Baseline Computed Tomography Images Research Articles

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.

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.

Prognostic impact of pretreatment skeletal muscle index and CONUT score in diffuse large B-cell Lymphoma

BackgroundAlthough the prognostic value of the Controlling Nutritional Status (CONUT) score in diffuse large B-cell lymphoma (DLBCL) has been reported in several previous studies, its clinical relevance for the presence of sarcopenia has not been assessed.MethodsIn this study, 305 DLBCL patients were reviewed. They were categorized into normal/mild (n = 219) and moderate/severe (n = 86) CONUT groups. Sarcopenia was assessed using the L3-skeletal muscle index measured by baseline computed tomography imaging. Based on CONUT score and sarcopenia, patients were grouped: A (normal/mild CONUT and no sarcopenia), B (either moderate/severe CONUT or sarcopenia, but not both), and C (both moderate/severe CONUT and sarcopenia).ResultsThe moderate/severe CONUT group showed higher rates of ≥ grade 3 febrile neutropenia, thrombocytopenia, non-hematologic toxicities, and early treatment discontinuation not related to disease progression, compared to the normal/mild CONUT group. The moderate/severe CONUT group had a lower complete response rate (58.1% vs. 80.8%) and shorter median overall survival (18.5 vs. 162.6 months) than the normal/mild group. Group C had the poorest prognosis with a median survival of 8.6 months, while groups A and B showed better outcomes (not reached and 60.1 months, respectively). Combining CONUT score and sarcopenia improved the predictive accuracy of the Cox regression model (C-index: 0.763), compared to the performance of using either CONUT score (C-index: 0.754) or sarcopenia alone (C-index: 0.755).ConclusionsIn conclusion, the moderate/severe CONUT group exhibited treatment intolerance, lower response, and poor prognosis. Additionally, combining CONUT score and sarcopenia enhanced predictive accuracy for survival outcomes compared to individual variables.

Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study

PurposePersistent nonsolid nodules (NSNs) usually exhibit an indolent course and may remain stable for several years; however, some NSNs grow quickly and require surgical excision. Therefore, identifying quantitative features capable of early discrimination between growing and nongrowing NSNs is becoming a crucial aspect of radiological analysis. The main purpose of this study was to evaluate the performance of an open-source software (ImageJ) to predict the future growth of NSNs detected in a Caucasian (Italian) population.Material and methodsWe retrospectively selected 60 NSNs with an axial diameter of 6–30 mm scanned with the same acquisition-reconstruction parameters and the same computed tomography (CT) scanner. Software-based analysis was performed on thin-section CT images using ImageJ. For each NSNs, several quantitative features were extracted from the baseline CT images. The relationships of NSN growth with quantitative CT features and other categorical variables were analyzed using univariate and multivariable logistic regression analyses.ResultsIn multivariable analysis, only the skewness and linear mass density (LMD) were significantly associated with NSN growth, and the skewness was the strongest predictor of growth. In receiver operating characteristic curve analyses, the optimal cutoff values of skewness and LMD were 0.90 and 19.16 mg/mm, respectively. The two predictive models that included the skewness, with or without LMD, exhibited an excellent power for predicting NSN growth.ConclusionAccording to our results, NSNs with a skewness value > 0.90, specifically those with a LMD > 19.16 mg/mm, should require closer follow-up due to their higher growth potential, and higher risk of becoming an active cancer.

