Association of imaging and pathological findings of breast cancer in very young women: Report of a twenty-year retrospective study.

PURPOSE: In this study, we aimed to assess the new trends in characteristics, molecular subtypes, and imaging findings of breast cancer in very young women. METHODS: We retrospectively reviewed the database of a primary breast cancer referral center in southern Iran in 342 cases of 30-year-old or younger women from 2001 to 2020. Pathologic data, including nuclear subtype and grade, tumor stage, presence of in situ cancer, imaging data including lesion type in mammogram and ultrasound, and treatment data were recorded. Descriptive statistics were applied. Differences between categorical values between groups were compared using Pearson's Chi-square test. RESULTS: The mean age was 27.89 years. The tumor type was invasive ductal carcinoma in 82 % of cases. Fourteen patients (4.4 %) had only in situ cancer, and 170 patients had in situ components (49.7 %). Molecular subtypes were available in 278 patients, including 117 (42.1 %) Luminal A, 64 (23.0 %) Luminal B, 58 (20.9 %) triple negative, and 39 (14 %) HER2 Enriched. In those with mammograms available, 63 (30.1 %) had no findings, 53 (25.3 %) had mass, 27 (12.9 %) had asymmetry, whether focal or global, 21 (10 %) had microcalcifications solely, and 45 (21.5 %) had more than one finding. Microcalcifications were significantly more common in Luminal cancers than HER2 and triple-negative cancers (p = 0.041). CONCLUSION: Our study shows the most common subtype to be Luminal A cancer, with 74 % of the tumors being larger than 2 cm at the time of diagnosis. Irregular masses with non-circumscribed margins were the most common imaging findings.

A case report on pulmonary metastasis of giant cell tumor mimicking arteriovenous malformation.

Giant cell tumor (GCT) is typically a benign tumor of the skeletal system that mainly presents with bone pain. Pulmonary metastasis is one of the distant presentations of GCT in patients who have previously undergone surgical resection of the tumor. Among the various presentations of pulmonary metastasis in GCT, lesions with arteriovenous malformation (AVM) features are rare and have only been reported in a few cases. In this case report, we present the case of a 29-year-old female patient who had previously undergone surgical resection of a GCT in her right lower extremity 4 years ago. The patient was referred to us with progressive dyspnea, and a lesion resembling an AVM was found during radiologic evaluation using chest computed tomography. Pathologic evaluation of the lesion after biopsy revealed that it was a metastasis of GCT presenting with vascular-like features in the lung. This study reports on a very rare occurrence of GCT pulmonary metastasis with an AVM appearance on imaging, highlighting the clinical importance of atypical presentations of pulmonary metastasis in patients with a history of GCT. Appropriate and timely screening and management of such lesions may prevent adverse outcomes such as massive hemorrhage and deterioration of lung function.

Enhanced myometrial vascularity and AVM: A review on diagnosis and management.

Uterine Arteriovenous malformations (AVM) are vascular disorders characterized by complex high-flow tangles of abnormal vessels connecting arteries and veins with bypassing capillaries. Recently, the terminology applied to describe uterine AVMs has been modified. Most AVMs are acquired. The term enhanced myometrial vascularity (EMV) is used to describe any condition in which any uterine pathology may lead to increased myometrial vascularity regardless of the absence or presence of residual tissue of gestation.

Prediction of In-Hospital Outcomes in Patients with Traumatic Brain Injury Using Computed Tomographic Scoring Systems: A Comparison Between Marshall, Rotterdam, and Neuroimaging Radiological Interpretation Systems.

OBJECTIVE: This study aimed to compare the prognostic value of Marshall, Rotterdam, and Neuroimaging Radiological Interpretation Systems (NIRIS) in predicting the in-hospital outcomes of patients with traumatic brain injury. METHODS: We identified 250 patients with traumatic brain injury in a retrospective single-center cohort from 2019 to 2020. Computed tomography (CT) scans were reviewed by two radiologists and scored according to three CT scoring systems. One-month outcomes were evaluated, including hospitalization, intensive care unit admission, neurosurgical procedure, and mortality. Logistic regression analysis was performed to identify scoring systems and outcome relationships. The best cutoff value was calculated using the receiver operating characteristic curve model. RESULTS: Eighteen patients (7.2%) died in the 1-month follow-up. The mean age and Glasgow Coma Scale of survivors differed significantly from nonsurvivors. Subarachnoid hemorrhage and compressed/absent cisterns were dead patients' most frequent CT findings. All three scoring systems had good discrimination power in mortality prediction (area under the receiver operating characteristic curve of the Marshall, Rotterdam, and NIRIS was 0.78, 0.86, and 0.84, respectively). Regarding outcome, three systems directly correlated with unfavorable outcome prediction. CONCLUSIONS: The Marshall, Rotterdam, and NIRIS are good predictive models for mortality and outcome prediction, with slight superiority of the Rotterdam in mortality prediction and the Marshall in intensive care unit admission and neurosurgical procedures.

