Ultrasensitive Method Enables Liquid Biopsy for the Precise Detection of Circulating MicroRNAs.

Due to invasive and serial examinations of bioactive molecules, liquid biopsy (LB) has emerged as a rapid and reliable solution for early disease detection and monitoring. Developing portable devices with high specificity and sensitivity for LB is highly valuable. To realize a generalized approach to increase the sensitivity of LB, we developed an ultrasensitive diagnostic biochip based on the amplification of miRNA by recombinase polymerase amplification and the significant enhancement of fluorescence signals by photonic crystal (PC) materials. The PCs-RPA biochip has a detection limit as low as 0.24 aM, a wide linear range of 8 orders of magnitude, and excellent specificity. Such advantages realize the accurate detection of circulating miRNAs with very low content in clinical serum samples for the precise diagnosis of nonsmall cell lung cancer.

18 F-FDG PET/CT Imaging of Isolated Myeloid Sarcoma in the Right Humerus.

ABSTRACT: Myeloid sarcoma is a neoplastic mass formed by the infiltration of primitive or immature myeloid cells into organs and tissues outside the bone marrow. It may occur before, at the same time, or manifest as the recurrence of acute myeloid leukemia, myelodysplastic syndromes, and chronic myeloproliferative syndromes. It may involve any organ or tissue, including skin, soft tissue, lymph nodes, and gastrointestinal tract and bone. Isolated humerus involvement is extremely rare. Herein, we present the FDG PET/CT findings of a rare case of isolate myeloid sarcoma in the right humerus, which showed only increased bone density with moderate FDG uptake.

Correlation between carcinoembryonic antigen (CEA) expression and EGFR mutations in non-small-cell lung cancer: a meta-analysis.

OBJECTIVES: The purpose of this meta-analysis was to investigate the relationship between serum carcinoembryonic antigen (CEA) expression and epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC). METHODS: Databases such as PubMed, Cochrane, EMBASE and Google Scholar were systematically searched to identify studies assessing the association of serum CEA expression with EGFR mutations. Across 19 studies, 4168 patients were included between CEA expression and EGFR mutations odds ratio (OR) conjoint analysis of correlations. RESULTS: Compared with CEA-negative NSCLC, CEA-positive tumors had an increased EGFR mutation rate (OR = 1.85, 95% confidence interval: 1.48-2.32, P < 0.00001). This association was observed in both stage IIIB/IV patients (OR = 1.60, 95% CI: 1.18-2.15, P = 0.002) and stage I-IIIA (OR = 1.67, 95% CI: 1.01-2.77, P = 0.05) patients. In addition, CEA expression was associated with exon 19 (OR = 1.97, 95% CI: 1.25-3.11, P = 0.003) and exon 21 (OR = 1.51, 95% CI: 1.07-2.12, P = 0.02) EGFR mutations. In ADC pathological type had also showed the correlation (OR = 1.84, 95% CI: 1.31-2.57, P = 0.0004). CONCLUSIONS: This meta-analysis indicated that serum CEA expression was associated with EGFR mutations in NSCLC patients. The results of this study suggest that CEA level may play a predictive role in the EGFR mutation status of NSCLC patients. Detecting serum CEA expression levels can give a good suggestion to those patients who are confused about whether to undergo EGFR mutation tests. Moreover, it may help better plan of the follow-up treatment.

Hotspots and frontiers in PSMA research for prostate cancer: a bibliometric and visualization analysis over the past 20 years.

