Incidence rate of occult lymph node metastasis in clinical T1-2N0M0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study : Original research.

BACKGROUND: This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data. METHODS: By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV). RESULTS: The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients. CONCLUSIONS: The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.

Radiomics model based on contrast-enhanced computed tomography imaging for early recurrence monitoring after radical resection of AFP-negative hepatocellular carcinoma.

BACKGROUND: Although radical surgical resection is the most effective treatment for hepatocellular carcinoma (HCC), the high rate of postoperative recurrence remains a major challenge, especially in patients with alpha-fetoprotein (AFP)-negative HCC who lack effective biomarkers for postoperative recurrence surveillance. Emerging radiomics can reveal subtle structural changes in tumors by analyzing preoperative contrast-enhanced computer tomography (CECT) imaging data and may provide new ways to predict early recurrence (recurrence within 2 years) in AFP-negative HCC. In this study, we propose to develop a radiomics model based on preoperative CECT to predict the risk of early recurrence after surgery in AFP-negative HCC. PATIENTS AND METHODS: Patients with AFP-negative HCC who underwent radical resection were included in this study. A computerized tool was used to extract radiomic features from the tumor region of interest (ROI), select the best radiographic features associated with patient's postoperative recurrence, and use them to construct the radiomics score (RadScore), which was then combined with clinical and follow-up information to comprehensively evaluate the reliability of the model. RESULTS: A total of 148 patients with AFP-negative HCC were enrolled in this study, and 1,977 radiographic features were extracted from CECT, 2 of which were the features most associated with recurrence in AFP-negative HCC. They had good predictive ability in both the training and validation cohorts, with an area under the ROC curve (AUC) of 0.709 and 0.764, respectively. Tumor number, microvascular invasion (MVI), AGPR and radiomic features were independent risk factors for early postoperative recurrence in patients with AFP-negative HCC. The AUCs of the integrated model in the training and validation cohorts were 0.793 and 0.791, respectively. The integrated model possessed the clinical value of predicting early postoperative recurrence in patients with AFP-negative HCC according to decision curve analysis, which allowed the classification of patients into subgroups of high-risk and low-risk for early recurrence. CONCLUSION: The nomogram constructed by combining clinical and imaging features has favorable performance in predicting the probability of early postoperative recurrence in AFP-negative HCC patients, which can help optimize the therapeutic decision-making and prognostic assessment of AFP-negative HCC patients.

Early detection of necrosis in low-enhanced pancreatic parenchyma using contrast-enhanced computed tomography was a better predictor of clinical outcomes than pancreatic inflammation: A multicentric cohort study of severe acute pancreatitis.

OBJECTIVES: We aim to assess the early use of contrast-enhanced computed tomography (CECT) of patients with severe acute pancreatitis (SAP) using the computed tomography severity index (CTSI) in prognosis prediction. The CTSI combines quantification of pancreatic and extrapancreatic inflammation with the extent of pancreatic necrosis. METHODS: Post-hoc retrospective analysis of a large, multicentric database (44 institutions) of SAP patients in Japan. The area under the curve (AUC) of the CTSI for predicting mortality and the odds ratio (OR) of the extent of pancreatic inflammation and necrosis were calculated using multivariable analysis. RESULTS: In total, 1097 patients were included. The AUC of the CTSI for mortality was 0.65 (95 % confidence interval [CI:] [0.59-0.70]; p < 0.001). In multivariable analysis, necrosis 30-50 % and >50 % in low-enhanced pancreatic parenchyma (LEPP) was independently associated with a significant increase in mortality, with OR 2.04 and 95 % CI 1.01-4.12 (P < 0.05) and OR 3.88 and 95 % CI 2.04-7.40 (P < 0.001), respectively. However, the extent of pancreatic inflammation was not associated with mortality, regardless of severity. CONCLUSIONS: The degree of necrosis in LEPP assessed using early CECT of SAP was a better predictor of mortality than the extent of pancreatic inflammation.

Early encapsulation of peripancreatic fluid/necrosis collections on imaging (CECT) in acute pancreatitis: influential factors and clinical significance for prognosis.

