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Background. The effects of oligopin as an antioxidant on polycystic ovarian morphology (PCOM) have not yet been examined. Therefore, the objective of this study was to evaluate the oligopin supplementation on PCOM among patients with polycystic ovarian syndrome (PCOS). Methods. This randomized, placebo-controlled trial was carried out at Shariati Hospital, Arash Hospital, and Yas Hospital, Tehran, Iran, to determine the effect of oligopin (50 mg/d) or placebo in PCOS patients. The ultrasonographic ovarian morphology was assessed in women aged 18–40 years, before and after 3 months of intervention. Results. Among 45 randomized participants, 32 participants, of whom 17 were in the oligopin group and 15 were in the placebo group completed the trial. There was only one adverse event in the oligopin group. The mean (standard deviation) age of the patients was 30.47 (6.30) years and the median (interquartile range) BMI was 27.50 (23.42–33.55). Three months of oligopin therapy significantly decreased ovarian stromal area (p=0.01) and stromal/total area (p=0.003). However, no significant differences were observed in the ovarian volume, ovarian area, 2–9 mm antral follicle counts, or peripheral follicle distribution pattern at 3 months. Conclusion. Among participants with PCOS, the use of oligopin (50 mg) daily, as compared with a placebo, resulted in improvement of the stromal area and stromal/total area at the end of the 3 months of treatment. Further studies are, however, needed to evaluate the longer-term efficacy and safety. This trial is registered with IRCT20140406017139N3.

Abstract 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.

Abstract 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.

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.

Breast cancer is one of the most common types of cancer in women. Screening mammography is a low‑dose X‑ray examination of breasts, which is conducted to detect breast cancer at early stages when the cancerous tumor is too small to be felt as a lump. Screening mammography is conducted for women with no symptoms of breast cancer, for early detection of cancer when the cancer is most treatable and consequently greatly reduce the death rate from the breast cancer. Screening mammography should be performed every year for women age 45-54, and every two years for women age 55 and older who are in good health. A mammogram is read by a radiologist to diagnose cancer. To assist radiologists in reading mammograms, computer-aided detection (CAD) systems have been developed which can identify suspicious lesions on mammograms. CADs can improve the accuracy and confidence level of radiologists in decision making and have been approved by FDA for clinical use. Traditional CAD systems work based on conventional machine learning (ML) and image processing algorithms. With recent advances in software and hardware resources, a great breakthrough in deep learning (DL) algorithms was followed, which revolutionized various engineering areas including medical technologies. Recently, DL models have been applied in CAD systems in mammograms and achieved outstanding performance. In contrast to conventional ML, DL algorithms eliminate the need for the tedious task of human-designed feature engineering, as they are capable of learning useful features automatically from the raw data (mammogram). One of the most common DL frameworks is the convolutional neural network (CNN). To localize lesions in a mammogram, a CNN should be applied in region‑based algorithms such as R‑CNN, Fast R‑CNN, Faster R‑CNN, and YOLO. Proper training of a DL‑based CAD requires a large amount of annotated mammogram data, where cancerous lesions have been marked by an experienced radiologist. This highlights the importance of establishing a large, annotated mammogram dataset for the development of a reliable CAD system. This article provides a brief review of the state‑of‑the‑art techniques for DL‑based CAD in mammography.

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.

Abstract 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.

Diffusion Magnetic Resonance Imaging (dMRI) has widely been used as a part of breast MRI protocols throughout the world, providing valuable information about breast tissue structures. This method has the potential to improve the characterization of benign and malignant breast lesions thereby guiding treatment decisions. DMRI as a non-contrast approach has certain benefits in comparison with the Dynamic Contrast Enhanced (DCE) method. Particularly, dMRI does not need intravenous contrast, which makes the imaging process faster and easier. Although there are still concerns about dMRI images quality, advances in the acquisition methods seem to be promising. More advanced dMRI strategies, such as Diffusion Tensor Imaging (DTI) and Intravoxel Incoherent Motion (IVIM), not only improve diagnosis accuracy, but also present new information about tissue perfusion. This review will present an overview of dMRI in the characterization of breast cancers.

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.

