Computed Tomography and Magnetic Resonance Imaging Signs of Chronic Liver Rejection: A Case-Control Study.

OBJECTIVE: In liver transplantation, chronic rejection is still poorly studied. This study aimed to investigate the role of imaging in its recognition. METHODS: This study is a retrospective observational case-control series. Patients with histologic diagnosis of chronic liver transplant rejection were selected; the last imaging examination (computed tomography or magnetic resonance imaging) before the diagnosis was evaluated. At least 3 controls were selected for each case; radiological signs indicative of altered liver function were analyzed. χ 2 Test with Yates correction was used to compare the rates of radiologic signs in the case and control groups, also considering whether patients suffered chronic rejection within or after 12 months. Statistical significance was set at P < 0.050. RESULTS: A total of 118 patients were included in the study (27 in the case group and 91 in the control group). Periportal edema was appreciable in 19 of 27 cases (70%) and in 6 of 91 controls (4%) ( P < 0.001); ascites and hepatomegaly were present in 14 of 27 cases (52%) and 12 of 27 cases (44%), respectively, and in 1 of 91 controls (1%) ( P < 0.001); splenomegaly was present in 13 of 27 cases (48%) and in 8 of 91 controls (10%) ( P < 0.001); and biliary tract dilatation was present in 13 of 27 cases (48%) and in 11 of 91 patients controls (5%) ( P < 0.001). In the controls, periportal edema was significantly less frequent beyond 12 months after transplant (1% vs 11%; P = 0.020); the other signs after 12 months were not significant. CONCLUSIONS: The identification of periportal edema, biliary dilatation, ascites, and hepatosplenomegaly can serve as potential warning signs of ongoing chronic liver rejection. It is especially important to investigate periportal edema if it is present 1 year or more after orthotopic liver transplantation.

Multimodal treatment with curative intent in a germline BRCA2 mutant metastatic ampullary adenocarcinoma: a case report.

BACKGROUND: Cancers of the Vater ampulla (ampullary cancers, ACs) account for less than 1% of all gastrointestinal tumors. ACs are usually diagnosed at advanced stage, with poor prognosis and limited therapeutic options. BRCA2 mutations are identified in up to 14% of ACs and, differently from other tumor types, therapeutic implications remain to be defined. Here, we report a clinical case of a metastatic AC patient in which the identification of a BRCA2 germline mutation drove a personalized multimodal approach with curative-intent. CASE PRESENTATION: A 42-year-old woman diagnosed with stage IV BRCA2 germline mutant AC underwent platinum-based first line treatment achieving major tumor response but also life-threatening toxicity. Based on this, as well as on molecular findings and expected low impact of available systemic treatment options, the patient underwent radical complete surgical resection of both primary tumor and metastatic lesions. Following an isolated retroperitoneal nodal recurrence, given the expected enhanced sensitivity to radiotherapy in BRCA2 mutant cancers, the patient underwent imaging-guided radiotherapy leading to long-lasting complete tumor remission. After more than 2 years, the disease remains radiologically and biochemically undetectable. The patient accessed a dedicated screening program for BRCA2 germline mutation carriers and underwent prophylactic bilateral oophorectomy. CONCLUSIONS: Even considering the intrinsic limitations of a single clinical report, we suggest that the finding of BRCA germline mutations in ACs should be taken into consideration, together with other clinical variables, given their potential association with remarkable response to cytotoxic chemotherapy that might be burdened with enhanced toxicity. Accordingly, BRCA1/2 mutations might offer the opportunity of personalizing treatment beyond PARP inhibitors up to the choice of a multimodal approach with curative-intent.

Chest computed tomography of suspected COVID-19 pneumonia in the Emergency Department: comparative analysis between patients with different vaccination status.