BACKGROUND: To identify the factors influencing the early encapsulation of peripancreatic fluid/necrosis collections via contrast-enhanced computed tomography (CECT) and to determine the clinical significance of early encapsulation for determining the prognosis of acute pancreatitis (AP) patients. METHODS: AP patients who underwent CECT between 4 and 10 days after disease onset were enrolled in this study. Early encapsulation was defined as a continuous enhancing wall around peripancreatic fluid/necrosis collections on CECT. Univariate and multivariate logistic regression analyses were performed to assess the associations between the variables and early encapsulation. Clinical outcomes were compared between the non-encapsulation and early encapsulation groups with 1:1 propensity score matching. RESULTS: A total of 289 AP patients were enrolled. The intra-observer and inter-observer agreement were considered good (kappa statistics of 0.729 and 0.614, respectively) for identifying early encapsulation on CECT. The ratio of encapsulation increased with time, with a ratio of 12.5% on day 5 to 48.7% on day 9. Multivariate logistic regression analysis revealed that the longer time from onset to CECT examination (OR 1.55, 95% CI 1.23-1.97), high alanine aminotransferase level (OR 0.98, 95% CI 0.97-0.99), and high APACHE II score (OR 0.89, 95% CI 0.81-0.98) were found to be independent factors associated with delayed encapsulation. The incidence of persistent organ failure was significantly lower in the early encapsulation group after matching (22.4% vs 6.1%, p = 0.043). However, there was no difference in the incidence of infected pancreatic necrosis, surgical intervention, or in-hospital mortality. CONCLUSIONS: AP patients without early encapsulation of peripancreatic fluid/necrosis collections have a greater risk of persistent organ failure. In addition to longer time, the high APACHE II score and elevated alanine aminotransferase level are factors associated with delayed encapsulation.

Diagnostic value of contrast-enhanced CT in clear cell renal cell carcinoma: a systematic review and meta-analysis.

OBJECTIVE: Contrast-enhanced computed tomography (CECT) improves lesion contrast with surrounding tissues through the injection of contrast agents. This enhancement allows for more precise lesion characterization, aiding in the early diagnosis of clear cell renal cell carcinoma (ccRCC). This meta-analysis aims to assess the diagnostic efficacy of CECT in ccRCC and to provide an ideal imaging examination method for the preoperative diagnosis of ccRCC. METHODS: We conducted a comprehensive search across six major online databases: PubMed, Web of Science, Cochrane Library, WANFANG DATA, China National Knowledge Infrastructure, and Chinese BioMedical Literature Database (CBM). The objective was to collate and analyze studies that evaluate the diagnostic utility of CECT in the identification of ccRCC. Meta-disc 1.4 and Stata 16.0 were used to conduct a meta-analysis and evaluate the diagnostic accuracy of CECT for ccRCC. RESULTS: The meta-analysis included 17 relevant studies investigating the diagnostic value of CECT for ccRCC. The combined sensitivity and specificity of CECT were 0.88 (95% confidence interval: 0.83-0.91) and 0.82 (95%CI: 0.75-0.87), respectively. Positive diagnostic likelihood ratio = 4.87 (95%CI: 3.47-6.84), negative diagnostic likelihood ratio = 0.15 (95%CI: 0.11-0.21), and diagnostic odds ratio = 32.67 (95%CI: 18.21-58.61). In addition, the area under the ROC curve was 0.92 (95%CI: 0.89-0.94), indicating that CECT has a decent discriminative ability in diagnosing ccRCC. CONCLUSIONS: CECT is recognized as a highly effective imaging tool for diagnosing ccRCC. It provides valuable guidance in the preoperative assessment and planning of surgical strategies for patients with ccRCC.

The value of CT radiomics combined with deep transfer learning in predicting the nature of gallbladder polypoid lesions.

