Reliability of Coronary Artery Calcium Severity Assessment on Non-Electrocardiogram-Gated CT: A Meta-Analysis

ObjectiveThe purpose of this meta-analysis was to investigate the pooled agreements of the coronary artery calcium (CAC) severities assessed by electrocardiogram (ECG)-gated and non-ECG-gated CT and evaluate the impact of the scan parameters.Materials and MethodsPubMed, EMBASE, and the Cochrane library were systematically searched. A modified Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to evaluate the quality of the studies. Meta-analytic methods were utilized to determine the pooled weighted bias, limits of agreement (LOA), and the correlation coefficient of the CAC scores or the weighted kappa for the categorization of the CAC severities detected by the two modalities. The heterogeneity among the studies was also assessed. Subgroup analyses were performed based on factors that could affect the measurement of the CAC score and severity: slice thickness, reconstruction kernel, and radiation dose for non-ECG-gated CT.ResultsA total of 4000 patients from 16 studies were included. The pooled bias was 62.60, 95% LOA were −36.19 to 161.40, and the pooled correlation coefficient was 0.94 (95% confidence interval [CI] = 0.89–0.97) for the CAC score. The pooled weighted kappa of the CAC severity was 0.85 (95% CI = 0.79–0.91). Heterogeneity was observed in the studies (I² > 50%, p < 0.1). In the subgroup analysis, the agreement between the CAC categorizations was better when the two CT examinations had reconstructions based on the same slice thickness and kernel.ConclusionThe pooled agreement of the CAC severities assessed by the ECG-gated and non-ECG-gated CT was excellent; however, it was significantly affected by scan parameters, such as slice thickness and the reconstruction kernel.     

A New Method for Aortic Valve Planimetry with High-Resolution 3-Dimensional MRI and Its Comparison with Conventional Cine MRI and Echocardiography for Assessing the Severity of Aortic Valvular Stenosis

ObjectiveWe aimed to compare the aortic valve area (AVA) calculated using fast high-resolution three-dimensional (3D) magnetic resonance (MR) image acquisition with that of the conventional two-dimensional (2D) cine MR technique.Materials and MethodsWe included 139 consecutive patients (mean age ± standard deviation [SD], 68.5 ± 9.4 years) with aortic valvular stenosis (AS) and 21 asymptomatic controls (52.3 ± 14.2 years). High-resolution T2-prepared 3D steady-state free precession (SSFP) images (2.0 mm slice thickness, 10 contiguous slices) for 3D planimetry (3DP) were acquired with a single breath hold during mid-systole. 2D SSFP cine MR images (6.0 mm slice thickness) for 2D planimetry (2DP) were also obtained at three aortic valve levels. The calculations for the effective AVA based on the MR images were compared with the transthoracic echocardiographic (TTE) measurements using the continuity equation.ResultsThe mean AVA ± SD derived by 3DP, 2DP, and TTE in the AS group were 0.81 ± 0.26 cm², 0.82 ± 0.34 cm², and 0.80 ± 0.26 cm², respectively (p = 0.366). The intra-observer agreement was higher for 3DP than 2DP in one observer: intraclass correlation coefficient (ICC) of 0.95 (95% confidence interval [CI], 0.94–0.97) and 0.87 (95% CI, 0.82–0.91), respectively, for observer 1 and 0.97 (95% CI, 0.96–0.98) and 0.98 (95% CI, 0.97–0.99), respectively, for observer 2. Inter-observer agreement was similar between 3DP and 2DP, with the ICC of 0.92 (95% CI, 0.89–0.94) and 0.91 (95% CI, 0.88–0.93), respectively. 3DP-derived AVA showed a slightly higher agreement with AVA measured by TTE than the 2DP-derived AVA, with the ICC of 0.87 (95% CI, 0.82–0.91) vs. 0.85 (95% CI, 0.79–0.89).ConclusionHigh-resolution 3D MR image acquisition, with single-breath-hold SSFP sequences, gave AVA measurement with low observer variability that correlated highly with those obtained by TTE.     

Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma

ObjectiveTo develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC).Materials and MethodsThis retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets.ResultsFive radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62–0.89) and 0.77 (95% CI, 0.65–0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40–0.67], p = 0.006), CE-T1WI (0.53 [95% CI, 0.40–0.67], p = 0.012), and clinical parameters (0.53 [95% CI, 0.39–0.67], p = 0.021) in the external validation set.ConclusionThe radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.     