Abstract WP53: The Assessment Of Brain Water Content At Posttraumatic Ischemia: Ct Perfusion Study

Background: The influence of cerebral edema and secondary insults on the clinical outcome of traumatic brain injury (TBI) is well known. The studies of the brain water homeostasis dynamics at TBI remain rare, which determines the relevance of our work. The purpose is to study the changes in brain water homeostasis after TBI of varying severity compared to the cerebral microcirculation parameters. Materials: This non-randomized retrospective single-center study complies with the Helsinki Declaration. One hundred twenty-eight patients with posttraumatic ischemia (PCI) after moderate-to-severe TBI in the middle cerebral artery territory who presented between July 2015 and February 2022 to our hospital were included. PCI was determined using perfusion computed tomography (CT), and brain edema was determined using net water uptake (NWU) on baseline CT images. The patients were divided according to Marshall’s classification. Multivariate linear regression models were performed to analyze data. Results: NWU in PCI zones was significantly higher than in non-ischemic zones (8.1% versus 4.2%; P &lt;0.001). In the multivariable regression analysis, the mean transit time increase was significantly and independently associated with higher NWU ( R 2 = 0,089, P &lt;0.01). In the PCI zone, cerebral blood flow (CBF), volume (CBV), and time to peak (TTP) were not significantly associated with NWU values ( P &gt;0.05). No significant differences existed between the NWU values in PCI foci in different Marshall groups ( P =0.308). Conclusion: The Marshall classification does not seem to be able to predict the progression of posttraumatic ischemia. The blood passage delay through the cerebral microvascular bed was significantly accompanied by brain tissue water uptake increase in the PCI focus.

Baseline Radiomic Signature to Estimate Overall Survival in Patients With NSCLC

IntroductionWe aimed to define a baseline radiomic signature associated with overall survival (OS) using baseline computed tomography (CT) images obtained from patients with NSCLC treated with nivolumab or chemotherapy. MethodsThe radiomic signature was developed in patients with NSCLC treated with nivolumab in CheckMate-017, -026, and -063. Nivolumab-treated patients were pooled and randomized to training, calibration, or validation sets using a 2:1:1 ratio. From baseline CT images, volume of tumor lesions was semiautomatically segmented, and 38 radiomic variables depicting tumor phenotype were extracted. Association between the radiomic signature and OS was assessed in the nivolumab-treated (validation set) and chemotherapy-treated (test set) patients in these studies. ResultsA baseline radiomic signature was identified using CT images obtained from 758 patients. The radiomic signature used a combination of imaging variables (spatial correlation, tumor volume in the liver, and tumor volume in the mediastinal lymph nodes) to output a continuous value, ranging from 0 to 1 (from most to least favorable estimated OS). Given a threshold of 0.55, the sensitivity and specificity of the radiomic signature for predicting 3-month OS were 86% and 77.8%, respectively. The signature was identified in the training set of patients treated with nivolumab and was significantly associated (p < 0.0001) with OS in patients treated with nivolumab or chemotherapy. ConclusionsThe radiomic signature provides an early readout of the anticipated OS in patients with NSCLC treated with nivolumab or chemotherapy. This could provide important prognostic information and may support risk stratification in clinical trials.

On Net Water Uptake in Posttraumatic Ischemia Foci.

The influence of cerebral edema and resultant secondary complications on the clinical outcome of traumatic brain injury (TBI) is well known. Clinical studies of brain water homeostasis dynamics in TBI are limited, which determines the relevance of our work. The purpose is to study changes in brain water homeostasis after TBI of varying severity compared to corresponding cerebral microcirculation parameters. This non-randomized retrospective single-center study complies with the Helsinki Declaration for patient's studies. The study included 128 patients with posttraumatic ischemia (PCI) after moderate-to-severe TBI in the middle cerebral artery territory who were admitted to the hospital between July 2015 and February 2022. PCI was evaluated by perfusion computed tomography (CT), and brain edema was determined using net water uptake (NWU) on baseline CT images. The patients were allocated according to Marshall's classification. Multivariate linear regression models were performed to analyze data. NWU in PCI areas were significantly higher than in patients with its absence (8.1% vs. 4.2%, accordingly; p<0.001). In the multivariable regression analysis, the mean transit time increase was significantly and independently associated with higher NWU (R2=0.089, p<0.01). In the PCI zone, cerebral blood flow, cerebral blood volume, and time to peak were not significantly associated with NWU values (p>0.05). No significant differences were observed between the NWU values in PCI foci in different Marshall groups (p=0.308). Marshall's classification does not predict the progression of posttraumatic ischemia. The blood passage delays through the cerebral microvascular bed is associated with brain tissue water content increase in the PCI focus.

