Presurgical Setting Research Articles

Prevention and management of hypertensive crises in children with pheochromocytoma and paraganglioma.

Hypertensive crises in pediatric patients are rare conditions. However, determining their precise prevalence is more challenging than in adults due to the heterogeneity in the definition itself. These crises frequently occur without a prior diagnosis of hypertension and may indicate an underlying cause of secondary hypertension, including pheochromocytoma/paraganglioma (PPGL). The mechanisms of hypertensive crises in the pediatric population with PPGL are directly related to different types of catecholamine excess. Noradrenergic tumors typically present with sustained hypertension due to their predominant action on α1-adrenoceptors in the vasculature. Conversely, adrenergic tumors, through epinephrine binding to β2-adrenoceptors in addition to stimulation of α1- and α2-adrenoceptors, more frequently cause paroxysmal hypertension. Furthermore, the biochemical phenotype also reflects the tumor localization and the presence of a genetic mutation. Recent evidence suggests that more than 80% of PPGL in pediatric cases have a hereditary background. PPGL susceptibility mutations are categorized into three clusters; mutations in cluster 1 are more frequently associated with a noradrenergic phenotype, whereas those in cluster 2 are associated with an adrenergic phenotype. Consequently, the treatment of hypertensive crises in pediatric patients with PPGL, reflecting the underlying pathophysiology, requires first-line therapy with alpha-blockers, potentially in combination with beta-blockers only in the case of tachyarrhythmia after adequate alpha-blockade. The route of administration for treatment depends on the context, such as intraoperative or pre-surgical settings, and whether it presents as a hypertensive emergency (elevated blood pressure with acute target organ damage), where intravenous administration of antihypertensive drugs is mandatory. Conversely, in cases of hypertensive urgency, if children can tolerate oral therapy, intravenous administration may initially be avoided. However, managing these cases is complex and requires careful consideration of the selection and timing of therapy administration, particularly in pediatric patients. Therefore, facing these conditions in tertiary care centers through interdisciplinary collaboration is advisable to optimize therapeutic outcomes.

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

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. 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). Seventy-seven liver metastases in 28 patients with a mean age of 60years (range 40-80years) 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. Normalized approach in CT radiomics analysis allows to predict RAS mutational status in colorectal liver metastases patients.

EANM practice guidelines for an appropriate use of PET and SPECT for patients with epilepsy

Epilepsy is one of the most frequent neurological conditions with an estimated prevalence of more than 50 million people worldwide and an annual incidence of two million. Although pharmacotherapy with anti-seizure medication (ASM) is the treatment of choice, ~30% of patients with epilepsy do not respond to ASM and become drug resistant. Focal epilepsy is the most frequent form of epilepsy. In patients with drug-resistant focal epilepsy, epilepsy surgery is a treatment option depending on the localisation of the seizure focus for seizure relief or seizure freedom with consecutive improvement in quality of life. Beside examinations such as scalp video/electroencephalography (EEG) telemetry, structural, and functional magnetic resonance imaging (MRI), which are primary standard tools for the diagnostic work-up and therapy management of epilepsy patients, molecular neuroimaging using different radiopharmaceuticals with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) influences and impacts on therapy decisions. To date, there are no literature-based praxis recommendations for the use of Nuclear Medicine (NM) imaging procedures in epilepsy. The aims of these guidelines are to assist in understanding the role and challenges of radiotracer imaging for epilepsy; to provide practical information for performing different molecular imaging procedures for epilepsy; and to provide an algorithm for selecting the most appropriate imaging procedures in specific clinical situations based on current literature. These guidelines are written and authorized by the European Association of Nuclear Medicine (EANM) to promote optimal epilepsy imaging, especially in the presurgical setting in children, adolescents, and adults with focal epilepsy. They will assist NM healthcare professionals and also specialists such as Neurologists, Neurophysiologists, Neurosurgeons, Psychiatrists, Psychologists, and others involved in epilepsy management in the detection and interpretation of epileptic seizure onset zone (SOZ) for further treatment decision. The information provided should be applied according to local laws and regulations as well as the availability of various radiopharmaceuticals and imaging modalities.