A computed tomography-based nomogram to predict pneumothorax caused by preoperative localization of ground glass nodules using hook wire

Objective:To develop and validate a CT-based nomogram to predict the occurrence of loculated pneumothorax due to hook wire placement.Methods:Patients (n = 177) were divided into pneumothorax (n = 72) and non-pneumothorax (n = 105) groups. Multivariable logistic regression analysis was applied to build a clinical prediction model using significant predictors identified by univariate analysis of imaging features and clinical factors. Receiver operating characteristic (ROC) was applied to evaluate the discrimination of the nomogram, which was calibrated using calibration curve.Results:Based on the results of multivariable regression analysis, transfissure approach [odds ratio (OR): 757.94; 95% confidence interval CI (21.20–27099.30) p < 0.0001], transemphysema [OR: 116.73; 95% CI (12.34–1104.04) p < 0.0001], localization of multiple nodules [OR: 8.04; 95% CI (2.09–30.89) p = 0.002], and depth of nodule [OR: 0.77; 95% CI (0.71–0.85) p < 0.0001] were independent risk factors for pneumothorax and were included in the predictive model (p < 0.05). The area under the ROC curve value for the nomogram was 0.95 [95% CI (0.92–0.98)] and the calibration curve indicated good consistency between risk predicted using the model and actual risk.Conclusion:A CT-based nomogram combining imaging features and clinical factors can predict the probability of pneumothorax before localization of ground-glass nodules. The nomogram is a decision-making tool to prevent pneumothorax and determine whether to proceed with further treatment.Advances in knowledge:A nomogram composed of transfissure, transemphysema, multiple nodule localization, and depth of nodule has been developed to predict the probability of pneumothorax before localization of GGNs.     

Accuracy of Digital Breast Tomosynthesis for Detecting Breast Cancer in the Diagnostic Setting: A Systematic Review and Meta-Analysis

ObjectiveTo compare the accuracy for detecting breast cancer in the diagnostic setting between the use of digital breast tomosynthesis (DBT), defined as DBT alone or combined DBT and digital mammography (DM), and the use of DM alone through a systematic review and meta-analysis.Materials and MethodsOvid-MEDLINE, Ovid-Embase, Cochrane Library and five Korean local databases were searched for articles published until March 25, 2020. We selected studies that reported diagnostic accuracy in women who were recalled after screening or symptomatic. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random effects model was used to estimate pooled sensitivity and specificity. We compared the diagnostic accuracy between DBT and DM alone using meta-regression and subgroup analyses by modality of intervention, country, existence of calcifications, breast density, Breast Imaging Reporting and Data System category threshold, study design, protocol for participant sampling, sample size, reason for diagnostic examination, and number of readers who interpreted the studies.ResultsTwenty studies (n = 44513) that compared DBT and DM alone were included. The pooled sensitivity and specificity were 0.90 (95% confidence interval [CI] 0.86–0.93) and 0.90 (95% CI 0.84–0.94), respectively, for DBT, which were higher than 0.76 (95% CI 0.68–0.83) and 0.83 (95% CI 0.73–0.89), respectively, for DM alone (p < 0.001). The area under the summary receiver operating characteristics curve was 0.95 (95% CI 0.93–0.97) for DBT and 0.86 (95% CI 0.82–0.88) for DM alone. The higher sensitivity and specificity of DBT than DM alone were consistently noted in most subgroup and meta-regression analyses.ConclusionUse of DBT was more accurate than DM alone for the diagnosis of breast cancer. Women with clinical symptoms or abnormal screening findings could be more effectively evaluated for breast cancer using DBT, which has a superior diagnostic performance compared to DM alone.     

Use of Artificial Intelligence for Reducing Unnecessary Recalls at Screening Mammography: A Simulation Study

ObjectiveTo conduct a simulation study to determine whether artificial intelligence (AI)-aided mammography reading can reduce unnecessary recalls while maintaining cancer detection ability in women recalled after mammography screening.Materials and MethodsA retrospective reader study was performed by screening mammographies of 793 women (mean age ± standard deviation, 50 ± 9 years) recalled to obtain supplemental mammographic views regarding screening mammography-detected abnormalities between January 2016 and December 2019 at two screening centers. Initial screening mammography examinations were interpreted by three dedicated breast radiologists sequentially, case by case, with and without AI aid, in a single session. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate for breast cancer diagnosis were obtained and compared between the two reading modes.ResultsFifty-four mammograms with cancer (35 invasive cancers and 19 ductal carcinomas in situ) and 739 mammograms with benign or negative findings were included. The reader-averaged AUC improved after AI aid, from 0.79 (95% confidence interval [CI], 0.74–0.85) to 0.89 (95% CI, 0.85–0.94) (p < 0.001). The reader-averaged specificities before and after AI aid were 41.9% (95% CI, 39.3%–44.5%) and 53.9% (95% CI, 50.9%–56.9%), respectively (p < 0.001). The reader-averaged sensitivity was not statistically different between AI-unaided and AI-aided readings: 89.5% (95% CI, 83.1%–95.9%) vs. 92.6% (95% CI, 86.2%–99.0%) (p = 0.053), although the sensitivities of the least experienced radiologists before and after AI aid were 79.6% (43 of 54 [95% CI, 66.5%–89.4%]) and 90.7% (49 of 54 [95% CI, 79.7%–96.9%]), respectively (p = 0.031). With AI aid, the reader-averaged recall rate decreased by from 60.4% (95% CI, 57.8%–62.9%) to 49.5% (95% CI, 46.5%–52.4%) (p < 0.001).ConclusionAI-aided reading reduced the number of recalls and improved the diagnostic performance in our simulation using women initially recalled for supplemental mammographic views after mammography screening.     

