Mapping research trends of transarterial chemoembolization for hepatocellular carcinoma from 2012 to 2021: A bibliometric analysis

BACKGROUND

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide and the second leading cause of cancer-related deaths. Transcatheter arterial chemoembolization (TACE) is a therapy where drugs aimed to slow or halt tumor development are injected into the artery supplying for HCC tissues. A comprehensive analysis of all the articles on TACE for HCC can give us a general understanding of the progress in this field and provide guidance for future research.

AIM

To analyze and visualize scientific results and research trends in TACE treatment for HCC.

METHODS

The "Web of Science" database was used to identify articles regarding TACE for the treatment of HCC from 2012 to 2021. VOSviewer and CiteSpace were used to analyze the publications trends, collaboration between countries/insti-tutions/authors, and the co-occurrence of keywords, keyword bursts, and references.

RESULTS

A total of 5728 original articles on TACE for HCC were retrieved. Regarding the volume of publications, the total number of yearly publications showed a generally increasing trend. China had the highest number of articles, while the United States achieved the highest Hirsch index and highest number of citations. The Sun Yat-sen University in China was most prolific institution. The most active author was Park, J.W from South Korea. The Journal of Vascular and Interventional Radiology (234 articles) was the most productive journal. There is a growing trend toward international collaboration in TACE for HCC. Cluster networks of co-cited references suggested that practice guidelines and targeted therapies are an essential theme in this field. In addition, cluster analysis based on keyword co-occurrence identified the research topic "prediction of TACE treatment" as a hotspot, and propensity score matching can be used to help investigators conduct innovative studies in the future.

CONCLUSION

The results of our bibliometric analysis provide the latest trends and hot topics in TACE therapy for HCC.

Development and validation of a nomogram based on biparametric MRI PI-RADS v2.1 and clinical parameters to avoid unnecessary prostate biopsies

BACKGROUND

Biparametric MRI (bpMRI) is a faster, contrast-free, and less expensive MRI protocol that facilitates the detection of prostate cancer. The aim of this study is to determine whether a biparametric MRI PI-RADS v2.1 score-based model could reduce unnecessary biopsies in patients with suspected prostate cancer (PCa).

METHODS

The patients who underwent MRI-guided biopsies and systematic biopsies between January 2020 and January 2022 were retrospectively analyzed. The development cohort used to derive the prediction model consisted of 275 patients. Two validation cohorts included 201 patients and 181 patients from 2 independent institutions. Predictive models based on the bpMRI PI-RADS v2.1 score (bpMRI score) and clinical parameters were used to detect clinically significant prostate cancer (csPCa) and compared by analyzing the area under the curve (AUC) and decision curves. Spearman correlation analysis was utilized to determine the relationship between International Society of Urological Pathology (ISUP) grade and clinical parameters/bpMRI score.

RESULTS

Logistic regression models were constructed using data from the development cohort to generate nomograms. By applying the models to the all cohorts, the AUC for csPCa was significantly higher for the bpMRI PI-RADS v2.1 score-based model than for the clinical model in both cohorts (p < 0.001). Considering the test trade-offs, urologists would agree to perform 10 fewer bpMRIs to avoid one unnecessary biopsy, with a risk threshold of 10-20% in practice. Correlation analysis showed a strong correlation between the bpMRI score and ISUP grade.

CONCLUSION

A predictive model based on the bpMRI score and clinical parameters significantly improved csPCa risk stratification, and the bpMRI score can be used to determine the aggressiveness of PCa prior to biopsy.

A Nomogram Based on Preoperative Lipiodol Deposition after Sequential Retreatment with Transarterial Chemoembolization to Predict Prognoses for Intermediate-Stage Hepatocellular Carcinoma

