Differences Between CT-Perfusion and Biphasic Contrast-enhanced CT for Detection and Characterization of Hepatocellular Carcinoma: Potential Explanations for Discrepant Cases.

AIM: To compare the diagnostic value of liver perfusion computed tomography (PCT) and biphasic contrast-enhanced CT (bpCECT) for detection and characterization of hepatocellular carcinoma (HCC), and to identify potential causes for inter-modal discrepancies. PATIENTS AND METHODS: In this retrospective study, 162 cases with a total of 325 HCC-typical lesions were evaluated using both PCT and bpCECT (mean time between examinations=15 days, range=0-13 days). HCC diagnosis was performed by multi-modality imaging including lesion growth at follow-up. For PCT, a total acquisition time of 40 s (26 measurements) each 1.5 s using 80 kV and 100 mAs, as well as 50 ml iodine contrast agent (at 5 ml/s) covering the entire liver was used. Mean arterial liver perfusion (ALP), portal venous perfusion (PVP) and hepatic arterial index (HPI) for both tumor and non-involved liver parenchyma; mean blood flow, blood volume and k-trans for tumor were quantified. Tumor localization, and size were registered. bpCECT consisted of unenhanced, arterial (30-33 s delay), and portal-venous (70-75 s) phases performed using 120 kV, 200-250 mAs, thin-slice reformates (<1 mm), 100 ml contrast agent (at 3 ml/s) followed by 50 ml saline flush. Finally, we divided the results according to detection by PCT only (i.e. missed by pbCECT), and by both PCT and pbCECT. RESULTS: PCT detected 272 lesions compared to 217 with bpCECT only. HCCs in liver segments 4 and 5 were significantly better detected by PCT (p<0.005). Furthermore, PCT detected significantly smaller HCCs than did bpCECT (p<0.001). Lesions detected by both methods had significantly higher mean ALP (p=0.03) and HPI (p=0.02), and lower mean PVP (p=0.01). Tumor blood flow, blood volume and k-trans proved not to be significant for lesion detection. The mean ALP, HPI, and PVP in inconspicuous cirrhotic liver were also not significant for lesion detection. The PVP(tumor)/HPI(liver) ratio of detected lesions was significantly higher for PCT alone (p=0.04). Pretreated, still vital lesions were better detected by bpCECT. CONCLUSION: Detection of smaller HCC lesions, lesions located in liver segments 4 and 5, as well as lesions presenting lower ALP and HPI, and higher PVP(tumor)/HPI(liver) ratio was better using both methods, emphasizing the important role of PCT.

Can a Novel Deep Neural Network Improve the Computer-Aided Detection of Solid Pulmonary Nodules and the Rate of False-Positive Findings in Comparison to an Established Machine Learning Computer-Aided Detection?

OBJECTIVE: The aim of this study was to compare the performance of 2 approved computer-aided detection (CAD) systems for detection of pulmonary solid nodules (PSNs) in an oncologic cohort. The first CAD system is based on a conventional machine learning approach (VD10F), and the other is based on a deep 3D convolutional neural network (CNN) CAD software (VD20A). METHODS AND MATERIALS: Nine hundred sixty-seven patients with a total of 2451 PSNs were retrospectively evaluated using the 2 different CAD systems. All patients had thin-slice chest computed tomography (0.6 mm) using 100 kV and 100 mAs and a high-resolution kernel (I50f). The CAD images generated by VD10F were transferred to the PACS for evaluation. The images generated by VD20A were evaluated using a Web browser-based viewer. Finally, a senior radiologist who was blinded for the CAD results examined the thin-slice images of every patient (ground truth). RESULTS: A total of 2451 PSNs were detected by the senior radiologist. CAD-VD10F detected 1401 true-positive, 143 false-negative, 565 false-positive (FP), and 342 true-negative PSNs, resulting in sensitivity of 90.7%, specificity of 37.7%, positive predictive value of 0.71, and negative predictive value of 0.70. CAD-VD20A detected 1381 true-positive, 163 false-negative, 337 FP, and 570 true-negative PSNs, resulting in sensitivity of 89.4%, specificity of 62.8%, positive predictive value of 0.80, and negative predictive value 0.77, respectively. The rate of FP per scan was 0.6 for CAD-VD10F and 0.3 for CAD-VD20A. CONCLUSIONS: The new deep learning-based CAD software (VD20A) shows similar sensitivity with the conventional CAD software (VD10F), but a significantly higher specificity.

Longitudinal monitoring of apparent diffusion coefficient (ADC) in myeloma patients with lower M-gradient levels undergoing systemic treatment and whole-body MRI monitoring.

