Subjective and objective effects of radioiodine therapy on the sense of smell.

PURPOSE: Evaluating the impact of radioiodine therapy (RIT) on olfactory function in thyroid cancer patients through quantitative and qualitative olfactory tests. METHOD: In this cohort study, patients with thyroid cancer were included. Demographic, clinical, and laboratory data were collected. To subjectively evaluate the olfactory changes aftter RIT, the Visual Analog Scale (VAS), Self-Reported Mini-Olfactory Questionnaire (self-MOQ), and the University of Washington Quality of Life Questionnaire (UW-QOL) were assessed. Out of UW-QOL questions those related to saliva, taste, and overall health condition were analysed. For objective assessment, patients underwent both the Butanol Threshold Test (BTT) and the a version of Smell Identification Test (SIT). Patients were assessed before, one month, and six months after RIT. RESULTS: Ninety eight patients were included (Male = 17). A statistically significant decrement was observed in olfaction based on the VAS, between the baseline and one (pvalue = 0.015) and six months (pvalue = 0.031) of follow-up. Additionally, saliva (pvalue = 0.001), taste (pvalue = 0.000), and overall health condition (pvalue = 0.010) significantly decreased one-month after RIT. The measures were not different between the baseline and 6-month follow up and the improvement of index of taste was significant from 1-month to 6-months follow ups (pvalue = 0.000). However, none of the objective tests (the BTT and the SIT) indicated a significant decline in olfaction during the follow up. CONCLUSION: A subjective RIT related decrease in smell function, taste, and saliva production was documented without any objective olfactory dysfunction.

Disparities in access to endovenous treatment options in chronic lower extremity superficial venous insufficiency: A national 7-year analysis.

OBJECTIVE: The goal of this study was to analyze trends in treatment access for chronic superficial venous disease and to identify disparities in care. METHODS: This retrospective study was exempt from institutional review board approval. The American College of Surgeon National Surgical Quality Improvement Program database was used to identify patients who underwent vein stripping (VS) and endovenous procedures for treatment of chronic superficial venous disease. Endovenous options included radiofrequency ablation (RFA) and laser ablation. Data was available from 2011 to 2018 and demographic information was extracted for each patient identified by Current Procedural Terminology codes. For all racial and ethnic groups, trend lines were plotted, and the relative rate of change was determined within each specified demographic. RESULTS: There were 21,025 patients included in the analysis. The overall mean age was 54.2 years, and the majority of patients were female (64.8%). In total, 27.9%, 55.2%, and 16.9% patients underwent VS, RFA, and laser ablation, respectively. Patients who received laser ablation were older (P < .001). Hispanic ethnicity was associated with significantly lower odds of receiving endovascular thermal ablation (EVTA) over VS (odds ratio [OR], 0.71; 95% confidence interval [CI], 0.64-0.78; P < .001). American Indian/Alaska Native patients were more likely to receive EVTA over VS (OR, 4.02; 95% CI, 2.48-6.86); similarly, Native Hawaiian/Pacific Islander patients were more likely to receive EVTA over VS, although this difference was not statistically significant (OR, 1.44; 95% CI, 0.93-2.27). On multinomial regression, Hispanic patients were less likely to receive RFA over VS, whereas American Indian/Alaskan Native patients were more likely to receive RFA over VS. In all racial and ethnic groups, the percentage of endovenous procedures increased, whereas vein stripping decreased. CONCLUSIONS: Based on a hospital-based dataset, demographic indicators, including age, sex, race, and ethnicity, are associated with differences in endovenous treatments for chronic superficial venous insufficiency suggesting disparities in obtaining minimally invasive treatment options among certain patient groups.

Quantitative peritumoral magnetic resonance imaging fingerprinting improves machine learning-based prediction of overall survival in colorectal cancer.

