CT pulmonary angiography-based scoring system to predict the prognosis of acute pulmonary embolism

Imaging-based risk stratification of patients with pulmonary embolism based on dual-energy CT-derived radiomics

BACKGROUND

Technological progress in the acquisition of medical images and the extraction of underlying quantitative imaging data has introduced exciting prospects for the diagnostic assessment of a wide range of conditions. This study aims to investigate the diagnostic utility of a machine learning classifier based on dual-energy computed tomography (DECT) radiomics for classifying pulmonary embolism (PE) severity and assessing the risk for early death.

METHODS

Patients who underwent CT pulmonary angiogram (CTPA) between January 2015 and March 2022 were considered for inclusion in this study. Based on DECT imaging, 107 radiomic features were extracted for each patient using standardized image processing. After dividing the dataset into training and test sets, stepwise feature reduction based on reproducibility, variable importance and correlation analyses were performed to select the most relevant features; these were used to train and validate the gradient-boosted tree models.

RESULTS

The trained machine learning classifier achieved a classification accuracy of .90 for identifying high-risk PE patients with an area under the receiver operating characteristic curve of .59. This CT-based radiomics signature showed good diagnostic accuracy for risk stratification in individuals presenting with central PE, particularly within higher risk groups.

CONCLUSION

Models utilizing DECT-derived radiomics features can accurately stratify patients with pulmonary embolism into established clinical risk scores. This approach holds the potential to enhance patient management and optimize patient flow by assisting in the clinical decision-making process. It also offers the advantage of saving time and resources by leveraging existing imaging to eliminate the necessity for manual clinical scoring.

Novel model integrating computed tomography-based image markers with genetic markers for discriminating radiation pneumonitis in patients with unresectable stage III non-small cell lung cancer receiving radiotherapy: a retrospective multi-center radiogenomics study

BACKGROUND

Chemoradiotherapy is a critical treatment for patients with locally advanced and unresectable non-small cell lung cancer (NSCLC), and it is essential to identify high-risk patients as early as possible owing to the high incidence of radiation pneumonitis (RP). Increasing attention is being paid to the effects of endogenous factors for RP. This study aimed to investigate the value of computed tomography (CT)-based radiomics combined with genomics in analyzing the risk of grade ≥ 2 RP in unresectable stage III NSCLC.

METHODS

In this retrospective multi-center observational study, 100 patients with unresectable stage III NSCLC who were treated with chemoradiotherapy were analyzed. Radiomics features of the entire lung were extracted from pre-radiotherapy CT images. The least absolute shrinkage and selection operator algorithm was used for optimal feature selection to calculate the Rad-score for predicting grade ≥ 2 RP. Genomic DNA was extracted from formalin-fixed paraffin-embedded pretreatment biopsy tissues. Univariate and multivariate logistic regression analyses were performed to identify predictors of RP for model development. The area under the receiver operating characteristic curve was used to evaluate the predictive capacity of the model. Statistical comparisons of the area under the curve values between different models were performed using the DeLong test. Calibration and decision curves were used to demonstrate discriminatory and clinical benefit ratios, respectively.

RESULTS

The Rad-score was constructed from nine radiomic features to predict grade ≥ 2 RP. Multivariate analysis demonstrated that histology, Rad-score, and XRCC1 (rs25487) allele mutation were independent high-risk factors correlated with RP. The area under the curve of the integrated model combining clinical factors, radiomics, and genomics was significantly higher than that of any single model (0.827 versus 0.594, 0.738, or 0.641). Calibration and decision curve analyses confirmed the satisfactory clinical feasibility and utility of the nomogram.

CONCLUSION

Histology, Rad-score, and XRCC1 (rs25487) allele mutation could predict grade ≥ 2 RP in patients with locally advanced unresectable NSCLC after chemoradiotherapy, and the integrated model combining clinical factors, radiomics, and genomics demonstrated the best predictive efficacy.

Validation of clinical-radiological scores for prognosis of mortality in acute pulmonary embolism

INTRODUCTION

Acute pulmonary embolism (APE) is a hazardous disorder with a high mortality. Combination of clinical, radiological, and serological parameters can improve risk stratification of APE. Most of the proposed combined scores were not validated in independent cohorts. Our aim was to validate the proposed clinical-radiological scores for prognosis of 7- and 30-day mortality in APE.

MATERIALS AND METHODS

Our sample comprised 531 patients with APE, mean age 64.8 ± 15.6 years, 221 (41.6%) females and 310 (58.4%) males. The following parameters were collected: Age and sex of the patients, mortality within the observation time of 30 days, simplified pulmonary embolism severity index (sPESI), pH troponin level (pg/ml), minimal systolic and diastolic blood pressures (mmHg), heart rate, O2 saturation, episodes of syncope, and need for vasopressors. On CT pulmonary angiography (CTPA), short axis ratio right ventricle/left ventricle (RV/LV), and reflux of contrast medium into the inferior vena cava were obtained. The following clinical-radiological scores were calculated: BOVA score, pulmonary embolism mortality score (PEMS), European Society of Cardiology (ESC) score, Kumamaru score, and Calgary acute pulmonary embolism (CAPE) score.

