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Magnetic Resonance Materials in Physics, Biology and Medicine
Published in: Magnetic Resonance Materials in Physics, Biology and Medicine 2/2020

Open Access 01-04-2020 | Breast Cancer | Research Article

Semi-automatic segmentation from intrinsically-registered 18F-FDG–PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE–MRI

Authors: Maren Marie Sjaastad Andreassen, Pål Erik Goa, Torill Eidhammer Sjøbakk, Roja Hedayati, Hans Petter Eikesdal, Callie Deng, Agnes Østlie, Steinar Lundgren, Tone Frost Bathen, Neil Peter Jerome

Published in: Magnetic Resonance Materials in Physics, Biology and Medicine | Issue 2/2020

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Abstract

Objectives

To investigate the reliability of simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI)-derived biomarkers using semi-automated Gaussian mixture model (GMM) segmentation on PET images, against conventional manual tumor segmentation on dynamic contrast-enhanced (DCE) images.

Materials and methods

Twenty-four breast cancer patients underwent PET/MRI (following 18F-fluorodeoxyglucose (18F-FDG) injection) at baseline and during neoadjuvant treatment, yielding 53 data sets (24 untreated, 29 treated). Two-dimensional tumor segmentation was performed manually on DCE–MRI images (manual DCE) and using GMM with corresponding PET images (GMM–PET). Tumor area and mean apparent diffusion coefficient (ADC) derived from both segmentation methods were compared, and spatial overlap between the segmentations was assessed with Dice similarity coefficient and center-of-gravity displacement.

Results

No significant differences were observed between mean ADC and tumor area derived from manual DCE segmentation and GMM–PET. There were strong positive correlations for tumor area and ADC derived from manual DCE and GMM–PET for untreated and treated lesions. The mean Dice score for GMM–PET was 0.770 and 0.649 for untreated and treated lesions, respectively.