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

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. 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). 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. Normalized approach in MRI radiomics analysis allows to predict RAS mutational status.

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

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. 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. 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%. 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.

Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging-Tractography in Resective Brain Surgery: Lesion Coverage Strategies and Patient Outcomes.

Diffusion tensor imaging (DTI)-tractography and functional magnetic resonance imaging (fMRI) have dynamically entered the presurgical evaluation context of brain surgery during the past decades, providing novel perspectives in surgical planning and lesion access approaches. However, their application in the presurgical setting requires significant time and effort and increased costs, thereby raising questions regarding efficiency and best use. In this work, we set out to evaluate DTI-tractography and combined fMRI/DTI-tractography during intra-operative neuronavigation in resective brain surgery using lesion-related preoperative neurological deficit (PND) outcomes as metrics. We retrospectively reviewed medical records of 252 consecutive patients admitted for brain surgery. Standard anatomical neuroimaging protocols were performed in 127 patients, 69 patients had additional DTI-tractography, and 56 had combined DTI-tractography/fMRI. fMRI procedures involved language, motor, somatic sensory, sensorimotor and visual mapping. DTI-tractography involved fiber tracking of the motor, sensory, language and visual pathways. At 1 month postoperatively, DTI-tractography patients were more likely to present either improvement or preservation of PNDs (p = 0.004 and p = 0.007, respectively). At 6 months, combined DTI-tractography/fMRI patients were more likely to experience complete PND resolution (p < 0.001). Low-grade lesion patients (N = 102) with combined DTI-tractography/fMRI were more likely to experience complete resolution of PNDs at 1 and 6 months (p = 0.001 and p < 0.001, respectively). High-grade lesion patients (N = 140) with combined DTI-tractography/fMRI were more likely to have PNDs resolved at 6 months (p = 0.005). Patients with motor symptoms (N = 80) were more likely to experience complete remission of PNDs at 6 months with DTI-tractography or combined DTI-tractography/fMRI (p = 0.008 and p = 0.004, respectively), without significant difference between the two imaging protocols (p = 1). Patients with sensory symptoms (N = 44) were more likely to experience complete PND remission at 6 months with combined DTI-tractography/fMRI (p = 0.004). The intraoperative neuroimaging modality did not have a significant effect in patients with preoperative seizures (N = 47). Lack of PND worsening was observed at 6 month follow-up in patients with combined DTI-tractography/fMRI. Our results strongly support the combined use of DTI-tractography and fMRI in patients undergoing resective brain surgery for improving their postoperative clinical profile.

ID: 207779 Peripheral Nerve Stimulation for the Treatment of Chronic Knee Pain: A Retrospective Case Series

Peripheral nerve stimulation (PNS) is a promising novel treatment modality for the management of chronic knee pain.1,2 Multiple studies have suggested that PNS is effective in the treatment of post-surgical knee pain.3-5 Fewer data, however, exist to support the use of PNS in the pre-surgical setting. This investigation seeks to clarify the potential clinical utility of PNS for the treatment of chronic knee pain amongst patients who have failed conservative measures.

Radiomics and Machine Learning Analysis Based on Magnetic Resonance Imaging in the Assessment of Colorectal Liver Metastases Growth Pattern.

