In:
PLOS ONE, Public Library of Science (PLoS), Vol. 15, No. 12 ( 2020-12-14), p. e0243839-
Abstract:
The preoperative imaging-based differentiation of primary central nervous system lymphomas (PCNSLs) and glioblastomas (GBs) is of high importance since the therapeutic strategies differ substantially between these tumors. In this study, we investigate whether the gamma distribution (GD) model is useful in this differentiation of PNCSLs and GBs. Twenty-seven patients with PCNSLs and 57 patients with GBs were imaged with diffusion-weighted imaging using 13 b-values ranging from 0 to 1000 sec/mm 2 . The shape parameter (κ) and scale parameter (θ) were obtained with the GD model. Fractions of three different areas under the probability density function curve (f1, f2, f3) were defined as follows: f1, diffusion coefficient (D) 〈 1.0×10 −3 mm 2 /sec; f2, D 〉 1.0×10 −3 and 〈 3.0×10 −3 mm 2 /sec; f3, D 〉 3.0 × 10 −3 mm 2 /sec. The GD model-derived parameters were compared between PCNSLs and GBs. Receiver operating characteristic (ROC) curve analyses were performed to assess diagnostic performance. The correlations with intravoxel incoherent motion (IVIM)-derived parameters were evaluated. The PCNSL group's κ (2.26 ± 1.00) was significantly smaller than the GB group's (3.62 ± 2.01, p = 0.0004). The PCNSL group's f1 (0.542 ± 0.107) was significantly larger than the GB group's (0.348 ± 0.132, p 〈 0.0001). The PCNSL group's f2 (0.372 ± 0.098) was significantly smaller than the GB group's (0.508 ± 0.127, p 〈 0.0001). The PCNSL group's f3 (0.086 ± 0.043) was significantly smaller than the GB group's (0.144 ± 0.062, p 〈 0.0001). The combination of κ, f1, and f3 showed excellent diagnostic performance (area under the curve, 0.909). The f1 had an almost perfect inverse correlation with D. The f2 and f3 had very strong positive correlations with D and f, respectively. The GD model is useful for the differentiation of GBs and PCNSLs.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0243839
DOI:
10.1371/journal.pone.0243839.g001
DOI:
10.1371/journal.pone.0243839.g002
DOI:
10.1371/journal.pone.0243839.g003
DOI:
10.1371/journal.pone.0243839.g004
DOI:
10.1371/journal.pone.0243839.g005
DOI:
10.1371/journal.pone.0243839.g006
DOI:
10.1371/journal.pone.0243839.t001
DOI:
10.1371/journal.pone.0243839.t002
DOI:
10.1371/journal.pone.0243839.s001
DOI:
10.1371/journal.pone.0243839.s002
DOI:
10.1371/journal.pone.0243839.s003
DOI:
10.1371/journal.pone.0243839.r001
DOI:
10.1371/journal.pone.0243839.r002
DOI:
10.1371/journal.pone.0243839.r003
DOI:
10.1371/journal.pone.0243839.r004
DOI:
10.1371/journal.pone.0243839.r005
DOI:
10.1371/journal.pone.0243839.r006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2020
detail.hit.zdb_id:
2267670-3
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