Critical analysis of the effect of various methodologies to compute breast cancer tumour blood flow-based texture features using first-pass 18F-FDG PET

Published:October 16, 2022DOI:


      • BF-based texture feature values are impacted by the feature computation methodology.
      • A relative rescaling led to better differentiation between pCR and non-pCR.
      • No global BF or metabolic parameter was significantly different between patients.
      • BF and metabolic parameters combined led to the best patient differentiation.



      Assessment of tumour blood flow (BF) heterogeneity using first-pass FDG PET/CT and textural feature (TF) analysis is an innovative concept. We aim to explore the relationship between BF heterogeneity measured with different TFs calculation methods and the response to neoadjuvant chemotherapy (NAC) in patients with newly diagnosed breast cancer (BC).


      One hundred and twenty-five patients were enrolled. Dynamic first-pass and delayed FDG PET/CT scans were performed before NAC. Nine TFs were calculated from perfusion and metabolic PET images using relative (RR) or absolute (AR) rescaling strategies with two textural matrix calculation methods. Patients were classified according to presence or absence of a pathologic complete response (pCR) after NAC. The relationship between BF texture features and conventional features were analysed using spearman correlations. The TFs’ differences between pCR and non-pCR groups were evaluated using Mann–Whitney tests and descriptive factorial discriminant analysis (FDA).


      Relation between tumour BF-based TFs and global BF parameters were globally similar to those observed for tumour metabolism. None of the TFs was significantly different between pCR and non-pCR groups in the Mann–Whitney analysis, after Benjamini–Hochberg correction. Using a RR led to better discriminations between responders and non-responders in the FDA analysis. The best results were obtained by combining all the PET features, including BF ones.


      A better differentiation of patients reaching a pCR was observed using a RR. Moreover, BF heterogeneity might bring a useful information when combined with metabolic PET parameters to predict the pCR after neoadjuvant chemotherapy.


