An approach to evaluate the quality of radiological reports in Head and Neck cancer loco-regional staging: experience of two Academic Hospitals.

OBJECTIVES: To evaluate the quality of the reports of loco-regional staging computed tomography (CT) or magnetic resonance imaging (MRI) in head and neck (H&N) cancer. METHODS: Consecutive reports of staging CT and MRI of all H&N cancer cases from 2018 to 2020 were collected. We created lists of quality indicators for tumor (T) for each district and for node (N). We marked these as 0 or 1 in the report calculating a report score (RS) and a maximum sum (MS) of each list. Two radiologists and two otolaryngologists in consensus classified reports as low quality (LQ) if the RS fell in the percentage range 0-59% of MS and as high quality (HQ) if it fell in the range 60-100%, annotating technique and district. We evaluated the distribution of reports in these categories. RESULTS: Two hundred thirty-seven reports (97 CT and 140 MRI) of 95 oral cavity, 52 laryngeal, 47 oropharyngeal, 19 hypo-pharyngeal, 14 parotid, and 10 nasopharyngeal cancers were included. Sixty-six percent of all the reports were LQ for T, 66% out of all the MRI reports, and 65% out of all CT reports were LQ. Eight-five percent of reports were HQ for N, 85% out of all the MRI reports, and 82% out of all CT reports were HQ. Reports of oral cavity, oro-nasopharynx, and parotid were LQ, respectively, in 76%, 73%, 100% and 92 out of cases. CONCLUSION: Reports of staging CT/MRI in H&N cancer were LQ for T description and HQ for N description.

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features.

Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann-Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, p < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, p < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.

Pseudo-pneumatosis of the gastrointestinal tract: its incidence and the accuracy of a checklist supported by artificial intelligence (AI) techniques to reduce the misinterpretation of pneumatosis.

PURPOSE: To assess the incidence of erroneous diagnosis of pneumatosis (pseudo-pneumatosis) in patients who underwent an emergency abdominal CT and to verify the performance of imaging features, supported by artificial intelligence (AI) techniques, to reduce this misinterpretation. METHODS: We selected 71 radiological reports where the presence of pneumatosis was considered definitive or suspected. Surgical findings, clinical outcomes, and reevaluation of the CT scans were used to assess the correct diagnosis of pneumatosis. We identified four imaging signs from literature, to differentiate pneumatosis from pseudo-pneumatosis: gas location, dissecting gas in the bowel wall, a circumferential gas pattern, and intramural gas beyond a gas-fluid/faecal level. Two radiologists reevaluated in consensus all the CT scans, assessing the four above-mentioned variables. Variable discriminative importance was assessed using the Fisher exact test. Accurate and statistically significant variables (p-value < 0.05, accuracy > 75%) were pooled using boosted Random Forests (RFs) executed using a Leave-One-Out cross-validation (LOO cv) strategy to obtain unbiased estimates of individual variable importance by permutation analysis. After the LOO cv, the comparison of the variable importance distribution was validated by one-sided Wilcoxon test. RESULTS: Twenty-seven patients proved to have pseudo-pneumatosis (error: 38%). The most significant features to diagnose pneumatosis were presence of dissecting gas in the bowel wall (accuracy: 94%), presence of intramural gas beyond a gas-fluid/faecal level (accuracy: 86%), and a circumferential gas pattern (accuracy: 78%). CONCLUSION: The incidence of pseudo-pneumatosis can be high. The use of a checklist which includes three imaging signs can be useful to reduce this overestimation.

Uncommon site of metastatic neuroblastoma in a 15-year-old girl: case report and description of sonographic and radiographic features.

BACKGROUND: Although most breast masses in children are benign, breast cancer must be considered in the differential diagnosis. The majority are represented by sarcomas and secondary lesions. Literature reports only four cases of neuroblastoma breast metastasis, with no emphasis on radiologic features. Our work aims to furnish a description of radiologic and sonographic features of neuroblastoma metastasis in the breast. CASE DESCRIPTION: A 15-year-old girl had a round nodular mass in the outer upper quadrant of the left breast that had rapidly enlarged over the last month. An ultrasound showed two subcutaneous nodules (3.8 cm and 1.3 cm in maximum diameter), with an irregular shape, heterogeneous echogenicity (isohypoechoic), and hyperechoic foci with a posterior acoustic shadow inside. Overall, the features were highly suspicious of secondary malignant lesions. Computed tomographic scan was performed and found a large retroperitoneal mass and multiple mixed secondary lesions to the spine and hip. A 14G core needle biopsy of breast masses was performed and showed a secondary localization of neuroblastoma. CONCLUSIONS: In adolescents, metastases are the most frequent cause of malignant breast masses. Ultrasound examination should be preferred as the first imaging tool. For the differential diagnosis of breast metastasis with benign masses, a rapid enlargement, a heterogeneous echogenicity, and intralesional hyperechogenic foci could be considered features of malignancy.

Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments.

Between January and October of 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has infected more than 34 million persons in a worldwide pandemic leading to over one million deaths worldwide (data from the Johns Hopkins University). Since the virus begun to spread, emergency departments were busy with COVID-19 patients for whom a quick decision regarding in- or outpatient care was required. The virus can cause characteristic abnormalities in chest radiographs (CXR), but, due to the low sensitivity of CXR, additional variables and criteria are needed to accurately predict risk. Here, we describe a computerized system primarily aimed at extracting the most relevant radiological, clinical, and laboratory variables for improving patient risk prediction, and secondarily at presenting an explainable machine learning system, which may provide simple decision criteria to be used by clinicians as a support for assessing patient risk. To achieve robust and reliable variable selection, Boruta and Random Forest (RF) are combined in a 10-fold cross-validation scheme to produce a variable importance estimate not biased by the presence of surrogates. The most important variables are then selected to train a RF classifier, whose rules may be extracted, simplified, and pruned to finally build an associative tree, particularly appealing for its simplicity. Results show that the radiological score automatically computed through a neural network is highly correlated with the score computed by radiologists, and that laboratory variables, together with the number of comorbidities, aid risk prediction. The prediction performance of our approach was compared to that that of generalized linear models and shown to be effective and robust. The proposed machine learning-based computational system can be easily deployed and used in emergency departments for rapid and accurate risk prediction in COVID-19 patients.