Discussion

Using PET/MRI, tumor area and mean ADC value estimated with a GMM–PET can replicate manual DCE tumor definition from MRI for monitoring neoadjuvant treatment response in breast cancer.
Appendix
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Literature
1.
2.
Cancer Registry of Norway (2018) Cancer in Norway 2017—cancer incidence, mortality, survival and prevalence in Norway. Accessed 10 Dec 2018
3.
Cortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N, Bonnefoi H, Cameron D, Gianni L, Valagussa P, Swain SM, Prowell T, Loibl S, Wickerham DL, Bogaerts J, Baselga J, Perou C, Blumenthal G, Blohmer J, Mamounas EP, Bergh J, Semiglazov V, Justice R, Eidtmann H, Paik S, Piccart M, Sridhara R, Fasching PA, Slaets L, Tang S, Gerber B, Geyer CE Jr, Pazdur R, Ditsch N, Rastogi P, Eiermann W, von Minckwitz G (2014) Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet 384(9938):164–172CrossRefPubMed
4.
Thomas E, Holmes FA, Smith TL, Buzdar AU, Frye DK, Fraschini G, Singletary SE, Theriault RL, McNeese MD, Ames F, Walters R, Hortobagyi GN (2004) The use of alternate, non-cross-resistant adjuvant chemotherapy on the basis of pathologic response to a neoadjuvant doxorubicin-based regimen in women with operable breast cancer: long-term results from a prospective randomized trial. J Clin Oncol 22(12):2294–2302CrossRefPubMed
5.
Graham LJ, Shupe MP, Schneble EJ, Flynt FL, Clemenshaw MN, Kirkpatrick AD, Gallagher C, Nissan A, Henry L, Stojadinovic A, Peoples GE, Shumway NM (2014) Current approaches and challenges in monitoring treatment responses in breast cancer. J Cancer 5(1):58–68CrossRefPubMedPubMedCentral
6.
Chu W, Jin W, Liu D, Wang J, Geng C, Chen L, Huang X (2018) Diffusion-weighted imaging in identifying breast cancer pathological response to neoadjuvant chemotherapy: a meta-analysis. Oncotarget 9(6):7088–7100CrossRefPubMed
7.
Partridge SC, Zhang Z, Newitt DC, Gibbs JE, Chenevert TL, Rosen MA, Bolan PJ, Marques HS, Romanoff J, Cimino L, Joe BN, Umphrey HR, Ojeda-Fournier H, Dogan B, Oh K, Abe H, Drukteinis JS, Esserman LJ, Hylton NM (2018) Diffusion-weighted MRI findings predict pathologic response in neoadjuvant treatment of breast cancer: the ACRIN 6698 multicenter trial. Radiology 289(3):618–627CrossRefPubMed
8.
Pickles MD, Gibbs P, Lowry M, Turnbull LW (2006) Diffusion changes precede size reduction in neoadjuvant treatment of breast cancer. Magn Reson Imaging 24(7):843–847CrossRefPubMed
9.
Nilsen L, Fangberget A, Geier O, Olsen DR, Seierstad T (2010) Diffusion-weighted magnetic resonance imaging for pretreatment prediction and monitoring of treatment response of patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. Acta Oncol (Stockholm, Sweden) 49(3):354–360CrossRef
10.
Sharma U, Danishad KK, Seenu V, Jagannathan NR (2009) Longitudinal study of the assessment by MRI and diffusion-weighted imaging of tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. NMR Biomed 22(1):104–113CrossRefPubMed
11.
12.
Sinha S, Lucas-Quesada FA, Sinha U, DeBruhl N, Bassett LW (2002) In vivo diffusion-weighted MRI of the breast: potential for lesion characterization. J Magn Reson Imaging 15(6):693–704CrossRefPubMed
13.
Guo Y, Cai Y-Q, Cai Z-L, Gao Y-G, An N-Y, Ma L, Mahankali S, Gao J-H (2002) Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J Magn Reson Imaging 16(2):172–178CrossRefPubMed
14.
Bickel H, Pinker K, Polanec S, Magometschnigg H, Wengert G, Spick C, Bogner W, Bago-Horvath Z, Helbich TH, Baltzer P (2017) Diffusion-weighted imaging of breast lesions: region-of-interest placement and different ADC parameters influence apparent diffusion coefficient values. Eur Radiol 27(5):1883–1892CrossRefPubMed
15.
Kim TH, Yoon JK, Kang DK, Lee SJ, Jung YS, Yim H, An YS (2015) Correlation between F-18 fluorodeoxyglucose positron emission tomography metabolic parameters and dynamic contrast-enhanced MRI-derived perfusion data in patients with invasive ductal breast carcinoma. Ann Surg Oncol 22(12):3866–3872CrossRefPubMed
16.
17.
18.
Nakajo M, Kajiya Y, Kaneko T, Kaneko Y, Takasaki T, Tani A, Ueno M, Koriyama C, Nakajo M (2010) FDG PET/CT and diffusion-weighted imaging for breast cancer: prognostic value of maximum standardized uptake values and apparent diffusion coefficient values of the primary lesion. Eur J Nucl Med Mol Imaging 37(11):2011–2020CrossRefPubMed
