Maximizing Mentorship Throughout Your Breast Imaging Career.

Unlike many other subspecialties in radiology, breast radiologists practice in a patient-facing and interdisciplinary environment where team building, communication, and leadership skills are critical. Although breast radiologists can improve these skills over time, strong mentorship can accelerate this process, leading to a more successful and satisfying career. In addition to providing advice, insight, feedback, and encouragement to mentees, mentors help advance the field of breast radiology by contributing to the development of the next generation of leaders. During the mentorship process, mentors continue to hone their listening, problem-solving, and networking skills, which in turn creates a more supportive and nurturing work environment for the entire breast care team. This article reviews important mentorship skills that are essential for all breast radiologists. Although some of the principles apply to all mentoring relationships, ensuring that every breast radiologist has the skills to be both an effective mentor and mentee is key to the future of the profession.

Auto-segmentation of Adult-Type Diffuse Gliomas: Comparison of Transfer Learning-Based Convolutional Neural Network Model vs. Radiologists.

Segmentation of glioma is crucial for quantitative brain tumor assessment, to guide therapeutic research and clinical management, but very time-consuming. Fully automated tools for the segmentation of multi-sequence MRI are needed. We developed and pretrained a deep learning (DL) model using publicly available datasets A (n = 210) and B (n = 369) containing FLAIR, T2WI, and contrast-enhanced (CE)-T1WI. This was then fine-tuned with our institutional dataset (n = 197) containing ADC, T2WI, and CE-T1WI, manually annotated by radiologists, and split into training (n = 100) and testing (n = 97) sets. The Dice similarity coefficient (DSC) was used to compare model outputs and manual labels. A third independent radiologist assessed segmentation quality on a semi-quantitative 5-scale score. Differences in DSC between new and recurrent gliomas, and between uni or multifocal gliomas were analyzed using the Mann-Whitney test. Semi-quantitative analyses were compared using the chi-square test. We found that there was good agreement between segmentations from the fine-tuned DL model and ground truth manual segmentations (median DSC: 0.729, std-dev: 0.134). DSC was higher for newly diagnosed (0.807) than recurrent (0.698) (p < 0.001), and higher for unifocal (0.747) than multi-focal (0.613) cases (p = 0.001). Semi-quantitative scores of DL and manual segmentation were not significantly different (mean: 3.567 vs. 3.639; 93.8% vs. 97.9% scoring ≥ 3, p = 0.107). In conclusion, the proposed transfer learning DL performed similarly to human radiologists in glioma segmentation on both structural and ADC sequences. Further improvement in segmenting challenging postoperative and multifocal glioma cases is needed.

Competency based curriculum for cardiovascular magnetic resonance: A position statement of the Society for Cardiovascular Magnetic Resonance.

This position statement guides cardiovascular magnetic resonance (CMR) imaging program directors and learners on the key competencies required for Level II and III CMR practitioners, whether trainees come from a radiology or cardiology background. This document is built upon existing curricula and was created and vetted by an international panel of cardiologists and radiologists on behalf of the Society for Cardiovascular Magnetic Resonance (SCMR).

Systematic training of LI-RADS CT v2018 improves interobserver agreements and performances in LR categorization for focal liver lesions.

AIM: To retrospectively explored whether systematic training in the use of Liver Imaging Reporting and Data System (LI-RADS) v2018 on computed tomography (CT) can improve the interobserver agreements and performances in LR categorization for focal liver lesions (FLLs) among different radiologists. MATERIALS AND METHODS: A total of 18 visiting radiologists and the liver multiphase CT images of 70 hepatic observations in 63 patients at high risk of HCC were included in this study. The LI-RADS v2018 training procedure included three thematic lectures, with an interval of 1 month. After each seminar, the radiologists had 1 month to adopt the algorithm into their daily work. The interobserver agreements and performances in LR categorization for FLLs among the radiologists before and after training were compared. RESULTS: After training, the interobserver agreements in classifying the LR categories for all radiologists were significantly increased for most LR categories (P < 0.001), except for LR-1 (P = 0.053). After systematic training, the areas under the curve (AUCs) for LR categorization performance for all participants were significantly increased for most LR categories (P < 0.001), except for LR-1 (P = 0.062). CONCLUSION: Systematic training in the use of the LI-RADS can improve the interobserver agreements and performances in LR categorization for FLLs among radiologists with different levels of experience.

Creation of a protective space between the rectum and prostate prior to prostate radiotherapy using a hydrogel spacer.

The placement of a polyethylene glycol (PEG) hydrogel spacer is a recently developed technique employed to reduce the radiation dose administered to the rectum during prostate radiotherapy. This procedure has been adopted by urologists and radiation oncologists involved in transperineal prostate biopsy and brachytherapy, and more recently by radiologists with experience in transperineal prostate procedures. Radiologists should be familiar with the product, which may be encountered on computed tomography (CT) or magnetic resonance imaging (MRI). Radiologists may wish to become involved in the delivery of this increasingly utilised procedure. This review familiarises radiologists with the technique and risks and benefits of the use of transperineal delivery of hydrogel spacers with imaging examples.