RESULTS

Overall, 31 patients (5.8%) died within seven and 64 patients (12%) within 30 days. All scores showed high negative prognostic values ranging from 89.0 to 99.0%. PEMS and CAPE score demonstrated the highest specificity for 7-day mortality (93.4% and 85.0%), PEMS and BOVA for 30-day mortality (94.2% and 90.4%). The highest sensitivity was observed for ESC 2019 (96.8% and 95.3%). Kumamaru and CAPE scores had low sensitivity. All scores had low positive and high negative predictive values.

CONCLUSION

For prognosis of 7- and 30-day mortality in APE, PEMS score has the highest specificity. ESC 2019 shows the highest sensitivity. All scores had low positive and high negative predictive values.

Severity prediction of pulmonary diseases using chest CT scans via cost-sensitive label multi-kernel distribution learning

BACKGROUND AND OBJECTIVES

The progression of pulmonary diseases is a complex progress. Timely predicting whether the patients will progress to the severe stage or not in its early stage is critical to take appropriate hospital treatment. However, this task suffers from the "insufficient and incomplete" data issue since it is clinically impossible to have adequate training samples for one patient at each day. Besides, the training samples are extremely imbalanced since the patients who will progress to the severe stage is far less than those who will not progress to the non-severe stage.

METHOD

We consider the severity prediction of pulmonary diseases as a time estimation problem based on CT scans. To handle the issue of "insufficient and incomplete" training samples, we introduced label distribution learning (LDL). Specifically, we generate a label distribution for each patient, making a CT image contribute to not only the learning of its chronological day, but also the learning of its neighboring days. In addition, a cost-sensitive mechanism is introduced to explore the imbalance data issue. To identify the importance of pulmonary segments in pulmonary disease severity prediction, multi-kernel learning in composite kernel space is further incorporated and particle swarm optimization (PSO) is used to find the optimal kernel weights.

RESULTS

We compare the performance of the proposed CS-LD-MKSVR algorithm with several classical machine learning algorithms and deep learning (DL) algorithms. The proposed method has obtained the best classification results on the in-house data, fully indicating its effectiveness in pulmonary disease severity prediction.

CONTRIBUTIONS

The severity prediction of pulmonary diseases is considered as a time estimation problem, and label distribution is introduced to describe the conversion time from non-severe stage to severe stage. The cost-sensitive mechanism is also introduced to handle the data imbalance issue to further improve the classification performance.

Incidence and Prognostic Role of Pleural Effusion in Patients with Pulmonary Embolism: A Systematic Review and Meta-Analysis

Background: Pleural effusion is a common pulmonary embolism (PE) complication, which has been documented to increase the risk of death in PE and relate to disease progression. However, the incidence of pleural effusion varies among studies and its association with PE outcome is still unclear. This study sought to determine the pooled incidence and prognostic value of pleural effusion events in patients with PE. Methods: We systematically searched the PubMed, EMBASE, SCOPE, Web of Science, Cochrane, LILACS, CINAHL, EBSCO, AMED, and OVID databases from the inception of each database to 7 September 2022 with a restriction on human studies, to identify studies assessing the association between pleural effusion and PE including all prospective and retrospective clinical studies. An exploratory meta-analysis was performed using a random-effects model. We evaluated the heterogeneity and performed subgroup analyses. Results: The final meta-analysis included 29 studies involving 13,430 PE patients. The pooled incidence of pleural effusion in PE patients was 41.2% (95% CI: 35.7–46.6%), which tended to be unilateral (pooled incidence: 60.8%, 95% CI: 45.7–75.8%) and small (pooled incidence: 85.9%, 95% CI: 82.6–89.1%). Pooled analysis using a random-effects model (I2 = 53.2%) showed that pleural effusion was associated with an increased risk of 30-day mortality (RR 2.19, 95% CI: 1.53–3.15, p < 0.001, I2 = 67.1%) and in-hospital mortality (RR 2.39, 95% CI: 1.85–3.09, p < 0.001, I2 = 37.1%) in patients with PE. Conclusions: Our meta-analysis found that PE patients had a high incidence of pleural effusion, which was usually unilateral and small. Pleural effusion generally increases 30-day and in-hospital mortality in patients with PE, and it is recommended that physicians be aware of the risk of death from PE, especially when patients have pleural effusion. Further investigations focusing on PE with pleural effusion are warranted.

Development and validation of metabolic scoring to individually predict prognosis and monitor recurrence early in gastric cancer: A large-sample analysis

PURPOSE

To develop and validate a simple metabolic score (Metabolic score, MS) for use in evaluating the prognosis of gastric cancer (GC) patients and dynamically monitor for early recurrence.