BACKGROUND: Prostate-specific membrane antigen (PSMA)-targeted imaging and therapy have significantly changed the management of patients with prostate cancer (PCa) at different disease stages. This advancement has attracted the attention of scholars, leading to a prolific output of scholarly publications. This study comprehensively outlines the knowledge framework associated with PSMA-based diagnosis and treatment of PCa through the application of bibliometric analysis, and discusses the potential research trends and foci. METHODS: Articles and reviews related to PSMA for prostate cancer from 2003 to 2022 were retrieved from Web of Science Core Collection. VOSviewer, Citespace, and R-bibliometrix were primarily employed to execute and visually represent co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references and keywords in this field. RESULTS: A total of 3830 papers were included. The papers on the field of PSMA-based PCa therapy and imaging had been continuously increased since 2003, but the rate has slowed from 2020. The United States made the largest contribution in this field, in terms of publications 997 (26.03%), H-index (110) and total citations (53,167 times). We identified the most productive institution were Technical University of Munich, and Australian institutions had become very active in recent years. Journal of Nuclear Medicine was the most prominent journal in this field. Professors Matthias Eiber and Martin G Pomper made great achievements, while Ali Afshar-Oromieh was the most co-cited author. According to the result of keywords and topics analysis, "ga-68 labeled psma ligand", "radiation dosimetry" and "HBED-CC" were major research areas in the near future, while "Extended pelvic lymph node dissection" was considered to be the future research foci. CONCLUSIONS: The field of psma-based PCa therapy and imaging is in the stage of vigorous development and has a bright prospect. The United States and Germany have achieved outstanding results in this area, while Australia has recently developed rapidly. It is foreseeable that more research foci will be lied in the early detection of pelvic lymph nodes and the multimodal imaging-guided surgery.

Electrochemiluminescent CdS Quantum Dots Biosensor for Cancer Mutation Detection at Different Positions on Linear DNA Analytes.

PCR-based techniques routinely employed for the detection of mutated linear DNA molecules, including circulating tumor DNA (ctDNA), require large nucleotide sections on both sides of the mutation for primer annealing. This means that DNA fragments with a mutation positioned closer to the extremities are unlikely to be detected. Thus, sensors capable of recognizing linear DNA with characteristic mutations closer to the ends would be advantageous over the state-of-the-art approaches. Here, an electrochemiluminescence-resonance energy transfer (ECL-RET) biosensor comprising capped CdS quantum dots and hairpin DNA probes labeled with Au nanoparticles was developed for the detection of epidermal growth factor receptor (EGFR) ctDNA carrying the critical T790M lung cancer mutation. The ECL-RET system detected different DNA molecules including single-stranded 18-nucleotides (nt) and 40-nt as well as double-stranded 100-nt with the single nucleotide polymorphism (SNP) coding for T790M located either in the middle or only 7 nt from one end. For all target DNA, the sensor's limits of detection (LODs) were in the aM range, with excellent selectivity. It was the case of 100-nt target linear ctDNA fragments with LODs of 8.1 and 3.4 aM when the EGFR T790M SNP was either in the middle or at the end, respectively. These results show that ECL-RET systems can sense mutations in DNA fragments that would remain undetected by standard techniques.

Risk factors for air embolism following computed tomography-guided percutaneous transthoracic needle biopsy: a systematic review and meta-analysis

To quantitatively analyze the risk factors for air embolism following computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) and qualitatively review their characteristics. The databases of PubMed, Embase, Web of Science, Wanfang Data, VIP information, and China National Knowledge Infrastructure were searched on January 4, 2021, for studies reporting the occurrence of air embolisms following CT-guided PTNB. After study selection, data extraction, and quality assessment, the characteristics of the included cases were qualitatively and quantitatively analyzed. A total of 154 cases of air embolism following CT-guided PTNB were reported. The reported incidence was 0.06% to 4.80%, and 35 (22.73%) patients were asymptomatic. An unconscious or unresponsive state was the most common symptom (29.87%). Air was most commonly found in the left ventricle (44.81%), and 104 (67.53%) patients recovered without sequelae. Air location (P < 0.001), emphysema (P = 0.061), and cough (P = 0.076) were associated with clinical symptoms. Air location (P = 0.015) and symptoms (P < 0.001) were significantly associated with prognosis. Lesion location [odds ratio (OR): 1.85, P = 0.017], lesion subtype (OR: 3.78, P = 0.01), pneumothorax (OR: 2.16, P = 0.003), hemorrhage (OR: 3.20, P < 0.001), and lesions located above the left atrium (OR: 4.35, P = 0.042) were significant risk factors for air embolism. Based on the current evidence, a subsolid lesion, being located in the lower lobe, the presence of pneumothorax or hemorrhage, and lesions located above the left atrium were significant risk factors for air embolism.

