The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already assumed pandemic proportions, affecting over 100 countries in few weeks. A global response is needed to prepare health systems worldwide. Covid-19 can be diagnosed both on chest X-ray and on computed tomography (CT). Asymptomatic patients may also have lung lesions on imaging. CT investigation in patients with suspicion Covid-19 pneumonia involves the use of the high-resolution technique (HRCT). Artificial intelligence (AI) software has been employed to facilitate CT diagnosis. AI software must be useful categorizing the disease into different severities, integrating the structured report, prepared according to subjective considerations, with quantitative, objective assessments of the extent of the lesions. In this communication, we present an example of a good tool for the radiologist (Thoracic VCAR software, GE Healthcare, Italy) in Covid-19 diagnosis (Pan et al. in Radiology, 2020. https ://doi.org/10.1148/radio l.20202 00370 ). Thoracic VCAR offers quantitative measurements of the lung involvement. Thoracic VCAR can generate a clear, fast and concise report that communicates vital medical information to referring physicians. In the post-processing phase, software, thanks to the help of a colorimetric map, recognizes the ground glass and differentiates it from consolidation and quantifies them as a percentage with respect to the healthy parenchyma. AI software therefore allows to accurately calculate the volume of each of these areas. Therefore, keeping in mind that CT has high diagnostic sensitivity in identifying lesions, but not specific for Covid-19 and similar to other infectious viral diseases, it is mandatory to have an AI software that expresses objective evaluations of the percentage of ventilated lung parenchyma compared to the affected one.

Objective To evaluate by means of regression models the relationships between baseline clinical and laboratory data and lung involvement on baseline chest CT and to quantify the thoracic disease using an artificial intelligence tool and a visual scoring system to predict prognosis in patients with COVID-19 pneumonia. Materials and methods This study included 103 (41 women and 62 men; 68.8 years of mean age—range, 29–93 years) with suspicious COVID-19 viral infection evaluated by reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) test. All patients underwent CT examinations at the time of admission in addition to clinical and laboratory findings recording. All chest CT examinations were reviewed using a structured report. Moreover, using an artificial intelligence tool we performed an automatic segmentation on CT images based on Hounsfield unit to calculate residual healthy lung parenchyma, ground-glass opacities (GGO), consolidations and emphysema volumes for both right and left lungs. Two expert radiologists, in consensus, attributed at the CT pulmonary disease involvement a severity score using a scale of 5 levels; the score was attributed for GGO and consolidation for each lung, and then, an overall radiological severity visual score was obtained summing the single score. Univariate and multivariate regression analysis was performed. Results Symptoms and comorbidities did not show differences statistically significant in terms of patient outcome. Instead, SpO2 was significantly lower in patients hospitalized in critical conditions or died while age, HS CRP, leukocyte count, neutrophils, LDH, d-dimer, troponin, creatinine and azotemia, ALT, AST and bilirubin values were significantly higher. GGO and consolidations were the main CT patterns (a variable combination of GGO and consolidations was found in 87.8% of patients). CT COVID-19 disease was prevalently bilateral (77.6%) with peripheral distribution (74.5%) and multiple lobes localizations (52.0%). Consolidation, emphysema and residual healthy lung parenchyma volumes showed statistically significant differences in the three groups of patients based on outcome (patients discharged at home, patients hospitalized in stable conditions and patient hospitalized in critical conditions or died) while GGO volume did not affect the patient's outcome. Moreover, the overall radiological severity visual score (cutoff ≥ 8) was a predictor of patient outcome. The highest value of R-squared (R2 = 0.93) was obtained by the model that combines clinical/laboratory findings at CT volumes. The highest accuracy was obtained by clinical/laboratory and CT findings model with a sensitivity, specificity and accuracy, respectively, of 88%, 78% and 81% to predict discharged/stable patients versus critical/died patients. Conclusion In conclusion, both CT visual score and computerized software-based quantification of the consolidation, emphysema and residual healthy lung parenchyma on chest CT images were independent predictors of outcome in patients with COVID-19 pneumonia.

Purpose: To compare different commercial software in the quantification of Pneumonia Lesions in COVID-19 infection and to stratify the patients based on the disease severity using on chest computed tomography (CT) images. Materials and methods: We retrospectively examined 162 patients with confirmed COVID-19 infection by reverse transcriptase-polymerase chain reaction (RT-PCR) test. All cases were evaluated separately by radiologists (visually) and by using three computer software programs: (1) Thoracic VCAR software, GE Healthcare, United States; (2) Myrian, Intrasense, France; (3) InferRead, InferVision Europe, Wiesbaden, Germany. The degree of lesions was visually scored by the radiologist using a score on 5 levels (none, mild, moderate, severe, and critic). The parameters obtained using the computer tools included healthy residual lung parenchyma, ground-glass opacity area, and consolidation volume. Intraclass coefficient (ICC), Spearman correlation analysis, and non-parametric tests were performed. Results: Thoracic VCAR software was not able to perform volumes segmentation in 26/162 (16.0%) cases, Myrian software in 12/162 (7.4%) patients while InferRead software in 61/162 (37.7%) patients. A great variability (ICC ranged for 0.17 to 0.51) was detected among the quantitative measurements of the residual healthy lung parenchyma volume, GGO, and consolidations volumes calculated by different computer tools. The overall radiological severity score was moderately correlated with the residual healthy lung parenchyma volume obtained by ThoracicVCAR or Myrian software, with the GGO area obtained by the ThoracicVCAR tool and with consolidation volume obtained by Myrian software. Quantified volumes by InferRead software had a low correlation with the overall radiological severity score. Conclusions: Computer-aided pneumonia quantification could be an easy and feasible way to stratify COVID-19 cases according to severity; however, a great variability among quantitative measurements provided by computer tools should be considered.