Background: Diffusion-weighted imaging (DWI) is one of methods in evaluation of breast lesions. We aimed to investigate the apparent diffusion coefficient (ADC) values in breast tumors and their accuracy in differentiating benign versus malignant lesions. Methods: In this cross-sectional study, 72 patients with 88 breast lesions were investigated by 1.5-T breast MRI from 2015 to 2017 in Athari Imaging Center in Tehran, Iran. Nearly all patients has undergone histopathology evaluation. One small region of interest (ROI) were placed on the most restricted region inside the solid part on the ADC map. Care was taken to avoid cystic or necrotic, fatty regions and hematoma inside the mass. A large round ROIs were placed in healthy fibroglandular tissue of contralateral breast ADC values were measured and compared in normal breast tissue and in most restricted parts of breast lesions (mass and non-mass). After determining cut-off for differentiation of benign and malignant lesions, sensitivity, specificity, accuracy, positive predictive value and negative predictive value were calculated. Results: Mean age of patients was 43.3 years. The average tumor size of benign and malignant lesions were calculated 26.0 mm, 35.3 mm respectively and 23 mm and 46 mm in mass and non-mass respectively. Invasive ductal carcinoma include the majority of pathology result (in 37.5% of the patients). Our results revealed that the measured ADC values in normal breast tissue were higher than breast lesions (P≤0.01). Mean ADC value in benign lesions was 1.40×10-3 mm²/s and for malignant lesion was 1.08×10-3 mm²/s. ADC value in the normal breast tissue was 1.79×10-3 mm2/s and was significantly higher than ADC value of breast lesions (benign and malignant). Cut-off value in non-mass was not valid, but in mass was 1.19×10-3 mm²/s with sensitivity, specificity, positive predictive value, negative predictive and accuracy of 89.7%, 83.8%, 87.5%, 86.6%, and 87.1% respectively. Conclusion: In DWI imaging, ADC value can differentiate benign and malignant masses with high sensitivity and specificity but not helpful in non-mass lesions.

Background: Pseudoaneurysm of the breast is a very uncommon disease mostly occurring following traumatic injuries including biopsy. Increasing the use of core needle biopsy in breast pathologies could lead to more cases of the iatrogenic pseudoaneurysm. Case presentation: Here we described a breast pseudoaneurysm case that occurred after core needle biopsy. The diagnosis was made by color doppler ultrasonography and MRI with contrast. Ultrasound-guided compression was used to treat the lesion. The lesion was followed up with ultrasonography and finally it was resolved. Conclusion: Although hematoma is a more common complication of breast biopsy, other complications including pseudoaneurysm should be kept in mind. Ultrasound is a useful technique for both the diagnosis and non-surgical treatment of the disease. Ultrasound-guided compression and follow up exams could be helpful to prevent invasive procedures.

Detecting metastatic cells in tumor/lymph node samples of breast cancer patients is extremely important for diagnosis. Here the authors develop a microelectronic biochip that detect the presence of invasive/metastatic cells in unprocessed biopsies and performs better than the current gold standards.

Purpose: To determine the relationship between color and spectral Doppler features of breast cancers and their biomarkers. Patients and Methods: From January 2017 to January 2018, 43 patients with breast cancer were enrolled. Age, existence of color flow in the Doppler ultrasound, color flow pattern, tumor size, and immunohistochemistry (IHC) subtypes were recorded. Results: Among 43 breast cancer patients, IHC profiles showed that 36 patients were estrogen (ER) positive, 30 patients were progesterone (PR) positive, and 12 patients were human epidermal growth factor receptor 2 (Her2) positive. The prevalence of biomarker groups in this study were as follows: luminal A, 21 patients (48.83%); luminal B, 15 (34.88%); Her 2 amplifier, 2 (4.65%); and triple negative, 5 (11.62%). Thirty-seven patients (86.04%) with malignant masses had detectable flow and six patients (13.95%) had no detectable flow. The ER-positive and PR-positive breast cancers had the highest vascular presence rate in color Doppler ultrasound but it was not statistically significant. Maximum vessel diameter in different biomarker groups and Doppler color patterns with various biomarkers showed no significant differences. Conclusion: It is not possible to predict breast cancer biomarker groups using available color Doppler features and indexes, so pathology with IHC is still required.

Introduction: Breast magnetic resonance imaging (BMRI) has been identified as a valuable modality in the diagnosis of breast cancer and monitoring the response to chemotherapy. The aim of this study was to evaluate the relative importance of different descriptors of breast masses in contrast-enhanced breast MRI. Material and methods: In a database of pathologically proven breast lesions, in total 433 masses in 312 patients detected by contrast-enhanced breast MRI were selected. All images were assessed according to the MRI BI-RADS lexicon and those with significant positive MRI findings (BI-RADS categories 3, 4, 5) were enrolled in the study. Results : Mean age of patients was 45.09 ±10.5 years. The most frequent BI-RADS score was 4 (60.7%), followed by 3 (27%). Among the morphologic descriptors of the enhancing masses, the findings most strongly associated with malignancy included spiculated margin (60.6%) and irregular shape (38%). Considering the dynamic descriptors, a wash-out pattern in the time-intensity curve was the most powerful finding associated with malignancy (27.9%). Among all breast MRI descriptors, the best odds ratio (OR) in association with malignancy was noted for speculated margin (OR = 10.2) followed by wash-out or plateau curves (OR = 6.1), size greater than 1 cm (OR = 4.3) and irregular shape (OR = 3.1). Conclusions : It seems that morphologic descriptors of MRI BI-RADS for enhancing masses are quite specific, while dynamic descriptors of the masses are highly sensitive. Appropriate consideration and combination of different BI-RADS findings could help in better characterization of enhancing masses on breast MRI, lowering the rate of false positive reports and avoiding unnecessary biopsies.