METHODS

We retrospectively collected general clinicopathological data of patients who underwent radical gastrectomy for GC between September 2012 and December 2017 in the Department of Gastric Surgery of the Fujian Medical University Union Hospital. Using a random forest algorithm to screen preoperative blood indicators into the Least absolute shrinkage and selection operator (LASSO) model, we developed a novel MS to predict prognosis.

RESULTS

Data of 1974 patients were used to develop and validate the model. Total cholesterol (TCHO), bilirubin (TBIL), direct bilirubin (DBIL), and 15 other metabolic indicators had significant predictive value for the prognosis using the random forest algorithm. In the overall population, 533 patients (27.0%) had high and 1441 (73%) had low MS status. High MS status was related to tumor progression. The KM curves of 3-year OS and RFS for training set patients showed low MS had a better prognosis than high MS (OS: 79.4% vs 59.7%, P < 0.001; RFS: 76.0% vs 56.2%, P < 0.001).

CONCLUSIONS

We have developed and validated MS to predict the long-term survival of GC patients and allow early monitoring of recurrence. This will provide physicians with simple, economical, and dynamic tumor monitoring information.

A Radiomics Nomogram for Non-Invasive Prediction of Progression-Free Survival in Esophageal Squamous Cell Carcinoma

To construct a prognostic model for preoperative prediction on computed tomography (CT) images of esophageal squamous cell carcinoma (ESCC), we created radiomics signature with high throughput radiomics features extracted from CT images of 272 patients (204 in training and 68 in validation cohort). Multivariable logistic regression was applied to build the radiomics signature and the predictive nomogram model, which was composed of radiomics signature, traditional TNM stage, and clinical features. A total of 21 radiomics features were selected from 954 to build a radiomics signature which was significantly associated with progression-free survival (p &lt; 0.001). The area under the curve of performance was 0.878 (95% CI: 0.831–0.924) for the training cohort and 0.857 (95% CI: 0.767–0.947) for the validation cohort. The radscore of signatures' combination showed significant discrimination for survival status. Radiomics nomogram combined radscore with TNM staging and showed considerable improvement over TNM staging alone in the training cohort (C-index, 0.770 vs. 0.603; p &lt; 0.05), and it is the same with clinical data (C-index, 0.792 vs. 0.680; p &lt; 0.05), which were confirmed in the validation cohort. Decision curve analysis showed that the model would receive a benefit when the threshold probability was between 0 and 0.9. Collectively, multiparametric CT-based radiomics nomograms provided improved prognostic ability in ESCC.

Development and validation of a 30-day death nomogram in patients with spontaneous cerebral hemorrhage: a retrospective cohort study

The purpose of this study was to establish and validate a nomogram to estimate the 30-day probability of death in patients with spontaneous cerebral hemorrhage. From January 2015 to December 2017, a cohort of 450 patients with clinically diagnosed cerebral hemorrhage was collected for model development. The minimum absolute contraction and the selection operator (lasso) regression model were used to select the strongest prediction of patients with cerebral hemorrhage. Discrimination and calibration were used to evaluate the performance of the resulting nomogram. After internal validation, the nomogram was further assessed in a different cohort containing 148 consecutive subjects examined between January 2018 and December 2018. The nomogram included five predictors from the lasso regression analysis, including: Glasgow coma scale (GCS), hematoma location, hematoma volume, white blood cells, and D-dimer. Internal verification showed that the model had good discrimination, (the area under the curve is 0.955), and good calibration [unreliability (U) statistic, p = 0.739]. The nomogram still showed good discrimination (area under the curve = 0.888) and good calibration [U statistic, p = 0.926] in the verification cohort data. Decision curve analysis showed that the prediction nomogram was clinically useful. The current study delineates a predictive nomogram combining clinical and imaging features, which can help identify patients who may die of cerebral hemorrhage.

Left atrial size measured on CT pulmonary angiography: another parameter of pulmonary embolism severity? A systematic review

We systematically review the potential role of left atrial (LA) size, evaluated at computed tomography angiography (CTA) in patients with acute pulmonary embolism (PE), as a new parameter of PE severity. A literature search based on PubMed (MEDLINE), Scopus, Cochrane library and Google Scholar databases was performed to locate previous published investigations reporting data on the severity of acute PE based on the evaluation of LA size (either volume, diameter or area). Six studies, corresponding to a total of 990 patients, published between 2012 and 2019 were included into the analysis. The severity of acute PE, in terms of hemodynamic impairment, increases with the reduction of the LA volume and a significant negative correlation was observed between the pulmonary artery obstruction index (PAOI) and the LA area. Similarly, the longest left-to-right as well as the anteroposterior diameters of the LA had a significant positive correlation with the PAOI index for both the measurement. The LA volume significantly decreased with the increasing of the PAOI index. Moreover, a lower LA volume was observed in those subjects with a saddle PE appearing as the best single parameter able to discriminate between patients having or not a saddle acute PE. Intriguingly, PE patients died within 30 days from the acute event had a significant small LA volume compared to survivors. Data obtained from the current medical literature seem to suggest that the evaluation of LA size evaluation could be a new parameter of PE severity. Further and larger prospective studies are needed to confirm preliminary findings.

