Risk Assessment and Radiomics Analysis in Magnetic Resonance Imaging of Pancreatic Intraductal Papillary Mucinous Neoplasms (IPMN).

Intraductal papillary mucinous neoplasms (IPMNs) are a very common incidental finding during patient radiological assessment. These lesions may progress from low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and even pancreatic cancer. The IPMN progression risk grows with time, so discontinuation of surveillance is not recommended. It is very important to identify imaging features that suggest LGD of IPMNs, and thus, distinguish lesions that only require careful surveillance from those that need surgical resection. It is important to know the management guidelines and especially the indications for surgery, to be able to point out in the report the findings that suggest malignant degeneration. The imaging tools employed for diagnosis and risk assessment are Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) with contrast medium. According to the latest European guidelines, MRI is the method of choice for the diagnosis and follow-up of patients with IPMN since this tool has a highest sensitivity in detecting mural nodules and intra-cystic septa. It plays a key role in the diagnosis of worrisome features and high-risk stigmata, which are associated with IPMNs malignant degeneration. Nowadays, the main limit of diagnostic tools is the ability to identify the precursor of pancreatic cancer. In this context, increasing attention is being given to artificial intelligence (AI) and radiomics analysis. However, these tools remain in an exploratory phase, considering the limitations of currently published studies. Key limits include noncompliance with AI best practices, radiomics workflow standardization, and clear reporting of study methodology, including segmentation and data balancing. In the radiological report it is useful to note the type of IPMN so as the morphological features, size, rate growth, wall, septa and mural nodules, on which the indications for surveillance and surgery are based. These features should be reported so as the surveillance time should be suggested according to guidelines.

Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.

PURPOSE: To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases. METHODS: Patient selection in a retrospective study was carried out from January 2018 to May 2021 considering the following inclusion criteria: patients subjected to surgical resection for liver metastases; proven pathological liver metastases; patients subjected to enhanced CT examination in the presurgical setting with a good quality of images; and RAS assessment as standard reference. A total of 851 radiomics features were extracted using the PyRadiomics Python package from the Slicer 3D image computing platform after slice-by-slice segmentation on CT portal phase by two expert radiologists of each individual liver metastasis performed first independently by the individual reader and then in consensus. Balancing technique was performed, and inter- and intraclass correlation coefficients were calculated to assess the between-observer and within-observer reproducibility of features. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers were considered. Moreover, features selection was performed before and after a normalized procedure using two different methods (3-sigma and z-score). RESULTS: Seventy-seven liver metastases in 28 patients with a mean age of 60 years (range 40-80 years) were analyzed. The best predictors, at univariate analysis for both normalized procedures, were original_shape_Maximum2DDiameter and wavelet_HLL_glcm_InverseVariance that reached an accuracy of 80%, an AUC ≥ 0.75, a sensitivity ≥ 80% and a specificity ≥ 70% (p value < < 0.01). However, a multivariate analysis significantly increased the accuracy in RAS prediction when a linear regression model (LRM) was used. The best performance was obtained using a LRM combining linearly 12 robust features after a z-score normalization procedure: AUC of 0.953, accuracy 98%, sensitivity 96%, specificity of 100%, PPV 100% and NPV 96% (p value < < 0.01). No statistically significant increase was obtained considering the tested machine learning both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in CT radiomics analysis allows to predict RAS mutational status in colorectal liver metastases patients.

Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging.

PURPOSE: To assess the efficacy of radiomics features, obtained by magnetic resonance imaging (MRI) with hepatospecific contrast agent, in pre-surgical setting, to predict RAS mutational status in liver metastases. METHODS: Patients with MRI in pre-surgical setting were enrolled in a retrospective study. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. The features were extracted considering the agreement with the Imaging Biomarker Standardization Initiative (IBSI). Balancing was performed through synthesis of samples for the underrepresented classes using the self-adaptive synthetic oversampling (SASYNO) approach. Inter- and intraclass correlation coefficients (ICC) were calculated to assess the between-observer and within-observer reproducibility of all radiomics characteristics. For continuous variables, nonparametric Wilcoxon-Mann-Whitney test was utilized. Benjamini and Hochberg's false discovery rate (FDR) adjustment for multiple testing was used. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Moreover, features selection were performed before and after a normalized procedure using two different methods (3-sigma and z-score). McNemar test was used to assess differences statistically significant between dichotomic tables. All statistical procedures were done using MATLAB R2021b Statistics and Machine Toolbox (MathWorks, Natick, MA, USA). RESULTS: Seven normalized radiomics features, extracted from arterial phase, 11 normalized radiomics features, from portal phase, 12 normalized radiomics features from hepatobiliary phase and 12 normalized features from T2-W SPACE sequence were robust predictors of RAS mutational status. The multivariate analysis increased significantly the accuracy in RAS prediction when a LRM was used, combining 12 robust normalized features extracted by VIBE hepatobiliary phase reaching an accuracy of 99%, a sensitivity 97%, a specificity of 100%, a PPV of 100% and a NPV of 98%. No statistically significant increase was obtained, considering the tested classifiers DT, KNN and SVM, both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in MRI radiomics analysis allows to predict RAS mutational status.