BACKGROUND: Computed tomography (CT) radiomics combined with deep transfer learning was used to identify cholesterol and adenomatous gallbladder polyps that have not been well evaluated before surgery. PURPOSE: To investigate the potential of various machine learning models, incorporating radiomics and deep transfer learning, in predicting the nature of cholesterol and adenomatous gallbladder polyps. MATERIAL AND METHODS: A retrospective analysis was conducted on clinical and imaging data from 100 patients with cholesterol or adenomatous polyps confirmed by surgery and pathology at our hospital between September 2015 and February 2023. Preoperative contrast-enhanced CT radiomics combined with deep learning features were utilized, and t-tests and least absolute shrinkage and selection operator (LASSO) cross-validation were employed for feature selection. Subsequently, 11 machine learning algorithms were utilized to construct prediction models, and the area under the ROC curve (AUC), accuracy, and F1 measure were used to assess model performance, which was validated in a validation group. RESULTS: The Logistic algorithm demonstrated the most effective prediction in identifying polyp properties based on 10 radiomics combined with deep learning features, achieving the highest AUC (0.85 in the validation group, 95% confidence interval = 0.68-1.0). In addition, the accuracy (0.83 in the validation group) and F1 measure (0.76 in the validation group) also indicated strong performance. CONCLUSION: The machine learning radiomics combined with deep learning model based on enhanced CT proves valuable in predicting the characteristics of cholesterol and adenomatous gallbladder polyps. This approach provides a more reliable basis for preoperative diagnosis and treatment of these conditions.

A deep learning approach to remove contrast from contrast-enhanced CT for proton dose calculation.

PURPOSE: Non-Contrast Enhanced CT (NCECT) is normally required for proton dose calculation while Contrast Enhanced CT (CECT) is often scanned for tumor and organ delineation. Possible tissue motion between these two CTs raises dosimetry uncertainties, especially for moving tumors in the thorax and abdomen. Here we report a deep-learning approach to generate NCECT directly from CECT. This method could be useful to avoid the NCECT scan, reduce CT simulation time and imaging dose, and decrease the uncertainties caused by tissue motion between otherwise two different CT scans. METHODS: A deep network was developed to convert CECT to NCECT. The network receives a 3D image from CECT images as input and generates a corresponding contrast-removed NCECT image patch. Abdominal CECT and NCECT image pairs of 20 patients were deformably registered and 8000 image patch pairs extracted from the registered image pairs were utilized to train and test the model. CTs of clinical proton patients and their treatment plans were employed to evaluate the dosimetric impact of using the generated NCECT for proton dose calculation. RESULTS: Our approach achieved a Cosine Similarity score of 0.988 and an MSE value of 0.002. A quantitative comparison of clinical proton dose plans computed on the CECT and the generated NCECT for five proton patients revealed significant dose differences at the distal of beam paths. V100% of PTV and GTV changed by 3.5% and 5.5%, respectively. The mean HU difference for all five patients between the generated and the scanned NCECTs was ∼4.72, whereas the difference between CECT and the scanned NCECT was ∼64.52, indicating a ∼93% reduction in mean HU difference. CONCLUSIONS: A deep learning approach was developed to generate NCECTs from CECTs. This approach could be useful for the proton dose calculation to reduce uncertainties caused by tissue motion between CECT and NCECT.

Machine learning radiomics to predict the early recurrence of intrahepatic cholangiocarcinoma after curative resection: A multicentre cohort study.

PURPOSE: Postoperative early recurrence (ER) leads to a poor prognosis for intrahepatic cholangiocarcinoma (ICC). We aimed to develop machine learning (ML) radiomics models to predict ER in ICC after curative resection. METHODS: Patients with ICC undergoing curative surgery from three institutions were retrospectively recruited and assigned to training and external validation cohorts. Preoperative arterial and venous phase contrast-enhanced computed tomography (CECT) images were acquired and segmented. Radiomics features were extracted and ranked through their importance. Univariate and multivariate logistic regression analysis was used to identify clinical characteristics. Various ML algorithms were used to construct radiomics-based models, and the predictive performance was evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis. RESULTS: 127 patients were included for analysis: 90 patients in the training set and 37 patients in the validation set. Ninety-two patients (72.4%) experienced recurrence, including 71 patients exhibiting ER. Male sex, microvascular invasion, TNM stage, and serum CA19-9 were identified as independent risk factors for ER, with the corresponding clinical model having a poor predictive performance (AUC of 0.685). Fifty-seven differential radiomics features were identified, and the 10 most important features were utilized for modelling. Seven ML radiomics models were developed with a mean AUC of 0.87 ± 0.02, higher than the clinical model. Furthermore, the clinical-radiomics models showed similar predictive performance to the radiomics models (AUC of 0.87 ± 0.03). CONCLUSION: ML radiomics models based on CECT are valuable in predicting ER in ICC.

