Baseline Computed Tomography Research Articles

Interventions to Reduce Imaging in Children With Minor Traumatic Head Injury: A Systematic Review.

Reducing unnecessary imaging in emergency departments (EDs) for children with minor traumatic brain injuries (mTBIs) has been encouraged. Our objective was to systematically review the effectiveness of interventions to decrease imaging in this population. Eight electronic databases and the gray literature were searched. Comparative studies assessing ED interventions to reduce imaging in children with mTBIs were eligible. Two independent reviewers screened studies, completed a quality assessment, and extracted data. The median of relative risks with interquartile range (IQR) are reported. A multivariable metaregression identified predictors of relative change in imaging. Twenty-eight studies were included, and most (79%) used before-after designs. The Pediatric Emergency Care Applied Research Network (PECARN) rule was the most common intervention (71%); most studies (75%) used multifaceted interventions (median components: 3; IQR: 1.75 to 4). Before-after studies assessing multi-faceted PECARN interventions reported decreased computed tomography (CT) head imaging (relative risk = 0.73; IQR: 0.60 to 0.89). Higher baseline imagine (P < .001) and additional intervention components (P = .008) were associated with larger imaging decreases. The limitations of this study include the inconsistent reporting of important outcomes and that the results are based on non-randomized studies. Implementing interventions in EDs with high baseline CT ordering using complex interventions was more likely to reduce head imaging in children with mTBIs. Including the PECARN decision rule in the intervention strategy decreased orders by a median of 27%. Further research could provide insight into which specific factors influence successful implementation and sustained effects.

Initial sarcopenia and body composition changes as prognostic factors in cervical cancer patients treated with concurrent chemoradiation: An artificial intelligence-based volumetric study

ObjectivesThis study aimed to investigate the influence of baseline sarcopenia and changes in body composition on survival during cervical cancer treatment. MethodsPatients diagnosed with stage IB1-IVB cervical cancer who underwent primary concurrent chemoradiation therapy (CCRT) between 2002 and 2022 were included. The exclusion criteria were prior radical hysterectomy, lack of pretreatment computed tomography (CT) imaging, or significant comorbidities. An artificial intelligence-based automatic segmentation program assessed body composition by analyzing CT images, defining L3 sarcopenia (L3 skeletal muscle index [SMI] <39cm2/m2) and volumetric sarcopenia (volumetric SMI <180.4 cm3/m3). Comparative and multivariate analyses identified the prognostic factors. The impact of body component changes during CCRT was explored. ResultsAmong 347 patients, there were 125 recurrences and 59 deaths (median follow-up, 50.5 months). Seven patients were excluded from the volumetric sarcopenia analysis because of incomplete baseline CT data, and 175 patients were included in the analysis of body composition changes. Patients with L3 sarcopenia had a lower 5-year progression-free survival (PFS) rate (55.6% vs. 66.2%, p = 0.027), while those with volumetric sarcopenia showed a poorer 5-year overall survival rate (76.5% vs. 85.1%, p = 0.036). Patients with total fat loss during CCRT had a worse 5-year PFS rate than those with total fat gain (61.9% vs. 73.8%, p = 0.029). Multivariate analyses revealed that total fat loss (adjusted hazard ratio [aHR], 2.172; 95% confidence interval [CI], 1.066–4.424; p = 0.033) was a significant factor for recurrence, whereas L3 sarcopenia was not. Volumetric sarcopenia increased the risk of death by 1.75-fold (aHR, 1.750; 95% CI, 1.012–3.025; p = 0.045). ConclusionsAmong patients with cervical cancer undergoing CCRT, initial volumetric sarcopenia and fat loss during treatment are survival risk factors. These findings suggest the potential importance of personalized supportive care, including tailored nutrition and exercise interventions.

Can the presence of aortic or coronary artery calcifications in the standard of care baseline CT or PET-CT scan of patients with Hodgkin or non-Hodgkin lymphoma be used as a predictor of MACE?