(1) Background: Conventional transarterial chemoembolization (cTACE) is the mainstay treatment for patients with Barcelona Clinic Liver Cancer (BCLC) B-stage hepatocellular carcinoma (HCC). However, BCLC B-stage patients treated with cTACE represent a prognostically heterogeneous population. We aim to develop and validate a lipiodol-deposition-based nomogram for predicting the long-term survival of BCLC B-stage HCC patients after sequential cTACE. (2) Methods: In this retrospective study, 229 intermediate-stage HCC patients from two hospitals were separately allocated to a training cohort (n = 142) and a validation cohort (n = 87); these patients underwent repeated TACE (≥4 TACE sessions) between May 2010 and May 2017. Lipiodol deposition was assessed by semiautomatic volumetric measurement with multidetector computed tomography (MDCT) before cTACE and was characterized by two ordinal levels: ≤50% (low) and >50% (high). A clinical lipiodol deposition nomogram was constructed based on independent risk factors identified by univariate and multivariate Cox regression analyses, and the optimal cutoff points were obtained. Prediction models were assessed by time-dependent receiver-operating characteristic curves, calibration curves, and decision curve analysis. (3) Results: The median number of TACE sessions was five (range, 4−7) in both cohorts. Before the TACE-3 sessions, the newly constructed nomogram based on lipiodol deposition achieved desirable diagnostic performance in the training and validation cohorts with AUCs of 0.72 (95% CI, 0.69−0.74) and 0.71 (95% CI, 0.68−0.73), respectively, and demonstrated higher predictive ability compared with previously published prognostic models (all p < 0.05). The prognostic nomogram obtained good clinical usefulness in predicting the patient outcomes after TACE. (4) Conclusions: Based on each pre-TACE lipiodol deposition, two sessions are recommended before abandoning cTACE or combining treatment for patients with intermediate-stage HCC. Furthermore, the nomogram based on pre-TACE-3 lipiodol deposition can be used to predict the prognoses of patients with BCLC B-stage HCC.

Pneumatosis cystoides intestinalis: A case report

RATIONALE

Pneumatosis cystoides intestinalis (PCI) is a rare condition characterized by multiple gas-filled cysts in the intestinal wall, and can be caused by many conditions.

PATIENT CONCERNS

We reported a-69-year-old man with a long history of chronic obstructive pulmonary disease was admitted to the gastroenterology department because of alternating bowel movement and intermittent bloody stool.

DIAGNOSES

Colonoscopy revealed multiple nodular protuberances covered with normal-looking mucosa in the ascending and proximal transverse colon. Abdominal computed tomography scan and endoscopic ultrasound revealed multiple gas-filled cystic lesions in the submucosa. The diagnosis of PCI was confirmed by cyst collapse after puncturing with a fine needle.

INTERVENTIONS AND OUTCOMES

Considering that the patient had no peritonitis or other complications, conservative approaches, including oxygen inhalation and oral probiotics, were used. The patient was transferred to the anorectal department after 5days of clinical observation in good condition to further treat hemorrhoids.

LESSONS

PCI is a rare condition that may be secondary to many other diseases. Because of its atypical clinical manifestations, it can be misdiagnosed as other diseases, such as polyps, inflammatory bowel disease, and even cancer. The diagnosis of PCI depends on computed tomography, colonoscopy, and endoscopic ultrasonography. Fine-needle aspiration may be helpful in the diagnosis and treatment of PCI.

Development and Validation of a Random Forest Risk Prediction Pneumothorax Model in Percutaneous Transthoracic Needle Biopsy

BACKGROUND Computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) is an effective means for diagnosing various thoracic diseases. Pneumothorax is the most common complication, and when it becomes life-threatening, urgent medical intervention is required. The purpose of this study was to develop and validate a model that can be used to predict postoperative pneumothorax following CT-guided PTNB. MATERIAL AND METHODS We enrolled 245 patients who completed CT-guided PTNB to develop the model. A random forest (RF) model was built using 15 risk factors (15-RFs). The 7 most critical risk factors (7-RFs) were extracted by feature selection and used to build a new model. The independent external validation data contained 97 patients. Logistic regression (LR), support vector machine (SVM), and decision tree (DT) models were also developed using both 15-RFs and 7-RFs, and their performance was compared with the RF models. RESULTS The length of the aerated lung traversed was identified as the most important risk factor for developing pneumothorax, followed by angle of pleural puncture, lesion depth, lesion size, age, procedure time, and sex. The RF model demonstrated better performance in the development and validation datasets when compared with the LR, SVM, and DT based on 15-RFs and 7-RFs. According to DeLong's test for difference in ROC curves, the RF models based on the 15-RFs and 7-RFs achieved similar classification performance (P>0.05). CONCLUSIONS This study demonstrated the feasibility of using the 7-RFs RF model for predicting postoperative pneumothorax before patients undergo CT-guided PTNB.

Can machine learning-based analysis of multiparameter MRI and clinical parameters improve the performance of clinically significant prostate cancer diagnosis?