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        • Cochet A.
        • Pigeonnat S.
        • Khoury B.
        • Vrigneaud J.-M.
        • Touzery C.
        • Berriolo-Riedinger A.
        • et al.
        Evaluation of breast tumor blood flow with dynamic first-pass 18F-FDG PET/CT: comparison with angiogenesis markers and prognostic factors.
        J Nucl Med. 2012; 53: 512-520
        • Mullani N.A.
        • Herbst R.S.
        • O’Neil R.G.
        • Gould K.L.
        • Barron B.J.
        • Abbruzzese J.L.
        Tumor blood flow measured by PET dynamic imaging of first-pass 18F-FDG uptake: a comparison with 15o-labeled water-measured blood flow.
        J Nucl Med. 2008; 49: 517-523
        • Mankoff D.A.
        • Dunnwald L.K.
        • Gralow J.R.
        • Ellis G.K.
        • Schubert E.K.
        • Tseng J.
        • et al.
        Changes in blood flow and metabolism in locally advanced breast cancer treated with neoadjuvant chemotherapy.
        J Nucl Med. 2003; 44: 1806-1814
        • Humbert O.
        • Riedinger J.-M.
        • Vrigneaud J.-M.
        • Kanoun S.
        • Dygai-Cochet I.
        • Berriolo-Riedinger A.
        • et al.
        18F-FDG PET-derived tumor blood flow changes after 1 cycle of neoadjuvant chemotherapy predicts outcome in Triple-Negative breast cancer.
        J Nucl Med. 2016; 57: 1707-1712
        • Humbert O.
        • Lasserre M.
        • Bertaut A.
        • Fumoleau P.
        • Coutant C.
        • Brunotte F.
        • et al.
        Breast cancer blood flow and metabolism on Dual-Acquisition18F-FDG PET: Correlation with tumor phenotype and neoadjuvant chemotherapy response.
        J Nucl Med. 2018; 59: 1035-1041
        • Mankoff D.A.
        • Dunnwald L.K.
        • Gralow J.R.
        • Ellis G.K.
        • Charlop A.
        • Lawton T.J.
        • et al.
        Blood flow and metabolism in locally advanced breast cancer: relationship to response to therapy.
        J Nucl Med. 2002; 43: 500-509
        • Dunnwald L.K.
        • Gralow J.R.
        • Ellis G.K.
        • Livingston R.B.
        • Linden H.M.
        • Specht J.M.
        • et al.
        Tumor metabolism and blood flow changes by positron emission tomography: relation to survival in patients treated with neoadjuvant chemotherapy for locally advanced breast cancer.
        J Clin Oncol. 2008; 26: 4449-4457
        • Tseng J.
        • Dunnwald L.K.
        • Schubert E.K.
        • Link J.M.
        • Minoshima S.
        • Muzi M.
        • et al.
        18F-FDG kinetics in locally advanced breast cancer: correlation with tumor blood flow and changes in response to neoadjuvant chemotherapy.
        J Nucl Med. 2004; 45: 1829-1837
        • Ha S.
        • Park S.
        • Bang J.-I.
        • Kim E.-K.
        • Lee H.-Y.
        Metabolic radiomics for pretreatment 18 F-FDG PET/CT to characterize locally advanced breast cancer: histopathologic characteristics, response to neoadjuvant chemotherapy, and prognosis.
        Sci Rep. 2017; 7: 1-11–017–01524–7
        • Payan N.
        • Presles B.
        • Brunotte F.
        • Coutant C.
        • Desmoulins I.
        • Vrigneaud J.-M.
        • et al.
        Biological correlates of tumor perfusion and its heterogeneity in newly diagnosed breast cancer using dynamic first-pass 18F-FDG PET/CT.
        Euro J Nucl Med Mol Imaging. 2019;
        • Castiglioni I.
        • Rundo L.
        • Codari M.
        • Di Leo G.
        • Salvatore C.
        • Interlenghi M.
        • et al.
        AI applications to medical images: From machine learning to deep learning.
        Phys Med. 2021; 83: 9-24
        • Arabi H.
        • AkhavanAllaf A.
        • Sanaat A.
        • Shiri I.
        • Zaidi H.
        The promise of artificial intelligence and deep learning in PET and SPECT imaging.
        Phys Med. 2021; 83: 122-137
        • Brooks F.J.
        On some misconceptions about tumor heterogeneity quantification.
        Eur J Nucl Med Mol Imaging. 2013; 40: 1292-1294–013–2430–y
        • van Velden F.H.P.
        • Kramer G.M.
        • Frings V.
        • Nissen I.A.
        • Mulder E.R.
        • et al.
        Repeatability of radiomic features in Non-Small-Cell lung cancer [18F]FDG-PET/CT studies: Impact of reconstruction and delineation.
        Mol Imaging Biol. 2016; 18: 788-795–016–0940–2
        • Yan J.
        • Chu-Shern J.L.
        • Loi H.Y.
        • Khor L.K.
        • Sinha A.K.
        • Quek S.T.
        • et al.
        Impact of image reconstruction settings on texture features in 18F-FDG PET.
        J Nucl Med. 2015; 56: 1667-1673
        • Leijenaar R.T.H.
        • Carvalho S.
        • Velazquez E.R.
        • van Elmpt W.J.C.
        • Parmar C.
