visvikis
  • Benjamin Koopmansch
  • Frédéric Lambert
  • Carla Coimbra
  • Laurence Seidel
  • Adelin Albert
  • Philippe Delvenne
  • Roland Hustinx
Original Article

Abstract

Purpose

The aim of this study was to investigate the prognostic value of baseline 18F-FDG PET/CT textural analysis in locally-advanced rectal cancer (LARC).

Methods

Eighty-six patients with LARC underwent 18F-FDG PET/CT before treatment. Maximum and mean standard uptake values (SUVmax and SUVmean), metabolic tumoral volume (MTV), total lesion glycolysis (TLG), histogram-intensity features, as well as 11 local and regional textural features, were evaluated. The relationships of clinical, pathological and PET-derived metabolic parameters with disease-specific survival (DSS), disease-free survival (DFS) and overall survival (OS) were assessed by Cox regression analysis. Logistic regression was used to predict the pathological response by the Dworak tumor regression grade (TRG) in the 66 patients treated with neoadjuvant chemoradiotherapy (nCRT).

Results

The median follow-up of patients was 41 months. Seventeen patients (19.7%) had recurrent disease and 18 (20.9 %) died, either due to cancer progression (n = 10) or from another cause while in complete remission (n = 8). DSS was 95% at 1 year, 93% at 2 years and 87% at 4 years. Weight loss, surgery and the texture parameter coarseness were significantly associated with DSS in multivariate analyses. DFS was 94 % at 1 year, 86 % at 2 years and 79 % at 4 years. From a multivariate standpoint, tumoral differentiation and the texture parameters homogeneity and coarseness were significantly associated with DFS. OS was 93% at 1 year, 87% at 2 years and 79% after 4 years. cT, surgery, SUVmean, dissimilarity and contrast from the neighborhood intensity-difference matrix (contrastNGTDM) were significantly and independently associated with OS. Finally, RAS-mutational status (KRAS and NRAS mutations) and TLG were significant predictors of pathological response to nCRT (TRG 3-4).

Conclusion

Textural analysis of baseline 18F-FDG PET/CT provides strong independent predictors of survival in patients with LARC, with better predictive power than intensity- and volume-based parameters. The utility of such features, especially coarseness, should be confirmed by larger clinical studies before considering their potential integration into decisional algorithms aimed at personalized medicine.

Keywords

18F-FDG PET/CT Textural analysis Tumor heterogeneity Radiomics Rectal cancer 

Notes

Acknowledgments

We thank our colleague André Frère, from the department of Gastro-enterology of the CHR of Liege, who granted us access to data from patients followed in his hospital, Sébastien Jodogne, from the department of Medical Physics of the CHU of Liege, for the design of the textural analysis software, and Stéphanie Gofflot, from the Biobank of the University of Liege, for providing tumoral samples for genetic analyzes.

Compliance with ethical standards

Conflicts of interest

None.

Ethical approval

All procedures were performed in accordance with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The study design and exemption from informed consent were approved by the Institutional Review Board of Liege University Hospital.

Informed consent

For this type of study formal consent is not required.

