Machine learning approaches in the prediction of positive axillary lymph nodes post neoadjuvant chemotherapy using MRI, CT, or ultrasound: A systematic review.

Background and objective: Neoadjuvant chemotherapy is a standard treatment approach for locally advanced breast cancer. Conventional imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound, have been used for axillary lymph node evaluation which is crucial for treatment planning and prognostication. This systematic review aims to comprehensively examine the current research on applying machine learning algorithms for predicting positive axillary lymph nodes following neoadjuvant chemotherapy utilizing imaging modalities, including MRI, CT, and ultrasound. Methods: A systematic search was conducted across databases, including PubMed, Scopus, and Web of Science, to identify relevant studies published up to December 2023. Articles employing machine learning algorithms to predict positive axillary lymph nodes using MRI, CT, or ultrasound data after neoadjuvant chemotherapy were included. The review follows the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, encompassing data extraction and quality assessment. Results: Seven studies were included, comprising 1502 patients. Four studies used MRI, two used CT, and one applied ultrasound. Two studies developed deep-learning models, while five used classic machine-learning models mainly based on multiple regression. Across the studies, the models showed high predictive accuracy, with the best-performing models combining radiomics and clinical data. Conclusion: This systematic review demonstrated the potential of utilizing advanced data analysis techniques, such as deep learning radiomics, in improving the prediction of positive axillary lymph nodes in breast cancer patients following neoadjuvant chemotherapy.

A New Model Including AMH Cut-off Levels to Predict Post-treatment Ovarian Function in Early Breast Cancer: A Prospective Cohort Study.

BACKGROUND: Breast cancer (BC) treatment decreases fertility capacity, but unnecessary fertility preservation procedures in women who would not be infertile after treatment would be a waste of time and resources and could cause the unwarranted exposure of cancer cells to exogenous sex hormones. It has been largely shown that post-treatment ovarian reserve is directly associated with pre-treatment anti-mullerian hormone levels (AMH0). A threshold for AMH0, or a model including AMH0 and patient characteristics that could distinguish the patients who will be infertile after treatments, still needs to be defined. Accordingly, this study was performed to specifically target this high-priority concern. METHODS: Women≤45 years old with newly diagnosed non-metastatic BC were entered in this multicenter prospective cohort study. AMH0 and two-year post-treatment AMH (AMH2) were measured, and hormonal patient features were recorded as well. Receiver operating characteristic (ROC) curve analysis, decision tree (DT), and random forest analyses were performed to find a cut-off point for AMH0 and define a model involving related features for the prediction of AMH2. RESULTS: The data from 84 patients were analyzed. ROC curve analysis revealed that AMH0>3 ng/mL (Area under the curve=0.69, 95% CI: 0.54‒0.84) was the best indicator for predicting AMH2≥0.7 (sensitivity=79%, specificity=60%). The best model detected by DT and random forest for predicting an AMH2>0.7 with a probability of 93% consisted of a combination of AMH0>3.3, menarche age<14, and age<31. CONCLUSION: This combination model can be used to withhold fertility preservation procedures in BC patients. Performing larger studies is suggested to further test this model.

Evaluation of Features in Probably Benign and Malignant Nonmass Enhancement in Breast MRI.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a highly sensitive breast imaging modality in detecting breast carcinoma. Nonmass enhancement (NME) is uniquely seen on MRI of the breast. The correlation between NME features and pathologic results has not been extensively explored. Our goal was to evaluate the characteristics of probably benign and suspicious NME lesions in MRI and determine which features are more associated with malignancy. We performed a retrospective research after approval by the hospital ethics committee on women who underwent breast MRI from March 2017 to March 2020 and identified 63 lesions of all 400 NME that were categorized as probably benign or suspicious according to the BI-RADS classification (version 2013). MRI features of NME findings including the location, size, distribution and enhancement pattern, kinetic curve, diffusion restriction, and also pathology result or 6-12-month follow-up MRI were evaluated and analyzed in each group (probably benign or suspicious NME). Vacuum-guided biopsies (VAB) were performed under mammographic or sonographic guidance and confirmed with MRI by visualization of the inserted clips. Segmental distribution and clustered ring internal enhancement were significantly associated with malignancy (p value<0.05), while linear distribution or homogeneous enhancement patterns were associated with benignity (p value <0.05). Additionally, the plateau and washout types in the dynamic curve were only seen in malignant lesions (p value <0.05). The presence of DWI restriction in NME lesions was also found to be a statistically important factor. Understanding the imaging findings of malignant NME is helpful to determine when biopsy is indicated. The correlation between NME features and pathologic results is critical in making appropriate management.

