Low-parameter supervised learning models can discriminate pseudoprogression and true progression in non-perfusion-based MRI.
Annu Int Conf IEEE Eng Med Biol Soc
; 2023: 1-4, 2023 07.
Article
in En
| MEDLINE
| ID: mdl-38083692
ABSTRACT
Discrimination of pseudoprogression and true progression is one challenge to the treatment of malignant gliomas. Although some techniques such as circulating tumor DNA (ctDNA) and perfusion-weighted imaging (PWI) demonstrate promise in distinguishing PsP from TP, we investigate robust and replicable alternatives to distinguish the two entities based on more widely-available media. In this study, we use low-parametric supervised learning techniques based on geographically-weighted regression (GWR) to investigate the utility of both conventional MRI sequences as well as a diffusion-weighted sequence (apparent diffusion coefficient or ADC) in the discrimination of PsP v TP. GWR applied to MRI modality pairs is a unique approach for small sample sizes and is a novel approach in this arena. From our analysis, all modality pairs involving ADC maps, and those involving post-contrast T1 regressed onto T2 showed potential promise. This work on ADC data adds to a growing body of research suggesting the predictive benefits of ADC, and suggests further research on the relationships between post-contrast T1 and T2.Clinical relevance- Few studies have investigated predictive potential of conventional MRI and ADC to detect PsP. Our study adds to the growing research on the topic and presents a new perspective to research by exploiting the utility of ADC in PsP v TP distinction. In addition, our GWR methodology for low-parametric supervised computer vision models demonstrates a unique approach for image processing of small sample sizes.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Magnetic Resonance Imaging
/
Glioma
Limits:
Humans
Language:
En
Journal:
Annu Int Conf IEEE Eng Med Biol Soc
Year:
2023
Document type:
Article