Purpose

To establish machine learning(ML) models for the diagnosis of clinically significant prostate cancer (csPC) using multiparameter magnetic resonance imaging (mpMRI), texture analysis (TA), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative analysis and clinical parameters and to evaluate the stability of these models in internal and temporal validation.

Methods

The dataset of 194 men was split into training (n = 135) and internal validation (n = 59) cohorts, and a temporal dataset (n = 58) was used for evaluation. The lesions with Gleason score ≥ 7 were defined as csPC. Logistic regression (LR), stepwise regression (SR), classical decision tree (cDT), conditional inference tree (CIT), random forest (RF) and support vector machine (SVM) models were established by combining mpMRI-TA, DCE-MRI and clinical parameters and validated by internal and temporal validation using the receiver operating characteristic (ROC) curve and Delong’s method.

Results

Eight variables were determined as important predictors for csPC, with the first three related to texture features derived from the apparent diffusion coefficient (ADC) mapping. RF, LR and SR models yielded larger and more stable area under the ROC curve values (AUCs) than other models. In the temporal validation, the sensitivity was lower than that of the internal validation (p < 0.05). There were no significant differences in specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV) and AUC (p > 0.05).

Conclusions

Each machine learning model in this study has good classification ability for csPC. Compared with internal validation, the sensitivity of each machine learning model in temporal validation was reduced, but the specificity, accuracy, PPV, NPV and AUCs remained stable at a good level. The RF, LR and SR models have better classification performance in the imaging-based diagnosis of csPC, and ADC texture-related parameters are of the highest importance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11548-021-02507-w.

Development of a computed tomography-based radiomics nomogram for prediction of transarterial chemoembolization refractoriness in hepatocellular carcinoma

BACKGROUND

Some patients with hepatocellular carcinoma (HCC) are more likely to experience disease progression despite continuous transarterial chemoembolization (TACE), which is called TACE refractoriness. At present, it is still difficult to predict TACE refractoriness, although some models/scoring systems have been developed. At present, radiological-based radiomics models have been successfully applied to predict cancer patient prognosis.

AIM

To develop and validate a computed tomography (CT)-based radiomics nomogram for the pre-treatment prediction of TACE refractoriness.

METHODS

This retrospective study consisted of a training dataset (n = 137) and an external validation dataset (n = 81) of patients with clinically/pathologically confirmed HCC who underwent repeated TACE from March 2009 to March 2016. Radiomics features were retrospectively extracted from preoperative CT images of the arterial phase. The pre-treatment radiomics signature was generated using least absolute shrinkage and selection operator Cox regression analysis. A CT-based radiomics nomogram incorporating clinical risk factors and the radiomics signature was built and verified by calibration curve and decision curve analyses. The usefulness of the CT-based radiomics nomogram was assessed by Kaplan-Meier curve analysis. We used the concordance index to conduct head-to-head comparisons of the radiomics nomogram with the other four models (Assessment for Retreatment with Transarterial Chemoembolization score; α-fetoprotein, Barcelona Clinic Liver Cancer, Child-Pugh, and Response score; CT-based radiomics signature; and clinical model). All analyses were conducted according to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis statement.

RESULTS

The median duration of follow-up was 61.3 mo (interquartile range, 25.5-69.3 mo) for the training cohort and 67.1 mo (interquartile range, 32.4-71.3 mo) for the validation cohort. The median number of TACE sessions was 4 (range, 3-7) in both cohorts. Eight radiomics features were chosen from 869 candidate features to build a radiomics signature. The CT-based radiomics nomogram included the radiomics score (hazard ratio = 3.9, 95% confidence interval: 3.1-8.8, P < 0.001) and four clinical factors and classified patients into high-risk (score > 3.5) and low-risk (score ≤ 3.5) groups with markedly different prognoses (overall survival: 12.3 mo vs 23.6 mo, P < 0.001). The accuracy of the nomogram was considerably higher than that of the other four models. The calibration curve and decision curve analyses verified the usefulness of the CT-based radiomics nomogram for clinical practice.

CONCLUSION

The newly constructed CT-based radiomics nomogram can be used for the pre-treatment prediction of TACE refractoriness, which may provide better guidance for decision making regarding further TACE treatment.

