Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm.

OBJECTIVES: To perform a comprehensive within-subject image quality analysis of abdominal CT examinations reconstructed with DLIR and to evaluate diagnostic accuracy compared to the routinely applied adaptive statistical iterative reconstruction (ASiR-V) algorithm. MATERIALS AND METHODS: Oncologic patients were prospectively enrolled and underwent contrast-enhanced CT. Images were reconstructed with DLIR with three intensity levels of reconstruction (high, medium, and low) and ASiR-V at strength levels from 10 to 100% with a 10% interval. Three radiologists characterized the lesions and two readers assessed diagnostic accuracy and calculated signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), figure of merit (FOM), and subjective image quality, the latter with a 5-point Likert scale. RESULTS: Fifty patients (mean age: 70 ± 10 years, 23 men) were enrolled and 130 liver lesions (105 benign lesions, 25 metastases) were identified. DLIR_H achieved the highest SNR and CNR, comparable to ASiR-V 100% (p ≥ .051). DLIR_M returned the highest subjective image quality (score: 5; IQR: 4-5; p ≤ .001) and significant median increase (29%) in FOM (p < .001). Differences in detection were identified only for lesions ≤ 0.5 cm: 32/33 lesions were detected with DLIR_M and 26 lesions were detected with ASiR-V 50% (p = .031). Lesion accuracy of was 93.8% (95% CI: 88.1, 97.3; 122 of 130 lesions) for DLIR and 87.7% (95% CI: 80.8, 92.8; 114 of 130 lesions) for ASiR-V 50%. CONCLUSIONS: DLIR yields superior image quality and provides higher diagnostic accuracy compared to ASiR-V in the assessment of hypovascular liver lesions, in particular for lesions ≤ 0.5 cm. CLINICAL RELEVANCE STATEMENT: Deep learning image reconstruction algorithm demonstrates higher diagnostic accuracy compared to iterative reconstruction in the identification of hypovascular liver lesions, especially for lesions ≤ 0.5 cm. KEY POINTS: • Iterative reconstruction algorithm impacts image texture, with negative effects on diagnostic capabilities. • Medium-strength deep learning image reconstruction algorithm outperforms iterative reconstruction in the diagnostic accuracy of ≤ 0.5 cm hypovascular liver lesions (93.9% vs 78.8%), also granting higher objective and subjective image quality. • Deep learning image reconstruction algorithm can be safely implemented in routine abdominal CT protocols in place of iterative reconstruction.

Ureteral wall thickness and distal ureteral density in patients with residual fragments after Ho:YAG laser semi-rigid ureterolithotripsy.

Recent data suggest that greater ureteral density distal to ureteral stones or increased ureteral wall thickness (UWT) can predict impacted stones. The aim of our study was to evaluate if patients with residual fragments present with greater ureteral density and larger UWT when compared to stone-free patients. From January onward, a consecutive series of patients undergoing semi rigid Ho:YAG laser ureterolithotripsy (ULT) for ureteral stones were enrolled. A non-contrast enhanced computed tomography (CT) scan was performed before the procedure to evaluate distal ureteral density (DUD) and wall ureteral thickness (UWT) at the site of ureteral stones. Patients with residual fragments were compared to stone-free patients using a matched-pair analysis (1:1 scenario). Cases were matched sequentially using the following criteria: age, gender, body mass index (BMI), stone length, hydronephrosis, location of stones, and mean Hounsfield unit (HU) of the stone. Overall, 160 patients were enrolled, mean age was 57.9 ± 14 years, mean BMI was 25.8 ± 4 kg/m2, mean length of the stone was 10.6 ± 4.9 mm, and mean UWT was 1.4 ± 1.6 mm. A total of 150/160 (94%) patients presented hydronephrosis; mean HU stone was 868 ± 327; mean DUD was 54 ± 17.8 HU. Ureteral distal density (51.7 vs 56.6; p = 0.535) and ureteral distal thickness (1.39 vs 1.54; p = 0.078) were similar in both groups of patients. In our study, the evaluation of distal ureteral density does not predict stone-free rate. Further studies should evaluate the role for preoperative computer tomography in predicting surgery outcome.

Post-Acute Sequelae of COVID-19 Pneumonia: Six-month Chest CT Follow-up.

