European Journal of Radiology Open最新文献

Objective

To evaluate the performance of mammography and breast ultrasonography to diagnose tumor recurrence in patients after breast conserving therapy.

Material and Methods

Imaging findings of 130 breast cancer patients treated by breast conserving therapy (BCT) who have followed up with mammography and ultrasonography at our center between 1 ^st January 2010 and 1st January 2016 were interpreted by two radiologists. The information of recurrent tumor and baseline data were blinded. Imaging interpretation followed the ACR Breast imaging-reporting and data system (BI-RADS) 5th edition guideline. Findings of mammography, breast ultrasonography, demographic data and histological data were recorded and analyzed.

Results

The presence of mass in mammography (P-value=0.025) and internal vascularity in mass in ultrasonography (P-value<0.001) were associated with recurrent tumor at the surgical bed. All the recurrent tumors were interpreted as BI-RADS 4 (71 patients) with sensitivity= 100%, specificity= 89.5%. BIRADS4 is significant in the diagnosis of recurrent breast cancer in BCT patients (AUC of the ROC curve = 0.742 and 95% CI=(0.7–0.79)).

Conclusion

The presence of mass in mammography and internal vascularity in the mass in ultrasonography are the imaging findings which were significantly related to recurrent tumor at surgical bed in patient with breast conserving treatment.

Introduction

High mammographic breast density (MBD) is an independent breast cancer risk factor. In organised breast screening settings, discussions are ongoing regarding the optimal clinical role of MBD to help guide screening decisions. The aim of this scoping review was to provide an overview of current practices incorporating MBD within population-based breast screening programmes and from professional organisations internationally.

Methods

This scoping review was conducted in accordance with the framework proposed by the Joanna Briggs Institute. The electronic databases, MEDLINE (PubMed), EMBASE, CINAHL Plus, Scopus, and Web of Science were systematically searched. Grey literature sources, websites of international breast screening programmes, and relevant government organisations were searched to identify further relevant literature. Data from identified materials were extracted and presented as a narrative summary.

Results

The search identified 78 relevant documents. Documents were identified for breast screening programmes in 18 countries relating to screening intervals for women with dense breasts, MBD measurement, reporting, notification, and guiding supplemental screening. Documents were identified from 18 international professional organisations with the majority of material relating to supplemental screening guidance for women with dense breasts. Key factors collated during the data extraction process as relevant considerations for MBD practices included the evidence base needed to inform decision-making processes and resources (healthcare system costs, radiology equipment, and workforce planning).

Conclusions

This scoping review summarises current practices and guidelines incorporating MBD in international population-based breast screening settings and highlights the absence of consensus between organised breast screening programmes incorporating MBD in current breast screening protocols.

Background

Structured reporting has been demonstrated to increase report completeness and to reduce error rate, also enabling data mining of radiological reports. Still, structured reporting is perceived by radiologists as a fragmented reporting style, limiting their freedom of expression.

Purpose

A deep learning-based natural language processing method was developed to automatically convert unstructured COVID-19 chest CT reports into structured reports.

Methods

Two hundred-two COVID-19 chest CT were retrospectively reviewed by two experienced radiologists, who wrote for each exam a free-form text radiological report and coherently filled the template provided by the Italian Society of Medical and Interventional Radiology, used as ground-truth. A semi-supervised convolutional neural network was implemented to extract 62 categorical variables from the report. Two iterations were carried-out, the first without fine-tuning, the second one performing a fine-tuning. The performance was measured using the mean accuracy and the F1 mean score. An error analysis was performed to identify errors entirely attributable to incorrect processing of the model.

Results

The algorithm achieved a mean accuracy of 93.7% and an F1 score 93.8% in the first iteration. Most of the errors were exclusively attributable to wrong inference (46%). In the second iteration the model achieved for both parameters 95,8% and percentage of errors attributable to wrong inference decreased to 26%.

Conclusions

The convolutional neural network achieved an optimal performance in the automated conversion of free-form text into structured radiological reports, overcoming all the limitation attributed to structured reporting and finally paving the way for data mining of radiological report.

Rationale and objective

To compare burnout and stressors of breast radiologists prior to and during the COVID-19 pandemic.

Materials and methods

Members of the Society of Breast Imaging were emailed an IRB-approved survey in January 2021 during the COVID–19 pandemic. Survey included questions from the Maslach Burnout Inventory and specific stressors including work pace, work-life balance, care of dependents, and financial strain. Data were compared to previous surveys prior to the pandemic.