Computed tomography-based score model/nomogram for predicting technical and midterm outcomes in transjugular intrahepatic portosystemic shunt treatment for symptomatic portal cavernoma

BACKGROUND

Transjugular intrahepatic portosystemic shunt (TIPS) may be technically difficult in patients with cavernous transformation of the portal vein (CTPV). Computed tomography (CT) is widely used for assessing the situation of the portal vein and its tributaries before TIPS, and an ultrasound-based Yerdel grading system has been developed, which is deemed useful for liver transplantation. Therefore, we hypothesized that a CT-based CTPV scoring system could be useful for predicting technical and midterm outcomes in TIPS treatment for symptomatic portal cavernoma.

AIM

To investigate the clinical significance of a CT-based score model/nomogram for predicting technical success and midterm outcome in TIPS treatment for symptomatic CTPV.

METHODS

Patients with symptomatic CTPV who had undergone TIPS from January 2010 to June 2017 were retrospectively analysed. The CTPV was graded with a score of 1-4 based on contrast-CT imaging findings of the diseased vessel. Outcome measures were technical success rate, stent patency rate, and midterm survival. Cohen’s kappa statistic, the Kaplan-Meier and log-rank tests, and uni- and multivariable analyses were performed. A nomogram was constructed and verified by calibration and decision curve analysis.

RESULTS

A total of 76 patients (45 men and 31 women; mean age, 52.3 ± 14.7 years) were enrolled in the study. The inter-reader agreement (κ) of the CTPV score was 0.81. TIPS was successfully placed in 78% of patients (59/76). The independent predictor of technical success was CTPV score (odds ratio [OR] = 5.56, 95% confidence interval [CI]: 3.55-9.67, P = 0.002). The independent predictors of primary TIPS patency were CTPV score and splenectomy (OR = 9.22, 95%CI: 4.78-13.45, P = 0.009; OR = 4.67, 95%CI: 2.59-7.44, P = 0.017). The survival rates differed significantly between the TIPS success and failure groups. The clinical nomogram was made up of patient age, model for end-stage liver disease score, and CTPV score. The calibration curves and decision curve analysis verified the usefulness of the CTPV score-based nomogram for clinical practice.

CONCLUSION

TIPS should be considered a safe and feasible therapy for patients with symptomatic CTPV. Furthermore, the CT-based score model/nomogram might aid interventional radiologists in therapeutic decision-making.

Diagnostic Performance of Biparametric MRI for Detection of Prostate Cancer: A Systematic Review and Meta-Analysis

OBJECTIVE

The purpose of this study was to perform a systematic review and meta-analysis to estimate the diagnostic performance of biparametric MRI (bpMRI) for detection of prostate cancer (PCa).

MATERIALS AND METHODS

Two independent reviewers performed a systematic review of the literature published from January 2000 to July 2017 by using predefined search terms. The standard of pathologic reference was established at prostatectomy or prostate biopsy. The numbers of true- and false-positive and true- and false-negative results were extracted. The Quality Assessment of Diagnostic Accuracy Studies tool was used to assess the quality of the selected studies. Statistical analysis included pooling of diagnostic accuracy, meta-regression, subgroup analysis, head-to-head comparison, and identification of publication bias.

RESULTS

Thirty-three studies were used for general data pooling. The overall sensitivity was 0.81 (95% CI, 0.76-0.85), and overall specificity was 0.77 (95% CI, 0.69-0.84). As for clinically relevant PCa, bpMRI maintained high diagnostic value (AUC, 0.85; 95% CI, 0.82-0.88). There was no evidence of publication bias (p = 0.67). From head-to-head comparison for detection of PCa, multiparametric MRI (mpMRI) had significantly higher pooled sensitivity (0.85; 95% CI, 0.78-0.93) than did bpMRI (0.80; 95% CI, 0.71-0.90) (p = 0.01). However, the pooled specificity values were not significantly different (mpMRI, 0.77 [95% CI, 0.58-0.95]; bpMRI, 0.80 [95% CI, 0.64-0.96]; p = 0.82).

CONCLUSION

The results of this meta-analysis suggest that bpMRI has high diagnostic accuracy in the detection of PCa and maintains a high detection rate for clinically relevant PCa. However, owing to high heterogeneity among the included studies, caution is needed in applying the results of the meta-analysis.

Clinical Application of Biparametric MRI Texture Analysis for Detection and Evaluation of High-Grade Prostate Cancer in Zone-Specific Regions

OBJECTIVE

The purpose of this study was to investigate the performance of biparametric MRI texture analysis (TA) in detecting and evaluating high-grade prostate cancer in zone-specific regions.