Purpose: To identify differences in chest computed tomography (CT) of the symptomatic coronavirus disease 2019 (COVID-19) population according to the patients' severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination status (non-vaccinated, vaccinated with incomplete or complete vaccination cycle). Material and methods: CT examinations performed in the Emergency Department (ED) in May-November 2021 for suspected COVID-19 pneumonia with a positive SARS-CoV-2 test were retrospectively included. Personal data were compared for vaccination status. One 13-year experienced radiologist and two 4th-year radiology residents independently evaluated chest CT scans according to CO-RADS and ACR COVID classifications. In possible COVID-19 pneumonia cases, defined as CO-RADS 3 to 5 (ACR indeterminate and typical) by each reader, high involvement CT score (≥ 25%) and CT patterns (presence of ground glass opacities, consolidations, crazy paving areas) were compared for vaccination status. Results: 184 patients with known vaccination status were included in the analysis: 111 non-vaccinated (60%) for SARS-CoV-2 infection, 21 (11%) with an incomplete vaccination cycle, and 52 (28%) with a complete vaccination cycle (6 different vaccine types). Multivariate logistic regression showed that the only factor predicting the absence of pneumonia (CO-RADS 1 and ACR negative cases) for the 3 readers was a complete vaccination cycle (OR = 12.8-13.1compared to non-vaccinated patients, p ≤ 0.032). Neither CT score nor CT patterns of possible COVID-19 pneumonia showed any statistically significant correlation with vaccination status for the 3 readers. Conclusions: Symptomatic SARS-CoV-2-infected patients with a complete vaccination cycle had much higher odds of showing a negative CT chest examination in ED compared to non-vaccinated patients. Neither CT involvement nor CT patterns of interstitial pneumonia showed differences across different vaccination status.

Radiomics predicts response of individual HER2-amplified colorectal cancer liver metastases in patients treated with HER2-targeted therapy.

The aim of our study was to develop and validate a machine learning algorithm to predict response of individual HER2-amplified colorectal cancer liver metastases (lmCRC) undergoing dual HER2-targeted therapy. Twenty-four radiomics features were extracted after 3D manual segmentation of 141 lmCRC on pretreatment portal CT scans of a cohort including 38 HER2-amplified patients; feature selection was then performed using genetic algorithms. lmCRC were classified as nonresponders (R-), if their largest diameter increased more than 10% at a CT scan performed after 3 months of treatment, responders (R+) otherwise. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values in correctly classifying individual lesion and overall patient response were assessed on a training dataset and then validated on a second dataset using a Gaussian naïve Bayesian classifier. Per-lesion sensitivity, specificity, NPV and PPV were 89%, 85%, 93%, 78% and 90%, 42%, 73%, 71% respectively in the testing and validation datasets. Per-patient sensitivity and specificity were 92% and 86%. Heterogeneous response was observed in 9 of 38 patients (24%). Five of nine patients were carriers of nonresponder lesions correctly classified as such by our radiomics signature, including four of seven harboring only one nonresponder lesion. The developed method has been proven effective in predicting behavior of individual metastases to targeted treatment in a cohort of HER2 amplified patients. The model accurately detects responder lesions and identifies nonresponder lesions in patients with heterogeneous response, potentially paving the way to multimodal treatment in selected patients. Further validation will be needed to confirm our findings.

Clinical relevance of the left brachiocephalic vein anatomy for vascular access in dialysis patients.

INTRODUCTION: Most hemodialysis patients start renal replacement therapy with a central venous catheter (CVC). The left internal jugular vein (LIJV) is the second-choice vein for CVC positioning, after the right IJV. However, to reach the right atrium, the CVC must pass through the left brachiocephalic vein (LBV), which also drains blood from the left arm through the subclavian vein. The purpose of this study is to describe how the anatomy of the central venous system and in particular that of the LBV affects vascular access in hemodialysis patients. MATERIALS AND METHODS: Three-dimensional (3D) virtual model reconstructions of the central thoracic veins of three hemodialysis patients were obtained from contrast-enhanced computed tomography scans acquired in the venous phase. The images were exported as DICOM files and loaded on open-source software for visualizing and analyzing the medical imaging (3D Slicer, Windows version 4.8.1). RESULTS: As expected, the 3D reconstructions showed that the LBV has a tortuous path with three main angulations that could be associated with external compression and stenosis. These could determine the difficulties and increased risks of venous injury during CVC placement, and an increased risk of medium to long-term catheter-associated vein thrombosis and stenosis. CONCLUSIONS: The anatomical features of the LBV indicate that the path of a CVC from the LIJV to the right atrium is tortuous and can easily be complicated by vein injury, negatively affecting the creation of future arterio-venous vascular accesses in the left arm.