A machine and human reader study on AI diagnosis model safety under attacks of adversarial images.

While active efforts are advancing medical artificial intelligence (AI) model development and clinical translation, safety issues of the AI models emerge, but little research has been done. We perform a study to investigate the behaviors of an AI diagnosis model under adversarial images generated by Generative Adversarial Network (GAN) models and to evaluate the effects on human experts when visually identifying potential adversarial images. Our GAN model makes intentional modifications to the diagnosis-sensitive contents of mammogram images in deep learning-based computer-aided diagnosis (CAD) of breast cancer. In our experiments the adversarial samples fool the AI-CAD model to output a wrong diagnosis on 69.1% of the cases that are initially correctly classified by the AI-CAD model. Five breast imaging radiologists visually identify 29%-71% of the adversarial samples. Our study suggests an imperative need for continuing research on medical AI model's safety issues and for developing potential defensive solutions against adversarial attacks.

Performance and educational training of radiographers in lung nodule or mass detection: Retrospective comparison with different deep learning algorithms.

ABSTRACT: The aim of this investigation was to compare the diagnostic performance of radiographers and deep learning algorithms in pulmonary nodule/mass detection on chest radiograph.A test set of 100 chest radiographs containing 53 cases with no pathology (normal) and 47 abnormal cases (pulmonary nodules/masses) independently interpreted by 6 trained radiographers and deep learning algorithems in a random order. The diagnostic performances of both deep learning algorithms and trained radiographers for pulmonary nodules/masses detection were compared.QUIBIM Chest X-ray Classifier, a deep learning through mass algorithm that performs superiorly to practicing radiographers in the detection of pulmonary nodules/masses (AUCMass: 0.916 vs AUCTrained radiographer: 0.778, P < .001). In addition, heat-map algorithm could automatically detect and localize pulmonary nodules/masses in chest radiographs with high specificity.In conclusion, the deep-learning based computer-aided diagnosis system through 4 algorithms could potentially assist trained radiographers by increasing the confidence and access to chest radiograph interpretation in the age of digital age with the growing demand of medical imaging usage and radiologist burnout.

Improving radiologist's ability in identifying particular abnormal lesions on mammograms through training test set with immediate feedback.

It has been shown that there are differences in diagnostic accuracy of cancer detection on mammograms, from below 50% in developing countries to over 80% in developed world. One previous study reported that radiologists from a population in Asia displayed a low mammographic cancer detection of 48% compared with over 80% in developed countries, and more importantly, that most lesions missed by these radiologists were spiculated masses or stellate lesions. The aim of this study was to explore the performance of radiologists after undertaking a training test set which had been designed to improve the capability in detecting a specific type of cancers on mammograms. Twenty-five radiologists read two sets of 60 mammograms in a standardized mammogram reading room. The first test set focused on stellate or spiculated masses. When radiologists completed the first set, the system displayed immediate feedback to the readers comparing their performances in each case with the truth of cancer cases and cancer types so that the readers could identify individual-based errors. Later radiologists were asked to read the second set of mammograms which contained different types of cancers including stellate/spiculated masses, asymmetric density, calcification, discrete mass and architectural distortion. Case sensitivity, lesion sensitivity, specificity, receiver operating characteristics (ROC) and Jackknife alternative free-response receiver operating characteristics (JAFROC) were calculated for each participant and their diagnostic accuracy was compared between two sessions. Results showed significant improvement among radiologists in case sensitivity (+ 11.4%; P < 0.05), lesion sensitivity (+ 18.7%; P < 0.01) and JAFROC (+ 11%; P < 0.01) in the second set compared with the first set. The increase in diagnostic accuracy was also recorded in the detection of stellate/spiculated mass (+ 20.6%; P < 0.05). This indicated that the performance of radiologists in detecting malignant lesions on mammograms can be improved if an appropriate training intervention is applied after the readers' weakness and strength are identified.

Critical Evaluation of Interventional Neuroradiology Fellowship Program Websites in North America.