A Computed Tomography-based Radiomics Nomogram for Predicting Osteoporotic Vertebral Fractures: A Longitudinal Study.

Fractures are a serious consequence of osteoporosis in older adults. However, few longitudinal studies have shown the role of computed tomography (CT)-based radiomics in predicting osteoporotic fractures. We evaluated the performance of a CT radiomics-based model for osteoporotic vertebral fractures (OVFs) in a longitudinal study. A total of 7906 individuals without OVF older than 50 years, and who underwent CT scans between 2016 and 2019 were enrolled and followed up until 2021. Seventy-two cases of new OVF were identified. A total of 144 people without OVF during follow-up were selected as controls. Radiomics features were extracted from baseline CT images. CT values of trabecular bone, and area and density of erector spinae were determined. Cox regression analysis was used to identify the independent associated factors. The predictive performance of the nomogram was assessed using the receiver operating characteristic curve, calibration curve, and decision curve. CT value of vertebra (adjusted hazard ratio (aHR) = 2.04; 95% CI, 1.07-3.89), radiomics score (aHR = 6.56; 95% CI, 3.47-12.38), and area of erector spinae (aHR = 1.68; 95% CI, 1.02-2.78) were independently associated with OVF. Radscore was associated with severe OVF (aHR = 6.00; 95% CI, 2.78-12.93). The nomogram showed good discrimination with a C-index of 0.82 (95% CI, 0.77-0.87). The area under the curve of nomogram and radscore were both higher than osteoporosis + muscle area for 3-year and 4-year risk of fractures (P < .05). The decision curve also demonstrated that the radiomics nomogram was useful. Bone radiomics is associated with OVF, and the nomogram based on radiomics signature and muscle provides a tool for the prediction of OVF.

Quantitative CT analysis to predict esophageal fistula in patients with advanced esophageal cancer treated by chemotherapy or chemoradiotherapy

BackgroundEsophageal fistula is one of the most serious complications of chemotherapy or chemoradiotherapy (CRT) for advanced esophageal cancer. This study aimed to evaluate the performance of quantitative computed tomography (CT) analysis and to establish a practical imaging model for predicting esophageal fistula in esophageal cancer patients treated with chemotherapy or chemoradiotherapy.MethodsThis study retrospectively enrolled 204 esophageal cancer patients (54 patients with fistula, 150 patients without fistula) and all patients were allocated to the primary and validation cohorts according to the time of inclusion in a 1:1 ratio. Ulcer depth, tumor thickness and length, and minimum and maximum enhanced CT values of esophageal cancer were measured in pretreatment CT imaging. Logistic regression analysis was used to evaluate the associations of CT quantitative measurements with esophageal fistula. Receiver operating characteristic curve (ROC) analysis was also used.ResultsLogistic regression analysis showed that independent predictors of esophageal fistula included tumor thickness [odds ratio (OR) = 1.167; p = 0.037], the ratio of ulcer depth to adjacent tumor thickness (OR = 164.947; p < 0.001), and the ratio of minimum to maximum enhanced CT value (OR = 0.006; p = 0.039) in the primary cohort at baseline CT imaging. These predictors were used to establish a predictive model for predicting esophageal fistula, with areas under the receiver operating characteristic curves (AUCs) of 0.946 and 0.841 in the primary and validation cohorts, respectively. The quantitative analysis combined with T stage for predicting esophageal fistula had AUCs of 0.953 and 0.917 in primary and validation cohorts, respectively.ConclusionQuantitative pretreatment CT analysis has excellent performance for predicting fistula formation in esophageal cancer patients who treated by chemotherapy or chemoradiotherapy.

Skeletal Muscle Adiposity and Lung Function Trajectory in the Severe Asthma Research Program.

