Browsing by Subject "Future tumor burden"

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    Narrative review: predicting future molecular and clinical profiles of prostate cancer in the United States
    (AME Publishing, 2021-03) Santoni, Matteo; Cimadamore, Alessia; Massari, Francesco; Sorgentoni, Giulia; Cheng, Liang; Lopez-Beltran, Antonio; Battelli, Nicola; Montironi, Rodolfo; Pathology and Laboratory Medicine, School of Medicine
    Prostate cancer represents the most frequent tumor in men, accounting for the 21% of all diagnosed tumors, with 191,930 new cases and 33,330 deaths estimated in 2020. Advanced prostate cancer represents a heterogeneous disease, ranging from hormone naive or hormone sensitive to castration resistant. The therapeutic armamentarium for this disease has been implemented in the last years by novel hormonal therapies and chemotherapies. However, the percentage of patients who achieve complete responses still results negligible. On this scenario, the design of clinical trials investigating new therapeutic approaches represent a dramatic medical need. Predicting cancer incidence may be fundamental to design specific clinical trials, to optimize the allocation of economic resources, and to plan future cancer control programs. ERG, SPOP and DDR genes alterations can act as therapeutic targets in prostate cancer patients and can be tested to identify a gene-selected patient population to enrol in specific trials. According to our predictions, ERG gene fusions will be the most predominant molecular subtype, accounting for 69,050 new cases in 2030. Mutation in SPOP gene will be diagnosed in 16,512 tumors, corresponding to the number of cases associated with alterations in DDR genes (including 7,956 BRCA2 mutated tumors). In this article, we analyzed and discussed the future molecular and clinical profiles of prostate cancer in the United States, aimed to describe a series of distinct subpopulations and to quantify potential clinical trial candidates in the next years.
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    Predicting future cancer burden in the United States by artificial neural networks
    (Taylor & Francis, 2021) Piva, Francesco; Tartari, Francesca; Giulietti, Matteo; Aiello, Marco Maria; Cheng, Liang; Lopez-Beltran, Antonio; Mazzucchelli, Roberta; Cimadamore, Alessia; Cerqueti, Roy; Battelli, Nicola; Montironi, Rodolfo; Santoni, Matteo; Pathology and Laboratory Medicine, School of Medicine
    Aims: To capture the complex relationships between risk factors and cancer incidences in the US and predict future cancer burden. Materials & methods: Two artificial neural network (ANN) algorithms were adopted: a multilayer feed-forward network (MLFFNN) and a nonlinear autoregressive network with eXogenous inputs (NARX). Data on the incidence of the four most common tumors (breast, colorectal, lung and prostate) from 1992 to 2016 (available from National Cancer Institute online datasets) were used for training and validation, and data until 2050 were predicted. Results: The rapid decreasing trend of prostate cancer incidence started in 2010 will continue until 2018-2019; it will then slow down and reach a plateau after 2050, with several differences among ethnicities. The incidence of breast cancer will reach a plateau in 2030, whereas colorectal cancer incidence will reach a minimum value of 35 per 100,000 in 2030. As for lung cancer, the incidence will decrease from 50 per 100,000 (2017) to 31 per 100,000 in 2030 and 26 per 100,000 in 2050. Conclusion: This up-to-date prediction of cancer burden in the US could be a crucial resource for planning and evaluation of cancer-control programs.