A Virtual Reality Environment to Visualize Three-Dimensional Patient-Specific Models by a Mobile Head-Mounted Display.

INTRODUCTION: With the availability of low-cost head-mounted displays (HMDs), virtual reality environments (VREs) are increasingly being used in medicine for teaching and clinical purposes. Our aim was to develop an interactive, user-friendly VRE for tridimensional visualization of patient-specific organs, establishing a workflow to transfer 3-dimensional (3D) models from imaging datasets to our immersive VRE. MATERIALS AND METHODS: This original VRE model was built using open-source software and a mobile HMD, Samsung Gear VR. For its validation, we enrolled 33 volunteers: morphologists (n = 11), trainee surgeons (n = 15), and expert surgeons (n = 7). They tried our VRE and then filled in an original 5-point Likert-type scale 6-item questionnaire, considering the following parameters: ease of use, anatomy comprehension compared with 2D radiological imaging, explanation of anatomical variations, explanation of surgical procedures, preoperative planning, and experience of gastrointestinal/neurological disorders. Results in the 3 groups were statistically compared using analysis of variance. RESULTS: Using cross-sectional medical imaging, the developed VRE allowed to visualize a 3D patient-specific abdominal scene in 1 hour. Overall, the 6 items were evaluated positively by all groups; only anatomy comprehension was statistically significant different among the 3 groups. CONCLUSIONS: Our approach, based on open-source software and mobile hardware, proved to be a valid and well-appreciated system to visualize 3D patient-specific models, paving the way for a potential new tool for teaching and preoperative planning.

Depressed fractures of the skull due to direct kick and the contribution of 3D CT reconstruction.

Depressed skull fractures occur when broken bones displace inward, meaning that a portion of the outer table of the fracture line lies below the normal anatomical position of the inner table. They typically result from force trauma, when the skull is struck by an object with a moderately large amount of kinetic energy but a small surface area, or when an object with a large amount of kinetic energy impacts only a small area of the skull. In the present case, a depressed fracture of the frontal bone was detected at the autopsy of a 52-year-old man who, according to the belated confession of the assailant, was kicked in the head. The assailant was wearing sneakers. Could such a fracture be caused "just" by a kick? In this case it was possible due to an extraordinarily thin cranial vault (0.2 cm frontal, 0.3 cm occipital), which allowed the fractures to occur from a kinetic force that might not have been sufficient with a normal cranial vault thickness. An important role in the forensic analysis of the case was played by the 3D CT reconstruction.

Dual-energy CT applications on liver imaging: what radiologists and radiographers should know? A systematic review.

PURPOSE: This review aims to provide a comprehensive summary of DECT techniques, acquisition workflows, and post-processing methods. By doing so, we aim to elucidate the advantages and disadvantages of DECT compared to conventional single-energy CT imaging. METHODS: A systematic search was conducted on MEDLINE/EMBASE for DECT studies in liver imaging published between 1980 and 2024. Information regarding study design and endpoints, patient characteristics, DECT technical parameters, radiation dose, iodinated contrast agent (ICA) administration and postprocessing methods were extracted. Technical parameters, including DECT phase, field of view, pitch, collimation, rotation time, arterial phase timing (from injection), and venous timing (from injection) from the included studies were reported, along with formal narrative synthesis of main DECT applications for liver imaging. RESULTS: Out of the initially identified 234 articles, 153 met the inclusion criteria. Extensive variability in acquisition parameters was observed, except for tube voltage (80/140 kVp combination reported in 50% of articles) and ICA administration (1.5 mL/kg at 3-4 mL/s, reported in 91% of articles). Radiation dose information was provided in only 40% of articles (range: 6-80 mGy), and virtual non-contrast imaging (VNC) emerged as a common strategy to reduce the radiation dose. The primary application of DECT post-processed images was in detecting focal liver lesions (47% of articles), with predominance of study focusing on hepatocellular carcinoma (HCC) (27%). Furthermore, a significant proportion of the articles (16%) focused on enhancing DECT protocols, while 15% explored metastasis detection. CONCLUSION: Our review recommends using 80/140 kVp tube voltage with 1.5 mL/kg ICA at 3-4 mL/s flow rate. Post-processing should include low keV-VMI for enhanced lesion detection, IMs for tumor iodine content evaluation, and VNC for dose reduction. However, heterogeneous literature hinders protocol standardization.