To assess Radiomics and Machine Learning Analysis in Liver Colon and Rectal Cancer Metastases (CRLM) Growth Pattern, we evaluated, retrospectively, a training set of 51 patients with 121 liver metastases and an external validation set of 30 patients with a single lesion. All patients were subjected to MRI studies in pre-surgical setting. For each segmented volume of interest (VOI), 851 radiomics features were extracted using PyRadiomics package. Nonparametric test, univariate, linear regression analysis and patter recognition approaches were performed. The best results to discriminate expansive versus infiltrative front of tumor growth with the highest accuracy and AUC at univariate analysis were obtained by the wavelet_LHH_glrlm_ShortRunLowGray Level Emphasis from portal phase of contrast study. With regard to linear regression model, this increased the performance obtained respect to the univariate analysis for each sequence except that for EOB-phase sequence. The best results were obtained by a linear regression model of 15 significant features extracted by the T2-W SPACE sequence. Furthermore, using pattern recognition approaches, the diagnostic performance to discriminate the expansive versus infiltrative front of tumor growth increased again and the best classifier was a weighted KNN trained with the 9 significant metrics extracted from the portal phase of contrast study, with an accuracy of 92% on training set and of 91% on validation set. In the present study, we have demonstrated as Radiomics and Machine Learning Analysis, based on EOB-MRI study, allow to identify several biomarkers that permit to recognise the different Growth Patterns in CRLM.

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

To assess the efficacy of radiomics features obtained by T2-weighted sequences to predict clinical outcomes following liver resection in colorectal liver metastases patients. 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 61years of median age and 121 liver metastases. The validation cohort consisted a total of 30 patients with single lesion with 60years 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. 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%. 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.

EOB-MR Based Radiomics Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases.

Simple SummaryThe aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. 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. These results were confirmed by external validation dataset. We obtained a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences.The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. This retrospective analysis was approved by the local Ethical Committee board of National Cancer of Naples, IRCCS “Fondazione Pascale”. Radiological databases were interrogated from January 2018 to May 2021 in order to select patients with liver metastases with pathological proof and EOB-MRI study in pre-surgical setting. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest by 2 expert radiologists, 851 radiomics features were extracted as median values using PyRadiomics. non-parametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. The best predictor to discriminate expansive versus infiltrative front of tumor growth was HLH_glcm_MaximumProbability extraxted on VIBE_FA30 with an accuracy of 84%, a sensitivity of 83%, and a specificity of 82%. The best predictor to discriminate tumor budding was Inverse Variance obtained by the original GLCM matrix extraxted on VIBE_FA30 with an accuracy of 89%, a sensitivity of 96% and a specificity of 65%. The best predictor to differentiate the mucinous type of tumor was the HHL_glszm_ZoneVariance extraxted on VIBE_FA30 with an accuracy of 85%, a sensitivity of 46% and a specificity of 95%. The best predictor to identify tumor recurrence was the LHL_glcm_Correlation extraxted on VIBE_FA30 with an accuracy of 86%, a sensitivity of 52% and a specificity of 97%. The best linear regression model was obtained in the identification of the tumor growth front considering the height textural significant metrics by VIBE_FA10 (an accuracy of 89%; sensitivity of 93% and a specificity of 82%). Considering significant texture metrics tested with pattern recognition approaches, the best performance for each outcome was reached by a KNN in the identification of recurrence with the 3 textural significant features extracted by VIBE_FA10 (AUC of 91%, an accuracy of 93%; sensitivity of 99% and a specificity of 77%). 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.

Contrast MR-Based Radiomics and Machine Learning Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases: A Preliminary Study.