MATERIALS AND METHODS

A retrospective study included 184 consecutively registered biopsy-naive patients in whom prostate cancer was suspected who were undergoing multiparametric prostate MRI. MR images were scored and evaluated by two readers using the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) and biparametric MRI TA in separate sessions. Interobserver agreement on PI-RADSv2 score and textural parameters of biparametric MRI was evaluated. The logistic regression model based on TA was built for different zones of the prostate. ROC analysis was used to compare the TA-based model with other parameters alone. The correlation of each parameter with Gleason score of high-grade prostate cancer was also assessed.

RESULTS

Reader reliability ranged from moderate to good for PI-RADSv2 (Cohen κ = 0.525-0.616) and from good to excellent for textural metrics (intraclass correlation coefficient, 0.745-0.925). Diagnostic performance was significantly improved by use of the TA-based model (transition zone AUC, 0.87; peripheral zone AUC, 0.89) in comparison with PI-RADSv2 and other texture parameters alone. For the transition zone, entropy had moderate to good correlation with the Gleason score of high-grade prostate cancer (r = 0.562, p = 0.004). In the peripheral zone, entropy (r = 0.614, p = 0.003) and inertia (r = 0.663, p = 0.002) had moderate to good correlations with Gleason score.

CONCLUSION

The results of this clinical study indicate that a TA-based model that includes biparametric MRI can be used for identifying high-grade prostate cancer and that specific parameters extracted from TA may be additional tools for assessing tumor aggressiveness.

Lymph nodes regression grade is a predictive marker for rectal cancer after neoadjuvant therapy and radical surgery

Neoadjuvant therapy (NT) for rectal cancer (RC) reduces primary tumors and involved lymph nodes. While a prognostic value of tumor regression grade (TRG) has been identified, involved lymph node regression grade (LRG) has not been systematically evaluated. Here, we evaluated the association of LRG with oncologic outcomes of RC patients after NT followed by radical surgery. 347 patients with locally advanced RC who received NT and then underwent radical surgery were retrospectively recruited between 2004 and 2011. Response to NT was evaluated by a 3-tier LRG and TRG based on the ratio of residual tumor to fibrosis. LRG was assessed in all patients (LRG 0, 170 patients [49.0%]; LRG 1, 100 patients [28.8%]; and LRG 2, 77 patients [22.2%]). LRG correlated with 5-year distant metastasis and 5-year disease free survival (p=0.029 and 0.023, respectively). LRG also correlated with TRG (p=0.017). We conclude that the LRG system may be an independent predictive factor of long-term oncologic outcomes of rectal cancer patients after NT and radical surgery.

A Systematic Review and Meta-Analysis of C-Arm Cone-Beam CT-Guided Percutaneous Transthoracic Needle Biopsy of Lung Nodules

Background

A systematic review and meta-analysis of all available publications was performed to evaluate the diagnostic accuracy of percutaneous transthoracic needle biopsy (PTNB) using a C-Arm Cone-Beam CT (CBCT) system in patients with lung nodules.

Material/Methods

Thedatabases of PUBMED, OVID, EBSCO, EMBASE, and China National Knowledge Infrastructure (CNKI) were systematically searched for relevant original articles on the diagnostic accuracy of CBCT-guided PTNB for the diagnosis of nodules in the lungs. Diagnostic indices including sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and diagnostic score (DS) were calculated. Moreover,summary receiver operating characteristic curves (SROC) were constructed with Stata (version 13.0), Rev Man (version 5.3), and Meta-disc (version 1.4) software. Other clinical indices such as incidence of complications were also recorded.

Results

Eight studies met the inclusion and exclusion criteria for the meta-analysis. The pooled sensitivity, specificity, PLR, NLR, DOR, DS, and SROC with 95% confidence intervals were 0.96 (0.93–0.98), 1.00 (0.91–1.00), 711.15 (9.48–53325.89), 0.04 (0.02–0.07), 16585.29 (284.88–9.7e+05), 9.72 (5.65–13.78), and 0.99 (0.97–0.99), respectively. The incidence of pneumothorax and hemorrhage was 10–29.27% and 1.22–47.25%, respectively.

Conclusions

CBCT-guided PTNB has an acceptable rate of complications and is associated with a reasonable radiation exposure. Moreover, it is a highly accurate and safe technique for the diagnosis of lung nodules and can be recommended to be used in routine clinical practice.

