A 45-year-old-man presented a slightly painful proptosis and diplopia for 7 months. He had been kept elsewhere on oral steroids without evidence of any clinical response over an 8-week period with suspected diagnosis of an inflammatory pseudotumor upon referral to our clinic. An intraoperative biopsy positive for primary liposarcoma was followed by debulking surgery. Exenteration and radiotherapy were performed after pathologic confirmation of this diagnosis. No recurrence has been observed after 2 years of follow-up. We underline the importance of an accurate an early diagnosis in the management of this tumor, delayed in this case because of therapy with steroids.

<b><i>Objective:</i></b> The aim of this study was to investigate the association between rhegmatogenous retinal detachment (RRD) and solar radiation in northwestern Spain. <b><i>Methods:</i></b> All RRD cases in Pontevedra from 2008 and 2014 were retrospectively analyzed. Climatological data from 4 weather stations in the area were collected. The association between RRD incidence and solar radiation was investigated. <b><i>Results:</i></b> A total of 256 RRD cases were identified. There was a seasonal variation in the incidence of RRD with a maximum number of incident cases observed in June and July and a minimum number of cases observed in January and December. An association was found between RRD incidence and solar radiation both monthly (<i>p</i> = 0.004) and bimonthly (<i>p</i> = 0.057). The right eye was more frequently affected than the left eye (<i>p</i> = 0.035). RD cases other than rhegmatogenous showed neither seasonality nor association with radiation. <b><i>Conclusions:</i></b> Solar radiation may play a role in RRD genesis in our area. Laterality could be related to the amount of radiation reaching each eye.

A 84-year-old-woman presented a painless eyelid mass in her right eyelid. A biopsy was made and the anatomopathologic study showed a spiradenoma with malignant changes. The patient rejected any kind of treatment in spite of the prognosis of the lesion. Radiological and pathological features of this infrequent eyelid tumour are discussed.

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Background How to treat a disease remains to be the most common type of clinical question. Obtaining evidence-based answers from biomedical literature is difficult. Analogical reasoning with embeddings from deep learning (embedding analogies) may extract such biomedical facts, although the state-of-the-art focuses on pair-based proportional (pairwise) analogies such as man:woman::king:queen (“queen = −man +king +woman”). Objective This study aimed to systematically extract disease treatment statements with a Semantic Deep Learning (SemDeep) approach underpinned by prior knowledge and another type of 4-term analogy (other than pairwise). Methods As preliminaries, we investigated Continuous Bag-of-Words (CBOW) embedding analogies in a common-English corpus with five lines of text and observed a type of 4-term analogy (not pairwise) applying the 3CosAdd formula and relating the semantic fields person and death: “dagger = −Romeo +die +died” (search query: −Romeo +die +died). Our SemDeep approach worked with pre-existing items of knowledge (what is known) to make inferences sanctioned by a 4-term analogy (search query −x +z1 +z2) from CBOW and Skip-gram embeddings created with a PubMed systematic reviews subset (PMSB dataset). Stage1: Knowledge acquisition. Obtaining a set of terms, candidate y, from embeddings using vector arithmetic. Some n-gram pairs from the cosine and validated with evidence (prior knowledge) are the input for the 3cosAdd, seeking a type of 4-term analogy relating the semantic fields disease and treatment. Stage 2: Knowledge organization. Identification of candidates sanctioned by the analogy belonging to the semantic field treatment and mapping these candidates to unified medical language system Metathesaurus concepts with MetaMap. A concept pair is a brief disease treatment statement (biomedical fact). Stage 3: Knowledge validation. An evidence-based evaluation followed by human validation of biomedical facts potentially useful for clinicians. Results We obtained 5352 n-gram pairs from 446 search queries by applying the 3CosAdd. The microaveraging performance of MetaMap for candidate y belonging to the semantic field treatment was F-measure=80.00% (precision=77.00%, recall=83.25%). We developed an empirical heuristic with some predictive power for clinical winners, that is, search queries bringing candidate y with evidence of a therapeutic intent for target disease x. The search queries -asthma +inhaled_corticosteroids +inhaled_corticosteroid and -epilepsy +valproate +antiepileptic_drug were clinical winners, finding eight evidence-based beneficial treatments. Conclusions Extracting treatments with therapeutic intent by analogical reasoning from embeddings (423K n-grams from the PMSB dataset) is an ambitious goal. Our SemDeep approach is knowledge-based, underpinned by embedding analogies that exploit prior knowledge. Biomedical facts from embedding analogies (4-term type, not pairwise) are potentially useful for clinicians. The heuristic offers a practical way to discover beneficial treatments for well-known diseases. Learning from deep learning models does not require a massive amount of data. Embedding analogies are not limited to pairwise analogies; hence, analogical reasoning with embeddings is underexploited.

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