Simple SummaryThe objective of the study was to evaluate the radiomics features obtained by contrast MRI studies as prognostic biomarkers in colorectal liver metastases patients to predict clinical outcomes following liver resection. We demonstrated a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences. Moreover, considering linear regression models or neural network classifiers in a multivariate approach was possible to increase the performance in terms of accuracy, sensitivity, and specificity.Purpose: To assess radiomics features efficacy obtained by arterial and portal MRI phase in the prediction of clinical outcomes in the colorectal liver metastases patients, evaluating recurrence, mutational status, pathological characteristic (mucinous and tumor budding) and surgical resection margin. Methods: This retrospective analysis was approved by the local Ethical Committee board, and radiological databases were used to select patients with colorectal liver metastases with pathological proof and MRI study in a pre-surgical setting after neoadjuvant chemotherapy. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest on MRI by two expert radiologists, 851 radiomics features were extracted as median values using the PyRadiomics tool. Non-parametric Kruskal-Wallis test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The best predictor to discriminate expansive versus infiltrative tumor growth front was wavelet_LHH_glrlm_ShortRunLowGrayLevelEmphasis extracted on portal phase with accuracy of 82%, sensitivity of 84%, and specificity of 77%. The best predictor to discriminate tumor budding was wavelet_LLH_firstorder_10Percentile extracted on portal phase with accuracy of 92%, a sensitivity of 96%, and a specificity of 81%. The best predictor to differentiate the mucinous type of tumor was the wavelet_LLL_glcm_ClusterTendency extracted on portal phase with accuracy of 88%, a sensitivity of 38%, and a specificity of 100%. The best predictor to identify the recurrence was the wavelet_HLH_ngtdm_Complexity extracted on arterial phase with accuracy of 90%, a sensitivity of 71%, and a specificity of 95%. The best linear regression model was obtained in the identification of mucinous type considering the 13 textural significant metrics extracted by arterial phase (accuracy of 94%, sensitivity of 77% and a specificity of 99%). The best results were obtained in the identification of tumor budding with the eleven textural significant features extracted by arterial phase using a KNN (accuracy of 95%, sensitivity of 84%, and a specificity of 99%). Conclusions: Our results confirmed the capacity of radiomics to identify as biomarkers and several prognostic features that could affect the treatment choice in patients with liver metastases in order to obtain a more personalized approach.

Surgical hand antisepsis- comparison of alcohol based hand rub (propan-2-ol 45 gm, propan-1-ol 30 gm, mecetronium ethyl-sulphate 0.2gm) and povidone iodine scrub (7.5%)

Introduction: One of the important causes of morbidity and mortality in post-surgical period is surgical site infection [1]. The main carrier of nosocomial and multi-drug resistant bacterial infection in hospital setting are healthcare workers [2]. Surgical site infections are avoidable with proper methodical antisepsis. The traditionally used disinfectant for hand antisepsis is povidone iodine. Newer Alcohol based hand rubs are present in commercial market, there are very few clinical studies that suggest the efficacy and comparison between povidone-iodine and alcohol based hand rubs in the pre surgical settings. There is a need to evaluate the antimicrobial based comparison between the hand antisepsis methods for better surgeon compliance and providing quality treatments to the patients. This study provides a basis in opting for a better alternative amongst the presurgical hand antisepsis methods. Methods: An observational study was conducted at a tertiary care rural hospital. Patients undergoing clean surgeries were included in the study. The subjects representing the surgeons and the paramedical staff (n=200) were divided into two groups of 100 each (povidone iodine and alcohol based hand rub). Samples were then collected using sterile gloves and sterile culture tubes and were sent to the microbiology department for obtaining cultures. The efficacy of surgical hand antisepsis methods were compared, surgical site infection occurrences if found were accessed for seniority of surgeon, noted to deduce any relationship with the organism found on hand swabs and results were calculated. Result: In concern with the two groups of hand antisepsis, bacterial growth profile was obtained, results showed a positive culture of 7% of which 4.5% positive cultures were found in Group A(povidone iodine) for Staphylococcus, Acinetobacter, Pseudomonas spp. and Streptococcus and 2.5% belonged to group B (Alcohol based hand rub) for Staphylococcus and Klebsiella. The statistical comparison of both groups A and B was done to calculate the p value which came out to be 0.267 which is not significant. Conclusion: In 7% hand swabs the culture was positive for bacteria, with staphylococcus being the most common isolated commensal. No significant difference in efficacy of hand antisepsis was established in reducing the bacteria in surgical hand antisepsis protocol. No association was found between type of hand scrub and seniority of the surgeons. Since there was no incidence of surgical site infection in the patients, the better alternative between types of hand rub could not be deduced.

