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With the widespread dissemination of picture archiving and communication systems (PACSs) in hospitals, the amount of imaging data is rapidly increasing. Effective image retrieval systems are required to manage these complex and large image databases. The authors reviewed the past development and the present state of medical image retrieval systems including text-based and content-based systems. In order to provide a more effective image retrieval service, the intelligent content-based retrieval systems combined with semantic systems are required.

We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner. Recent pre-training methods in NLP focus on learning either bottom or top-level language representations: contextualized word representations derived from language model objectives at one extreme and a whole sequence representation learned by order classification of two given textual segments at the other.However, these models are not directly encouraged to capture representations of intermediate-size structures that exist in natural languages such as sentences and the relationships among them. To that end, we propose a new approach to encourage learning of a contextualized sentence-level representation by shuffling the sequence of input sentences and training a hierarchical transformer model to reconstruct the original ordering. Through experiments on downstream tasks such as GLUE, SQuAD, and DiscoEval, we show that this feature of our model improves the performance of the original BERT by large margins.

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Background: Although sarcopenia has been reported to predict survival in cancer patients, its impact on patients who received immune checkpoint inhibitors (ICIs) has not been thoroughly investigated. This systematic review aimed to assess the long-term oncologic impact of sarcopenia on patients who received ICIs. Methods: A systematic review of studies indexed in the PubMed, Embase, and Cochrane databases, up to April 1, 2021, was conducted. Studies that reported hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS) based on sarcopenia in patients treated with ICIs were included. The inverse variance method was used with a random-effects model for data analysis. Results: A total of 1284 patients from 14 studies were included. Among the patients who received ICIs, patients with sarcopenia had a significant increase in overall mortality compared to patients without sarcopenia in univariate analyses (HR = 1.66, 95% CI = 1.20–2.29, p = 0.002) and in adjusted HRs (HR = 1.55, 95% CI = 1.15–2.10, p = 0.004). The same results were obtained for PFS by both univariate analysis (HR = 1.75, 95% CI = 1.37–2.23, p < 0.001) and adjusted HRs (HR = 1.63, 95% CI 1.28–2.09, p < 0.001). Conclusions: Sarcopenia appears to be an effective biomarker for predicting long-term oncologic outcomes in patients receiving ICI therapy and hence plays an important role when making treatment decisions. However, the fundamental role of this association with survival should be further investigated in large cohorts and clinical trials.