Quantitative Computed Tomography and Response to Pronation in COVID-19 ARDS.

BACKGROUND: The use of prone position (PP) has been widespread during the COVID-19 pandemic. Whereas it has demonstrated benefits, including improved oxygenation and lung aeration, the factors influencing the response in terms of gas exchange to PP remain unclear. In particular, the association between baseline quantitative computed tomography (CT) scan results and gas exchange response to PP in invasively ventilated subjects with COVID-19 ARDS is unknown. The present study aimed to compare baseline quantitative CT results between subjects responding to PP in terms of oxygenation or CO2 clearance and those who did not. METHODS: This was a single-center, retrospective observational study including critically ill, invasively ventilated subjects with COVID-19-related ARDS admitted to the ICUs of Niguarda Hospital between March 2020-November 2021. Blood gas samples were collected before and after PP. Subjects in whom the PaO2 /FIO2 increase was ≥ 20 mm Hg after PP were defined as oxygen responders. CO2 responders were defined when the ventilatory ratio (VR) decreased during PP. Automated quantitative CT analyses were performed to obtain tissue mass and density of the lungs. RESULTS: One hundred twenty-five subjects were enrolled, of which 116 (93%) were O2 responders and 51 (41%) CO2 responders. No difference in quantitative CT characteristics and oxygen were observed between responders and non-responders (tissue mass 1,532 ± 396 g vs 1,654 ± 304 g, P = .28; density -544 ± 109 HU vs -562 ± 58 HU P = .42). Similar findings were observed when dividing the population according to CO2 response (tissue mass 1,551 ± 412 g vs 1,534 ± 377 g, P = .89; density -545 ± 123 HU vs -546 ± 94 HU, P = .99). CONCLUSIONS: Most subjects with COVID-19-related ARDS improved their oxygenation at the first pronation cycle. The study suggests that baseline quantitative CT scan data were not associated with the response to PP in oxygenation or CO2 in mechanically ventilated subjects with COVID-19-related ARDS.

Advancements in Neuroendocrine Neoplasms: Imaging and Future Frontiers.

Neuroendocrine neoplasms (NENs) are a diverse group of tumors with varying clinical behaviors. Their incidence has risen due to increased awareness, improved diagnostics, and aging populations. The 2019 World Health Organization classification emphasizes integrating radiology and histopathology to characterize NENs and create personalized treatment plans. Imaging methods like CT, MRI, and PET/CT are crucial for detection, staging, treatment planning, and monitoring, but each of them poses different interpretative challenges and none are immune to pitfalls. Treatment options include surgery, targeted therapies, and chemotherapy, based on the tumor type, stage, and patient-specific factors. This review aims to provide insights into the latest developments and challenges in NEN imaging, diagnosis, and management.

Focal Liver Lesions in Budd-Chiari Syndrome: Spectrum of Imaging Findings.

Budd-Chiari syndrome (BCS) is a rare clinical entity characterized by hepatic venous outflow obstruction, resulting in liver congestion and subsequent chronic parenchymal damage. This condition often leads to the development of focal liver lesions, including benign focal nodular hyperplasia-like regenerative nodules, hepatocellular carcinoma, and perfusion-related pseudo-lesions. Computed tomography, ultrasound, and magnetic resonance are the commonly employed imaging modalities for the follow-up of BCS patients and for the detection and characterization of new-onset lesions. The accurate differentiation between benign and malignant nodules is crucial for optimal patient management and treatment planning. However, it can be challenging due to the variable and overlapping characteristics observed. This review aims to provide a comprehensive overview of the imaging features and differential diagnosis of focal liver lesions in BCS, emphasizing the key findings and discussing the challenges associated with their interpretation, with the purpose of facilitating the subsequent clinical decision-making.

Impact of COVID-19 Pandemic on the Workload of Diagnostic Radiology: A 2-Year Observational Study in a Tertiary Referral Hospital.