Aim: To investigate magnetic resonance imaging (MRI)-based peritumoral texture features as prognostic indicators of survival in patients with colorectal liver metastasis (CRLM). Methods: From 2007-2015, forty-eight patients who underwent MRI within 3 months prior to initiating treatment for CRLM were identified. Clinicobiological prognostic variables were obtained from electronic medical records. Ninety-four metastatic hepatic lesions were identified on T1-weighted post-contrast images and volumetrically segmented. A total of 112 radiomic features (shape, first-order, texture) were derived from a 10 mm region surrounding each segmented tumor. A random forest model was applied, and performance was tested by receiver operating characteristic (ROC). Kaplan-Meier analysis was utilized to generate the survival curves. Results: Forty-eight patients (male:female = 23:25, age 55.3 years ± 18 years) were included in the study. The median lesion size was 25.73 mm (range 8.5-103.8 mm). Microsatellite instability was low in 40.4% (38/94) of tumors, with Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation detected in 68 out of 94 (72%) tumors. The mean survival was 35 months ± 21 months, and local disease progression was observed in 35.5% of patients. Univariate regression analysis identified 42 texture features [8 first order, 5 gray level dependence matrix (GLDM), 5 gray level run time length matrix (GLRLM), 5 gray level size zone matrix (GLSZM), 2 neighboring gray tone difference matrix (NGTDM), and 17 gray level co-occurrence matrix (GLCM)] independently associated with metastatic disease progression (P < 0.03). The random forest model achieved an area under the curve (AUC) of 0.88. Conclusions: MRI-based peritumoral heterogeneity features may serve as predictive biomarkers for metastatic disease progression and patient survival in CRLM.

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer.

Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.

CT Texture Analysis in Nonalcoholic Fatty Liver Disease (NAFLD).

Background: Nonalcoholic fatty liver disease (NAFLD) is the most common form of liver disease worldwide. There are limited biomarkers that can detect progression from simple steatosis to nonalcoholic steatohepatitis (NASH). The purpose of our study was to utilize CT texture analysis to distinguish steatosis from NASH. Methods: 16 patients with NAFLD (38% male, median (interquartile range): age 57 (48-64) years, BMI 37.5 (35.0-46.8) kg/m2) underwent liver biopsy and abdominal non-contrast CT. CT texture analysis was performed to quantify gray-level tissue summaries (e.g., entropy, kurtosis, skewness, and attenuation) using commercially available software (TexRad, Cambridge England). Logistic regression analyses were performed to quantify the association between steatosis/NASH status and CT texture. ROC curve analysis was performed to determine sensitivity, specificity, AUC, 95% CIs, and cutoff values of texture parameters to differentiate steatosis from NASH. Results: By histology, 6/16 (37%) of patients had simple steatosis and 10/16 (63%) had NASH. Patients with NASH had lower entropy (median, interquartile range (IQR): 4.3 (4.1, 4.8) vs. 5.0 (4.9, 5.2), P = 0.013) and lower mean value of positive pixels (MPP) (34.4 (21.8, 52.2) vs. 66.5 (57.0, 70.7), P = 0.009) than those with simple steatosis. Entropy values below 4.73 predict NASH with 100% (95%CI: 67-100%) specificity and 80% (50-100%) sensitivity, AUC: 0.88. MPP values below 54.0 predict NASH with 100% (67-100%) specificity and 100% (50-100%) sensitivity, AUC 0.90. Conclusion: Our study provides preliminary evidence that CT texture analysis may serve as a novel imaging biomarker for disease activity in NAFLD and the discrimination of steatosis and NASH.

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma.

Pretreatment LDH is a standard prognostic biomarker for advanced melanoma and is associated with response to ICI. We assessed the role of machine learning-based radiomics in predicting responses to ICI and in complementing LDH for prognostication of metastatic melanoma. From 2008-2022, 79 patients with 168 metastatic hepatic lesions were identified. All patients had arterial phase CT images 1-month prior to initiation of ICI. Response to ICI was assessed on follow-up CT at 3 months using RECIST criteria. A machine learning algorithm was developed using radiomics. Maximum relevance minimum redundancy (mRMR) was used to select features. ROC analysis and logistic regression analyses evaluated performance. Shapley additive explanations were used to identify the variables that are the most important in predicting a response. mRMR selection revealed 15 features that are associated with a response to ICI. The machine learning model combining both radiomics features and pretreatment LDH resulted in better performance for response prediction compared to models that included radiomics or LDH alone (AUC of 0.89 (95% CI: [0.76-0.99]) vs. 0.81 (95% CI: [0.65-0.94]) and 0.81 (95% CI: [0.72-0.91]), respectively). Using SHAP analysis, LDH and two GLSZM were the most predictive of the outcome. Pre-treatment CT radiomic features performed equally well to serum LDH in predicting treatment response.