Comparing Texture Analysis of Apparent Diffusion Coefficient MRI in Hepatocellular Adenoma and Hepatocellular Carcinoma.

AIM: This study aimed to assess the effectiveness of using MRI-apparent diffusion coefficient (ADC) map-driven radiomics to differentiate between hepatocellular adenoma (HCA) and hepatocellular carcinoma (HCC) features. MATERIALS AND METHODS: The study involved 55 patients with liver tumors (20 with HCA and 35 with HCC), featuring 106 lesions equally distributed between hepatic carcinoma and hepatic adenoma who underwent texture analysis on ADC map MR images. The analysis identified several imaging features that significantly differed between the HCA and HCC groups. Four classification models were compared for distinguishing HCA from HCC including linear support vector machine (linear-SVM), radial basis function SVM (RBF-SVM), random forest (RF), and k-nearest neighbor (KNN). RESULTS: The k-nearest neighbor (KNN) classifier displayed the top accuracy (0.89) and specificity (0.90). Linear-SVM and KNN classifiers showcased the leading sensitivity (0.88) for both, with the KNN classifier achieving the highest precision (0.9). In comparison, the conventional interpretation had lower sensitivity (70.1%) and specificity (77.9%). CONCLUSION: The study found that utilizing ADC maps for texture analysis in MR images is a viable method to differentiate HCA from HCC, yielding promising results in identified texture features.

Evidence Supporting Diagnostic Value of Liver Imaging Reporting and Data System for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review and Meta-analysis.

Background: Based on the Liver Imaging Data and Reporting System (LI-RADS) guidelines, Hepatocellular Carcinoma (HCC) can be diagnosed using imaging criteria in patients at risk of HCC. Objective: This study aimed to assess the diagnostic value of LI-RADS in high-risk patients with HCC. Material and Methods: This systematic review is conducted on international databases, including Google Scholar, Web of Science, PubMed, Embase, PROQUEST, and Cochrane Library, with appropriate keywords. Using the binomial distribution formula, the variance of each study was calculated, and all the data were analyzed using STATA version 16. The pooled sensitivity and specificity were determined using a random-effects meta-analysis approach. Also, we used the chi-squared test and I2 index to calculate heterogeneity among studies, and Funnel plots and Egger tests were used for evaluating publication bias. Results: The pooled sensitivity was estimated at 0.80 (95% CI 0.76-0.84). According to different types of Liver Imaging Reporting and Data Systems (LI-RADS), the highest pooled sensitivity was in version 2018 (0.83 (95% CI 0.79-0.87) (I2: 80.6%, P of chi 2 test for heterogeneity <0.001 and T2: 0.001). The pooled specificity was estimated as 0.89 (95% CI 0.87-0.92). According to different types of LI-RADS, the highest pooled specificity was in version 2014 (93.0 (95% CI 89.0-96.0) (I2: 81.7%, P of chi 2 test for heterogeneity <0.001 and T2: 0.001). Conclusion: LI-RADS can assist radiologists in achieving the required sensitivity and specificity in high-risk patients suspected to have HCC. Therefore, this strategy can serve as an appropriate tool for identifying HCC.

Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects' prognosis.

