Emergency Breast Imaging, What Radiologists Need To Know

Awareness by the general radiologist of the various emergent conditions of the breast would enable a better management and appropriate referral, rather than postponing management till a breast radiologist is available for consultation. Early referrals are essential to prevent deterioration of complications including severe infection and even sepsis. There has been a lack of consensus in the past regarding appropriate management and delays in treatment have resulted in worse outcomes which could have been avoided.

Image-Guided Breast Interventions: Biopsy and Beyond

Breast interventions primarily comprise of biopsy of the suspicious breast lesions to obtain accurate pathological diagnosis. Generally, image-guided breast biopsy is required for nonpalpable lesions, however, even in palpable lesions, image-guided biopsy should be performed as it improves the accuracy of diagnosis. Image-guided breast interventions have progressed well beyond biopsy, making the radiologist an important part of the multidisciplinary management of breast cancer. Preoperative localization of nonpalpable abnormalities guides optimal surgical excision to obtain negative margins without sacrificing the normal tissue. Ablative procedures for breast cancer treatment such as radiofrequency ablation (RFA) and high-intensity focused ultrasound ablation can sometimes replace surgery in older patients with comorbidities. This article enumerates and describes the expanding spectrum of image-guided interventions performed by breast radiologist.

The concordance in lesion detection and characteristics between the Anatomical Intelligence and conventional breast ultrasound Scan method

Background The aim of this study was to investigate the concordance in lesion detection, between conventional Handhold Ultrasound (HHUS) and The Anatomical Intelligence for Breast ultrasound scan method. Result The AI-breast showed the absolute agreement between the resident and an experienced breast radiologist. The ICC for the scan time, number, clockface location, distance to the nipple, largest diameter and mean diameter of the lesion obtained by a resident and an experienced breast radiologist were 0.7642, 0.7692, 0.8651, 0.8436, 0.7502, 0.8885, respectively. The ICC of the both practitioners of AI-breast were 0.7971, 0.7843, 0.9283, 0.8748, 0.7248, 0.8163, respectively. The k value of Anatomical Intelligence breast between experienced breast radiologist and resident in these image characteristics of boundary, morphology, aspect ratio, internal echo, and BI-RADS assessment were 0.7424, 0.7217, 0.6741, 0.6419, 0.6241, respectively. The k value of the two readers of AI-breast were 0.6531, 0.6762, 0.6439, 0.6137, 0.5981, respectively. Conclusion The anatomical intelligent breast US scanning method has excellent reproducibility in recording the lesion location and the distance from the nipple, which may be utilized in the lesions surveillance in the future.

Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions

Objectives We aimed to assess the performance of radiomics and machine learning (ML) for classification of non-cystic benign and malignant breast lesions on ultrasound images, compare ML’s accuracy with that of a breast radiologist, and verify if the radiologist’s performance is improved by using ML. Methods Our retrospective study included patients from two institutions. A total of 135 lesions from Institution 1 were used to train and test the ML model with cross-validation. Radiomic features were extracted from manually annotated images and underwent a multistep feature selection process. Not reproducible, low variance, and highly intercorrelated features were removed from the dataset. Then, 66 lesions from Institution 2 were used as an external test set for ML and to assess the performance of a radiologist without and with the aid of ML, using McNemar’s test. Results After feature selection, 10 of the 520 features extracted were employed to train a random forest algorithm. Its accuracy in the training set was 82% (standard deviation, SD, ± 6%), with an AUC of 0.90 (SD ± 0.06), while the performance on the test set was 82% (95% confidence intervals (CI) = 70–90%) with an AUC of 0.82 (95% CI = 0.70–0.93). It resulted in being significantly better than the baseline reference (p = 0.0098), but not different from the radiologist (79.4%, p = 0.815). The radiologist’s performance improved when using ML (80.2%), but not significantly (p = 0.508). Conclusions A radiomic analysis combined with ML showed promising results to differentiate benign from malignant breast lesions on ultrasound images. Key Points • Machine learning showed good accuracy in discriminating benign from malignant breast lesions • The machine learning classifier’s performance was comparable to that of a breast radiologist • The radiologist’s accuracy improved with machine learning, but not significantly

Neurofibroma in the Breast: Diagnosis and Management Considerations

Neurofibroma (NF) of the breast is an uncommon benign entity that occurs sporadically or in association with neurofibromatosis type 1 (NF1). Sporadic NF of the breast is very rare and can present at any age. Neurofibroma of the breast associated with NF1 is more common. Neurofibroma commonly presents as oval, circumscribed masses that overlap with many benign entities. The histopathologic diagnosis of NF of the breast can present a management dilemma for the breast radiologist. An NF that is not associated with NF1 has good post-resection prognosis if superficial, sporadic, and solitary. However, NF of the breast diagnosed in an otherwise healthy patient should prompt evaluation for NF1 and formal genetic risk assessment. Patients diagnosed with NF1 have a higher lifetime risk for developing breast cancer and therefore may benefit from both initiating screening mammography at a younger age and supplemental screening MRI.

Transitioning to Practice: Getting up to Speed in Efficiency and Accuracy

The transition from trainee to breast radiologist is challenging. The many new responsibilities that breast radiologists acquire while establishing themselves as clinicians may increase stress and anxiety. Taking inventory of existing knowledge and skills and addressing deficits toward the end of one’s training can be beneficial. New breast radiologists should expect to be slower and gain proficiency in the first several years out of training. Having realistic expectations for oneself with respect to screening mammography interpretation and following up on the subsequent diagnostic imaging workup of screening callback examinations can increase competence and confidence. Familiarity with the available literature to guide management in the diagnostic setting can increase efficiency. Planning ahead for localizations and biopsies also allows for efficiency while alleviating anxiety. Ultimately, adapting to a new work environment using a collaborative approach with primary healthcare providers, pathologists, and surgeons while remembering to have mentors within and beyond the field of radiology allows for a more successful transition.

Patient Diversity in Breast Imaging: Barriers and Potential Solutions

Recent reports have highlighted disparities in breast cancer care related to patient diversity. Breast radiologists represent the face of breast imaging and are key players in advocating for patients to reduce these disparities. Diversity-related barriers for breast imaging patients, as they journey from screening to survivorship, include impediments to access and quality of care, gaps in communication, and lack of knowledge in both providers and patients. Potential strategies for overcoming these specific barriers include “culturally tailored” nurse navigators, mobile mammography, improved communication, patient and provider education, and breast radiologist involvement in advocacy efforts promoting diversity. As current trends in recommendations and guidelines for breast imaging include more numerous and advanced imaging modalities, it is important to acknowledge and address diversity-related disparities.