Application of contrast-enhanced CT radiomics in prediction of early recurrence of locally advanced oesophageal squamous cell carcinoma after trimodal therapy.

BACKGROUND: Early recurrence of oesophageal squamous cell carcinoma (SCC) is defined as recurrence after surgery within 1 year, and appears as local recurrence, distant recurrence, and lymph node positive and disseminated recurrence. Contrast-enhanced computed tomography (CECT) is recommended for diagnosis of primary tumor and initial staging of oesophageal SCC, but it cannot be used to predict early recurrence. It is reported that radiomics can help predict preoperative stages of oesophageal SCC, lymph node metastasis before operation, and 3-year overall survival of oesophageal SCC patients following chemoradiotherapy by extracting high-throughput quantitative features from CT images. This study aimed to develop models based on CT radiomics and clinical features of oesophageal SCC to predict early recurrence of locally advanced cancer. METHODS: We collected electronic medical records and image data of 197 patients with confirmed locally advanced oesophageal SCC. These patients were randomly allocated to 137 patients in the training cohort and 60 in the test cohort. 352 radiomics features were extracted by delineating region-of-interest (ROI) around the lesion on CECT images and clinical signature was generated by medical records. The radiomics model, clinical model, the combined model of radiomics and clinical features were developed by radiomics features and/or clinical characteristics. Predicting performance of the three models was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1 score. RESULTS: Eleven radiomics features and/or six clinical signatures were selected to build prediction models related to recurrence of locally advanced oesophageal SCC after trimodal therapy. The AUC of integration of radiomics and clinical models was better than that of radiomics or clinical model for the training cohort (0.821 versus 0.754 or 0.679, respectively) and for the validation cohort (0.809 versus 0.646 or 0.658, respectively). Integrated model of radiomics and clinical features showed good performance in predicting early recurrence of locally advanced oesophageal SCC for both the training and validation cohorts (accuracy = 0.730 and 0.733, and F-1score = 0.730 and 0.778, respectively). CONCLUSIONS: The integrated model of CECT radiomics and clinical features may be a potential imaging biomarker to predict early recurrence of locally advanced oesophageal SCC after trimodal therapy.

CT radiomics features to predict lymph node metastasis in advanced esophageal squamous cell carcinoma and to discriminate between regional and non-regional lymph node metastasis: a case control study.

BACKGROUND: Prediction of lymph node status in esophageal squamous cell carcinoma (ESCC) is critical for clinical decision making. In clinical practice, computed tomography (CT) has been frequently used to assist in the preoperative staging of ESCC. Texture analysis can provide more information to reflect potential biological heterogeneity based on CT. A nomogram for the preoperative diagnosis of lymph node metastasis in patients with resectable ESCC has been previously developed. However, to the best of our knowledge, no reports focus on developing CT radiomics features to discriminate ESCC patients with regional lymph node metastasis (RLNM) and non-regional lymph node metastasis (NRLNM). We, therefore, aimed to develop CT radiomics models to predict lymph node metastasis (LNM) in advanced ESCC and to discriminate ESCC between RLNM and NRLNM. METHODS: This study enrolled 334 patients with pathologically confirmed advanced ESCC, including 152 patients without LNM and 182 patients with LNM, and 103 patients with RLNM and 79 patients NRLNM. Radiomics features were extracted from CT data for each patient. The least absolute shrinkage and selection operator (LASSO) model and independent samples t-tests or Mann-Whitney U tests were exploited for dimension reduction and selection of radiomics features. Optimal radiomics features were chosen using multivariable logistic regression analysis. The discriminating performance was assessed by area under the receiver operating characteristic curve (AUC) and accuracy. RESULTS: The radiomics features were developed based on multivariable logistic regression and were significantly associated with LNM status in both the training and validation cohorts (P<0.001). The radiomics models could differentiate between patients with and without LNM (AUC =0.79 and 0.75, and accuracy =0.75 and 0.71 in the training and validation cohorts, respectively). In patients with LNM, the radiomics features could effectively differentiate between RLNM and NRLNM (AUC =0.98 and 0.95, and accuracy =0.94 and 0.83 in the training and validation cohorts, respectively). CONCLUSIONS: CT radiomics features could help predict the LNM status of advanced ESCC patients and effectively discriminate ESCC between RLNM and NRLNM.

