Headache, Loss of Smell, and Visual Disturbances: Symptoms of SARS-CoV-2 Infection? A Case Report.

OBJECTIVE: The purpose of this case report is to describe the main components of the history and physical examination that led to idiopathic intracranial hypertension differential diagnosis, which initially was investigated as COVID-19. METHODS (CASE DESCRIPTION): A 28-year-old woman complaining of constant headache and loss of smell and taste was suspected as SARS-CoV-2 infection by her general practitioner. She underwent 3 molecular swab tests, all negative, then decided to seek her physical therapist for relieving headache. RESULTS: The full cranial nerve examination revealed impaired olfactory (CNI), abducens (CN VI), and facial (CN VII) nerves, leading the physical therapist to refer the patient to a neurosurgeon for a suspected central nervous system involvement. The neurosurgeon prescribed a detailed MRI and an ophthalmologic examination, which allowed for the final diagnosis of idiopathic intracranial hypertension. CONCLUSION: An urgent lumbo-peritoneal shunting surgery resolved the patient's symptoms and saved her sight. Despite the ongoing COVID-19 pandemic, health care professionals must pay attention to properly investigating patients' signs and symptoms using comprehensive clinical reasoning, considering the screening for referral to specialist medical attention. IMPACT: A thorough physical examination is required for every patient even if patients' signs and symptoms are in line with apparent common and widespread pathologies. Cranial nerve evaluation is an essential component of the physical therapist assessment and decision-making process. The ongoing pandemic highlighted the fundamental assistance of physical therapists toward physicians in the screening and management of musculoskeletal diseases.

A Thoracic Outlet Syndrome That Concealed a Glioblastoma. Findings from a Case Report.

Background: Glioblastoma is the most frequent and aggressive malignant brain tumor among adults. Unfortunately, its symptoms can vary considerably depending on the size, location and the anatomic structures of the involved brain. Case report: A 58-year-old male amateur cyclist who suffered from sharp arm pain was examined for a thoracic outlet syndrome due to a previous clavicle fracture. Because of ambiguous results of the neck and nerve plexus imaging, he was referred to a neurosurgeon who properly suspected a brain tumor. The neuroimaging of the brain shown a 3 cm disploriferative mass with a blood enhancement within the left parietal lobe. The mass was urgently removed, and its histologic analysis stated a grade 4 glioblastoma. Conclusion: This case report highlights the differential diagnosis process and the teamwork approach needed to diagnose a rare presentation of a brain glioblastoma, which started its symptoms mimicking a thoracic outlet syndrome caused by a previous bone fracture.

The process of EUSOMA certification improves the quality of breast cancer care: the experience of an Italian EUSOMA certificated Breast Unit (BU) according to the results of a cross-sectional study.

INTRODUCTION: The adherence to recognized guidelines and the constant monitoring of performance throughout quality indicators (QIs) are strategic tools to improve the quality of care. The study is aimed to assess the effect of the EUSOMA (European Society of Breast Cancer Specialists) certification process on the quality of breast cancer care of an EUSOMA certified Breast Unit (BU) of Northern Italy. MATERIALS AND METHODS: Seventeen mandatory and recommended EUSOMA QIs, based on 594, were analysed for the years 2015-2018. Univariate logistic regression models were performed to compare QIs performance in the years before and after obtaining the EUSOMA certification (2015-6 vs. 2017-8). RESULTS: Compared to the years 2015-6, the second period of BU activity showed a higher number of QIs achieving both the minimum standard (15 vs. 11) and the 100% of completeness (6 vs. 1). There was a significant improvement of the two QIs evaluating the proportion of Ductal Carcinoma in situ receiving just an operation (from 76% to 95.2%; p=0.033) and the completeness of the prognostic characterisation of invasive cancers (from 94.6% to 99.5%; p=0.022). Conversely, the QI related to the endocrine-sensitive invasive carcinoma receiving adjuvant hormonal therapy dropped from 92.1% to 85.9% (p=0.042) and was significantly lower for patients over 74 compared to those aged ≤54 (73.8% vs. 94.7%; p<0.0001 Fisher's exact test). CONCLUSIONS: The EUSOMA certification process enhanced the clinical practice, promoting a tailored-patient primary systemic or adjuvant therapy and avoiding unnecessary invasive surgical and local-regional treatments.

Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis.

The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computer-aided diagnosis (CAD) vs human judgment alone in characterizing solitary pulmonary nodules (SPNs) at computed tomography (CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator (BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic (ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions (P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs (15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses (mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization.

Solid pulmonary nodule risk assessment and decision analysis: comparison of four prediction models in 285 cases.

OBJECTIVES: The aim of this study was to compare classification results from four major risk prediction models in a wide population of incidentally detected solitary pulmonary nodules (SPNs) which were selected to crossmatch inclusion criteria for the selected models. METHODS: A total of 285 solitary pulmonary nodules with a definitive diagnosis were evaluated by means of four major risk assessment models developed from non-screening populations, namely the Mayo, Gurney, PKUPH and BIMC models. Accuracy was evaluated by receiver operating characteristic (ROC) area under the curve (AUC) analysis. Each model's fitness to provide reliable help in decision analysis was primarily assessed by adopting a surgical threshold of 65 % and an observation threshold of 5 % as suggested by ACCP guidelines. RESULTS: ROC AUC values, false positives, false negatives and indeterminate nodules were respectively 0.775, 3, 8, 227 (Mayo); 0.794, 41, 6, 125 (Gurney); 0.889, 42, 0, 144 (PKUPH); 0.898, 16, 0, 118 (BIMC). CONCLUSIONS: Resultant data suggests that the BIMC model may be of greater help than Mayo, Gurney and PKUPH models in preoperative SPN characterization when using ACCP risk thresholds because of overall better accuracy and smaller numbers of indeterminate nodules and false positive results. KEY POINTS: • The BIMC and PKUPH models offer better characterization than older prediction models • Both the PKUPH and BIMC models completely avoided false negative results • The Mayo model suffers from a large number of indeterminate results.