ArticleMedical physics2025
Tissue classification from raw diffusion-weighted images using machine learning.
Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Tissue classification from raw diffusion-weighted images using machine learning.Medical physics · 2025Article
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8 authors.
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Abstract
backgroundIn diffusion-weighted imaging (DWI), a large collection of diffusion models is available to provide insights into tissue characteristics. However, these models are limited by predefined assumptions and computational challenges, potentially hindering the full extraction of information from the diffusion MR signal. PURPOSE: This study aimed at developing a MOdel-free Diffusion-wEighted MRI (MODEM) method for tissue differentiation by using a machine learning (ML) algorithm based on raw diffusion images without relying on any specific diffusion model. MODEM has been applied to both simulation data and cervical cancer diffusion images and compared with several diffusion models.
methodsWith Institutional Review Board approval, 54 cervical cancer patients (median age, 52 years; age range, 29-73 years) participated in the study, including 26 in the early FIGO (International Federation of Gynecology and Obstetrics) stage (IB, 16; IIA, 10) and 28 the late stage (IIB, 8; IIIB, 14; IIIC, 1; IVA, 3; IVB, 2). The participants underwent DWI with 17 b-values (0 to 4500 s/mm
resultsFor the simulation dataset, MODEM produced a higher AUC and better accuracy, particularly in scenarios where the noise level exceeded 5%. For the cervical cancer dataset, MODEM yielded the highest AUC and accuracy in cervical cancer detection (AUC, 0.976; accuracy, 91.9%) and cervical cancer staging (AUC, 0.773; accuracy, 69.2%), significantly outperforming any of the diffusion models (p < 0.05).
conclusionsMODEM is useful for cervical cancer detection and staging and offers considerable advantages over analytical diffusion models for tissue characterization.
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