        • Hoekstra O.S.
        • et al.
        Stability of FDG-PET radiomics features: an integrated analysis of test-retest and inter-observer variability.
        Acta Oncol. 2013; 52: 1391-1397
        • Galavis P.E.
        • Hollensen C.
        • Jallow N.
        • Paliwal B.
        • Jeraj R.
        Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters.
        Acta Oncol. 2010; 49: 1012-1016
        • Leijenaar R.T.H.
        • Nalbantov G.
        • Carvalho S.
        • van Elmpt W.J.C.
        • Troost E.G.C.
        • Boellaard R.
        • et al.
        The effect of SUV discretization in quantitative FDG-PET radiomics: the need for standardized methodology in tumor texture analysis.
        Sci Rep. 2015; 5: 11075
        • Pfaehler E.
        • Beukinga R.J.
        • de Jong J.R.
        • Riemer H.J.
        • Slump C.H.
        • Rudi A.J.
        • et al.
        Repeatability of 18 F-FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method.
        Med Phys. 2019; 46: 665-678
        • Hatt M.
        • Tixier F.
        • Pierce L.
        • Kinahan P.E.
        • Le Rest C.C.
        • Visvikis D.
        Characterization of PET/CT images using texture analysis: the past, the present…any future?.
        Eur J Nucl Med Mol Imaging. 2017; 44: 151-165–016–3427–0
        • Zwanenburg A.
        • Vallières M.
        • Abdalah M.A.
        • Aerts H.J.
        • Andrearczyk V.
        • Apte A.
        • Ashrafinia S.
        • Bakas S.
        • Beukinga R.J.
        • Boellaard R.
        • et al.
        The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping.
        Radiology. 2020; 295: 328-338
        • Branchini M.
        • Zorz A.
        • Zucchetta P.
        • Bettinelli A.
        • De Monte F.
        • Cecchin D.
        • et al.
        Impact of acquisition count statistics reduction and SUV discretization on PET radiomic features in pediatric 18F-FDG-PET/MRI examinations.
        Phys Med. 2019; 59: 117-126
        • Presotto L.
        • Bettinardi V.
        • De Bernardi E.
        • Belli M.
        • Cattaneo G.
        • Broggi S.
        • et al.
        PET textural features stability and pattern discrimination power for radiomics analysis: An “ad-hoc” phantoms study.
        Phys Med. 2018; 50: 66-74
        • Tixier F.
        • Le Rest C.C.
        • Hatt M.
        • Albarghach N.
        • Pradier O.
        • Metges J.-P.
        • 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: 369-378
        • Tixier F.
        • Hatt M.
        • Valla C.
        • Fleury V.
        • Lamour C.
        • Ezzouhri S.
        • et al.
        Visual versus quantitative assessment of intratumor 18F-FDG PET uptake heterogeneity: Prognostic value in Non-Small cell lung cancer.
        J Nucl Med. 2014; 55: 1235-1241
        • Orlhac F.
        • Soussan M.
        • Chouahnia K.
        • Martinod E.
        • Buvat I.
        18F-FDG PET-derived textural indices reflect tissue-specific uptake pattern in non-small cell lung cancer.
        PLoS One. 2015; 10: e0145063
        • Hatt M.
        • Majdoub M.
        • Vallières M.
        • Tixier F.
        • Le Rest C.C.
        • 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: 38-44
        • Shen W.-C.
        • Chen S.-W.
        • Liang J.-A.
        • Hsieh T.-C.
        • Yen K.-Y.
        • Kao C.-H.
        Fluorodeoxyglucose positron emission tomography for the textural features of cervical cancer associated with lymph node metastasis and histological type.
        Eur J Nucl Med Mol Imaging. 2017; 44: 1721-1731–017–3697–1
        • Amin M.B.
        • Edge S.B.
        • Greene F.L.
        • Byrd D.R.
        • Brookland R.K.
        • et al.
        AJCC Cancer staging manual.
        Springer, 2018
        • Mullani N.A.
        • Gould K.L.
        First-pass measurements of regional blood flow with external detectors.
        J Nucl Med. 1983; 24: 577-581
        • Schaefer A.
        • Kremp S.
        • Hellwig D.
        • Rübe C.
        • Kirsch C.-M.
        • Nestle U.
        A contrast-oriented algorithm for FDG-PET-based delineation of tumour volumes for the radiotherapy of lung cancer: derivation from phantom measurements and validation in patient data.
        Eur J Nucl Med Mol Imaging. 2008; 35: 1989-1999–008–0875–1
        • Foster B.
        • Bagci U.
        • Mansoor A.
        • Xu Z.
        • Mollura D.J.
        A review on segmentation of positron emission tomography images.
        Comput Biol Med. 2014; 50: 76-96
        • Desseroit M.-C.
        • Tixier F.
        • Weber W.A.
        • Siegel B.A.
        • Le Rest C.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. 2016; 58: 406-411
        • Hatt M.