OBJECTIVE: To investigate the accessibility and content of interventional neuroradiology (INR) fellowship program websites in North America. METHODS: We obtained a list of INR/endovascular surgical neuroradiology (ESN) fellowship programs from the Accreditation Council for Graduate Medical Education, the Committee on Advanced Subspecialty Training, the Society of NeuroInterventional Surgery, and the Neurosurgical Fellowship Training Program Directory websites. Individual program websites were evaluated for 27 different fellow recruitment and education criteria. U.S. programs were grouped based on census region and national ranking, and differences between these groups with regard to fellow recruitment and education characteristics were analyzed using nonparametric statistics. RESULTS: A total of 79 INR/ESN fellowship websites were evaluated for presence of fellow recruitment and education features. Approximately one third of all features pertinent to recruitment (32.11%) and approximately 1 in 5 features regarding education (19.11%) were described in these websites. Program description (69.6%), program coordinator/administrator contact e-mail (59.5%), program director's name (59.5%), program eligibility requirements (51.9%), research opportunities (40.5%), and faculty listing (39.2%) were among the most frequently described features, whereas details about parking (1.3%), interview day itinerary (1.3%), meal allowance (2.5%), retirement and benefits (3.8%), and call schedule (5.1%) were the least frequently described features. There was no significant difference between surveyed features and programs when stratified by U.S. census region, neurosurgery/neurology hospital rankings, or accreditation status. CONCLUSIONS: INR/ESN fellowship website content is variable across North America and there is room for improvement to develop and enhance comprehensiveness of program website content.

Association between tears of the posterior root of the medial meniscus and far posterior femoral condyle osteoarthritis.

PURPOSE: We aimed to evaluate whether tears of the posterior horn of the medial meniscus root ligament (PHMM RL) are associated with osteoarthritis of the far posterior femoral condyles (FPFC). METHODS: Retrospective review of 1158 patients who underwent arthroscopy identified 49 patients with confirmed tears of the medial meniscus posterior root ligament attachment. Preoperative magnetic resonance imaging (MRI) studies were reviewed to identify advanced osteoarthritis involving the medial and lateral FPFC. Control patients (n=48) had no meniscal tears confirmed by arthroscopy. Cases and controls were age- and sex-matched exactly 1:1. One case patient was excluded since there was no age- and sex-matched control available. The International Cartilage Research Society (ICRS) MRI cartilage grade was recorded for the medial and lateral FPFC. Associations were evaluated using univariate and multivariable conditional logistic regression analyses. RESULTS: There were 48 case and 48 control patients (10 men in each group, 20.8%) with median age 53 years (range, 21-67). Medial FPFC ICRS Grade 2 or higher lesions were present in 34 (70.8%) of case patients and 16 (33.3%) of control patients. Lateral FPFC ICRS Grade 2 or higher lesions were present in 24 (50%) of case patients and 14 (28.2%) of control patients. Increased body mass index (BMI) was associated with PHMM RL tears (OR=1.11, 95% CI [1.01, 1.22], P = 0.020). MRI was 81.2% (39/48) sensitive and 91.2% (44/48) specific for detection of PHMM RL tears. PHMM RL tears were associated with Grade 2 or higher medial FPFC osteoarthritis (OR=10.00, 95% CI (2.34, 42.78), P < 0.001). This association remained after adjusting for BMI (OR=11.79, 95% CI [2.46, 56.53], P = 0.002). There was also an association between PHMM RL tears and lateral FPFC osteoarthritis, which persisted after adjusting for BMI (OR =3.00, 95% CI [1.07, 8.37], P = 0.036). CONCLUSION: PHMM RL tears are associated with advanced osteoarthritis of the FPFC. Radiologists identifying FPFC osteoarthritis should look carefully for PHMM RL tears.

A pictorial review on clinicopathologic and radiologic features of duodenal gastrointestinal stromal tumors.

Duodenal gastrointestinal stromal tumors (GISTs) are rare, and studies on their management are not sufficient. Owing to the complex anatomy of the duodenum and pancreatic head, GISTs can be misdiagnosed as pancreatic head tumors. Surgical resection is the first treatment for localized duodenal GISTs; thus, noninvasive imaging is important for the diagnosis and treatment of GISTs. Computed tomography, magnetic resonance imaging and endoscopic ultrasonography findings can be helpful for the diagnosis of duodenal GISTs and can help differentiate GISTs from other adjacent tumors.

Supervised Learning Occurs in Visual Perceptual Learning of Complex Natural Images.

There have been long-standing debates regarding whether supervised or unsupervised learning mechanisms are involved in visual perceptual learning (VPL) [1-14]. However, these debates have been based on the effects of simple feedback only about response accuracy in detection or discrimination tasks of low-level visual features such as orientation [15-22]. Here, we examined whether the content of response feedback plays a critical role for the acquisition and long-term retention of VPL of complex natural images. We trained three groups of human subjects (n = 72 in total) to better detect "grouped microcalcifications" or "architectural distortion" lesions (referred to as calcification and distortion in the following) in mammograms either with no trial-by-trial feedback, partial trial-by-trial feedback (response correctness only), or detailed trial-by-trial feedback (response correctness and target location). Distortion lesions consist of more complex visual structures than calcification lesions [23-26]. We found that partial feedback is necessary for VPL of calcifications, whereas detailed feedback is required for VPL of distortions. Furthermore, detailed feedback during training is necessary for VPL of distortion and calcification lesions to be retained for 6 months. These results show that although supervised learning is heavily involved in VPL of complex natural images, the extent of supervision for VPL varies across different types of complex natural images. Such differential requirements for VPL to improve the detectability of lesions in mammograms are potentially informative for the professional training of radiologists.