Deep learning-based automatic detection of tuberculosis disease in chest X-ray images.

Purpose: To train a convolutional neural network (CNN) model from scratch to automatically detect tuberculosis (TB) from chest X-ray (CXR) images and compare its performance with transfer learning based technique of different pre-trained CNNs. Material and methods: We used two publicly available datasets of postero-anterior chest radiographs, which are from Montgomery County, Maryland, and Shenzhen, China. A CNN (ConvNet) from scratch was trained to automatically detect TB on chest radiographs. Also, a CNN-based transfer learning approach using five different pre-trained models, including Inception_v3, Xception, ResNet50, VGG19, and VGG16 was utilized for classifying TB and normal cases from CXR images. The performance of models for testing datasets was evaluated using five performances metrics, including accuracy, sensitivity/recall, precision, area under curve (AUC), and F1-score. Results: All proposed models provided an acceptable accuracy for two-class classification. Our proposed CNN architecture (i.e., ConvNet) achieved 88.0% precision, 87.0% sensitivity, 87.0% F1-score, 87.0% accuracy, and AUC of 87.0%, which was slightly less than the pre-trained models. Among all models, Exception, ResNet50, and VGG16 provided the highest classification performance of automated TB classification with precision, sensitivity, F1-score, and AUC of 91.0%, and 90.0% accuracy. Conclusions: Our study presents a transfer learning approach with deep CNNs to automatically classify TB and normal cases from the chest radiographs. The classification accuracy, precision, sensitivity, and F1-score for the detection of TB were found to be more than 87.0% for all models used in the study. Exception, ResNet50, and VGG16 models outperformed other deep CNN models for the datasets with image augmentation methods.

Factors Predicting Outcome in Intensive Care Unit-Admitted COVID-19 Patients: Using Clinical, Laboratory, and Radiologic Characteristics.

PURPOSE: To investigate the factors contributing to mortality in coronavirus disease 2019 (COVID-19) patients admitted in the intensive care unit (ICU) and design a model to predict the mortality rate. METHOD: We retrospectively evaluated the medical records and CT images of the ICU-admitted COVID-19 patients who had an on-admission chest CT scan. We analyzed the patients' demographic, clinical, laboratory, and radiologic findings and compared them between survivors and nonsurvivors. RESULTS: Among the 121 enrolled patients (mean age, 62.2 ± 14.0 years; male, 82 (67.8%)), 41 (33.9%) survived, and the rest succumbed to death. The most frequent radiologic findings were ground-glass opacity (GGO) (71.9%) with peripheral (38.8%) and bilateral (98.3%) involvement, with lower lobes (94.2%) predominancy. The most common additional findings were cardiomegaly (63.6%), parenchymal band (47.9%), and crazy-paving pattern (44.4%). Univariable analysis of radiologic findings showed that cardiomegaly (p : 0.04), pleural effusion (p : 0.02), and pericardial effusion (p : 0.03) were significantly more prevalent in nonsurvivors. However, the extension of pulmonary involvement was not significantly different between the two subgroups (11.4 ± 4.1 in survivors vs. 11.9 ± 5.1 in nonsurvivors, p : 0.59). Among nonradiologic factors, advanced age (p : 0.002), lower O2 saturation (p : 0.01), diastolic blood pressure (p : 0.02), and hypertension (p : 0.03) were more commonly found in nonsurvivors. There was no significant difference between survivors and nonsurvivors in terms of laboratory findings. Three following factors remained significant in the backward logistic regression model: O2 saturation (OR: 0.91 (95% CI: 0.84-0.97), p : 0.006), pericardial effusion (6.56 (0.17-59.3), p : 0.09), and hypertension (4.11 (1.39-12.2), p : 0.01). This model had 78.7% sensitivity, 61.1% specificity, 90.0% positive predictive value, and 75.5% accuracy in predicting in-ICU mortality. CONCLUSION: A combination of underlying diseases, vital signs, and radiologic factors might have prognostic value for mortality rate prediction in ICU-admitted COVID-19 patients.

Spinal arteriovenous malformations causing Foix-Alajouanine syndrome, a case report and review of the literature.

