Robotic Major Hepatectomy in Elderly Patient.

BACKGROUND: the role of minimally invasive liver surgery has been progressively developed, with the practice increasing in safety and feasibility also with respect to major liver resections. The aim of this study was to analyze the feasibility and safety of major liver resection in elderly patients. METHODS: data from a multicentric retrospective database including 1070 consecutive robotic liver resections in nine European hospital centers were analyzed. Among these, 131 were major liver resections. Patients were also divided in two groups (<65 years old and ≥65 years old) and perioperative data were compared between the two groups. RESULTS: a total of 131 patients were included in the study. Operative time was 332 ± 125 min. Postoperative overall complications occurred in 27.1% of patients. Severe complications (Clavien Dindo ≥ 3) were 9.9%. Hospital stay was 6.6 ± 5.3 days. Patients were divided into two groups based on their age: 75 patients < 65 years old and 56 patients ≥ 65 years old. Prolonged pain, lung infection, intensive care stay, and 90-day readmission were worse in the elderly group. The two groups were matched for ASA and Charlson comorbidity score and, after statistical adjustment, postoperative data were similar between two groups. CONCLUSIONS: robotic major liver resection in elderly patients was associated with satisfying short-term outcomes.

Pancreatic Neuroendocrine Tumors: What Is the Best Surgical Option?

Background: Pancreatic neuroendocrine tumors (pNETs) represent a rare subset of pancreatic cancer. Functional tumors cause hormonal changes and clinical syndromes, while non-functional ones are often diagnosed late. Surgical management needs multidisciplinary planning, involving enucleation, distal pancreatectomy with or without spleen preservation, central pancreatectomy, pancreaticoduodenectomy or total pancreatectomy. Minimally invasive approaches have increased in the last decade compared to the open technique. The aim of this study was to analyze the current diagnostic and surgical trends for pNETs, to identify better interventions and their outcomes. Methods: The study adhered to the PRISMA guidelines, conducting a systematic review of the literature from May 2008 to March 2022 across multiple databases. Several combinations of keywords were used ("NET", "pancreatic", "surgery", "laparoscopic", "minimally invasive", "robotic", "enucleation", "parenchyma sparing") and relevant article references were manually checked. The manuscript quality was evaluated. Results: The study screened 3867 manuscripts and twelve studies were selected, primarily from Italy, the United States, and China. A total of 7767 surgically treated patients were collected from 160 included centers. The mean age was 56.3 y.o. Enucleation (EN) and distal pancreatectomy (DP) were the most commonly performed surgeries and represented 43.4% and 38.6% of the total interventions, respectively. Pancreatic fistulae, postoperative bleeding, re-operation, and follow-up were recorded and analyzed. Conclusions: Enucleation shows better postoperative outcomes and lower mortality rates compared to pancreaticoduodenectomy (PD) or distal pancreatectomy (DP), despite the similar risks of postoperative pancreatic fistulae (POPF). DP is preferred over enucleation for the pancreas body-tail, while laparoscopic enucleation is better for head pNETs.

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.

Treatments, prognostic factors, and genetic heterogeneity in advanced cholangiocarcinoma: A multicenter real-world study.

BACKGROUND AND AIMS: Cholangiocarcinoma (CCA), a rare and aggressive hepatobiliary malignancy, presents significant clinical management challenges. Despite rising incidence and evolving treatment options, prognosis remains poor, motivating the exploration of real-world data for enhanced understanding and patient care. METHODS: This multicenter study analyzed data from 120 metastatic CCA patients at three institutions from 2016 to 2023. Kaplan-Meier curves assessed overall survival (OS), while univariate and multivariate analyses evaluated links between clinical variables (age, gender, tumor site, metastatic burden, ECOG performance status, response to first-line chemotherapy) and OS. Genetic profiling was conducted selectively. RESULTS: Enrolled patients had a median age of 68.5 years, with intrahepatic tumors predominant in 79 cases (65.8%). Among 85 patients treated with first-line chemotherapy, cisplatin and gemcitabine (41.1%) was the most common regimen. Notably, one-third received no systemic treatment. After a median 14-month follow-up, 81 CCA-related deaths occurred, with a median survival of 13.1 months. Two clinical variables independently predicted survival: response to first-line chemotherapy (disease control vs. no disease control; HR: 0.27; 95% CI: 0.14-0.50; p < 0.0001) and metastatic involvement (>1 site vs. 1 site; HR: 1.99; 95% CI: 1.04-3.80; p = 0.0366). The three most common genetic alterations involved the ARID1A, tp53, and CDKN2A genes. CONCLUSIONS: Advanced CCA displays aggressive clinical behavior, emphasizing the need for treatments beyond chemotherapy. Genetic diversity supports potential personalized therapies. Collaborative research and deeper CCA biology understanding are crucial to enhance patient outcomes in this challenging malignancy.

