Fertility preservation is an important issue for patients in reproductive age with early stage cervical cancer. In view of recent developments, our purpose was to review and discuss available surgical alternatives. A literature search was conducted using PUBMED, including papers between 1980 and December 2011. In patients with stage IA1 cervical cancer, conization is a valid alternative. Patients with stage IA2-IB1 disease can be conservatively treated by radical trachelectomy. This is as well-established conservative approach and appears to be safe and effective in allowing a high chance of conception. Prematurity is the most serious issue in pregnancies following trachelectomy. Less invasive options such as simple trachelectomy or conization seem to be feasible for stages IA2-IB1, but more and better evidence is needed. Neoadjuvant therapy might allow conservative surgery to be performed also in patients with more extensive lesions. Ovarian transposition is important when adjuvant radiation is needed. In conclusion, available literature shows that there are interesting fertility-sparing treatment alternatives to the “golden standard” for the management of early cervical cancer in young women.

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Attention-based methods for deep neural networks constitute a technique that has attracted increased interest in recent years. Attention mechanisms can focus on important parts of a sequence and, as a result, enhance the performance of neural networks in a variety of tasks, including sentiment analysis, emotion recognition, machine translation and speech recognition. In this work, we study attention-based models built on recurrent neural networks (RNNs) and examine their performance in various contexts of sentiment analysis. Self-attention, global-attention and hierarchical-attention methods are examined under various deep neural models, training methods and hyperparameters. Even though attention mechanisms are a powerful recent concept in the field of deep learning, their exact effectiveness in sentiment analysis is yet to be thoroughly assessed. A comparative analysis is performed in a text sentiment classification task where baseline models are compared with and without the use of attention for every experiment. The experimental study additionally examines the proposed models’ ability in recognizing opinions and emotions in movie reviews. The results indicate that attention-based models lead to great improvements in the performance of deep neural models showcasing up to a 3.5% improvement in their accuracy.

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