Image-Guided Intraoperative Assessment of Surgical Margins in Oral Cavity Squamous Cell Cancer: A Diagnostic Test Accuracy Review.

(1) Background: The assessment of resection margins during surgery of oral cavity squamous cell cancer (OCSCC) dramatically impacts the prognosis of the patient as well as the need for adjuvant treatment in the future. Currently there is an unmet need to improve OCSCC surgical margins which appear to be involved in around 45% cases. Intraoperative imaging techniques, magnetic resonance imaging (MRI) and intraoral ultrasound (ioUS), have emerged as promising tools in guiding surgical resection, although the number of studies available on this subject is still low. The aim of this diagnostic test accuracy (DTA) review is to investigate the accuracy of intraoperative imaging in the assessment of OCSCC margins. (2) Methods: By using the Cochrane-supported platform Review Manager version 5.4, a systematic search was performed on the online databases MEDLINE-EMBASE-CENTRAL using the keywords "oral cavity cancer, squamous cell carcinoma, tongue cancer, surgical margins, magnetic resonance imaging, intraoperative, intra-oral ultrasound". (3) Results: Ten papers were identified for full-text analysis. The negative predictive value (cutoff < 5 mm) for ioUS ranged from 0.55 to 0.91, that of MRI ranged from 0.5 to 0.91; accuracy analysis performed on four selected studies showed a sensitivity ranging from 0.07 to 0.75 and specificity ranging from 0.81 to 1. Image guidance allowed for a mean improvement in free margin resection of 35%. (4) Conclusions: IoUS shows comparable accuracy to that of ex vivo MRI for the assessment of close and involved surgical margins, and should be preferred as the more affordable and reproducible technique. Both techniques showed higher diagnostic yield if applied to early OCSCC (T1-T2 stages), and when histology is favorable.

Prediction of vertebral fractures in cancer patients undergoing hormone deprivation therapies: Reliability of who fracture risk assessment tool (frax) and bone mineral density in real-life clinical practice.

Background and Objective: Prediction of fractures in cancer survivors exposed to hormone-deprivation therapies (HDTs) is a challenge since bone loss is rapid and severe, and determinants of fractures in this setting are still largely unknown. In this study we investigated reliability of the WHO Fracture Risk Assessment Tool (FRAX) and bone mineral density (BMD) to identify subjects developing vertebral fractures during HDTs. Design: Five-hundred-twenty-seven consecutive subjects (429 females with breast cancer, 98 males with prostate cancer; median age 61 years), under HDTs for at least 6 months, were evaluated for vertebral fractures by a radiological and morphometric approach, in relationship with FRAX score, body mass index (BMI), BMD, age and duration of HDTs. Results: Vertebral fractures were found in 140 subjects (26.6%) and spine deformity index was significantly associated with duration of HDTs (rho 0.38; p < 0.001). Only in females, vertebral fractures were significantly associated with FRAX score for major fractures [OR 1.08; P < 0.001]. The best cut-off of FRAX score for major fractures, as calculated by receiving operating characteristic (ROC) analysis was 6.35%. In males, however, vertebral fractures were significantly and independently associated with BMI ≥ 25 Kg/m2 (OR 17.63; P < 0.001), BMD T-score below -1.0 SD at any skeletal site (OR 7.79; P < 0.001) and gonadotropin-releasing hormone agonists (GnRHa) plus abiraterone treatment (OR 11.51; P = 0.001). Conclusions: FRAX and BMD may be useful for predicting vertebral fractures in subjects undergoing HDTs, but the thresholds seem to be lower than those used in the general population. High BMI is a determinant of vertebral fractures in males under HDT.

Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment.

Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.

Comparison between 1.5 and 3.0 Tesla magnetic resonance enterography for the assessment of disease activity and complications in ileo-colonic Crohn's disease.

BACKGROUND: Magnetic resonance imaging (MRI) can assess disease activity and severity in Crohn's disease (CD). Three-Tesla magnetic resonance (3T) increases signal-to-noise ratio (SNR) and reduces time of image acquisition (IAT). Whether 3T increases the accuracy of MRI in CD compared to 1.5T is unknown. AIM: We aimed to compare prospectively the accuracy of 3 and 1.5T in ileo-colonic CD patients. METHODS: Twenty-six patients with ileo-colonic CD underwent 1.5 and 3T MR enterography at the same time. Ileocolonoscopy was the reference standard for luminal disease. Sensitivity, specificity and accuracy of MRI in evaluating six signs of active and complicated disease (localization, thickening, enhancement, strictures, entero-enteric fistulas, and ulcers) were calculated for both techniques. RESULTS: Three-Tesla resulted as sensitive, specific, and accurate as 1.5T in detecting disease location (accuracy 0.93 vs. 0.86), bowel wall thickening and enhancement (accuracy 0.92 vs. 0.80 for both parameters), strictures (accuracy 0.90 vs. 0.80) and entero-enteric fistulas (accuracy 0.92 vs. 0.92). 3T was superior to 1.5T in detecting ulcers (0.76 vs. 0.42, P < 0.05). SNR resulted higher in 3T, and IAT resulted shorter than 1.5. CONCLUSIONS: We found that 3T is equally accurate as 1.5T in evaluating ileo-colonic CD. Because of superiority in detecting mucosal ulcers, 3T should be preferred in patients with ileo-colonic CD.