Abstract Background Major adverse cardiovascular events (MACE), commonly defined as acute myocardial infarction, acute coronary syndrome/ischemic heart disease requiring revascularization, stroke, and heart failure, represent a significant cause of mortality among patients with cancer. With increased rates of both cancer and cancer survivorship, the identification of new predictive markers for the development of MACE in this population is crucial for implementing effective preventive and risk-mitigation strategies. Purpose To determine whether the presence of calcifications in the coronary arteries (CAC) or aorta (CA) of lymphoma patients, as reported in standard-of-care PET-CT (Positron Emission Tomography-Computed Tomography) or chest CT (Computed Tomography) scan, can predict the occurrence of MACE. Methods A Retrospective analysis was conducted on a cohort of patients diagnosed with Hodgkin or non-Hodgkin lymphoma and treated with anthracycline-based chemotherapy. Patients were diagnosed from January 1, 2013, and followed through to June 30, 2023. The reference point for the study was the radiologist’s report of the PET-CT or chest CT conducted before the initiation of anthracycline therapy. Patients who did not undergo a PET-CT or CT before the start of treatment, and/or developed MACE before treatment initiation were excluded. Univariate and multivariate adjusted Cox regression models were employed to assess whether the presence of CA and CAC was associated with the development of MACE. Associations with outcomes were evaluated using the Kaplan-Meier method and Cox regression. Results 326 patients were included with a mean age of 55 years (range: 52-60), predominantly male 201 (61%) and white 314 (96%), CAC was reported in 75 patients (23%) and CA in 18 (6%). In the univariate regression model, a statistically significant association was found between the presence of both CA and CAC with the risk of MACE. CAC showed a stronger association with the development of MACE than CA (HR: 3.7 [1-7.9], p=0.0005 vs HR: 2.9 [1- 8.5], p=0.050). In multivariable analysis CAC emerged as the strongest predictor of MACE (HR: 2.9 [1.3 – 6.4], p=0.01), after accounting for hypertension, diabetes, dyslipidemia, and GFR &amp;lt;60 (Table 1), while the association with CA attenuated (HR: 2.01 [0.7-6.5], p=0.2010) and was no longer significant (Table 2). Conclusion CAC in the standard-of-care PET/-CT scans was a predictor of MACE in lymphoma patients treated with anthracyclines, while the association with CA weakened in multivariable analyses and was likely underpowered given the small number of patients with CA. If replicated, this finding warrants closer surveillance of this group of patients and can be considered as a non-invasive cardiovascular risk marker.

A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as ground-glass nodules based on follow-up CT imaging.

Different pathological stages of lung adenocarcinoma require different surgical strategies and have varying prognoses. Predicting their invasiveness is clinically important. This study aims to develop a nomogram to predict the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules (GGNs) based on follow-up computed tomography (CT) imaging. We retrospectively collected data of 623 GGNs from 601 patients who underwent two follow-up chest CT scans and were confirmed as lung adenocarcinoma by postoperative pathology between June 2017 and August 2023. These patients were randomly divided into training and testing sets in a 7:3 ratio. Eighty-seven GGNs from 86 patients who underwent surgery between September 2023 and April 2024 were prospectively collected as a validation set. The volume, mean density, solid component volume (SV), percentage of solid component (PSC), and mass of GGNs were evaluated using the InferRead CT Lung software. Patients were classified into Group A (atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma) and Group B (invasive adenocarcinoma). Three predictive models were established: model 1 utilized clinical characteristics and morphological features on pre-surgical CT, model 2 incorporated clinical characteristics, morphological features and quantitative parameters on pre-surgical CT, and model 3 utilized all selected features on baseline and pre-surgical CT. Model 3 achieved a satisfying area under the curves values of 0.911, 0.893, and 0.932 in the training, testing, and validation sets, respectively, demonstrating superior predictive performance than model1 (0.855, 0.858, and 0.816) and model2 (0.895, 0.891, and 0.903). A nomogram was constructed based on model 3. Calibration curves showed a good fit, and decision curve analysis showed that the nomogram was clinically useful. The nomogram based on morphological features and quantitative parameters from follow-up CT images showed good discrimination and calibration abilities in predicting the invasiveness of lung adenocarcinoma manifesting as GGNs.

Solid pseudopapillary neoplasms of the pancreas (SPNs): diagnostic accuracy of CT and CT imaging features

PurposeTo summarize the abdominal computed tomography (CT) imaging and clinicopathological data of patients with SPNs of the pancreas and analyze the accuracy of preoperative CT diagnosis and features.Materials and methodsBetween June 2006 and June 2023, CT images of 120 histopathologically proven SPNs in the pancreas were retrospectively reviewed. Fifteen features, including age, sex, and CT-determined features, were included in a multiple stepwise regression analysis. The correlations between features and SPNs, including odds ratios (ORs) and 95% confidence intervals (CIs), were evaluated.ResultsAmong the 120 patients, the diagnostic accuracy of CT was 43.3%. The baseline CT results of patients with a correct diagnosis and misdiagnosis revealed significant differences in sex (P = 0.043), age (P = 0.004), boundary (P = 0.037) and encapsulation (P = 0.002) between the two groups. The preoperative imaging diagnostic accuracy was significantly greater in females than in males (47.9% vs. 25.0%, P = 0.043). The immunohistochemical indices did not significantly differ between the two groups. The results of univariate analysis revealed significant differences in sex (P = 0.048), age (P = 0.014), tumor length (P = 0.023), tumor boundaries (P = 0.039) and capsule type (P = 0.003). The results of multivariate analysis revealed that encapsulation was closely related to the diagnostic accuracy of CT (P = 0.04).ConclusionsThe accuracy of CT in the diagnosis of SPNs is low, but a length‒diameter ratio of the tumor approaching 1.0, encapsulation and clear boundaries are important CT-determined features. The capsule is an independent CT predictor in the diagnosis of SPNs.