The clinical utility of a memory specialization index in epilepsy surgery patients with unilateral hippocampal sclerosis.

Although group studies provide some support for the material-specific model of memory function, there are considerable individual variations in memory function in people with temporal lobe epilepsy, even in those with the same underlying pathology. In this proof-of-concept study, we examined the sensitivity and specificity of a single measure of an individual's relative strength for the encoding of verbal or visual learning. Six hundred ninety-two patients with left hemisphere language dominance and unilateral hippocampal sclerosis completed verbal and visual encoding tasks with similar test structures as part of their presurgical evaluation. Three hundred one patients had right hippocampal sclerosis (RHS), and 391 patients had left hippocampal sclerosis (LHS). A memory specialization index (MSI) was calculated by subtracting the Visual Learning z-score from the Verbal Learning z-score. A positive value on the MSI indicates a relative strength in verbal learning. A negative score indicates a relative strength in visual learning. Employing cut-offs of ±1, the MSI had a positive predictive value of 71% (confidence interval [CI] 95% 0.64-0.77) for LHS and 64% (CI 95% 0.55-0.74) for RHS and was superior to the standalone z-scores from the verbal and visual tests in each respect. In the LHS group, the MSI was significantly correlated with age and duration of epilepsy. Older patients who had a longer duration of epilepsy were more likely to demonstrate a similar level of impairment in both verbal and visual learning, with a decreasing discrepancy between the scores on the two tasks over time. Our MSI provides a measure with high specificity for RHS. The pattern of strengths and weaknesses in visual and verbal encoding may evolve with age and duration of epilepsy, and clinicians should be aware of these factors when interpreting the lateralizing significance of test scores, particularly in a presurgical setting.

Molecular Genetics of Follicular-Derived Thyroid Cancer.

Simple SummaryThyroid tumors that derive from follicular cells are not a homogeneous entity, showing variable morphological appearance and different degrees of differentiation. Molecular markers are useful for both diagnostic purposes and prognostic stratification of patients. In presurgical setting, molecular testing of indeterminate thyroid nodules on aspirates provides useful diagnostic information; the molecular analysis on tumor tissues can also reveal the presence of genetic alterations related to patients’ prognosis. In recent years, the molecular characterization of these tumors has acquired even more importance thanks to the introduction of targeted drugs. This review summarizes the current literature on the molecular landscape of follicular-derived thyroid tumors.Thyroid cancer is the most common type of endocrine-related malignancy, whose incidence rates have increased dramatically in the last few decades. Neoplasms of follicular origin generally have excellent prognosis, with the exception of less differentiated tumors. Follicular-derived thyroid cancer can manifest as a variety of morphologically distinct entities, characterized by various degrees of differentiation and invasiveness. Histological evaluation is thus crucial for the definition of patients’ prognosis. However, within each histological subtype, tumor behavior can be highly variable, and, in this respect, molecular characterization can provide insightful information to refine the risk stratification of tumors. In addition to the importance of its prognostic role, molecular testing can be used to support the differential diagnosis of thyroid nodules in the absence of marked cyto-morphological aberrations. Finally, with the advent of targeted drugs, the presence of molecular alterations will guide the therapeutic strategies for patients with advanced tumors who do not respond to standard treatment. This review aims to describe the genetic landscape of follicular-derived thyroid tumors also highlighting differences across histological subtypes.

Risk of preoperative understaging of duodenal neuroendocrine neoplasms: a plea for caution in the treatment strategy.

