Background: The aim of this study was to evaluate the accuracy of three-dimensional (3D) printed liver models developed by a cost-effective approach for establishing validity of using these models in a clinical setting.Methods: Fifteen patients undergoing laparoscopic liver resection in a single surgical department were included. Patient-specific, 1-1 scale 3D printed liver models including the liver, tumor, and vasculature were created from contrast-enhanced computed tomography (CT) images using a cost-effective approach. The 3D models were subsequently CT scanned, 3D image post-processing was performed, and these 3D computer models (MCT) were compared to the original 3D models created from the original patient images (PCT).3D computer models of each type were co-registered using a point set registration method. 3D volume measurements of the liver and lesions were calculated and compared for each set. In addition, Hausdorff distances were calculated and surface quality was compared by generated heatmaps. Results:The median liver volume in MCT was 1,281.84 [interquartile range (IQR) =296.86] cm 3 , and 1,448.03 (IQR =413.23) cm 3 in PCT. Analysis of differences between surfaces showed that the median value of mean Hausdorff distances for liver parenchyma was 1.92 mm. Bland-Altman plots revealed no significant bias in liver volume and diameters of hepatic veins and tumor location. Median errors of all measured vessel diameters were smaller than CT slice height. There was a slight trend towards undersizing anatomical structures, although those errors are most likely due to source imaging. Conclusions:We have confirmed the accuracy of 3D printed liver models created by using the low-cost method. 3D models are useful tools for pre-operative planning and intra-operative guidance. Future research in this field should continue to move towards clinical trials for assessment of the impact of these models on pre-surgical planning decisions and perioperative outcomes.

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IntroductionNeuroendocrine neoplasms including neuroendocrine tumors (NETs) are often diagnosed as primary disseminated or inoperable. In those cases, systemic extensive therapy is necessary, but radical treatment is unlikely. As described in the literature, in some selected cases, peptide receptor radionuclide therapy (PRRT) may be used as a first-line/neoadjuvant therapy that allows further successful surgery. Such treatment may enable a reduction of total tumor burden or allow a radical treatment which improves the final outcomes.AimThis study aims to assess whether neoadjuvant PRRT could be a treatment option for patients with initially unresectable NETs.MethodsAmong the group of 114 patients treated with PRRT between the years 2005 and 2020, in 32 cases, it was the first-line therapy, mainly due to massive disease burden at the time of diagnosis. Among them, nine patients received PRRT as the first-line treatment due to the primary inoperable tumors with the intention of preoperative reduction of the tumor size in order to allow for a surgical treatment.ResultsNeoadjuvant PRRT enabled surgery in four out of nine (45%) patients. Finally, in two out of four cases, the goal (radical surgery) has been achieved.ConclusionPRRT may be considered not only as a palliative but also as a neoadjuvant therapy in advanced, somatostatin-positive NETs that were initially inoperable.

Objectives The aim of this study was to evaluate impact of 3D printed models on decision-making in context of laparoscopic liver resections (LLR) performed with intraoperative ultrasound (IOUS) guidance. Methods Nineteen patients with liver malignances (74% were colorectal cancer metastases) were prospectively qualified for LLR or radiofrequency ablation in a single center from April 2017 to December 2018. Models were 3DP in all cases based on CT and facilitated optical visualization of tumors' relationships with portal and hepatic veins. Planned surgical extent and its changes were tracked after CT analysis and 3D model inspection, as well as intraoperatively using IOUS. Results Nineteen patients were included in the analysis. Information from either 3DP or IOUS led to changes in the planned surgical approach in 13/19 (68%) patients. In 5/19 (26%) patients, the 3DP model altered the plan of the surgery preoperatively. In 4/19 (21%) patients, 3DP independently changed the approach. In one patient, IOUS modified the plan post-3DP. In 8/19 (42%) patients, 3DP model did not change the approach, whereas IOUS did. In total, IOUS altered surgical plans in 9 (47%) cases. Most of those changes (6/9; 67%) were caused by detection of additional lesions not visible on CT and 3DP. Conclusions 3DP can be helpful in planning complex and major LLRs and led to changes in surgical approach in 26.3% (5/19 patients) in our series. 3DP may serve as a useful adjunct to IOUS. Key Points • 3D printing can help in decision-making before major and complex resections in patients with liver cancer. • In 5/19 patients, 3D printed model altered surgical plan preoperatively. • Most surgical plan changes based on intraoperative ultrasonography were caused by detection of additional lesions not visible on CT and 3D model.

The aim of this study was to compare the results of automatic assessment of high resolution computed tomography (HRCT) by artificial intelligence (AI) in 150 patients from three subgroups: pneumonia in the course of COVID-19, bronchopneumonia and atypical pneumonia. The volume percentage of inflammation and the volume percentage of “ground glass” were significantly higher in the atypical (respectively, 11.04%, 8.61%) and the COVID-19 (12.41%, 10.41%) subgroups compared to the bronchopneumonia (5.12%, 3.42%) subgroup. The volume percentage of consolidation was significantly higher in the COVID-19 (2.95%) subgroup compared to the atypical (1.26%) subgroup. The percentage of “ground glass” in the volume of inflammation was significantly higher in the atypical (89.85%) subgroup compared to the COVID-19 (79.06%) subgroup, which in turn was significantly higher compared to the bronchopneumonia (68.26%) subgroup. HRCT chest images, analyzed automatically by artificial intelligence software, taking into account the structure including “ground glass” and consolidation, significantly differ in three subgroups: COVID-19 pneumonia, bronchopneumonia and atypical pneumonia. However, the partial overlap, particularly between COVID-19 pneumonia and atypical pneumonia, may limit the usefulness of automatic analysis in differentiating the etiology. In our future research, we plan to use artificial intelligence for objective assessment of the dynamics of pulmonary lesions during COVID-19 pneumonia.