BACKGROUND: Artificial intelligence (AI)-aided analysis of chest CT expedites the quantification of abnormalities and may facilitate the diagnosis and assessment of the prognosis of subjects with COVID-19. OBJECTIVES: This study investigates the performance of an AI-aided quantification model in predicting the clinical outcomes of hospitalized subjects with COVID-19 and compares it with radiologists' performance. SUBJECTS AND METHODS: A total of 90 subjects with COVID-19 (men, n = 59 [65.6%]; age, 52.9±16.7 years) were recruited in this cross-sectional study. Quantification of the total and compromised lung parenchyma was performed by two expert radiologists using a volumetric image analysis software and compared against an AI-assisted package consisting of a modified U-Net model for segmenting COVID-19 lesions and an off-the-shelf U-Net model augmented with COVID-19 data for segmenting lung volume. The fraction of compromised lung parenchyma (%CL) was calculated. Based on clinical results, the subjects were divided into two categories: critical (n = 45) and noncritical (n = 45). All admission data were compared between the two groups. RESULTS: There was an excellent agreement between the radiologist-obtained and AI-assisted measurements (intraclass correlation coefficient = 0.88, P < 0.001). Both the AI-assisted and radiologist-obtained %CLs were significantly higher in the critical subjects (P = 0.009 and 0.02, respectively) than in the noncritical subjects. In the multivariate logistic regression analysis to distinguish the critical subjects, an AI-assisted %CL ≥35% (odds ratio [OR] = 17.0), oxygen saturation level of <88% (OR = 33.6), immunocompromised condition (OR = 8.1), and other comorbidities (OR = 15.2) independently remained as significant variables in the models. Our proposed model obtained an accuracy of 83.9%, a sensitivity of 79.1%, and a specificity of 88.6% in predicting critical outcomes. CONCLUSIONS: AI-assisted measurements are similar to quantitative radiologist-obtained measurements in determining lung involvement in COVID-19 subjects.

The association of metformin and aspirin intake with mammographic breast density: A cross-sectional study.

Background: Our purpose was to investigate the association between Mammographic breast density (MBD), a known strong marker for breast cancer and metformin and aspirin use and duration of use alone or simultaneously, in a sample of Iranian women considering other confounding factors. Methods: In a cross-sectional study, 712 individuals were selected out of women referred to two university hospitals for screening mammography. Participants' information was collected with a questionnaire. Four-category density scale (a = almost entirely fatty, b = scattered fibroglandular densities, c= heterogeneously dense, and d = extremely dense) was categorized as low (a&b) and high (c&d) density. Results: The mean age of the participants was 49.80 ± 7.26 years. Sixty-five percent of women belonged to the high and 35% to the low MBD category. Both aspirin and metformin had a significantly negative association with MBD, however, when confounding factors were entered into the models, only aspirin after adjustment for age and BMI had an inverse association with MBD (OR = 0.53, 95% CI: 0.35-0.94). Simultaneous use of metformin and aspirin (OR = 0.44, 95 %CI: 0.17-1.12) was associated with lower MBD. Furthermore, in women who used metformin (OR = 0.23, 95% CI: 0.09-0.62) and aspirin (OR= 0.35, 95% CI: 0.17-0.72) for 2 to 5 years, MBD was significantly lower. However, after the adjustment of confounding factors, these associations were not statistically significant. Conclusion: It seems metformin and aspirin intakes are associated with MBD. However, further studies with more sample size are needed.

Assessment of LI-RADS efficacy in classification of hepatocellular carcinoma and benign liver nodules using DCE-MRI features and machine learning.