Associated Pleural and Pericardial Effusions: An Extensive Differential Explored

Concurrent pleural and pericardial effusions are not an unusual finding, but their differential diagnosis remains uncertain. Medline-based review identified an extensive list of infectious, inflammatory, neoplastic, iatrogenic, and myriad other etiologies. A single retrospective study had addressed this presentation. Several principles of a diagnostic workup are suggested, acknowledging that a significant minority of patients may not require a comprehensive workup and remain 'idiopathic'.

A COVID-19 risk score combining chest CT radiomics and clinical characteristics to differentiate COVID-19 pneumonia from other viral pneumonias

With the continued transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) throughout the world, identification of highly suspected COVID-19 patients remains an urgent priority. In this study, we developed and validated COVID-19 risk scores to identify patients with COVID-19. In this study, for patient-wise analysis, three signatures, including the risk score using radiomic features only, the risk score using clinical factors only, and the risk score combining radiomic features and clinical variables, show an excellent performance in differentiating COVID-19 from other viral-induced pneumonias in the validation set. For lesion-wise analysis, the risk score using three radiomic features only also achieved an excellent AUC value. In contrast, the performance of 130 radiologists based on the chest CT images alone without the clinical characteristics included was moderate as compared to the risk scores developed. The risk scores depicting the correlation of CT radiomics and clinical factors with COVID-19 could be used to accurately identify patients with COVID-19, which would have clinically translatable diagnostic and therapeutic implications from a precision medicine perspective.

Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study

Background

Accurate diagnosis of high-grade branching type intraductal papillary mucinous neoplasms (BD-IPMNs) is challenging in clinical setting. We aimed to construct and validate a nomogram combining clinical characteristics and radiomic features for the preoperative prediction of low and high-grade in BD-IPMNs.

Methods

Two hundred and two patients from three medical centers were enrolled. The high-grade BD-IPMN group comprised patients with high-grade dysplasia and invasive carcinoma in BD-IPMN (n = 50). The training cohort comprised patients from the first medical center (n = 103), and the external independent validation cohorts comprised patients from the second and third medical centers (n = 48 and 51). Within 3 months prior to surgery, all patients were subjected to magnetic resonance examination. The volume of interest was delineated on T1-weighted (T1-w) imaging, T2-weighted (T2-w) imaging, and contrast-enhanced T1-weighted (CET1-w) imaging, respectively, on each tumor slice. Quantitative image features were extracted using MITK software (G.E.). The Mann-Whitney U test or independent-sample t-test, and LASSO regression, were applied for data dimension reduction, after which a radiomic signature was constructed for grade assessment. Based on the training cohort, we developed a combined nomogram model incorporating clinical variables and the radiomic signature. Decision curve analysis (DCA), a receiver operating characteristic curve (ROC), a calibration curve, and the area under the ROC curve (AUC) were used to evaluate the utility of the constructed model based on the external independent validation cohorts.

Results

To predict tumor grade, we developed a nine-feature-combined radiomic signature. For the radiomic signature, the AUC values of high-grade disease were 0.836 in the training cohort, 0.811 in external validation cohort 1, and 0.822 in external validation cohort 2. The CA19–9 level and main pancreatic duct size were identified as independent parameters of high-grade of BD-IPMNs using multivariate logistic regression analysis. The CA19–9 level and main pancreatic duct size were then used to construct the radiomic nomogram. Using the radiomic nomogram, the high-grade disease-associated AUC values were 0.903 (training cohort), 0.884 (external validation cohort 1), and 0.876 (external validation cohort 2). The clinical utility of the developed nomogram was verified using the calibration curve and DCA.

Conclusions

The developed radiomic nomogram model could effectively distinguish high-grade patients with BD-IPMNs preoperatively. This preoperative identification might improve treatment methods and promote personalized therapy in patients with BD-IPMNs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-021-00395-6.

Pulmonary Vascular Volume Estimated by Automated Software is a Mortality Predictor after Acute Pulmonary Embolism

Fundamento:

A embolia pulmonar aguda (EPA) tem desfecho clínico variável. A angiotomografia computadorizada (angio-CT) é considerada o padrão-ouro para o diagnóstico.

Objetivo:

Avaliar se o volume vascular pulmonar (VVP) quantificado por software automatizado é um preditor de mortalidade após EPA.

Métodos:

Estudo de coorte retrospectivo no qual a imagem da angio-CT de 61 pacientes com EPA foi reanalisada. O VVP e o volume pulmonar (VP) foram estimados automaticamente pelo software Yacta. Calculamos o VVP ajustado pela razão: VVP(cm³)/VP(litros). Parâmetros prognósticos clássicos da angio-CT (carga embólica; razão do diâmetro do ventrículo direito/ventrículo esquerdo; razão do diâmetro da artéria pulmonar/aorta; desvio do septo interventricular; infarto pulmonar e refluxo de contraste na veia hepática) foram avaliados. A mortalidade em 1 mês foi o desfecho analisado. Consideramos um valor de p <0,05 como estatisticamente significativo.

