OBJECTIVES: To evaluate the effect of artificial intelligence-assisted compressed sensing (ACS) acceleration on MRI radiomic feature extraction and performance of diagnostic staging models for nasopharyngeal carcinoma (NPC) in comparison with conventional parallel imaging (PI). METHODS: A total of 64 patients with newly diagnosed NPC underwent 3.0T MRI using axial T1-weighted (T1W), T2-weighted (T2W), and contrast-enhanced T1-weighted (CE-T1W) sequences. Both PI and ACS protocols were performed using identical imaging parameters. The total scan time for the 3 sequences in ACS group was 227 s, representing a 30% reduction from 312 s in the PI group. Eighteen first-order and 75 texture features were extracted using Pyradiomics. Intraclass correlation coefficients (ICCs) were calculated to assess the agreement between the two acceleration methods. After feature selection using the least absolute shrinkage and selection operator (LASSO), random forest regression models were constructed to distinguish early-stage (T1 and T2) from advanced-stage (T3 and T4) NPC. The diagnostic performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. RESULTS: ACS-accelerated images demonstrated good radiomic reproducibility, with 86.0% (240/279) of features showing good agreement (ICC>0.75), with mean ICCs for T1W, T2W and CE-T1W sequences of 0.91±0.09, 0.89±0.13 and 0.88±0.11, respectively. The staging prediction models achieved similar AUCs for ACS and PI (0.89 vs 0.90, P=0.991). CONCLUSIONS The MRI radiomic features extracted using ACS and PI techniques are highly consistent, and the ACS-based model shows comparable diagnostic performance to the PI-based model, but ACS significantly reduces the scan time and provides an efficient and reliable acceleration strategy for radiomics in NPC.
Humans ; Nasopharyngeal Neoplasms/diagnosis* ; Magnetic Resonance Imaging/methods* ; Nasopharyngeal Carcinoma ; Neoplasm Staging ; Artificial Intelligence ; Carcinoma ; Female ; Male ; Middle Aged ; Adult ; Radiomics

Objective To explore the diagnostic value of MRE for differentiating hepatic benign and malignant tumors.Methods 36 patients with liver tumor (a total of 39 lesions,including 20 hepatocellular carcinomas,7 hemangiomas,5 cholangiocellular carcinomas,3 metastases,2 hepatic angiomyolipomas,1 carcinosarcoma,1 castleman’s disease)and 9 healthy volunteers were evaluated with MRE.The elastogram were generated with FUNCTOOL post processing program.The mean value of elasticity of hepatic malignant tumors,hepatic benign tumors,hepatic parenchyma around the malignant tumors,hepatic parenchyma around the benign tumors and the normal liver of healthy volunteers were measured and compared.Results The mean value of elasticity of malignant tumors [(7.39±1.70)kPa]was significantly higher than these of benign tumors [(4.11±0.37)kPa,P < 0.001],peripheral parenchyma around the malignant tumors [(3.50±0.73)kPa,P < 0.001],peripheral parenchyma around the benign tumors [(2.61±0.45)kPa,P < 0.001] and normal liver of healthy volunteers [(2.38±0.24)kPa,P <0.001].The mean value of elasticity of parenchyma around the malignant tumors [(3.50±0.73)kPa]was significantly higher than that of hepatic parenchyma around the benign tumors [(2.61 ± 0.45)kPa,P <0.001].The mean value of elasticity of hepatic parenchyma around the benign tumors [(2.61±0.45)kPa]was slightly higher than normal liver of healthy volunteers [(2.38±0.24)kPa],and there was no significant difference between the two (P >0.05).A cutoff value of 5.08 kPa can accurately differentiate malignant tumors from benign tumors and normal liver parenchyma.Conclusion MRE could be used in diagnosis of hepatic focal tumors,which is helpful for differentiating benign and malignant liver tumors.