Size and PEG Length-Controlled PEGylated Monocrystalline Superparamagnetic Iron Oxide Nanocomposite for MRI Contrast Agent.

OBJECTIVE: PEGylated superparamagnetic iron oxide (SPIO) is the most promising alternatives to gadolinium-based contrast agents (GBCAs) in MRI. This paper is to explore the imaging effects of PEGylated SPIO, which is influenced by particle sizes and surface polyethylene glycol (PEG) coating, using as MRI contrast agents at different magnetic field intensities. METHODS: Firstly, nine PEGylated monocrystalline SPIO nanoparticles with different nanocrystal sizes and different molecular weights PEG coating were prepared, and then physical and biological properties were analyzed. Finally, MRI imaging in vivo was performed to observe the imaging performance. RESULTS: Nine PEGylated monocrystalline SPIO nanoparticles have good relaxivities, serum stability, and biosecurity. At the same time, they show different imaging characteristics at different magnetic field intensities. Eight-nanometer SPIO@PEG5k is an effective T 2 contrast agent at 3.0 T (r 2/r 1 = 14.0), is an ideal T 1-T 2 dual-mode contrast agent at 1.5 T (r 2/r 1 = 6.52), and is also an effective T 1 contrast agent at 0.5 T (r 2/r 1 = 2.49), while 4-nm SPIO@PEG5k is a T 1-T 2 dual-mode contrast agent at 3.0 T (r 2/r 1 = 5.24), and is a useful T 1 contrast agent at 0.5 T (r 2/r 1 = 1.74) and 1.5 T (r 2/r 1 = 2.85). MRI studies in vivo at 3.0 T further confirm that 4-nm SPIO@PEG5k displays excellent T 1-T 2 dual-mode contrast enhancement, whereas 8-nm SPIO@PEG5k only displays T 2 contrast enhancement. CONCLUSION: PEGylated SPIOs with different nanocrystal sizes and PEG coating can be used as T 1, T 2, or T 1-T 2 dual-mode contrast agents to meet the clinical demands of MRI at specific magnetic fields.

The characteristics of acute necrotizing pancreatitis in different age stages: An MRI study.

PURPOSE: To study the characteristics of acute necrotizing pancreatitis (ANP) in different age stages and their correlations with the clinical outcomes using magnetic resonance imaging (MRI). METHOD: MRI of 716 patients with acute pancreatitis was retrospectively reviewed to assess the incidence and characteristics of ANP. On MRI, ANP was classified into three subtypes: extrapancreatic necrosis (EPN) alone, pancreatic necrosis (PN) alone and combined necrosis. The extent of necrosis was also quantified on MRI. All patients were divided into three age groups, that is, young,middle-aged and elderly groups, and these characteristics of ANP were compared among the three age groups. The endpoints of patients' clinical outcome were compared among different age groups and different characteristics of ANP. RESULTS: Of the 716 patients, 129(18 %) were identified as ANP on MRI. The prevalence of ANP in the elderly group was the highest (28.9 %, p < 0.05). The patients in the middle-age and the elderly groups exhibited a higher risk of combined necrosis (56.9 %, 55.8 %; respectively), and elderly patients more frequently had extensive extrapancreatic involvement compared with young patients (65.9 % vs 21.4 %; p = 0.004); however, PN alone was more common in young patients. These characteristics of ANP were significantly bound up with clinical outcomes. CONCLUSIONS: Different subtypes of ANP have different outcomes. More importantly, age needs to be considered as a factor of special concern in development of ANP.

CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: a case control study.