Cross-reactive CD8+ T cell responses to tumor-associated antigens (TAAs) and homologous microbiota-derived antigens (MoAs).

BACKGROUND: We have recently shown extensive sequence and conformational homology between tumor-associated antigens (TAAs) and antigens derived from microorganisms (MoAs). The present study aimed to assess the breadth of T-cell recognition specific to MoAs and the corresponding TAAs in healthy subjects (HS) and patients with cancer (CP). METHOD: A library of > 100 peptide-MHC (pMHC) combinations was used to generate DNA-barcode labelled multimers. Homologous peptides were selected from the Cancer Antigenic Peptide Database, as well as Bacteroidetes/Firmicutes-derived peptides. They were incubated with CD8 + T cells from the peripheral blood of HLA-A*02:01 healthy individuals (n = 10) and cancer patients (n = 16). T cell recognition was identified using tetramer-staining analysis. Cytotoxicity assay was performed using as target cells TAP-deficient T2 cells loaded with MoA or the paired TuA. RESULTS: A total of 66 unique pMHC recognized by CD8+ T cells across all groups were identified. Of these, 21 epitopes from microbiota were identified as novel immunological targets. Reactivity against selected TAAs was observed for both HS and CP. pMHC tetramer staining confirmed CD8+ T cell populations cross-reacting with CTA SSX2 and paired microbiota epitopes. Moreover, PBMCs activated with the MoA where shown to release IFNγ as well as to exert cytotoxic activity against cells presenting the paired TuA. CONCLUSIONS: Several predicted microbiota-derived MoAs are recognized by T cells in HS and CP. Reactivity against TAAs was observed also in HS, primed by the homologous bacterial antigens. CD8+ T cells cross-reacting with MAGE-A1 and paired microbiota epitopes were identified in three subjects. Therefore, the microbiota can elicit an extensive repertoire of natural memory T cells to TAAs, possibly able to control tumor growth ("natural anti-cancer vaccination"). In addition, non-self MoAs can be included in preventive/therapeutic off-the-shelf cancer vaccines with more potent anti-tumor efficacy than those based on TAAs.

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.

An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies.

The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019-2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.

PURPOSE: We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases. METHODS: Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon-Mann-Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. RESULTS: The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%. CONCLUSIONS: Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.

Hepatocellular carcinoma (HCC) tumor microenvironment is more suppressive than colorectal cancer liver metastasis (CRLM) tumor microenvironment.

BACKGROUND AND PURPOSE: While HCC is an inflammation-associated cancer, CRLM develops on permissive healthy liver microenvironment. To evaluate the immune aspects of these two different environments, peripheral blood-(PB), peritumoral-(PT) and tumoral tissues-(TT) from HCC and CRLM patients were evaluated. METHODS: 40 HCC and 34 CRLM were enrolled and freshly TT, PT and PB were collected at the surgery. PB-, PT- and TT-derived CD4+CD25+ Tregs, M/PMN-MDSC and PB-derived CD4+CD25- T-effector cells (Teffs) were isolated and characterized. Tregs' function was also evaluated in the presence of the CXCR4 inhibitor, peptide-R29, AMD3100 or anti-PD1. RNA was extracted from PB/PT/TT tissues and tested for FOXP3, CXCL12, CXCR4, CCL5, IL-15, CXCL5, Arg-1, N-cad, Vim, CXCL8, TGFß and VEGF-A expression. RESULTS: In HCC/CRLM-PB, higher number of functional Tregs, CD4+CD25hiFOXP3+ was detected, although PB-HCC Tregs exert a more suppressive function as compared to CRLM Tregs. In HCC/CRLM-TT, Tregs were highly represented with activated/ENTPD-1+Tregs prevalent in HCC. As compared to CRLM, HCC overexpressed CXCR4 and N-cadherin/vimentin in a contest rich in arginase and CCL5. Monocytic MDSCs were highly represented in HCC/CRLM, while high polymorphonuclear MDSCs were detected only in HCC. Interestingly, the function of CXCR4-PB-Tregs was impaired in HCC/CRLM by the CXCR4 inhibitor R29. CONCLUSION: In HCC and CRLM, peripheral blood, peritumoral and tumoral tissues Tregs are highly represented and functional. Nevertheless, HCC displays a more immunosuppressive TME due to Tregs, MDSCs, intrinsic tumor features (CXCR4, CCL5, arginase) and the contest in which it develops. As CXCR4 is overexpressed in HCC/CRLM tumor/TME cells, CXCR4 inhibitors may be considered for double hit therapy in liver cancer patients.

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.

Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics.