Background The long-term post-acute pulmonary sequelae of COVID-19 remain unknown. Purpose To evaluate lung injury in patients affected by COVID-19 pneumonia at the 6-month follow-up CT examination compared with the baseline chest CT examination. Materials and Methods From March 19, 2020, to May 24, 2020, patients with moderate to severe COVID-19 pneumonia who had undergone baseline chest CT were prospectively enrolled at their 6-month follow-up. The CT qualitative findings, semiquantitative Lung Severity Score (LSS), and the well-aerated lung volume at quantitative chest CT (QCCT) analysis were analyzed. The performance of the baseline LSS and QCCT findings for predicting fibrosis-like changes (reticular pattern and/or honeycombing) at the 6-month follow-up chest CT examination was tested by using receiver operating characteristic curves. Univariable and multivariable logistic regression analyses were used to test clinical and radiologic features that were predictive of fibrosis-like changes. The multivariable analysis was performed with clinical parameters alone (clinical model), radiologic parameters alone (radiologic model), and the combination of clinical and radiologic parameters (combined model). Results One hundred eighteen patients who had undergone baseline chest CT and agreed to undergo follow-up chest CT at 6 months were included in the study (62 women; mean age, 65 years ± 12 [standard deviation]). At follow-up chest CT, 85 of 118 (72%) patients showed fibrosis-like changes and 49 of 118 (42%) showed ground-glass opacities. The baseline LSS (>14) and QCCT findings (≤3.75 L and ≤80%) showed excellent performance for predicting fibrosis-like changes at follow-up chest CT. In the multivariable analysis, the areas under the curve were 0.89 (95% CI: 0.77, 0.96) for the clinical model, 0.81 (95% CI: 0.68, 0.9) for the radiologic model, and 0.92 (95% CI: 0.81, 0.98) for the combined model. Conclusion At 6-month follow-up chest CT, 72% of patients showed late sequelae, in particular fibrosis-like changes. The baseline Lung Severity Score and the well-aerated lung volume at quantitative chest CT (QCCT) analysis showed excellent performance for predicting fibrosis-like changes at the 6-month chest CT (area under the curve, >0.88). Male sex, cough, lymphocytosis, and the well-aerated lung volume at QCCT analysis were significant predictors of fibrosis-like changes at 6 months, demonstrating an inverse correlation (area under the curve, 0.92). © RSNA, 2021 See also the editorial by Wells and Devaraj in this issue.

Typical and atypical COVID-19 computed tomography findings.

In December 2019 a novel coronavirus, named severe acute respiratory syndrome coronavirus 2 was identified and the disease associated was named coronavirus disease 2019 (COVID-19). Fever, cough, myalgia, fatigue associated to dyspnea represent most common clinical symptoms of the disease. The reference standard for diagnosis of severe acute respiratory syndrome coronavirus 2 infection is real time reverse-transcription polymerase chain reaction test applied on respiratory tract specimens. Despite of lower specificity, chest computed tomography (CT), as reported in manifold scientific studies, showed high sensitivity, therefore it may help in the early detection, management and follow-up of COVID-19 pneumonia. Patients affected by COVID-19 pneumonia usually showed on chest CT some typical features, such as: Bilateral ground glass opacities characterized by multilobe involvement with posterior and peripheral distribution; parenchymal consolidations with or without air bronchogram; interlobular septal thickening; crazy paving pattern, represented by interlobular and intralobular septal thickening surrounded by ground-glass opacities; subsegmental pulmonary vessels enlargement (> 3 mm). Halo sign, reversed halo sign, cavitation and pleural or pericardial effusion represent some of atypical findings of COVID-19 pneumonia. On the other hand lymphadenopathy's and bronchiectasis' frequency is unclear, indeed conflicting data emerged in literature. Radiologists play a key role in recognition of high suspicious findings of COVID-19 on chest CT, both typical and atypical ones. Thus, the aim of this review is to illustrate typical and atypical CT findings of COVID-19.

External validation of Imamura nomogram as a tool to predict preoperatively laser semi-rigid ureterolithotripsy outcomes.