Early temporal changes on CT chest in patients with COVID-19 pneumonia

Introduction: Computed tomography (CT) scan of the chest helps in the evaluation, prognostication, and follow-up of patients with Corona virus disease-19 (COVID-19) pneumonia.Objectives: To study the early temporal evolution of findings on CT chest in patients with COVID-19 pneumonia at 6 weeks follow up.Material and methods: A retrospective analysis was performed on 40 patients with confirmed COVID-19 pneumonia who underwent baseline chest CT scans followed by a repeat scan at six weeks. Patients were quantitatively assessed using an objective CT severity score and qualitatively assessed using imaging parameters like consolidation, ground glass opacities (GGOs), parenchymal bands, septal thickening, nodules and crazy paving pattern.Results: Mean age of our study population was 58.5 ± 10.2 years with 80% being males. The baseline CT scan revealed a total median CT severity score interquartile range (IQR) of 16/25 (range: 14-20) while the follow-up scan after six weeks revealed a median score (IQR) of 14/25 (range: 8-19). This difference was statistically significant (p=0.04). The difference in CT severity scores of bilateral upper lobes was statistically significant (p=0.04 on right side and p=0.009 on left side); however, the difference in scores of bilateral lower lobes was not statistically significant (p&gt;0.05). The common imaging patterns on baseline imaging were GGO (97.5%), con-solidation (85%), septal thickening (85%), parenchymal bands (60%) and nodules (32.5%), which showed an interval resolution in the follow-up CT scan.Conclusion: Most of the patients with COVID-19 pneumonia showed interval resolution of lung findings seen on short-term follow-up CT scan.

A Cadaveric Study: Does Ankle Positioning Affect the Quality of Anatomic Syndesmosis Reduction?

The objective of this study was to compare the quality of syndesmotic reduction with the ankle in maximal dorsiflexion versus neutral plantarflexion (normal resting position). Baseline computed tomography (CT) imaging of 10 cadaveric ankle specimens from 5 donors was obtained with the ankles placed in normal resting position. Two fellowship-trained orthopaedic surgeons disrupted the syndesmosis of each ankle specimen. All ankles were then placed in neutral plantarflexion and were subsequently reduced with thumb pressure under direct visualization through an anterolateral approach and stabilized with one 0.062-inch K-wire placed from lateral to medial in a quadricortical fashion across the syndesmosis. Postreduction CT scans were then obtained with the ankle in normal resting position. This process was repeated with the ankles placed in maximal dorsiflexion during reduction and stabilization. Postreduction CT scans were then obtained with the ankles placed in normal resting position. All postreduction CT scans were compared with baseline CT imaging using mixed-effects linear regression with significance set at P < 0.05. Syndesmotic reduction and stabilization in maximal dorsiflexion led to increased external rotation of the fibula compared with baseline scans [13.0 ± 5.4 degrees (mean ± SD) vs. 7.5 ± 2.4 degrees, P = 0.002]. There was a tendency toward lateral translation of the fibula with the ankle reduced in maximal dorsiflexion (3.3 ± 1.0 vs. 2.7 ± 0.7 mm, P = 0.096). No other statistically significant differences between measurements of reduction with the ankle placed in neutral plantarflexion or maximal dorsiflexion compared with baseline were present (P > 0.05). Reducing the syndesmosis with the ankle in maximal dorsiflexion may lead to malreduction with external rotation of the fibula. There was no statistically significant difference in reduction quality with the ankle placed in neutral plantarflexion compared with baseline. Future studies should assess the clinical implications of ankle positioning during syndesmotic fixation.