Pretreatment staging is the milestone for planning either surgical or endoscopic treatment in duodenal neuroendocrine neoplasms (dNENs). Herein, a series of surgically treated dNEN patients was evaluated to assess the concordance between the pre- and postsurgical staging. Retrospective analysis of patients with a histologically confirmed diagnosis of dNENs, who underwent surgical resection observed at eight Italian tertiary referral centers. The presurgical TNM stage, based on the radiological and functional imaging, was compared with the pathological TNM stage, after surgery. From 2000 to 2019, 109 patients were included. Sixty-six patients had G1, 26 a G2, 7 a G3 dNEN (Ki-67 not available in 10 patients). In 46/109 patients (42%) there was disagreement between the pre- and postsurgical staging, being it understaged in 42 patients (38%), overstaged in 4 (3%). As regards understaging, in 25 patients (22.9%), metastatic loco-regional nodes (N) resulted undetected at both radiological and functional imaging. Understaging due to the presence of distal micrometastases (M) was observed in 2 cases (1.8%). Underestimation of tumor extent (T) was observed in 12 patients (11%); in three cases the tumor was understaged both in T and N extent. Conventional imaging has a poor detection rate for loco-regional nodes and micrometastases in the presurgical setting of the dNENs. These results represent important advice when local conservative approaches, such as endoscopy or local surgical excision are considered and it represents a strong recommendation to include endoscopic ultrasound in the preoperative tools for a more accurate local staging.

Abstract PS12-26: “Bridge” neoadjuvant endocrine therapy for early stage breast cancer patients during COVID-19 at an academic hospital in NYC: Lessons learned

Abstract Background: Neoadjuvant endocrine therapy has traditionally been considered a treatment option for locally advanced and/or surgically high-risk women with hormone positive disease. Early stage hormone-positive breast cancer, on the other hand, is usually managed with upfront surgery, with post-operative hormone therapy as a risk-reducing adjunct. During the COVID-19 pandemic, however, widespread closures of operating rooms throughout the country resulted in many breast cancer patients being offered presurgical endocrine therapy as a bridge to surgery. We explored the demographic and clinicopathologic characteristics of these patients and quantified their rate of uptake. Methods: The Institutional Breast Cancer Database was queried for all patients who were diagnosed with ER+ stage 0, I, or II breast cancer and were offered presurgical endocrine therapy (tamoxifen or aromatase inhibitor) by a medical oncologist from 3/12/2020 to 4/30/2020. Variables of interest included demographics, tumor characteristics, and rate of medication uptake and compliance. Results: Of 192 newly diagnosed breast cancer patients seen at NYU Perlmutter Cancer Center during this time period, 136 patients had early stage ER+ breast cancer. Forty-five patients had not yet undergone surgery, and were recommended to receive presurgical hormonal therapy as a bridge given the COVID-19 pandemic (Table 1). The average age was 60.5 years old (SD=13.8 years, range 31-89), and all were female. Thirty-four of 44 patients were post-menopausal (75.6%), while 10 were premenopausal (22.2%), and one was perimenopausal (2.2%). Twenty-six patients were white (57.8%), 12 were black (26.7%) 3 were Asian (6.7%), and 4 were other (8.9%). Thirty-four patients (75.6%) had invasive disease, while 8 had ductal carcinoma in situ (DCIS, 17.8%), and 3 had DCIS with microinvasion (6.7%). Nine patients (20%) did not take the medication for various reasons: 1 contracted COVID-19, 1 refused any treatment, 1 decided to transfer care out of state, 1 preferred to take a homeopathic remedy instead of endocrine therapy, 1 preferred to wait for surgery without medication, and 4 were scheduled for surgery sooner than anticipated and did not start the medication. The remaining 36 patients (80%) took medication for an average of 43.6 days (SD=27.3 days, range 9-101 days) prior to surgery. Twenty-eight patients (77.8%) took an aromatase inhibitor, and 8 (22.2%) took tamoxifen. Forty-two patients have now undergone surgery (93.3%); the remainder include the patient who is refusing all treatment, the patient who transferred out of state, and one patient who has not yet scheduled surgery, but is reportedly still taking an aromatase inhibitor. Conclusion: Improving adherence to long-term adjuvant endocrine therapy is an urgent need as patient acceptance is low. Reported completion rates range around 50%, and have not been improved by educational or technology-based interventions. The unique situation posed by the current COVID-19 pandemic has temporarily changed the management of early-stage breast cancer, and resulted in a high initial acceptance of endocrine therapy (80%), although duration is shorter in this presurgical setting. Further investigations will evaluate length of use, the psychosocial and behavioral factors that influence willingness to take endocrine therapy, and apply these lessons to management of early-stage hormone-positive breast cancer. Patient Demographics and Tumor CharacteristicsVariablesTotal (N=45)%Median Age (years)60.5 (31-89)RaceWhite2657.8Black1226.7Asian36.7Other48.9Menopause StatusPre-menopausal1022.2Peri-menopausal12.2Post-menopausal3475.6Mean BMI (kg/m2)28.3 (17.8-46.5)PalpabilityNon-palpable3066.7Palpable1533.3Mammographic Breast DensityEntirely Fatty24.4Scattered Fibroglandular1737.8Heterogeneously Dense2453.3Extremely Dense24.4HistologyDCIS817.8DCIS with microinvasion36.7Invasive carcinoma3475.6Clinical Stage0817.8I2760II1022.2Endocrine Therapy(N=36)Tamoxifen822.2Aromatase Inhibitor2877.8Mean Duration of Endocrine Therapy (days)43.6 (9-101) Citation Format: Cindy Cen, Freya Schnabel, Sylvia Adams, Magdalena Plasilova, Janet Yeh, Marleen Meyers, James Speyer, Elliot Belenkov, Maryann Kwa, Yelena Novik, Elena Katz, Amber Guth. “Bridge” neoadjuvant endocrine therapy for early stage breast cancer patients during COVID-19 at an academic hospital in NYC: Lessons learned [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS12-26.