Resultados:

Sete mortes (11%) ocorreram entre os 61 pacientes durante 1 mês de seguimento. O VVP ajustado <23cm³/L foi um preditor independente de mortalidade na análise univariada (odds ratio [OR]: 26; intervalo de confiança de 95% [IC95%]: 3-244; p=0,004) e na análise multivariada (OR ajustado: 19 [IC95%: 1,3-270]; p=0,03). Os parâmetros clássicos da angio-CT não foram associados à mortalidade em 1 mês nesta amostra. O VVP ajustado <23cm³/L apresentou sensibilidade de 86%, especificidade de 82%, valor preditivo negativo de 94% e valor preditivo positivo de 64% para identificação dos pacientes que morreram.

Conclusão:

VVP ajustado <23cm³/L foi um preditor independente de mortalidade após EPA. Esse parâmetro mostrou melhor desempenho prognóstico do que os outros achados clássicos da angio-CT. (Arq Bras Cardiol. 2020; 115(5):809-818)

A Novel Nomogram Model Based on Cone-Beam CT Radiomics Analysis Technology for Predicting Radiation Pneumonitis in Esophageal Cancer Patients Undergoing Radiotherapy

Purpose

We quantitatively analyzed the characteristics of cone-beam computed tomography (CBCT) radiomics in different periods during radiotherapy (RT) and then built a novel nomogram model integrating clinical features and dosimetric parameters for predicting radiation pneumonitis (RP) in patients with esophageal squamous cell carcinoma (ESCC).

Methods

At our institute, a retrospective study was conducted on 96 ESCC patients for whom we had complete clinical feature and dosimetric parameter data. CBCT images of each patient in three different periods of RT were obtained, the images were segmented using both lungs as the region of interest (ROI), and 851 image features were extracted. The least absolute shrinkage selection operator (LASSO) was applied to identify candidate radiomics features, and logistic regression analyses were applied to construct the rad-score. The optimal period for the rad-score, clinical features, and dosimetric parameters were selected to construct the nomogram model and then the receiver operating characteristic (ROC) curve was used to evaluate the prediction capacity of the model. Calibration curves and decision curves were used to demonstrate the discriminatory and clinical benefit ratios, respectively.

Results

The relative volume of total lung treated with ≥5 Gy (V5), mean lung dose (MLD), and tumor stage were independent predictors of RP and were finally incorporated into the nomogram. When the three time periods were modeled, the first period was better than the others. In the primary cohort, the area under the ROC curve (AUC) was 0.700 (95% confidence interval (CI) 0.568–0.832), and in the independent validation cohort, the AUC was 0.765 (95% CI 0.588–0.941). In the nomogram model that integrates clinical features and dosimetric parameters, the AUC in the primary cohort was 0.836 (95% CI 0.700–0.918), and the AUC in the validation cohort was 0.905 (95% CI 0.799–1.000). The nomogram model exhibits excellent performance. Calibration curves indicate a favorable consistency between the nomogram prediction and the actual outcomes. The decision curve exhibits satisfactory clinical utility.

Conclusion

The radiomics model based on early lung CBCT is a potentially valuable tool for predicting RP. V5, MLD, and tumor stage have certain predictive effects for RP. The developed nomogram model has a better prediction ability than any of the other predictors and can be used as a quantitative model to predict RP.

Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI

Background

Currently, how to accurately determine the patient prognosis after a percutaneous coronary intervention (PCI) remains unclear and may vary among populations, hospitals, and datasets. The aim of this study was to establish a prediction model of in-hospital mortality risk after primary PCI in patients with acute ST-elevated myocardial infarction (STEMI).

Methods

This was a multicenter, observational study of patients with acute STEMI who underwent primary PCI. The outcome was in-hospital mortality. The least absolute shrinkage and selection operator (LASSO) method was used to select the features that were the most significantly associated with the outcome. A regression model was built using the selected variables to select the significant predictors of mortality. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.

Results

Totally, 1169 and 316 patients were enrolled in the training and validation sets, respectively. Fourteen predictors were identified by the LASSO analysis: sex, Killip classification, left main coronary artery disease (LMCAD), grading of thrombus, TIMI classification, slow flow, application of IABP, administration of β-blocker, ACEI/ARB, symptom-to-door time (SDT), symptom-to-balloon time (SBT), syntax score, left ventricular ejection fraction (LVEF), and CK-MB peak. The mortality risk prediction nomogram achieved good discrimination for in-hospital mortality (training set: C-statistic = 0.987; model calibration: P = 0.722; validation set: C-statistic = 0.984, model calibration: P = 0.669). Area under the curve (AUC) values for the training and validation sets are 0.987 (95% CI: 0.981–0.994, P = 0.003) and 0.990 (95% CI: 0.987–0.998, P = 0.007), respectively. DCA shows that the nomogram can achieve good net benefit.