Supplementary material

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References

  1. 1.
    Ferlay J, Steliarova-Foucher E, Lortet-Tieulent J, Rosso S, Coebergh JW, Comber H, et al. Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer. 2013;49(6):1374–403.  https://doi.org/10.1016/j.ejca.2012.12.027.CrossRefPubMedGoogle Scholar
  2. 2.
    NCCN Guidelines version 3.2017 Rectal Cancer. https://www.nccn.org/professionals/physician_gls/pdf/rectal.pdf.
  3. 3.
    Compton CC, Fielding LP, Burgart LJ, Conley B, Cooper HS, Hamilton SR, et al. Prognostic factors in colorectal cancer. College of American Pathologists Consensus Statement 1999. Arch Pathol Lab Med. 2000;124(7):979–94.  https://doi.org/10.1043/0003-9985(2000)124<0979:PFICC>2.0.CO;2.PubMedGoogle 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.  https://doi.org/10.1200/JCO.2012.45.3258.CrossRefPubMedGoogle Scholar
  5. 5.
    Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology. 2013;266(1):177–84.  https://doi.org/10.1148/radiol.12120254.CrossRefPubMedGoogle Scholar
  6. 6.
    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.  https://doi.org/10.1245/s10434-014-3753-z.CrossRefPubMedGoogle Scholar
  7. 7.
    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.  https://doi.org/10.1007/s00280-013-2114-0.CrossRefPubMedGoogle Scholar
  8. 8.
    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.  https://doi.org/10.1097/RLU.0000000000000917.CrossRefPubMedGoogle Scholar
  9. 9.
    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.  https://doi.org/10.1097/DCR.0b013e318244a666.CrossRefPubMedGoogle Scholar
  10. 10.
    Dworak O, Keilholz L, Hoffmann A. Pathological features of rectal cancer after preoperative radiochemotherapy. Int J Colorectal Dis. 1997;12(1):19–23.CrossRefPubMedGoogle Scholar
  11. 11.
    Martin ST, Heneghan HM, Winter DC. Systematic review and meta-analysis of outcomes following pathological complete response to neoadjuvant chemoradiotherapy for rectal cancer. Br J Surg. 2012;99(7):918–28.  https://doi.org/10.1002/bjs.8702.CrossRefPubMedGoogle Scholar
  12. 12.
    Fokas E, Liersch T, Fietkau R, Hohenberger W, Hess C, Becker H, et al. Downstage migration after neoadjuvant chemoradiotherapy for rectal cancer: the reverse of the Will Rogers phenomenon? Cancer. 2015;121(11):1724–7.  https://doi.org/10.1002/cncr.29260.CrossRefPubMedGoogle Scholar
  13. 13.
    Das P, Skibber JM, Rodriguez-Bigas MA, Feig BW, Chang GJ, Wolff RA, et al. Predictors of tumor response and downstaging in patients who receive preoperative chemoradiation for rectal cancer. Cancer. 2007;109(9):1750–5.  https://doi.org/10.1002/cncr.22625.CrossRefPubMedGoogle Scholar
  14. 14.
    Ryan JE, Warrier SK, Lynch AC, Ramsay RG, Phillips WA, Heriot AG. Predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review. Colorectal Dis. 2016;18(3):234–46.  https://doi.org/10.1111/codi.13207.CrossRefPubMedGoogle Scholar
  15. 15.
    Joye I, Deroose CM, Vandecaveye V, Haustermans K. The role of diffusion-weighted MRI and (18)F-FDG PET/CT in the prediction of pathologic complete response after radiochemotherapy for rectal cancer: a systematic review. Radiother Oncol. 2014;113(2):158–65.  https://doi.org/10.1016/j.radonc.2014.11.026.CrossRefPubMedGoogle Scholar
  16. 16.
    Hatt M, van Stiphout R, le Pogam A, Lammering G, Visvikis D, Lambin P. Early prediction of pathological response in locally advanced rectal cancer based on sequential 18F-FDG PET. Acta Oncol. 2013;52(3):619–26.  https://doi.org/10.3109/0284186X.2012.702923.CrossRefPubMedGoogle Scholar
  17. 17.
    Li QW, Zheng RL, Ling YH, Wang QX, Xiao WW, Zeng ZF, et al. Prediction of tumor response after neoadjuvant chemoradiotherapy in rectal cancer using (18)fluorine-2-deoxy-D-glucose positron emission tomography-computed tomography and serum carcinoembryonic antigen: a prospective study. Abdom Radiol (NY). 2016;41(8):1448–55.  https://doi.org/10.1007/s00261-016-0698-7.CrossRefGoogle Scholar
  18. 18.
    van Stiphout RG, Valentini V, Buijsen J, Lammering G, Meldolesi E, van Soest J, et al. Nomogram predicting response after chemoradiotherapy in rectal cancer using sequential PETCT imaging: a multicentric prospective study with external validation. Radiother Oncol. 2014;113(2):215–22.  https://doi.org/10.1016/j.radonc.2014.11.002.CrossRefPubMedGoogle Scholar