Involvement of transverse mesocolon is associated with development of colonic fistula in patients with acute necrotizing pancreatitis.

BACKGROUND: Involvement of transverse mesocolon (TM) during acute necrotizing pancreatitis(ANP) indicates that inflammation has spread from retroperitoneal space to peritoneum. Nevertheless, the impact of TM involvement, as confirmed by contrast-enhanced computed tomography (CECT), on local complications and clinical outcomes was poorly investigated. PURPOSE: This study aimed to explore the association between CECT-diagnosed TM involvement and the development of colonic fistula in a cohort of ANP patients. METHODS: This is a single-center, retrospective cohort study involving ANP patients admitted from January 2020 to December 2020. TM involvement was diagnosed by two experienced radiologists. The study subjects were enrolled consecutively and divided into two groups: TM involvement and non-TM involvement. The primary outcome was colonic fistula during the index admission. Clinical outcomes were compared between the two groups, and the association between the TM involvement and the development of colonic fistula was assessed using multivariable analysis to adjust for baseline unbalances. RESULTS: A total of 180 patients with ANP were enrolled, and 86 (47.8%) patients had TM involvement. The incidence of the colonic fistula is significantly higher in patients with TM involvement (16.3% vs. 5.3%;p = 0.017). Moreover, the length of hospital stay was 24(13,68) days in patients with TM involvement and 15(7,31) days in those not (p = 0.001). Analysis of multivariable logistic regression revealed that TM involvement is an independent risk factor for the development of colonic fistula (odds ratio: 10.253, 95% CI: 2.206-47.650, p = 0.003). CONCLUSION: TM involvement in ANP patients is associated with development of colonic fistula in ANP patients.

Prediction model based on radiomics and clinical features for preoperative lymphovascular invasion in gastric cancer patients.

Background: We explored whether a model based on contrast-enhanced computed tomography radiomics features and clinicopathological factors can evaluate preoperative lymphovascular invasion (LVI) in patients with gastric cancer (GC) with Lauren classification. Methods: Based on clinical and radiomic characteristics, we established three models: Clinical + Arterial phase_Radcore, Clinical + Venous phase_Radcore and a combined model. The relationship between Lauren classification and LVI was analyzed using a histogram. Results: We retrospectively analyzed 495 patients with GC. The areas under the curve of the combined model were 0.8629 and 0.8343 in the training and testing datasets, respectively. The combined model showed a superior performance to the other models. Conclusion: CECT-based radiomics models can effectively predict preoperative LVI in GC patients with Lauren classification.

Added value of CE-CT radiomics to predict high Ki-67 expression in hepatocellular carcinoma.