Global burden of prostate cancer attributable to smoking among males in 204 countries and territories, 1990-2019.

INTRODUCTION: Understanding the latest global spatio-temporal pattern of prostate cancer burden attributable to smoking can help guide effective global health policy. This study aims to elucidate the trends in smoking-related prostate cancer from 1990 to 2019 using Global Burden of Disease (GBD) 2019 study data. METHODS: Data on prostate cancer attributable to smoking were extracted from Global Burden of Disease Study (GBD) 2019. The numbers and age-standardized rates on smoking-related prostate cancer mortality (ASMR) and disability-adjusted life years (ASDR) were analyzed by year, age, region, country, and socio-demographic index (SDI) level. Estimated annual percentage change (EAPC) was calculated to evaluate the temporal trends of ASMR and ASDR from 1990 to 2019. RESULTS: Of all prostate cancer deaths and DALYs globally in 2019, 6% and 6.6% were attributable to smoking, which contributed to 29,298 (95% CI 12,789 to 46,609) deaths and 571,590 (95% CI 253,490 to 917,820) disability-adjusted life-years (DALYs) in 2019. The number of smoking-related deaths and DALYs showed an upward trend, increasing by half from 1990 to 2019, while ASMR and ASDR declined in five sociodemographic indexes (SDI) regions, with the fastest decline in high SDI regions. For geographical regions, Western Europe and East Asia were the high-risk areas of prostate cancer deaths and DALYs attributable to smoking, among which China and the United States were the countries with the heaviest burden. The ASMR has decreased in all age groups, with the fastest decrease occurring in 75-79 years old. The ASMR or ASDR tended to increase in countries with the lowest SDI, but declined in countries with the highest SDI. The EAPC in ASMR or ASDR was highly negatively correlated with Human Development Index (HDI) in 2019, with coefficients 0.46. CONCLUSION: The number of smoking-related prostate cancer deaths and DALYs continued to increase globally, whereas its ASMR and ASDR have been decreasing. This substantial progress is particularly significant in developed regions and vary across geographic regions. Medical strategies to prevent and reduce the burden should be adjusted and implemented based on country-specific disease prevalence.

Predicting N2 lymph node metastasis in presurgical stage I-II non-small cell lung cancer using multiview radiomics and deep learning method.

BACKGROUND: Accurate diagnosis of N2 lymph node status of the resectable stage I-II non-small cell lung cancer (NSCLC) before surgery is crucial, while there is lack of corresponding method clinically. PURPOSE: To develop and validate a model to quantitively predict the N2 lymph node metastasis in presurgical clinical stage I-II NSCLC using multiview radiomics and deep learning method. METHODS: In this study, 140 NSCLC patients were enrolled and randomly divided into training and test sets. Univariate and multiple analysis method were used step by step to establish the clinical model; Then a multiview radiomics modeling scheme was designed, in which the optimal input feature set was determined by subcategorizing radiomics features (C1: original; C2: LoG and C3: wavelet) and comparison of corresponding radiomics model. The minimum-redundancy maximum-relevance (mRMR) selection and the least absolute shrinkage and selection operator (LASSO) algorithm were used for the feature selection and construction of each radiomics model (Rad). Next, an end-to-end ResNet18 architecture and transfer learning techniques were designed to construct a deep learning model (DL). Subsequently, the screened clinical risk factors and constructed Rad and DL models were combined and compared and a nomogram was constructed. Finally, the diagnostic performance of all constructed models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Delong test, Calibration analysis, Hosmer-Lemeshow test, and decision curves, respectively. RESULTS: Carcinoma embryonic antigen (CEA) level and spiculation were screened to make up the Clinical model, while seven radiomics features in the optimal input feature set C2 + C3 were selected to construct the Rad. DL was constructed by training on 1.8 million natural images and small sample data of our N2 lymph node volume of interest (VOI) images. Except for the Clinical model, all other models showed good predictive accuracy and consistency in both training set and test set. DL (area under curve (AUC): 0.83) was better than Rad (AUC: 0.76) in predictive accuracy, but their difference was not significant (p = 0.45). The combined models showed better diagnostic performance than the model only clinical or image risk factors were used (AUC for Clinical, Rad + DL, Rad + Clinical, DL + Clinical, and Rad + DL + Clinical were respectively 0.66, 0.86, 0.82, 0.86, and 0.88). Finally, the Rad + DL + Clinical model with the best diagnostic performance was selected to draw the final nomogram for clinical use. CONCLUSION: This study proposes a nomogram based on multiview radiomics, deep learning, and clinical features that can be efficiently used to quantitively predict presurgical N2 diseases in patients with clinical stage I-II NSCLC.