Purpose: The current study aimed to evaluate the efficiency of dynamic contrast-enhanced (DCE) MRI visual features in classifying benign liver nodules and hepatocellular carcinoma (HCC) using a machine learning model. Methods: 115 LI-RADS3, 137 LI-RADS4, and 140 LI-RADS5 nodules were included (392 nodules from 245 patients), which were evaluated by follow-up imaging for LR-3 and pathology results for LR-4 and LR-5 nodules. Data was collected retrospectively from 3 T and 1.5 T MRI scanners. All the lesions were categorized into 124 benign and 268 HCC lesions. Visual features included tumor size, arterial-phase hyper-enhancement (APHE), washout, lesion segment, mass/mass-like, and capsule presence. Gini-importance method extracted the most important features to prevent over-fitting. Final dataset was split into training(70%), validation(10%), and test dataset(20%). The SVM model was used to train the classifying algorithm. For model validation, 5-fold cross-validation was utilized, and the test data set was used to assess the final accuracy. The area under the curve and receiver operating characteristic curves were used to assess the performance of the classifier model. Results: For test dataset, the accuracy, sensitivity, and specificity values for classifying benign and HCC lesions were 82%,84%, and 81%, respectively. APHE, washout, tumor size, and mass/mass-like features significantly differentiated benign and HCC lesions with p-value < .001. Conclusions: The developed classification model employing DCE-MRI features showed significant performance of visual features in classifying benign and HCC lesions. Our study also highlighted the significance of mass and mass-like features in addition to LI-RADS categorization. For future work, this study suggests developing a deep-learning algorithm for automatic lesion segmentation and feature assessment to reduce lesion categorization errors.

Abbreviated breast MRI for evaluating breast cancer before initiation of neoadjuvant chemotherapy: A cross-sectional study.

Background: Although, there are accumulating evidence about diagnostic role of abbreviated breast magnetic resonance imaging (MRI) in screening setting, the implementation of abbreviated MRI in staging of breast cancer has been poorly elucidated. Objective: To evaluate the diagnostic performance of abbreviated breast MRI in estimating extent of disease before initiation of neoadjuvant chemotherapy. Methods: A total of 54 patients with biopsy-proven main lesion referred to evaluate by standard protocol breast MRI before initiation of neoadjuvant chemotherapy were retrospectively enrolled. From a standard protocol, a data set of abbreviated protocol consisting fat-saturated T1-weighted (T1W) pre-contrast and first two fat-saturated T1W post-contrast series with reconstruction of their subtraction including maximum intensity projection (MIP) were obtained and interpreted. The concordance rate of abbreviated with standard protocol (as a reference standard) were compared. Diagnostic accuracy, sensitivity, specificity, and positive and negative predictive value were calculated, as well. Results: The maximum size of the main mass was 38.6 ± 17.3 and 40.7 ± 17.9 for abbreviated and standard protocol, respectively. All of the main mass was detected by abbreviated protocol with 100% concordance. Concordance was 98.1% and 94.4% in terms of multifocal/multicentric status and for estimating of NME, respectively. The abbreviated protocol has high sensitivity and specificity with more than 90% value regarding main mass detection, measurement of the maximum size of the main mass, determination of multifocal/multicenter status and NAC involvement. Conclusion: Abbreviated protocol may be a reliable surrogate for standard protocol breast MRI in evaluating extent of breast cancer.

Diagnostic accuracy of intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) MRI to differentiate benign from malignant breast lesions: A systematic review and meta-analysis.

PURPOSE: Magnetic resonance imaging (MRI) can reduce the need for unnecessary invasive diagnostic tests by nearly half. In this meta-analysis, we investigated the diagnostic accuracy of intravoxel incoherent motion modeling (IVIM) and dynamic contrast-enhanced (DCE) MRI in differentiating benign from malignant breast lesions. METHOD: We systematically searched PubMed, EMBASE, and Scopus. We included English articles reporting diagnostic accuracy for both sequences in differentiating benign from malignant breast lesions. Articles were assessed by quality assessment of diagnostic accuracy studies-2 (QUADAS-2) questionnaire. We used a bivariate effects model for standardized mean difference (SMD) analysis and diagnostic test accuracy analysis. RESULTS: Ten studies with 537 patients and 707 (435 malignant and 272 benign) lesions were included. The D, f, Ktrans, and Kep mean values significantly differ between benign and malignant lesions. The pooled sensitivity (95 % confidence interval) and specificity were 86.2 % (77.9 %-91.7 %) and 70.3 % (56.5 %-81.1 %) for IVIM, and 93.8 % (85.3 %-97.5 %) and 68.1 % (52.7 %-80.4 %) for DCE, respectively. Combined IVIM and DCE depicted the highest area under the curve of 0.94, with a sensitivity and specificity of 91.8 % (82.8 %-96.3 %) and 87.6 % (73.8 %-94.7 %), respectively. CONCLUSIONS: Combined IVIM and DCE had the highest diagnostic accuracy, and multiparametric MRI may help reduce unnecessary benign breast biopsy.