  19. 19.
    Leccisotti L, Gambacorta MA, de Waure C, Stefanelli A, Barbaro B, Vecchio FM, et al. The predictive value of 18F-FDG PET/CT for assessing pathological response and survival in locally advanced rectal cancer after neoadjuvant radiochemotherapy. Eur J Nucl Med Mol Imaging. 2015;42(5):657–66.  https://doi.org/10.1007/s00259-014-2820-9.CrossRefPubMedGoogle Scholar
  20. 20.
    Zhang C, Tong J, Sun X, Liu J, Wang Y, Huang G. 18F-FDG-PET evaluation of treatment response to neo-adjuvant therapy in patients with locally advanced rectal cancer: a meta-analysis. Int J Cancer. 2012;131(11):2604–11.  https://doi.org/10.1002/ijc.27557.CrossRefPubMedGoogle Scholar
  21. 21.
    Maffione AM, Marzola MC, Capirci C, Colletti PM, Rubello D. Value of (18)F-FDG PET for predicting response to neoadjuvant therapy in rectal cancer: systematic review and meta-analysis. AJR Am J Roentgenol. 2015;204(6):1261–8.  https://doi.org/10.2214/AJR.14.13210.CrossRefPubMedGoogle Scholar
  22. 22.
    Rymer B, Curtis NJ, Siddiqui MR, Chand M. FDG PET/CT can assess the response of locally advanced rectal cancer to neoadjuvant chemoradiotherapy: evidence from meta-analysis and systematic review. Clin Nucl Med. 2016;41(5):371–5.  https://doi.org/10.1097/RLU.0000000000001166.CrossRefPubMedGoogle Scholar
  23. 23.
    Bedard PL, Hansen AR, Ratain MJ, Siu LL. Tumour heterogeneity in the clinic. Nature. 2013;501(7467):355–64.  https://doi.org/10.1038/nature12627.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Hatt M, Tixier F, Visvikis D, Cheze Le Rest C. Radiomics in PET/CT: more than meets the eye? J Nucl Med. 2017;58(3):365–6.  https://doi.org/10.2967/jnumed.116.184655.CrossRefPubMedGoogle Scholar
  25. 25.
    Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–6.  https://doi.org/10.1016/j.ejca.2011.11.036.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    El Naqa I, Grigsby P, Apte A, Kidd E, Donnelly E, Khullar D, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit. 2009;42(6):1162–71.  https://doi.org/10.1016/j.patcog.2008.08.011.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges JP, et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med. 2011;52(3):369–78.  https://doi.org/10.2967/jnumed.110.082404.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Cheng NM, Fang YH, Chang JT, Huang CG, Tsan DL, Ng SH, et al. Textural features of pretreatment 18F-FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma. J Nucl Med. 2013;54(10):1703–9.  https://doi.org/10.2967/jnumed.112.119289.CrossRefPubMedGoogle Scholar
  29. 29.
    Cook GJ, Yip C, Siddique M, Goh V, Chicklore S, Roy A, et al. Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med. 2013;54(1):19–26.  https://doi.org/10.2967/jnumed.112.107375.CrossRefPubMedGoogle Scholar
  30. 30.
    Hatt M, Majdoub M, Vallieres 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.  https://doi.org/10.2967/jnumed.114.144055.CrossRefPubMedGoogle Scholar
  31. 31.
    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.  https://doi.org/10.1007/s00259-016-3314-8.CrossRefPubMedGoogle Scholar
  32. 32.
    Bundschuh RA, Dinges J, Neumann L, Seyfried M, Zsoter N, Papp L, et al. Textural parameters of tumor heterogeneity in (1)(8)F-FDG PET/CT for therapy response assessment and prognosis in patients with locally advanced rectal cancer. J Nucl Med. 2014;55(6):891–7.  https://doi.org/10.2967/jnumed.113.127340.CrossRefPubMedGoogle Scholar
  33. 33.
    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.  https://doi.org/10.1007/s00259-015-3180-9.CrossRefPubMedGoogle Scholar
  34. 34.
    Hatt M, Cheze le Rest C, Turzo A, Roux C, Visvikis D. A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET. IEEE Trans Med Imaging. 2009;28(6):881–93.  https://doi.org/10.1109/TMI.2008.2012036.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Hatt M, Cheze-le Rest C, van Baardwijk A, Lambin P, Pradier O, Visvikis D. Impact of tumor size and tracer uptake heterogeneity in (18)F-FDG PET and CT non-small cell lung cancer tumor delineation. J Nucl Med. 2011;52(11):1690–7.  https://doi.org/10.2967/jnumed.111.092767.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Hatt M, Cheze Le Rest C, Albarghach N, Pradier O, Visvikis D. PET functional volume delineation: a robustness and repeatability study. Eur J Nucl Med Mol Imaging. 2011;38(4):663–72.  https://doi.org/10.1007/s00259-010-1688-6.CrossRefPubMedGoogle Scholar
