In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 7 ( 2023-7-21), p. e0282573-
Abstract:
Clinical prognostic scoring systems have limited utility for predicting treatment outcomes in lymphomas. We therefore tested the feasibility of a deep-learning (DL)-based image analysis methodology on pre-treatment diagnostic computed tomography (dCT), low-dose CT (lCT), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) images and rule-based reasoning to predict treatment response to chimeric antigen receptor (CAR) T-cell therapy in B-cell lymphomas. Pre-treatment images of 770 lymph node lesions from 39 adult patients with B-cell lymphomas treated with CD19-directed CAR T-cells were analyzed. Transfer learning using a pre-trained neural network model, then retrained for a specific task, was used to predict lesion-level treatment responses from separate dCT, lCT, and FDG-PET images. Patient-level response analysis was performed by applying rule-based reasoning to lesion-level prediction results. Patient-level response prediction was also compared to prediction based on the international prognostic index (IPI) for diffuse large B-cell lymphoma. The average accuracy of lesion-level response prediction based on single whole dCT slice-based input was 0.82 + 0.05 with sensitivity 0.87 + 0.07, specificity 0.77 + 0.12, and AUC 0.91 + 0.03. Patient-level response prediction from dCT, using the “Majority 60%” rule, had accuracy 0.81, sensitivity 0.75, and specificity 0.88 using 12-month post-treatment patient response as the reference standard and outperformed response prediction based on IPI risk factors (accuracy 0.54, sensitivity 0.38, and specificity 0.61 (p = 0.046)). Prediction of treatment outcome in B-cell lymphomas from pre-treatment medical images using DL-based image analysis and rule-based reasoning is feasible. This approach can potentially provide clinically useful prognostic information for decision-making in advance of initiating CAR T-cell therapy.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0282573
DOI:
10.1371/journal.pone.0282573.g001
DOI:
10.1371/journal.pone.0282573.g002
DOI:
10.1371/journal.pone.0282573.t001
DOI:
10.1371/journal.pone.0282573.t002
DOI:
10.1371/journal.pone.0282573.s001
DOI:
10.1371/journal.pone.0282573.s002
DOI:
10.1371/journal.pone.0282573.s003
DOI:
10.1371/journal.pone.0282573.s004
DOI:
10.1371/journal.pone.0282573.s005
DOI:
10.1371/journal.pone.0282573.s006
DOI:
10.1371/journal.pone.0282573.s007
DOI:
10.1371/journal.pone.0282573.s008
DOI:
10.1371/journal.pone.0282573.s009
DOI:
10.1371/journal.pone.0282573.s010
DOI:
10.1371/journal.pone.0282573.s011
DOI:
10.1371/journal.pone.0282573.s012
DOI:
10.1371/journal.pone.0282573.s013
DOI:
10.1371/journal.pone.0282573.s014
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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