Prevalence and Correlation between MRI Findings and Outcome of Conservative Treatment in Primary Idiopathic Frozen Shoulder.

Objectives: Primary idiopathic frozen shoulder (FS) causes pain and stiffness in the shoulder joint. Over time, this disease causes restriction of shoulder motion. We undertook this study to evaluate possible correlation of MRI findings with outcome of conservative management in FS. Methods: A total of 65 cases participated in prospective cohort study. The correlation of MRI findings obtained before commencing the treatment with outcome of non-operative management (Mean of ROM, VAS, SST and OSS) was evaluated. Results: Anterior extracapsular edema significantly correlated with FF, EXR, VAS (a) and OSS. The effusion in humeral side of axillary recess significantly correlated with ROM restriction in ABD, EXR. Capsular thickness of glenoid portion showed good significance with FF, ABD, VAS (a) and OSS. Increased thickness of CHL showed negative correlation with improvement of EXR (P=0.049) (r=-0.617). Thickening of IGHL showed negative correlation with improvement of ABD (p=0.005 r=-0.862) and FF (p=0.007 r=-0.831). Mean Height of Axillary recess (HAR) was 7.2mm (3.5-11mm). HAR showed negative correlation with VAS pain scale (P=0.036) (r=-0.682) and OSS (P=0.038) (r=-0.668). Conclusion: Thickness of the joint capsule and effusion at the axillary fold are important factors for refractory frozen shoulder. We can recommend MRI for refractive cases and low threshold of expectation can be set for conservative management in patients with above findings.

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.

Worldwide Knowledge About Interventional Radiology Among Medical Students: Findings of a Comprehensive Review.

PURPOSE: The objective of this comprehensive review is to investigate the studies assessing the interventional radiology knowledge among medical students worldwide and inspect the feasible solutions for improving their perspective on this specialty. METHODS: A comprehensive literature search was accomplished on PubMed, Scopus, Web of science, and Embase databases. The quality of eligible articles was assessed with the QATSDD assessment tool. RESULTS: After screening of articles, 29 studies were included. All the included articles were published between 2009 and 2021, with 20 studies performed in 2017 and thereafter. Interventional radiologists' duties, procedures, and training pathways were the main subjects investigated in surveys. The interest and exposure to interventional radiology were also assessed. All the studies indicated that the knowledge about interventional radiology compared with other specialties is weak. In 14 studies, over 50% of participants reported their interventional radiology knowledge as either poor or none. The most reason for not considering interventional radiology residency was lack of awareness during medical schools. Studies which held a tutorial course reported the students' awareness and interest improved significantly after the course. CONCLUSION: The present evidence indicates that although the current awareness of interventional radiology is low, timely didactic courses in medical schools can improve this condition significantly, lead to graduation of more oriented referring physicians, and also attract more fit applicants to interventional radiology residency programs.

A novel deep learning model for breast lesion classification using ultrasound Images: A multicenter data evaluation.

PURPOSE: Breast cancer is one of the major reasons of death due to cancer in women. Early diagnosis is the most critical key for disease screening, control, and reducing mortality. A robust diagnosis relies on the correct classification of breast lesions. While breast biopsy is referred to as the "gold standard" in assessing both the activity and degree of breast cancer, it is an invasive and time-consuming approach. METHOD: The current study's primary objective was to develop a novel deep-learning architecture based on the InceptionV3 network to classify ultrasound breast lesions. The main promotions of the proposed architecture were converting the InceptionV3 modules to residual inception ones, increasing their number, and altering the hyperparameters. In addition, we used a combination of five datasets (three public datasets and two prepared from different imaging centers) for training and evaluating the model. RESULTS: The dataset was split into the train (80%) and test (20%) groups. The model achieved 0.83, 0.77, 0.8, 0.81, 0.81, 0.18, and 0.77 for the precision, recall, F1 score, accuracy, AUC, Root Mean Squared Error, and Cronbach's α in the test group, respectively. CONCLUSIONS: This study illustrates that the improved InceptionV3 can robustly classify breast tumors, potentially reducing the need for biopsy in many cases.