19.
Byun BH, Noh WC, Lim I, Lee SS, Cho AR, Park JA, Kim KM, Kim HA, Kim EK, Kim BI, Choi CW, Lim SM (2013) A new method for apparent diffusion coefficient measurement using sequential (18)F-FDG PET and MRI: correlation with histological grade of invasive ductal carcinoma of the breast. Ann Nucl Med 27(8):720–728CrossRefPubMed
20.
Kitajima K, Yamano T, Fukushima K, Miyoshi Y, Hirota S, Kawanaka Y, Miya M, Doi H, Yamakado K, Hirota S (2016) Correlation of the SUVmax of FDG-PET and ADC values of diffusion-weighted MR imaging with pathologic prognostic factors in breast carcinoma. Eur J Radiol 85(5):943–949CrossRefPubMed
21.
Daisne JF, Duprez T, Weynand B, Lonneux M, Hamoir M, Reychler H, Gregoire V (2004) Tumor volume in pharyngolaryngeal squamous cell carcinoma: comparison at CT, MR imaging, and FDG PET and validation with surgical specimen. Radiology 233(1):93–100CrossRefPubMed
22.
Im HJ, Bradshaw T, Solaiyappan M, Cho SY (2018) Current methods to define metabolic tumor volume in positron emission tomography: which one is better? Nucl Med Mol Imaging 52(1):5–15CrossRefPubMed
23.
Schelling M, Avril N, Nahrig J, Kuhn W, Romer W, Sattler D, Werner M, Dose J, Janicke F, Graeff H, Schwaiger M (2000) Positron emission tomography using [(18)F]Fluorodeoxyglucose for monitoring primary chemotherapy in breast cancer. J Clin Oncol 18(8):1689–1695CrossRefPubMed
24.
Holland D, Kuperman JM, Dale AM (2010) Efficient correction of inhomogeneous static magnetic field-induced distortion in Echo Planar Imaging. Neuroimage 50(1):175–183CrossRefPubMed
25.
Martinez-Moller A, Souvatzoglou M, Delso G, Bundschuh RA, Chefd’hotel C, Ziegler SI, Navab N, Schwaiger M, Nekolla SG (2009) Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data. J Nucl Med 50(4):520–526CrossRefPubMed
26.
Klein S, Staring M, Murphy K, Viergever MA, Pluim JP (2010) Elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 29(1):196–205CrossRefPubMed
27.
Drzezga A, Souvatzoglou M, Eiber M, Beer AJ, Furst S, Martinez-Moller A, Nekolla SG, Ziegler S, Ganter C, Rummeny EJ, Schwaiger M (2012) First clinical experience with integrated whole-body PET/MR: comparison to PET/CT in patients with oncologic diagnoses. J Nucl Med 53(6):845–855CrossRefPubMed
28.
Geoff McLachlan DP (2000) Finite mixture models. Wiley series in probability and statistics. Applied probability and statistics. Wiley, New York
29.
Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302CrossRef
30.
Benjamini Y, Hochberg Y (2000) On the adaptive control of the false discovery rate in multiple testing with independent statistics. J Educ Behav Stat 25(1):60–83CrossRef
31.
Nogueira L, Brandao S, Matos E, Nunes RG, Ferreira HA, Loureiro J, Ramos I (2015) Region of interest demarcation for quantification of the apparent diffusion coefficient in breast lesions and its interobserver variability. Diagn Interv Radiol 21(2):123–127CrossRefPubMedPubMedCentral
32.
Nonomura Y, Yasumoto M, Yoshimura R, Haraguchi K, Ito S, Akashi T, Ohashi I (2001) Relationship between bone marrow cellularity and apparent diffusion coefficient. J Magn Reson Imaging 13(5):757–760CrossRefPubMed
33.
Yoshikawa MI, Ohsumi S, Sugata S, Kataoka M, Takashima S, Mochizuki T, Ikura H, Imai Y (2008) Relation between cancer cellularity and apparent diffusion coefficient values using diffusion-weighted magnetic resonance imaging in breast cancer. Radiat Med 26(4):222–226CrossRefPubMed
34.
Ito K, Kato T, Ohta T, Tadokoro M, Yamada T, Ikeda M, Nishino M, Ishigaki T, Gambhir S (1996) Fluorine-18 fluoro-2-deoxyglucose positron emission tomography in recurrent rectal cancer: relation to tumour size and cellularity. Eur J Nucl Med 23(10):1372–1377CrossRefPubMed
35.
Higashi T, Tamaki N, Torizuka T, Nakamoto Y, Sakahara H, Kimura T, Honda T, Inokuma T, Katsushima S, Ohshio G, Imamura M, Konishi J (1998) FDG uptake, GLUT-1 glucose transporter and cellularity in human pancreatic tumors. J Nucl Med 39(10):1727–1735PubMed
36.