Rationale: Extrapulmonary manifestations of asthma, including fatty infiltration in tissues, may reflect systemic inflammation and influence lung function and disease severity. Objectives: To determine if skeletal muscle adiposity predicts lung function trajectory in asthma. Methods: Adult SARP III (Severe Asthma Research Program III) participants with baseline computed tomography imaging and longitudinal postbronchodilator FEV1% predicted (median follow-up 5 years [1,132 person-years]) were evaluated. The mean of left and right paraspinous muscle density (PSMD) at the 12th thoracic vertebral body was calculated (Hounsfield units [HU]). Lower PSMD reflects higher muscle adiposity. We derived PSMD reference ranges from healthy control subjects without asthma. A linear multivariable mixed-effects model was constructed to evaluate associations of baseline PSMD and lung function trajectory stratified by sex. Measurements and Main Results: Participants included 219 with asthma (67% women; mean [SD] body mass index, 32.3 [8.8] kg/m2) and 37 control subjects (51% women; mean [SD] body mass index, 26.3 [4.7] kg/m2). Participants with asthma had lower adjusted PSMD than control subjects (42.2 vs. 55.8 HU; P < 0.001). In adjusted models, PSMD predicted lung function trajectory in women with asthma (β = -0.47 Δ slope per 10-HU decrease; P = 0.03) but not men (β = 0.11 Δ slope per 10-HU decrease; P = 0.77). The highest PSMD tertile predicted a 2.9% improvement whereas the lowest tertile predicted a 1.8% decline in FEV1% predicted among women with asthma over 5 years. Conclusions: Participants with asthma have lower PSMD, reflecting greater muscle fat infiltration. Baseline PSMD predicted lung function decline among women with asthma but not men. These data support an important role of metabolic dysfunction in lung function decline.

Readiness of Healthcare Systems to Generate Real-World Evidence: Reliability of CT Radiographic End Points for Evaluation of First-Line Systemic Treatment.

Regulatory agencies such as the US Food and Drug Administration and health technology assessment bodies are increasingly using real-world evidence (RWE). The ability of healthcare systems to reliably generate response rate and progression-free survival from real-world data is unknown. We examined the capacity of a single-payer system to provide RWE by evaluating the frequency of computed tomography (CT) imaging during standard first-line metastatic systemic treatment of breast, colorectal, and lung cancer. A 1-year cohort of patients with metastatic-at-diagnosis breast, colorectal, and lung cancer treated with first-line systemic therapy (excluding hormone therapy) referred to BC Cancer in 2016 was retrospectively reviewed for first-line treatment and CT imaging. Duration of first-line treatment was calculated from the first to the last dose of therapy. CT imaging was counted from the start of therapy to 8 weeks after the last therapy dose. A cohort of 664 patients was identified from the BC Cancer Registry. Distribution of metastatic disease at diagnosis was breast (n = 82), colorectal (n = 214), and lung (n = 368) cancer. For breast, colorectal, and lung cancer, there was a baseline CT within 4 weeks of treatment initiation in 59%, 51%, and 48% of patients, with median duration of first-line treatment of 14.6, 25.3, and 11.9 weeks and median CT imaging interval of 9.1, 9.0, and 6.1 weeks. In our publicly funded healthcare system, availability of baseline CT imaging was 48% to 59% and the frequency of assessment ranged from 6.1 to 9.1 weeks, subject to patterns of practice and resource availability. Our system was not capable of providing RWE for image-based end points. Alternative end points should be considered to capitalize on the wealth of real-world data.

CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy.