Machine Learning-Based Radiomic Features on Pre-Ablation MRI as Predictors of Pathologic Response in Patients with Hepatocellular Carcinoma Who Underwent Hepatic Transplant.

BACKGROUND: The aim was to investigate the role of pre-ablation tumor radiomics in predicting pathologic treatment response in patients with early-stage hepatocellular carcinoma (HCC) who underwent liver transplant. METHODS: Using data collected from 2005-2015, we included adult patients who (1) had a contrast-enhanced MRI within 3 months prior to ablation therapy and (2) underwent liver transplantation. Demographics were obtained for each patient. The treated hepatic tumor volume was manually segmented on the arterial phase T1 MRI images. A vector with 112 radiomic features (shape, first-order, and texture) was extracted from each tumor. Feature selection was employed through minimum redundancy and maximum relevance using a training set. A random forest model was developed based on top radiomic and demographic features. Model performance was evaluated by ROC analysis. SHAP plots were constructed in order to visualize feature importance in model predictions. RESULTS: Ninety-seven patients (117 tumors, 31 (32%) microwave ablation, 66 (68%) radiofrequency ablation) were included. The mean model for end-stage liver disease (MELD) score was 10.5 ± 3. The mean follow-up time was 336.2 ± 179 days. Complete response on pathology review was achieved in 62% of patients at the time of transplant. Incomplete pathologic response was associated with four features: two first-order and two GLRM features using univariate logistic regression analysis (p < 0.05). The random forest model included two radiomic features (diagnostics maximum and first-order maximum) and four clinical features (pre-procedure creatinine, pre-procedure albumin, age, and gender) achieving an AUC of 0.83, a sensitivity of 82%, a specificity of 67%, a PPV of 69%, and an NPV of 80%. CONCLUSIONS: Pre-ablation MRI radiomics could act as a valuable imaging biomarker for the prediction of tumor pathologic response in patients with HCC.

Clinical characteristics and MRI-based phenotypes of perianal abscess formation in children with fistulizing Crohn's Disease.

Purpose: The aim of this study was to explore potential correlation of the MR imaging features and clinical characteristics with formation of perianal abscess in children with Crohn's perianal fistulas (CPF). Methods: From 2010 to 2020, pediatric patients with CPF diagnosis on their first pelvic MRI were identified retrospectively. All patients were divided into two groups based on the presence or absence of perianal abscess. Baseline clinical and MRI characteristics were recorded for each patient. All the statistical calculations were performed using R (version 3.6.3). Results: A total of 60 patients [F:M 17:43, median age 14 years (IQR 10-15), ranging 3-18 years] were included in this study. Forty-four abscesses were identified in 36/60 children (mean volume 3 ± 8.6 ml, median 0.3 ml). In 24/60 patients with perianal disease, no abscess was detected on the MRI. Ten patients (28%) showed perianal abscess on pelvic MRI at the initial diagnosis. The rate of active disease on colonoscopy (visible ulcerations/aphthous ulcers) was similar in both groups (95% vs. 94%). With regards to disease location, the majority of patients (40/60, 66.6%) in both groups had ileocolonic CD. All patients without abscess had a single perianal fistula (n = 24; 3 simple and 21 complex fistulae), however, patients with perianal abscess tended to have >1 fistulous tracts (n = 50 fistulas; all complex, 27 single, 10 double and 1 triple). Intersphincteric fistula was the most common fistula type in both groups (79% and 66%, p = 0.1). The total length of fistula (3.8 ± 1.7 vs. 2.8 ± 0.8 cm, p = 0.006) and presence of multiple external openings (n = 25 vs. 7, p = 0.019) were significantly higher in patients with abscesses, and fistula length >3.3 cm showed 80% specificity and 83% PPV for the presence of perianal abscess. Fistulas were symptomatic (pain, bleeding or drainage) at similar rates in both groups (68% and 70%, p = 0.1). Conclusion: Pediatric patients with CPF who develop perianal abscess have a distinct imaging phenotype defined by longer fistula length (>3.3 cm), multiple skin openings and multiple fistulous tracts (≥2) on MRI. Patients who have these features but does not have an abscess on imaging may merit more aggressive treatment (and close monitoring) to prevent the development of an abscess.

Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers.

Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.

Olfactory Dysfunction is Associated with More Severe Clinical Course in COVID-19.

To perform a quantitative olfactory test in positive COVID19 RT-PCR admitted patients and asymptomatic ones, to evaluate the association between hyposmia and disease severity. This is a Cross sectional study. Ninety-one patients including 68 inpatients and 23 asymptomatic healthcare workers with positive COVID-19 RT-PCRs. Methods: Demographics and clinical characteristics were collected. Iran Smell Identification Test (IR-SIT), a highly accurate 6-odorant test was used to evaluate the reliability of self-reported hyposmia and determine the correlation of the measured olfactory dysfunction with disease severity. Twenty-two of 91 patients (24%) reported hyposmia, while 41/91 (45%) patients had measurable olfactory dysfunction (IR-SIT score 1-4, p < 0.05). Mean age of the 68 inpatients and 23 asymptomatic patients were 43.97 ± 16.13 years; M:F 43:25, and 43.87 ± 12.76 years; M:F 8:15 respectively. Of 68 patients, 20 were graded as severe, and 48/68 had mild course of disease. IR-SIT detected hyposmia in 80% of patients with severe disease, and 50% with mild disease, respectively. The risk of disease severity was significantly increased for patients with olfactory dysfunction and was detected 4 times higher when compared to patients with mild disease (OR 4, 95% CI: 1.166-13.728, p = 0.028). Olfactory Dysfunction was present in 80% of patients with severe course. The risk of disease severity is significantly increased with olfactory dysfunction in admitted patients.

Quantitative tumor heterogeneity MRI profiling improves machine learning-based prognostication in patients with metastatic colon cancer.

OBJECTIVES: Intra-tumor heterogeneity has been previously shown to be an independent predictor of patient survival. The goal of this study is to assess the role of quantitative MRI-based measures of intra-tumor heterogeneity as predictors of survival in patients with metastatic colorectal cancer. METHODS: In this IRB-approved retrospective study, we identified 55 patients with stage 4 colon cancer with known hepatic metastasis on MRI. Ninety-four metastatic hepatic lesions were identified on post-contrast images and manually volumetrically segmented. A heterogeneity phenotype vector was extracted from each lesion. Univariate regression analysis was used to assess the contribution of 110 extracted features to survival prediction. A random forest-based machine learning technique was applied to the feature vector and to the standard prognostic clinical and pathologic variables. The dataset was divided into a training and test set at a ratio of 4:1. ROC analysis and confusion matrix analysis were used to assess classification performance. RESULTS: Mean survival time was 39 ± 3.9 months for the study population. A total of 22 texture features were associated with patient survival (p < 0.05). The trained random forest machine learning model that included standard clinical and pathological prognostic variables resulted in an area under the ROC curve of 0.83. A model that adds imaging-based heterogeneity features to the clinical and pathological variables resulted in improved model performance for survival prediction with an AUC of 0.94. CONCLUSIONS: MRI-based texture features are associated with patient outcomes and improve the performance of standard clinical and pathological variables for predicting patient survival in metastatic colorectal cancer. KEY POINTS: • MRI-based tumor heterogeneity texture features are associated with patient survival outcomes. • MRI-based tumor texture features complement standard clinical and pathological variables for prognosis prediction in metastatic colorectal cancer. • Agglomerative hierarchical clustering shows that patient survival outcomes are associated with different MRI tumor profiles.

Performance of simultaneous multi-slice accelerated diffusion-weighted imaging for assessing focal renal lesions in pediatric patients with tuberous sclerosis complex.

