Background So far, only a few studies evaluated the correlation between CT features and clinical outcome in patients with COVID-19 pneumonia. Purpose To evaluate CT ability in differentiating critically ill patients requiring invasive ventilation from patients with less severe disease. Methods We retrospectively collected data from patients admitted to our institution for COVID-19 pneumonia between March 5th-24th. Patients were considered critically ill or non-critically ill, depending on the need for mechanical ventilation. CT images from both groups were analyzed for the assessment of qualitative features and disease extension, using a quantitative semiautomatic method. We evaluated the differences between the two groups for clinical, laboratory and CT data. Analyses were conducted on a per-protocol basis. Results 189 patients were analyzed. PaO 2 /FIO 2 ratio and oxygen saturation (SaO 2 ) were decreased in critically ill patients. At CT, mixed pattern (ground glass opacities (GGO) and consolidation) and GGO alone were more frequent respectively in critically ill and in non-critically ill patients (p < 0.05). Lung volume involvement was significantly higher in critically ill patients (38.5 % vs. 5.8 %, p < 0.05). A cut-off of 23.0 % of lung involvement showed 96 % sensitivity and 96 % specificity in distinguishing critically ill patients from patients with less severe disease. The fraction of involved lung was related to lactate dehydrogenase (LDH) levels, PaO 2 /FIO 2 ratio and SaO 2 (p < 0.05). Conclusion Lung disease extension, assessed using quantitative CT, has a significant relationship with clinical severity and may predict the need for invasive ventilation in patients with COVID-19.
Purpose The assessment of Programmed death-ligand 1 (PD-L1) expression has become a game changer in the treatment of patients with advanced non-small cell lung cancer (NSCLC). We aimed to investigate the ability of Radiomics applied to computed tomography (CT) in predicting PD-L1 expression in patients with advanced NSCLC. Methods By applying texture analysis, we retrospectively analyzed 72 patients with advanced NSCLC. The datasets were randomly split into a training cohort (2/3) and a validation cohort (1/3). Forty radiomic features were extracted by manually drawing tumor volumes of interest (VOIs) on baseline contrast-enhanced CT. After selecting features on the training cohort, two predictive models were created using binary logistic regression, one for PD-L1 values ≥ 50% and the other for values between 1 and 49%. The two models were analyzed with ROC curves and tested in the validation cohort. Results The Radiomic Score (Rad-Score) for PD-L1 values ≥ 50%, which consisted of Skewness and Low Gray-Level Zone Emphasis (GLZLM_LGZE), presented a cut-off value of − 0.745 with an area under the curve (AUC) of 0.811 and 0.789 in the training and validation cohort, respectively. The Rad-Score for PD-L1 values between 1 and 49% consisted of Sphericity, Skewness, Conv_Q3 and Gray Level Non-Uniformity (GLZLM_GLNU), showing a cut-off value of 0.111 with AUC of 0.763 and 0.806 in the two population, respectively. Conclusion Rad-Scores obtained from CT texture analysis could be useful for predicting PD-L1 expression and guiding the therapeutic choice in patients with advanced NSCLC.
Objectives: To investigate the role of quantitative Magnetic Resonance Imaging (MRI) in preoperative assessment of tumor aggressiveness in patients with endometrial cancer, correlating multiple parameters obtained from diffusion and dynamic contrast-enhanced (DCE) MR sequences with conventional histopathological prognostic factors and inflammatory tumour infiltrate. Methods: Forty-four patients with biopsy-proven endometrial cancer underwent preoperative MR imaging at 3T scanner, including DCE imaging, diffusion-weighted imaging (DWI) and intravoxel incoherent motion imaging (IVIM). Images were analyzed on dedicated post-processing workstations and quantitative parameters were extracted: Ktrans, Kep, Ve and AUC from the DCE; ADC from DWI; diffusion D, pseudo diffusion D*, perfusion fraction f from IVIM and tumour volume from DWI. The following histopathological data were obtained after surgery: histological type, grading (G), lympho-vascular invasion (LVI), lymph node status, FIGO stage and inflammatory infiltrate. Results: ADC was significantly higher in endometrioid histology, G1-G2 (low grade), and stage IA. Significantly higher D* were found in endometrioid subptype, negative lymph nodes and stage IA. The absence of LVI is associated with higher f values. Ktrans and Ve values were significantly higher in low grade. Higher D*, f and AUC occur with the presence of chronic inflammatory cells, D * was also able to distinguish chronic from mixed type of inflammation. Larger volume was significantly correlated with the presence of mixed-type inflammation, LVI, positive lymph nodes and stage ≥IB. Conclusions: Quantitative biomarkers obtained from pre-operative DWI, IVIM and DCE-MR examination are an in vivo representation of the physiological and microstructural characteristics of endometrial carcinoma allowing to obtain the fundamental parameters for stratification into Risk Classes. Advances in knowledge: Quantitative imaging biomarkers obtained from DWI, DCE, and IVIM may improve preoperative prognostic stratification in patients with endometrial cancer leading to a more informed therapeutic choice.