Developing a nomogram based on multiparametric magnetic resonance imaging for forecasting high-grade prostate cancer to reduce unnecessary biopsies within the prostate-specific antigen gray zone

BACKGROUND

Since 1980s the application of Prostate specific antigen (PSA) brought the revolution in prostate cancer diagnosis. However, it is important to underline that PSA is not the ideal screening tool due to its low specificity, which leads to the possible biopsy for the patient without High-grade prostate cancer (HGPCa). Therefore, the aim of this study was to establish a predictive nomogram for HGPCa in patients with PSA 4-10 ng/ml based on Prostate Imaging Reporting and Data System version 2 (PI-RADS v2), MRI-based prostate volume (PV), MRI-based PV-adjusted Prostate Specific Antigen Density (adjusted-PSAD) and other traditional classical parameters.

METHODS

Between January 2014 and September 2015, Of 151 men who were eligible for analysis were formed the training cohort. A prediction model for HGPCa was built by using backward logistic regression and was presented on a nomogram. The prediction model was evaluated by a validation cohort between October 2015 and October 2016 (n = 74). The relationship between the nomogram-based risk-score as well as other parameters with Gleason score (GS) was evaluated. All patients underwent 12-core systematic biopsy and at least one core targeted biopsy with transrectal ultrasonographic guidance.

RESULTS

The multivariate analysis revealed that patient age, PI-RADS v2 score and adjusted-PSAD were independent predictors for HGPCa. Logistic regression (LR) model had a larger AUC as compared with other parameters alone. The most discriminative cutoff value for LR model was 0.36, the sensitivity, specificity, positive predictive value and negative predictive value were 87.3, 78.4, 76.3, and 90.4%, respectively and the diagnostic performance measures retained similar values in the validation cohort (AUC 0.82 [95% CI, 0.76-0.89]). For all patients with HGPCa (n = 50), adjusted-PSAD and nomogram-based risk-score were positively correlated with the GS of HGPCa in PSA gray zone (r = 0.455, P = 0.002 and r = 0.509, P = 0.001, respectively).

CONCLUSION

The nomogram based on multiparametric magnetic resonance imaging (mp-MRI) for forecasting HGPCa is effective, which could reduce unnecessary prostate biopsies in patients with PSA 4-10 ng/ml and nomogram-based risk-score could provide a more robust parameter of assessing the aggressiveness of HGPCa in PSA gray zone.

Developing a new PI-RADS v2-based nomogram for forecasting high-grade prostate cancer

AIM

To establish a predictive nomogram for high-grade prostate cancer (HGPCa) in biopsy-naive patients based on the Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2), magnetic resonance imaging (MRI)-based prostate volume (PV), MRI-based PV-adjusted prostate-specific antigen density (PSAD), and other classical parameters.

MATERIAL AND METHODS

Between August 2014 and August 2015, 158 men who were eligible for analysis were included as the training cohort. A prediction model for HGPCa was built using backward logistic regression and was presented on a nomogram. The prediction model was evaluated by a validation cohort between September 2015 and March 2016 (n=89). Histology of all lesions was obtained with MRI-directed transrectal ultrasound (TRUS)-guided targeted and sectoral biopsy.

RESULTS

The multivariate analysis revealed that patient age, PI-RADS v2 score, and adjusted PSAD were independent predictors for HGPCa. The most discriminative cut-off value for the logistic regression model was 0.33; the sensitivity, specificity, positive predictive value, and negative predictive value were 83.3%, 87.4%, 88.4%, and 81.2%, respectively. The diagnostic performance measures retained similar values in the validation cohort (AUC=0.83).

CONCLUSION

The nomogram for forecasting HGPCa is effective and potentially reducing harm from unnecessary prostate biopsy and over-diagnosis.

Diffusion-Weighted Imaging for Pretreatment Evaluation and Prediction of Treatment Effect in Patients Undergoing CT-Guided Injection for Lumbar Disc Herniation

OBJECTIVE

To determine whether a change in apparent diffusion coefficient (ADC) value could predict early response to CT-guided Oxygen-Ozone (O2-O3) injection therapy in patients with unilateral mono-radiculopathy due to lumbar disc herniation.