OBJECTIVES: Longitudinal assessment of changes in apparent diffusion coefficient (ADC)-values in multiple myeloma (MM) patients and their potential role for classifying disease activity. METHODS: Retrospective analysis of whole-body-MRI data in 73 stage III MM patients undergoing systemic treatment. Bone marrow involvement was evaluated using a standardized unenhanced 4-sequences whole-body-MRI protocol. We measured ADC-values in focal lesions (FL) and diffusely involved bone marrow (DIBM) areas. Response to treatment was based on the course of hematologic parameters. The time points of MRI-examinations were baseline, 1st (mean, 3 months), 2nd (mean, 10 months), and 3rd (mean, 18 months) follow up (FU). RESULTS: Mean IgG and IgA serum values at baseline were 2.1 mg/dl and 1.8 mg/dl, respectively. Patients were classified into responders (n = 59) and non-responders (n = 34). Some patients were re-enrolled for new treatment regimens as they became therapy-refractory. Patterns of medullary involvement were focal (n = 44), diffuse (n = 61) and mixed (n = 30). In FL, a subgroup of myeloma patients undergoing short-term 1st FU experienced a significant increase in ADC in responders (p = 0.001), but not in non-responders (p = 0.9). In the further course of the study, ADC levels decreased continuously in responders (p = 0.02) and increased slightly in non-responders (p = 0.8). In patients with DIBM, ADC values decreased in the responders (p < 0.001) and in the non-responders (p = 0.78). An ADC cut-off value of 0.5-0.6 × 10-3  mm2/s for diagnosing inactive disease at follow-up proved unreliable. CONCLUSIONS: In myeloma-patients with lower tumor burden, the longitudinal course of ADC-values is predictable only for FL whereas for DIBM ADC-changes considerably overlap between responders and non-responders and are not indicative for assessment of the disease activity.

Paraneoplastic syndrome in undifferentiated embryonic sarcoma of the liver.

BACKGROUND: The undifferentiated embryonic sarcoma of the liver (UESL) is a rare, aggressive tumor mainly affecting children. Since UESL has no specific clinical symptoms or imaging characteristics, many cases of UESL are diagnosed late. The paraneoplastic leukemoid reaction (PLR) is a very rare concomitant of oncological patients associated with poor prognosis. This report describes the clinical course of a patient combining these two rare entities and describes the diagnostic challenges and dynamics of paraneoplastic syndrome. CASE PRESENTATION: We report a case of UESL in a 46-year-old male who became clinically conspicuous due to pronounced B symptoms. CT and MRI showed a suspicious unifocal liver lesion. As the histological analysis of a tissue sample did not reveal a clear result, an 18F-FDG-PET-CT examination was performed. In addition to a high glucose metabolism of the liver lesion, an increased glucose metabolism in the entire bone marrow was observed. This finding was considered as pronounced paraneoplasia and laparotomy with liver segment resection followed. Immediately after resection of the tumor the paraneoplastic symptoms completely declined and the patient showed no signs of recurrence in the 1-year follow-up. CONCLUSIONS: Although UESL is rare and predominantly affects children, this diagnosis should always be considered for unclear unifocal cystic liver lesions, regardless of the patient's age, as appropriate treatment has a good prognosis.

A multiscale modelling approach to assess the impact of metabolic zonation and microperfusion on the hepatic carbohydrate metabolism.

The capacity of the liver to convert the metabolic input received from the incoming portal and arterial blood into the metabolic output of the outgoing venous blood has three major determinants: The intra-hepatic blood flow, the transport of metabolites between blood vessels (sinusoids) and hepatocytes and the metabolic capacity of hepatocytes. These determinants are not constant across the organ: Even in the normal organ, but much more pronounced in the fibrotic and cirrhotic liver, regional variability of the capillary blood pressure, tissue architecture and the expression level of metabolic enzymes (zonation) have been reported. Understanding how this variability may affect the regional metabolic capacity of the liver is important for the interpretation of functional liver tests and planning of pharmacological and surgical interventions. Here we present a mathematical model of the sinusoidal tissue unit (STU) that is composed of a single sinusoid surrounded by the space of Disse and a monolayer of hepatocytes. The total metabolic output of the liver (arterio-venous glucose difference) is obtained by integration across the metabolic output of a representative number of STUs. Application of the model to the hepatic glucose metabolism provided the following insights: (i) At portal glucose concentrations between 6-8 mM, an intra-sinusoidal glucose cycle may occur which is constituted by glucose producing periportal hepatocytes and glucose consuming pericentral hepatocytes, (ii) Regional variability of hepatic blood flow is higher than the corresponding regional variability of the metabolic output, (iii) a spatially resolved metabolic functiogram of the liver is constructed. Variations of tissue parameters are equally important as variations of enzyme activities for the control of the arterio-venous glucose difference.