Diagnostic Values of the Liver Imaging Reporting and Data System in the Detection and Characterization of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis.

This review was undertaken to assess the diagnostic value of the Liver Imaging Reporting and Data System (LI-RADS) in patients with a high risk of hepatocellular carcinoma (HCC). Google Scholar, PubMed, Web of Science, Embase, PROQUEST, and Cochrane Library, as the international databases, were searched with appropriate keywords. Using the binomial distribution formula, the variance of all studies was calculated, and using Stata version 16 (StataCorp LLC, College Station, TX, USA), the obtained data were analyzed. Using a random-effect meta-analysis approach, we determined the pooled sensitivity and specificity. Utilizing the funnel plot and Begg's and Egger's tests, we assessed publication bias. The results exhibited pooled sensitivity and pooled specificity of 0.80% and 0.89%, respectively, with a 95% confidence interval (CI) of 0.76-0.84 and 0.87-0.92, respectively. The 2018 version of LI-RADS showed the greatest sensitivity (0.83%; 95% CI 0.79-0.87; I 2 = 80.6%; P < 0.001 for heterogeneity; T 2 = 0.001). The maximum pooled specificity was detected in LI-RADS version 2014 (American College of Radiology, Reston, VA, USA; 93.0%; 95% CI 89.0-96.0; I 2 = 81.7%; P < 0.001 for heterogeneity; T 2 = 0.001). In this review, the results of estimated sensitivity and specificity were satisfactory. Therefore, this strategy can serve as an appropriate tool for identifying HCC.

Association between 2D trans-perineal ultrasound and physical examination in evaluation of ani function.

PURPOSE: To compare the correlation between 2D transperineal ultrasonography and physical examination (intravaginal palpation) for assessing pelvic floor and levator ani function. METHODS: Due to symptoms of pelvic floor disorder, 40 women between the ages of 29 and 75 were enrolled in this study as candidates for urodynamic and structural evaluation of the pelvic floor. A pelvic floor gynaecologist and radiologist assessed the levator ani function via physical examination (graded based on the Oxford Grading System) and transperineal 2D ultrasound, respectively. RESULTS: The ultrasound parameters for calculating the Levator Ani Index (LAI) demonstrate a difference between the anteroposterior dimension of the levator hiatus (r = 0.691, p < 0.001) and the cranial shift of muscle (r = 0.499, p < 0.001) at rest and during a squeezing manoeuvre in the mid-sagittal plane. Reduced anteroposterior diameter of the hiatus and increased cranial shift were associated with a higher Oxford Physical Examination Score (OPES). The association between LAI and OPES was independent of baseline variables such as age, BMI, number of births, and the presence of incontinence symptoms. CONCLUSION: Measures such as the LAI can be used to quantify the function of the levator ani muscle, which may be useful for evaluating the efficacy of pelvic floor physiotherapy and exercise.

Value of Conventional MRI, DCE-MRI, and DWI-MRI in the Discrimination of Metastatic from Non-Metastatic Lymph Nodes in Rectal Cancer: A Systematic Review and Meta-Analysis Study.