Conclusions

A novel nomogram was developed and is a simple and accurate tool for predicting the risk of in-hospital mortality in patients with acute STEMI who underwent primary PCI.

Clinical relevance of bilateral pleural effusion in patients with acute pulmonary embolism

BACKGROUND

The clinical relevance of bilateral pleural effusion (BPE) in patients with acute pulmonary embolism (PE) is unclear.

AIMS

To describe characteristics of patients with acute PE that present with BPE.

METHODS

Patients with acute PE were retrospectively analysed and divided into three groups: without pleural effusion, unilateral pleural effusion and bilateral effusion. Clinical, laboratory and radiological characteristics were compared between the three groups.

RESULTS

The study population (n = 343) consisted of unilateral effusion group (n = 83), BPE group (n = 94) and without effusion group (n = 166). Several variables were noted in higher proportion (%), in the BPE group in comparison to both the unilateral effusion and without effusion groups: heart failure (17.0 vs 7.2 vs 6.7, P = 0.017), hypoalbuminaemia (59.3 vs 39.5 vs 25.6, P ˂ 0.001), PE occurrence in-hospital setting (51 vs 25.6 vs 15.1, P ˂ 0.001), major operation (31 vs 19.2 vs 15.2, P = 0.01) and mechanical ventilation (13.0 vs 4.9 vs 4.2, P = 0.019). Norton scale scores were found to be lower among patients with BPE in comparison to both patients with unilateral and without pleural effusion (15.55 vs 16.92 vs 17.36, P = 0.006). After adjusting confounding variables, patients with BPE have lower probability for in-hospital survival in comparison to both patients with unilateral pleural effusion (odds ratio = 0.30, 95% confidence interval 0.12-0.79), and patients without pleural effusion (odds ratio = 0.26, 95% confidence interval 0.11-0.61).

CONCLUSIONS

BPE in patients with acute PE may have significant clinical implications. It may signify serious underlying comorbidities which contribute to higher in-hospital mortality in comparison to both patients with unilateral pleural effusion and patients without pleural effusion.

Computed Tomography Findings Associated With 30-Day Mortality in Patients With Malignant Superior Vena Cava Syndrome

OBJECTIVE

The objective of this study was to identify radiological and clinical factors associated with early mortality in malignant superior vena cava syndrome (SVCS).

MATERIALS AND METHODS

Chest computed tomography studies of 127 patients with malignancy-associated SVCS were retrospectively reviewed. Involvement of SVC and tributaries, pleural and pericardial effusions, pulmonary artery involvement, and ancillary findings were documented. Univariate and multivariate models determined associations between radiological and clinical variables, and 30-day mortality.

RESULTS

Thirty-day mortality rate was 16.5% (n = 21). Factors associated with 30-day mortality on univariate analysis included age, cancer stage, SVCS clinical severity, left jugular vein obstruction, number of involved veins, pulmonary arteries involvement, and presence of pleural effusions. Age, SVCS clinical severity, number of veins involved, and pleural effusions were positively associated with 30-day mortality on multivariate analysis.

CONCLUSIONS

Selected clinical and radiological variables are associated with early death in malignant SVCS. These factors may identify a subgroup of patients who may benefit from treatment escalation.

EXTraction of EMR numerical data: an efficient and generalizable tool to EXTEND clinical research

Background

Electronic medical records (EMR) contain numerical data important for clinical outcomes research, such as vital signs and cardiac ejection fractions (EF), which tend to be embedded in narrative clinical notes. In current practice, this data is often manually extracted for use in research studies. However, due to the large volume of notes in datasets, manually extracting numerical data often becomes infeasible. The objective of this study is to develop and validate a natural language processing (NLP) tool that can efficiently extract numerical clinical data from narrative notes.

Results

To validate the accuracy of the tool EXTraction of EMR Numerical Data (EXTEND), we developed a reference standard by manually extracting vital signs from 285 notes, EF values from 300 notes, glycated hemoglobin (HbA1C), and serum creatinine from 890 notes. For each parameter of interest, we calculated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F₁ score of EXTEND using two metrics.

(1) completion of data extraction, and (2) accuracy of data extraction compared to the actual values in the note verified by chart review. At the note level, extraction by EXTEND was considered correct only if it accurately detected and extracted all values of interest in a note.

Using manually-annotated labels as the gold standard, the note-level accuracy of EXTEND in capturing the numerical vital sign values, EF, HbA1C and creatinine ranged from 0.88 to 0.95 for sensitivity, 0.95 to 1.0 for specificity, 0.95 to 1.0 for PPV, 0.89 to 0.99 for NPV, and 0.92 to 0.96 in F₁ scores. Compared to the actual value level, the sensitivity, PPV, and F₁ score of EXTEND ranged from 0.91 to 0.95, 0.95 to 1.0 and 0.95 to 0.96.

Conclusions

EXTEND is an efficient, flexible tool that uses knowledge-based rules to extract clinical numerical parameters with high accuracy. By increasing dictionary terms and developing new rules, the usage of EXTEND can easily be expanded to extract additional numerical data important in clinical outcomes research.