Is robotic liver resection feasible in patients with lesions in close proximity to major vessels? A propensity score matching analysis.

INTRODUCTION: Robotic surgery is widely diffused in the surgical field and is becoming increasingly prevalent, however several aspects need more detailed assessment. One of them concerns the role of robotic liver surgery for lesions in contact with major vascular (CMV) pedicles. The aim of our study is to evaluate and compare intra and post operative outcomes in patients undergoing robotic liver resections between lesions in contact or free from major vessels. METHODS: A multicentric retrospective study was performed including 1030 patients who underwent robotic liver resection. Patients were divided into two groups according to vascular contact. Intra and post-operative outcomes were compared between the groups before and after Propensity Score Matching. RESULTS: After propensity score matching 889 patients were included in the study. Among these lesions, 595 were not in contact with major vessels (NCMV) and 294 were in contact with major vessels (CMV). Use of Pringle Manoeuvre was more associated with CMV resections (49.8 % vs 31.2 %, p = 0,0001). No differences in terms of operative time, conversion rate, morbidity and type of complications were observed after PSM. CONCLUSION: The presents study shows how robotic surgery is a valid and safe technique also for resection of tumors close to vascular pedicles.

Dynamic surgical anatomy using 3D reconstruction technology in complex hepato-biliary surgery with vascular involvement. Results from an international multicentric survey.

INTRODUCTION: Three-dimensional liver modeling can lead to substantial changes in choosing the type and extension of liver resection. This study aimed to explore whether 3D reconstruction helps to better understand the relationship between liver tumors and neighboring vascular structures compared to standard 2D CT scan images. METHODS: Contrast-enhanced CT scan images of 11 patients suffering from primary and secondary hepatic tumors were selected. Twenty-three experienced HBP surgeons participated to the survey. A standardized questionnaire outlining 16 different vascular structures (items) having a potential relationship with the tumor was provided. Intraoperative and histopathological findings were used as the reference standard. The proper hypothesis was that 3D accuracy is greater than 2D. As a secondary endpoint, inter-raters' agreement was explored. RESULTS: The mean difference between 3D and 2D, was 2.6 points (SE: 0.40; 95 % CI: 1.7-3.5; p < 0.0001). After sensitivity analysis, the results favored 3D visualization as well (mean difference 1.7 points; SE: 0.32; 95 % CI: 1.0-2.5; p = 0.0004). The inter-raters' agreement was moderate for both methods (2D: W = 0.45; 3D: W = 0.44). CONCLUSION: 3D reconstruction may give a significant contribution to better understanding liver vascular anatomy and the precise relationship between the tumor and the neighboring structures.

Integrated plasma metabolomics and lipidomics profiling highlights distinctive signature of hepatocellular carcinoma in HCV patients.

BACKGROUND: Early diagnosis of hepatocellular carcinoma (HCC) is essential towards the improvement of prognosis and patient survival. Circulating markers such as α-fetoprotein (AFP) and micro-RNAs represent useful tools but still have limitations. Identifying new markers can be fundamental to improve both diagnosis and prognosis. In this approach, we harness the potential of metabolomics and lipidomics to uncover potential signatures of HCC. METHODS: A combined untargeted metabolomics and lipidomics plasma profiling of 102 HCV-positive patients was performed by HILIC and RP-UHPLC coupled to Mass Spectrometry. Biochemical parameters of liver function (AST, ALT, GGT) and liver cancer biomarkers (AFP, CA19.9 e CEA) were evaluated by standard assays. RESULTS: HCC was characterized by an elevation of short and long-chain acylcarnitines, asymmetric dimethylarginine, methylguanine, isoleucylproline and a global reduction of lysophosphatidylcholines. A supervised PLS-DA model showed that the predictive accuracy for HCC class of metabolomics and lipidomics was superior to AFP for the test set (100.00% and 94.40% vs 55.00%). Additionally, the model was applied to HCC patients with AFP values < 20 ng/mL, and, by using only the top 20 variables selected by VIP scores achieved an Area Under Curve (AUC) performance of 0.94. CONCLUSION: These exploratory findings highlight how metabo-lipidomics enables the distinction of HCC from chronic HCV conditions. The identified biomarkers have high diagnostic potential and could represent a viable tool to support and assist in HCC diagnosis, including AFP-negative patients.

