AI-Based Quantitative CT Analysis of Temporal Changes According to Disease Severity in COVID-19 Pneumonia.

OBJECTIVE: To quantitatively evaluate computed tomography (CT) parameters of coronavirus disease 2019 (COVID-19) pneumonia an artificial intelligence (AI)-based software in different clinical severity groups during the disease course. METHODS: From March 11 to April 15, 2020, 51 patients (age, 18-84 years; 28 men) diagnosed and hospitalized with COVID-19 pneumonia with a total of 116 CT scans were enrolled in the study. Patients were divided into mild (n = 12), moderate (n = 31), and severe (n = 8) groups based on clinical severity. An AI-based quantitative CT analysis, including lung volume, opacity score, opacity volume, percentage of opacity, and mean lung density, was performed in initial and follow-up CTs obtained at different time points. Receiver operating characteristic analysis was performed to find the diagnostic ability of quantitative CT parameters for discriminating severe from nonsevere pneumonia. RESULTS: In baseline assessment, the severe group had significantly higher opacity score, opacity volume, higher percentage of opacity, and higher mean lung density than the moderate group (all P ≤ 0.001). Through consecutive time points, the severe group had a significant decrease in lung volume (P = 0.006), a significant increase in total opacity score (P = 0.003), and percentage of opacity (P = 0.007). A significant increase in total opacity score was also observed for the mild group (P = 0.011). Residual opacities were observed in all groups. The involvement of more than 4 lobes (sensitivity, 100%; specificity, 65.26%), total opacity score greater than 4 (sensitivity, 100%; specificity, 64.21), total opacity volume greater than 337.4 mL (sensitivity, 80.95%; specificity, 84.21%), percentage of opacity greater than 11% (sensitivity, 80.95%; specificity, 88.42%), total high opacity volume greater than 10.5 mL (sensitivity, 95.24%; specificity, 66.32%), percentage of high opacity greater than 0.8% (sensitivity, 85.71%; specificity, 80.00%) and mean lung density HU greater than -705 HU (sensitivity, 57.14%; specificity, 90.53%) were related to severe pneumonia. CONCLUSIONS: An AI-based quantitative CT analysis is an objective tool in demonstrating disease severity and can also assist the clinician in follow-up by providing information about the disease course and prognosis according to different clinical severity groups.

Diagnosis of Invasive Fungal Diseases in Hematological Malignancies: A Critical Review of Evidence and Turkish Expert Opinion (TEO-2).

One of the most problematic issues in hematological malignancies is the diagnosis of invasive fungal diseases. Especially, the difficulty of mycological diagnosis and the necessity of immediate intervention in molds have led to the adoption of "surrogate markers" that do not verify but rather strongly suggest fungal infection. The markers commonly used are galactomannan (GM), beta-glucan, and imaging methods. Although there are numerous studies on these diagnostic approaches, none of these markers serve as a support for the clinician, as is the case in human immunodeficiency virus (HIV) or cytomegalovirus (CMV) infections. This paper has been prepared to explain the diagnostic tests. As molecular tests have not been standardized and are not used routinely in the clinics, they will not be mentioned here.

Two in one: Overlapping CT findings of COVID-19 and underlying lung diseases.

Coronavirus disease 2019 (COVID-19) is associated with pneumonia and has various pulmonary manifestations on computed tomography (CT). Although COVID-19 pneumonia is usually seen as bilateral predominantly peripheral ground-glass opacities with or without consolidation, it can present with atypical radiological findings and resemble the imaging findings of other lung diseases. Diagnosis of COVID-19 pneumonia is much more challenging for both clinicians and radiologists in the presence of pre-existing lung disease. The imaging features of COVID-19 and underlying lung disease can overlap and obscure the findings of each other. Knowledge of the radiological findings of both diseases and possible complications, correct diagnosis, and multidisciplinary consensus play key roles in the appropriate management of diseases. In this pictorial review, the chest CT findings are presented of patients with underlying lung diseases and overlapping COVID-19 pneumonia and the various reasons for radiological lung abnormalities in these patients are discussed.

Value of Dynamic 18F-FDG PET/CT in Predicting the Success of Neoadjuvant Chemotherapy in Patients with Locally Advanced Breast Cancer: A Prospective Study.

