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Published in: European Journal of Nuclear Medicine and Molecular Imaging 4/2019

01-04-2019 | Rectal Cancer | Original Article

Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features

Authors: V. Giannini, S. Mazzetti, I. Bertotto, C. Chiarenza, S. Cauda, E. Delmastro, C. Bracco, A. Di Dia, F. Leone, E. Medico, A. Pisacane, D. Ribero, M. Stasi, D. Regge

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 4/2019

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Abstract

Purpose

Pathological complete response (pCR) following neoadjuvant chemoradiotherapy or radiotherapy in locally advanced rectal cancer (LARC) is reached in approximately 15–30% of cases, therefore it would be useful to assess if pretreatment of 18F-FDG PET/CT and/or MRI texture features can reliably predict response to neoadjuvant therapy in LARC.

Methods

Fifty-two patients were dichotomized as responder (pR+) or non-responder (pR-) according to their pathological tumor regression grade (TRG) as follows: 22 as pR+ (nine with TRG = 1, 13 with TRG = 2) and 30 as pR- (16 with TRG = 3, 13 with TRG = 4 and 1 with TRG = 5). First-order parameters and 21 second-order texture parameters derived from the Gray-Level Co-Occurrence matrix were extracted from semi-automatically segmented tumors on T2w MRI, ADC maps, and PET/CT acquisitions. The role of each texture feature in predicting pR+ was assessed with monoparametric and multiparametric models.

Results

In the mono-parametric approach, PET homogeneity reached the maximum AUC (0.77; sensitivity = 72.7% and specificity = 76.7%), while PET glycolytic volume and ADC dissimilarity reached the highest sensitivity (both 90.9%). In the multiparametric analysis, a logistic regression model containing six second-order texture features (five from PET and one from T2w MRI) yields the highest predictivity in distinguish between pR+ and pR- patients (AUC = 0.86; sensitivity = 86%, and specificity = 83% at the Youden index).