Evaluation of volume changes following lateral window maxillary sinus floor elevation using Minics software.

Background: Significant volume changes at the site of lateral window maxillary sinus floor elevation have been reported 6 months postoperatively, with stabilization thereafter. However, at present, there is no consensus regarding the gold standard to assess the shape and volume of the bone graft site after implantation. This study aimed to analyze volume changes in the lateral window maxillary sinus floor elevation region using Minics software. Methods: We analyzed 40 patients who underwent lateral window maxillary sinus floor elevation surgery at the Stomatology Department of Binhaiwan Central Hospital, Dongguan, China between 2017 and 2020. Twenty patients underwent lateral window maxillary sinus floor elevation with simultaneous implantation, while 20 patients underwent lateral window maxillary sinus floor elevation with delayed implantation 6 months later. Minics software was used for three-dimensional analysis of the elevation site on the day after surgery (T1) and 6 months after surgery (T2) in both groups. Results: The elevation site volume was reduced 6 months after lateral window maxillary sinus floor elevation. The differences in the length, width, height from the tip, and volume of the implant between T1 and T2 were statistically significant. Conclusions: The length, width, height, and volume of the lateral window maxillary sinus floor elevation region were reduced using Minics software, suggesting that the volume of the elevation site should be enlarged as much as possible during the operation to prevent volume shrinkage.

Comparison of CT-Guided Core Needle Biopsy in Pulmonary Ground-Glass and Solid Nodules Based on Propensity Score Matching Analysis.

Purpose: To compare the diagnostic accuracy and safety of computed tomography (CT)-guided core needle biopsy (CNB) between pulmonary ground-glass and solid nodules using propensity score matching (PSM) method and determine the relevant risk factors. Methods: This was a single-center retrospective cohort study using data from 665 patients who underwent CT-guided CNB of pulmonary nodules in our hospital between May 2019 and May 2021, including 39 ground-glass nodules (GGNs) and 626 solid nodules. We used a 1:4 PSM analysis to compared the diagnostic yields and complications rates of CT-guided CNB between 2 groups. Results: After PSM, 170 cases involved in the comparison (34 GGNs vs 136 solid nodules) were randomly matched (1:4) by patient demographics, clinical history, lesion characteristics, and procedure-related factors. There was no statistically significant difference in the diagnostic yields and complications rates between 2 groups. Significant pneumothorax incidence increase was noted at small lesion size, deep lesion location, and traversing interlobar fissure (P < .05). Post-biopsy hemorrhage was a protective factor for pneumothorax (P < .05). The size/proportion of consolidation of GGN did not influence the diagnostic accuracy and complication incidence (P > .05). Conclusions: The accuracy and safety of CT-guided CNB were comparable for ground-glass and solid nodules and the size/proportion of consolidation of GGN may be not a relevant risk factor. The biopsy should avoid traversing interlobar fissure as far as possible. Smaller lesion size and deeper lesion location may lead to higher pneumothorax rate and post-biopsy hemorrhage may be a protective factor for pneumothorax.

