Introduction: At present there is no predictive value univocally associated with the success of chemotherapy. Biomarkers produced by ovarian cancer (HE4 and Ca125) could have a good prognostic significance. The aim of this study is to prove the ability of biomarkers to identify patients with the highest risk of non-optimal response during the chemotherapy, and to predict which patients will most likely develop recurrence of disease. Methods: We analyzed 78 patients with epithelial ovarian cancers who underwent surgery in the biennium 2016–2017. All the patients underwent chemotherapy after surgery or interval debulking surgery following neoadjuvant therapy. Serum levels of HE4 and Ca125 were measured at diagnosis and at each cycle of chemotherapy. We established the degree of response to the treatment by computed tomography scan, and the patients were followed up (median: 10 months). The parameters of progression-free survival and disease-free survival were related to serum levels of biomarkers. Results: Both CA125 and HE4 values became negative at the fourth cycle in the patients with good response to chemotherapy. HE4 increased earlier than Ca125. The parameters that best correlated with a long progression-free survival were: negativization of the marker after the third cycle of chemotherapy (HE4: odds ratio (OR) 5.5; Ca125: OR 9.1) and biomarker serum levels lower than the mean value in the affected population at the time of diagnosis (HE4: OR 3.4; Ca125: OR 3.7). Conclusions: We can conclude that the monitoring of HE4 and Ca125 during chemotherapy, especially at the third cycle, is recommended, because their variation is a good prognostic factor.

No abstract

Background. Positron emission tomography (PET) has proven clinical utility both in the initial and relapse staging phase, but this technique is controversial during pregnancy. The objective of this review is to provide a compendium of available information on the use of PET during pregnancy. Materials and methods. A systematic literature review was conducted from 1 January 2004 until 20 May 2021. A total of 4 small series and 9 case reports consisting of 25 cases were selected. Results. During the first trimester, the fetus is most sensitive to ionization damage, so lower doses are recommended (2.6E-02 mGy/MBq). Fetal-effective doses are higher in this period and the average fetal dose (4.06 ± 3.22 mGy) remains significantly below the threshold for deterministic effects. During the second and third trimesters, recommended doses are higher (1.4E-02 mGy/MBq at 6 months, and 6.9E-03 mGy/MBq at 9 months of gestation). 18F-FDG activity was distributed to the whole fetus with a prevalence of myocardial tissue in seven cases. The use of special precautions, such as PET-magnetic resonance (MR) and urinary bladder catheterization, reduces the amount of radioactive tracer. Breastfeeding interruption is not recommended. Conclusions. 18F-FDG PET is not contraindicated in pregnancy, but multidisciplinary discussion is necessary and strict precautions are recommended.

Introduction In patients affected by epithelial ovarian cancer (EOC) complete cytoreduction (CC) has been associated with higher survival outcomes. Artificial intelligence (AI) systems have proved clinical benefice in different areas of healthcare. Objective To systematically assemble and analyze the available literature on the use of AI in patients affected by EOC to evaluate its applicability to predict CC compared to traditional statistics. Material and Methods Data search was carried out through PubMed, Scopus, Ovid MEDLINE, Cochrane Library, EMBASE, international congresses and clinical trials. The main search terms were: Artificial Intelligence AND surgery/cytoreduction AND ovarian cancer. Two authors independently performed the search by October 2022 and evaluated the eligibility criteria. Studies were included when data about Artificial Intelligence and methodological data were detailed. Results A total of 1899 cases were analyzed. Survival data were reported in 2 articles: 92% of 5-years overall survival (OS) and 73% of 2-years OS. The median area under the curve (AUC) resulted 0,62. The model accuracy for surgical resection reported in two articles reported was 77,7% and 65,8% respectively while the median AUC was 0,81. On average 8 variables were inserted in the algorithms. The most used parameters were age and Ca125. Discussion AI revealed greater accuracy compared against the logistic regression models data. Survival predictive accuracy and AUC were lower for advanced ovarian cancers. One study analyzed the importance of factors predicting CC in recurrent epithelial ovarian cancer and disease free interval, retroperitoneal recurrence, residual disease at primary surgery and stage represented the main influencing factors. Surgical Complexity Scores resulted to be more useful in the algorithms than pre-operating imaging. Conclusion AI showed better prognostic accuracy if compared to conventional algorithms. However further studies are needed to compare the impact of different AI methods and variables and to provide survival informations.