Conclusions

If preliminary results of this study are confirmed, pretreatment PET and MRI could be useful to personalize patient treatment, e.g., avoiding toxicity of neoadjuvant therapy in patients predicted pR-.
Literature
1.
go back to reference Siegel R, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin. 2017;67:7–30.CrossRef Siegel R, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin. 2017;67:7–30.CrossRef
2.
go back to reference NCCN Clinical Practice Guidelines in Oncology: Rectal Cancer, Version 1.2016. (2016). NCCN.org. Accessed 11 Jan 2019. NCCN Clinical Practice Guidelines in Oncology: Rectal Cancer, Version 1.2016. (2016). NCCN.​org. Accessed 11 Jan 2019.
3.
go back to reference Sauer R, Becker H, Hohenberger W, et al. Preoperative versus postoperative chemoradiotherapy for rectal cancer. N Engl J Med. 2004;351(17):1731–40.CrossRefPubMed Sauer R, Becker H, Hohenberger W, et al. Preoperative versus postoperative chemoradiotherapy for rectal cancer. N Engl J Med. 2004;351(17):1731–40.CrossRefPubMed
4.
go back to reference Li Y, Wang J, Ma X, Tan L, Yan Y, Xue C, et al. A review of neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Int J Biol Sci. 2016;12(8):1022–31.CrossRefPubMedPubMedCentral Li Y, Wang J, Ma X, Tan L, Yan Y, Xue C, et al. A review of neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Int J Biol Sci. 2016;12(8):1022–31.CrossRefPubMedPubMedCentral
5.
go back to reference Maas M, Nelemans PJ, Valentini V, et al. Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol. 2010;11:835–44.CrossRefPubMed Maas M, Nelemans PJ, Valentini V, et al. Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol. 2010;11:835–44.CrossRefPubMed
6.
go back to reference 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. Eur J Cancer. 2012;48(4):441–6.CrossRefPubMedPubMedCentral 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. Eur J Cancer. 2012;48(4):441–6.CrossRefPubMedPubMedCentral
7.
go back to reference Diehn M, Nardini C, Wang DS, McGovern S, Jayaraman M, Liang Y, et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A. 2008;105(13):5213–8.CrossRefPubMedPubMedCentral Diehn M, Nardini C, Wang DS, McGovern S, Jayaraman M, Liang Y, et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A. 2008;105(13):5213–8.CrossRefPubMedPubMedCentral
8.
go back to reference Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative. Nat Commun. 2014;5:4006.CrossRefPubMedPubMedCentral Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative. Nat Commun. 2014;5:4006.CrossRefPubMedPubMedCentral
9.
go back to reference Coroller TP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RT, Hermann G, et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol. 2015;114(3):345–50.CrossRefPubMedPubMedCentral Coroller TP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RT, Hermann G, et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol. 2015;114(3):345–50.CrossRefPubMedPubMedCentral
10.
go back to reference Jalil O, Afaq A, Ganeshan B, Patel UB, Boone D, Endozo R, et al. Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy. Color Dis. 2017;19(4):349–62.CrossRef Jalil O, Afaq A, Ganeshan B, Patel UB, Boone D, Endozo R, et al. Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy. Color Dis. 2017;19(4):349–62.CrossRef
11.
go back to reference Liu L, Liu Y, Xu L, Li Z, Lv H, Dong N, et al. Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer. J Magn Reson Imaging. 2017a;45(6):1798–808.CrossRefPubMed Liu L, Liu Y, Xu L, Li Z, Lv H, Dong N, et al. Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer. J Magn Reson Imaging. 2017a;45(6):1798–808.CrossRefPubMed
12.
go back to reference De Cecco CN, Ganeshan B, Ciolina M, Rengo M, Meinel FG, Musio D, et al. Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance. Investig Radiol. 2015;50(4):239–45.CrossRef De Cecco CN, Ganeshan B, Ciolina M, Rengo M, Meinel FG, Musio D, et al. Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance. Investig Radiol. 2015;50(4):239–45.CrossRef
13.
go back to reference Cusumano D, Dinapoli N, Boldrini L, Chiloiro G, Gatta R, Masciocchi C, et al. Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer. Radiol Med. 2017;123:286.CrossRefPubMed Cusumano D, Dinapoli N, Boldrini L, Chiloiro G, Gatta R, Masciocchi C, et al. Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer. Radiol Med. 2017;123:286.CrossRefPubMed
14.
go back to reference 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. 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.
15.
go back to reference Bundschuh R, 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.CrossRefPubMed Bundschuh R, 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.CrossRefPubMed