CT-Guided Percutaneous Core Needle Biopsy in Typing and Subtyping Lung Cancer: A Comparison to Surgery.

Background: Lung cancer histologic types and subtypes are closely associated with treatment selection and prognosis prediction. In this study, we aim to evaluate the suitability of computed tomography-guided percutaneous core needle biopsy (CT-guided PCNB) in typing and subtyping lung cancer. Methods: From August 2007 to December 2015, the patients who underwent CT-guided PCNB and lung lesion resection were retrospectively collected and analyzed. All pathological sections were reassessed in consensus by 2 junior pathologists (group A) and 2 senior pathologists (group B), respectively. All cases were diagnosed on 3 levels: first, malignant and benign diagnosis; second, histologic types diagnosis; and third, histologic subtypes diagnosis and compared with surgery results. Pearson chi-square test was used to compare the differences of diagnostic accuracy between pathologists in group A and group B. Results: A cohort of 160 patients was included in this study. On the first level, the diagnostic accuracy was 90.63% (group A) and 94.38% (group B), (P = .20). On the second level, the diagnostic accuracy for malignant lesions, adenocarcinoma (ADC), and squamous cell carcinoma (SQC) were, respectively, 72.66%, 84.72%, and 69.05% (group A) and 76.98%, 90.28%, and 71.43% (group B) (P > .05). On the third level, the diagnostic accuracy for ADC subtypes were 26.39% (group A) and 55.56% (group B) (P < 0.01); for SQC subtypes were 28.57% (group A) and 38.10% (group B) (P = 0.36). Conclusion: Small specimens obtained by CT-guided PCNB were suitable for the diagnosis of lung cancer histologic types, which may contribute to the selection of a suitable treatment strategy for the unresectable lung cancers. While for the diagnosis of subtypes, discussion with experienced pathologists was recommended.

A Predictive Model to Differentiate Between Second Primary Lung Cancers and Pulmonary Metastasis.

RATIONALE AND OBJECTIVES: To develop and validate a nomogram for differentiating second primary lung cancers (SPLCs) from pulmonary metastases (PMs). MATERIALS AND METHODS: A total of 261 lesions from 253 eligible patients were included in this study. Among them, 195 lesions (87 SPLCs and 108 PMs) were used in the training cohort to establish the diagnostic model. Twenty-one clinical or imaging features were used to derive the model. Sixty-six lesions (32 SPLCs and 34 PMs) were included in the validation set. RESULTS: After analysis, age, lesion distribution, type of lesion, air bronchogram, contour, spiculation, and vessel convergence sign were considered to be significant variables for distinguishing SPLCs from PMs. Subsequently, these variables were selected to establish a nomogram. The model showed good distinction in the training set (area under the curve = 0.97) and the validation set (area under the curve = 0.92). CONCLUSION: This study found that the nomogram calculated from clinical and radiological characteristics could accurately classify SPLCs and PMs.

An electrochemiluminescence resonance energy transfer biosensor for the detection of circulating tumor DNA from blood plasma.

A liquid biopsy is a noninvasive approach for detecting double-stranded circulating tumor DNA (ctDNA) of 90-320 nucleotides in blood plasma from patients with cancer. Most techniques employed for ctDNA detection are time consuming and require expensive DNA purification kits. Electrochemiluminescence resonance energy transfer (ECL-RET) biosensors exhibit high sensitivity, a wide response range, and are promising for straightforward sensing applications. Until now, ECL-RET biosensors have been designed for sensing short single-stranded oligonucleotides of less than 45 nucleotides. In this work, an ECL-RET biosensor comprising graphitic carbon nitride quantum dots was assessed for the amplification-free detection in the blood plasma of DNA molecules coding for the EGFR L858R mutation, which is associated with non-small-cell lung cancer. Following a low-cost pre-treatment, the highly specific ECL-RET biosensor quantified double-stranded EGFR L858R DNA of 159 nucleotides diluted into the blood within a linear range of 0.01 fM to 1 pM, demonstrating its potential for noninvasive biopsies.