MATERIALS AND METHODS

A total of 52 patients with unilateral mono-radiculopathy received a single intradiscal (3 mL) and periganglionic (5 mL) injection of an O2-O3 mixture. An ADC index of the involved side to the intact side was calculated using the following formula: pre-treatment ADC index = ([ADC involved side - ADC intact side] / ADC intact side) × 100. We analyzed the relationship between the pre-treatment Oswestry Disability Index (ODI) and the ADC index. In addition, the correlation between ODI recovery ratio and ADC index was investigated. The sensitivity and specificity of the ADC index for predicting response in O2-O3 therapy was determined.

RESULTS

Oswestry Disability Index and the ADC index was not significantly correlated (r = -0.125, p = 0.093). The ADC index and ODI recovery ratio was significantly correlated (r = 0.819, p < 0.001). When using 7.10 as the cut-off value, the ADC index obtained a sensitivity of 86.3% and a specificity of 82.9% for predicting successful response to therapy around the first month of follow-up.

CONCLUSION

This preliminary study demonstrates that the patients with decreased ADC index tend to show poor improvement of clinical symptoms. The ADC index may be a useful indicator to predict early response to CT-guided O2-O3 injection therapy in patients with unilateral mono-radiculopathy due to lumbar disc herniation.

CT-guided core needle biopsy of pleural lesions: evaluating diagnostic yield and associated complications

Objective

The purpose of this study was to retrospectively evaluate the diagnostic accuracy and complications of CT-guided core needle biopsy (CT-guided CNB) of pleural lesion and the possible effects of influencing factors.

Materials and Methods

From September 2007 to June 2013, 88 consecutive patients (60 men and 28 women; mean [± standard deviation] age, 51.1 ± 14.4 years; range, 19-78 years) underwent CT-guided CNB, which was performed by two experienced chest radiologists in our medical center. Out of 88 cases, 56 (63%) were diagnosed as malignant, 28 (31%) as benign and 4 (5%) as indeterminate for CNB of pleural lesions. The final diagnosis was confirmed by either histopathological diagnosis or clinical follow-up. The diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and complication rates were statistically evaluated. Influencing factors (patient age, sex, lesion size, pleural-puncture angle, patient position, pleural effusion, and number of pleural punctures) were assessed for their effect on accuracy of CT-guided CNB using univariate and subsequent multivariate analysis.

Results

Diagnostic accuracy, sensitivity, specificity, PPV, and NPV were 89.2%, 86.1%, 100%, 100%, and 67.8%, respectively. The influencing factors had no significant effect in altering diagnostic accuracy. As far as complications were concerned, occurrence of pneumothorax was observed in 14 (16%) out of 88 patients. Multivariate analysis revealed lesion size/pleural thickening as a significant risk factor (odds ratio [OR]: 8.744, p = 0.005) for occurrence of pneumothorax. Moreover, presence of pleural effusion was noted as a significant protective factor (OR: 0.171, p = 0.037) for pneumothorax.

Conclusion

CT-guided CNB of pleural lesion is a safe procedure with high diagnostic yield and low risk of significant complications.

Meta-analysis of quantitative diffusion-weighted MR imaging in differentiating benign and malignant pancreatic masses

There have been numerous studies done to explore the diagnostic performance of quantitative diffusion-weighted (DW) MR imaging to differentiate between benign and malignant pancreatic masses. However, the results have been inconsistent. We performed a meta-analysis to investigate whether DW-MR imaging can differentiate between these two diseases. Databases including MEDLINE, EMBASE and Cochrane Library were utilized to find relevant articles published between January 2001 and January 2014. A Stata version 12.0 and a Meta-Disc version 1.4 were used to describe primary results. Twelve studies with 594 patients, which fulfilled the inclusion criteria, were enrolled for the analysis. The pooled sensitivity and specificity of DW imaging was 0.91 (95% CI: 0.84, 0.95) and 0.86 (95% CI: 0.76, 0.93) respectively. The area under the curve of the summary receiver operating characteristic was 0.95 (95% CI: 0.93, 0.96). The results indicated that DW imaging might be a valuable tool for differentiating benign and malignant pancreatic masses.

Usefulness of DWI in preoperative assessment of deep myometrial invasion in patients with endometrial carcinoma: a systematic review and meta-analysis

BACKGROUND

The objective of this study was to perform a systematic review and a meta-analysis in order to estimate the diagnostic accuracy of diffusion weighted imaging (DWI) in the preoperative assessment of deep myometrial invasion in patients with endometrial carcinoma.