BACKGROUND: Today, despite many studies on the diagnosis of metastasis to lymph nodes (LNs) in Rectal Cancer (RC), its diagnosis is still very challenging for radiologists. The purpose of the present study was to the assessment of the diagnostic value of conventional MRI, DCE-MRI, and DWI-MRI in the discrimination of metastatic from non-metastatic lymph nodes in RC. METHODS: In the present meta-analysis study, we surveyed international databases including PubMed, Scopus, Embase, and Science Direct with appropriate keywords. Using the binomial distribution formula, the variance of each study was calculated and the data were analyzed using STATA version 14. Finally, the results of the studies were entered into the random-effects meta-analysis.  Also, we used the chi-squared test and I2 index to calculate heterogeneity among studies, and for evaluating publication bias, Funnel plots and Egger tests were used. RESULTS: 31 articles published between 2005 and 2021, comprising 2517 patients were included in the present study. The sensitivity and specificity of DCE-MRI were 83% (74% to 80%), and 86% (80% to 93%), respectively with PPV 84% (76% to 89%) and NPV 88% (79% to 95%). Also, the sensitivity and specificity of DWI-MRI were 81% (74% to 88%), and 74% (78% to 91%), respectively with PPV 63% (54% to 74%), NPV 85% (77% to 93%), AUC 80 % (75% to 86%) and accuracy 82% (75% to 88%). For conventional MRI, the sensitivity 74% (67% to 80%), specificity 77% (71% to 83%), PPV 62% (48% to 69%), NPV 70% (62% to 77%), AUC 78% (72% to 83%) and 71% accuracy (68% to 78%) was obtained. CONCLUSION: Based on our finding DCE-MRI is the most suitable technique for the discrimination of metastatic lymph nodes in rectal cancer.

Diagnostic value of T2 and diffusion-weighted imaging (DWI) in local staging of endometrial cancer.

BACKGROUND: Endometrial cancer (EC) is the eighth most prevalent cancer globally. T2-weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) help anatomical localization and local staging of lesions. The present study was performed to assess the diagnostic value of the simultaneous use of T2 and DWI techniques in EC evaluation. METHODS: Seventy-eight histopathological-proven EC cases were included in this study. Patients were assessed using a complete MRI exam, including T2 and DWI. The myometrial invasion, cervical, serosal or adnexal, vaginal or parametrial, and pelvic lymph node involvements and accuracy, sensitivity, specificity, and positive and negative predictive values were evaluated in each sequence distinctly and was compared with the pathology findings and full standard protocol using post-contrast multiphasic contrast-enhanced series. RESULTS: Deep myometrial invasion in EC cases was detected in 38.5% by T2-DWI and 37.2% by pathology. The pathology diagnosed cervical, serosal, and vaginal involvements and pelvic lymph node metastases in 20.5%, 7.7%, 6.4% and 11.5% of cases respectively, while the numbers for T2-DWI were 26.9%, 7.7%, 7.7%, and 15.4%, respectively. The accuracy, sensitivity, specificity, PPV, and NPV of T2-DWI in the diagnosis of myometrial invasion were 93.5%, 93.1%, 93.8%, 90%, and 93.8%, respectively. A slightly higher Kappa coefficient of DWI (0.973) in the diagnosis of myometrial invasion was identified compared to T2 (0.946). The T2-DWI technique had a 52.6% intraclass correlation coefficient in the diagnosis of IA stage. CONCLUSION: The simultaneous consideration of T2 and DWI technique may signify a noninvasive, rapid, safe, and accurate approach for precisely assessing myometrial invasion and EC staging. Elimination of intravenous contrast material result in prevention of contrast related side effects beside significant cost reduction for health care systems and patients with a comparable result to contrast enhanced MRI.

Our experience using synthesized mammography vs full field digital mammography in population-based screening.