BACKGROUND: This study aimed to develop a computed tomography (CT) model to predict Ki-67 expression in hepatocellular carcinoma (HCC) and to examine the added value of radiomics to clinico-radiological features. METHODS: A total of 208 patients (training set, n = 120; internal test set, n = 51; external validation set, n = 37) with pathologically confirmed HCC who underwent contrast-enhanced CT (CE-CT) within 1 month before surgery were retrospectively included from January 2014 to September 2021. Radiomics features were extracted and selected from three phases of CE-CT images, least absolute shrinkage and selection operator regression (LASSO) was used to select features, and the rad-score was calculated. CE-CT imaging and clinical features were selected using univariate and multivariate analyses, respectively. Three prediction models, including clinic-radiologic (CR) model, rad-score (R) model, and clinic-radiologic-radiomic (CRR) model, were developed and validated using logistic regression analysis. The performance of different models for predicting Ki-67 expression was evaluated using the area under the receiver operating characteristic curve (AUROC) and decision curve analysis (DCA). RESULTS: HCCs with high Ki-67 expression were more likely to have high serum α-fetoprotein levels (P = 0.041, odds ratio [OR] 2.54, 95% confidence interval [CI]: 1.04-6.21), non-rim arterial phase hyperenhancement (P = 0.001, OR 15.13, 95% CI 2.87-79.76), portal vein tumor thrombus (P = 0.035, OR 3.19, 95% CI: 1.08-9.37), and two-trait predictor of venous invasion (P = 0.026, OR 14.04, 95% CI: 1.39-144.32). The CR model achieved relatively good and stable performance compared with the R model (AUC, 0.805 [95% CI: 0.683-0.926] vs. 0.678 [95% CI: 0.536-0.839], P = 0.211; and 0.805 [95% CI: 0.657-0.953] vs. 0.667 [95% CI: 0.495-0.839], P = 0.135) in the internal and external validation sets. After combining the CR model with the R model, the AUC of the CRR model increased to 0.903 (95% CI: 0.849-0.956) in the training set, which was significantly higher than that of the CR model (P = 0.0148). However, no significant differences were found between the CRR and CR models in the internal and external validation sets (P = 0.264 and P = 0.084, respectively). CONCLUSIONS: Preoperative models based on clinical and CE-CT imaging features can be used to predict HCC with high Ki-67 expression accurately. However, radiomics cannot provide added value.

Deep learning-assisted diagnosis of parotid gland tumors by using contrast-enhanced CT imaging.

OBJECTIVES: Imaging interpretation of the benignancy or malignancy of parotid gland tumors (PGTs) is a critical consideration prior to surgery in view of therapeutic and prognostic values of such discrimination. This study investigates the application of a deep learning-based method for preoperative stratification of PGTs. MATERIALS AND METHODS: Using the 3D DenseNet-121 architecture and a dataset consisting of 117 volumetric arterial-phase contrast-enhanced CT scans, we developed a binary classifier for PGT distinction and tested it. We compared the discriminative performance of the model on the test set to that of 12 junior and 12 senior head and neck clinicians. Besides, potential clinical utility of the model was evaluated by measuring changes in unassisted and model-assisted performance of junior clinicians. RESULTS: The model finally reached the sensitivity, specificity, PPV, NPV, F1-score of 0.955 (95% CI 0.751-0.998), 0.667 (95% CI 0.241-0.940), 0.913 (95% CI 0.705-0.985), 0.800 (95% CI 0.299-0.989) and 0.933, respectively, comparable to that of practicing clinicians. Furthermore, there were statistically significant increases in junior clinicians' specificity, PPV, NPV and F1-score in differentiating benign from malignant PGTs when unassisted and model-assisted performance of junior clinicians were compared. CONCLUSION: Our results provide evidence that deep learning-based method may offer assistance for PGT's binary distinction.

Late adverse events to iodinated contrast media in patients treated with IL-2: a safety report from the Danish Renal Carcinoma Group (DaRenCa) study - 1.

BACKGROUND: A higher incidence of late adverse events (LAEs) to iodinated contrast media in interleukin-2 (IL-2)-treated patients has been reported. PURPOSE: To assess the incidence of LAEs after administration of iodinated contrast media in patients with metastatic renal cell carcinoma (mRCC) treated with IL-2. MATERIAL AND METHODS: Patients were randomized to treatment with IL-2 and interferon-α with/without bevacizumab in the Danish Renal Carcinoma Group study - 1. Patients underwent a computed tomography (CT) scan at baseline, at one month, at three months, and every third month until RECIST 1.1 defined progression. LAEs due to iodinated contrast media were systematically registered according to the Common Terminology Criteria for Adverse Events classification. RESULTS: In total, 89 patients were included and underwent a total of 507 contrast-enhanced CT scans. An overall incidence of 46 (9.1%) LAEs was observed in 38 of 89 (42.7%) patients; 3 LAEs at baseline (3.4% of all baseline scans), 39 (13.9%) LAEs during IL-2-based therapies, and 4 (2.9%) LAEs after termination of IL-based therapies. There was no difference in progression-free survival, overall survival, and treatment response in patients experiencing LAEs compared to patients without LAEs (P = 0.2, P = 0.5, and P = 0.6, respectively). CONCLUSION: Patients with mRCC demonstrated a higher incidence of LAEs after administration of iodinated contrast during ongoing IL-2 therapy, indicating that iodinated contrast media may cause a recall phenomenon of IL-2 toxicities in patients with mRCC. Treatment with IL-2 should not be a contraindication for contrast-enhanced scans in patients with mRCC but expertise and vigilance are required.