Sonazoid™ versus SonoVue® for Diagnosing Hepatocellular Carcinoma Using Contrast-Enhanced Ultrasound in At-Risk Individuals: A Prospective,Single-Center,Intraindividual, Noninferiority Study

ObjectiveTo determine whether Sonazoid-enhanced ultrasound (SZUS) was noninferior to SonoVue-enhanced ultrasound (SVUS) in diagnosing hepatocellular carcinoma (HCC) using the same diagnostic criteria.Materials and MethodsThis prospective, single-center, noninferiority study ({"type":"clinical-trial","attrs":{"text":"NCT04847726","term_id":"NCT04847726"}}NCT04847726) enrolled 105 at-risk participants (71 male; mean age ± standard deviation, 63 ± 11 years; range, 26–86 years) with treatment-naïve solid hepatic nodules (≥ 1 cm). All participants underwent same-day SZUS (experimental method) and SVUS (control method) for one representative nodule per participant. Images were interpreted by three readers (the operator and two independent readers). All malignancies were diagnosed histopathologically, while the benignity of other lesions was confirmed by follow-up stability or pathology. The primary endpoint was per-lesion diagnostic accuracy for HCC pooled across three readers using the conventional contrast-enhanced ultrasound diagnostic criteria, including arterial phase hyperenhancement followed by mild (assessed within 2 minutes after contrast injection) and late (≥ 60 seconds with a delay of 5 minutes) washout. The noninferiority delta was -10%p. Furthermore, different time delays were compared as washout criteria in SZUS, including delays of 2, 5, and > 10 minutes.ResultsA total of 105 lesions (HCCs [n = 61], non-HCC malignancies [n = 19], and benign [n = 25]) were evaluated. Using the 5-minutes washout criterion, per-lesion accuracy of SZUS pooled across the three readers (72.4%; 95% confidence interval [CI], 64.1%–79.3%) was noninferior to that of SVUS (71.4%; 95% CI, 63.1%–78.6%), meeting the statistical criterion for non-inferiority (difference of 0.95%p; 95% CI, -3.8%p–5.7%p). The arterial phase hyperenhancement combined with the 5-minutes washout criterion showed the same sensitivity as that of the > 10-minutes criterion (59.0% vs. 59.0%, p = 0.989), and the specificities were not significantly different (90.9% vs. 86.4%, p = 0.072).ConclusionSZUS was noninferior to SVUS for diagnosing HCC in at-risk patients using the same diagnostic criteria. No significant improvement in HCC diagnosis was observed by extending the washout time delay from 5 to 10 minutes.     

How to Combine Diffusion-Weighted and T2-Weighted Imaging for MRI Assessment of Pathologic Complete Response to Neoadjuvant Chemoradiotherapy in Patients with Rectal Cancer?

ObjectiveAdequate methods of combining T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) to assess complete response (CR) to chemoradiotherapy (CRT) for rectal cancer are obscure. We aimed to determine an algorithm for combining T2WI and DWI to optimally suggest CR on MRI using visual assessment.Materials and MethodsWe included 376 patients (male:female, 256:120; mean age ± standard deviation, 59.7 ± 11.1 years) who had undergone long-course CRT for rectal cancer and both pre- and post-CRT high-resolution rectal MRI during 2017–2018. Two experienced radiologists independently evaluated whether a tumor signal was absent, representing CR, on both post-CRT T2WI and DWI, and whether the pre-treatment DWI showed homogeneous hyperintensity throughout the lesion. Algorithms for combining T2WI and DWI were as follows: ‘AND,’ if both showed CR; ‘OR,’ if any one showed CR; and ‘conditional OR,’ if T2WI showed CR or DWI showed CR after the pre-treatment DWI showed homogeneous hyperintensity. Their efficacies for diagnosing pathologic CR (pCR) were determined in comparison with T2WI alone.ResultsSixty-nine patients (18.4%) had pCR. AND had a lower sensitivity without statistical significance (vs. 62.3% [43/69]; 59.4% [41/69], p = 0.500) and a significantly higher specificity (vs. 87.0% [267/307]; 90.2% [277/307], p = 0.002) than those of T2WI. Both OR and conditional OR combinations resulted in a large increase in sensitivity (vs. 62.3% [43/69]; 81.2% [56/69], p < 0.001; and 73.9% [51/69], p = 0.008, respectively) and a large decrease in specificity (vs. 87.0% [267/307]; 57.0% [175/307], p < 0.001; and 69.1% [212/307], p < 0.001, respectively) as compared with T2WI, ultimately creating additional false interpretations of CR more frequently than additional identification of patients with pCR.ConclusionAND combination of T2WI and DWI is an appropriate strategy for suggesting CR using visual assessment of MRI after CRT for rectal cancer.     