Neuroendocrine neoplasms (NENs) are a group of lesions originating from cells of the diffuse neuroendocrine system. NENs may involve different sites, including the gastrointestinal tract (GEP-NENs). The incidence and prevalence of GEP-NENs has been constantly rising thanks to the increased diagnostic power of imaging and immuno-histochemistry. Despite the plethora of biochemical markers and imaging techniques, the prognosis and therapeutic choice in GEP-NENs still represents a challenge, mainly due to the great heterogeneity in terms of tumor lesions and clinical behavior. The concept that biomedical images contain information about tissue heterogeneity and pathological processes invisible to the human eye is now well established. From this substrate comes the idea of radiomics. Computational analysis has achieved promising results in several oncological settings, and the use of radiomics in different types of GEP-NENs is growing in the field of research, yet with conflicting results. The aim of this narrative review is to provide a comprehensive update on the role of radiomics on GEP-NEN management, focusing on the main clinical aspects analyzed by most existing reports: predicting tumor grade, distinguishing NET from other tumors, and prognosis assessment.

Impact of endobiliary radiofrequency ablation on survival of patients with unresectable cholangiocarcinoma: a narrative review.

Cholangiocarcinoma (CCA) is a rare cancer originating from the biliary epithelium and accounts for about 3% of all gastrointestinal malignancies. Unfortunately, the majority of patients are not eligible for surgical resection at the time of diagnosis, because of the locally advanced stage or metastatic disease. The overall survival time of unresectable CCA is generally less than 1 year, despite current chemotherapy regimens. Biliary drainage is often required as a palliative treatment for patients with unresectable CCA. Recurrent jaundice and cholangitis tend to occur because of reobstruction of the biliary stents. This not only jeopardizes the efficacy of chemotherapy, but also causes significant morbidity and mortality. Effective control of tumor growth is crucial for prolonging stent patency and consequently patient survival. Recently, endobiliary radiofrequency ablation (ERFA) has been experimented as a treatment modality to reduce tumor mass, and delay tumor growth, extending stent patency. Ablation is accomplished by means of high-frequency alternating current which is released from the active electrode of an endobiliary probe placed in a biliary stricture. It has been shown that tumor necrosis releases intracellular particles which are highly immunogenic and activate antigen-presenting cells, enhancing local immunity directed against the tumor. This immunogenic response could potentially enhance tumor suppression and be responsible for improved survival of patients with unresectable CCA who undergo ERFA. Several studies have demonstrated that ERFA is associated with an increased median survival of approximately 6 months in patients with unresectable CCA. Furthermore, recent data support the hypothesis that ERFA could ameliorate the efficacy of chemotherapy administered to patients with unresectable CCA, without increasing the risk of complications. This narrative review discusses the results of the studies published in recent years and focuses on the impact that ERFA could have on overall survival of patients with unresectable cholangiocarcinoma.

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.

The prognostic role of p53 mutations in metastatic colorectal cancer: A systematic review and meta-analysis.

INTRODUCTION: P53 is one of the most frequently mutated genes in colorectal cancer (CRC). The present study was undertaken to provide a solid estimate of the prognostic value of p53 mutations in metastatic CRC patients. METHODS: This meta-analysis was done in accordance to the Preferred Reporting Item For Systematic Reviews and Meta-Analysis 2020 guidelines. Studies in English published in the last ten years were searched through PubMed and Google Scholar. Final selection criteria were: 1) association with overall survival, 2) presence of Hazard Ratios (HRs) with 95% Confidence Intervals (CIs). The articles were evaluated for quality and risk of bias using the Newcastle-Ottawa Scale and QUIPS tool, respectively. The meta-analysis was conducted with random-effects model according to the Hartung-Knapp-Sidik-Jonkman method and results were depicted in classical Forest plots. Studies heterogeneity was determined by I2 and Tau2 statistics. The relationship between p53 mutation and clinic-pathological variables was examined using the χ2 test. RESULTS: Nine articles met the eligibility criteria and went to the final analysis. Sample size ranged from 51 to 1043 patients. All studies were retrospective. The Newcastle Ottawa Scale score was > 6 in all studies, QUIPS risk of bias was low in 6, moderate in 3 studies. Only three studies analysed the entire p53 gene coding region. The DNA sequencing technological platforms varied from Sanger to NGS sequencing techniques. The p53 mutational frequencies ranged from 35.0 % to 73.0 %. A strong association (p < 0.0001) emerged between p53 alteration and left-sided CRC. The final pooled HR (p53 mutated vs p53 wild-type tumors) for overall survival was 1.30 (95 % CI: 0.75-2.25) at random-effects model. CONCLUSIONS: The available evidence does not support a prognostic role for p53 in metastatic CRC patients. Prospective studies, with larger sample sizes and consistent and harmonized methodology, are needed to explore the prognostic role of p53 in metastatic CRC patients.