Microfocus computed tomography for fetal postmortem imaging: an overview.

Over the last few years, fetal postmortem microfocus computed tomography (micro-CT) imaging has increased in popularity for both diagnostic and research purposes. Micro-CT imaging could be a substitute for autopsy, particularly in very early gestation fetuses for whom autopsy can be technically challenging and is often unaccepted by parents. This article provides an overview of the latest research in fetal postmortem micro-CT imaging with a focus on diagnostic accuracy, endovascular staining approaches, placental studies and the reversibility of staining. It also discusses new methods that could prove helpful for micro-CT of larger fetuses. While more research is needed, contrast-enhanced micro-CT has the potential to become a suitable alternative to fetal autopsy. Further research using this novel imaging tool could yield wider applications, such as its practise in imaging rare museum specimens.

A deep learning and radiomics fusion model based on contrast-enhanced computer tomography improves preoperative identification of cervical lymph node metastasis of oral squamous cell carcinoma.

OBJECTIVES: In this study, we constructed and validated models based on deep learning and radiomics to facilitate preoperative diagnosis of cervical lymph node metastasis (LNM) using contrast-enhanced computed tomography (CECT). MATERIALS AND METHODS: CECT scans of 100 patients with OSCC (217 metastatic and 1973 non-metastatic cervical lymph nodes: development set, 76 patients; internally independent test set, 24 patients) who received treatment at the Peking University School and Hospital of Stomatology between 2012 and 2016 were retrospectively collected. Clinical diagnoses and pathological findings were used to establish the gold standard for metastatic cervical LNs. A reader study with two clinicians was also performed to evaluate the lymph node status in the test set. The performance of the proposed models and the clinicians was evaluated and compared by measuring using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). RESULTS: A fusion model combining deep learning with radiomics showed the best performance (ACC, 89.2%; SEN, 92.0%; SPE, 88.9%; and AUC, 0.950 [95% confidence interval: 0.908-0.993, P < 0.001]) in the test set. In comparison with the clinicians, the fusion model showed higher sensitivity (92.0 vs. 72.0% and 60.0%) but lower specificity (88.9 vs. 97.5% and 98.8%). CONCLUSION: A fusion model combining radiomics and deep learning approaches outperformed other single-technique models and showed great potential to accurately predict cervical LNM in patients with OSCC. CLINICAL RELEVANCE: The fusion model can complement the preoperative identification of LNM of OSCC performed by the clinicians.

Differential diagnosis and prognosis of small renal masses: association with collateral vessels detected using contrast-enhanced computed tomography.