Oligo-metastatic neoPlasms from the gastro-intestinal tract: iDentIfiCaTIon of cliNical and molecular drivers: the PREDICTION study.

BACKGROUND: Metastatic disease in tumors originating from the gastrointestinal tract can exhibit varying degrees of tumor burden at presentation. Some patients follow a less aggressive disease course, characterized by a limited number of metastatic sites, referred to as "oligo-metastatic disease" (OMD). The precise biological characteristics that define the oligometastatic behavior remain uncertain. In this study, we present a protocol designed to prospectively identify OMD, with the aim of proposing novel therapeutic approaches and monitoring strategies. METHODS: The PREDICTION study is a monocentric, prospective, observational investigation. Enrolled patients will receive standard treatment, while translational activities will involve analysis of the tumor microenvironment and genomic profiling using immunohistochemistry and next-generation sequencing, respectively. The first primary objective (descriptive) is to determine the prevalence of biological characteristics in OMD derived from gastrointestinal tract neoplasms, including high genetic concordance between primary tumors and metastases, a significant infiltration of T lymphocytes, and the absence of clonal evolution favoring specific driver genes (KRAS and PIK3CA). The second co-primary objective (analytic) is to identify a prognostic score for true OMD, with a primary focus on metastatic colorectal cancer. The score will comprise genetic concordance (> 80%), high T-lymphocyte infiltration, and the absence of clonal evolution favoring driver genes. It is hypothesized that patients with true OMD (score 3+) will have a lower rate of progression/recurrence within one year (20%) compared to those with false OMD (80%). The endpoint of the co-primary objective is the rate of recurrence/progression at one year. Considering a reasonable probability (60%) of the three factors occurring simultaneously in true OMD (score 3+), using a significance level of α = 0.05 and a test power of 90%, the study requires a minimum enrollment of 32 patients. DISCUSSION: Few studies have explored the precise genetic and biological features of OMD thus far. In clinical settings, the diagnosis of OMD is typically made retrospectively, as some patients who undergo intensive treatment for oligometastases develop polymetastatic diseases within a year, while others do not experience disease progression (true OMD). In the coming years, the identification of true OMD will allow us to employ more personalized and comprehensive strategies in cancer treatment. TRIAL REGISTRATION: ClinicalTrials.gov ID NCT05806151.

Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment.

OBJECTIVE: The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated. METHODS: The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed. RESULTS: The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence. CONCLUSIONS: The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.

Survival benefit of second line therapies for recurrent hepatocellular carcinoma: repeated hepatectomy, thermoablation and second-line transplant referral in a real life national scenario.

BACKGROUND: Despite second-line transplant(SLT) for recurrent hepatocellular carcinoma(rHCC) leads to the longest survival after recurrence(SAR), its real applicability has never been reported. The aim was to compare the SAR of SLT versus repeated hepatectomy and thermoablation(CUR group). METHODS: Patients were enrolled from the Italian register HE.RC.O.LE.S. between 2008 and 2021. Two groups were created: CUR versus SLT. A propensity score matching (PSM) was run to balance the groups. RESULTS: 743 patients were enrolled, CUR = 611 and SLT = 132. Median age at recurrence was 71(IQR 6575) years old and 60(IQR 53-64, p < 0.001) for CUR and SLT respectively. After PSM, median SAR for CUR was 43 months(95%CI = 37 - 93) and not reached for SLT(p < 0.001). SLT patients gained a survival benefit of 9.4 months if compared with CUR. MilanCriteria(MC)-In patients were 82.7% of the CUR group. SLT(HR 0.386, 95%CI = 0.23 - 0.63, p < 0.001) and the MELD score(HR 1.169, 95%CI = 1.07 - 1.27, p < 0.001) were the only predictors of mortality. In case of MC-Out, the only predictor of mortality was the number of nodules at recurrence(HR 1.45, 95%CI= 1.09 - 1.93, p = 0.011). CONCLUSION: It emerged an important transplant under referral in favour of repeated hepatectomy or thermoablation. In patients with MC-Out relapse, the benefit of SLT over CUR was not observed.