Pulmonary arteries: imaging of pulmonary embolism and beyond

The pulmonary arteries are not just affected by thrombus. Various acquired and congenital conditions can also affect the pulmonary arteries. In this review we discuss cross sectional imaging modalities utilized for the imaging of the pulmonary arteries. Acquired pulmonary artery entities, including pulmonary artery sarcoma (PAS), vasculitis, aneurysm, and arteriovenous malformations, and congenital anomalies in adults, including proximal interruption of the pulmonary artery, pulmonary sling, pulmonary artery stenosis, and idiopathic dilatation of the pulmonary trunk, are also discussed. An awareness of these entities and their imaging findings is important for radiologists interpreting chest imaging.

A Combined Nomogram Model to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors

PURPOSE

The purpose of this study is to develop and validate a nomogram model combing radiomics features and clinical characteristics to preoperatively differentiate grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (pNET).Experimental Design: A total of 137 patients who underwent contrast-enhanced CT from two hospitals were included in this study. The patients from the second hospital (n = 51) were selected as an independent validation set. The arterial phase in contrast-enhanced CT was selected for radiomics feature extraction. The Mann-Whitney U test and least absolute shrinkage and selection operator regression were applied for feature selection and radiomics signature construction. A combined nomogram model was developed by incorporating the radiomics signature with clinical factors. The association between the nomogram model and the Ki-67 index and rate of nuclear mitosis were also investigated respectively. The utility of the proposed model was evaluated using the ROC, area under ROC curve (AUC), calibration curve, and decision curve analysis (DCA). The Kaplan-Meier (KM) analysis was used for survival analysis.

RESULTS

An eight-feature-combined radiomics signature was constructed as a tumor grade predictor. The nomogram model combining the radiomics signature with clinical stage showed the best performance (training set: AUC = 0.907; validation set: AUC = 0.891). The calibration curve and DCA demonstrated the clinical usefulness of the proposed nomogram. A significant correlation was observed between the developed nomogram and Ki-67 index and rate of nuclear mitosis, respectively. The KM analysis showed a significant difference between the survival of predicted grade 1 and grade 2/3 groups (P = 0.002).

CONCLUSIONS

The combined nomogram model developed could be useful in differentiating grade 1 and grade 2/3 tumor in patients with pNETs.

MRI features predict p53 status in lower-grade gliomas via a machine-learning approach

Background

P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images.

Methods

Preoperative MR images were retrospectively obtained from 272 patients with primary grade II/III gliomas. The patients were randomly allocated in a 2:1 ratio to a training (n = 180) or validation (n = 92) set. A total of 431 radiomic features were extracted from each patient. The lest absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomic signature construction. Subsequently, a machine-learning model to predict p53 status was established using the selected features and a Support Vector Machine classifier. The predictive performance of all individual features and the model was calculated using receiver operating characteristic curves in both the training and validation sets.

Results

The p53-related radiomic signature was built using the LASSO algorithm; this procedure consisted of four first-order statistics or related wavelet features (including Maximum, Median, Minimum, and Uniformity), a shape and size-based feature (Spherical Disproportion), and ten textural features or related wavelet features (including Correlation, Run Percentage, and Sum Entropy). The prediction accuracies based on the area under the curve were 89.6% in the training set and 76.3% in the validation set, which were better than individual features.

Conclusions

These results demonstrate that MR image texture features are predictive of p53 mutation status in lower-grade gliomas. Thus, our procedure can be conveniently used to facilitate presurgical molecular pathological diagnosis.

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We established a p53-related radiomic signature in lower-grade gliomas based on LASSO algorithm.

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We developed a machine-learning model using the radiomic signature and a support vector machine.

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P53 mutation status of lower-grade gliomas was predicted effectively based on our machine-learning model.

Development of a preprocedure nomogram for predicting contrast-induced acute kidney injury after coronary angiography or percutaneous coronary intervention

Most of the risk models for predicting contrast-induced acute kidney injury (CI-AKI) are available for postcontrast exposure prediction, thus have limited values in practice. We aimed to develop a novel nomogram based on preprocedural features for early prediction of CI-AKI in patients after coronary angiography (CAG) or percutaneous coronary intervention (PCI). A total of 245 patients were retrospectively reviewed from January 2015 to January 2017. Least absolute shrinkage and selection operator (Lasso) regression model was applied to select most strong predictors for CI-AKI. The CI-AKI risk score was calculated for each patient as a linear combination of selected predictors that were weighted by their respective coefficients. The discrimination of nomogram was assessed by C-statistic. The occurrence of CI-AKI was 13.9% (34 out of 245). We identified ten predictors including sex, diabetes mellitus, lactate dehydrogenase level, C-reactive protein, years since drinking, chronic kidney disease (CKD), stage of CKD, stroke, acute myocardial infarction, and systolic blood pressure. The CI-AKI prediction nomogram obtained good discrimination (C-statistic, 0.718, 95%CI: 0.637-0.800, p = 7.23 × 10^-5). The cutoff value of CI-AKI risk score was -1.953. Accordingly, the novel nomogram we developed is a simple and accurate tool for preprocedural prediction of CI-AKI in patients undergoing CAG or PCI.