Results

The response rate was 25% (261/1061) for those who opened the email. Of the respondents, 74% (194/261) were female, 82% (214/261) were white, 73% (191/261) were full time, 71% (185/261) were fellowship trained, 41% (106/261) had more than 20 years of experience, and 30% (79/261) were in academic practice.

Respondents in 2021 reported frequent levels of depersonalization (2.2) and emotional exhaustion (3.4) while reporting frequent levels of personal accomplishment (5.3), a protective factor. These values were nearly identical before the pandemic in 2020: (2.2, 3.5, 5.3, respectively, p = .9). Respondents rated practicing faster than they would like as the highest stressor; however, 5 of the 6 stressors improved after the pandemic onset (p < .05). Conversely, participants perceived these stresses had gotten slightly worse since the pandemic (p < .01). Almost 50% of respondents reported they were considering leaving their practice; the most common reason was work/life balance.

Conclusion

Burnout in breast radiologists remains frequent but unchanged during the COVID-19 pandemic. While participants perceived that some stressors were worse during the pandemic, there was slight improvement in most stressors between the pre-pandemic and pandemic cohorts.

Background

The new immunotherapies have not only changed the oncological therapeutic approach but have also made it necessary to develop new imaging methods for assessing the response to treatment. Delta radiomics consists of the analysis of radiomic features variation between different medical images, usually before and after therapy.

Purpose

This review aims to evaluate the role of delta radiomics in the immunotherapy response assessment.

Methods

A systematic search was performed in PubMed, Scopus, and Web Of Science using “delta radiomics AND immunotherapy” as search terms. The included articles' methodological quality was measured using the Radiomics Quality Score (RQS) tool.

Results

Thirteen articles were finally included in the systematic review. Overall, the RQS of the included studies ranged from 4 to 17, with a mean RQS total of 11,15 ± 4,18 with a corresponding percentage of 30.98 ± 11.61 %. Eleven articles out of 13 performed imaging at multiple time points. All the included articles performed feature reduction. No study carried out prospective validation, decision curve analysis, or cost-effectiveness analysis.

Conclusions

Delta radiomics has been demonstrated useful in evaluating the response in oncologic patients undergoing immunotherapy. The overall quality was found law, due to the lack of prospective design and external validation. Thus, further efforts are needed to bring delta radiomics a step closer to clinical implementation.

Purpose

To evaluate the stand-alone diagnostic performances of AI-CAD and outcomes of AI-CAD detected abnormalities when applied to the mammographic interpretation workflow.

Methods

From January 2016 to December 2017, 6499 screening mammograms of 5228 women were collected from a single screening facility. Historic reads of three radiologists were used as radiologist interpretation. A commercially-available AI-CAD was used for analysis. One radiologist not involved in interpretation had retrospectively reviewed the abnormality features and assessed the significance (negligible vs. need recall) of the AI-CAD marks. Ground truth in terms of cancer, benign or absence of abnormality was confirmed according to histopathologic diagnosis or negative results on the next-round screen.

Results

Of the 6499 mammograms, 6282 (96.7%) were in the negative, 189 (2.9%) were in the benign, and 28 (0.4%) were in the cancer group. AI-CAD detected 5 (17.9%, 5 of 28) of the 9 cancers that were intially interpreted as negative. Of the 648 AI-CAD recalls, 89.0% (577 of 648) were marks seen on examinations in the negative group, and 267 (41.2%) of the AI-CAD marks were considered to be negligible. Stand-alone AI-CAD has significantly higher recall rates (10.0% vs. 3.4%, P < 0.001) with comparable sensitivity and cancer detection rates (P = 0.086 and 0.102, respectively) when compared to the radiologists’ interpretation.

Conclusion

AI-CAD detected 17.9% additional cancers on screening mammography that were initially overlooked by the radiologists. In spite of the additional cancer detection, AI-CAD had significantly higher recall rates in the clinical workflow, in which 89.0% of AI-CAD marks are on negative mammograms.

Objectives

To develop a mutation-based radiomics signature to predict response to imatinib in Gastrointestinal Stromal Tumors (GISTs).

Methods

Eighty-two patients with GIST were enrolled in this retrospective study, including 52 patients from one center that were used to develop the model, and 30 patients from a second center to validate it. Reference standard was the mutational status of tyrosine-protein kinase (KIT) and platelet-derived growth factor α (PDGFRA). Patients were dichotomized in imatinib sensitive (group 0 - mutation in KIT or PDGFRA, different from exon 18-D842V), and imatinib non-responsive (group 1 - PDGFRA exon 18-D842V mutation or absence of mutation in KIT/PDGFRA). Initially, 107 texture features were extracted from the tumor masks of baseline computed tomography scans. Different machine learning methods were then implemented to select the best combination of features for the development of the radiomics signature.