Management of cutaneous melanoma: radiologists challenging and risk assessment.

Melanoma patient remains a challenging for the radiologist, due to the difficulty related to the management of a patient more often in an advanced stage of the disease. It is necessary to determine a stratification of risk, optimizing the means, with diagnostic tools that should be optimized in relation to the type of patient, and improving knowledge. Staging and risk assessment procedures are determined by disease presentation at diagnosis. Melanoma staging is a critical tool to assist clinical decision-making and prognostic assessment. It is used for clinical trial design, eligibility, stratification, and analysis. The current standard for regional lymph nodes staging is represented by the sentinel lymph node excision biopsy procedure. For staging of distant metastases, PET-CT has the highest sensitivity and diagnostic odds ratio. Similar trend is observed during melanoma surveillance. The advent of immunotherapy, which has improved patient outcome, however, has determined new issues for radiologists, partly due to atypical response patterns, partly due to adverse reactions that must be identified as soon as possible for the correct management of the patient. The main objectives of the new ir-criteria are to standardize the assessment between different trials. However, these ir-criteria do not take into account all cases of atypical response patterns, as hyperprogression or dissociated responses. None of these criteria has actually been uniformly adopted in routine. The immune-related adverse events (irAEs) can involve various organs from head to toe. It is crucial for radiologists to know the imaging appearances of this condition, to exclude recurrent or progressive disease and for pneumonitis, since it could be potentially life-threatening toxicity, resulting in pneumonitis-related deaths in early phase trials, to allow a proper patient management.

Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases.

PURPOSE: To assess the efficacy of radiomics features obtained by T2-weighted sequences to predict clinical outcomes following liver resection in colorectal liver metastases patients. METHODS: This retrospective analysis was approved by the local Ethical Committee board and radiological databases were interrogated, from January 2018 to May 2021, to select patients with liver metastases with pathological proof and MRI study in pre-surgical setting. The cohort of patients included a training set and an external validation set. The internal training set included 51 patients with 61 years of median age and 121 liver metastases. The validation cohort consisted a total of 30 patients with single lesion with 60 years of median age. For each volume of interest, 851 radiomics features were extracted as median values using PyRadiomics. Nonparametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbours (KNN), artificial neural network (NNET) and decision tree (DT)) were considered. RESULTS: The best predictor to discriminate expansive versus infiltrative front of tumour growth was obtained by wavelet_LHL_gldm_DependenceNonUniformityNormalized with an accuracy of 82%; to discriminate high grade versus low grade or absent was the wavelet_LLH_glcm_Imc1 with accuracy of 88%; to differentiate the mucinous type of tumour was the wavelet_LLH_glcm_JointEntropy with accuracy of 92% while to identify tumour recurrence was the wavelet_LLL_glcm_Correlation with accuracy of 85%. Linear regression model increased the performance obtained with respect to the univariate analysis exclusively in the discrimination of expansive versus infiltrative front of tumour growth reaching an accuracy of 90%, a sensitivity of 95% and a specificity of 80%. Considering significant texture metrics tested with pattern recognition approaches, the best performance was reached by the KNN in the discrimination of the tumour budding considering the four textural predictors obtaining an accuracy of 93%, a sensitivity of 81% and a specificity of 97%. CONCLUSIONS: Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases.