3-Dimensional Echocardiographic Prediction of Left Ventricular Outflow Tract Area Prior to Transcatheter Mitral Valve Replacement

BackgroundNew postprocessing software facilitates 3-dimensional (3D) echocardiographic determination of mitral annular (MA) and neo–left ventricular outflow tract (neo-LVOT) dimensions in patients undergoing transcatheter mitral valve replacement (TMVR). ObjectivesThis study aims to test the accuracy of 3D echocardiographic analysis as compared to baseline computed tomography (CT). MethodsA total of 105 consecutive patients who underwent TMVR at 2 tertiary care centers between October 2017 and May 2023 were retrospectively included. A virtual valve was projected in both baseline CT and 3D transesophageal echocardiography (TEE) using dedicated software. MA dimensions were measured in baseline images and neo-LVOT dimensions were measured in baseline and postprocedural images. All measurements were compared to baseline CT as a reference. The predicted neo-LVOT area was correlated with postprocedural peak LVOT gradients. ResultsThere was no significant bias in baseline neo-LVOT prediction between both imaging modalities. TEE significantly underestimated MA area, perimeter, and medial-lateral dimension compared to CT. Both modalities significantly underestimated the actual neo-LVOT area (mean bias pre/post TEE: 25.6 mm2, limit of agreement: −92.2 mm2 to 143.3 mm2; P < 0.001; mean bias pre/post CT: 28.3 mm2, limit of agreement: −65.8 mm2 to 122.4 mm2; P = 0.046), driven by neo-LVOT underestimation in the group treated with dedicated mitral valve bioprosthesis. Both CT- and TEE-predicted-neo-LVOT areas exhibited an inverse correlation with postprocedural LVOT gradients (r2 = 0.481; P < 0.001 for TEE and r2 = 0.401; P < 0.001 for CT). ConclusionsTEE-derived analysis provides comparable results with CT-derived metrics in predicting the neo-LVOT area and peak gradient after TMVR.

Multi-modality imaging parameters that predict rapid tumor regression in head and neck radiotherapy

Background and purposeVolume regression during radiotherapy can indicate patient-specific treatment response. We aimed to identify pre-treatment multimodality imaging (MMI) metrics from positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT) that predict rapid tumor regression during radiotherapy in human papilloma virus (HPV) associated oropharyngeal carcinoma. Materials and methodsPre-treatment FDG PET-CT, diffusion-weighted MRI (DW-MRI), and intra-treatment (at 1, 2, and 3 weeks) MRI were acquired in 72 patients undergoing chemoradiation therapy for HPV+ oropharyngeal carcinoma. Nodal gross tumor volumes were delineated on longitudinal images to measure intra-treatment volume changes. Pre-treatment PET standardized uptake value (SUV), CT Hounsfield Unit (HU), and non-gaussian intravoxel incoherent motion DW-MRI metrics were computed and correlated with volume changes. Intercorrelations between MMI metrics were also assessed using network analysis. Validation was carried out on a separate cohort (N = 64) for FDG PET-CT. ResultsSignificant correlations with volume loss were observed for baseline FDG SUVmean (Spearman ρ = 0.46, p < 0.001), CT HUmean (ρ = 0.38, p = 0.001), and DW-MRI diffusion coefficient, Dmean (ρ = -0.39, p < 0.001). Network analysis revealed 41 intercorrelations between MMI and volume loss metrics, but SUVmean remained a statistically significant predictor of volume loss in multivariate linear regression (p = 0.01). Significant correlations were also observed for SUVmean in the validation cohort in both primary (ρ = 0.30, p = 0.02) and nodal (ρ = 0.31, p = 0.02) tumors. ConclusionsMultiple pre-treatment imaging metrics were correlated with rapid nodal gross tumor volume loss during radiotherapy. FDG-PET SUV in particular exhibited significant correlations with volume regression across the two cohorts and in multivariate analysis.

Advanced Gastric Cancer: CT Radiomics Prediction of Lymph Modes Metastasis After Neoadjuvant Chemotherapy.

This study aims to create and assess machine learning models for predicting lymph node metastases following neoadjuvant treatment in advanced gastric cancer (AGC) using baseline and restaging computed tomography (CT). We evaluated CT images and pathological data from 158 patients with resected stomach cancer from two institutions in this retrospective analysis. Patients were eligible for inclusion if they had histologically proven gastric cancer. They had received neoadjuvant chemotherapy, with at least 15 lymph nodes removed. All patients received baseline and preoperative abdominal CT and had complete clinicopathological reports. They were divided into two cohorts: (a) the primary cohort (n = 125) for model creation and (b) the testing cohort (n = 33) for evaluating models' capacity to predict the existence of lymph node metastases. The diagnostic ability of the radiomics-model for lymph node metastasis was compared to traditional CT morphological diagnosis by radiologist. The radiomics model based on the baseline and preoperative CT images produced encouraging results in the training group (AUC 0.846) and testing cohort (AUC 0.843). In the training cohort, the sensitivity and specificity were 81.3% and 77.8%, respectively, whereas in the testing cohort, they were 84% and 75%. The diagnostic sensitivity and specificity of the radiologist were 70% and 42.2% (using baseline CT) and 46.3% and 62.2% (using preoperative CT). In particular, the specificity of radiomics model was higher than that of conventional CT in diagnosing N0 cases (no lymph node metastasis). The CT-based radiomics model could assess lymph node metastasis more accurately than traditional CT imaging in AGC patients following neoadjuvant chemotherapy.