Diffusion-Weighted MRI of Malignant versus Benign Portal Vein Thrombosis

ObjectiveTo validate the diffusion-weighted MRI (DWI) for differentiation of benign from malignant portal vein thrombosis.ResultsThe mean ADC ± standard deviation of bland and malignant PVT were 1.00 ± 0.39 × 10^-3 mm²/sec and 0.92 ± 0.25 × 10^-3 mm²/sec, respectively; without significant difference (p = 0.799). The area under ROC curve for ADC was 0.520. An ADC value of > 1.35 × 10^-3 mm²/sec predicted bland PVT with a specificity of 94.6% (95% confidence interval [CI]: 84.9–98.9%) and a sensitivity of 22.2% (95% CI: 6.4–47.6%), respectively.ConclusionDue to the wide range and considerable overlap of the ADCs, DWI cannot differentiate the benign from malignant thrombi efficiently.     

Early detection treatment response for head and neck carcinomas using intravoxel incoherent motion-magnetic resonance imaging: a meta-analysis

Objectives:To evaluate the diagnostic accuracy of intravoxel incoherent motion-MRI (IVIM-MRI) for predicting the treatment response in head and neck squamous cell carcinomas (HNSCC) patients.Methods:A comprehensive literature search was performed to identify original articles on diagnostic performance of IVIM in predicting treatment response in HNSCC patients receiving chemoradiotherapy. The IVIM parameters studied were diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), and apparent diffusion coefficient. Summary estimates of diagnostic accuracy were obtained by using a random-effects model. Of 65 studies screened, 8 studies with 347 patients were finally included.Results:The pooled sensitivities and specificities were 76% [95% confidence interval (CI) 69–82%] and 81% (95% CI 70–89%) for pre-treatment D, and 70% (95% CI 58–80%) and 82% (95% CI 66–92%) for △D, respectively. In addition, the sensitivities and specificities ranged from 41.7 to 94% and 67 to 100% for pre-treatment f, and from 55.7 to 76.5% and 72.2 to 93.3% for pre-treatment apparent diffusion coefficient, respectively.Conclusions:The diffusion-related coefficients pre-treatment D and △D demonstrated good accuracy in predicting early treatment response in HNSCC patients. However, because of the variability in reference test and other limitations of included literature, further investigation is needed before implementing any IVIM strategy into clinical practice.     

Chemoradiation-induced changes in systemic inflammatory markers and their prognostic significance in oesophageal squamous cell carcinoma

Objective:Chemoradiation (CRT) may induce a change in systemic inflammatory state which could affect clinical outcomes in oesophageal cancer. We aimed to evaluate the changes and prognostic significance of systemic inflammatory markers following definitive CRT in oesophageal squamous cell carcinoma.Methods:A total of 53 patients treated with concurrent CRT were included in this retrospective analysis. We compared neutrophils, lymphocytes, platelets, neutrophil–lymphocyte ratio (NLR) and platelet–lymphocyte ratio (PLR) before and after CRT using Wilcoxon signed-rank test. Overall survival (OS) and progression-free survival (PFS) were calculated. Univariable and multivariable survival analysis were performed using Cox regression analysis. Clinical univariable survival prognostic factors with p < 0.1 were included in a multivariable cox regression analysis for backward stepwise model selection.Results:Both NLR (median ∆+2.8 [IQR −0.11, 8.62], p < 001) and PLR (median ∆+227 [81.3–523.5], p < 0.001) increased significantly after CRT. Higher levels of pre-CRT, post-CRT and change (∆) in NLR and PLR were associated with inferior OS and PFS. Post-CRT NLR (HR 1.04, 95% CI 1.02–1.07, p < 0.001), post-CRT platelets (HR 1.03, 95% CI 1.01–1.05, p = 0.005), cT-stage (HR 3.83, 95% CI 1.39–10.60, p = 0.01) and RT dose (HR 0.41, 95% CI 0.21–0.81, p = 0.01) were independent prognostic factors for OS in multivariable analysis. Change in NLR (HR 1.04, 95% CI 1.01–1.06, p = 0.001), post-CRT platelets (HR 1.03, 95% CI 1.01–1.05, p = 0.002), cT-stage (HR 3.98, 95% CI 1.55–10.25, p = 0.004) and RT dose (HR 0.41, 95% CI 0.21–0.80, p = 0.009) were independent prognostic factors for PFS.Conclusion:Both NLR and PLR increased following definitive CRT. Post-CRT NLR and ∆NLR were associated with adverse survival in oesophageal SCC.Advances in knowledge:We showed that CRT increased PLR and NLR, possibly reflecting a systemic inflammatory state which were associated with poor clinical outcomes in oesophageal SCC.     