Role of chest CT scan in patients with preexisting cancer and COVID-19 pneumonia.

BACKGROUND: Detection of COVID-19 in cancer patients is challenging due to probable preexisting pulmonary infiltration caused by many infectious and non-infectious etiologies. We evaluated chest CT scan findings of COVID-19 pneumonia in cancer patients and explored its prognostic role in mortality. METHODS: We studied 266 COVID-19 patients with a history of cancer diagnosis between 2020 and 2022. Chest CT images were reported based on Radiological Society of North America (RSNA) structural report and the CT score and pattern of involvement were noted. We used multivariate logistic regression models to determine the association between CT scan findings and mortality of the cancer COVID-19 patients. RESULTS: The mean age was 56.48 (± 18.59), and 53% were men. Gastrointestinal (29.3%), hematologic (26.3%), and breast (10.5%) cancers were the most frequent types of cancer. The prevalence of atypical or indeterminate findings in the chest CT was 42.8%. Most radiologic findings were consolidation mixed with ground-glass opacity (44.4%), pleural effusion (33.5%), and pure ground-glass opacity (19.5%). The risk of death was higher among those who had typical chest CT for COVID-19 (OR 3.47; 95% CI 1.14-8.98) and those who had a severity of score higher than 18 (OR 1.89; 95% CI 1.07-3.34). Also, presence of consolidation (P value 0.040), pleural effusion (P value 0.000), centrilobular nodules (P value 0.013), and architectural distortion (P value 0.005) were associated with a poorer prognosis. CONCLUSION: Less than half of COVID-19 patients with a history of cancer had typical imaging features of COVID-19. Radiologists should be aware of atypical, rare, or subtle chest CT findings in patients with pre-existing cancer.

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.

Comparing efficacy and safety of P013, a proposed pertuzumab biosimilar, with the reference product in HER2-positive breast cancer patients: a randomized, phase III, equivalency clinical trial.

BACKGROUND: Breast cancer is the most frequently diagnosed cancer and the leading reason for cancer-related death among women. Neoadjuvant treatment with dual-HER2 (human epidermal growth factor receptor 2) blockade has shown promising effects in this regard. The present study aimed to compare the efficacy and safety of a proposed pertuzumab biosimilar with the reference pertuzumab. METHODS: This randomized, phase III, multicenter, equivalency clinical trial was conducted on chemotherapy-naive women with HER2-positive breast cancer. Patients were randomly assigned (1:1) to receive six cycles of either P013 (CinnaGen, Iran) or the originator product (Perjeta, Roche, Switzerland) along with trastuzumab, carboplatin, and docetaxel every 3 weeks. Patients were stratified by cancer type (operable, locally advanced, inflammatory) and hormone receptor status. The primary endpoint was breast pathologic complete response (bpCR). Secondary endpoints included comparisons of total pCR, overall response rate (ORR), breast-conserving surgery (BCS), safety, and immunogenicity. RESULTS: Two hundred fourteen patients were randomized to treatment groups. bpCR rate in the per-protocol population was 67.62% in the P013 and 71.57% in the reference drug groups. Based on bpCR, P013 was equivalent to the reference pertuzumab with a mean difference of - 0.04 (95% CI: - 0.16, 0.09). Secondary endpoints were also comparable between the two groups. CONCLUSIONS: The proposed biosimilar P013 was equivalent to the reference product in terms of efficacy. The safety of both medications was also comparable.

Chest CT Scan Features to Predict COVID-19 Patients' Outcome and Survival.

