In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 3 ( 2023-3-31), p. e0282710-
Abstract:
We investigated prospectively whether, in cervical cancer (CC) treated with concurrent chemoradiotherapy (CCRT), the Apparent diffusion coefficient (ADC) histogram and texture parameters and their change rates during treatment could predict prognosis. Methods Fifty-seven CC patients treated with CCRT at our institution were included. They underwent MRI scans up to four times during the treatment course (1st, before treatment [n = 41], 2nd, at the start of image-guided brachytherapy (IGBT) [n = 41] , 3rd, in the middle of IGBT [n = 27], 4th, after treatment [n = 53] ). The entire tumor was manually set as the volume of interest (VOI) manually in the axial images of the ADC map by two radiologists. A total of 107 image features (morphology features 14, histogram features 18, texture features 75) were extracted from the VOI. The recurrence prediction values of the features and their change rates were evaluated by Receiver operating characteristics (ROC) analysis. The presence or absence of local and distant recurrence within two years was set as an outcome. The intraclass correlation coefficient (ICC) was also calculated. Results The change rates in kurtosis between the 1 st and 3 rd , and 1 st and 2 nd MRIs, and the change rate in grey level co-occurrence matrix_cluster shade between the 2 nd and 3 rd MRIs showed particularly high predictive powers (area under the ROC curve = 0.785, 0.759, and 0.750, respectively), which exceeded the predictive abilities of the parameters obtained from pre- or post-treatment MRI only. The change rate in kurtosis between the 1 st and 2 nd MRIs had good reliability (ICC = 0.765). Conclusions The change rate in ADC kurtosis between the 1 st and 2 nd MRIs was the most reliable parameter, enabling us to predict prognosis early in the treatment course.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0282710
DOI:
10.1371/journal.pone.0282710.g001
DOI:
10.1371/journal.pone.0282710.g002
DOI:
10.1371/journal.pone.0282710.g003
DOI:
10.1371/journal.pone.0282710.g004
DOI:
10.1371/journal.pone.0282710.g005
DOI:
10.1371/journal.pone.0282710.t001
DOI:
10.1371/journal.pone.0282710.t002
DOI:
10.1371/journal.pone.0282710.t003
DOI:
10.1371/journal.pone.0282710.t004
DOI:
10.1371/journal.pone.0282710.t005
DOI:
10.1371/journal.pone.0282710.s001
DOI:
10.1371/journal.pone.0282710.s002
DOI:
10.1371/journal.pone.0282710.s003
DOI:
10.1371/journal.pone.0282710.s004
DOI:
10.1371/journal.pone.0282710.s005
DOI:
10.1371/journal.pone.0282710.s006
DOI:
10.1371/journal.pone.0282710.s007
DOI:
10.1371/journal.pone.0282710.s008
DOI:
10.1371/journal.pone.0282710.r001
DOI:
10.1371/journal.pone.0282710.r002
DOI:
10.1371/journal.pone.0282710.r003
DOI:
10.1371/journal.pone.0282710.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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