METHODS

Studies evaluating DWI for the detection of deep myometrial invasion in patients with endometrial carcinoma were systematically searched for in the MEDLINE, EMBASE, and Cochrane Library from January 1995 to January 2014. Methodologic quality was assessed by using the Quality Assessment of Diagnostic Accuracy Studies tool. Bivariate random-effects meta-analytic methods were used to obtain pooled estimates of sensitivity, specificity, diagnostic odds ratio (DOR) and receiver operating characteristic (ROC) curves. The study also evaluated the clinical utility of DWI in preoperative assessment of deep myometrial invasion.

RESULTS

Seven studies enrolling a total of 320 individuals met the study inclusion criteria. The summary area under the ROC curve was 0.91. There was no evidence of publication bias (P = 0.90, bias coefficient analysis). Sensitivity and specificity of DWI for detection of deep myometrial invasion across all studies were 0.90 and 0.89, respectively. Positive and negative likelihood ratios with DWI were 8 and 0.11 respectively. In patients with high pre-test probabilities, DWI enabled confirmation of deep myometrial invasion; in patients with low pre-test probabilities, DWI enabled exclusion of deep myometrial invasion. The worst case scenario (pre-test probability, 50%) post-test probabilities were 89% and 10% for positive and negative DWI results, respectively.

CONCLUSION

DWI has high sensitivity and specificity for detecting deep myometrial invasion and more importantly can reliably rule out deep myometrial invasion. Therefore, it would be worthwhile to add a DWI sequence to the standard MRI protocols in preoperative evaluation of endometrial cancer in order to detect deep myometrial invasion, which along with other poor prognostic factors like age, tumor grade, and LVSI would be useful in stratifying high risk groups thereby helping in the tailoring of surgical approach in patient with low risk of endometrial carcinoma.

Value of diffusion-weighted imaging in distinguishing pancreatic carcinoma from mass-forming chronic pancreatitis: a meta-analysis

BACKGROUND

Several previous studies have shown that diffusion-weighted imaging (DWI) can provide additional information for focal pancreatic lesions by demonstrating more restricted diffusion in solid malignant tumors than in chronic pancreatitis, which can be indicated by a decreased apparent diffusion coefficient (ADC). However, these studies have a modest sample size and convey inconclusive results. The aim of this study was to determine, in a meta-analysis, the diagnostic performance of quantitative diffusion-weighted magnetic resonance imaging in distinguishing pancreatic carcinoma from mass-forming chronic pancreatitis.

METHODS

We determined the sensitivities and specificities across studies. A summary receiver operator characteristic (sROC) curve was constructed to calculate the area under the curve (AUC).

RESULTS

The pooled sensitivity of DWI was 0.86 (95% CI: 0.80-0.91) and the pooled specificity was 0.82 (95% CI: 0.72-0.89). The AUC of the sROC was 0.91 (95% CI: 0.88-0.93).

CONCLUSIONS

DWI may be a potentially technically feasible tool for differentiating pancreatic carcinoma from mass-forming chronic pancreatitis. However, large-scale randomized control trials are necessary to assess its clinical value.

Value of diffusion-weighted imaging in distinguishing pancreatic carcinoma from mass-forming chronic pancreatitis: a meta-analysis

BACKGROUND

Several previous studies have shown that diffusion-weighted imaging (DWI) can provide additional information for focal pancreatic lesions by demonstrating more restricted diffusion in solid malignant tumors than in chronic pancreatitis, which can be indicated by a decreased apparent diffusion coefficient (ADC). However, these studies have a modest sample size and convey inconclusive results. The aim of this study was to determine, in a meta-analysis, the diagnostic performance of quantitative diffusion-weighted magnetic resonance imaging in distinguishing pancreatic carcinoma from mass-forming chronic pancreatitis.

METHODS

We determined the sensitivities and specificities across studies. A summary receiver operator characteristic (sROC) curve was constructed to calculate the area under the curve (AUC).

RESULTS

The pooled sensitivity of DWI was 0.86 (95% CI: 0.80-0.91) and the pooled specificity was 0.82 (95% CI: 0.72-0.89). The AUC of the sROC was 0.91 (95% CI: 0.88-0.93).

CONCLUSIONS

DWI may be a potentially technically feasible tool for differentiating pancreatic carcinoma from mass-forming chronic pancreatitis. However, large-scale randomized control trials are necessary to assess its clinical value.