Volumetric analysis of acute uncomplicated type B aortic dissection using an automated deep learning aortic zone segmentation model

BackgroundMachine learning techniques have shown excellent performance in three-dimensional medical image analysis, but have not been applied to acute uncomplicated type B aortic dissection (auTBAD) using Society for Vascular Surgery (SVS) and Society of Thoracic Surgeons (STS)-defined aortic zones. The purpose of this study was to establish a trained, automatic machine learning aortic zone segmentation model to facilitate performance of an aortic zone volumetric comparison between patients with auTBAD based on the rate of aortic growth. MethodsPatients with auTBAD and serial imaging were identified. For each patient, imaging characteristics from two computed tomography (CT) scans were analyzed: (1) the baseline CT angiography (CTA) at the index admission and (2) either the most recent surveillance CTA or the most recent CTA before an aortic intervention. Patients were stratified into two comparative groups based on aortic growth: rapid growth (diameter increase of ≥5 mm/year) and no or slow growth (diameter increase of <5 mm/year). Deidentified images were imported into an open source software package for medical image analysis and images were annotated based on SVS/STS criteria for aortic zones. Our model was trained using four-fold cross-validation. The segmentation output was used to calculate aortic zone volumes from each imaging study. ResultsOf 59 patients identified for inclusion, rapid growth was observed in 33 patients (56%) and no or slow growth was observed in 26 patients (44%). There were no differences in baseline demographics, comorbidities, admission mean arterial pressure, number of discharge antihypertensives, or high-risk imaging characteristics between groups (P > .05 for all). Median duration between baseline and interval CT was 1.07 years (interquartile range [IQR], 0.38-2.57). Postdischarge aortic intervention was performed in 13 patients (22%) at a mean of 1.5 ± 1.2 years, with no difference between the groups (P > .05). Among all patients, the largest relative percent increases in zone volumes over time were found in zone 4 (13.9%; IQR, −6.82 to 35.1) and zone 5 (13.4%; IQR, −7.78 to 37.9). There were no differences in baseline zone volumes between groups (P > .05 for all). The average Dice coefficient, a performance measure of the model output, was 0.73. Performance was best in zone 5 (0.84) and zone 9 (0.91). ConclusionsWe describe an automatic deep learning segmentation model incorporating SVS-defined aortic zones. The open source, trained model demonstrates concordance to the manually segmented aortas with the strongest performance in zones 5 and 9, providing a framework for further clinical applications. In our limited sample, there were no differences in baseline aortic zone volumes between patients with rapid growth and patients with no or slow growth.

Significance of imaging-detected extranodal extension (iENE) in locally advanced head and neck squamous cell carcinoma (LASCCHN) treated with induction chemotherapy followed by chemoradiotherapy.

6034 Background: Extranodal extension (ENE) of nodal metastasis is a significant prognostic factor in p16-negative SCCHN and is classified as N3b by the AJCC 8th edition. Therefore, pretreatment determination of ENE has significant clinical implications in SCCHN, and iENE has just recently been proposed. We previously discussed association with pathological ENE and iENE (Jpn J Radiol. 2020;38(6):489-506.). However, the role of iENE in non-surgical sequential therapy remains unclear. Methods: We retrospectively reviewed patients with LASCCHN originating from the oropharynx, hypopharynx, and larynx who received enhanced computed tomography (CT), then treated with induction chemotherapy (IC) with paclitaxel, carboplatin, and cetuximab followed by chemoradiotherapy (CRT) from 2013 to 2022 in our hospital. Two radiologists specializing in head and neck cancer blindly annotated the status of iENE in baseline CT images by the previously reported criteria (Oral Oncol. 2022;125:105716.). Multivariate analysis variables for event-free survival (EFS) and overall survival (OS) included the presence or absence of iENE, a response to IC, clinical T-category, performance status, smoking status, etc. Results: In the 88 patients, 67 (76.1%) had iENE and 21 (23.9%) did not at baseline. In the former and latter group, stage II/III/IV were 10.4%/26.9%/62.7% and 0%/28.6%/71.4%, HPV-positive were 37.3% and 28.6%, respectively. With the median follow-up of 37.4 months (range: 6.7-108.8), the former had significantly shorter EFS (3-y EFS: 41.9% vs. 75.6%, hazard ratio [HR]; 2.9 (1.2-7.4), p-value; 0.02) and OS (3-y OS: 72.8% vs. 100%, HR; Inf (0.01-Inf), p-value=0.003). Multivariate analysis identified the presence of iENE (HR for EFS: 2.80, 95%CI: 0.97-8.05, HR for OS: 2.93, 95%CI: 1.01-8.44) and unresponsiveness to IC (HR for EFS: 2.47, 95%CI: 1.31-4.68, HR for OS: 2.87, 95%CI: 1.13-7.26) as mutually independent unfavorable prognostic factors for both EFS and OS. Furthermore, classification based on the two factors could identify the population with a worse prognosis (Table). Conclusions: In the sequential therapy of IC followed by CRT, the current study revealed for the first time that subjects with an iENE at baseline, together with an unsatisfactory response to IC would require special attention, such as more intensified post-treatment follow-up as well as additional therapeutic interventions to improve their prognosis. [Table: see text]