BACKGROUND: Computed tomography (CT) is commonly used in all stages of oesophageal squamous cell carcinoma (SCC) management. Compared to basic CT features, CT radiomic features can objectively obtain more information about intratumour heterogeneity. Although CT radiomics has been proved useful for predicting treatment response to chemoradiotherapy in oesophageal cancer, the best way to use CT radiomic biomarkers as predictive markers for determining resectability of oesophageal SCC remains to be developed. This study aimed to develop CT radiomic features related to resectability of oesophageal SCC with five predictive models and to determine the most predictive model. METHODS: Five hundred ninety-one patients with oesophageal SCC undergoing contrast-enhanced CT were enrolled in this study, and were composed by 270 resectable cases and 321 unresectable cases. Of the 270 resectable oesophageal SCCs, 91 cases were primary resectable tumours; and the remained 179 cases received neoadjuvant therapy after CT, shrank on therapy, and changed to resectable tumours. Four hundred thirteen oesophageal SCCs including 189 resectable cancers and 224 unresectable cancers were randomly allocated to the training cohort; and 178 oesophageal SCCs including 81 resectable tumours and 97 unresectable tumours were allocated to the validation group. Four hundred ninety-five radiomic features were extracted from CT data for identifying resectability of oesophageal SCC. Useful radiomic features were generated by dimension reduction using least absolute shrinkage and selection operator. The optimal radiomic features were chosen using multivariable logistic regression, random forest, support vector machine, X-Gradient boost and decision tree classifiers. Discriminating performance was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1score. RESULTS: Eight radiomic features were selected to create radiomic models related to resectability of oesophageal SCC (P-values < 0.01 for both cohorts). Multivariable logistic regression model showed the best performance (AUC = 0.92 ± 0.04 and 0.87 ± 0.02, accuracy = 0.87 and 0.86, and F-1score = 0.93 and 0.86 in training and validation cohorts, respectively) in comparison with any other model (P-value < 0.001). Good calibration was observed for multivariable logistic regression model. CONCLUSION: CT radiomic models could help predict resectability of oesophageal SCC, and multivariable logistic regression model is the most predictive model.

Accurate identification of myocardial viability after myocardial infarction with novel manganese chelate-based MR imaging.

We developed a novel manganese (Mn2+ ) chelate for magnetic resonance imaging (MRI) assessment of myocardial viability in acute and chronic myocardial infarct (MI) models, and compared it with Gadolinium-based delay enhancement MRI (Gd3+ -DEMRI) and histology. MI was induced in 14 rabbits by permanent occlusion of the left circumflex coronary artery. Gd3+ -DEMRI and Mn2+ chelate-based delayed enhancement MRI (Mn2+ chelate-DEMRI) were performed at 7 days (acute MI, n = 8) or 8 weeks (chronic MI, n = 6) after surgery with sequential injection of 0.15 mmol/kg Gd3+ and Mn2+ chelate. The biodistribution of Mn2+ in tissues and blood was measured at 1.5 and 24 h. Blood pressure, heart rate (HR), left ventricular (LV) function, and infarct fraction (IF) were analyzed, and IF was compared with the histology. The Mn2+ chelate group maintained a stable hemodynamic status during experiment. For acute and chronic MI, all rabbits survived without significant differences in HR or LV function before and after injection of Mn2+ chelate or Gd3+ (p > 0.05). Mn2+ chelate mainly accumulated in the kidney, liver, spleen, and heart at 1.5 h, with low tissue uptake and urine residue at 24 h after injection. In the acute MI group, there was no significant difference in IF between Mn2+ chelate-DEMRI and histology (22.92 ± 2.21% vs. 21.79 ± 2.25%, respectively, p = 0.87), while Gd3+ -DEMRI overestimated IF, as compared with histology (24.54 ± 1.73%, p = 0.04). In the chronic MI group, there was no significant difference in IF between the Mn2+ chelate-DEMRI, Gd3+ -DEMRI, and histology (29.50 ± 11.39%, 29.95 ± 9.40%, and 29.00 ± 10.44%, respectively, p > 0.05), and all three were well correlated (r = 0.92-0.96, p < 0.01). We conclude that the use of Mn2+ chelate-DEMRI is reliable for MI visualization and identifies acute MI more accurately than Gd3+ -DEMRI.

Sinistral Portal Hypertension in Acute Pancreatitis: A Magnetic Resonance Imaging Study.