BACKGROUND: Diffusion-weighted imaging (DWI) is a useful MRI technique to characterize abdominal lesions in children, but long acquisition times can lead to image degradation. Simultaneous multi-slice accelerated DWI is a promising technique to shorten DWI scan times. OBJECTIVE: To test the feasibility of simultaneous multi-slice DWI of the kidneys in pediatric patients with tuberous sclerosis complex (TSC) and to evaluate the accelerated protocol regarding image quality and quantitative apparent diffusion coefficient (ADC) values compared to standard echoplanar DWI sequence. MATERIALS AND METHODS: We included 33 children and adolescents (12 female, 21 male; mean age 10±5 years) with TSC and renal cyst or angiomyolipoma on 3-tesla (T) MRI from 2017 to 2019. All studies included both free-breathing standard echoplanar DWI and simultaneous multi-slice DWI sequences. Subjective and quantitative image quality was evaluated using a predefined 5-point scale. ADC values were obtained for all renal cysts and angiomyolipomas ≥5 mm. All statistical analysis was performed using Stata/SE v15.1. RESULTS: Simultaneous multi-slice DWI ADC values were slightly lower compared to standard echoplanar DWI for both renal cysts and angiomyolipomas (mean difference 0.05×10-3 mm2/s, 95% confidence interval [CI] 0.40-0.50 and 0.024×10-3 mm2/s, 95% CI 0.17-0.21, respectively, with P>0.1). Our results showed that renal lesions with ADC values >1.69×10-3 mm2/s were all cysts, whereas lesions with values <1.16×10-3 mm2/s were all angiomyolipomas. However, ADC values could not discriminate between lipid-rich and lipid-poor angiomyolipomas (P>0.1, for both sequences). CONCLUSION: A 55% reduction in scan time was achieved using simultaneous multi-slice DWI for abdominal imaging in children with TSC, with near identical image quality as standard DWI. These results suggest that multi-slice techniques should be considered more broadly as an MRI acceleration technique in children.

Factors influencing cumulative radiation dose from percutaneous intra-abdominal abscess drainage in the setting of inflammatory bowel disease.

PURPOSE: Patients with inflammatory bowel disease (IBD) are at risk for intra-abdominal abscesses requiring CT-guided drainage. These patients are at baseline risk of high cumulative radiation exposure from imaging, which may be exacerbated by CT-guided drainage. This study aimed to determine the radiation dose associated with percutaneous drainage in the setting of IBD and identify risk factors associated with high exposure. METHODS: An IRB-approved single-center retrospective study was performed to identify patients with IBD who underwent percutaneous abscess drainage over a 5-year period. An episode of drainage was defined from drain placement to removal, with all intervening procedures and diagnostic CT scans included in the cumulative radiation dose. RESULTS: The mean cumulative effective dose for a drainage episode was 47.50 mSv. The mean duration of a drainage episode was 68.7 days. Patients with a cumulative dose greater than 50 mSv required higher number of follow-up visits compared to patients with less than 50 mSv (6.9 vs. 3.5, p = 0.003*). Patients with higher cumulative dose were also more likely to require drain upsize (54% vs. 13%, p = 0.01*) or additional drain placement (63% vs 24%, p = 0.03*) compared to patients with lower dose. CONCLUSION: Intra-abdominal abscess drainage may be associated with significant cumulative radiation exposure. Requirement of drain upsizing or additional drain placement were associated with higher cumulative radiation dose, which may be related to more severe underlying inflammatory bowel disease.

Genetic reduction of cilium length by targeting intraflagellar transport 88 protein impedes kidney and liver cyst formation in mouse models of autosomal polycystic kidney disease.