Quantitative Ultrasound Radiofrequency Data Analysis for the Assessment of Hepatic Steatosis in Nonalcoholic Fatty Liver Disease Using Magnetic Resonance Imaging Proton Density Fat Fraction as the Reference Standard

ObjectiveTo investigate the diagnostic performance of quantitative ultrasound (US) parameters for the assessment of hepatic steatosis in patients with nonalcoholic fatty liver disease (NAFLD) using magnetic resonance imaging proton density fat fraction (MRI-PDFF) as the reference standard.Materials and MethodsIn this single-center prospective study, 120 patients with clinically suspected NAFLD were enrolled between March 2019 and January 2020. The participants underwent US examination for radiofrequency (RF) data acquisition and chemical shift-encoded liver MRI for PDFF measurement. Using the RF data analysis, the attenuation coefficient (AC) based on tissue attenuation imaging (TAI) (AC-TAI) and scatter-distribution coefficient (SC) based on tissue scatter-distribution imaging (TSI) (SC-TSI) were measured. The correlations between the quantitative US parameters (AC and SC) and MRI-PDFF were evaluated using Pearson correlation coefficients. The diagnostic performance of AC-TAI and SC-TSI for detecting hepatic fat contents of ≥ 5% (MRI-PDFF ≥ 5%) and ≥ 10% (MRI-PDFF ≥ 10%) were assessed using receiver operating characteristic (ROC) analysis. The significant clinical or imaging factors associated with AC and SC were analyzed using linear regression analysis.ResultsThe participants were classified based on MRI-PDFF: < 5% (n = 38), 5–10% (n = 23), and ≥ 10% (n = 59). AC-TAI and SC-TSI were significantly correlated with MRI-PDFF (r = 0.659 and 0.727, p < 0.001 for both). For detecting hepatic fat contents of ≥ 5% and ≥ 10%, the areas under the ROC curves of AC-TAI were 0.861 (95% confidence interval [CI]: 0.786–0.918) and 0.835 (95% CI: 0.757–0.897), and those of SC-TSI were 0.964 (95% CI: 0.913–0.989) and 0.935 (95% CI: 0.875–0.972), respectively. Multivariable linear regression analysis showed that MRI-PDFF was an independent determinant of AC-TAI and SC-TSI.ConclusionAC-TAI and SC-TSI derived from quantitative US RF data analysis yielded a good correlation with MRI-PDFF and provided good performance for detecting hepatic steatosis and assessing its severity in NAFLD.     

Immune Checkpoint Inhibitor with or without Radiotherapy in Melanoma Patients with Brain Metastases: A Systematic Review and Meta-Analysis