BACKGROUND: Active surveillance (AS) is one of the treatment methods for patients with small renal masses (SRMs; < 4 cm), including renal cell carcinomas (RCCs). However, some small RCCs may exhibit aggressive neoplastic behaviors and metastasize. Little is known about imaging biomarkers capable of identifying potentially aggressive small RCCs. Contrast-enhanced computed tomography (CECT) often detects collateral vessels arising from neoplastic angiogenesis in RCCs. Therefore, this study aimed to evaluate the association between SRM differential diagnoses and prognoses, and the detection of collateral vessels using CECT. METHODS: A total of 130 consecutive patients with pathologically confirmed non-metastatic SRMs (fat-poor angiomyolipomas [fpAMLs; n = 7] and RCCs [n = 123]) were retrospectively enrolled. Between 2011 and 2019, SRM diagnoses in these patients were confirmed after biopsy or surgical resection. All RCCs were surgically resected. Regardless of diameter, a collateral vessel (CV) was defined as any blood vessel connecting the tumor from around the kidney using CECT. First, we analyzed the role of CV-detection in differentiating between fpAML and RCC. Then, we evaluated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of RCC diagnosis based on CV-detection using CECT. We also assessed the prognostic value of CV-detection using the Fisher exact test, and Kaplan-Meier method and the log-rank test. RESULTS: The sensitivity, specificity, PPV, NPV, and accuracy of CV-detection for the diagnosis of small RCCs was 48.5, 45.5, 100, 100, and 9.5% respectively. Five of 123 (4.1%) patients with RCC experienced recurrence. CV-detection using CECT was the only significant factor associated with recurrence (p = 0.0177). Recurrence-free survival (RFS) was significantly lower in patients with CV compared with in those without CV (5-year RFS 92.4% versus 100%, respectively; p = 0.005). In addition, critical review of the CT images revealed the CVs to be continuous with the venous vessels around the kidney. CONCLUSIONS: The detection of CVs using CECT is useful for differentiating between small fpAMLs and RCCs. CV-detection may also be applied as a predictive parameter for small RCCs prone to recurrence after surgical resection. Moreover, AS could be suitable for small RCCs without CVs.

Hematologic and inflammatory parameters for determining severity of odontogenic infections at admission: a retrospective study.

BACKGROUND: Severe odontogenic infections in the head and neck region, especially necrotizing soft tissue infection (NSTI) and deep neck abscess, are potentially fatal due to their delayed diagnosis and treatment. Clinically, it is often difficult to distinguish NSTI and deep neck abscess in its early stage from cellulitis, and the decision to perform contrast-enhanced computed tomography imaging for detection is often a challenge. This retrospective case-control study aimed to examine the utility of routine blood tests as an adjunctive diagnostic tool for NSTI in the head and neck region and deep neck abscesses. METHODS: Patients with severe odontogenic infections in the head and neck region that required hospitalization were classified into four groups. At admission, hematologic and inflammatory parameters were calculated according to the blood test results. In addition, a decision tree analysis was performed to detect NSTI and deep neck abscesses. RESULTS: There were 271 patients, 45.4% in Group I (cellulitis), 22.5% in Group II (cellulitis with shallow abscess formation), 27.3% in Group III (deep neck abscess), and 4.8% in Group IV (NSTI). All hematologic and inflammatory parameters were higher in Groups III and IV. The Laboratory Risk Indicator for Necrotizing Fasciitis score, with a cut-off value of 6 and C-reactive protein (CRP) + the neutrophil-to-lymphocyte ratio (NLR), with a cut-off of 27, were remarkably useful for the exclusion diagnosis for Group IV. The decision tree analysis showed that the systemic immune-inflammation index (SII) of ≥ 282 or < 282 but with a CRP + NLR of ≥ 25 suggests Group III + IV and the classification accuracy was 89.3%. CONCLUSIONS: Hematologic and inflammatory parameters calculated using routine blood tests can be helpful as an adjunctive diagnostic tool in the early diagnosis of potentially fatal odontogenic infections. An SII of ≥ 282 or < 282 but with a CRP + NLR of ≥ 25 can be useful in the decision-making for performing contrast-enhanced computed tomography imaging.

Morphologic Severity of Acute Pancreatitis on Imaging Is Independently Associated with Opioid Dose Requirements in Hospitalized Patients.

BACKGROUND: Prior studies have evaluated clinical characteristics associated with opioid dose requirements in hospitalized patients with acute pancreatitis (AP) but did not incorporate morphologic findings on CT imaging. AIMS: We sought to determine whether morphologic severity on imaging is independently associated with opioid dose requirements in AP. METHODS: Adult inpatients with a diagnosis of AP from 2006 to 2017 were reviewed. The highest modified CT severity index (MCTSI) score and the daily oral morphine equivalent (OME) for each patient over the first 7 days of hospitalization were used to grade the morphologic severity of AP and calculate mean OME per day(s) of treatment (MOME), respectively. Multiple regression analysis was used to evaluate the association of MOME with MCSTI. RESULTS: There were 249 patients with AP, of whom 196 underwent contrast-enhanced CT. The mean age was 46 ± 13.6 years, 57.9% were male, and 60% were black. The mean MOME for the patient cohort was 60 ± 52.8 mg/day. MCTSI (ß = 3.5 [95% CI 0.3, 6.7], p = 0.03), early hemoconcentration (ß = 21 [95% CI 4.6, 39], p = 0.01) and first episode of AP (ß = - 17 [95% CI - 32, - 2.7], p = 0.027) were independently associated with MOME. Among the 19 patients undergoing ≥ 2 CT scans, no significant differences in MOME were seen between those whose MCTSI score increased (n = 12) versus decreased/remained the same (n = 7). CONCLUSION: The morphologic severity of AP positively correlated with opioid dose requirements. No difference in opioid dose requirements were seen between those who did versus those who did not experience changes in their morphologic severity.