Polycystin-1 (PC1) and -2 (PC2), products of the PKD1 and PKD2 genes, are mutated in autosomal dominant polycystic kidney disease (ADPKD). They localize to the primary cilia; however, their ciliary function is in dispute. Loss of either the primary cilia or PC1 or PC2 causes cyst formation. However, loss of both cilia and PC1 or PC2 inhibits cyst growth via an unknown pathway. To help define a pathway, we studied cilium length in human and mouse kidneys. We found cilia are elongated in kidneys from patients with ADPKD and from both Pkd1 and Pkd2 knockout mice. Cilia elongate following polycystin inactivation. The role of intraflagellar transport proteins in Pkd1-deficient mice is also unknown. We found that inactivation of Ift88 (a gene expressing a core component of intraflagellar transport) in Pkd1 knockout mice, as well as in a new Pkd2 knockout mouse, shortened the elongated cilia, impeded kidney and liver cystogenesis, and reduced cell proliferation. Multi-stage in vivo analysis of signaling pathways revealed ß-catenin activation as a prominent, early, and sustained event in disease onset and progression in Pkd2 single knockout but not in Pkd2.Ift88 double knockout mouse kidneys. Additionally, AMPK, mTOR and ERK pathways were altered in Pkd2 single knockout mice but only AMPK and mTOR pathway alteration were rescued in Pkd2.Ift88 double knockout mice. Thus, our findings advocate an essential role of polycystins in the structure and function of the primary cilia and implicate ß-catenin as a key inducer of cystogenesis downstream of the primary cilia. Our data suggest that modulating cilium length and/or its associated signaling events may offer novel therapeutic approaches for ADPKD.

Reducing Radiation Dose and Contrast Medium Volume With Application of Dual-Energy CT in Children and Young Adults.

OBJECTIVE. The purpose of this study was to assess if dual-source dual-energy CT (DS-DECT) can be used with lower radiation doses and contrast material volumes than single-energy CT (SECT) in children and young adults. MATERIALS AND METHODS. This retrospective study included 85 consecutive children and young adults (age range, 1 month old to 19 years old; 81 male, 70 female) who underwent contrast-enhanced DS-DECT of the chest (n = 41) or the abdomen and pelvis (n = 44) on second- or third-generation dual-source CT scanners (Somatom Flash or Force, Siemens Healthineers) for clinically indicated reasons. We included 66 age-, sex-, body region-, and weight-matched patients who underwent SECT on the same scanner. Patients were scanned with either SECT (with automatic exposure control using both CARE kV [Siemens Healthineers] and CARE Dose 4D [Siemens Healthineers]) or DS-DECT (with CARE Dose 4D). Two pediatric radiologists assessed clinical indications, radiologic findings, image quality, and any study limitations (noise or artifacts). Patient demographics (age, sex, weight), scan parameters (tube voltage, tube current-time product, pitch, section thickness), CT dose descriptors (volume CT dose index, dose-length product, size-specific dose estimate [SSDE]), and contrast material volume were recorded. Descriptive statistics, paired t test, and Cohen kappa test were performed. RESULTS. Mean patient ages and weights ± SD in DS-DECT (10 ± 6 years old, 38 ± 23 kg) and SECT (11 ± 7 years old, 43 ± 29 kg) groups were not significantly different (p > 0.05). Respective SSDEs for chest DS-DECT (4.0 ± 2.1 mGy), chest SECT (6.1 ± 4.4 mGy), abdomen-pelvis DS-DECT (5.0 ± 5.0 mGy), and abdomen-pelvis SECT (8.3 ± 4.0 mGy) were significantly different (p = 0.003-0.005). Contrast material volume for DS-DECT examinations was 19-22% lower compared with the weight- and body region-matched scans obtained with SECT. Image quality of DECT was acceptable in all patients. CONCLUSION. In children and young adults, chest and abdomen-pelvis DS-DECT enables substantial radiation dose and contrast volume reductions compared with weight- and region-matched SECT.

CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept.