ObjectiveImmune checkpoint inhibitor (ICI) therapy has shown activity against melanoma brain metastases. Recently, promising results have also been reported for ICI combination therapy and ICI combined with radiotherapy. We aimed to evaluate radiologic response and adverse event rates of these therapeutic options by a systematic review and meta-analysis.Materials and MethodsA systematic literature search of Ovid-MEDLINE and EMBASE was performed up to October 12, 2019 and included studies evaluating the intracranial objective response rates (ORRs) and/or disease control rates (DCRs) of ICI with or without radiotherapy for treating melanoma brain metastases. We also evaluated safety-associated outcomes.ResultsEleven studies with 14 cohorts (3 with ICI combination therapy; 5 with ICI combined with radiotherapy; 6 with ICI monotherapy) were included. ICI combination therapy {pooled ORR, 53% (95% confidence interval [CI], 44–61%); DCR, 57% (95% CI, 49–66%)} and ICI combined with radiotherapy (pooled ORR, 42% [95% CI, 31–54%]; DCR, 85% [95% CI, 63–95%]) showed higher local efficacy compared to ICI monotherapy (pooled ORR, 15% [95% CI, 11–20%]; DCR, 26% [95% CI, 21–32%]). The grade 3 or 4 adverse event rate was significantly higher with ICI combination therapy (60%; 95% CI, 52–67%) compared to ICI monotherapy (11%; 95% CI, 8–17%) and ICI combined with radiotherapy (4%; 95% CI, 1–19%). Grade 3 or 4 central nervous system (CNS)-related adverse event rates were not different (9% in ICI combination therapy; 8% in ICI combined with radiotherapy; 5% in ICI monotherapy).ConclusionICI combination therapy or ICI combined with radiotherapy showed better local efficacy than ICI monotherapy for treating melanoma brain metastasis. The grade 3 or 4 adverse event rate was highest with ICI combination therapy, and the CNS-related grade 3 or 4 event rate was similar. Prospective trials will be necessary to compare the efficacy of ICI combination therapy and ICI combined with radiotherapy.     

Optimal Duration of MRI Follow-up to Safely Identify Middle Ear Residual Cholesteatoma

BACKGROUND AND PURPOSE:Previous studies have demonstrated the usefulness of non-EPI DWI for detection of residual cholesteatoma. However, limited data are available to determine the suitable duration of imaging follow-up after a first MR imaging with normal findings has been obtained. The present study aimed to determine the optimal duration of non-EPI DWI follow-up for residual cholesteatoma.MATERIALS AND METHODS:A retrospective, monocentric study was performed between 2013 and 2019 and included all participants followed up after canal wall up tympanoplasty with at least 2 non-EPI DWI examinations performed on the same 1.5T MR imaging scanner. MR images were reviewed independently by 2 radiologists. Sensitivity and specificity values were calculated as a function of time after the operation. Receiver operating characteristic curves were analyzed to determine the optimal follow-up duration.RESULTS:We analyzed 47 MRIs from 17 participants. At the end of the individual follow-up period, a residual cholesteatoma had been found in 41.1% of cases. The follow-up duration ranged from 20 to 198 months (mean, 65.9 [SD, 43.9] months). Participants underwent between 2 and 5 non-EPI DWI examinations. Analyses of the receiver operating characteristic curves revealed that the optimal diagnostic value of non-EPI DWI occurred 56 months after the operation when the first MR imaging performed a mean of 17.3 (SD, 6.8) months after the operation had normal findings (sensitivity = 0.71; specificity = 0.7, Youden index = 0.43).CONCLUSIONS:Repeat non-EPI DWI is required to detect slow-growing middle ear residual cholesteatomas. We, therefore, recommend performing non-EPI DWI for at least the first 5 years after the initial operation.

The development of DWI has profoundly changed the management of middle ear cholesteatomas. An increasing number of surgeons no longer systematically perform second-look surgery, and MR imaging follow-up is performed if revision surgery is not needed to treat conductive hearing loss. Numerous studies have evaluated the sensitivity and specificity of EPI DWI and non-EPI DWI sequences for the detection of residual cholesteatoma. Non-EPI DWI sequences offer the best sensitivity and specificity and are suitable for the detection of residual cholesteatomas as small as 2 mm.¹ A recent meta-analysis of 26 studies concerning non-EPI DWI showed a pooled sensitivity and specificity of 0.91 (95% CI, 0.87–0.95) and 0.92 (95% CI, 0.86–0.96), respectively.¹ Another meta-analysis reported a similar pooled sensitivity and specificity of 0.89 (95% CI, 0.52–0.99) and 0.93 (95% CI, 0.81–0.98), respectively.²However, data on the diagnostic value of non-EPI DWI sequences regarding the optimal timing after the initial operation remain limited. Lingam et al³ reported a sensitivity of 0.91 (95% CI, 0.79–0.97) and a specificity of 0.88 (95% CI, 0.69–0.97) with a median time to MR imaging of 5.4 months after the operation. Khemani et al⁴ found a sensitivity of 0.82 (95% CI, 0.63–0.94) and a specificity of 0.90 (95% CI, 0.55–1.00) when MR imaging was performed 10–24 months after the operation.Most authors agree that imaging follow-up should not start <12 months postsurgery,^5-8 to reduce the number of false-negatives due to residual cholesteatomas measuring <2 mm.⁹ Nevertheless, the optimal duration of follow-up necessary to exclude the existence of a residual cholesteatoma if the findings of the first MR imaging are considered normal is unclear. In a recent retrospective series, follow-up non-EPI DWI detected residual cholesteatoma in 12 of 88 patients only after a mean interval of 3.8 years after the initial cholesteatoma surgery (median, 3.7 years; range, 1.6–7.9 years).¹⁰ Pai et al¹⁰ suggested that imaging follow-up should be performed for a minimum of 5 years postoperatively, without defining how this was calculated.To provide more information on the optimal imaging follow-up duration, we describe the long-term follow-up imaging of participants with ≥2 non-EPI DWI examinations for residual middle ear cholesteatoma. The sensitivity and specificity values were calculated as a function of the duration of the follow-up, and the receiver operating characteristic curves were analyzed to determine the optimal follow-up time.     