Objectives: This prospective study was planned to compare the predictive value of dynamic 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in locally advanced breast cancer patients (LABC) receiving neoadjuvant chemotherapy (NAC). Methods: Twenty seven patients with LABC [median age: 47, (26-66)] underwent a dynamic 18F-FDG PET study at baseline, and after 2-3 cycles of (NAC) were included (interim). Maximum standardized uptake value (SUVmax) values and SUV ratios for the 2nd, 5th, 10th, and 30th minutes and dynamic curve slope (SL) values and SL ratios were measured using 18F-FDG dynamic data. In addition, the values of SUVmean (2minSUVmean), SULpeak (2minSULpeak), metabolic volume (2minVol), and total lesion glycolysis (2minTLG) were measured for the first 2 min. Percent changes between baseline and interim studies were calculated and compared with the pathological results as the pathological complete response (PCR) or the pathological non-complete response (non-PCR). Receiver operating characteristic curves were obtained to calculate the area under the curve to predict PCR. Optimal threshold values were calculated to discriminate between PCR and non-PCR groups. Results: Baseline study SUV 30 (p=0.044), SUV 30/2 (p=0.041), SUV 30/5 (p=0.049), SUV 30/10 (p=0.021), SL 30/2 (p=0.029) and SL 30/5 (p=0.027) values were statistically significant different between PCR and non-PCR groups. The percentage changes of 2minVol between PCR and non-PCR groups were statistically significant. For the threshold value of -67.6% change in 2minVol, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 87.2%, 77.8%, 63.6%, 93.3%, and 80.7%, respectively (area under the curve: 0.826, p=0.009). Conclusion: Semiquantitative parameters for dynamic 18F-FDG PET can predict PCR. % changes in 2minVol can identify non-responding patients better than other parameters.

Outcomes of high-risk breast lesions diagnosed using image-guided core needle biopsy: results from a multicenter retrospective study.

PURPOSE: The clinical management of high-risk lesions using image-guided biopsy is challenging. This study aimed to evaluate the rates at which such lesions were upgraded to malignancy and identify possible predictive factors for upgrading high-risk lesions. METHODS: This retrospective multicenter analysis included 1.343 patients diagnosed with high-risk lesions using an image-guided core needle or vacuum-assisted biopsy (VAB). Only patients managed using an excisional biopsy or with at least one year of documented radiological follow-up were included. For each, the Breast Imaging Reporting and Data System (BI-RADS) category, number of samples, needle thickness, and lesion size were correlated with malignancy upgrade rates in different histologic subtypes. Pearson's chi-squared test, the Fisher-Freeman-Halton test, and Fisher's exact test were used for the statistical analyses. RESULTS: The overall upgrade rate was 20.6%, with the highest rates in the subtypes of intraductal papilloma (IP) with atypia (44.7%; 55/123), followed by atypical ductal hyperplasia (ADH) (38.4%; 144/375), lobular neoplasia (LN) (12.7%; 7/55), papilloma without atypia (9.4%; 58/611), flat epithelial atypia (FEA) (8.7%; 10/114), and radial scars (RSs) (4.6%; 3/65). There was a significant relationship between the upgrade rate and BI-RADS category, number of samples, and lesion size Lesion size was the most predictive factor for an upgrade in all subtypes. CONCLUSION: ADH and atypical IP showed considerable upgrade rates to malignancy, requiring surgical excision. The LN, IP without atypia, pure FEA, and RS subtypes showed lower malignancy rates when the BI-RADS category was lower and in smaller lesions that had been adequately sampled using VAB. After being discussed in a multidisciplinary meeting, these cases could be managed with follow-up instead of excision.

Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings.

PURPOSE: The aim of this study was to evaluate visual and software-based quantitative assessment of parenchymal changes and normal lung parenchyma in patients with coronavirus disease 2019 (COVID-19) pneumonia. The secondary aim of the study was to compare the radiologic findings with clinical and laboratory data. METHODS: Patients with COVID-19 who underwent chest computed tomography (CT) between March 11, 2020 and April 15, 2020 were retrospectively evaluated. Clinical and laboratory findings of patients with abnormal findings on chest CT and PCR-evidence of COVID-19 infection were recorded. Visual quantitative assessment score (VQAS) was performed according to the extent of lung opacities. Software-based quantitative assessment of the normal lung parenchyma percentage (SQNLP) was automatically quantified by a deep learning software. The presence of consolidation and crazy paving pattern (CPP) was also recorded. Statistical analyses were performed to evaluate the correlation between quantitative radiologic assessments, and clinical and laboratory findings, as well as to determine the predictive utility of radiologic findings for estimating severe pneumonia and admission to intensive care unit (ICU). RESULTS: A total of 90 patients were enrolled. Both VQAS and SQNLP were significantly correlated with multiple clinical parameters. While VQAS >8.5 (sensitivity, 84.2%; specificity, 80.3%) and SQNLP <82.45% (sensitivity, 83.1%; specificity, 84.2%) were related to severe pneumonia, VQAS >9.5 (sensitivity, 93.3%; specificity, 86.5%) and SQNLP <81.1% (sensitivity, 86.5%; specificity, 86.7%) were predictive of ICU admission. Both consolidation and CPP were more commonly seen in patients with severe pneumonia than patients with nonsevere pneumonia (P = 0.197 for consolidation; P < 0.001 for CPP). Moreover, the presence of CPP showed high specificity (97.2%) for severe pneumonia. CONCLUSION: Both SQNLP and VQAS were significantly related to the clinical findings, highlighting their clinical utility in predicting severe pneumonia, ICU admission, length of hospital stay, and management of the disease. On the other hand, presence of CPP has high specificity for severe COVID-19 pneumonia.