Background: Providing efficient care for infectious coronavirus disease 2019 (COVID-19) patients requires an accurate and accessible tool to medically optimize medical resource allocation to high-risk patients. Purpose: To assess the predictive value of on-admission chest CT characteristics to estimate COVID-19 patients' outcome and survival time. Materials and Methods: Using a case-control design, we included all laboratory-confirmed COVID-19 patients who were deceased, from June to September 2020, in a tertiary-referral-collegiate hospital and had on-admission chest CT as the case group. The patients who did not die and were equivalent in terms of demographics and other clinical features to cases were considered as the control (survivors) group. The equivalency evaluation was performed by a fellowship-trained radiologist and an expert radiologist. Pulmonary involvement (PI) was scored (0-25) using a semiquantitative scoring tool. The PI density index was calculated by dividing the total PI score by the number of involved lung lobes. All imaging parameters were compared between case and control group members. Survival time was recorded for the case group. All demographic, clinical, and imaging variables were included in the survival analyses. Results: After evaluating 384 cases, a total of 186 patients (93 in each group) were admitted to the studied setting, consisting of 126 (67.7%) male patients with a mean age of 60.4 ± 13.6 years. The PI score and PI density index in the case vs. the control group were on average 8.9 ± 4.5 vs. 10.7 ± 4.4 (p value: 0.001) and 2.0 ± 0.7 vs. 2.6 ± 0.8 (p value: 0.01), respectively. Axial distribution (p value: 0.01), cardiomegaly (p value: 0.005), pleural effusion (p value: 0.001), and pericardial effusion (p value: 0.04) were mostly observed in deceased patients. Our survival analyses demonstrated that PI score ≥ 10 (p value: 0.02) and PI density index ≥ 2.2 (p value: 0.03) were significantly associated with a lower survival rate. Conclusion: On-admission chest CT features, particularly PI score and PI density index, are potential great tools to predict the patient's clinical outcome.

A Spectrum of Ultrasound and MR Imaging of Fetal Gastrointestinal Abnormalities: Part 2 Anorectal Malformation, Liver, and Abdominal Wall Anomalies.

Ultrasound (US) and magnetic resonance imaging (MRI) are two modalities for diagnosing fetal gastrointestinal (GI) anomalies. Ultrasound (US) is the modality of choice. MRI can be used as a complementary method. Despite its expanding utilization in central nervous system (CNS) fetal malformation, MRI has not yet been established for evaluation of fetal GI abnormalities. Therefore, more attention should be paid to the clinical implications of MRI investigations following screening by US.

A Spectrum of Ultrasound and MR Imaging of Fetal Gastrointestinal Abnormalities: Part 1 Esophagus to Colon.

Ultrasound (US) and magnetic resonance imaging (MRI) are two modalities for diagnosing fetal gastrointestinal (GI) anomalies. Ultrasound (US) is the modality of choice. MRI can be used as a complementary method. Despite its expanding utilization in central nervous system (CNS) fetal malformation, MRI has not yet been established for evaluation of fetal GI abnormalities. Therefore, more attention should be paid to the clinical implications of MRI investigations following screening by US.

Comparison of fetal lung maturation in fetuses with intrauterine growth restriction with control group, using lung volume, lung/liver and lung/muscle signal intensity and apparent diffusion coefficient ratios on different magnetic resonance imaging sequences.

PURPOSE: To compare lung volume, lung apparent diffusion coefficient (ADC) and signal intensity ratio (SIR) on different magnetic resonance imaging (MRI) sequences between intrauterine growth restriction (IUGR) fetuses and the control group. MATERIALS AND METHODS: 49 IUGR and 58 non-IUGR fetuses were imaged using 3 Tesla MRI units. Total lung volume (TLV), lung/liver SIR (LLSIR) and lung/muscle SIR (LMSIR) in T1 and T2-weighted sequences and lung/liver ADC ratio (LLADCR) and lung/muscle ADC ratio (LMADCR) were assessed. RESULTS: LLSIR and LMSIR were significantly higher in the T1-weighted sequence (p-value: .03) and LLADCR and LMADCR were significantly lower on diffusion-weighted imaging (DWI) in IUGR fetuses compared to the control group (p-value: .01). There was no significant difference in SIRs in the T2-weighted sequence between the two groups. Although TLV was increased with gestational age in both groups, it was significantly lower in the IUGR group (mean: 82 ± 22.7 ml vs. 110.8 ± 18 ml, p-value: <.001). CONCLUSION: The T1-weighted sequence and DWI seem to be better than the T2-weighted sequence for assessing the faint difference of lung maturity between groups. However, SIR differences were not as meaningful as TLV differences and this could be related to the complex maturation process in IUGR fetuses as the effect of higher endogenous corticosteroids.