MRI Predictors of Malignant Transformation in Patients with Inverted Papilloma: A Decision Tree Analysis Using Conventional Imaging Features and Histogram Analysis of Apparent Diffusion Coefficients

ObjectivePreoperative differentiation between inverted papilloma (IP) and its malignant transformation to squamous cell carcinoma (IP-SCC) is critical for patient management. We aimed to determine the diagnostic accuracy of conventional imaging features and histogram parameters obtained from whole tumor apparent diffusion coefficient (ADC) values to predict IP-SCC in patients with IP, using decision tree analysis.Materials and MethodsIn this retrospective study, we analyzed data generated from the records of 180 consecutive patients with histopathologically diagnosed IP or IP-SCC who underwent head and neck magnetic resonance imaging, including diffusion-weighted imaging and 62 patients were included in the study. To obtain whole tumor ADC values, the region of interest was placed to cover the entire volume of the tumor. Classification and regression tree analyses were performed to determine the most significant predictors of IP-SCC among multiple covariates. The final tree was selected by cross-validation pruning based on minimal error.ResultsOf 62 patients with IP, 21 (34%) had IP-SCC. The decision tree analysis revealed that the loss of convoluted cerebriform pattern and the 20th percentile cutoff of ADC were the most significant predictors of IP-SCC. With these decision trees, the sensitivity, specificity, accuracy, and C-statistics were 86% (18 out of 21; 95% confidence interval [CI], 65–95%), 100% (41 out of 41; 95% CI, 91–100%), 95% (59 out of 61; 95% CI, 87–98%), and 0.966 (95% CI, 0.912–1.000), respectively.ConclusionDecision tree analysis using conventional imaging features and histogram analysis of whole volume ADC could predict IP-SCC in patients with IP with high diagnostic accuracy.     

Performance of Prediction Models for Diagnosing Severe Aortic Stenosis Based on Aortic Valve Calcium on Cardiac Computed Tomography: Incorporation of Radiomics and Machine Learning

ObjectiveWe aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms.Materials and MethodsWe retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score.ResultsThe radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869–0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815–0.948] and 0.899 [95% CI, 0.820–0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant (p > 0.05 for all).ConclusionModels based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.     

Diagnostic accuracy of apparent diffusion coefficient (ADC) value in differentiating malignant from benign solid liver lesions: a systematic review and meta-analysis

Objectives:We undertook a systematic review and meta-analysis of the diagnostic performance of mean apparent diffusion coefficient (ADC) values derived by diffusion-weighted (DW)-MRI in the characterization of solid benign and malignant liver lesions, and to assess their value in discriminating these lesions in daily routine practice.Methods:A systematic review of PubMed, Embase, Scopus, and Web of Science was conducted to retrieve studies that used ADC values for differentiating solid benign/dysplastic nodules and malignant liver lesions. A bivariate random-effects model with pooled sensitivity and specificity values with 95% CI (confidence interval) was used. This meta-analysis was performed on the per-lesion basis. Summary receiver operating characteristic (SROC) plot and area under curve (AUC) were created.Results:A total of 14 original articles were retrieved. The combined (95% CI) sensitivity and specificity of mean ADC values for differentiating solid benign from malignant lesions were 78% (67–86%) and 74% (64–81%), respectively. The pooled (95% CI) positive and negative LRs were respectively 3 (2.3–3.8) and 0.3 (0.21–0.43). The DOR (95% CI) was 10 (7–15). The AUC (95% CI) of the SROC plot was 82% (78–85%). Reporting bias was negligible (p value of regression test = 0.36). Mean size of malignant lesions and breathing pattern of MRI were found to be sources of heterogeneity of pooled sensitivity.Conclusion:ADC measurement independently may not be an optimal diagnostic imaging method for differentiating solid malignant from solid benign hepatic lesions. The meta-analysis showed that ADC measurement had moderate diagnostic accuracy for characterizing solid liver lesions. Further prospective and comparative studies with pre-specified ADC thresholds could be performed to investigate the best MRI protocol and ADC threshold for characterizing solid liver lesions.Advances in knowledge:ADC measurement by DW-MRI does not have a good diagnostic performance to differentiate solid malignant from solid benign lesions. Therefore, we suggest not using ADC values in clinical practice to evaluate solid liver lesions.     

