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The article presents a case study of a 52-year-old woman who has a lesion of a rib. It mentions that the patient was diagnosed with mucinous carcinoma of the right breast. She underwent a modified radical mastectomy with axillary dissection and simultaneous reconstruction with an expander in the submuscular plane.

Background: Radiology reports are typically written in a free-text format, making clinical information difficult to extract and use. Recently, the adoption of structured reporting (SR) has been recommended by various medical societies thanks to the advantages it offers, e.g. standardization, completeness, and information retrieval. We propose a pipeline to extract information from Italian free-text radiology reports that fits with the items of the reference SR registry proposed by a national society of interventional and medical radiology, focusing on CT staging of patients with lymphoma., Methods: Our work aims to leverage the potential of Natural Language Processing and Transformer-based models to deal with automatic SR registry filling. With the availability of 174 Italian radiology reports, we investigate a rule-free generative Question Answering approach based on the Italian-specific version of T5: IT5. To address information content discrepancies, we focus on the six most frequently filled items in the annotations made on the reports: three categorical (multichoice), one free-text (free-text), and two continuous numerical (factual). In the preprocessing phase, we encode also information that is not supposed to be entered. Two strategies (batch-truncation and ex-post combination) are implemented to comply with the IT5 context length limitations. Performance is evaluated in terms of strict accuracy, f1, and format accuracy, and compared with the widely used GPT-3.5 Large Language Model. Unlike multichoice and factual, free-text answers do not have 1-to-1 correspondence with their reference annotations. For this reason, we collect human-expert feedback on the similarity between medical annotations and generated free-text answers, using a 5-point Likert scale questionnaire (evaluating the criteria of correctness and completeness)., Results: The combination of fine-tuning and batch splitting allows IT5 ex-post combination to achieve notable results in terms of information extraction of different types of structured data, performing on par with GPT-3.5. Human-based assessment scores of free-text answers show a high correlation with the AI performance metrics f1 (Spearman's correlation coefficients>0.5, p-values<0.001) for both IT5 ex-post combination and GPT-3.5. The latter is better at generating plausible human-like statements, even if it systematically provides answers even when they are not supposed to be given., Conclusions: In our experimental setting, a fine-tuned Transformer-based model with a modest number of parameters (i.e., IT5, 220 M) performs well as a clinical information extraction system for automatic SR registry filling task. It can extract information from more than one place in the report, elaborating it in a manner that complies with the response specifications provided by the SR registry (for multichoice and factual items), or that closely approximates the work of a human-expert (free-text items); with the ability to discern when an answer is supposed to be given or not to a user query., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether artificial intelligence working with chest X-ray (CXR) scans and clinical data can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. Indeed, further to induce lower radiation dose than computed tomography (CT), CXR is a simpler and faster radiological technique, being also more widespread. In this respect, we present three approaches that use features extracted from CXR images, either handcrafted or automatically learnt by convolutional neuronal networks, which are then integrated with the clinical data. As a further contribution, this work introduces a repository that collects data from 820 patients enrolled in six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, suggesting that clinical data and images have the potential to provide useful information for the management of patients and hospital resources., Competing Interests: Declaration of Competing Interest Authors declare that they have no conflict of interest., (Copyright © 2021. Published by Elsevier B.V.)

Compartmental tongue surgery (CTS) is a surgical technique that removes the compartments (anatomo-functional units) containing the primary tumor, eliminating the disease and potential muscular, vascular, glandular and lymphatic pathways of spread and recurrence. Compartment boundaries are defined as each hemi-tongue bounded by the lingual septum, the stylohyoid ligament and muscle, and the mylohyoid muscle. In this non-randomized retrospective study we evaluated the oncologic efficacy of CTS in patients with squamous cell carcinoma (SCCA) of the tongue treated from 1995 to 2008. We evaluated 193 patients with primary, previously untreated cT2-4a, cN0, cN+, M0 SCCA with no contraindication to anesthesia and able to give informed consent. Fifty patients treated between October 1995 and July 1999 received standard surgery (resection margin >1cm); 143 patients treated between July 1999 and January 2008 received CTS. Study endpoints were: 5-year local disease-free, locoregional disease-free and overall survival. After 5years, local disease control was achieved in 88.4% of CTS patients (16.8% improvement on standard surgery); locoregional disease control in 83.5% (24.4% improvement) and overall survival was 70.7% (27.3% improvement). The markedly improved outcomes in CTS patients, compared to those treated by standard surgery, suggest CTS as an important new approach in the surgical management of tongue cancer., (Copyright © 2010 Elsevier Ltd. All rights reserved.)