The goal of this study was to evaluate the effectiveness of radiomics signatures on pre-treatment computed tomography (CT) images of lungs to predict the tumor responses of non-small cell lung cancer (NSCLC) patients treated with first-line chemotherapy, targeted therapy, or a combination of both. This retrospective study included 322 NSCLC patients who were treated with first-line chemotherapy, targeted therapy, or a combination of both. Of these patients, 224 were randomly assigned to a cohort to help develop the radiomics signature. A total of 1946 radiomics features were obtained from each patient's CT scan. The top-ranked features were selected by the Minimum Redundancy Maximum Relevance (MRMR) feature-ranking method and used to build a lightweight radiomics signature with the Random Forest (RF) classifier. The independent predictive (IP) features (AUC > 0.6, p value < 0.05) were further identified from the top-ranked features and used to build a refined radiomics signature by the RF classifier. Its prediction performance was tested on the validation cohort, which consisted of the remaining 98 patients. The initial lightweight radiomics signature constructed from 15 top-ranked features had an AUC of 0.721 (95% CI, 0.619-0.823). After six IP features were further identified and a refined radiomics signature was built, it had an AUC of 0.746 (95% CI, 0.646-0.846). Radiomics signatures based on pre-treatment CT scans can accurately predict tumor response in NSCLC patients after first-line chemotherapy or targeted therapy treatments. Radiomics features could be used as promising prognostic imaging biomarkers in the future. The radiomics signature extracted from baseline CT images in patients with NSCLC can predict response to first-line chemotherapy, targeted therapy, or both treatments with an AUC = 0.746 (95% CI, 0.646-0.846). The radiomics signature could be used as a new biomarker for quantitative analysis in radiology, which might provide value in decision-making and to define personalized treatments for cancer patients.

Body Composition as an Independent Predictive and Prognostic Biomarker in Advanced Urothelial Carcinoma Patients Treated with Immune Checkpoint Inhibitors.

Several immune checkpoint inhibitors (ICIs) are approved for the treatment of advanced urothelial carcinoma (UC). There are limited biomarkers for ICI-treated patients with UC. We investigated the association between body composition and clinical outcomes in ICI-treated UC patients. We conducted a retrospective analysis of 70 ICI-treated patients with advanced UC at Winship Cancer Institute from 2015 to 2020. Baseline computed tomography images within 2 months of ICI initiation were collected at mid-L3 and muscle and fat compartments (subcutaneous, intermuscular, and visceral) were segmented using SliceOMatic v5.0 (TomoVision, Magog, Canada). A prognostic body composition risk score (high: 0-1, intermediate: 2-3, or low-risk: 4) was created based on the β coefficient from the multivariate Cox model (MVA) following best-subset variable selection. Our body composition risk score was skeletal muscle index (SMI) + 2 × attenuated skeletal muscle (SM) mean + visceral fat index (VFI). Concordance statistics (C-statistics) were used to quantify the discriminatory magnitude of the predictive model. Most patients (70%) were men and the majority received ICIs in the second- (46%) or third-line (21%) setting. High-risk patients had significantly shorter overall survival (OS; hazard ratio [HR], 6.72; p<.001), progression-free survival (HR, 5.82; p<.001), and lower chance of clinical benefit (odds ratio [OR], 0.02; p=.003) compared with the low-risk group in MVA. The C-statistics for our body composition risk group and myosteatosis analyses were higher than body mass index for all clinical outcomes. Body composition variables such as SMI, SM mean, and VFI may be prognostic and predictive of clinical outcomes in ICI-treated patients with UC. Larger, prospective studies are warranted to validate this hypothesis-generating data. This study developed a prognostic body composition risk scoring system using radiographic biomarkers for patients with bladder cancer treated with immunotherapy. The study found that the high-risk patients had significantly worse clinical outcomes. Notably, the study's model was better at predicting outcomes than body mass index. Importantly, these results suggest that radiographic measures of body composition should be considered for inclusion in updated prognostic models for patients with urothelial carcinoma treated with immunotherapy. These findings are useful for practicing oncologists in the academic or community setting, particularly given that baseline imaging is routine for patients starting on treatment with immunotherapy.