The objective of this systematic review was to assess the results of radiomics for prediction of overall survival (OS) and progression free survival (PFS) in ovarian cancer (OC) patients. A secondary objective was to evaluate the findings of papers that based their analyses on inter-site heterogeneity. This systematic review was conducted according to the PRISMA statement. After the initial retrieval of 145 articles, the final systematic review comprised six articles. Association between radiomic features and OS was evaluated in 3/6 studies (50%); all articles showed a significant association between radiomic features and OS. Association with PFS was evaluated in 5/6 (83%) articles; the period of follow-up ranged between six and 36 months. All the articles showed significant association between radiomic models and PFS. Inter-site textural features were used for analysis in 2/6 (33%) articles. They demonstrated that high levels of inter-site textural heterogeneity were significantly associated with incomplete surgical resection in breast cancer gene-negative patients, and that lower heterogeneity was associated with complete resectability. There were some differences among papers in methodology; for example, only 3/6 (50%) articles included validation cohorts. In conclusion, radiomic models have demonstrated promising results as predictors of survival in OC patients, although larger studies are needed to allow clinical applicability.
Purpose Recently coronavirus disease (COVID-19) caused a global pandemic, characterized by acute respiratory distress syndrome (ARDS). The aim of our study was to detect pulmonary embolism (PE) in patients with severe form of COVID-19 infection using pulmonary CT angiography, and its associations with clinical and laboratory parameters. Methods From March to December 2020, we performed a prospective monocentric study collecting data from 374 consecutive patients with confirmed SARS-CoV-2 infection, using real-time reverse-transcriptase polymerase-chain-reaction (rRT-PCR) assay of nasopharyngeal swab specimens. We subsequently selected patients with at least two of the following inclusion criteria: (1) severe acute respiratory symptoms (such as dyspnea, persistent cough, fever > 37.5 °C, fatigue, etc.); (2) arterial oxygen saturation ≤ 93% at rest; (3) elevated D-dimer (≥ 500 ng/mL) and C-reactive protein levels (≥ 0.50 mg/dL); and (4) presence of comorbidities. A total of 63/374 (17%) patients met the inclusion criteria and underwent CT angiography during intravenous injection of iodinated contrast agent (Iomeprol 400 mgI/mL). Statistical analysis was performed using Wilcoxon rank-sum and Chi-square tests. Results About, 26/60 patients (40%) were found positive for PE at chest CT angiography. In these patients, D-dimer and CRP values were significantly higher, while a reduction in SaO2 < 93% was more common than in patients without PE (P < 0.001). Median time between illness onset and CT scan was significantly longer (15 days; P < 0.001) in patients with PE. These were more likely to be admitted to the Intensive Care Unit (19/26 vs. 11/34 patients; P < 0.001) and required mechanical ventilation more frequently than those without PE (15/26 patients vs. 9/34 patients; P < 0.001). Vascular enlargement was significantly more frequent in patients with PE than in those without (P = 0.041). Conclusions Our results pointed out that patients affected by severe clinical features of COVID-19 associated with comorbidities and significant increase of D-dimer levels developed acute mono- or bi-lateral pulmonary embolism in 40% of cases. Therefore, the use of CT angiography rather than non-contrast CT should be considered in these patients, allowing a better evaluation, that can help the management and improve the outcomes.