PURPOSE: The purpose of this study is to evaluate the Radiomics and Machine Learning Analysis based on MRI in the assessment of Liver Mucinous Colorectal Metastases.Query METHODS: The cohort of patients included a training set (121 cases) and an external validation set (30 cases) with colorectal liver metastases with pathological proof and MRI study enrolled in this approved study retrospectively. About 851 radiomics features were extracted as median values by means of the PyRadiomics tool on volume on interest segmented manually by two expert radiologists. Univariate analysis, linear regression modelling and pattern recognition methods were used as statistical and classification procedures. RESULTS: The best results at univariate analysis were reached by the wavelet_LLH_glcm_JointEntropy extracted by T2W SPACE sequence with accuracy of 92%. Linear regression model increased the performance obtained respect to the univariate analysis. The best results were obtained by a linear regression model of 15 significant features extracted by the T2W SPACE sequence with accuracy of 94%, a sensitivity of 92% and a specificity of 95%. The best classifier among the tested pattern recognition approaches was k-nearest neighbours (KNN); however, KNN achieved lower precision than the best linear regression model. CONCLUSIONS: Radiomics metrics allow the mucinous subtype lesion characterization, in order to obtain a more personalized approach. We demonstrated that the best performance was obtained by T2-W extracted textural metrics.

On the CO2 gain in on-line hemodiafiltration.

BACKGROUND: The dialysis bath holds up to 90 mmHg carbon dioxide (CO2 ) in order to keep pH low and salts in their soluble forms. CO2 crosses the dialyzer membrane and diffuses to patients. In post-dilution on-line hemodiafiltration (HDF) many liters of CO2 -containing dialysis bath - in the form of infusate - are delivered directly to patients bypassing the filtering membrane, but the precise amount of CO2 delivered is unknown. METHODS: To gain insights on this issue 18 outpatients undergoing their regular on-line HDF were investigated by means of blood gas analysis. RESULTS: Arterial pre-dialysis samples show slight hypocapnia (35.40 ± 3.22 mmHg) consistent with the secondary compensatory response to metabolic acidosis. In blood coming back to patients (venous line of extracorporeal circuit) pCO2 doubled, amounting to 69 ± 5.5 mmHg (P < .0001 with respect to pre-dialysis values) hence in on-line HDF a CO2 gain does occur. Turning off the infusate flux pump, pCO2 decreased to 63.1 ± 5.8 mmHg (P = .004) meaning that delivery of infusate in post-dilution mode significantly contributes to CO2 gain, albeit by a small amount. CONCLUSION: On-line HDF is featured by CO2 delivery to patients, in part dragged by the infusate.

Intrahepatic cholangiocarcinoma and its differential diagnosis at MRI: how radiologist should assess MR features.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is the second most common type of primary hepatic malignancy. Aim of this work is to analyse the features of ICC and its differential diagnosis at MRI, assessing two categories intraparenchymal and peribiliary lesions. METHODS: The study population included 88 patients with histological diagnosis of ICCs: 61 with mass-forming type, 23 with periductal-infiltrating tumours and 4 with intraductal-growing type. As a control study groups, we identified: 86 consecutive patients with liver colorectal intrahepatic metastases (mCRC) (groups A); 35 consecutive patients with peribiliary metastases (groups B); 62 consecutive patients (groups C) with hepatocellular carcinoma (HCC); 18 consecutive patients (groups D) with combined hepatocellular cholangiocarcinoma (cHCC-CCA); and 26 consecutive patients (groups E) with hepatic hemangioma. For all lesions, magnetic resonance (MR) features were assessed according to Liver Imaging Reporting and Data System (LI-RADS) version 2018. The liver-specific gadolinium ethoxybenzyl dimeglumine-EOB (Primovist, Bayer Schering Pharma, Germany), was employed. Chi-square test was employed to analyse differences in percentage values of categorical variable, while the nonparametric Kruskal-Wallis test was used to test for statistically significant differences between the median values of the continuous variables. However, false discovery rate adjustment according to Benjamin and Hochberg for multiple testing was considered. RESULTS: T1- and T2-weighted signal intensity (SI), restricted diffusion, transitional phase (TP) and hepatobiliary phase (HP) aspects allowed the differentiation between study group (mass-forming ICCs) and each other control group (A, C, D, E) with statistical significance, while arterial phase (AP) appearance allowed the differentiation between study group and the control groups C and D with statistical significance and PP appearance allowed the differentiation between study group and the control groups A, C and D with statistical significance. Instead, no MR feature allowed the differentiation between study group (periductal-infiltrating type) and control group B. CONCLUSION: T1 and T2 W SI, restricted diffusion, TP and HP appearance allowed the differentiation between mass-forming ICCs and mimickers with statistical significance, while AP appearance allowed the differentiation between study group and the control groups C and D with statistical significance and PP appearance allowed the differentiation between study group and the control groups A, C and D.

Removal of non-steroidal anti-inflammatory drugs from water by zeolite-rich composites: The interference of inorganic anions on the ibuprofen and naproxen adsorption.