The Role of Medical Imaging in COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic launched in the third decade of the twenty-first century and continued to present time to cause the worst challenges the modern medicine has ever encountered. Medical imaging is an essential part of the universal fight against this pandemic. In the absence of documented treatment and vaccination, early accurate diagnosis of infected patients is the backbone of this pandemic management. This chapter reviews different aspects of medical imaging in the context of COVID-19.

A Practical, Clinical User-Friendly Format for Breast Ultrasound Report.

OBJECTIVE: Breast ultrasound (BUS) is often performed as an adjunct to mammography in breast cancer screening or for evaluating breast lesions. Our aim was to design a practical and user-friendly format for BUS that could include the details of the Breast Imaging Reporting and Data System. MATERIALS AND METHODS: As a team of radiologists and surgeons trained in the management of breast diseases, we gathered and carried out the project in four phases-literature search and collection of present report formats, summarizing key points and preparing the first draft, seeking expert opinion and preparing the final format, and pilot testing-followed by a survey was answered by the research team's radiologists and surgeons. RESULTS: It produced a list of items to be stated in the BUS report, the final BUS report format, and the pilot format guide. Then, the radiologists used the format in three active ultrasound units in university-affiliated centers, and reports were referred to the surgeons. At the end of the project, the survey showed a high degree of ease of use, clarity, conciseness, comprehensiveness, and well-classified structure of the report format; but radiologists believed that the new organization took more time. CONCLUSION: We propose our design as a user-friendly and practical format for BUS reports. It should be used for a longer time and by various ultrasound centers in order to ascertain its benefits.

Recent progress in immunotherapy of breast cancer targeting the human epidermal growth factor receptor 2 (HER2).

OBJECTIVE: Breast cancer is responsible for most of the cancer-induced deaths in women around the world. The current review will discuss different approaches of targeting HER2, an epidermal growth factor overexpressed in 30% of breast cancer cases. DATA SOURCES: We conducted a search on Pubmed and Scopus databases to find studies relevant to HER2+ breast cancers and targeting HER2 as means of immunotherapy. Out of 1043 articles, 105 studies were included in this review. DATA SUMMARY: As well as the introduction of HER2 and breast cancer subtypes, we discussed various aspects of HER2-targeting immunotherapy including monoclonal antibodies, Antibody-drug conjugates (ADCs), Chimeric Antigen Receptor (CAR) T-cells and vaccines. CONCLUSIONS: Despite several ways of controlling breast cancer, the need to investigate new drugs and approaches seems to be much significant as this cancer still has a heavy burden on people's health and survival.

Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system.

OBJECTIVE: Proposing a scoring tool to predict COVID-19 patients' outcomes based on initially assessed clinical and CT features. METHODS: All patients, who were referred to a tertiary-university hospital respiratory triage (March 27-April 26, 2020), were highly clinically suggestive for COVID-19 and had undergone a chest CT scan were included. Those with positive rRT-PCR or highly clinically suspicious patients with typical chest CT scan pulmonary manifestations were considered confirmed COVID-19 for additional analyses. Patients, based on outcome, were categorized into outpatient, ordinary-ward admitted, intensive care unit (ICU) admitted, and deceased; their demographic, clinical, and chest CT scan parameters were compared. The pulmonary chest CT scan features were scaled with a novel semi-quantitative scoring system to assess pulmonary involvement (PI). RESULTS: Chest CT scans of 739 patients (mean age = 49.2 ± 17.2 years old, 56.7% male) were reviewed; 491 (66.4%), 176 (23.8%), and 72 (9.7%) cases were managed outpatient, in an ordinary ward, and ICU, respectively. A total of 439 (59.6%) patients were confirmed COVID-19 cases; their most prevalent chest CT scan features were ground-glass opacity (GGO) (93.3%), pleural-based peripheral distribution (60.3%), and multi-lobar (79.7%), bilateral (76.6%), and lower lobes (RLL and/or LLL) (89.1%) involvement. Patients with lower SpO2, advanced age, RR, total PI score or PI density score, and diffuse distribution or involvement of multi-lobar, bilateral, or lower lobes were more likely to be ICU admitted/expired. After adjusting for confounders, predictive models found cutoffs of age ≥ 53, SpO2 ≤ 91, and PI score ≥ 8 (15) for ICU admission (death). A combination of all three factors showed 89.1% and 95% specificity and 81.9% and 91.4% accuracy for ICU admission and death outcomes, respectively. Solely evaluated high PI score had high sensitivity, specificity, and NPV in predicting the outcome as well. CONCLUSION: We strongly recommend patients with age ≥ 53, SpO2 ≤ 91, and PI score ≥ 8 or even only high PI score to be considered as high-risk patients for further managements and care plans. KEY POINTS: • Chest CT scan is a valuable tool in prioritizing the patients in hospital triage. • A more accurate and novel 35-scale semi-quantitative scoring system was designed to predict the COVID-19 patients' outcome. • Patients with age ≥ 53, SpO2 ≤ 91, and PI score ≥ 8 or even only high PI score should be considered high-risk patients.

Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT.

The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID-) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis.

Propagation-Based Phase-Contrast CT of the Breast Demonstrates Higher Quality Than Conventional Absorption-Based CT Even at Lower Radiation Dose.

RATIONALE AND OBJECTIVES: Propagation-based phase-contrast CT (PB-CT) is an advanced X-ray imaging technology that exploits both refraction and absorption of the transmitted X-ray beam. This study was aimed at optimizing the experimental conditions of PB-CT for breast cancer imaging and examined its performance relative to conventional absorption-based CT (AB-CT) in terms of image quality and radiation dose. MATERIALS AND METHODS: Surgically excised breast mastectomy specimens (n = 12) were scanned using both PB-CT and AB-CT techniques under varying imaging conditions. To evaluate the radiological image quality, visual grading characteristics (VGC) analysis was used in which 11 breast specialist radiologists compared the overall image quality of PB-CT images with respect to the corresponding AB-CT images. The area under the VGC curve was calculated to measure the differences between PB-CT and AB-CT images. RESULTS: The highest radiological quality was obtained for PB-CT images using a 32 keV energy X-ray beam and by applying the Homogeneous Transport of Intensity Equation phase retrieval with the value of its parameter γ set to one-half of the theoretically optimal value for the given materials. Using these optimized conditions, the image quality of PB-CT images obtained at 4 mGy and 2 mGy mean glandular dose was significantly higher than AB-CT images at 4 mGy (AUCVGC = 0.901, p = 0.001 and AUCVGC = 0.819, p = 0.011, respectively). CONCLUSION: PB-CT achieves a higher radiological image quality compared to AB-CT even at a considerably lower mean glandular dose. Successful translation of the PB-CT technique for breast cancer imaging can potentially result in improved breast cancer diagnosis.

Correlation of apparent diffusion coefficient values and peritumoral edema with pathologic biomarkers in patients with breast cancer.

PURPOSE: To investigate the relationship between breast cancer imaging features on magnetic resonance imaging (MRI) and histopathological characteristics. METHODS AND MATERIALS: We prospectively enrolled 46 patients who underwent 1.5-T MRI with 68 breast malignant lesions from 2017 until 2019. Peritumoral edema was determined based on visual assessment on T2 weighted imaging. Lesions were categorized into two groups: A: with edema (48 lesions) and B: without edema (20 lesions). RESULTS: The tumor size was not different among two groups but multifocal-multicentric lesions were more common in the group A (70% vs. 35%). The axillary lymph nodes are most involved in group A. ER and PR positive lesions were more common in group B (90% vs. 56.3%) but in the group A, HER2 positive lesions were found to be more common (31.3% vs. 15%). The mean ADC value in tumors and peritumoral regions were lower (0.97 × 10-3 mm2/s, P = 0.023) and higher (1.85 × 10-3 mm2/s, P < 0.0001) in group A, respectively. Peritumoral ADC value was significantly higher in HER2-positive group. CONCLUSION: Breast carcinomas with peritumoral edema were found to be more multifocal-multicentric, with higher prevalence of axillary lymph node involvement, more HER 2-positive, with lower prevalence of ER/PR-positive, lower tumoral ADC and higher peritumoral ADC values.