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Purpose: To investigate the potential of intravoxel incoherent motion diffusion-weighted imaging (IVIM) for preoperative prediction of lymphovascular invasion (LVI) in gastric cancer (GC)., Methods: This study prospectively enrolled 90 patients (62 males, 28 females, 60.79 ± 9.99 years old) who received radical gastrostomy. Abdominal MRI examinations including IVIM were performed within 1 week before surgery. Patients were divided into LVI-positive and -negative group according to pathological diagnosis after surgery. The apparent diffusion coefficient (ADC) and IVIM parameters, including true diffusion coefficient (D), pseudodiffusion coefficient (D*), and pseudodiffusion fraction (f), were compared between the two groups. The relationship between MRI parameters and LVI was studied by Spearman's correlation analysis. Multivariable logistic regression analysis was used to screen independent predictors of LVI. Receiver-operating characteristic curve analyses were applied to evaluate the efficacy., Results: The ADC, D in LVI-positive group were lower, whereas tumor thickness and f parameter in LVI-positive group were higher than those in LVI-negative group, and they were statistically correlated with LVI (p < 0.05). D, f and tumor thickness were independent risk factors of LVI. The area under the curve of ADC, D, f, thickness, and the combined parameter (D + f + thickness) were 0.667, 0.754, 0.695, 0.792, and 0.876, respectively. The combined parameter demonstrated higher efficacy than any other parameters (p < 0.05)., Conclusion: The ADC, D, and f can effectively distinguish LVI status of GC. The D, f and thickness were independent predictors. The combination of the three predictors further improved the efficacy., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: To investigate the efficacy of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the early prediction of the pathologic response to neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC)., Methods: Fifty patients with LAGC who were treated with NAC followed by radical gastrectomy were enrolled. Uncontrasted and DCE-MRI were performed within 1 week before NAC. According to tumor regression grading (TRG), patients were labeled as responders (TRG = 0 + 1) and non-responders (TRG = 2 + 3). Apparent diffusion coefficients (ADC) and DCE-MRI kinetics (K trans , V e , and K ep ) were compared between the two groups. Logistic regression analysis was performed to screen independent factors to predict the NAC efficacy. The relationship between MRI parameters and TRG was studied by Spearman's correlation analysis. Receiver-operating characteristic curve analyses were applied to evaluate the efficacy., Results: ADC, K trans , and K ep values were higher in responders than in non-responders (p < 0.05) and correlated with TRG (p < 0.05). The ADC and K ep values were independent markers for predicting TRG. The area under the curve, sensitivities, specificities of ADC, K trans , K ep , and ADC + K ep were 0.813, 0.699, 0.709, 0.886;73.64%, 65.54%, 63.21%, 70.37%; 86.47%, 54.97%, 79.47%, 95.65%; respectively. ADC + K ep demonstrated a higher efficacy than K trans and K ep (p = 0.012, 0.011), but without improvement compared with ADC (p > 0.05)., Conclusion: Both DWI and DCE-MRI can effectively predict the pathologic response to NAC in LAGC. A combination of ADC and K ep increased the efficacy, and ADC is the most valuable imaging parameter., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background and Objective: To develop a machine-learning model by integrating clinical and imaging modalities for predicting tumor response and survival of hepatocellular carcinoma (HCC) with transarterial chemoembolization (TACE)., Methods: 140 HCC patients with TACE were retrospectively included from two centers. Tumor response were evaluated using modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria. Response-related radiomics scores (Rad-scores) were constructed on T2-weighted images (T 2 WI) and dynamic contrast-enhanced (DCE) imaging separately, and then integrated with conventional clinic-radiological variables into a logistic regression (LR) model for predicting tumor response. LR model was trained in 94 patients in center 1 and independently tested in 46 patients in center 2., Results: Among 4 MRI sequences, T 2 WI achieved better performance than DCE (area under the curve [AUC] 0.754 vs 0.602 to 0.752). LR model by combining Rad-score on T 2 WI with Barcelona Clinic Liver Cancer (BCLC) stage and albumin-bilirubin (ALBI) grade resulted in an AUC of 0.813 in training and 0.781 in test for predicting tumor response. In survival analysis, progression-free survival (PFS) and overall survival (OS) presented significant difference between LR-predicted responders and non-responders. The ALBI grade and BCLC stage were independent predictors of PFS; and LR-predicted response, ALBI grade, satellite node, and BCLC stage were independent predictors of OS. The resulting Cox model produced concordance-indexes of 0.705 and 0.736 for predicting PFS and OS, respectively., Conclusions: The model combined MRI radiomics with clinical factors demonstrated favorable performance for predicting tumor response and clinical outcomes, thus may help personalized clinical management., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)