Composites of two natural zeolites - clinoptilolite and phillipsite, and cationic surfactants (cetylpyridinium chloride and Arquad® 2HT-75) were tested for the removal of two emerging contaminants - ibuprofen and naproxen. For each zeolite-rich rock, two different modifications of the zeolitic surfaces were prepared (monolayer and bilayer surfactant coverage). The influence of the initial drug concentrations and contact time on adsorption of these drugs was followed in buffer solution. The Langmuir model showed the highest adsorption capacity for the composite characterized by a bilayered surfactant at the clinoptilolite surface: 19.7 mg/g and 16.1 mg/g for ibuprofen and naproxen, respectively. Also, to simulate real systems, drug adsorption isotherms were conducted in natural water (Grindstone creek water - Columbia, Missouri, USA) by using the best performing adsorbent; in this case, a slight decrease of drug adsorption was recorded. Kinetic runs were performed in distilled water as well as in the presence of ions such as sulfates and bicarbonates; also, in this case, the interfering agents defined an adsorption decrease for bilayer composites.

Evaluation of Expected Ventilatory Response to Metabolic Acidosis in Severely Ill Patients.

INTRODUCTION: Winters' formula (pCO2 =1.5*HCO3 +8) is used worldwide to predict the ventilatory response to metabolic acidosis, namely to predict the pCO2 value complying with reduction of serum bicarbonate concentration (HCO3 ). This equation was obtained half a century ago in mostly pediatric subjects. Subsequently different and inconsistent rules have been suggested. The study was done to verify the reliability of Winters' formula in severely ill patients with respect of other modern and commonly used formulas. METHODS: We applied Winters' formula and some other formulas to a dataset of arterial gas analysis from 29 severely ill malaria patients (about half of them requiring ICU or hemodialysis). The expected pCO2 value was computed by each formula and the root mean square error (RMSE) was measured. Beyond predicting the expected pCO2 value, expected range of values was also computed (as expected value ± each own error) and agreement with the best fit equation (± its error) was assessed. RESULTS: In this dataset featured by metabolic acidosis of moderate degree (mean pH 7.2, mean HCO3 : 15.3 mmol/l) a strong positive linear relationship between pCO2 and HCO3 was found (R squared =0.97). The best fit linear equation was in form of pCO2 = 1.28*HCO3 +11.55. Winters' formula exhibits the lowest RMSE (1 mmHg) and shows the better agreement (Cohen's kappa=0.7) with the best fit equation Conclusions: Winters' formula can still profitably used to compute the expected pCO2 value and in turn to infer mixed (metabolic plus respiratory) acid-base disorders in severely ill patients.

FGFR Fusions in Cancer: From Diagnostic Approaches to Therapeutic Intervention.

Fibroblast growth factor receptors (FGFRs) are tyrosine kinase receptors involved in many biological processes. Deregulated FGFR signaling plays an important role in tumor development and progression in different cancer types. FGFR genomic alterations, including FGFR gene fusions that originate by chromosomal rearrangements, represent a promising therapeutic target. Next-generation-sequencing (NGS) approaches have significantly improved the discovery of FGFR gene fusions and their detection in clinical samples. A variety of FGFR inhibitors have been developed, and several studies are trying to evaluate the efficacy of these agents in molecularly selected patients carrying FGFR genomic alterations. In this review, we describe the most frequent FGFR aberrations in human cancer. We also discuss the different approaches employed for the detection of FGFR fusions and the potential role of these genomic alterations as prognostic/predictive biomarkers.

Radiofrequency Ablation and Microwave Ablation in Liver Tumors: An Update.