Choi BB, Kim SH, Kang BJ, Lee JH, Song BJ, Jeong SH, Yim HW (2012) Diffusion-weighted imaging and FDG PET/CT: predicting the prognoses with apparent diffusion coefficient values and maximum standardized uptake values in patients with invasive ductal carcinoma. World J Surg Oncol 10:126CrossRefPubMedPubMedCentral
37.
Inglese M, Cavaliere C, Monti S, Forte E, Incoronato M, Nicolai E, Salvatore M, Aiello M (2019) A multi-parametric PET/MRI study of breast cancer: evaluation of DCE-MRI pharmacokinetic models and correlation with diffusion and functional parameters. NMR Biomed 32(1):e4026CrossRefPubMed
38.
Muz B, de la Puente P, Azab F, Azab AK (2015) The role of hypoxia in cancer progression, angiogenesis, metastasis, and resistance to therapy. Hypoxia (Auckl) 3:83–92CrossRef
39.
Daisne JF, Duprez T, Weynand B (2004) Tumor volume in pharyngolaryngeal squamous cell carcinoma: comparison at CT, MR imaging, and FDG PET and validation with surgical specimen. Radiology 233:93CrossRefPubMed
40.
Sridhar P, Mercier G, Tan J, Truong MT, Daly B, Subramaniam RM (2014) FDG PET metabolic tumor volume segmentation and pathologic volume of primary human solid tumors. Am J Roentgenol 202(5):1114–1119CrossRef
41.
Burger IA, Vargas HA, Apte A, Beattie BJ, Humm JL, Gonen M, Larson SM, Ross Schmidtlein C (2014) PET quantification with a histogram derived total activity metric: superior quantitative consistency compared to total lesion glycolysis with absolute or relative SUV thresholds in phantoms and lung cancer patients. Nucl Med Biol 41(5):410–418CrossRefPubMedPubMedCentral
42.
Burger IA, Casanova R, Steiger S, Husmann L, Stolzmann P, Huellner MW, Curioni A, Hillinger S, Schmidtlein CR, Soltermann A (2016) 18F-FDG PET/CT of non-small cell lung carcinoma under neoadjuvant chemotherapy: background-based adaptive-volume metrics outperform TLG and MTV in predicting histopathologic response. J Nucl Med 57(6):849–854CrossRefPubMed
43.
Werner-Wasik M, Nelson AD, Choi W (2012) What is the best way to contour lung tumors on PET scans? Multiobserver validation of a gradient-based method using a NSCLC digital PET phantom. Int J Radiat Oncol Biol Phys 82:1164CrossRefPubMed
44.
Pinker K, Helbich TH, Morris EA (2017) The potential of multiparametric MRI of the breast. Br J Radiol 90(1069):20160715CrossRefPubMed
45.
Chen JH, Feig B, Agrawal G, Yu H, Carpenter PM, Mehta RS, Nalcioglu O, Su MY (2008) MRI evaluation of pathologically complete response and residual tumors in breast cancer after neoadjuvant chemotherapy. Cancer 112(1):17–26CrossRefPubMed
46.
Gulani V, Calamante F, Shellock FG, Kanal E, Reeder SB (2017) Gadolinium deposition in the brain: summary of evidence and recommendations. Lancet Neurol 16(7):564–570CrossRefPubMed
47.
Kim HR, Jung HK, Ko KH, Kim SJ, Lee KS (2016) Mammography, US, and MRI for preoperative prediction of extensive intraductal component of invasive breast cancer: interobserver variability and performances. Clin Breast Cancer 16(4):305–311CrossRefPubMed
48.
Riola-Parada C, Garcia-Canamaque L, Perez-Duenas V, Garcerant-Tafur M, Carreras-Delgado JL (2016) Simultaneous PET/MRI vs PET/CT in oncology. A systematic review. Rev Esp Med Nucl Imagen Mol 35(5):306–312PubMed
49.
Aristophanous M, Penney BC, Martel MK, Pelizzari CA (2007) A Gaussian mixture model for definition of lung tumor volumes in positron emission tomography. Med Phys 34(11):4223–4235CrossRefPubMed
50.
Adejolu M, Huo L, Rohren E, Santiago L, Yang WT (2012) False-positive lesions mimicking breast cancer on FDG PET and PET/CT. Am J Roentgenol 198(3):W304–W314CrossRef
Metadata
Title
Semi-automatic segmentation from intrinsically-registered 18F-FDG–PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE–MRI
Authors
Maren Marie Sjaastad Andreassen
Pål Erik Goa
Torill Eidhammer Sjøbakk
Roja Hedayati
Hans Petter Eikesdal
Callie Deng
Agnes Østlie
Steinar Lundgren
Tone Frost Bathen
Neil Peter Jerome
Publication date
01-04-2020
Publisher
Springer International Publishing
Published in
Magnetic Resonance Materials in Physics, Biology and Medicine / Issue 2/2020
Print ISSN: 0968-5243
Electronic ISSN: 1352-8661
DOI
https://doi.org/10.1007/s10334-019-00778-8

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