Pediatric Bleeding Questionnaire as a predictor of perioperative bleeding in patients with low factor VII levels

IntroductionFactor VII is an essential blood coagulation factor, yet the minimum level of Factor VII activity (FVII:C) for hemostasis is not well defined. Therefore, patients with seemingly low FVII:C levels diagnosed on presurgical screening are often exposed to potentially unnecessary treatment to mitigate bleeding risk that may not exist. The Pediatric Bleeding Questionnaire (PBQ) a bleeding risk assessment tool has not been validated in patients with low FVII:C. ObjectiveTo assess the utility of the PBQ as a bleeding risk predictor in patients with low FVII:C level undergoing surgical procedures. MethodsPatients with low FVII:C levels diagnosed in the pre-surgical setting with a completed PBQ were eligible. Patient’s FVII:C, PBQ score, surgery type, perioperative hemostatic measures and bleeding events were recorded. ResultsFifty-Two patients (mean age 10.6 years) with mean FVII:C of 39.7% were evaluated. All but 3 patients had a PBQ score ≤2. No perioperative bleeding prophylaxis was administered to 41 (78.8%) patients. None of the patients reported prolonged perioperative bleeding. ConclusionIndividuals with low FVII:C detected on presurgical screening are not at high risk for bleeding in the absence of a personal bleeding history and the PBQ did not predict bleeding risk in this setting.

A New Quantitative Classification of the Extrahepatic Biliary Tract Related to Cystic Duct Implantation