This article provides an overview of radiofrequency ablation (RFA) and microwave ablation (MWA) for treatment of primary liver tumors and hepatic metastasis. Only studies reporting RFA and MWA safety and efficacy on liver were retained. We found 40 clinical studies that satisfied the inclusion criteria. RFA has become an established treatment modality because of its efficacy, reproducibility, low complication rates, and availability. MWA has several advantages over RFA, which may make it more attractive to treat hepatic tumors. According to the literature, the overall survival, local recurrence, complication rates, disease-free survival, and mortality in patients with hepatocellular carcinoma (HCC) treated with RFA vary between 53.2 ± 3.0 months and 66 months, between 59.8% and 63.1%, between 2% and 10.5%, between 22.0 ± 2.6 months and 39 months, and between 0% and 1.2%, respectively. According to the literature, overall survival, local recurrence, complication rates, disease-free survival, and mortality in patients with HCC treated with MWA (compared with RFA) vary between 22 months for focal lesion >3 cm (vs. 21 months) and 50 months for focal lesion ≤3 cm (vs. 27 months), between 5% (vs. 46.6%) and 17.8% (vs. 18.2%), between 2.2% (vs. 0%) and 61.5% (vs. 45.4%), between 14 months (vs. 10.5 months) and 22 months (vs. no data reported), and between 0% (vs. 0%) and 15% (vs. 36%), respectively. According to the literature, the overall survival, local recurrence, complication rates, and mortality in liver metastases patients treated with RFA (vs. MWA) are not statistically different for both the survival times from primary tumor diagnosis and survival times from ablation, between 10% (vs. 6%) and 35.7% (vs. 39.6), between 1.1% (vs. 3.1%) and 24% (vs. 27%), and between 0% (vs. 0%) and 2% (vs. 0.3%). MWA should be considered the technique of choice in selected patients, when the tumor is ≥3 cm in diameter or is close to large vessels, independent of its size. IMPLICATIONS FOR PRACTICE: Although technical features of the radiofrequency ablation (RFA) and microwave ablation (MWA) are similar, the differences arise from the physical phenomenon used to generate heat. RFA has become an established treatment modality because of its efficacy, reproducibility, low complication rates, and availability. MWA has several advantages over RFA, which may make it more attractive than RFA to treat hepatic tumors. The benefits of MWA are an improved convection profile, higher constant intratumoral temperatures, faster ablation times, and the ability to use multiple probes to treat multiple lesions simultaneously. MWA should be considered the technique of choice when the tumor is ≥3 cm in diameter or is close to large vessels, independent of its size.

Cetuximab, irinotecan and fluorouracile in fiRst-line treatment of immunologically-selected advanced colorectal cancer patients: the CIFRA study protocol.

BACKGROUND: Combination of chemotherapies (fluoropirimidines, oxaliplatin and irinotecan) with biologic drugs (bevacizumab, panitumumab, cetuximab) have improved clinical responses and survival of metastatic colorectal cancer (mCRC). However, patients' selection thorough the identification of predictive factors still represent a challange. Cetuximab (Erbitux®), a chimeric monoclonal antibody binding to the Epidermal Growth Factor Receptor (EGFR), belongs to the Immunoglobulins (Ig) grade 1 subclass able to elicite both in vitro and in vivo the Antibody-Dependent Cell-mediated Cytotoxicity (ADCC). ADCC is the cytotoxic killing of antibody-coated target cells by immunologic effectors. The effector cells express a receptor for the Fc portion of these antibodies (FcγR); genetic polymorphisms of FcγR modify the binding affinity with the Fc of IgG1. Interestingly, the high-affinity FcγRIIIa V/V is associated with increased ADCC in vitro and in vivo. Thus, ADCC could partially account for cetuximab activity. METHODS/DESIGN: CIFRA is a single arm, open-label, phase II study assessing the activity of cetuximab in combination with irinotecan and fluorouracile in FcγRIIIa V/V patients with KRAS, NRAS, BRAF wild type mCRC. The study is designed with a two-stage Simon model based on a hypothetical higher response rate (+ 10%) of FcγRIIIa V/V patients as compared to previous trials (about 60%) assuming ADCC as one of the possible mechanisms of cetuximab action. The test power is 95%, the alpha value of the I-type error is 5%. With these assumptions the sample for passing the first stage is 14 patients with > 6 responses and the final sample is 34 patients with > 18 responses to draw positive conclusions. Secondary objectives include toxicity, responses' duration, progression-free and overall survival. Furthermore, an associated translational study will assess the patients' cetuximab-mediated ADCC and characterize the tumor microenvironment. DISCUSSION: The CIFRA study will determine whether ADCC contributes to cetuximab activity in mCRC patients selected on an innovative immunological screening. Data from the translational study will support results' interpretation as well as provide new insights in host-tumor interactions and cetuximab activity. TRIAL REGISTRATION: The CIFRA trial (version 0.0, June 21, 2018) has been registered into the NIH-US National Library of Medicine, ClinicalTrials.gov database with the identifier number ( NCT03874062 ).