Background: Synthesized Mammogram (SM) from Digital Breast Tomosynthesis (DBT) images is introduced to replace the routine Full Field Digital Mammography (FFDM) to reduce radiation dose. Purpose: to compare the conspicuity of cancer related findings between SM and FFDM and combination of these methods with DBT. Methods: The study was conducted in a tertiary breast imaging center, where 200 women referred for screening were enrolled in the study sequentially. Patients underwent FFDM and DBT simultaneously and a two-year follow-up was done. Data was evaluated for Breast Imaging Reporting and Data System (BI-RADS) score, breast density, mass lesions, calcification, and focal asymmetry by two expert breast radiologists. Comparison between different methods was made by Cohen Kappa test. Results: 22 patients with likely malignant findings went under biopsy. Taking histopathologic findings and two-year follow up as reference, the overall sensitivity and specificity for FFDM+DBT (86.1 and 88.9 respectively) and SM+DBT (86.1 and 88.2) didn't show a meaningful difference. Comparing SM and FFDM, calcification in 20 subjects were overlooked on SM, but later detected when combined with DBT. Considering breast composition and BI-RADS categorization, an excellent agreement existed between the readers. Conclusion: Screening with SM+DBT shows comparable results with FFDM+DBT considering BI-RADS categorization of the patients. Although SM showed slightly inferior sensitivity compared to FFDM, after combining DBT with SM no malignant appearing calcification or mass lesion was missed.

Does MRI have added value in ultrasound-detected BIRADS-3 breast masses in candidates for assisted reproductive therapy?

Background: Ultrasound-detected breast lesions with probably benign features are a great challenge for clinicians, especially in breasts with dense composition. We aimed to investigate the finding of two radiologic modalities on these lesions. Methods: This retrospective cross-sectional study recruited patients including (1) candidates of assisted reproductive therapy (ART), (2) patients with prior high-risk lesions, and (3) the "suspected" BIRADS-3 masses referring to masses that US BIRADS-3 was not compatible with the clinical breast exam. The degree of agreement in diagnosing BIRADS-3 lesions between two modalities of magnetic resonance imaging (MRI) and ultrasonography (US), and comparison of the lesions in US and MRI were the study variables. Results: A total number of 123 lesions in 67 patients with a median age of 38 (IQR: 11, range: 17-67). In the examination by MRI, 107 (87.0 %) lesions were BIRADS-3 indicating the agreement level between these two modalities. The median size of the lesions in US was 9 mm (IQR: 5, range: 3-43) and 9 mm (IQR: 10, range: 4-46) in MRI. The measured size of the lesions between the two modalities was highly correlated (Spearman correlation coefficient: 0.889, P-value < 0.001). MRI evaluation revealed two cases of deep lesions which were missed in the US imaging. Conclusions: This study found relatively high agreement values between US and MRI in detecting BIRADS-3 breast lesions in candidates for ART or patients with prior high-risk lesions. Also, MRI could downgrade about one-tenth of the cases to a lower BIRADS level and resolved the need for closer follow-up.

Implementation of abbreviated breast MRI in diagnostic and screening settings.

BACKGROUND: Abbreviated magnetic resonance imaging (MRI) includes fewer sequences than standard MRI, which could be utilized for breast cancer detection. PURPOSE: To evaluate the diagnostic accuracy of abbreviated MRI protocol in screening and diagnostic settings. MATERIAL AND METHODS: All women with screening and diagnostic (problem-solving and preoperative staging) MRI examination were recruited from 2017 to 2020. Two expert radiologists assessed designed abbreviated protocol (fat-saturated T1-weighted [T1W] pre-contrast and two first fat-saturated T1W post-contrast series with reconstruction of their subtraction) including maximum intensity projection (MIP) and then evaluated standard protocol of breast MRI. Associated findings, including axillary lymphadenopathy and invasion to nipple, skin, or pectoralis muscle were also evaluated. The concordance rate of abbreviated with standard protocol in screening and diagnostic settings were also compared, based on BI-RADS classification. Diagnostic accuracy, sensitivity, specificity, and positive and negative predictive value were calculated. RESULTS: A total of 108 (26.5%) of 408 patients (mean age = 43 ± 9 years) were classified as BI-RADS 4-5 and considered positive findings based on suspicious enhancement (mass or non-mass enhancement). Compared to standard protocol, abbreviated protocol revealed >98% accuracy in the diagnostic setting as well as 100% accuracy in the screening setting. Concordance rates in screening and diagnostic settings were 99.6% and 98.1%, respectively. There was no discordance between abbreviated and standard protocol in the evaluation of associated findings. CONCLUSION: Abbreviated MRI protocol possesses substantial diagnostic accuracy in both screening and diagnostic settings. Additional information provided by standard protocol might not require for cancer detection.

