Prediction of prostate cancer aggressiveness using 18F-Fluciclovine (FACBC) PET and multisequence multiparametric MRI View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-06-10

AUTHORS

Parisa Movahedi, Harri Merisaari, Ileana Montoya Perez, Pekka Taimen, Jukka Kemppainen, Anna Kuisma, Olli Eskola, Jarmo Teuho, Jani Saunavaara, Marko Pesola, Esa Kähkönen, Otto Ettala, Timo Liimatainen, Tapio Pahikkala, Peter Boström, Hannu Aronen, Heikki Minn, Ivan Jambor

ABSTRACT

The aim of this prospective single-institution clinical trial (NCT02002455) was to evaluate the potential of advanced post-processing methods for 18F-Fluciclovine PET and multisequence multiparametric MRI in the prediction of prostate cancer (PCa) aggressiveness, defined by Gleason Grade Group (GGG). 21 patients with PCa underwent PET/CT, PET/MRI and MRI before prostatectomy. DWI was post-processed using kurtosis (ADCk, K), mono- (ADCm), and biexponential functions (f, Dp, Df) while Logan plots were used to calculate volume of distribution (VT). In total, 16 unique PET (VT, SUV) and MRI derived quantitative parameters were evaluated. Univariate and multivariate analysis were carried out to estimate the potential of the quantitative parameters and their combinations to predict GGG 1 vs >1, using logistic regression with a nested leave-pair out cross validation (LPOCV) scheme and recursive feature elimination technique applied for feature selection. The second order rotating frame imaging (RAFF), monoexponential and kurtosis derived parameters had LPOCV AUC in the range of 0.72 to 0.92 while the corresponding value for VT was 0.85. The best performance for GGG prediction was achieved by K parameter of kurtosis function followed by quantitative parameters based on DWI, RAFF and 18F-FACBC PET. No major improvement was achieved using parameter combinations with or without feature selection. Addition of 18F-FACBC PET derived parameters (VT, SUV) to DWI and RAFF derived parameters did not improve LPOCV AUC. More... »

PAGES

9407

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-020-66255-8

DOI

http://dx.doi.org/10.1038/s41598-020-66255-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1128343546

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/32523075


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22 schema:description The aim of this prospective single-institution clinical trial (NCT02002455) was to evaluate the potential of advanced post-processing methods for 18F-Fluciclovine PET and multisequence multiparametric MRI in the prediction of prostate cancer (PCa) aggressiveness, defined by Gleason Grade Group (GGG). 21 patients with PCa underwent PET/CT, PET/MRI and MRI before prostatectomy. DWI was post-processed using kurtosis (ADCk, K), mono- (ADCm), and biexponential functions (f, Dp, Df) while Logan plots were used to calculate volume of distribution (VT). In total, 16 unique PET (VT, SUV) and MRI derived quantitative parameters were evaluated. Univariate and multivariate analysis were carried out to estimate the potential of the quantitative parameters and their combinations to predict GGG 1 vs >1, using logistic regression with a nested leave-pair out cross validation (LPOCV) scheme and recursive feature elimination technique applied for feature selection. The second order rotating frame imaging (RAFF), monoexponential and kurtosis derived parameters had LPOCV AUC in the range of 0.72 to 0.92 while the corresponding value for VT was 0.85. The best performance for GGG prediction was achieved by K parameter of kurtosis function followed by quantitative parameters based on DWI, RAFF and 18F-FACBC PET. No major improvement was achieved using parameter combinations with or without feature selection. Addition of 18F-FACBC PET derived parameters (VT, SUV) to DWI and RAFF derived parameters did not improve LPOCV AUC.
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30 CT
31 DWI
32 GGG 1
33 Gleason grade group
34 Logan plot
35 MRI
36 PCA
37 PET
38 PET/CT
39 PET/MRI
40 Raff
41 addition
42 advanced post-processing methods
43 aggressiveness
44 aim
45 analysis
46 better performance
47 biexponential function
48 cancer aggressiveness
49 clinical trials
50 combination
51 corresponding values
52 cross-validation scheme
53 distribution
54 elimination technique
55 feature selection
56 frame
57 function
58 grade group
59 group
60 improvement
61 k parameter
62 kurtosis
63 kurtosis function
64 logistic regression
65 major improvements
66 method
67 mono
68 monoexponential
69 multiparametric MRI
70 multivariate analysis
71 order
72 parameter combinations
73 parameters
74 patients
75 performance
76 plots
77 post-processing method
78 potential
79 prediction
80 prostate cancer aggressiveness
81 prostatectomy
82 quantitative parameters
83 range
84 recursive feature elimination technique
85 regression
86 scheme
87 second order
88 selection
89 single-institution clinical trial
90 technique
91 trials
92 unique pets
93 validation scheme
94 values
95 volume
96 volume of distribution
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