RATIONALE AND OBJECTIVES: To evaluate the impact of COVID-19 pandemic on diagnostic imaging workload in a tertiary referral hospital. MATERIALS AND METHODS: Radiological examinations performed in pre-pandemic period (2015-2019) and in pandemic period (2020-2021) were retrospectively included. Based on epidemiological data and restriction measures, four pandemic waves were identified. For each of them, the relative change (RC) in workload was calculated and compared to the 5-year averaged workload in the corresponding pre-COVID-19 periods. Workload variations were also assessed according to technique (radiographs, CT, MRI, ultrasounds), body district (chest, abdomen, breast, musculoskeletal, head/neck, brain/spine, cardiovascular) and care setting (inpatient, outpatient, emergency imaging, pre-admission imaging). RESULTS: A total of 1384380 examinations were included. In 2020 imaging workload decreased (RC = -11%) compared to the average of the previous 5 years, while in 2021 only a minimal variation (RC = +1%) was observed. During first wave, workload was reduced for all modalities, body regions and types of care setting (RC from -86% to -10%), except for CT (RC = +3%). In subsequent waves, workload increased only for CT (mean RC = +18%) and, regarding body districts, for breast (mean RC = +23%) and cardiovascular imaging (mean RC = +23%). For all other categories, a workload comparable to pre-pandemic period was almost only restored in the fourth wave. In all pandemics periods workload decrease was mainly due to reduced outpatient activity (p < 0.001), while inpatient and emergency imaging was increased (p < 0.001). CONCLUSION: Evaluating imaging workload changes throughout COVID-19 pandemic helps to understand the response dynamics of radiological services and to improve institutional preparedness to face extreme contingency.

Clinical Impact of Spontaneous Portosystemic Shunts in Liver Transplantation: A Comprehensive Assessment Through Total Shunt Area Measurement.

BACKGROUND: The impact of spontaneous portosystemic shunts (SPSSs) on natural history of cirrhotic patients was recently evaluated through the measurement of total shunt area (TSA), a novel tool that allows a comprehensive assessment of SPSSs extension, identifying a direct correlation of higher TSA with lower patient survival. The role of SPSSs in liver transplant (LT) is still debated: we sought to investigate the clinical impact of TSA on the development of early allograft dysfunction (EAD), acute kidney injury (AKI), postoperative complications, and graft and patient survival following LT. METHODS: Preoperative imaging of 346 cirrhotic patients undergoing primary LT between 2015 and 2020 were retrospectively revised, recording the size and anatomy of each SPSS to calculate TSA. The impact of TSA and selected patient and donor characteristics on the development of EAD, AKI, and clinically relevant complications was evaluated through univariate and multivariate logistic regression, whereas their effect on graft and patient survival was investigated through Cox regression analysis. RESULTS: A TSA exceeding 78.54 mm 2 resulted as an independent risk factor for the development of EAD (odds ratio [OR]: 2.327; P = 0.003), grade 3 AKI (OR: 2.093; P = 0.041), and clinically relevant complications (OR: 1.962; P = 0.015). Moreover, higher TSA was significantly related to early graft and patient survivals, emerging as an independent risk factor for 12-mo graft loss (hazard ratio: 3.877; P = 0.007) and patient death (hazard ratio: 2.682; P = 0.018). CONCLUSIONS: Higher TSA emerged as a significant risk factor for worse postoperative outcomes following LT, supporting the need for careful hemodynamic assessment and management of patients presenting multiple/larger shunts.

Echo-Endoscopy Combined with Virtual Reality: A Whole Perspective of Laparoscopic Common Bile Duct Exploration in Children.

Introduction: Endoscopic procedures are performed more frequently in children due to technological advances that can be safely performed in an adequate setting with a support of a multidisciplinary team. Pediatric indications for ERCP (endoscopic retrograde cholangiopancreatography) and EUS (endoscopic ultrasound) occur mainly due to congenital malformations. In a pediatric case series, we report the application of EUS combined with duodenoscopy, eventually associated with ERCP and minimally invasive surgery, highlighting the importance of defining a tailored dedicated management pathway for each patient. Patients and methods: A series of 12 patients, managed at our Center in the last three years, were evaluated, and their management was discussed. Results: EUS was performed in eight patients and permitted the differential diagnosis of duplication cysts and the visualization of the biliary tree and pancreatic anatomy. ERCP was attempted in five patients: in one case, it permitted the preservation of pancreatic tissue, postponing surgery and in three patients, it was technically unfeasible. MIS (minimally invasive surgery) was performed in seven patients, two with laparoscopic common bile duct exploration (LCBDE). Precise anatomical definition and the possibility of surgical simulation and team sharing were evaluated under VR HMD (Virtual Reality Head Mounted Display) in four cases. Conclusions: Exploration of the common bile duct in children differs from that of the adult population and combines echo-endoscopy and ERCP. The integrated use of minimally invasive surgery in the pediatric area is necessary for the whole management perspective in complex malformations and small patients. The introduction in the clinical practice of a preoperative study with Virtual Reality allows a better survey of the malformation and a tailored treatment.

Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches.

BACKGROUND: To develop a pipeline for automatic extraction of quantitative metrics and radiomic features from lung computed tomography (CT) and develop artificial intelligence (AI) models supporting differential diagnosis between coronavirus disease 2019 (COVID-19) and other viral pneumonia (non-COVID-19). METHODS: Chest CT of 1,031 patients (811 for model building; 220 as independent validation set (IVS) with positive swab for severe acute respiratory syndrome coronavirus-2 (647 COVID-19) or other respiratory viruses (384 non-COVID-19) were segmented automatically. A Gaussian model, based on the HU histogram distribution describing well-aerated and ill portions, was optimised to calculate quantitative metrics (QM, n = 20) in both lungs (2L) and four geometrical subdivisions (GS) (upper front, lower front, upper dorsal, lower dorsal; n = 80). Radiomic features (RF) of first (RF1, n = 18) and second (RF2, n = 120) order were extracted from 2L using PyRadiomics tool. Extracted metrics were used to develop four multilayer-perceptron classifiers, built with different combinations of QM and RF: Model1 (RF1-2L); Model2 (QM-2L, QM-GS); Model3 (RF1-2L, RF2-2L); Model4 (RF1-2L, QM-2L, GS-2L, RF2-2L). RESULTS: The classifiers showed accuracy from 0.71 to 0.80 and area under the receiving operating characteristic curve (AUC) from 0.77 to 0.87 in differentiating COVID-19 versus non-COVID-19 pneumonia. Best results were associated with Model3 (AUC 0.867 ± 0.008) and Model4 (AUC 0.870 ± 0.011. For the IVS, the AUC values were 0.834 ± 0.008 for Model3 and 0.828 ± 0.011 for Model4. CONCLUSIONS: Four AI-based models for classifying patients as COVID-19 or non-COVID-19 viral pneumonia showed good diagnostic performances that could support clinical decisions.

A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia.

BACKGROUND: The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model. METHODS: LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model. RESULTS: Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81. CONCLUSIONS: Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts. KEY POINTS: We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.

Stem Cell Injection for Complex Refractory Perianal Fistulas in Crohn's Disease: A Single Center Initial Experience.

From 30 to 70% of patients with Crohn's disease (CD) may develop perianal fistulas during their lifetime. The medical and surgical management of this complication is challenging, and its treatment still gives unsatisfactory results. However, recent studies on adipose-derived mesenchymal stem cells have proven their anti-inflammatory and immuno-modulatory potential, representing a new promising tool in the treatment of such stubborn disease. We report our initial experience with three patients who had recurrent perianal CD treated with local infiltration of stem cell darvadstrocel (Alofisel). All the patients had a long history of perianal disease refractory to multiple medical and surgical treatments. The preoperative workup included transperineal ultrasound (TP-US), pelvic MRI, and colonoscopy that ruled out active proctitis in all the patients. The post-treatment follow-up included clinical assessment at 1, 3, and 6 months with repeated MRI and TP-US at 6 months. At 6 months, 2 patients had a clinical response despite radiological persistence of fistula tracts, while one patient presented perianal fistula recurrence complicated by perianal abscess. Although our experience is limited to 3 patients and a short follow-up, our results confirm that darvadstrocel injection is a safe procedure, with a good clinical response in most of the patients, but that it apparently had no effect on the anatomical modification of the fistula tracts. Long-term results, with a rigorous assessment of anatomical lesions, are still needed to support the promising data of the literature.