Purpose: In soft tissues, tumors have generally different sound speeds than normal tissues, so that sound speed images could be used to characterize breast cancer and to study the tumor invasion process. Ultrasound Computed Tomography (UCT) has a great potential to provide quantitative estimations of physical properties of normal and abnormal tissues that provides accurate characterization capability for breast cancer. The goal of this study is a comparison of two different detector arrangement and image reconstruction filters on image quality parameters in fan-beam back projection method to introduce the modified arrangement and filter to achieve high-quality USCT images. Materials and Methods: Two USCT systems with two different detector arrangement of the ultrasound array, ring-shape and parallel shape, have been simulated and Point Spread Function (PSF), Modulate Transform Function (MTF) and spatial resolution are three image quality parameters, have been investigated in both systems. In this study, a modified filter introduced that apply on the sinogram in two dimensions in spite of the Ram-Lak that apply on sinogram in one dimension. Results: These study results show that the spatial resolution affected by not only detector arrangement but also reconstruction filters. In addition, applying the modified filter improves the resolution of the ring-shaped system and destroy the resolution of the line one and we have higher resolution in the modified filter than Ram-Lak. Conclusion: The result suggests that to have high-resolution USCT images, the difference filters specially modified filter can be used and ring-shaped system has higher resolution images than line one.

Tamoxifen is a popular medication used in the adjuvant therapy of hormone sensitive breast cancer. In this case report we describe a 39 years old woman presented with ovarian hyperstimulation on the underlying thamoxifen treatment and consecutive ovarian torsion.

Neonatal MR templates are appropriate for brain structural analysis and spatial normalization. However, they do not provide the essential accurate details of cranial bones and fontanels-sutures. Distinctly, CT images provide the best contrast for bone definition and fontanels-sutures. In this paper, we present, for the first time, an approach to create a fully registered bimodal MR-CT head template for neonates with a gestational age of 39 to 42 weeks. Such a template is essential for structural and functional brain studies, which require precise geometry of the head including cranial bones and fontanels-sutures. Due to the special characteristics of the problem (which requires inter-subject inter-modality registration), a two-step intensity-based registration method is proposed to globally and locally align CT images with an available MR template. By applying groupwise registration, the new neonatal CT template is then created in full alignment with the MR template to build a bimodal MR-CT template. The mutual information value between the CT and the MR template is 1.17 which shows their perfect correspondence in the bimodal template. Moreover, the average mutual information value between normalized images and the CT template proposed in this study is 1.24±0.07. Comparing this value with the one reported in a previously published approach (0.63±0.07) demonstrates the better generalization properties of the new created template and the superiority of the proposed method for the creation of CT template in the standard space provided by MR neonatal head template. The neonatal bimodal MR-CT head template is freely downloadable from https://www.u-picardie.fr/labo/GRAMFC.

Purpose: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is an effective tool for detection and characterization of breast lesions. Qualitative assessment of suspicious breast DCE-MRI is problematic and operator dependent. The purpose of this study is to evaluate diagnostic efficacy of the representative characteristic parameters, extracted from kinetic curves of DCE-MRI, for discrimination between benign from malignant suspicious breast tumors. Methods: Pre-operative DCE-MR images of twenty-six histopathological approved breast lesions were analyzed. The images were reviewed by an expert radiologist and the regions of interests (ROI)s were selected on the most solid part of the lesion.Semi-quantitative kinetic parameters, namely: maximum signal enhancement (SI), max 60 initial area under the curve (IAUC), time to peak (TTP), wash in rate (WIR), wash out rate (WOR) and signal enhancement ratio (SER), were calculated within each ROI. Mean values of the calculated features among benign and malignant groups were compared using student’s t-test. Finally, a classification was performed employing support vector machines (SVM) using each of the parameters and their combinations in order to investigate the efficacy of the parameters in distinguishing between benign from malignant tumors. Results: The performance of the classification procedure employing the combination of semi-quantitative features with (p-value< 0.001) was evaluated by means of several measures, including accuracy, sensitivity, specificity, positive predictive value and negative predictive value which returned amounts of 97.5%, 96.49%,100%, 100% and 95.61% respectively. Conclusion: In conclusion, semi-quantitative analysis of the characteristic kinetic curves of suspicious breast lesions derived from SVM classifier provides an effective lesion classification in breast DCE-MR images.