CT and MRI Liver Imaging Reporting and Data System Version 2018 for Hepatocellular Carcinoma: A Systematic Review With Meta-Analysis

PurposeThe aim of this study was to determine the diagnostic performance of the LR-5 category for hepatocellular carcinoma (HCC) and the pooled proportion of HCC in each Liver Imaging Reporting and Data System (LI-RADS) category with CT and MRI, using LI-RADS version 2018.MethodsThe MEDLINE, Embase, and Scopus databases were searched from inception to December 7, 2019, for studies reporting the diagnostic accuracy of LI-RADS version 2018 for HCC. Risk for bias and concerns regarding applicability were assessed using the Quality Assessment of Diagnostic Accuracy Studies–2 tool. Random-effects models were used to determine the summary estimates of the diagnostic performance of LR-5 and the pooled proportion of HCC for each LI-RADS category.ResultsFourteen studies were included in the final analysis, consisting of 2,708 observations with 1,841 HCCs. The pooled per-observation sensitivity and specificity of the LR-5 category for diagnosing HCC were 70% (95% confidence interval [CI], 61%-78%) and 91% (95% CI, 89%-93%), respectively. No HCCs were reported for LR-1 and LR-2. The pooled proportions of HCC were 31% (95% CI, 12%-50%) for LR-3, 64% (95% CI, 47%-80%) for LR-4, 95% (95% CI, 93%-96%) for LR-5, 54% (95% CI, 30%-77%) for LR-TIV, and 33% (95% CI, 21%-46%) for LR-M. The proportions of HCC were significantly different among the LI-RADS categories (P = .022).ConclusionsThe LR-5 category of LI-RADS version 2018 provided moderate sensitivity and high specificity for diagnosing HCC. Higher LI-RADS categories from LR-3 to LR-5 included greater proportions of HCC.     

CT Fractional Flow Reserve for the Diagnosis of Myocardial Bridging-Related Ischemia: A Study Using Dynamic CT Myocardial Perfusion Imaging as a Reference Standard

ObjectiveTo investigate the diagnostic performance of CT fractional flow reserve (CT-FFR) for myocardial bridging-related ischemia using dynamic CT myocardial perfusion imaging (CT-MPI) as a reference standard.Materials and MethodsDynamic CT-MPI and coronary CT angiography (CCTA) data obtained from 498 symptomatic patients were retrospectively reviewed. Seventy-five patients (mean age ± standard deviation, 62.7 ± 13.2 years; 48 males) who showed myocardial bridging in the left anterior descending artery without concomitant obstructive stenosis on the imaging were included. The change in CT-FFR across myocardial bridging (ΔCT-FFR, defined as the difference in CT-FFR values between the proximal and distal ends of the myocardial bridging) in different cardiac phases, as well as other anatomical parameters, were measured to evaluate their performance for diagnosing myocardial bridging-related myocardial ischemia using dynamic CT-MPI as the reference standard (myocardial blood flow < 100 mL/100 mL/min or myocardial blood flow ratio ≤ 0.8).ResultsΔCT-FFR_systolic (ΔCT-FFR calculated in the best systolic phase) was higher in patients with vs. without myocardial bridging-related myocardial ischemia (median [interquartile range], 0.12 [0.08–0.17] vs. 0.04 [0.01–0.07], p < 0.001), while CT-FFR_systolic (CT-FFR distal to the myocardial bridging calculated in the best systolic phase) was lower (0.85 [0.81–0.89] vs. 0.91 [0.88–0.96], p = 0.043). In contrast, ΔCT-FFR_diastolic (ΔCT-FFR calculated in the best diastolic phase) and CT-FFR_diastolic (CT-FFR distal to the myocardial bridging calculated in the best diastolic phase) did not differ significantly. Receiver operating characteristic curve analysis showed that ΔCT-FFR_systolic had largest area under the curve (0.822; 95% confidence interval, 0.717–0.901) for identifying myocardial bridging-related ischemia. ΔCT-FFR_systolic had the highest sensitivity (91.7%) and negative predictive value (NPV) (97.8%). ΔCT-FFR_diastolic had the highest specificity (85.7%) for diagnosing myocardial bridging-related ischemia. The positive predictive values of all CT-related parameters were low.ConclusionΔCT-FFR_systolic reliably excluded myocardial bridging-related ischemia with high sensitivity and NPV. Myocardial bridging showing positive CT-FFR results requires further evaluation.