Enhancing cancer prediction in challenging screen-detected incident lung nodules using time-series deep learning

Lung cancer screening (LCS) using annual computed tomography (CT) scanning significantly reduces mortality by detecting cancerous lung nodules at an earlier stage. Deep learning algorithms can improve nodule malignancy risk stratification. However, they have typically been used to analyse single time point CT data when detecting malignant nodules on either baseline or incident CT LCS rounds. Deep learning algorithms have the greatest value in two aspects. These approaches have great potential in assessing nodule change across time-series CT scans where subtle changes may be challenging to identify using the human eye alone. Moreover, they could be targeted to detect nodules developing on incident screening rounds, where cancers are generally smaller and more challenging to detect confidently.Here, we show the performance of our Deep learning-based Computer-Aided Diagnosis model integrating Nodule and Lung imaging data with clinical Metadata Longitudinally (DeepCAD-NLM-L) for malignancy prediction. DeepCAD-NLM-L showed improved performance (AUC = 88%) against models utilizing single time-point data alone. DeepCAD-NLM-L also demonstrated comparable and complementary performance to radiologists when interpreting the most challenging nodules typically found in LCS programs. It also demonstrated similar performance to radiologists when assessed on out-of-distribution imaging dataset. The results emphasize the advantages of using time-series and multimodal analyses when interpreting malignancy risk in LCS.

Prospective study for the early detection of lung carcinoma in patients with HIV infection (GESIDA study 8815)

IntroductionLung cancer (LC) screening detects tumors early. The prospective GESIDA 8815 study was designed to assess the usefulness of this strategy in HIV + people (PLHIV) by performing a low-radiation computed tomography (CT) scan. Patients and methods371 heavy smokers patients were included (>20 packs/year), >45 years old and with a CD4+ <200 mm3 nadir. One visit and CT scan were performed at baseline and 4 for follow-up time annually. Results329 patients underwent the baseline visit and CT (CT0) and 206 completed the study (CT1 = 285; CT2 = 259; CT3 = 232; CT4 = 206). All were receiving ART. A total >8 mm lung nodules were detected, and 9 early-stage PCs were diagnosed (4 on CT1, 2 on CT2, 1 on CT3 and 2 on CT4). There were no differences between those who developed LC and those who did not in sex, age, CD4+ nadir, previous lung disease, family history, or amount of packets/year. At each visit, other pathologies were diagnosed, mainly COPD, calcified coronary artery and residual tuberculosis lesions. At the end of the study, 38 patients quit smoking and 75 reduced their consumption. Two patients died from LC and 16 from other causes (p = 0.025). ConclusionsThe design of the present study did not allow us to define the real usefulness of the strategy. Adherence to the test progressively decreased over time. The diagnosis of other thoracic pathologies is very frequent. Including smokers in an early diagnosis protocol for LC could help to quit smoking.

Detection of Traumatic Ankle Arthrotomies: Computed Tomography Scan Versus Saline Load Test.

Periarticular wounds present a common diagnostic dilemma for emergency providers and orthopedic surgeons because traumatic arthrotomies (TA) often necessitate different management from superficial soft tissue wounds. Historically, TA have been diagnosed with the saline load test (SLT). Computed tomography (CT) scan has been studied as an alternative to SLT in diagnosing TA in several joints, but there are limited data specifically pertaining to the ankle. This study aimed to compare the ability of a CT scan to identify an ankle TA versus a traditional SLT. The hypothesis was that there would be no significant difference between a CT scan and SLT in diagnosing ankle TA in a cadaveric model. This cadaveric study used 10 thawed fresh-frozen cadaveric ankles. A baseline CT scan was performed to ensure no intra-articular air existed before simulated TA. After the baseline CT, a 1 cm TA was created in the anterolateral arthroscopy portal site location. The ankles then underwent a postarthrotomy CT scan to evaluate for the presence of intra-articular air. After the CT scan, a 30 mL SLT was performed using the anteromedial portal site location. After arthrotomy, intra-articular air was visualized in 7 of 10 cadavers in the postarthrotomy CT scan. All the ankles had fluid extravasation during the SLT with <10 mL of saline. The sensitivity of the SLT for TA was 100% versus 70% for the CT scan. The SLT was more sensitive in diagnosing 1-cm ankle TA than a CT scan in a cadaveric model.