16.
go back to reference Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol. 2015;60(14):5471–96.CrossRefPubMed Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol. 2015;60(14):5471–96.CrossRefPubMed
17.
go back to reference Mandard A, Dalibard F, Mandard JC, Marnay J, Henry-Amar M, Petiot JF, et al. Pathologic assessment of tumor regression after preoperative chemoradiotherapy of esophageal carcinoma. Clinicopathol Correl Cancer. 1994;73(11):2680–6. Mandard A, Dalibard F, Mandard JC, Marnay J, Henry-Amar M, Petiot JF, et al. Pathologic assessment of tumor regression after preoperative chemoradiotherapy of esophageal carcinoma. Clinicopathol Correl Cancer. 1994;73(11):2680–6.
18.
go back to reference Engels B, De Paoli A, Cattari G, Munoz F, Vagge S, Norkus D, et al. Preoperative radiotherapy with a simultaneous integrated boost compared to chemoradiation therapy for T3-4 rectal cancer: interim analysis of a multicentric randomized trial. Int J Radiat Oncol Biol Phys. 2014;90(1):S22–3.CrossRef Engels B, De Paoli A, Cattari G, Munoz F, Vagge S, Norkus D, et al. Preoperative radiotherapy with a simultaneous integrated boost compared to chemoradiation therapy for T3-4 rectal cancer: interim analysis of a multicentric randomized trial. Int J Radiat Oncol Biol Phys. 2014;90(1):S22–3.CrossRef
19.
go back to reference Boellaard R, O’Doherty MJ, Weber WA, Mottaghy FM, Lonsdale MN, Stroobants SG, et al. FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0. Eur J Nucl Med Mol Imaging. 2010;37(1):181–200.CrossRefPubMed Boellaard R, O’Doherty MJ, Weber WA, Mottaghy FM, Lonsdale MN, Stroobants SG, et al. FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0. Eur J Nucl Med Mol Imaging. 2010;37(1):181–200.CrossRefPubMed
20.
go back to reference Johnson HJ, McCormick M, Ibanez L. The ITK software guide. 3rd ed. New York: Kitware Inc.; 2013. Johnson HJ, McCormick M, Ibanez L. The ITK software guide. 3rd ed. New York: Kitware Inc.; 2013.
21.
go back to reference Brambilla M, Matheoud R, Basile C, Bracco C, Castiglioni I, Cavedon C, et al. An adaptive thresholding method for BTV estimation incorporating PET reconstruction parameters: a multicenter study of the robustness and the reliability. Comput Math Methods Med. 2015;2015:571473.CrossRefPubMedPubMedCentral Brambilla M, Matheoud R, Basile C, Bracco C, Castiglioni I, Cavedon C, et al. An adaptive thresholding method for BTV estimation incorporating PET reconstruction parameters: a multicenter study of the robustness and the reliability. Comput Math Methods Med. 2015;2015:571473.CrossRefPubMedPubMedCentral
22.
24.
go back to reference Chizi B, Maimon O. Dimension reduction and feature selection. In: Maimon O, Rokach L, editors. Data mining and knowledge discovery handbook. Boston: Springer; 2005. p. 93–111.CrossRef Chizi B, Maimon O. Dimension reduction and feature selection. In: Maimon O, Rokach L, editors. Data mining and knowledge discovery handbook. Boston: Springer; 2005. p. 93–111.CrossRef
25.
go back to reference Soh L, Tsatsoulis C. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans Geosci Remote Sens. 1999;37(2):780–95.CrossRef Soh L, Tsatsoulis C. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans Geosci Remote Sens. 1999;37(2):780–95.CrossRef
26.
go back to reference Haralick RM, Shanmugam K. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;SMC-3:610–21.CrossRef Haralick RM, Shanmugam K. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;SMC-3:610–21.CrossRef
27.
go back to reference Clausi DA. An analysis of co-occurrence texture statistics as a function of grey level quantization. Can J Remote Sens. 2002;28(1):45–62.CrossRef Clausi DA. An analysis of co-occurrence texture statistics as a function of grey level quantization. Can J Remote Sens. 2002;28(1):45–62.CrossRef
28.
go back to reference Dinapoli N, Barbaro B, Gatta R, Chiloiro G, Casà C, Masciochi C, et al. Magnetic resonance, vendor-independent, intensity histogram analysis predicting. Int J Radiat Oncol Biol Phys. 2018;102:765.CrossRefPubMed Dinapoli N, Barbaro B, Gatta R, Chiloiro G, Casà C, Masciochi C, et al. Magnetic resonance, vendor-independent, intensity histogram analysis predicting. Int J Radiat Oncol Biol Phys. 2018;102:765.CrossRefPubMed
29.
go back to reference Ng F, Kozarski R, Ganeshan B, Goh V. Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol. 2013;82(2):342–8.CrossRefPubMed Ng F, Kozarski R, Ganeshan B, Goh V. Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol. 2013;82(2):342–8.CrossRefPubMed