OBJECTIVE: The aim of the study was to study the prevalence and characteristics of sinistral portal hypertension (SPH) in acute pancreatitis (AP) and its correlation with the severity of AP. METHODS: Retrospectively studied 633 patients with AP admitted to our institution and underwent magnetic resonance imaging (MRI). Diagnosis of SPH was based on clinical manifestations, laboratory tests, and MRI. The venous system and pancreatitis were evaluated on T1 weighted imaging, T2 weighted imaging, and dynamic-enhancement MRI. Data on patients' demographics, etiology, organ failure, MR severity index, and clinical outcomes were all collected. RESULTS: The SPH was detected in 21 patients (3.3%, 21/633). There was no statistical difference in organ failure between patients with SPH and without SPH (P > 0.05). The prevalence of SPH in males and females was 5.1% (17/336) versus 1.3% (4/297) (χ(2) = 6.775, P = 0.009), in edematous and necrotizing AP was 0.4% (2/510) versus 15.5% (19/123) (χ(2) = 65.413, P = 0.000), and in mild, moderate, and severe AP, based on MR severity index, were 0.6% (2/334) versus 2.9% (8/276) versus 47.8% (11/23) (χ(2) = 55.977, P = 0.000), respectively. CONCLUSIONS: The SPH rarely occurs in AP, and its risk is higher in males. Its presence is strongly associated with the local conditions of pancreatitis.

Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis.

OBJECTIVES: To predict the recurrence of acute pancreatitis (AP) by constructing a radiomics model of contrast-enhanced computed tomography (CECT) at AP first attack. METHODS: We retrospectively enrolled 389 first-attack AP patients (271 in the primary cohort and 118 in the validation cohort) from three tertiary referral centers; 126 and 55 patients endured recurrent attacks in each cohort. Four hundred twelve radiomics features were extracted from arterial and venous phase CECT images, and clinical characteristics were gathered to develop a clinical model. An optimal radiomics signature was chosen using a multivariable logistic regression or support vector machine. The radiomics model was developed and validated by incorporating the optimal radiomics signature and clinical characteristics. The performance of the radiomics model was assessed based on its calibration and classification metrics. RESULTS: The optimal radiomics signature was developed based on a multivariable logistic regression with 10 radiomics features. The classification accuracy of the radiomics model well predicted the recurrence of AP for both the primary and validation cohorts (87.1% and 89.0%, respectively). The area under the receiver operating characteristic curve (AUC) of the radiomics model was significantly better than that of the clinical model for both the primary (0.941 vs. 0.712, p = 0.000) and validation (0.929 vs. 0.671, p = 0.000) cohorts. Good calibration was observed for all the models (p > 0.05). CONCLUSIONS: The radiomics model based on CECT performed well in predicting AP recurrence. As a quantitative method, radiomics exhibits promising performance in terms of alerting recurrent patients to potential precautions. KEY POINTS: • The incidence of recurrence after an initial episode of acute pancreatitis is high, and quantitative methods for predicting recurrence are lacking. • The radiomics model based on contrast-enhanced computed tomography performed well in predicting the recurrence of acute pancreatitis. • As a quantitative method, radiomics exhibits promising performance in terms of alerting recurrent patients to the potential need to take precautions.

Genetic Polymorphisms: A Novel Perspective on Acute Pancreatitis.

Acute pancreatitis (AP) is a complex disease that results in significant morbidity and mortality. For many decades, it has compelled researchers to explore the exact pathogenesis and the understanding of the pathogenesis of AP has progressed dramatically. Currently, premature trypsinogen activation and NF-κB activation for inflammation are two remarkable hypotheses for the mechanism of AP. Meanwhile, understanding of the influence of genetic polymorphisms has resulted in tremendous development in the understanding of the advancement of complex diseases. Now, genetic polymorphisms of AP have been noted gradually and many researchers devote themselves to this emerging area. In this review, we comprehensively describe genetic polymorphisms combined with the latest hypothesis of pathogenesis associated with AP.

Molecular Imaging with MRI: Potential Application in Pancreatic Cancer.

Despite the variety of approaches that have been improved to achieve a good understanding of pancreatic cancer (PC), the prognosis of PC remains poor, and the survival rates are dismal. The lack of early detection and effective interventions is the main reason. Therefore, considerable ongoing efforts aimed at identifying early PC are currently being pursued using a variety of methods. In recent years, the development of molecular imaging has made the specific targeting of PC in the early stage possible. Molecular imaging seeks to directly visualize, characterize, and measure biological processes at the molecular and cellular levels. Among different imaging technologies, the magnetic resonance (MR) molecular imaging has potential in this regard because it facilitates noninvasive, target-specific imaging of PC. This topic is reviewed in terms of the contrast agents for MR molecular imaging, the biomarkers related to PC, targeted molecular probes for MRI, and the application of MRI in the diagnosis of PC.