Small liver lesions in oncologic patients: characterization with CT, MRI and contrast-enhanced US

Focal liver lesions (FLLs) are frequently discovered during ultrasound examinations either in healthy subjects without a clinical history of cancer or during staging or follow-up procedures in oncologic patients or in routine surveillance of hepatopathic patients. In oncologic patients, the liver is the most common target of metastatic disease and accurate detection and characterisation of FLLs is prognostically fundamental during the initial staging as well as before and after pre-operative chemotherapy, as it can help to identify patients who are most likely to benefit from liver surgery. Moreover, early detection of primary or secondary liver malignancies increases the possibility of curative surgical resection or successful percutaneous ablation. As many FLLs in these patients are benign, a precise and preferably non-invasive method of differentiation from malignant metastatic nodules is needed. Moreover, the continuous follow-up of cancer patients requires an easily available, reliable and cost-effective diagnostic tool for the detection and characterization of FLLs

Use of contrast agents in oncological imaging: magnetic resonance imaging

Magnetic resonance plays a leading role in the management of oncology patients, providing superior contrast resolution and greater sensitivity compared with other techniques, which enables more accurate tumor identification, characterization and staging. Contrast agents are widely used in clinical magnetic resonance imaging; approximately 40–50% of clinical scans are contrast enhanced. Most contrast agents are based on the paramagnetic gadolinium ion Gd(3+), which is chelated to avoid the toxic effects of free gadolinium. Multiple factors such as molecule structure, molecule concentration, dose, field strength and temperature determine the longitudinal and transverse relaxation rates (R1 and R2, respectively) and thus the T1- and T2-relaxivities of these chelates. These T1- and T2-relaxivities, together with their pharmacokinetic properties (i.e. distribution and concentration in the area of interest), determine the radiologic efficacy of the gadolinium-based contrast agents

Contrast-enhanced ultrasonography of the pancreas

[No abstract available

Machine Learning Prediction of Radiofrequency Thermal Ablation Efficacy: A New Option to Optimize Thyroid Nodule Selection

Background: Radiofrequency (RF) is a therapeutic modality for reducing the volume of large benign thyroid nodules. If thermal therapies are interpreted as an alternative strategy to surgery, critical issues in their use are represented by the extent of nodule reduction and by the durability of nodule reduction over a long period of time. Objective: To assess the ability of machine learning to discriminate nodules with volume reduction rate (VRR) &lt; or ≥50% at 12 months following RF treatment. Methods: A machine learning model was trained with a dataset of 402 cytologically benign thyroid nodules subjected to RF at six Italian Institutions. The model was trained with the following variables: baseline nodule volume, echostructure, macrocalcalcifications, vascularity, and 12-month VRR. Results: After training, the model could distinguish between nodules having VRR &lt;50% from those having VRR ≥50% in 85% of cases (accuracy: 0.85; 95% confidence interval [CI]: 0.80–0.90; sensitivity: 0.70; 95% CI: 0.62–0.75; specificity: 0.99; 95% CI: 0.98–1.0; positive predictive value: 0.95; 95% CI: 0.92–0.98; negative predictive value: 0.95; 95% CI: 0.92–0.98). Conclusions: This study demonstrates that a machine learning model can reliably identify those nodules that will have VRR &lt; or ≥50% at 12 months after one RF treatment session. Predicting which nodules will be poor or good responders represents valuable data that may help physicians and patients decide on the best treatment option between thermal ablation and surgery or in predicting if more than one session might be necessary to obtain a significant volume reduction.</jats:p