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.

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.

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.

Clinical Evaluation of a 2-Minute Ultrafast Brain MR Protocol for Evaluation of Acute Pathology in the Emergency and Inpatient Settings.

BACKGROUND AND PURPOSE: The use of MR imaging in emergency settings has been limited by availability, long scan times, and sensitivity to motion. This study assessed the diagnostic performance of an ultrafast brain MR imaging protocol for evaluation of acute intracranial pathology in the emergency department and inpatient settings. MATERIALS AND METHODS: Sixty-six adult patients who underwent brain MR imaging in the emergency department and inpatient settings were included in the study. All patients underwent both the reference and the ultrafast brain MR protocols. Both brain MR imaging protocols consisted of T1-weighted, T2/T2*-weighted, FLAIR, and DWI sequences. The ultrafast MR images were reconstructed by using a machine-learning assisted framework. All images were reviewed by 2 blinded neuroradiologists. RESULTS: The average acquisition time was 2.1 minutes for the ultrafast brain MR protocol and 10 minutes for the reference brain MR protocol. There was 98.5% agreement on the main clinical diagnosis between the 2 protocols. In head-to-head comparison, the reference protocol was preferred in terms of image noise and geometric distortion (P < .05 for both). The ultrafast ms-EPI protocol was preferred over the reference protocol in terms of reduced motion artifacts (P < .01). Overall diagnostic quality was not significantly different between the 2 protocols (P > .05). CONCLUSIONS: The ultrafast brain MR imaging protocol provides high accuracy for evaluating acute pathology while only requiring a fraction of the scan time. Although there was greater image noise and geometric distortion on the ultrafast brain MR protocol images, there was significant reduction in motion artifacts with similar overall diagnostic quality between the 2 protocols.

Diagnostic evaluation of deep learning accelerated lumbar spine MRI.

BACKGROUND AND PURPOSE: Deep learning (DL) accelerated MR techniques have emerged as a promising approach to accelerate routine MR exams. While prior studies explored DL acceleration for specific lumbar MRI sequences, a gap remains in comprehending the impact of a fully DL-based MRI protocol on scan time and diagnostic quality for routine lumbar spine MRI. To address this, we assessed the image quality and diagnostic performance of a DL-accelerated lumbar spine MRI protocol in comparison to a conventional protocol. METHODS: We prospectively evaluated 36 consecutive outpatients undergoing non-contrast enhanced lumbar spine MRIs. Both protocols included sagittal T1, T2, STIR, and axial T2-weighted images. Two blinded neuroradiologists independently reviewed images for foraminal stenosis, spinal canal stenosis, nerve root compression, and facet arthropathy. Grading comparison employed the Wilcoxon signed rank test. For the head-to-head comparison, a 5-point Likert scale to assess image quality, considering artifacts, signal-to-noise ratio (SNR), anatomical structure visualization, and overall diagnostic quality. We applied a 15% noninferiority margin to determine whether the DL-accelerated protocol was noninferior. RESULTS: No significant differences existed between protocols when evaluating foraminal and spinal canal stenosis, nerve compression, or facet arthropathy (all p > .05). The DL-spine protocol was noninferior for overall diagnostic quality and visualization of the cord, CSF, intervertebral disc, and nerve roots. However, it exhibited reduced SNR and increased artifact perception. Interobserver reproducibility ranged from moderate to substantial (κ = 0.50-0.76). CONCLUSION: Our study indicates that DL reconstruction in spine imaging effectively reduces acquisition times while maintaining comparable diagnostic quality to conventional MRI.

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.

Radiation dose reduction in chest dual-energy computed tomography: effect on image quality and diagnostic information / Redução da dose de radiação na tomografia computadorizada de dupla energia do tórax: efeito na qualidade das imagens e na informação diagnóstica

Abstract Objective: To determine whether dual-energy computed tomography (DECT) of the chest can be performed at a reduced radiation dose, with an emphasis on images generated with post-processing techniques. Materials and Methods: In 21 patients undergoing DECT of the chest in a dual-source scanner, an additional image series was acquired at a reduced radiation dose. Four thoracic radiologists assessed both image series for image quality, normal thoracic structures, as well as pulmonary and mediastinal abnormalities, on virtual monochromatic images at 40 keV and 60 keV. Data were analyzed with Student's t-test, kappa statistics, analysis of variance, and the Wilcoxon signed-rank test. Results: The overall image quality of 60 keV virtual monochromatic images at a reduced radiation dose was considered optimal in all patients, and no abnormalities were missed. Contrast enhancement and lesion detection performance were comparable between reduced-dose images at 40 keV and standard-of-care images at 60 keV. The intraobserver and interobserver agreement were both good. The mean volumetric CT dose index (CTDIvol), size-specific dose estimate (SSDE), dose-length product (DLP), and effective dose (ED) for reduced-dose DECT were 3.0 ± 0.6 mGy, 4.0 ± 0.6 mGy, 107 ± 30 mGy.cm, and 1.5 ± 0.4 mSv, respectively. Conclusion: DECT of the chest can be performed at a reduced radiation dose (CTDIvol < 3 mGy) without loss of diagnostic information.

Resumo Objetivo: Verificar se a tomografia computadorizada de dupla energia (TCDE) do tórax pode ser realizada com baixas doses de radiação, com ênfase em imagens pós-processadas. Materiais e Métodos: Em 21 pacientes submetidos a DECT do tórax foi adicionada uma série de imagens adquiridas com baixas doses de radiação. Quatro radiologistas com especialidade em tórax avaliaram a qualidade, visualização de estruturas torácicas normais e também anormalidades pulmonares e mediastinais das imagens monocromáticas de baixa energia (40 e 60 keV). Os dados foram analisados utilizando t-test, estatística kappa, análise de variância e teste Wilcoxon. Resultados: A qualidade das imagens monocromáticas de baixa energia (60 keV) com doses reduzidas foi considerada ótima para todos os pacientes e nenhuma anormalidade no tórax foi perdida. O realce pelo contraste e a performance de detecção de lesões foram similares nas imagens com radiação reduzida e com radiação padrão. Boa concordância intra-avaliadores e interavaliadores foi observada. A média dos parâmetros CTDIvol, SSDE, DLP e ED para TCDE de baixa dose foram 3,0 ± 0,6 mGy, 4,0 ± 0,6 mGy, 107 ± 30 mGy.cm e 1,5 ± 0,4 mSv, respectivamente. Conclusão: TCDE do tórax pode ser realizada com baixas doses de radiação (CTDIvol < 3 mGy), sem perder informações diagnósticas.