Background: Gallbladder cancer (GBC) is a rare entity with a poor prognosis, usually discovered late due to nonspecific symptoms; therefore, over the last years, attention has been focused on identifying the risk factors for developing this malignancy in order to provide an early diagnosis, as well as new prognostic factors in order to modulate the long-term evolution of such cases. The aim of this review is to discuss both major risk factors and prognostic factors in GBC for a better understanding and integration of relevant and currently available information. Methods: A literature search was performed using Cochrane Library, PubMed, Google Scholar, Elsevier, and Web of Science; studies published after the year of 2000, in English, were reviewed. Results: Over time, risk factors associated with the development of GBC have been identified, which outline the profile of patients with this disease. The most important prognostic factors in GBC remain TNM staging, safety margin, and R0 status, along with perineural invasion and lymphovascular invasion. Both the technique and experience of the surgeons and a pathological examination that ensures final staging are particularly important and increase the chances of survival of the patients. Conclusions: improvements in surgical techniques and pathological analyses might provide better and more consistent guidance for medical staff in the management of patients with GBC.

Ovarian cancer remains one of the most lethal gynaecological malignancies affecting women worldwide; therefore, attention has been focused on identifying new prognostic factors which might help the clinician to select cases who could benefit most from surgery versus cases in which neoadjuvant systemic therapy followed by interval debulking surgery should be performed. The aim of the current paper is to identify whether preoperative inflammation could serve as a prognostic factor for advanced-stage ovarian cancer. Material and methods: The data of 57 patients who underwent to surgery for advanced-stage ovarian cancer between 2014 and 2020 at the Cantacuzino Clinical Hospital were retrospectively reviewed. The receiver operating characteristic curve was used to determine the optimal cut-off value of different inflammatory markers for the overall survival analysis. The analysed parameters were the preoperative level of CA125, monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR) and systemic inflammation index (SII). Results: Baseline CA125 > 780 µ/mL, NLR ≥ 2.7, MLR > 0.25, PLR > 200 and a systemic immune inflammation index (SII, defined as platelet × neutrophil–lymphocyte ratio) ≥ 84,1000 were associated with significantly worse disease-free and overall survival in a univariate analysis. In a multivariate analysis, MLR and SII were significantly associated with higher values of overall survival (p < 0.0001 and p = 0.0124); meanwhile, preoperative values of CA125, PLR and MLR were not associated with the overall survival values (p = 0.5612, p = 0.6137 and p = 0.1982, respectively). In conclusion, patients presenting higher levels of MLR and SII preoperatively are expected to have a poorer outcome even if complete debulking surgery is performed and should be instead considered candidates for neoadjuvant systemic therapy followed by interval surgery.

Background: Despite improving surgical techniques and achieving more often complete debulking procedures, certain patients with advanced-stage ovarian cancer still have a very poor prognosis. The aim of the current paper is to investigate whether inflammatory and nutritional status can predict the long-term outcomes of ovarian cancer patients. Methods: A retrospective analysis of 57 cases diagnosed with advanced-stage ovarian cancer submitted to surgery as first intent therapy was carried out. In all cases, the preoperative status was determined by calculating the CRP/albumin ratio, as well as the Glasgow score, the modified Glasgow score and the prognostic nutritional index. Results: Patients presenting higher values of the CRP/albumin ratio, with a higher Glasgow score, modified Glasgow score and prognostic nutritional index (PNI), were more frequently associated with incomplete debulking surgery, a higher peritoneal carcinomatosis index and poorer overall survival (20 months versus 9 months for the CRP/albumin ratio p = 0.011, 42 versus 27 versus 12 months for the Glasgow score p = 0.042, 50 versus 19 versus 12 months for the modified Glasgow score, p = 0.001, and 54 months versus 21 months, p = 0.011 for the prognostic nutritional index). Conclusions: A strong relationship between the nutritional and inflammatory status in advanced-stage ovarian cancer seems to exist.

Liver transplantation is the last life-saving solution for patients with end stage liver disease. The discrepancy between waiting list and available organs has led to the appearance of extended donation criteria and the development of several scores (Child-Pugh score, MELD score, DRI score, SOFT score), in order to find the most suitable donor-recipient match. But none of these scores can predict survival after transplantation. Artificial Intelligence (AI) has recently been shown as an excellent tool for the study of the liver and comes in this aid with its various methods (random forest, artificial neural networks, decision tree, Bayesian networks, and support vector machine). Materials and Methods. By reviewing the literature (mostly retrospective multicenter studies), we aimed to establish if the AI is a proper or even a more accurate method of predicting posttransplant survival, in comparison with the existing linear statistical models. Results. Machine learning showed better results than several current scoring systems that use either isolated donor/recipient scores or combined donor/recipient factors. The advantages of this model are its capacity for analyzing both linear and nonlinear relationships between features and outcomes, its robustness of overfitting by design, and built-in insights into feature importance aiding model explainability. Nevertheless, machine learning has its limitations because it requires large amounts of data, which can be difficult to obtain, it also requires high levels of technical skill, can be difficult to engineer and it’s expensive. Conclusion. AI may have significant potential in aiding clinical decision-making during liver transplantation, including donor-recipient matching.