Evaluation of chest CT-scan appearances of COVID-19 according to RSNA classification system.

Background: The Radiologic Society of North America (RSNA) divides patients into four sections: negative, atypical, indeterminate, and typical coronavirus disease 2019 (COVID-19) pneumonia based on their computed tomography (CT) scan findings. Herein, we evaluate the frequency of the chest CT-scan appearances of COVID-19 according to each RSNA categorical group. Methods: A total of 90 patients with real-time reverse transcriptase-polymerase chain reaction (RT-PCR)-confirmed COVID-19 were enrolled in this study and differences in age, sex, cardiac characteristics, and imaging features of lung parenchyma were evaluated in different categories of RSNA classification. Results: According to the RSNA classification 87.8, 5.56, 4.44, and 2.22% of the patients were assigned as typical, indeterminate, atypical, and negative, respectively. The proportion of "atypical" patients was higher in the patients who had mediastinal lymphadenopathy and pleural effusion. Moreover, ground-glass opacity (GGO) and consolidation were more pronounced in the lower lobes and left lung compared to the upper lobes and right lung, respectively. While small nodules were mostly seen in the atypical group, small GGO was associated with the typical group, especially when it is present in the right lung and indeterminate group. Conclusion: Regardless of its location, non-round GGO is the most prevalent finding in the typical group of the RSNA classification systems. Mediastinal lymphadenopathy, pleural effusion, and small nodules are mostly observed in the atypical group and small GGO in the right lung is mostly seen in the typical group.

Diagnostic value of diffusion-weighted magnetic resonance imaging in discriminating between metastatic and non-metastatic pelvic lymph nodes in endometrial cancer.

BACKGROUND: Researchers have recently focused on assessing the accuracy of diffusion-weighted magnetic resonance imaging (DW-MRI) in predicting pelvic lymph node metastases in gynecological malignancies. PURPOSE: To evaluate the diagnostic value of DW-MRI in discriminating between metastatic and non-metastatic pelvic lymph nodes in endometrial cancer patients. MATERIAL AND METHODS: This retrospective database study was conducted with 33 women aged 30-84 years with pathologically proven endometrial cancer that had been assessed by DW-MRI before their first treatment initiation at our referral hospital from March 2016 to April 2019. The diffusion technique (b = 50, 400, and 1000 mm2/s) was used in the imaging, and continuous apparent diffusion coefficient (ADC) metrics (ADCmin, ADCmax, ADCmean, ADCSD, and rADC) were compared between the metastatic and non-metastatic lymph nodes. RESULTS: In total, 48 lymph nodes from 33 patients were assessed. All metastatic lymph nodes were restricted, while among the non-metastatic lymph nodes, only 19.3% were restricted. Considering pathological reports of metastatic and non-metastatic lymph nodes as the gold standard, DWI-related restricted and non-restricted features had a sensitivity of 80.6%, a specificity of 100%, and an accuracy of 87.5% to discriminate between a metastatic and non-metastatic pattern. ADC metrics of ADCmin, ADCmax, ADCmean, ADCSD, and rADC showed high values enabling differentiation between metastatic and non-metastatic lymph nodes. The best cut-off values were 0.7 × 10-3, 1.2 × 10-3, 1.01 × 10-3, 123, and 0.78, respectively. CONCLUSION: DW-MRI is a useful quantitative tool for differentiating between metastatic and benign lymph nodes in endometrial cancer patients.