Foix-Alajouanine syndrome is a rare progressive form of spinal AVM predominantly affecting the lower thoracic and/or lumbosacral regions. This study aims to describe the imaging findings of spinal AVM causing Foix-Alajouanine syndrome and to review the literature. We present a 48-year-old man with progressive back pain, leg weakness, and gait imbalance without urinary retention. We discuss the clinical and imaging findings and the significance of MRI in establishing the diagnosis. A definitive diagnosis of spinal AVM requires radiographic demonstration of the vascular anomaly. Despite the high sensitivity of angiography for the diagnosis of spinal AVM, the result of the study may be inconclusive and/or negative. The key MRI findings are the presence of abnormally dilated perimedullary vessels with signal voids from a high-velocity flow on T1 and T2 weighted images.

Identification of a novel homozygous mutation in the DDR2 gene from a patient with spondylo-meta-epiphyseal dysplasia by whole exome sequencing.

OBJECTIVES: The spondylo-meta-epiphyseal dysplasia (SMED) short limbs-hand type is a rare autosomal recessive disease, which is characterized by premature calcification leading to severe disproportionate short stature and various skeletal changes. Defective function of a conserved region encoding discoidin domain receptor tyrosine kinase 2 (DDR2 protein) by the discoidin domain-containing receptor 2 (DDR2 gene) is cause of this disease. The purpose of present study was to investigate disease-causing mutations on DDR2 gene in an Iranian family with SMED, and predict the DDR2 protein molecular mechanism in development of SMED. MATERIALS AND METHODS: In the present study, we evaluated a 2-year-old male with SMED. Detection of genetic changes in the studied patient was performed using Whole-Exome Sequencing (WES). PCR direct sequencing was performed for analysis of co-segregation of variants with the disease in family. Finally, in silico study was performed for further identification of molecular function of the identified genetic variant. RESULTS: We detected a novel splice-site mutation (NM_001014796: exon9: c.855+1G>A; NM_006182: exon8: c.855+1G>A) in DDR2 gene of the studied patient using WES. This mutation was exclusively detected in patients with homozygous SMED, not in healthy people. The effects of detected mutation on functions of DDR2 protein was predicted using in silico study. CONCLUSION: The causative mutation in studied patient with SMED was identified using Next-generation sequencing (NGS), successfully. The identified novel mutation in DDR2 gene can be useful in prenatal diagnosis (PND) of SMED, preimplantation genetic diagnosis (PGD), and genetic counseling.

Accurate Estimation of Breast Tumor Size: A Comparison Between Ultrasonography, Mammography, Magnetic Resonance Imaging, and Associated Contributing Factors.

OBJECTIVE: This study aimed to provide further evidence on the accuracy of tumor size estimates and influencing factors. MATERIALS AND METHODS: In this cross-sectional study, patients with a biopsy-proven diagnosis of breast cancer referred to our hospital to obtain a preoperative magnetic resonance imaging (MRI) between 2015 and 2016 were included. Data from 76 breast cancer patients with 84 lesions were collected. All participants underwent ultrasonography and MRI, and their mammograms (MGMs) were reevaluated for tumor size estimation. Measurements by the three imaging modalities were compared with the pathologically determined tumor size to assess their accuracy. Influencing factors such as surgical management, molecular and histopathological subtypes, and Breast Imaging Reporting and Data System enhancement types in MRI were also assessed. RESULTS: The rates of concordance with the gold standard were 64.3%, 76.2%, and 82.1% for MGM, ultrasound (US), and MRI measurements, respectively. Therefore, the highest concordance rate was observed in MRI-based estimates. Among the discordant cases, US and MGM underestimation were more prevalent (70%); nevertheless, MRI showed significant overestimation (80%). Tumor size estimates in patients whose MRIs presented with either non-mass enhancement [p=0.030; odds ratio (OR)=17.2; 95% confidence interval (CI): 1.3-225.9] or mass lesion with non-mass enhancement (p=0.001; OR=51.0; 95% CI: 5.0-518.4) were more likely to be discordant with pathological measurements compared with those in cases with only mass lesion on their MRIs. CONCLUSION: MRI was more accurate than either US or MGM in estimating breast tumor size but had the highest overestimation rate. Therefore, caution should be practiced in interpreting data obtained from subjects whose MRIs present with non-mass enhancement or mass lesion with non-mass enhancement.

Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images.