Results

The best performance was obtained with the 5 features selected by the ANOVA model and the Bayes classifier, using a threshold of 0.36. With this setting the radiomics signature had an accuracy and precision for sensitive patients of 82 % (95 % CI:60–95) and 90 % (95 % CI:73–97), respectively. Conversely, a precision of 80 % (95 % CI:34–97) was obtained in non-responsive patients using a threshold of 0.9. Indeed, with the latter setting 4 patients out of 5 were correctly predicted as non-responders.

Conclusions

The results are a first step towards using radiomics to improve the management of patients with GIST, especially when tumor tissue is unavailable for molecular analysis or when molecular profiling is inconclusive.

Purpose

The aim of our study is to evaluate artificial intelligence (AI) support in pelvic fracture diagnosis on X-rays, focusing on performance, workflow integration and radiologists’ feedback in a spoke emergency hospital.

Materials and methods

Between August and November 2021, a total of 235 sites of fracture or suspected fracture were evaluated and enrolled in the prospective study. Radiologist’s specificity, sensibility accuracy, positive and negative predictive values were compared to AI. Cohen's kappa was used to calculate the agreement between AI and radiologist. We also reviewed the AI workflow integration process, focusing on potential issues and assessed radiologists’ opinion on AI via a survey.

Results

The radiologist performance in accuracy, sensitivity and specificity was better than AI but McNemar test demonstrated no statistically significant difference between AI and radiologist’s performance (p = 0.32). Calculated Cohen’s K of 0.64.

Conclusion

Contrary to expectations, our preliminary results did not prove a real improvement of patient outcome nor in reporting time but demonstrated AI high NPV (94,62%) and non-inferiority to radiologist performance. Moreover, the commercially available AI algorithm used in our study automatically learn from data and so we expect a progressive performance improvement. AI could be considered as a promising tool to rule-out fractures (especially when used as a “second reader”) and to prioritize positive cases, especially in increasing workload scenarios (ED, nightshifts) but further research is needed to evaluate the real impact on the clinical practice.

Background

A detailed understanding of the non-perfused volume (NPV) evolution after prostate ablation therapy is lacking. The impact of different diseased prostate tissues on NPV evolution post-ablation is unknown.

Purpose

To characterize the NPV evolution for three treatment groups undergoing heat-based prostate ablation therapy, including benign prostatic hyperplasia (BPH), primary prostate cancer (PCa), and radiorecurrent PCa.

Materials and methods

Study design and data analysis were performed retrospectively. All patients received MRI-guided transurethral ultrasound ablation (TULSA). 21 BPH, 28 radiorecurrent PCa and 40 primary PCa patients were included. Using the T1-weighted contrast-enhanced MR image, the NPV was manually contoured by an experienced radiologist. All patients received an MRI immediately following the ablation. Follow-up included MRI at 3- and 12 months for BPH and radiorecurrent PCa patients and at 6- and 12 months for primary PCa patients.

Results

A significant difference between BPH and radiorecurrent PCa patients was observed at three months (p < 0.0001, Wilcoxon rank sum test), with the median NPV decreasing by 77 % for BPH patients but increasing by 4 % for radiorecurrent PCa patients. At six months, the median NPV decreased by 97 % for primary PCa. Across all groups, although 40 % of patients had residual NPV at 12 months, it tended to be < 1 mL.

Conclusion

The resolution of necrotic tissue after ablation was markedly slower for irradiated than treatment-naïve prostate tissue. These results may account for the increased toxicity observed after radiorecurrent salvage therapy. By 12 months, most necrotic prostate tissue had disappeared in every treatment group.

Contrast agents is used in magnetic resonance imaging (MRI) to improve the visibility of the details of the organ structures. Gadolinium-based contrast agent (GBCA) has been used since 1988 in MRI for diagnostic and follow-up of patients, the gadolinium good properties make it an effective choice for enhance the signal in MRI by increase its intensity and shortening the relaxation time of the proton. Recently, many studies show a gadolinium deposition in different human organs due to release of free gadolinium various body organs or tissue, which led to increased concern about the use of gadolinium agents, in this study, the potential diseases that may affect the patient and side effects that appear on the patient and related to accumulation of gadolinium were clarified, the study focused on the organs such as brain and bones in which gadolinium deposition was found and the lesions associated with it, and the diseases associated with gadolinium retention includes Nephrogenic Systemic Fibrosis (NSF) and Gadolinium deposition disease (GDD). Some studies tended to improve the contrast agents by developing a new non-gadolinium agents or development of next-generation gadolinium agents. In this review article the latest knowledge about MRI contrast agent.