Can lymphovascular invasion be predicted by contrast-enhanced CT imaging features in patients with esophageal squamous cell carcinoma? A preliminary retrospective study.

BACKGROUND: To investigate the value of contrast-enhanced CT (CECT)-derived imaging features in predicting lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC) patients. METHODS: One hundred and ninety-seven patients with postoperative pathologically confirmed esophageal squamous cell carcinoma treated in our hospital between January 2017 and January 2019 were enrolled in our study, including fifty-nine patients with LVI and one hundred and thirty-eight patients without LVI. The CECT-derived imaging features of all patients were analyzed. The CECT-derived imaging features were divided into quantitative features and qualitative features. The quantitative features consisted of the CT attenuation value of the tumor (CTVTumor), the CT attenuation value of the normal esophageal wall (CTVNormal), the CT attenuation value ratio of the tumor-to-normal esophageal wall (TNR), the CT attenuation value difference between the tumor and normal esophageal wall (ΔTN), the maximum thickness of the tumor measured by CECT (Thickness), the maximum length of the tumor measured by CECT (Length), and the gross tumor volume measured by CECT (GTV). The qualitative features consisted of an enhancement pattern, tumor margin, enlarged blood supply or drainage vessels to the tumor (EVFDT), and tumor necrosis. For the clinicopathological characteristics and CECT-derived imaging feature analysis, the chi-squared test was used for categorical variables, the Mann-Whitney U test was used for continuous variables with a nonnormal distribution, and the independent sample t-test was used for the continuous variables with a normal distribution. The trend test was used for ordinal variables. The association between LVI status and CECT-derived imaging features was analyzed by univariable logistic analysis, followed by multivariable logistic regression and receiver operating characteristic (ROC) curve analysis. RESULTS: The CTVTumor, TNR, ΔTN, Thickness, Length, and GTV in the group with LVI were higher than those in the group without LVI (P < 0.05). A higher proportion of patients with heterogeneous enhancement pattern, irregular tumor margin, EVFDT, and tumor necrosis were present in the group with LVI (P < 0.05). As revealed by the univariable logistic analysis, the CECT-derived imaging features, including CTVTumor, TNR, ΔTN and enhancement pattern, Thickness, Length, GTV, tumor margin, EVFDT, and tumor necrosis were associated with LVI status (P < 0.05). Only the TNR (OR 8.655; 95% CI 2.125-37.776), Thickness (OR 6.531; 95% CI 2.410-20.608), and tumor margin (OR 4.384; 95% CI 2.004-9.717) were independent risk factors for LVI in the multivariable logistic regression analysis. The ROC curve analysis incorporating the above three CECT-derived imaging features showed that the area under the curve obtained by the multivariable logistic regression model was 0.820 (95% CI 0.754-0.885). CONCLUSION: The CECT-derived imaging features, including TNR, Thickness, tumor margin, and their combination, can be used as predictors of LVI status for patients with ESCC.

Nose-shaped mass in the ascending aorta.

A 79-year-old man was referred for urgent coronary artery bypass grafting. Contrast-enhanced computed tomography revealed an atypically nose-shaped contrast defect, which intraoperatively turned out to be an atheromatous plaque. Its preoperative detection allowed us to prevent an adverse cerebral event. This case highlights that a thorough preoperative work-up should ideally include a CT angiography, in patients where atherosclerotic changes are to be expected.