Comparison Between Contrast-Enhanced Computed Tomography and Contrast-Enhanced Magnetic Resonance Imaging With Magnetic Resonance Cholangiopancreatography for Resectability Assessment in Extrahepatic Cholangiocarcinoma

Contrast-Enhanced Ultrasound in the Diagnosis of Solid Renal Lesions

The availability of imaging methods has enabled increased detection of kidney lesions, which are a common clinical problem. It is estimated that more than half of patients over the age of 50 have at least one undetermined mass in the kidney. The appropriate characterization and diagnosis of lesions imaged in the kidney allows for proper therapeutic management. Previously, contrast-enhanced computed tomography (CT) and contrast-enhanced magnetic resonance imaging (MRI) have been used in their extended diagnosis. However, the limitations of these techniques, such as radiation exposure, renal toxicity, and allergies to contrast agents, must be considered. Contrast-enhanced ultrasound (CEUS) is increasingly being used as an examination to resolve interpretive doubts that arise with other diagnostic methods. Indeed, it can be considered both as a problem-solving technique for diagnosing and distinguishing lesions and as a technique used for observation in preservative treatment. Evaluation of the enhancement curve over time on CEUS examination can help to differentiate malignant renal cell carcinoma (RCC) subtypes that should be resected from benign lesions, such as oncocytoma or angiomyolipoma (AML), in which surgery can be avoided. It allows for distinguishing between benign and malignant tumors, renal and pseudotumors, and solid and cystic tumors. Therefore, with recent advances in ultrasound technology, CEUS has emerged as a fast, reliable, and cost-effective imaging tool in the preoperative evaluation and diagnosis of solid renal masses.

Minor hepatectomy combined with cholangioplasty and cholangiojejunostomy for Bismuth II hilar cholangiocarcinoma: A propensity score matching analysis

BACKGROUND

The optimal surgical approach for Bismuth II hilar cholangiocarcinoma (HCCA) remains controversial. This study compared perioperative and oncological outcomes between minor and major hepatectomy.

MATERIALS AND METHODS

One hundred and seventeen patients with Bismuth II HCCA who underwent hepatectomy and cholangiojejunostomy between January 2018 and December 2022 were retrospectively investigated. Propensity score matching created a cohort of 62 patients who underwent minor (n = 31) or major (n = 31) hepatectomy. Perioperative outcomes, complications, quality of life, and survival outcomes were compared between the groups. Continuous data are expressed as the mean ± standard deviation, categorical variables are presented as n (%).

RESULTS

Minor hepatectomy had a significantly shorter operation time (245.42 ± 54.31 vs. 282.16 ± 66.65 min; P = 0.023), less intraoperative blood loss (194.19 ± 149.17 vs. 315.81 ± 256.80 mL; P = 0.022), a lower transfusion rate (4 vs. 11 patients; P = 0.038), more rapid bowel recovery (17.77 ± 10.00 vs. 24.94 ± 9.82 h; P = 0.005), and a lower incidence of liver failure (1 vs. 6 patients; P = 0.045). There were no significant between-group differences in wound infection, bile leak, bleeding, pulmonary infection, intra-abdominal fluid collection, and complication rates. Postoperative laboratory values, length of hospital stay, quality of life scores, 3-year overall survival (25.8 % vs. 22.6 %; P = 0.648), and 3-year disease-free survival (12.9 % vs. 16.1 %; P = 0.989) were comparable between the groups.

CONCLUSION

In this propensity score-matched analysis, overall survival and disease-free survival were comparable between minor and major hepatectomy in selected patients with Bismuth II HCCA. Minor hepatectomy was associated with a shorter operation time, less intraoperative blood loss, less need for transfusion, more rapid bowel recovery, and a lower incidence of liver failure. Besides, this findings need confirmation in a large-scale, multicenter, prospective randomized controlled trial with longer-term follow-up.

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

PURPOSE

We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases.

METHODS

Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon-Mann-Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered.

RESULTS

The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%.

CONCLUSIONS

Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.