Background: Multiparametric MRI (mpMRI) of the scrotum has been established as a useful second-line diagnostic tool for the investigation of scrotal diseases. Recently, recommendations on clinical indications for scrotal MRI were issued by the Scrotal and Penile Imaging Working Group of the European Society of Urogenital Radiology. Objective: To update current research on when to ask for an MRI of the scrotum. Methods: PubMed database was searched for original articles and reviews published during 2010-2021.Results: Eighty-three articles fulfilled the search criteria. Scrotal MRI is mainly recommended after inconclusive US findings or inconsistent with the clinical examination and should be asked in the following cases: differentiation between intratesticular and paratesticular lesions (in rare cases of uncertain US findings), characterization of paratesticular and intratesticular lesions (when US findings are indeterminate), discrimination between germ cell and sex cord-stromal testicular tumors, local staging of testicular malignancies (in patients planned for testis-sparing surgery), differentiation between seminomas and non-seminomatous tumors (when immediate chemotherapy is planned and orchiectomy is delayed), assessment of acute scrotum and scrotal trauma (rarely needed, in cases of non-diagnostic US findings) and detection and localization of undescended testes (in cases of inconlusive US findings). Although preliminary data show promising results in the evaluation of male infertility, no established role for mpMRI still exists. Conclusion:Multiparametric MRI of the scrotum, by assessing morphologic and functional data represents a valuable problem-solving tool, helping to improve our understanding on the nature of scrotal pathology and the process of spermatogenesis. The technique may improve patient care and reduce the number of unnecessary surgical procedures.
Purpose: Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions. Methods: Preoperative CTs of patients with HGSOC treated at four referral centers were retrospectively acquired and manually segmented. Hand-crafted features and deep radiomics features were extracted respectively by dedicated software (MODDICOM) and a dedicated convolutional neural network (CNN). Features were selected with and without prior harmonization (ComBat harmonization), and models were built using different machine learning algorithms, including clinical variables. Results: We included 218 patients. Radiomic models showed low performance in predicting both BRCA mutation (AUC in test set between 0.46 and 0.59) and 1-year relapse (AUC in test set between 0.46 and 0.56); deep learning models demonstrated similar results (AUC in the test of 0.48 for BRCA and 0.50 for relapse). The inclusion of clinical variables improved the performance of the radiomic models to predict BRCA mutation (AUC in the test set of 0.74). Conclusions: In our multicentric dataset, representative of a real-life clinical scenario, we could not find a good radiomic predicting model for PFS and BRCA mutational status, with both traditional radiomics and deep learning, but the combination of clinical and radiomic models improved model performance for the prediction of BRCA mutation. These findings highlight the need for standardization through the whole radiomic pipelines and robust multicentric external validations of results.
Structured reporting systems for endometriotic disease are gaining a central role in diagnostic imaging: our aim is to evaluate applicability and the feasibility of the recent ENZIAN score (2020) assessed by MRI. A total of 60 patients with suspected tubo–ovarian/deep endometriosis were retrospectively included in our study according to the following criteria: availability of MR examination; histopathological results from laparoscopic or surgical treatment; patients were not assuming estro-progestin or progestin therapy. Three different readers (radiologists with 2-, 5-, and 20-years of experience in pelvic imaging) have separately assigned a score according to the ENZIAN score (revised 2020) for all lesions detected by magnetic resonance imaging (MRI). Our study showed a high interobserver agreement and feasibility of the recent ENZIAN score applied to MRI; on the other hand, our experience highlighted some limitations mainly due to MRI’s inability to assess tubal patency and mobility, as required by the recent score (2020). In view of the limitations which arose from our study, we propose a modified MRI-ENZIAN score that provides a complete structured reporting system, more suitable for MRI. The high interobserver agreement of the recent ENZIAN score applied to MRI confirms its validity as a complete staging system for endometriosis, offering a shared language between radiologists and surgeons.
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