  37. 37.
    Haralick R, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973:610–21.Google Scholar
  38. 38.
    Yu H, Caldwell C, Mah K, Mozeg D. Coregistered FDG PET/CT-based textural characterization of head and neck cancer for radiation treatment planning. IEEE Trans Med Imaging. 2009;28(3):374–83.  https://doi.org/10.1109/TMI.2008.2004425.CrossRefPubMedGoogle Scholar
  39. 39.
    Amadasun M, King R. Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern. 1989;19:1264–74.CrossRefGoogle Scholar
  40. 40.
    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.  https://doi.org/10.2967/jnumed.111.099127.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    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 morphological components of non-small cell lung cancer tumors: a repeatability analysis in a prospective multi-center cohort. J Nucl Med. 2017;58(3):406–11.  https://doi.org/10.2967/jnumed.116.180919. CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    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.  https://doi.org/10.1007/s11307-016-0940-2.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Lu L, Lv W, Jiang J, Ma J, Feng Q, Rahmim A, et al. Robustness of radiomic features in [11C]Choline and [18F]FDG PET/CT imaging of nasopharyngeal carcinoma: impact of segmentation and discretization. Mol Imaging Biol. 2016;18(6):935–45.  https://doi.org/10.1007/s11307-016-0973-6.CrossRefPubMedGoogle Scholar
  44. 44.
    Doumou G, Siddique M, Tsoumpas C, Goh V, Cook GJ. The precision of textural analysis in (18)F-FDG-PET scans of oesophageal cancer. Eur Radiol. 2015;25(9):2805–12.  https://doi.org/10.1007/s00330-015-3681-8.CrossRefPubMedGoogle Scholar
  45. 45.
    Yan J, Chu-Shern JL, Loi HY, Khor LK, Sinha AK, Quek ST, et al. Impact of image reconstruction settings on texture features in 18F-FDG PET. J Nucl Med. 2015;56(11):1667–73.  https://doi.org/10.2967/jnumed.115.156927.CrossRefPubMedGoogle Scholar
  46. 46.
    Pyka T, Bundschuh RA, Andratschke N, Mayer B, Specht HM, Papp L, et al. Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy. Radiat Oncol. 2015;10:100.  https://doi.org/10.1186/s13014-015-0407-7.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    JS O, Kang BC, Roh JL, Kim JS, Cho KJ, Lee SW, et al. Intratumor textural heterogeneity on pretreatment (18)F-FDG PET images predicts response and survival after chemoradiotherapy for hypopharyngeal cancer. Ann Surg Oncol. 2015;22(8):2746–54.  https://doi.org/10.1245/s10434-014-4284-3.CrossRefGoogle Scholar
  48. 48.
    van Rossum PS, Fried DV, Zhang L, Hofstetter WL, van Vulpen M, Meijer GJ, et al. The incremental vValue of subjective and quantitative assessment of 18F-FDG PET for the prediction of pathologic complete response to preoperative chemoradiotherapy in esophageal cancer. J Nucl Med. 2016;57(5):691–700.  https://doi.org/10.2967/jnumed.115.163766.CrossRefPubMedGoogle Scholar
  49. 49.
    Ypsilantis PP, Siddique M, Sohn HM, Davies A, Cook G, Goh V, et al. Predicting response to neoadjuvant chemotherapy with PET imaging using convolutional neural networks. PLoS One. 2015;10(9):e0137036.  https://doi.org/10.1371/journal.pone.0137036.CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Pyka T, Gempt J, Hiob D, Ringel F, Schlegel J, Bette S, et al. Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas. Eur J Nucl Med Mol Imaging. 2016;43(1):133–41.  https://doi.org/10.1007/s00259-015-3140-4.CrossRefPubMedGoogle Scholar
  51. 51.
    Sauer R, Becker H, Hohenberger W, Rodel C, Wittekind C, Fietkau R, et al. Preoperative versus postoperative chemoradiotherapy for rectal cancer. N Engl J Med. 2004;351(17):1731–40.  https://doi.org/10.1056/NEJMoa040694.CrossRefPubMedGoogle Scholar
  52. 52.
    Weiser MR, Quah HM, Shia J, Guillem JG, Paty PB, Temple LK, et al. Sphincter preservation in low rectal cancer is facilitated by preoperative chemoradiation and intersphincteric dissection. Ann Surg. 2009;249(2):236–42.  https://doi.org/10.1097/SLA.0b013e318195e17c.CrossRefPubMedGoogle Scholar
  53. 53.
    Maffione AM, Ferretti A, Grassetto G, Bellan E, Capirci C, Chondrogiannis S, et al. Fifteen different 18F-FDG PET/CT qualitative and quantitative parameters investigated as pathological response predictors of locally advanced rectal cancer treated by neoadjuvant chemoradiation therapy. Eur J Nucl Med Mol Imaging. 2013;40(6):853–64.  https://doi.org/10.1007/s00259-013-2357-3.CrossRefPubMedGoogle Scholar