Myths and facts about artificial intelligence: why machine- and deep-learning will not replace interventional radiologists.

Artificial intelligence (AI) is revolutionizing healthcare and transforming the clinical practice of physicians across the world. Radiology has a strong affinity for machine learning and is at the forefront of the paradigm shift, as machines compete with humans for cognitive abilities. AI is a computer science simulation of the human mind that utilizes algorithms based on collective human knowledge and the best available evidence to process various forms of inputs and deliver desired outcomes, such as clinical diagnoses and optimal treatment options. Despite the overwhelmingly positive uptake of the technology, warnings have been published about the potential dangers of AI. Concerns have been expressed reflecting opinions that future medicine based on AI will render radiologists irrelevant. Thus, how much of this is based on reality? To answer these questions, it is important to examine the facts, clarify where AI really stands and why many of these speculations are untrue. We aim to debunk the 6 top myths regarding AI in the future of radiologists.

Imaging manifestations of immune-related adverse effects in checkpoint inhibitor therapies: A primer for the radiologist.

Immune checkpoint inhibitors are monoclonal antibodies directed against cellular pathways on T-cells to treat different types of malignancies. This new therapy can cause immune-related adverse events that can involve almost any organ system. This article will review clinical presentations, molecular mechanisms and imaging manifestations of adverse events caused by checkpoint inhibitors and also illustrate the pseudoprogression tumor response pattern.

MRI in pregnant patients with suspected abdominal and pelvic cancer: a practical guide for radiologists.

The incidence of abdominal and pelvic cancer in pregnancy is low, but it is rising as the population of pregnant women gets older. Depending on disease stage, gestational age and patient's preference, active surveillance as well as surgery and chemotherapy are feasible options during pregnancy. Correct diagnosis and staging of the tumor is crucial for choosing the best therapeutic approach. Moreover, a reproducible modality to assess the treatment response is requested. Magnetic resonance imaging (MRI) is commonly used with good results for the local staging and treatment response evaluation of most abdominal and pelvic cancers in nonpregnant patients, and it is considered relatively safe during pregnancy. The purpose of this article is to analyze the most relevant topics regarding the use of MRI in pregnant women with abdominal and pelvic cancer. We discuss MRI safety during pregnancy, including the use of gadolinium-based contrast agents (GBCAs), how to prepare the patient for the exam and MRI technique. This will be followed by a brief review on the most common malignancies diagnosed during pregnancy and their MRI appearance.

MRI evaluation of extramural vascular invasion by inexperienced radiologists.

OBJECTIVE: We proposed to determine whether the performance of inexperienced radiologists in determining extramural vascular invasion (EMVI) in rectal cancer on MRI can be promoted by means of targeted training. METHODS: 230 rectal cancer patients who underwent pre-operative chemoradiotherapy were included. Pre-therapy and post-therapy MR images and pathology EMVI evaluation were available for cases. 230 cases were randomly divided into 150 training cases and 80 testing cases, including 40 testing case A and 40 testing case B. Four radiologists were included for MRI EMVI evaluation, who were divided into targeted training group and non-targeted training group. The two groups evaluated testing case A at baseline, 3 month and 6 month, evaluated testing case B at 6 month. The main outcome was agreement with expert-reference for pre-therapy and post-therapy evaluation, the other outcome was accuracy with pathology for post-therapy evaluation. RESULTS: After 6 months of training, targeted training group showed statistically higher agreement with expert-reference than non-targeted training group for both pre-therapy and post-therapy MRI EMVI evaluation of testing case A and testing case B, all p < 0.05. Targeted training group also showed significantly higher accuracy with pathology than non-targeted training group for post-therapy evaluation of testing case A and testing case B after 6 months of training, all p < 0.05. CONCLUSION: The diagnostic performance for MRI EMVI evaluation could be promoted by targeted training for inexperienced radiologist. ADVANCES IN KNOWLEDGE: This study provided the first evidence that after 6 month targeted training, inexperienced radiologists demonstrated improved diagnostic performance, with a 20% increase in agreement with expert-reference for both pre-therapy and post-therapy MRI EMVI evaluation and also a 20% increase in or accuracy with pathology for post-therapy evaluation, while inexperienced radiologists could not gain obvious improvement in MRI EMVI evaluation through the same period of regular clinical practice. It indicated that targeted training may be necessary for helping inexperienced radiologist to acquire adequate experience for the MRI EMVI evaluation of rectal cancer, especially for radiologist who works in a medical unit where MRI EMVI diagnosis is uncommon.