INTRODUCTION: To assess the performance of pre-ablation computed tomography texture features of adrenal metastases to predict post-treatment local progression and survival in patients who underwent ablation using machine learning as a prediction tool. MATERIALS AND METHODS: This is a pilot retrospective study of patients with adrenal metastases undergoing ablation. Clinical variables were collected. Thirty-two texture features were extracted from manually segmented adrenal tumors. A univariate cox proportional hazard model was used for prediction of local progression and survival. A linear support vector machine (SVM) learning technique was applied to the texture features and clinical variables, with leave-one-out cross-validation. Receiver operating characteristic analysis and the area under the curve (AUC) were used to assess performance between using clinical variables only versus clinical variables and texture features. RESULTS: Twenty-one patients (61% male, age 64.1 ± 10.3 years) were included. Mean time to local progression was 29.8 months. Five texture features exhibited association with progression (p < 0.05). The SVM model based on clinical variables alone resulted in an AUC of 0.52, whereas the SVM model that included texture features resulted in an AUC 0.93 (p = 0.01). Mean overall survival was 35 months. Fourteen texture features were associated with survival in the univariate model (p < 0.05). While the trained SVM model based on clinical variables resulted in an AUC of 0.68, the SVM model that included texture features resulted in an AUC of 0.93 (p = 0.024). DISCUSSION: Pre-ablation texture analysis and machine learning improve local tumor progression and survival prediction in patients with adrenal metastases who undergo ablation.

Texture Analysis of Magnetic Resonance Enterography Contrast Enhancement Can Detect Fibrosis in Crohn Disease Strictures.

OBJECTIVE: The aim of the study was to investigate if texture analysis of contrast-enhanced magnetic resonance enterography (MRE) images can determine Crohn disease (CD) stricture histologic type. MATERIALS AND METHODS: A radiology report database query identified 25 pediatric patients with established CD who underwent MRE followed by bowel resection within 30 days. MRE images were reviewed to identify strictures on enteric phase T1-weighted fat-suppressed images, that were matched with sites of histologic sectioning. Regions of interest were drawn over the bowel wall and texture analysis was performed using TexRAD software (Cambridge, UK), with skewness, mean, entropy and standard deviation parameters assessed. A pathologist reviewed all stricture histology specimens to assess for active mucosal inflammation and mural fibrosis. Multivariate logistic regression and analysis of variance were performed to identify texture features associated with stricture fibrosis. RESULTS: Sixty-four bowel segments from 25 patients (mean age 16 ±â€Š2 years) with imaging-histologic correlation were included. Of note, all strictures included had undergone surgical resection with MRE imaging available within 30 days. The histologic distribution of these bowel segments included 9 segments that showed active inflammation without fibrosis, 23 segments that showed only fibrosis, and 32 mixed segments with concomitant active inflammation and fibrosis. Bivariate regression analysis demonstrated that skewness, standard deviation, entropy, and mean texture analysis features are independently associated with stricture fibrosis. Stepwise logistic regression showed that the combination of mean, skewness, and entropy texture predicted stricture fibrosis with a goodness-of-fit value of 0.995. A combination of threshold values for these 3 texture analysis parameters was able to correctly classify 100% of the strictures in the study cohort for presence (55/55) and absence (9/9) of fibrosis. CONCLUSIONS: MRE texture analysis (MRE-TA) texture features can differentiate CD stricture types and accurately detect fibrosis.

Can fully iterative reconstruction technique enable routine abdominal CT at less than 1 mSv?

OBJECTIVE: We assessed the effect of the forward projected model-based reconstruction technique (FIRST) on lesion detection of routine abdomen CT at <1 mSv. MATERIALS AND METHODS: Thirty-seven adult patients gave written informed consent for acquisition of low-dose CT (LDCT) immediately after their clinically-indicated, standard of care dose (SDCT), routine abdomen CT on a 640-slice MDCT (Aquillion One, Canon Medical System). The LDCT series were reconstructed with FIRST (at STD (Standard) and STR (Strong) levels), and SDCT series with filtered back projection (FBP). Two radiologists assessed lesions in LD-FBP and FIRST images followed by SDCT images. Then, SDCT and LDCT were compared for presence of artifacts in a randomized and blinded fashion. Patient demographics, size and radiation dose descriptors (CTDIvol, DLP) were recorded. Descriptive statistics and inter-observer variability were calculated for data analysis. RESULTS: Mean CTDIvol for SDCT and LDCT were 13 ± 4.7 mGy and 2.2 ± 0.8 mGy, respectively. There were 46 true positive lesions detected on SDCT. Radiologists detected 38/46 lesions on LD-FIRST-STD compared to 26/46 lesions on LD-FIRST-STR. The eight lesions (liver and kidney cysts, pancreatic lesions, sub-cm peritoneal lymph node) missed on LD-FIRST-STD were seen in patients with BMI > 25.8 kg/m2. Diagnostic confidence for lesion assessment was optimal in LD-FIRST-STD setting in most patients regardless of their size. The inter-observer agreement (kappa-value) for overall image quality were 0.98 and 0.84 for LD-FIRST-STD and STR levels, respectively. CONCLUSION: FIRST enabled optimal lesion detection in routine abdomen CT at less than 1 mSv radiation dose in patients with body mass less than ≤25.8 kg/m2.