Low-Intensity Focused Ultrasound-Augmented Multifunctional Nanoparticles for Integrating Ultrasound Imaging and Synergistic Therapy of Metastatic Breast Cancer.

The metastasis of breast cancer is believed to have a negative effect on its prognosis. Benefiting from the remarkable deep-penetrating and noninvasive characteristics, sonodynamic therapy (SDT) demonstrates a whole series of potential leading to cancer treatment. To relieve the limitation of monotherapy, a multifunctional nanoplatform has been explored to realize the synergistic treatment efficiency. Herein, we establish a novel multifunctional nano-system which encapsulates chlorin e6 (Ce6, for SDT), perfluoropentane (PFP, for ultrasound imaging), and docetaxel (DTX, for chemotherapy) in a well-designed PLGA core-shell structure. The synergistic Ce6/PFP/DTX/PLGA nanoparticles (CPDP NPs) featured with excellent biocompatibility and stability primarily enable its further application. Upon low-intensity focused ultrasound (LIFU) irradiation, the enhanced ultrasound imaging could be revealed both in vitro and in vivo. More importantly, combined with LIFU, the nanoparticles exhibit intriguing antitumor capability through Ce6-induced cytotoxic reactive oxygen species as well as DTX releasing to generate a concerted therapeutic efficiency. Furthermore, this treating strategy actives a strong anti-metastasis capability by which lung metastatic nodules have been significantly reduced. The results indicate that the SDT-oriented nanoplatform combined with chemotherapy could be provided as a promising approach in elevating effective synergistic therapy and suppressing lung metastasis of breast cancer.

Invasive Prediction of Ground Glass Nodule Based on Clinical Characteristics and Radiomics Feature.

Objective: To explore the diagnostic value of CT radiographic images and radiomics features for invasive classification of lung adenocarcinoma manifesting as ground-glass nodules (GGNs) in computer tomography (CT). Methods: A total of 312 GGNs were enrolled in this retrospective study. All GGNs were randomly divided into training set (n = 219) and test set (n = 93). Univariate and multivariate logistic regressions were used to establish a clinical model, while the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct the radiomics model. A combined model was finally built by combining these two models. The performance of these models was assessed in both training and test set. A combined nomogram was developed based on the combined model and evaluated with its calibration curves and C-index. Results: Diameter [odds ratio (OR), 1.159; p ＜ 0.001], lobulation (OR, 2.953; p = 0.002), and vascular changes (OR, 3.431; p ＜ 0.001) were retained as independent predictors of the invasive adenocarcinoma (IAC) group. Eleven radiomics features were selected by mRMR and LASSO method to established radiomics model. The clinical model and radiomics mode showed good predictive ability in both training set and test set. When two models were combined, the diagnostic area under the curve (AUC) value was higher than the single clinical or radiomics model (training set: 0.86 vs. 0.83 vs. 0.82; test set: 0.80 vs. 0.78 vs. 0.79). The constructed combined nomogram could effectively quantify the risk degree of 3 image features and Rad score with a C-index of 0.855 (95%: 0.805∼0.905). Conclusion: Radiographic and radiomics features show high accuracy in the invasive diagnosis of GGNs, and their combined analysis can improve the diagnostic efficacy of IAC manifesting as GGNs. The nomogram, serving as a noninvasive and accurate predictive tool, can help judge the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.

Radiomics Study for Discriminating Second Primary Lung Cancers From Pulmonary Metastases in Pulmonary Solid Lesions.