BACKGROUND: We aimed to validate Imamura nomogram for prediction of stone free rate in patients undergoing ureterolithotripsy (ULT). METHODS: From January 2013 to June 2016, patients undergoing laser semi-rigid ULT were prospectively enrolled at our center. All patients were preoperatively assessed with clinical history, blood samples, uranalysis and non-contrast enhanced computed tomography (CT). Treatment efficacy was assessed 1 month later by non-contrast enhanced CT. ROC curve was used to evaluate the performance characteristics of Imamura nomogram. RESULTS: Overall, we enrolled 275 patients. Median age was 55 years (IQR: 46/64), median length of stone was 9.8 mm (IQR: 7.5/12). Pyuria was detected in 6/275 (2.1%) patients. Stones were located at ureteropelvic junction in 55/275 (19%) patients, proximal ureter in 74/275 (26%) patients, middle and distal ureter in 66/275 (24%) patients and 82/275 (30%) patients, respectively. At 1-month follow-up, 209/275 (76%) patients were stone free. Imamura nomogram presented an AUC of 0.67 (95% CI: 0.580-0.761) for the prediction of stone free rate. At the best cut-off value of 75%, sensitivity was 76%, specificity was 55%, positive predictive value (PPV) was 83% and negative predictive value was 45%. CONCLUSIONS: We firstly validated Imamura nomogram in a European cohort study. It proved a reasonable accuracy (area under curve: 0.67) and a good PPV (83%). Further studies should confirm our results to support the routine clinical use of Imamura nomogram as a tool to predict ULT outcomes.

The role of contrast enhanced computed tomography in the diagnosis of necrotizing fasciitis and comparison with the laboratory risk indicator for necrotizing fasciitis (LRINEC).

PURPOSE: To evaluate the diagnostic efficacy of contrast enhanced computed tomography (CECT) in emergency departments for diagnosis of necrotizing fasciitis (NF) and for differential diagnosis of other musculoskeletal infections; to correlate radiological findings with the laboratory risk indicator for necrotizing fasciitis (LRINEC). MATERIALS AND METHODS: 7 radiological parameters to be analysed on CECT scans were established, exams of 36 patients with proven diagnosis of NF (n 12) and other musculoskeletal infections (n 24) were retrospectively reviewed; LRINEC score was calculated. Fisher's test and Spearman's and Kendall's coefficients of rank correlations were performed. RESULTS: Two parameters were found to be strongly associated with the diagnosis of NF: involvement of the fascia (Spearman's ρ of 0.888, p < 0.001) and lack of fascial enhancement (Spearman's ρ of 0.672, p < 0.001). LRINEC score did not show strong association with the presence of fasciitis NF (Spearman's ρ of 0.490, p = 0.0024). CONCLUSION: Computed tomography (CT) parameters, which are significantly associated with the diagnosis of NF, are the involvement of the fascia and its lack of enhancement; LRINEC score could be high (>5) also in other musculoskeletal infections. Final diagnosis of necrosis among the fascia is surgical. Presence of gas is not a specific sign of necrotizing fasciitis being present in other musculoskeletal infections. CT could easily discriminate NF from other musculoskeletal infections, adds an important value to clinical and laboratory tests in diagnosis of NF in an emergency context when magnetic resonance imaging, which is superior to CT in this discernment, could not be performed.

A Synchronous Pancreatic Metastasis from Renal Clear Cell Carcinoma, with Unusual CT Characteristics, Completely Regressed after Therapy with Sunitinib.

We present a case report of a 75-years-old woman affected by renal clear cell carcinoma with a synchronous pancreatic metastasis and a metachronous lung metastasis. This case has two peculiarities. First the pancreatic metastasis was treated just with medical therapy, that is, Sunitinib, instead of the surgical therapy that is mostly considered. Secondly, the pancreatic lesion showed different characteristics on the computed tomography scan compared to the usual pancreatic metastases from renal clear cell carcinoma. The pancreatic metastasis totally regressed after medical treatment and nowadays, four years after the diagnosis, the patient is disease-free.

A case of postsurgical necrotizing fasciitis invading the rectus abdominis muscle and review of the literature.

Necrotizing fasciitis is a life-threatening, soft tissue infection and an early diagnosis is needed to permit a prompt surgical and medical intervention. Due to the high fatal potential of the disease complications, the radiologist should distinguish necrotizing fasciitis from the most common soft tissue infections, in order to permit a prompt surgical and medical treatment. We present a case of a wide necrotizing fasciitis who presented at our emergency department and we also provide the basic tools, through a review of the literature, for the general radiologist to distinguish, with computed tomography and magnetic resonance imaging, necrotizing fasciitis from the most common infections that could present during our routine practice.