Advanced CT Techniques for Decreasing Radiation Dose, Reducing Sedation Requirements, and Optimizing Image Quality in Children.

CT is an invaluable diagnostic tool for pediatric patients; however, concerns have arisen about the potential risks of ionizing radiation associated with diagnostic imaging in young patients, particularly for pediatric populations that may require serial CT examinations. Recent attention has also been focused on the immediate and long-term risks of administration of anesthetic medications to infants and young children who require sedation to undergo imaging examinations. These concerns can be mitigated with use of advanced CT techniques that can decrease scan time and radiation dose while preserving image quality. In this article, current state-of-the-art CT acquisition techniques are reviewed as part of a comprehensive strategy to reduce radiation dose, decrease sedation needs, and optimize image quality in infants and young children. Three imaging strategies are discussed, including (a) dual-energy CT (DECT), (b) imaging with a low tube potential, and (c) rapid scanning. Consolidating multiphase imaging protocols into a single phase with virtual nonenhanced imaging on DECT scanners, as well as use of low tube voltage, can reduce the radiation dose while increasing the conspicuity of contrast material-enhanced structures with a reduced volume of iodinated contrast material and a reduced rate of injection. Rapid scanning techniques with either ultrahigh pitch at dual-source CT or with wide-area detector single-source CT facilitate scanning without the need for sedation in many children. ©RSNA, 2019 See discussion on this article by Szczykutowicz .

Computed tomography texture features can discriminate benign from malignant lymphadenopathy in pediatric patients: a preliminary study.

BACKGROUND: Differentiation of benign from malignant lymphadenopathy remains challenging in pediatric radiology. Textural analysis (TA) quantitates heterogeneity of tissue signal intensities and has been applied to analysis of CT images. OBJECTIVE: The purpose of this study was to establish whether CT textural analysis of enlarged lymph nodes visualized on pediatric CT can distinguish benign from malignant lymphadenopathy. MATERIALS AND METHODS: We retrospectively identified enlarged lymph nodes measuring 10-20 mm on contrast-enhanced CTs of patients age 18 years and younger that had been categorized as benign or malignant based on the known diagnoses. We placed regions of interest (ROIs) over lymph nodes of interest and performed textural analysis with and without feature size filtration. We then calculated test performance characteristics for TA features, along with multivariate logistic regression modeling using Akaike Information Criterion (AIC) minimization, to determine the optimal thresholds for distinguishing benign from malignant lymphadenopathy. RESULTS: We identified 34 enlarged malignant nodes and 29 benign nodes from 63 patients within the 10- to 20-mm size range. Filtered image TA exhibited 82.4% sensitivity, 86.2% specificity and 84.1% accuracy for detecting malignant lymph nodes using mean and entropy parameters, whereas unfiltered TA exhibited 88.2% sensitivity, 72.4% specificity and 81.0% accuracy using mean and mean value of positive pixels parameters. CONCLUSION: This preliminary study demonstrates that the use of TA features improves the utility of pediatric CT to distinguish benign from malignant lymphadenopathy. The addition of TA to pediatric CT protocols has great potential to aid the characterization of indeterminate lymph nodes. If definitive differentiation between benign and malignant lymphadenopathy is possible by TA, it has the potential to reduce the need for follow-up imaging and tissue sampling, with reduced associated radiation exposure. However future studies are needed to confirm the clinical applicability of TA in distinguishing benign from malignant lymphadenopathy.