PMID- 38077485
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231211
IS  - 1178-7007 (Print)
IS  - 1178-7007 (Electronic)
IS  - 1178-7007 (Linking)
VI  - 16
DP  - 2023
TI  - Application of Interpretable Machine Learning Models Based on Ultrasonic 
      Radiomics for Predicting the Risk of Fibrosis Progression in Diabetic Patients 
      with Nonalcoholic Fatty Liver Disease.
PG  - 3901-3913
LID - 10.2147/DMSO.S439127 [doi]
AB  - INTRODUCTION: Patients with nonalcoholic fatty liver disease (NAFLD) and type 2 
      diabetes mellitus (T2DM) face a significant risk of hepatic fibrosis. Liver 
      stiffness measurement (LSM) is commonly used to exclude advanced fibrosis, but 
      its effectiveness in predicting fibrosis progression, especially in initially 
      fibrosis-free patients, remains under-investigated. Although radiomics and 
      machine learning (ML) models show promise in interpreting intricate data and 
      predicting clinical outcomes, their application in assessing the fibrosis 
      progression risk has not been fully explored. This study aimed to address this 
      gap by developing and validating ML-based models to identify patients at risk of 
      fibrosis progression using clinical data and multimodal radiomics features, 
      thereby enhancing NAFLD and T2DM management. METHODS: The study involved a 
      retrospective analysis of 618 diabetic patients with NAFLD. These patients were 
      divided into training and external validation cohorts. Based on LSM values, 
      patients were classified into "Low-risk" and "Fibrosis-risk" groups. Radiomics 
      features from multimodal ultrasound imaging were extracted, standardized, and 
      utilized to develop various ML models. The models were internally validated based 
      on these radiomics or clinical data, and the optimal model's feature importance 
      was analyzed using the Shapley Additive Explanations (SHAP) approach, followed by 
      external validation. RESULTS: Of the 618 patients, 18.1% demonstrated an 
      LSM>/=6.5kPa, indicating a higher risk of hepatic fibrosis. The study identified 25 
      significant fibrosis-related radiomics features, with the support vector machine 
      (SVM) model demonstrating superior performance in both internal and external 
      validations. The SHAP analysis identified five key determinants of fibrosis risk, 
      which included three radiomics features from shear wave elastography (SWE) and 
      two from grayscale imaging. CONCLUSION: This study demonstrates the utility of an 
      SVM model based on radiomics features derived from SWE and grayscale imaging for 
      predicting fibrosis progression in diabetic patients with NAFLD, thereby enabling 
      timely and effective therapeutic interventions.
CI  - (c) 2023 Meng et al.
FAU - Meng, Fei
AU  - Meng F
AUID- ORCID: 0009-0000-5988-5868
AD  - Department of Ultrasound, Xuan Cheng City Central Hospital, Xuancheng, Anhui, 
      People's Republic of China.
FAU - Wu, Qin
AU  - Wu Q
AD  - Department of Ultrasound, Xuan Cheng City Central Hospital, Xuancheng, Anhui, 
      People's Republic of China.
FAU - Zhang, Wei
AU  - Zhang W
AD  - Department of Ultrasound, Xuan Cheng City Central Hospital, Xuancheng, Anhui, 
      People's Republic of China.
FAU - Hou, Shirong
AU  - Hou S
AD  - Department of Ultrasound, Xuan Cheng City Central Hospital, Xuancheng, Anhui, 
      People's Republic of China.
LA  - eng
PT  - Journal Article
DEP - 20231202
PL  - New Zealand
TA  - Diabetes Metab Syndr Obes
JT  - Diabetes, metabolic syndrome and obesity : targets and therapy
JID - 101515585
PMC - PMC10700041
OTO - NOTNLM
OT  - hepatic fibrosis
OT  - machine learning
OT  - nonalcoholic fatty liver disease
OT  - radiomics
OT  - support vector machine
OT  - type 2 diabetes mellitus
COIS- The authors have no conflicts of interest to declare.
EDAT- 2023/12/11 12:42
MHDA- 2023/12/11 12:43
PMCR- 2023/12/02
CRDT- 2023/12/11 06:31
PHST- 2023/10/07 00:00 [received]
PHST- 2023/11/22 00:00 [accepted]
PHST- 2023/12/11 12:43 [medline]
PHST- 2023/12/11 12:42 [pubmed]
PHST- 2023/12/11 06:31 [entrez]
PHST- 2023/12/02 00:00 [pmc-release]
AID - 439127 [pii]
AID - 10.2147/DMSO.S439127 [doi]
PST - epublish
SO  - Diabetes Metab Syndr Obes. 2023 Dec 2;16:3901-3913. doi: 10.2147/DMSO.S439127. 
      eCollection 2023.