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The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.

To assess the performance of the second reading of chest compute tomography (CT) examinations by expert radiologists in patients with discordance between the reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) test for COVID-19 viral pneumonia and the CT report. Three hundred and seventy-eight patients were included in this retrospective study (121 women and 257 men; 71 years median age, with a range of 29–93 years) and subjected to RT-PCR tests for suspicious COVID-19 infection. All patients were subjected to CT examination in order to evaluate the pulmonary disease involvement by COVID-19. CT images were reviewed first by two radiologists who identified COVID-19 typical CT patterns and then reanalyzed by another two radiologists using a CT structured report for COVID-19 diagnosis. Weighted k values were used to evaluate the inter-reader agreement. The median temporal window between RT-PCRs execution and CT scan was zero days with a range of (−9, 11) days. The RT-PCR test was positive in 328/378 (86.8%). Discordance between RT-PCR and CT findings for viral pneumonia was revealed in 60 cases. The second reading changed the CT diagnosis in 16/60 (26.7%) cases contributing to an increase the concordance with the RT-PCR. Among these 60 cases, eight were false negative with positive RT-PCR, and 36 were false positive with negative RT-PCR. Sensitivity, specificity, positive predictive value and negative predictive value of CT were respectively of 97.3%, 53.8%, 89.0%, and 88.4%. Double reading of CT scans and expert second readers could increase the diagnostic confidence of radiological interpretation in COVID-19 patients.

We performed a pilot study to evaluate the use of MRI delta texture analysis (D-TA) as a methodological item able to predict the frequency of complete pathological responses and, consequently, the outcome of patients with locally advanced rectal cancer addressed to neoadjuvant chemoradiotherapy (C-RT) and subsequently, to radical surgery. In particular, we carried out a retrospective analysis including 100 patients with locally advanced rectal adenocarcinoma who received C-RT and then radical surgery in three different oncological institutions between January 2013 and December 2019. Our experimental design was focused on the evaluation of the gross tumor volume (GTV) at baseline and after C-RT by means of MRI, which was contoured on T2, DWI, and ADC sequences. Multiple texture parameters were extracted by using a LifeX Software, while D-TA was calculated as percentage of variations in the two time points. Both univariate and multivariate analysis (logistic regression) were, therefore, carried out in order to correlate the above-mentioned TA parameters with the frequency of pathological responses in the examined patients’ population focusing on the detection of complete pathological response (pCR, with no viable cancer cells: TRG 1) as main statistical endpoint. ROC curves were performed on three different datasets considering that on the 21 patients, only 21% achieved an actual pCR. In our training dataset series, pCR frequency significantly correlated with ADC GLCM-Entropy only, when univariate and binary logistic analysis were performed (AUC for pCR was 0.87). A confirmative binary logistic regression analysis was then repeated in the two remaining validation datasets (AUC for pCR was 0.92 and 0.88, respectively). Overall, these results support the hypothesis that D-TA may have a significant predictive value in detecting the occurrence of pCR in our patient series. If confirmed in prospective and multicenter trials, these results may have a critical role in the selection of patients with locally advanced rectal cancer who may benefit form radical surgery after neoadjuvant chemoradiotherapy.

The family of painful osteocytic tumors includes osteoblastomas and osteoid osteomas—these lesions are considered benign, but they could produce a significant painful symptomatology. Usually, people affected are between 20 s and 30 s. When symptomatic, an effective treatment is mandatory for the management of these lesions to allow for a ful quality of life. The possibilities of treatment range from chirurgical en-block resection (procedure of surgical oncology aiming to remove a tumoral mass in its entirety, completely surrounded by a continuous layer of healthy tissue) to interventional approaches that, nowadays, are considered the most affordable and sustainable in terms of effectiveness, recovery after procedure, and for bone structure sparing. The main techniques used for osteoid osteomas and osteoblastomas are radio frequency ablation (RFA) and magnetic resonance-guided focused ultrasound (MRgFUS): the most important difference between these approaches is the needleless approach of MRgFUS, which further reduces the minimal invasiveness of RFA (and the related consequences) and the absence of exposure to ionizing radiation. Despite their high efficacy, a recurrence of pathology may occur due to a failure in therapy. In light of this, describing the various possibilities of follow up protocols and the imaging aspects of recurrence or incomplete treatment is mandatory. In the scenario given in the literature, many authors have tried to asses an organized follow up protocol of these patients, but many of them did not undergo periodical magnetic resonance (MR) or computerized tomography (CT) because of the lack of symptomatology. However, even if it seems that clinical evolution is central, different papers describe the protocol useful to detect eventual relapse. The aim of our manuscript is to review the various possibilities of follow-up of these patients and to bring together the most salient aspects found during the management of these osteocytic bone lesions.