Developing Virtual Reality Head Mounted Display (HMD) Set-Up for Thoracoscopic Surgery of Complex Congenital Lung MalFormations in Children.

Video assisted thoracoscopic surgery (VATS) has been adopted in pediatric age for the treatment of congenital lung malformations (CLM). The success of VATS in pediatrics largely depends on the surgeon's skill ability to understand the airways, vascular system and lung parenchyma anatomy in CLM. In the last years, virtual reality (VR) and 3-dimensional (3D) printing of organ models and VR head mounted display (HMD) technologies have been introduced for completion of preoperative planning in adult patients. To date no reports about the use of VR HMD technologies in a pediatric setting are available. The aim of this report is to introduce a VR HMD model in VATS procedure to improve the quality of care in children with CLM. VR HMD set-up for planning thoracoscopic surgery was performed in a series of pediatric patients with diagnosis of CLM. The preoperative VR HMD evaluation allowed a navigation into the malformation with the aim to explore, interact, and make the surgeon more confident and skilled to answer to the traps. A development of surgical simulations models and teaching program dedicated to education and training in pediatric VATS is suitable among the pediatric surgery community. Further studies should demonstrate all the benefits of such technology in pediatric patients submitted to VATS procedure.

Delta-Radiomics Predicts Response to First-Line Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients with Liver Metastases.

The purpose of this paper is to develop and validate a delta-radiomics score to predict the response of individual colorectal cancer liver metastases (lmCRC) to first-line FOLFOX chemotherapy. Three hundred one lmCRC were manually segmented on both CT performed at baseline and after the first cycle of first-line FOLFOX, and 107 radiomics features were computed by subtracting textural features of CT at baseline from those at timepoint 1 (TP1). LmCRC were classified as nonresponders (R-) if they showed progression of disease (PD), according to RECIST1.1, before 8 months, and as responders (R+), otherwise. After feature selection, we developed a decision tree statistical model trained using all lmCRC coming from one hospital. The final output was a delta-radiomics signature subsequently validated on an external dataset. Sensitivity, specificity, positive (PPV), and negative (NPV) predictive values in correctly classifying individual lesions were assessed on both datasets. Per-lesion sensitivity, specificity, PPV, and NPV were 99%, 94%, 95%, 99%, 85%, 92%, 90%, and 87%, respectively, in the training and validation datasets. The delta-radiomics signature was able to reliably predict R- lmCRC, which were wrongly classified by lesion RECIST as R+ at TP1, (93%, averaging training and validation set, versus 67% of RECIST). The delta-radiomics signature developed in this study can reliably predict the response of individual lmCRC to oxaliplatin-based chemotherapy. Lesions forecasted as poor or nonresponders by the signature could be further investigated, potentially paving the way to lesion-specific therapies.

Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model.

Growing evidence suggests that artificial intelligence tools could help radiologists in differentiating COVID-19 pneumonia from other types of viral (non-COVID-19) pneumonia. To test this hypothesis, an R-AI classifier capable of discriminating between COVID-19 and non-COVID-19 pneumonia was developed using CT chest scans of 1031 patients with positive swab for SARS-CoV-2 (n = 647) and other respiratory viruses (n = 384). The model was trained with 811 CT scans, while 220 CT scans (n = 151 COVID-19; n = 69 non-COVID-19) were used for independent validation. Four readers were enrolled to blindly evaluate the validation dataset using the CO-RADS score. A pandemic-like high suspicion scenario (CO-RADS 3 considered as COVID-19) and a low suspicion scenario (CO-RADS 3 considered as non-COVID-19) were simulated. Inter-reader agreement and performance metrics were calculated for human readers and R-AI classifier. The readers showed good agreement in assigning CO-RADS score (Gwet's AC2 = 0.71, p < 0.001). Considering human performance, accuracy = 78% and accuracy = 74% were obtained in the high and low suspicion scenarios, respectively, while the AI classifier achieved accuracy = 79% in distinguishing COVID-19 from non-COVID-19 pneumonia on the independent validation dataset. The R-AI classifier performance was equivalent or superior to human readers in all comparisons. Therefore, a R-AI classifier may support human readers in the difficult task of distinguishing COVID-19 from other types of viral pneumonia on CT imaging.