The added value of apparent diffusion coefficient assessments in O-RADS MRI evaluation for characterizing ovarian masses with solid components.

Background: Integrating diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) measurements with existing MR imaging protocols improves the differentiation between benign and malignant adnexal lesions. We aimed to assess the additional value of quantitative ADC in diagnosing adnexal masses classified by the O-RADS-MRI score and evaluate the impact on diagnostic performance.

Methods: This retrospective cohort study analyzed 159 patients with 218 ovarian masses, classified into benign, borderline, and malignant groups via histopathological evaluation.

Computer-Aided Detection (CADe) and Segmentation Methods for Breast Cancer Using Magnetic Resonance Imaging (MRI).

Breast cancer continues to be a major health concern, and early detection is vital for enhancing survival rates. Magnetic resonance imaging (MRI) is a key tool due to its substantial sensitivity for invasive breast cancers. Computer-aided detection (CADe) systems enhance the effectiveness of MRI by identifying potential lesions, aiding radiologists in focusing on areas of interest, extracting quantitative features, and integrating with computer-aided diagnosis (CADx) pipelines.

A Case of Primary Ovarian Primitive Neuroectodermal Tumor.

Primitive neuroectodermal tumors (PNET) are a family of poorly differentiated malignant neoplasms of neuroectodermal origin. According to the location of origin, PNETs could be further categorized as central or peripheral. Peripheral PNET (pPNET) is an uncommon type that accounts for 1% of all soft tissue sarcomas and occurs outside the central and sympathetic nervous systems.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Implications of Sex Difference in CT Scan Findings and Outcome of Patients with COVID-19 Pneumonia.

Purpose: The novel coronavirus pandemic has caused significant morbidity and mortality since December 2019. Although the role of chest CT for diagnosing coronavirus disease 2019 (COVID-19) pneumonia is still debatable, the modality has been used in scenarios of constrained reverse-transcription polymerase chain reaction (RT-PCR) testing. The epidemiologic reports indicate an unexplored difference between men and women in disease severity.

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.

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.

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.

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.