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.

Treatment options for PNET liver metastases: a systematic review.

BACKGROUND: Pancreatic neuroendocrine tumors (PNETs) are rare pancreatic neoplasms. About 40-80% of patients with PNET are metastatic at presentation, usually involving the liver (40-93%). Liver metastasis represents the most significant prognostic factor. The aim of this study is to present an up-to-date review of treatment options for patients with liver metastases from PNETs. METHODS: A systematic literature search was performed using the PubMed database to identify all pertinent studies published up to May 2018. RESULTS: The literature search evaluated all the therapeutic options for patients with liver metastases of PNETs, including surgical treatment, loco-regional therapies, and pharmacological treatment. All the different treatment options showed particular indications in different presentations of liver metastases of PNET. Surgery remains the only potentially curative therapeutic option in patients with PNETs and resectable liver metastases, even if relapse rates are high. Efficacy of medical treatment has increased with advances in targeted therapies, such as everolimus and sunitinib, and the introduction of radiolabeled somatostatin analogs. Several techniques for loco-regional control of metastases are available, including chemo- or radioembolization. CONCLUSIONS: Treatment of patients with PNET metastases should be multidisciplinary and must be personalized according to the features of individual patients and tumors.