BACKGROUND: The objective of this study was to assess the value of quantitative radiomics features in discriminating second primary lung cancers (SPLCs) from pulmonary metastases (PMs). METHODS: This retrospective study enrolled 252 malignant pulmonary nodules with histopathologically confirmed SPLCs or PMs and randomly assigned them to a training or validation cohort. Clinical data were collected from the electronic medical records system. The imaging and radiomics features of each nodule were extracted from CT images. RESULTS: A rad-score was generated from the training cohort using the least absolute shrinkage and selection operator regression. A clinical and radiographic model was constructed using the clinical and imaging features selected by univariate and multivariate regression. A nomogram composed of clinical-radiographic factors and a rad-score were developed to validate the discriminative ability. The rad-scores differed significantly between the SPLC and PM groups. Sixteen radiomics features and four clinical-radiographic features were selected to build the final model to differentiate between SPLCs and PMs. The comprehensive clinical radiographic-radiomics model demonstrated good discriminative capacity with an area under the curve of the receiver operating characteristic curve of 0.9421 and 0.9041 in the respective training and validation cohorts. The decision curve analysis demonstrated that the comprehensive model showed a higher clinical value than the model without the rad-score. CONCLUSION: The proposed model based on clinical data, imaging features, and radiomics features could accurately discriminate SPLCs from PMs. The model thus has the potential to support clinicians in improving decision-making in a noninvasive manner.

Subclassification of BI-RADS 4 Magnetic Resonance Lesions: A Systematic Review and Meta-Analysis.

OBJECTIVE: This research aims to investigate and evaluate the diagnostic efficacy of magnetic resonance imaging (MRI) in classifying Breast Imaging Reporting and Data System (BI-RADS) 4 lesions into subcategories: 4a, 4b, and 4c, so as to limit biopsies of suspected lesions in the breast. METHODS: The PubMed, Web of Science, Embase, and Cochrane Library foreign language databases were searched for literature published between January 2000 and July 2018. After analyzing the selection, data extraction, and quality assessment, a meta-analysis was performed, including data pooling, heterogeneity testing, and meta-regression. RESULTS: Fourteen articles, including 18 studies, met the inclusion criteria. The diagnostic efficacy of MRI for BI-RADS 4-weighted summary assay sensitivity and specificity were estimated at 0.95 [95% confidence interval (CI), 0.89-0.98] and 0.87 (95% CI, 0.81-0.91), respectively. The positive and negative likelihood ratios were 7.1 (95% CI, 4.7-10.7) and 0.06 (95% CI, 0.02-0.14), respectively. The diagnostic odds ratio was 126 (95% CI, 37-426), and the area under the receiver operating characteristic curve was 0.95 (95% CI, 0.93-0.97). The malignancy ratio of BI-RADS 4a, 4b, and 4c and malignancy range were 2.5% to 18.3%, 23.5% to 57.1%, and 58.0% to 95.2%, respectively. CONCLUSION: Risk stratification of suspected lesions (BI-RADS categories 4a, 4b, and 4c) can be achieved by MRI. The MRI is an effective auxiliary tool to further subclassify BI-RADS 4 lesions and prevent unnecessary biopsy of BI-RADS 4a lesions.

Magnetic Resonance Imaging Texture Analysis in Differentiating Benign and Malignant Breast Lesions of Breast Imaging Reporting and Data System 4: A Preliminary Study.

RATIONALE AND OBJECTIVES: This novel study aims to investigate texture parameters in distinguishing malignant and benign breast lesions classified as Breast Imaging Reporting and Data System 4 in dynamic contrast-enhanced magnetic resonance imaging (MRI). MATERIALS AND METHODS: This retrospective study included 203 patients with 136 breast cancer and 67 benign lesions who underwent breast MRI between November 23, 2016, and August 27, 2018. Co-occurrence matrix-based texture features were extracted from each lesion on T1-weighted contrast-enhanced MRI using MatLab software. The association between texture parameters and breast lesions was analyzed, and the diagnostic model for breast cancer was created. Classification performance was evaluated by the area under the receiver operating characteristic curve, sensitivity, and specificity. RESULTS: Significant differences were seen between malignant and benign lesions for a number of textural features, including contrast, correlation, autocorrelation, dissimilarity, cluster shade, and cluster performance (P < 0.05). After the analysis of the multicollinearity, 5 texture features (contrast, correlation, dissimilarity, cluster shade, and cluster performance) were included for the next principal component analysis. The differentiation accuracy of breast cancer based on the diagnostic model was 0.948 (95% confidence interval, 0.908-0.974). CONCLUSIONS: Texture features that measure randomness, heterogeneity, or homogeneity may reflect underlying growth patterns of breast lesions and show great difference in malignant and benign lesions. Therefore, texture analysis may be a valuable assisted tool for diagnostic analysis on breast.