30.
go back to reference Liu Y, Liu S, Qu F, Li Q, Cheng R, Ye Z. Tumor heterogeneity assessed by texture analysis on contrast-enhanced CT in lung adenocarcinoma: association with pathologic grade. Oncotarget. 2017b;8(32):53664–74.PubMedPubMedCentral Liu Y, Liu S, Qu F, Li Q, Cheng R, Ye Z. Tumor heterogeneity assessed by texture analysis on contrast-enhanced CT in lung adenocarcinoma: association with pathologic grade. Oncotarget. 2017b;8(32):53664–74.PubMedPubMedCentral
31.
go back to reference Lubner MG, Stabo N, Lubner SJ, del Rio AM, Song C, Halberg RB, et al. CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging. 2015;40(7):2331–7.CrossRefPubMed Lubner MG, Stabo N, Lubner SJ, del Rio AM, Song C, Halberg RB, et al. CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging. 2015;40(7):2331–7.CrossRefPubMed
32.
go back to reference Shen C, Liu Z, Guan M, Song J, Lian Y, Wang S, et al. 2D and 3D CT radiomics features prognostic performance comparison in non-small cell lung cancer. Transl Oncol. 2017;10(6):886–94.CrossRefPubMedPubMedCentral Shen C, Liu Z, Guan M, Song J, Lian Y, Wang S, et al. 2D and 3D CT radiomics features prognostic performance comparison in non-small cell lung cancer. Transl Oncol. 2017;10(6):886–94.CrossRefPubMedPubMedCentral
33.
go back to reference Henderson S, Purdie C, Michie C, Evans A, Lerski R, Johnston M, et al. Interim heterogeneity changes measured using entropy texture features on T2-weighted MRI at 3.0 T are associated with pathological response to neoadjuvant chemotherapy in primary breast cancer. Eur Radiol. 2017;27(11):4602–11.CrossRefPubMedPubMedCentral Henderson S, Purdie C, Michie C, Evans A, Lerski R, Johnston M, et al. Interim heterogeneity changes measured using entropy texture features on T2-weighted MRI at 3.0 T are associated with pathological response to neoadjuvant chemotherapy in primary breast cancer. Eur Radiol. 2017;27(11):4602–11.CrossRefPubMedPubMedCentral
34.
go back to reference Giannini V, Mazzetti S, Marmo A, Montemurro F, Regge D, Martincich L. A computer-aided diagnosis (CAD) scheme for pretreatment prediction of pathological response to neoadjuvant therapy using dynamic contrast-enhanced MRI texture features. Br J Radiol. 2017;90(1077):20170269.CrossRefPubMedPubMedCentral Giannini V, Mazzetti S, Marmo A, Montemurro F, Regge D, Martincich L. A computer-aided diagnosis (CAD) scheme for pretreatment prediction of pathological response to neoadjuvant therapy using dynamic contrast-enhanced MRI texture features. Br J Radiol. 2017;90(1077):20170269.CrossRefPubMedPubMedCentral
35.
go back to reference Vignati A, Mazzetti S, Giannini V, Russo F, Bollito E, Porpiglia F, et al. Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness. Phys Med Biol. 2015;60(7):2685–701.CrossRefPubMed Vignati A, Mazzetti S, Giannini V, Russo F, Bollito E, Porpiglia F, et al. Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness. Phys Med Biol. 2015;60(7):2685–701.CrossRefPubMed
36.
go back to reference Skogen K, Schulz A, Dormagen JB, Ganeshan B, Helseth E, Server A. Diagnostic performance of texture analysis on MRI in grading cerebral gliomas. Eur J Radiol. 2016;85(4):824–9.CrossRefPubMed Skogen K, Schulz A, Dormagen JB, Ganeshan B, Helseth E, Server A. Diagnostic performance of texture analysis on MRI in grading cerebral gliomas. Eur J Radiol. 2016;85(4):824–9.CrossRefPubMed
37.
go back to reference Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K. Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol. 2012;67(2):157–64.CrossRefPubMed Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K. Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol. 2012;67(2):157–64.CrossRefPubMed
38.
go back to reference Ganeshan B, Burnand K, Young R, Chatwin C, Miles K. Dynamic contrast-enhanced texture analysis of the liver: initial assessment in colorectal cancer. Investig Radiol. 2011;46(3):160–8.CrossRef Ganeshan B, Burnand K, Young R, Chatwin C, Miles K. Dynamic contrast-enhanced texture analysis of the liver: initial assessment in colorectal cancer. Investig Radiol. 2011;46(3):160–8.CrossRef
Metadata
Title
Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features
Authors
V. Giannini
S. Mazzetti
I. Bertotto
C. Chiarenza
S. Cauda
E. Delmastro
C. Bracco
A. Di Dia
F. Leone
E. Medico
A. Pisacane
D. Ribero
M. Stasi
D. Regge
Publication date
01-04-2019
Publisher
Springer Berlin Heidelberg
Published in
European Journal of Nuclear Medicine and Molecular Imaging / Issue 4/2019
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-018-4250-6

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