BackgroundKnowledge regarding biliary anatomy and its variations, including the cystic duct (CD), is important in the pre-surgical setting and for predicting biliary diseases. However, no large series has focused on CD evaluation using a quantitative analysis. The primary aim of this prospective study was to create a ‘taxonomic’ classification of CD anatomy in a large cohort of subjects who underwent magnetic resonance cholangiopancreatography (MRCP). The secondary aim was to evaluate the correlations between extrahepatic bile duct (EHBD) variants and biliary diseases. MethodsWe enrolled patients who underwent MRCP for different clinical indications from January 2017 to May 2019. Demographical, anatomical and clinical data were evaluated using statistical analyses, as appropriate. The anatomical assessment of EHBD was performed using the standard classification for CD in low, medium, and high insertions, and the lengths of CD to the duodenal papilla (DP), and EHBD was determined to conduct a new quantitative analysis. ResultsThe final study population comprised 1004 subjects. A new classification for EHBD as per the percentile distribution of the ratio CDDP/EHBD was designed, and the following categories were obtained: type 1 (below the 25th percentile) for CDDP/EHBD ratio ≤ 50%; type 2 (25th to 75th percentile) for CDDP/EHBD ratio 51–75% and type 3 (above the 75th percentiles) for CDDP/EHBD ratio > 75%. Type 1 of the new classification of CD implantation was significantly superior in terms of the detection of low, medial and intra-pancreatic CD that was significantly correlated with a high risk of choledochal lithiasis in comparison with the standard classification (P < 0.001). ConclusionsThe new classification of CD implantation enables identification of the vast majority of intra-pancreatic CDs that are correlated with a high risk of choledochal lithiasis in a single category (type 1) that is easy to identify using imaging.

Abbreviated MRI protocol for colorectal liver metastases: How the radiologist could work in pre surgical setting.

BackgroundMRI is the most reliable imaging modality that allows to assess liver metastases. Our purpose is to compare the per-lesion and per-patient detection rate of gadoxetic acid-(Gd-EOB) enhanced liver MRI and fast MR protocol including Diffusion Weighted Imaging (DWI) and T2-W Fat Suppression sequence in the detection of liver metastasis in pre surgical setting.MethodsOne hundred and eight patients with pathologically proven liver metastases (756 liver metastases) underwent Gd-EOBMRI were enrolled in this study. Three radiologist independently graded the presence of liver lesions on a five-point confidence scale assessed only abbreviated protocol (DWI and sampling perfection with application-optimized contrasts using different flip angle evolution (SPACE) fat suppressed sequence) and after an interval of more than 2 weeks the conventional study (all acquired sequences). Per-lesion and per-patient detection rate of metastases were calculated. Weighted к values were used to evaluate inter-reader agreement of the confidence scale regarding the presence of the lesion.ResultsMRI detected 732 liver metastases. All lesions were identified both by conventional study as by abbreviated protocol. In terms of per-lesion detection rate of liver metastasis, all three readers had higher detection rate both with abbreviated protocol and with standard protocol with Gd-EOB (96.8% [732 of 756] vs. 96.5% [730 of 756] for reader 1; 95.8% [725 of 756] vs. 95.2% [720 of 756] for reader 2; 96.5% [730 of 756] vs. 96.5% [730 of 756] for reader 3). Inter-reader agreement of lesions detection rate between the three radiologists was excellent (k range, 0.86–0.98) both for Gd-EOB MRI and for Fast protocol (k range, 0.89–0.99).ConclusionAbbreviated protocol showed the same detection rate than conventional study in detection of liver metastases.

Personalized treatment for differentiated thyroid cancer: current data and new perspectives.

More conservative and personalized treatment options have been developed in recent years to face the rising diagnosis of low-risk differentiated thyroid carcinoma (DTC). The present review describes the change towards a more risk-adapted management either in the treatment or in the follow-up of DTC. Particular attention is given to the innovations introduced by the latest guidelines for low-risk tumors, starting from the most appropriate extension of surgery up to the postoperative management. The emerging role of active surveillance for low-risk microcarcinoma is discussed, as well as the development of percutaneous strategies in the setting of malignant thyroid disease. The recent use of approved new systemic target therapies for advanced radioiodine refractory thyroid cancer is reported, together with the description of new compounds in trial. Finally, we provide some considerations to improve the risk evaluation in a presurgical setting, especially related to the rising role of genetics, to enable better risk-based cancer management and personalized treatment choices.