        • Tixier F.
        • Cheze Le Rest C.
        • Pradier O.
        • Visvikis D.
        Robustness of intratumour F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma.
        Eur J Nucl Med Mol Imaging. 2013; 40: 1662-1671–013–2486–8
        • Yoo T.S.
        • Ackerman M.J.
        • Lorensen W.E.
        • Schroeder W.
        • Chalana V.
        • Aylward S.
        • et al.
        Engineering and algorithm design for an image processing api: a technical report on ITK–the insight toolkit.
        Stud Health Technol Inform. 2002; 85: 586-592–1–60750–929–5–586
        • R. Core Team T.S.
        R: A language and environment for statistical computing.
        R Foundation for Statistical Computing, Vienna, Austria2022 (URL
        • Akoglu H.
        User’s guide to correlation coefficients.
        Turkish J Emerg Med. 2018; 18: 91-93
        • Lemarignier C.
        • Martineau A.
        • Teixeira L.
        • Vercellino L.
        • Espié M.
        • Merlet P.
        • et al.
        Correlation between tumour characteristics, SUV measurements, metabolic tumour volume, TLG and textural features assessed with F-FDG PET in a large cohort of oestrogen receptor-positive breast cancer patients.
        Eur J Nucl Med Mol Imaging. 2017; 44: 1145-1154–017–3641–4
        • Orlhac F.
        • Nioche C.
        • Soussan M.
        • Buvat I.
        Understanding changes in tumor texture indices in PET: A comparison between visual assessment and index values in simulated and patient data.
        J Nucl Med. 2017; 58: 387-392
        • Orlhac F.
        • Thézé B.
        • Soussan M.
        • Boisgard R.
        • Buvat I.
        Multiscale texture analysis: From 18F-FDG PET images to histologic images.
        J Nucl Med. 2016; 57: 1823-1828
        • Orlhac F.
        • Soussan M.
        • Maisonobe J.-A.
        • Garcia C.A.
        • Vanderlinden B.
        • Buvat I.
        Tumor texture analysis in 18f-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis.
        J Nucl Med. 2014; 55: 414-422
        • Forgács A.
        • Béresová M.
        • Garai I.
        • Lassen M.L.
        • Beyer T.
        • DiFranco M.D.
        • et al.
        Impact of intensity discretization on textural indices of [18F]FDG-PET tumour heterogeneity in lung cancer patients.
        Phys Med Biol. 2019; 64: 125016–6560/ab2328
        • Cheng L.
        • Zhang J.
        • Wang Y.
        • Xu X.
        • Zhang Y.
        • Zhang Y.
        • et al.
        Textural features of 18F-FDG PET after two cycles of neoadjuvant chemotherapy can predict pCR in patients with locally advanced breast cancer.
        Ann Nucl Med. 2017; 31: 544-552–017–1184–1
        • Humbert O.
        • Berriolo-Riedinger A.
        • Riedinger J.M.
        • Coudert B.
        • Arnould L.
        • Cochet A.
        • et al.
        Changes in 18F-FDG tumor metabolism after a first course of neoadjuvant chemotherapy in breast cancer: influence of tumor subtypes.
        Ann. Oncol. 2012; 23: 2572-2577
        • Hulikal N.
        • Gajjala S.R.
        • Kalawat T.
        • Kadiyala S.
        • Kottu R.
        Predicting response to neoadjuvant chemotherapy using 18f FDG PET-CT in patients with locally advanced breast cancer.
        Asian Pacific J Can Prevent APJCP. 2020; 21: 93
        • Lee I.H.
        • Lee S.J.
        • Lee J.
        • Jung J.H.
        • Park H.Y.
        • Jeong S.Y.
        • et al.
        Utility of 18 F-FDG PET/CT for predicting pathologic complete response in hormone receptor-positive, HER2-negative breast cancer patients receiving neoadjuvant chemotherapy.
        BMC Cancer. 2020; 20: 1-10–24641/v1
        • Walker M.D.
        • Asselin M.-C.
        • Julyan P.J.
        • Feldmann M.
        • Talbot P.S.
        • Jones T.
        • et al.
        Bias in iterative reconstruction of low-statistics PET data: benefits of a resolution model.
        Phys Med Biol. 2011; 56: 931-949
        • Wang T.
        • Lei Y.
        • Fu Y.
        • Curran W.J.
        • Liu T.
        • Nye J.A.
        • et al.
        Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.
        Phys Med. 2020; 76: 294-306
        • Huerga C.
        • Morcillo A.
        • Alejo L.
        • Marín A.
        • Obesso A.
        • Travaglio D.
        • et al.
        Role of correlated noise in textural features extraction.
        Phys Med. 2021; 91: 87-98
        • Aide N.
        • Salomon T.
        • Blanc-Fournier C.
        • Grellard J.-M.
        • Levy C.
        • Lasnon C.
        Implications of reconstruction protocol for histo-biological characterisation of breast cancers using FDG-pet radiomics.
        EJNMMI Res. 2018; 8: 114–018–0466–5