Search

Background: To develop and validate a nomogram model based on Gd-EOB-DTPA enhanced MRI for differentiation between hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) showing iso- or hyperintensity in the hepatobiliary phase (HBP). Methods: A total of 75 patients with 49 HCCs and 26 FNHs randomly divided into a training cohort (n = 52: 34 HCC; 18 FNH) and an internal validation cohort (n = 23: 15 HCC; 8 FNH). A total of 37 patients (n = 37: 25 HCC; 12 FNH) acted as an external test cohort. The clinical and imaging characteristics between HCC and FNH groups in the training cohort were compared. The statistically significant parameters were included into the FAE software, and a multivariate logistic regression classifier was used to identify independent predictors and establish a nomogram model. Receiver operating characteristic (ROC) curves were used to evaluate the prediction ability of the model, while the calibration and decision curves were used for model validation. Subanalysis was used to compare qualitative and quantitative characteristics of patients with chronic hepatitis and cirrhosis between the HCC and FNH groups. Results: In the training cohort, gender, age, enhancement rate in the arterial phase (AP), focal defects in uptake were significant predictors for HCC showing iso- or hyperintensity in the HBP. In the training cohort, area under the curve (AUC), sensitivity and specificity of the nomogram model were 0.989(95%CI: 0.967-1.000), 97.1% and 94.4%. In the internal validation cohort, the above three indicators were 0.917(95%CI: 0.782-1.000), 93.3% and 87.5%. In the external test cohort, the above three indicators were 0.960(95%CI: 0.905-1.000), 84.0% and 100.0%. The results of subanalysis showed that age was the independent predictor in the patients with chronic hepatitis and cirrhosis between HCC and FNH groups. Conclusions: Gd-EOB-DTPA enhanced MRI nomogram model may be useful for discriminating HCC and FNH showing iso- or hyperintensity in the HBP before surgery. [ABSTRACT FROM AUTHOR]

Background: This study aims to develop and validate an artificial intelligence (AI)-aided Prostate Imaging Reporting and Data System (PI-RADSAI) for prostate cancer (PCa) diagnosis based on MRI. Methods: The deidentified MRI data of 1540 biopsy-naïve patients were collected from four centres. PI-RADSAI is a two-stage, human-in-the-loop AI capable of emulating the diagnostic acumen of subspecialists for PCa on MRI. The first stage uses a UNet-Seg model to detect and segment biopsy-candidate prostate lesions, whereas the second stage leverages UNet-Seg segmentation is trained specifically with subspecialist' knowledge-guided 3D-Resnet to achieve an automatic AI-aided diagnosis for PCa. Results: In the independent test set, UNet-Seg identified 87.2% (628/720) of target lesions, with a Dice score of 44.9% (range, 22.8–60.2%) in segmenting lesion contours. In the ablation experiment, the model trained with the data from three centres was superior (kappa coefficient, 0.716 vs. 0.531) to that trained with single-centre data. In the internal and external tests, the triple-centre PI-RADSAI model achieved an overall agreement of 58.4% (188/322) and 60.1% (92/153) with a referential subspecialist in scoring target lesions; when one-point margin of error was permissible, the agreement rose to 91.3% (294/322) and 97.3% (149/153), respectively. In the paired test, PI-RADSAI outperformed 5/11 (45.5%) and matched the performance of 3/11 (27.3%) general radiologists in achieving a clinically significant PCa diagnosis (area under the curve, internal test, 0.801 vs. 0.770, p < 0.01; external test, 0.833 vs. 0.867, p = 0.309). Conclusions: Our closed-loop PI-RADSAI outperforms or matches the performance of more than 70% of general readers in the MRI assessment of PCa. This system might provide an alternative to radiologists and offer diagnostic benefits to clinical practice, especially where subspecialist expertise is unavailable. [ABSTRACT FROM AUTHOR]

Background: Dual‐phenotype hepatocellular carcinoma (DPHCC) is highly aggressive and difficult to distinguish from hepatocellular carcinoma (HCC). Purpose: To develop and validate clinical and radiomics models based on contrast‐enhanced MRI for the preoperative diagnosis of DPHCC. Study type: Retrospective. Population: A total of 87 patients with DPHCC and 92 patients with non‐DPHCC randomly divided into a training cohort (n = 125: 64 non‐DPHCC; 61 DPHCC) and a validation cohort (n = 54: 28 non‐DPHCC; 26 DPHCC). Field Strength/Sequence: A 3.0 T; dynamic contrast‐enhanced MRI with time‐resolved T1‐weighted imaging sequence. Assessment: In the clinical model, the maximum tumor diameter and hepatitis B virus (HBV) were independent risk factors of DPHCC. In the radiomics model, a total of 1781 radiomics features were extracted from tumor volumes of interest (VOIs) in the arterial phase (AP) and portal venous phase (PP) images. For feature reduction and selection, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were used. Clinical, AP, PP, and combined radiomics models were established using machine learning algorithms (support vector machine [SVM], logistic regression [LR], and logistic regression‐least absolute shrinkage and selection operator [LR‐LASSO]) and their discriminatory efficacy assessed and compared. Statistical Tests: The independent sample t test, Mann–Whitney U test, Chi‐square test, regression analysis, receiver operating characteristic curve (ROC) analysis, Pearson correlation analysis, the Delong test. A P value < 0.05 was considered statistically significant. Results: In the validation cohort, the combined radiomics model (area under the curve [AUC] = 0.908, 95% confidence interval [CI]: 0.831–0.985) showed the highest diagnostic performance. The AUCs of the PP (AUC = 0.879, 95% CI: 0.779–0.979) and combined radiomics models were significantly higher than that of clinical model (AUC = 0.685, 95% CI: 0.526–0.844). There were no significant differences in AUC between AP or PP radiomics model and combined radiomics model (P = 0.286, 0.180 and 0.543). Conclusion: MRI radiomics models may be useful for discriminating DPHCC from non‐DPHCC before surgery. Evidence Level: 4 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Byline: Chun-Hong. Hu, Su. Hu, Xi. Gao, Chun-Ming. Sun, Wen-Juan. Gan, Yun-Lian. Liu, Fang. Wen, Qi-Chun. Dai, Ping. Li Objective: To investigate the feasibility of tissue similarity map (TSM)-based relative [...]