Exploration and validation of radiomics signature as an independent prognostic biomarker in stage III-IVb nasopharyngeal carcinoma

There is no consensus on specific prognostic biomarkers potentially improving survival of nasopharyngeal carcinoma (NPC), especially in advanced-stage disease. The prognostic value of MRI-based radiomics signature is unclear. A total of 970 quantitative features were extracted from the tumor of 100 untreated NPC patients (stage III-IVb) (discovery set: n = 70, validation set: n = 30). We then applied least absolute shrinkage and selection operator (lasso) regression to select features that were most associated with progression-free survival (PFS). Candidate prognostic biomarkers included age, gender, overall stage, hemoglobin, platelet counts and radiomics signature. We developed model 1 (without radiomics signature) and model 2 (with radiomics signature) in the discovery set and then tested in the validation set. Multivariable Cox regression analysis was used to yield hazard ratio (HR) of each potential biomarker. We found the radiomics signature stratified patients in the discovery set into a low or high risk group for PFS (HR = 5.14, p < 0.001) and was successfully validated for patients in the validation set (HR = 7.28, p = 0.015). However, the other risk factors showed no significantly prognostic value (all p-values for HR, > 0.05). Accordingly, pretreatment MRI-based radiomics signature is a non-invasive and cost-effective prognostic biomarker in advanced NPC patients, which would improve decision-support in cancer care.

Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma

Purpose: To identify MRI-based radiomics as prognostic factors in patients with advanced nasopharyngeal carcinoma (NPC).Experimental Design: One-hundred and eighteen patients (training cohort: n = 88; validation cohort: n = 30) with advanced NPC were enrolled. A total of 970 radiomics features were extracted from T2-weighted (T2-w) and contrast-enhanced T1-weighted (CET1-w) MRI. Least absolute shrinkage and selection operator (LASSO) regression was applied to select features for progression-free survival (PFS) nomograms. Nomogram discrimination and calibration were evaluated. Associations between radiomics features and clinical data were investigated using heatmaps.Results: The radiomics signatures were significantly associated with PFS. A radiomics signature derived from joint CET1-w and T2-w images showed better prognostic performance than signatures derived from CET1-w or T2-w images alone. One radiomics nomogram combined a radiomics signature from joint CET1-w and T2-w images with the TNM staging system. This nomogram showed a significant improvement over the TNM staging system in terms of evaluating PFS in the training cohort (C-index, 0.761 vs. 0.514; P < 2.68 × 10^-9). Another radiomics nomogram integrated the radiomics signature with all clinical data, and thereby outperformed a nomogram based on clinical data alone (C-index, 0.776 vs. 0.649; P < 1.60 × 10^-7). Calibration curves showed good agreement. Findings were confirmed in the validation cohort. Heatmaps revealed associations between radiomics features and tumor stages.Conclusions: Multiparametric MRI-based radiomics nomograms provided improved prognostic ability in advanced NPC. These results provide an illustrative example of precision medicine and may affect treatment strategies. Clin Cancer Res; 23(15); 4259-69. ©2017 AACR.

Predictive value of perfusion defects on dual energy CTA in the absence of thromboembolic clots

BACKGROUND

To determine the predictive value of volumetrically measured lung perfusion defects (PDvol) and right ventricular dysfunction on dual-energy computed tomography angiography (DE-CTA) for predicting all cause mortality in patients suspected of pulmonary embolism (PE) but without evident thromboembolic clot on CTA.

METHODS

448 patients underwent DE-CTA on a 64-channel DSCT system between January 2007 and December 2012 for suspected PE, of which 115 were without detectable thromboembolic clot on CTA. Diagnostic performance for identifying patients at risk of dying was evaluated using ROC analysis. All-cause mortality was assessed via the hospital electronic medical records and/or consultation of the patient or the patient's primary care physician via phone call interviews. Sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and area under the curve (AUC) were determined for PDvol (volume of perfusion defects/total lung volume), transverse right ventricular to left ventricular diameter ratios (RV/LV) and for the combination of both tests.

RESULTS

Mortality was 38% within the investigated time period of 6 months. Patients who died had significantly higher PDvol (PDvol 28 ± 13% vs. 19 ± 12%, p < 0.001) and a non-significant difference in transverse RV/LV ratio (1.14 ± 0.37 vs. 1.06 ± 0.22, p = 0.159). The AUC was 0.71 for PDvol, 0.53 for RV/LV ratio, and 0.67 for the combination of PDvol and RV/LV ratio. PDvol remained a significant predictor after correcting for age.

CONCLUSIONS

In the absence of thromboembolic clots, PDvol at DE-CTA appears to be predictive for all cause mortality.