869 Early Signs of Elevated Intracranial Pressure (ICP) on Computed Tomography Correlate With Measured ICP in the Intensive Care Unit and Six-month Outcome Following Moderate to Severe TBI

INTRODUCTION: Identifying patients at risk for increased intracranial pressure (ICP) followingTBI via baseline computed tomography (CT) has not previously been validated in a prospective dataset. METHODS: Data were obtained from the Progesterone for Traumatic Brain Injury, Experimental Clinical Treatment (ProTECTIII) multicenter clinical trial. Baseline CT scans for 881 participants were individually reviewed by a blinded central neuroradiologist. Five signs of elevated ICP were measured (sulcal obliteration, lateral ventricle compression, 3rd ventricle compression, midline shift, and herniation). Associations between signs of increased ICP and outcomes (6-month functional outcome and mortality) were assessed. RESULTS: Sulcal obliteration (p = 0.029) and third ventricular compression (p = 0.039) were associated with elevated ICP. Univariate regression analyses indicated that increasing combinations of the five defined signs of elevated ICP were associated with mortality, poor functional outcome, and time to death. There was also an increased likelihood of mortality if patients required craniotomy (OR = 4.318, 95% Confidence Interval [1.330-16.030]) or hemicraniectomy (OR = 2.993 [1.109-8.482]). On multivariate regression analyses, hemorrhage location was associated with mortality (posterior fossa, OR = 3.208 [1.120-9.188] and basal ganglia, OR = 3.079 [1.178-8.077]). Volume of hemorrhage &gt;30cc was also associated with increased mortality, OR = 3.702 [1.575-8.956]). The proportion of patient hours with sustained ICP = 20 mmHg, and maximum ICP = 20 mmHg, were also directly correlated with increased mortality (OR = 64.99 [7.731-635.51]; and OR = 1.025 [1.004-1.047]). Poor functional outcome was predicted by concurrent presence of all five radiographic signs of elevated ICP (OR = 4.44 [1.514-14.183]) and presence of frontal lobe (OR = 2.951 [1.265-7.067]), subarachnoid (OR = 2.231 [1.067-4.717]), or intraventricular (OR = 2.249 [1.159-4.508]) hemorrhage. Time to death was modulated by total patient days of elevated ICP = 20 mmHg (Effect Size = 3.424 [1.500, 5.439]) in the first two weeks of hospitalization. CONCLUSIONS: ICP-related imaging variables can be prognosticate for moderate-severe TBI.

Important Radiologic and Clinical Factors for Predicting Overall Survival in Pancreatic Adenocarcinoma Patients Who Underwent FOLFIRINOX.

To predict poor overall survival (OS) in pancreatic adenocarcinoma (PAC) who underwent FOLFIRINOX (5-fluorouracil/leucovorin/irinotecan/oxaliplatin) using clinical and computed tomography (CT) findings. A total of 189 patients with PAC who received FOLFIRINOX were retrospectively included. Two reviewers assessed CT findings and resectability based on National Comprehensive Cancer Network guidelines. They determined tumor size changes according to Response Evaluation Criteria in Solid Tumors (RECIST 1.1). Delta measurements were performed. Clinical results, such as whether to perform surgery, were also investigated. A Cox proportional hazard model was used to identify significant predictors for OS. A CT-based nomogram was constructed to predict OS. Seventy-four patients (39.2%) underwent surgery. For OS, rim enhancement of PAC on baseline CT (hazard ratio [HR], 1.75; 95% confidence interval [CI], 1.10-2.77; P = 0.018), high delta tumor on baseline CT (HR, 2.46; 95% CI, 1.55-3.91; P < 0.001), progressive disease at follow-up CT (HR, 8.89; 95% CI, 2.94-26.87; P < 0.001), and without surgery (HR, 2.81; 95% CI, 1.49-5.30; P = 0.001) were important features related to poor prognosis. The nomogram showed good predictive ability for the survival. Both clinical and CT findings were useful for predicting OS after FOLFIRINOX in PAC.