  54. 54.
    Dos Anjos DA, Perez RO, Habr-Gama A, Sao Juliao GP, Vailati BB, Fernandez LM, et al. Semiquantitative volumetry by sequential PET/CT may improve prediction of complete response to neoadjuvant chemoradiation in patients with distal rectal cancer. Dis Colon Rectum. 2016;59(9):805–12.  https://doi.org/10.1097/DCR.0000000000000655.CrossRefPubMedGoogle Scholar
  55. 55.
    Bernhard EJ, Stanbridge EJ, Gupta S, Gupta AK, Soto D, Bakanauskas VJ, et al. Direct evidence for the contribution of activated N-ras and K-ras oncogenes to increased intrinsic radiation resistance in human tumor cell lines. Cancer Res. 2000;60(23):6597–600.PubMedGoogle Scholar
  56. 56.
    McKenna WG, Muschel RJ, Gupta AK, Hahn SM, Bernhard EJ. The RAS signal transduction pathway and its role in radiation sensitivity. Oncogene. 2003;22(37):5866–75.  https://doi.org/10.1038/sj.onc.1206699.CrossRefPubMedGoogle Scholar
  57. 57.
    Clancy C, Burke JP, Coffey JC. KRAS mutation does not predict the efficacy of neo-adjuvant chemoradiotherapy in rectal cancer: a systematic review and meta-analysis. Surg Oncol. 2013;22(2):105–11.  https://doi.org/10.1016/j.suronc.2013.02.001.CrossRefPubMedGoogle Scholar
  58. 58.
    Duldulao MP, Lee W, Nelson RA, Li W, Chen Z, Kim J, et al. Mutations in specific codons of the KRAS oncogene are associated with variable resistance to neoadjuvant chemoradiation therapy in patients with rectal adenocarcinoma. Ann Surg Oncol. 2013;20(7):2166–71.  https://doi.org/10.1245/s10434-013-2910-0.CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Chow OS, Kuk D, Keskin M, Smith JJ, Camacho N, Pelossof R, et al. KRAS and combined KRAS/TP53 mutations in locally advanced rectal cancer are independently associated with decreased response to neoadjuvant therapy. Ann Surg Oncol. 2016;23(8):2548–55.  https://doi.org/10.1245/s10434-016-5205-4.CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D. Characterization of PET/CT images using texture analysis: the past, the present... any future? Eur J Nucl Med Mol Imaging. 2017;44(1):151–65.  https://doi.org/10.1007/s00259-016-3427-0.CrossRefPubMedGoogle Scholar
  61. 61.
    Lovinfosse P, Koopmansch B, Lambert F, Jodogne S, Kustermans G, Hatt M, et al. (18)F-FDG PET/CT imaging in rectal cancer: relationship with the RAS mutational status. Br J Radiol. 2016;89(1063):20160212.  https://doi.org/10.1259/bjr.20160212.CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJ. Machine learning methods for quantitative radiomic biomarkers. Sci Rep. 2015;5:13087.  https://doi.org/10.1038/srep13087.CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Chalkidou A, O’Doherty MJ, Marsden PK. False discovery rates in PET and CT studies with texture features: a systematic review. PLoS One. 2015;10(5):e0124165.  https://doi.org/10.1371/journal.pone.0124165.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Pierre Lovinfosse
    • 1
  • Marc Polus
    • 2
  • Daniel Van Daele
    • 2
  • Philippe Martinive
    • 3
  • Frédéric Daenen
    • 4
  • Mathieu Hatt
    • 5
  • Dimitris Visvikis
    • 5
  • Benjamin Koopmansch
    • 6
  • Frédéric Lambert
    • 6
  • Carla Coimbra
    • 7
  • Laurence Seidel
    • 8
  • Adelin Albert
    • 8
  • Philippe Delvenne
    • 9
  • Roland Hustinx
    • 1
  1. 1.Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics CHUUniversity of LiègeLiegeBelgium
  2. 2.Department of Gastro-enterologyCentre Hospitalier Universitaire de LiègeLiègeBelgium
  3. 3.Division of Radiation Oncology, Department of Medical PhysicsCHU and University of LiègeLiègeBelgium
  4. 4.Department of Nuclear MedicineCentre Hospitalier Régional de la CitadelleLiègeBelgium
  5. 5.LaTIM, INSERM UMR 1101BrestFrance
  6. 6.Center for Human Genetic, Molecular Haemato-Oncology UnitUniLab Liège, Centre Hospitalier Universitaire de LiègeLiègeBelgium
  7. 7.Department of Abdominal Surgery and TransplantationCentre Hospitalier Universitaire de LiègeLiègeBelgium
  8. 8.Department of Biostatistics and Medico-economic InformationCentre Hospitalier Universitaire de LiègeLiègeBelgium
  9. 9.Department of PathologyCentre Hospitalier Universitaire de LiègeLiègeBelgium

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