OBJECTIVE: Pneumonia is a lung infection and causes the inflammation of the small air sacs (Alveoli) in one or both lungs. Proper and faster diagnosis of pneumonia at an early stage is imperative for optimal patient care. Currently, chest X-ray is considered as the best imaging modality for diagnosing pneumonia. However, the interpretation of chest X-ray images is challenging. To this end, we aimed to use an automated convolutional neural network-based transfer-learning approach to detect pneumonia in paediatric chest radiographs. METHODS: Herein, an automated convolutional neural network-based transfer-learning approach using four different pre-trained models (i.e. VGG19, DenseNet121, Xception, and ResNet50) was applied to detect pneumonia in children (1-5 years) chest X-ray images. The performance of different proposed models for testing data set was evaluated using five performances metrics, including accuracy, sensitivity/recall, Precision, area under curve, and F1 score. RESULTS: All proposed models provide accuracy greater than 83.0% for binary classification. The pre-trained DenseNet121 model provides the highest classification performance of automated pneumonia classification with 86.8% accuracy, followed by Xception model with an accuracy of 86.0%. The sensitivity of the proposed models was greater than 91.0%. The Xception and DenseNet121 models achieve the highest classification performance with F1-score greater than 89.0%. The plotted area under curve of receiver operating characteristics of VGG19, Xception, ResNet50, and DenseNet121 models are 0.78, 0.81, 0.81, and 0.86, respectively. CONCLUSION: Our data showed that the proposed models achieve a high accuracy for binary classification. Transfer learning was used to accelerate training of the proposed models and resolve the problem associated with insufficient data. We hope that these proposed models can help radiologists for a quick diagnosis of pneumonia at radiology departments. Moreover, our proposed models may be useful to detect other chest-related diseases such as novel Coronavirus 2019. ADVANCES IN KNOWLEDGE: Herein, we used transfer learning as a machine learning approach to accelerate training of the proposed models and resolve the problem associated with insufficient data. Our proposed models achieved accuracy greater than 83.0% for binary classification.

Identification of Two Novel Mutations in PKHD1 Gene from Two Families with Polycystic Kidney Disease by Whole Exome Sequencing.

BACKGROUND: Polycystic kidney disease (PKD) is an autosomal recessive disorder resulting from mutations in the PKHD1 gene on chromosome 6 (6p12), a large gene spanning 470 kb of genomic DNA. OBJECTIVE: The aim of the present study was to report newly identified mutations in the PKHD1 gene in two Iranian families with PKD. MATERIALS AND METHODS: Genetic alterations of a 3-month-old boy and a 27-year-old girl with PKD were evaluated using whole-exome sequencing. The PCR direct sequencing was performed to analyse the co-segregation of the variants with the disease in the family. Finally, the molecular function of the identified novel mutations was evaluated by in silico study. RESULTS: In the 3 month-old boy, a novel homozygous frameshift mutation was detected in the PKHD1 gene, which can cause PKD. Moreover, we identified three novel heterozygous missense mutations in ATIC, VPS13B, and TP53RK genes. In the 27-year-old woman, with two recurrent abortions history and two infant mortalities at early weeks due to metabolic and/or renal disease, we detected a novel missense mutation on PKHD1 gene and a novel mutation in ETFDH gene. CONCLUSION: In general, we have identified two novel mutations in the PKHD1 gene. These molecular findings can help accurately correlate genotype and phenotype in families with such disease in order to reduce patient births through preoperative genetic diagnosis or better management of disorders.

Patterns of brain activation during craving in heroin dependents successfully treated by methadone maintenance and abstinence-based treatments.

OBJECTIVE: Abstinence-based therapy (ABT) and methadone maintenance therapy (MMT) are common methods of treatment in heroin dependence as both suppress subjective feeling of drug craving. However, it is not clear whether the neural basis of craving suppression is similar in both types of treatments. In this study, we compared brain activation during pictorial presentation of heroin-related cues in ABT and MMT groups to understand the neural basis of drug craving in these groups. METHODS: Three groups of subjects (successful ABT and MMT clients and healthy control) underwent functional magnetic resonance imaging, while heroin-related cues and neutral cues were presented to them. In addition, subjective cue-elicited craving has been measured using drug drive questionnaire before and after imaging. RESULT: Self-report of craving was not different between ABT and MMT groups before and after scanning. Anterior cingulate cortex and inferior frontal gyrus showed higher activations in ABT than in healthy control. Inferior frontal gyrus and superior temporal gyrus showed higher activity in ABT than in MMT. Lingual gyrus and cerebellum showed higher activity in MMT than in healthy control. CONCLUSIONS: Heroin avoidance may be achieved by MMT or ABT; however, the neural mechanism underlying these therapeutic methods differs.