Correlation of CT parameters and PD-L1 expression status in gastric cancer.

We aimed to explore the correlation between routine computed tomography (CT) imaging features and programmed cell death ligand-1(PD-L1) expression status in gastric cancer and evaluate the predictive value of imaging parameters for this immunotherapy biomarker. Patients with gastric adenocarcinoma who underwent abdominal CT three-stage enhanced scan and PD-L1 immunohistochemical testing before treatment were retrospectively examined. All diagnoses were confirmed through pathology. According to the expression status of PD-L1, they were divided into the positive (CPS ≥ 5) or negative group (CPS < 5). Baseline CT imaging features were collected. Diagnostic performances of the different variables were evaluated using receiver operating characteristic (ROC) curve. In total, 67 patients (17 women and 50 men; mean age: 59.55 ± 10.22years) with gastric adenocarcinoma were included in the study. The overall stages, probability of maximum lymph node short diameter > 1cm and peak of lesion enhancement occurring in the arterial phase were statistically significant between the two groups (p < 0.05). Moreover, the arterial enhancement fraction (AEF) was significantly higher in the positive group than that in the negative group (p < 0.05), and ROC curve analysis showed that the AEF exhibited a high evaluation efficacy (area under the curve [AUC] = 0.724 [95% confidence interval (CI): 0.602-0.826]). The combined parameters had the best diagnostic efficacy (AUC = 0.825 [95%CI: 0.716-0.933]), sensitivity (75.00%), and specificity (81.40%). These findings confirm a correlation between CT imaging features and PD-L1 expression status in gastric cancer, and AEF may help evaluate high PD-L1 expression and select patients suitable for immunotherapy.

Wasserstein HOG: Local Directionality Extraction via Optimal Transport.

Directionally sensitive radiomic features including the histogram of oriented gradient (HOG) have been shown to provide objective and quantitative measures for predicting disease outcomes in multiple cancers. However, radiomic features are sensitive to imaging variabilities including acquisition differences, imaging artifacts and noise, making them impractical for using in the clinic to inform patient care. We treat the problem of extracting robust local directionality features by mapping via optimal transport a given local image patch to an iso-intense patch of its mean. We decompose the transport map into sub-work costs each transporting in different directions. To test our approach, we evaluated the ability of the proposed approach to quantify tumor heterogeneity from magnetic resonance imaging (MRI) scans of brain glioblastoma multiforme, computed tomography (CT) scans of head and neck squamous cell carcinoma as well as longitudinal CT scans in lung cancer patients treated with immunotherapy. By considering the entropy difference of the extracted local directionality within tumor regions, we found that patients with higher entropy in their images, had significantly worse overall survival for all three datasets, which indicates that tumors that have images exhibiting flows in many directions may be more malignant. This may seem to reflect high tumor histologic grade or disorganization. Furthermore, by comparing the changes in entropy longitudinally using two imaging time points, we found patients with reduction in entropy from baseline CT are associated with longer overall survival (hazard ratio = 1.95, 95% confidence interval of 1.4-2.8, p = 1.65e-5). The proposed method provides a robust, training free approach to quantify the local directionality contained in images.

Regression of Necrotic Lesions after Methotrexate Withdrawal in Patients with Methotrexate-Associated Lymphoproliferative Disorders: A Retrospective CT Study.

This retrospective study investigated whether necrotic lesions detected on a computed tomography (CT) scan are more regressive than non-necrotic lesions after methotrexate withdrawal in patients pathologically diagnosed with methotrexate-associated lymphoproliferative disorders (MTX-LPD). In total, 89 lesions extracted from 24 patients on CT scans were included in the analysis. All patients had been evaluated for the presence of necrosis within lesions via CT scan upon first suspicion of MTX-LPD (baseline CT scan). The percentage lesion size reduction between the baseline and initial follow-up CT scan was calculated. The association between necrosis within lesions and size changes was estimated via linear regression analyses using both crude and adjusted models. Necrosis was significantly more common in extranodal lesions (27 out of 30 lesions, 90%) than in nodal lesions (9 out of 59 lesions, 15%, p<0.001). In the crude model, the regression of necrotic lesions was 58.5% greater than that of non-necrotic lesions; the difference was statistically significant (p<0.001). Additionally, the longest diameter of necrotic lesions at the baseline CT scan was significantly greater than that of non-necrotic lesions (p<0.001). Based on the adjusted model, necrotic lesions showed 49.3% greater regression than non-necrotic lesions (p=0.017). Necrosis detected on a CT scan was found to be an independent predictor of regression after MTX withdrawal in patients with MTX-LPD.