Magnetic resonance spectroscopy imaging in diagnosis of suspicious prostate cancer: A meta-analysis.

BACKGROUND: This meta-analysis was conducted to assess the value of magnetic resonance spectroscopy imaging (MRSI) in the diagnosis of suspected prostate cancer (PC). METHODS: We identified all the relevant papers from the EMBASE, PubMed, EBSCO, Web of Science, and Cochrane Library databases and screened the reference lists. The quality assessment of diagnostic accuracy studies-version 2 tool was used to assess the study quality. Publication bias was analyzed using Deeks' funnel plot asymmetry test. We calculated the pooled sensitivities, specificities, positive likelihood ratios, negative likelihood ratios, diagnostic odds ratio (DOR), and 95% confidence intervals. The results were evaluated by summary receiver-operating characteristic curves (SROCs). Ultimately, a univariable meta-regression and subgroup analysis, Fagan plot, and likelihood matrix were used to analyze this review. RESULTS: A total of 19 articles, which were based on patient-level analysis of PC, were included. These studies had a pooled sensitivity, specificity, DOR, and an area under the SROC of 0.86, 0.78, 22, and 0.89, respectively, by patient-level analysis. From the likelihood matrix, the summary negative likelihood ratio and positive likelihood ratio for MRSI diagnosis of PC were concentrated on the right lower quadrant, which neither confirmed nor excluded the diagnosis of cancer. CONCLUSION: MRSI has a relative application value in the diagnosis of cases of suspected PC. While MRSI is still required for diagnosis along with other clinical data and comprehensive analysis.

CT and clinical characteristics that predict risk of EGFR mutation in non-small cell lung cancer: a systematic review and meta-analysis.

INTRODUCTION: To systematically analyze CT and clinical characteristics to find out the risk factors of epidermal growth factor receptor (EGFR) mutation in non-small cell lung cancer (NSCLC). Then the significant characteristics were used to set up a mathematic model to predict EGFR mutation in NSCLC. MATERIALS AND METHODS: PubMed, Web of Knowledge and EMBASE up to August 17, 2018 were systematically searched for relevant studies that investigated the evidence of association between CT and clinical characteristics and EGFR mutation in NSCLC. After study selection, data extraction, and quality assessment, the pooled odds ratios (ORs) were calculated. Then from May 2017 to August 2018, all NSCLC received EGFR mutation examination and CT examination in our hospital were chosen to test the prediction model by receiver operating characteristic (ROC) curves. RESULTS: Seventeen original studies met the inclusion criteria. The results showed that the ORs of ground-glass opacity (GGO), air bronchogram, pleural retraction, vascular convergence, smoking history, female gender were, respectively, 1.93 (P = 0.003), 2.09 (P = 0.03), 1.59 (P < 0.01), 1.61 (P = 0.001), 0.28 (P < 0.01), 0.35 (P < 0.01). The result of speculation, cavitation/bubble-like lucency, lesion shape, margin, pathological stage were, respectively, 1.19 (P = 0.32), 0.99 (P = 0.97), 0.82 (P = 0.42), 1.02 (P = 0.90), 0.77 (P = 0.30). 121 NSCLC received EGFR mutation test were included to test the prediction model. The mathematical model based on the results of meta-analysis was: 0.74 × air bronchogram + 0.46 × pleural retraction + 0.48 × vascular convergence - 1.27 × non-smoking history - 1.05 × female. The area under the ROC curve was 0.68. CONCLUSION: Based on the current evidence, GGO presence, air bronchogram, pleural retraction, vascular convergence were significant risk factors of EGFR mutation in NSCLC. And the prediction model can help to predict EGFR mutation status.