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Summary: The practice of treating candidates for liver transplantation (LT) for hepatocellular carcinoma (HCC), with locoregional therapies, is common in most transplant centers. However, for T1 tumors and expected waiting times to LT <6months, there is no evidence that these treatments are beneficial. For T2 tumors and for longer waiting times, neo-adjuvant treatments are usually performed with transarterial chemoembolization (TACE), ablation techniques and liver resection in selected cases. The treatment choice should be based on the BCLC staging system. At present, there is no evidence of the superiority of ablation/resection vs. TACE, but some studies showed better results of the former in achieving a complete response. The response to neo-adjuvant treatments should be evaluated through mRECIST criteria, but few studies adopted these criteria and properly analyzed factors affecting response. The simultaneous evaluation of the impact of neo-adjuvant therapies on dropout rate, post-LT HCC recurrence and patient survival is rarely reported. Tumor stage and volume, alpha-fetoprotein levels, response to treatments and liver function affect pre-LT outcomes. These same factors, together with vascular invasion and poor tumor differentiation, are major determinants of poor post-LT outcomes. Due to the low number of prospective studies with well-defined entry criteria and the variability of results, the role of downstaging is still to be defined. Novel molecular markers seem promising for the estimation of prognosis and/or response to treatments. With a persistent scarcity of organ donors, neo-adjuvant treatments can help identify patients with different probabilities of cancer progression, and consequently balance the priority of HCC and non-HCC-candidates through revised additional scores for HCC. [ABSTRACT FROM AUTHOR]

Background & Aims: Knowledge of pre-operative tumour grade is crucial in the management of hepatocellular carcinoma (HCC) because it can influence recurrence and survival after surgery. The accuracy of pre-operative needle core biopsy (NCB) in tumour grading has been assessed in only a few studies with conflicting results. Our aim was to determine the long-term safety and the overall accuracy of NCB in assessing tumour grading in subjects who had undergone liver resection for a single HCC. Methods: Eighty-one cirrhotic patients with HCC who had undergone NCB before liver resection were selected. Only patients with a single HCC and with at least a five-year-follow-up were included. Tumour grading was scored according to a modified Edmondson–Steiner classification: well/moderately (low grade) vs poorly-differentiated (high grade). Results: In the 81 patients with a solitary HCC (mean size 4.1±2.3cm) tumour grade was correctly identified by NCB in 74 out of 81 (91.4%) HCCs. NCB overall sensitivity and specificity were 65% and 98.1%, respectively, with a PPV of 92% and an NPV of 91%. No major complications (in particular tumour seeding) were observed. The overall survival rates at 1, 3, and 5years were 83%, 62%, and 44%, respectively; the recurrence rate after a 5-year-follow-up was 56.2% for low grade and 82.3% for high grade tumours (p <0.007). Conclusions: Pre-operative NCB can be performed on early (<5cm) HCC cirrhotic patients because it provides histologically useful information for HCC management with good accuracy and a low complication rate. [ABSTRACT FROM AUTHOR]

Background & Aims: Hepatocellular carcinoma (HCC) prognosis strongly depends upon nuclear grade and the presence of microscopic vascular invasion (MVI). The aim of this study was to develop an artificial neural network (ANN) that is able to predict tumour grade and MVI on the basis of non-invasive variables. Methods: Clinical, radiological, and histological data from 250 cirrhotic patients resected (n =200) or transplanted (n =50) for HCC were analyzed. ANN and logistic regression models were built on a training group of 175 randomly chosen patients and tested on the remaining testing group of 75. Receiver operating characteristics curve (ROC) and k-statistics were used to analyze model accuracy in the prediction of the final histological assessment of tumour grade (G1–G2 vs. G3–G4) and MVI (absent vs. present). Results: Pathologic examination showed G3–G4 in 69.6% of cases and MVI in 74.4%. Preoperative serum alpha-fetoprotein (AFP), tumour number, size, and volume were related to tumour grade and MVI (p <0.05) and were used for ANN building, whereas, tumour number did not enter into the logistic models. In the training group, ANN area under ROC curves (AUC) for tumour grade and MVI prediction were 0.94 and 0.92, both higher (p<0.001) than those of logistic models (0.85 for both). In the testing group, ANN correctly identified 93.3% of tumour grades (k =0.81) and 91% of MVI (k =0.73). Logistic models correctly identified 81% of tumour grades (k =0.55) and 85% of MVI (k =0.57). Conclusion: ANN identifies HCC tumour grades and MVI on the basis of preoperative variables more accurately than the conventional linear model and should be used for tailoring clinical management. [Copyright &y& Elsevier]