Radiomics-based model for predicting pathological complete response to neoadjuvant chemotherapy in muscle-invasive bladder cancer

To develop and validate a radiomics-based model for predicting response to neoadjuvant chemotherapy (NAC) using baseline computed tomography (CT) images in patients with muscle-invasive bladder cancer (MIBC). A radiomics signature for predicting pathological complete response (pCR) was developed using radiomics features selected by a random forest classifier on baseline CT images, and imaging predictors were identified in the training set (87 patients). By incorporating imaging predictors and radiomics signature, an imaging-based model was constructed using multivariate logistic regression analysis and validated in an independent validation set consisting of 48 patients with CT from outside institutions. The performance and clinical usefulness of the imaging-based model for predicting pCR were evaluated using area under the receiver operating characteristic curve (AUC) and decision curve analysis. Using a cut-off determined in the training set, the positive likelihood ratios of the imaging-based model were calculated and compared with imaging and histological predictors. The radiomics signature was developed based on six stable radiomics features. An imaging-based model incorporating radiomics signature, tumour shape, tumour size, and clinical stage showed good performance for predicting pCR in both the training (AUC, 0.85; 95% confidence interval [CI], 0.78-0.93) and validation (AUC, 0.75; 95% CI, 0.60-0.86) sets, providing a larger net benefit in decision curve analysis. The imaging-based model showed a higher positive likelihood ratio (1.91) for pCR than imaging and histological predictors (1.33-1.63). The radiomics-based model using baseline CT images may predict the response of patients with MIBC to NAC.

Red Cell Distribution Width is Associated with 30-day Mortality in Patients with Spontaneous Intracerebral Hemorrhage

BackgroundRed cell distribution width (RDW) has been associated with mortality and outcome in a wide variety of non-neurological and neurological diseases, namely in myocardial infarction and acute ischemic stroke, and the reason for this is not completely understood. We aimed to investigate RDW as a potential prognostic marker in patients with intracerebral hemorrhage (ICH).MethodsThis is a retrospective study of consecutive patients with acute non-traumatic ICH admitted to a single center during a 4-year period. We reviewed individual clinical records to collect demographic and baseline information, including RDW at admission, 3-month functional status, and incidence of death during follow-up. Baseline computed tomography imaging was reviewed to classify the location of ICH, and to measure ICH volume and perihematomal edema volume. Patients were divided according to quartile distribution of RDW (RDW-Q1-4).ResultsThe final study population consisted of 358 patients, median age 71 years (interquartile range [IQR] 60–80), 55% were male, and median Glasgow Coma Scale was 14 (IQR 10–15), with a mean follow-up of 17.6 months. Patients with higher RDW values were older (p = 0.003), more frequently presented with an active malignancy (p = 0.005), atrial fibrillation (p < 0.001), intraventricular hemorrhage (p = 0.048), and were anticoagulated (p < 0.001). Three-month functional independence was similar throughout RDW quartiles. RDW-Q4 was independently associated with increased 30-day mortality (adjusted odds ratio = 3.36, 95%CI = 1.48–7.62, p = 0.004), but not independently associated with increased mortality after 30 days (adjusted hazards ratio = 0.71, 95%CI = 0.29–1.73, p = 0.448).ConclusionsRDW is a robust and independent predictor of 30-day mortality in non-traumatic ICH patients, and further studies to understand this association are warranted.

Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma

For patients with locally advanced esophageal squamous cell carcinoma, neoadjuvant chemoradiation has been shown to improve long-term outcomes, but the treatment response varies among patients. Accurate pretreatment prediction of response remains an urgent need. To determine whether peritumoral radiomics features derived from baseline computed tomography images could provide valuable information about neoadjuvant chemoradiation response and enhance the ability of intratumoral radiomics to estimate pathological complete response. A total of 231 patients with esophageal squamous cell carcinoma, who underwent baseline contrast-enhanced computed tomography and received neoadjuvant chemoradiation followed by surgery at 2 institutions in China, were consecutively included. This diagnostic study used single-institution data between April 2007 and December 2018 to extract radiomics features from intratumoral and peritumoral regions and established intratumoral, peritumoral, and combined radiomics models using different classifiers. External validation was conducted using independent data collected from another hospital during the same period. Radiogenomics analysis using gene expression profile was done in a subgroup of the training set for pathophysiological explanation. Data were analyzed from June to December 2019. Computed tomography-based radiomics. The discriminative performances of radiomics models were measured by area under the receiver operating characteristic curve. Among the 231 patients included (192 men [83.1%]; mean [SD] age, 59.8 [8.7] years), the optimal intratumoral and peritumoral radiomics models yielded similar areas under the receiver operating characteristic curve of 0.730 (95% CI, 0.609-0.850) and 0.734 (0.613-0.854), respectively. The combined model was composed of 7 intratumoral and 6 peritumoral features and achieved better discriminative performance, with an area under the receiver operating characteristic curve of 0.852 (95% CI, 0.753-0.951), accuracy of 84.3%, sensitivity of 90.3%, and specificity of 79.5% in the test set. Gene sets associated with the combined model mainly involved lymphocyte-mediated immunity. The association of peritumoral area with response identification might be partially attributed to type I interferon-related biological process. A combination of peritumoral radiomics features appears to improve the predictive performance of intratumoral radiomics to estimate pathological complete response after neoadjuvant chemoradiation in patients with esophageal squamous cell carcinoma. This study underlines the significant application of peritumoral radiomics to assess treatment response in clinical practice.

Coronary and aortic calcification are associated with cardiovascular events on immune checkpoint inhibitor therapy

BackgroundAlthough the incidence of immune checkpoint inhibitor (ICI)-related cardiovascular (CV) toxicity is low, the overall burden of CV events after ICI is unknown. Risk factors for CV events after ICI have yet to be identified. ObjectivesWe sought to evaluate the association between vascular calcification on routine baseline computed tomography (CT) imaging and CV events following ICI. MethodThis was a single-center, retrospective cohort study of 76 patients referred to Cardio-Oncology with prior ICI treatment. Coronary and aortic calcification on non-gated chest and abdominal CT imaging were qualitatively assessed. The association of baseline clinical parameters and vascular calcification with symptomatic heart failure (HF), acute coronary syndrome, myocarditis, symptomatic arrhythmia, or pericardial effusion after ICI was evaluated. ResultsOver 11 months of follow-up, there were 80 CV events that occurred in 49 patients. Worse coronary and aortic calcification on pre-treatment CT imaging was seen in patients with a CV event (p = .018 and p = .014, respectively). There were no differences in traditional CV risk factors between those with and without a CV event. Eighteen patients (37%) were restarted on ICI therapy after a non- myocarditis or symptomatic systolic HF CV event without recurrent events or mortality over 13 months of follow-up. ConclusionsSymptomatic HF was the most common CV event seen after ICI therapy. Worse coronary and aortic calcification on baseline CT imaging was associated with CV events following ICI. With careful clinical evaluation, selected patients may be re-treated with ICI following a non- myocarditis or symptomatic systolic HF CV event.

Efficacy of cyclophosphamide, doxorubicin, and cisplatin for adenoid cystic carcinoma, and their relationship with the pre-chemotherapy tumor growth rate.

While surgical resection is the treatment of choice for adenoid cystic carcinoma (ACC), patients with metastasis and those who cannot undergo surgery receive palliative chemotherapy. However, the role of palliative chemotherapy is not clear yet. This study aimed to evaluate the efficacy of chemotherapy with cyclophosphamide, doxorubicin, and cisplatin (CAP) for patients with ACC; and to analyze the relationship between the pre-chemotherapy tumor growth rate (P-TGR) and treatment outcomes in patients with the recurred metastatic unresectable ACC. We retrospectively analyzed the clinical data and treatment outcomes of patients who diagnosed ACC and treated with CAP chemotherapy. Response evaluation was performed using computed tomography (CT) images obtained before and after chemotherapy according to the RECIST 1.1. P-TGR was defined as the difference of the sum of the largest diameter of the target lesion per unit of time between the pre-baseline and baseline CT images. Fourteen patients with ACC who were treated with CAP were enrolled. Median patient age was 49 years, and the patients received a median of 5 CAP treatment cycles. Two patients achieved partial response (PR) and 10 patients showed stable disease. Response rate was 14.3%, and the disease control rate was 85.7%. Median progression-free survival was 5.7 months (95% CI: 4.3 to not reached) and the median overall survival was 23.4 months (95% CI: 12.9 to not reached). A low P-TGR was associated with a good response to CAP (correlation coefficient, 0.56). Palliative CAP chemotherapy demonstrated a modest anti-cancer effect for ACC. A low P-TGR was associated with a good response to CAP chemotherapy.

Quantitative CT Analysis for Predicting the Behavior of Part-Solid Nodules with Solid Components Less than 6 mm: Size, Density and Shape Descriptors

Persistent part-solid nodules (PSNs) with a solid component &lt;6 mm usually represent minimally invasive adenocarcinomas and are significantly less aggressive than PSNs with a solid component ≥6 mm. However, not all PSNs with a small solid component behave in the same way: some nodules exhibit an indolent course, whereas others exhibit more aggressive behavior. Thus, predicting the future behavior of this subtype of PSN remains a complex and fascinating diagnostic challenge. The main purpose of this study was to apply open-source software to investigate which quantitative computed tomography (CT) features may be useful for predicting the behavior of a select group of PSNs. We retrospectively selected 50 patients with a single PSN with a solid component &lt;6 mm and diameter &lt;15 mm. Computerized analysis was performed using ImageJ software for each PSN and various quantitative features were calculated from the baseline CT images. The area, perimeter, mean Feret diameter, linear mass density, circularity and solidity were significantly related to nodule growth (p ≤ 0.031). Therefore, quantitative CT analysis was helpful for predicting the future behavior of a select group of PSNs with a solid component &lt;6 mm and diameter &lt;15 mm.

The Prognostic Role of TNM Staging Compared With Tumor Volume and Number of Pleural Sites in Malignant Pleural Mesothelioma

BackgroundAge, sex, stage, histotype, and surgery are the most recognized prognostic factors for malignant pleural mesothelioma (MPM). Tumor volume (TV) was suggested as an alternative prognostic evaluation. We aimed to assess the prognostic role of Tumor, Node, Metastases (TNM) versus TV and number of pleural sites (NPS). Patients and MethodsInformation on stage, TV, and NPS was collected for 52 MPM patients (pts) at our institution from 2009 to 2012. Baseline computed tomography imaging was performed to define TNM, TV, and NPS. Pts were divided in 3 stage groups: early (I-II), III, and IV. A dedicated computer system calculated TV. Pts were divided in 2 groups according to mean baseline TV (483 cm3). NPS was defined on the basis of the NPS macroscopically involved by disease (1-3). The association between TNM, tumor size (T), TV, NPS, TV and NPS, and overall survival was assessed using Cox models adjusted for age, sex, histology, and treatment. ResultsMost pts were male; mean age was 62 years. We showed an association between TV, TNM, and T. Stage III (hazard ratio [HR], 4.71; P = .02) and IV (HR, 7.40; P < .01), T3 (HR, 5.07; P < .01) and T4 (HR, 5.09; P < .01), TV > 483 cm3 (HR, 3.47; P < .01) and NPS 2 (HR, 3.00; P = .08) and 3 (HR, 6.05; P < .01) were predictive of worse survival. However, the TV and NPS combination performed better than TV, NPS, and TNM alone as a prognostic classifier. ConclusionWe showed that TV is related to TNM staging and T, in particular. Improved prognostic performance might be achievable using quantitative clinical staging combining TV and NPS.