MRI of the Lactating Breast

The breasts undergo marked physiologic changes during lactation that can make conventional imaging evaluation with mammography and US challenging. MRI can be a valuable diagnostic aid to differentiate physiologic and benign processes from malignancy in patients who are lactating. In addition, MRI may allow more accurate delineation of disease involvement than does conventional imaging and assists in locoregional staging, screening of the contralateral breast, assessment of response to neoadjuvant chemotherapy, and surgical planning. Although the American College of Radiology recommends against patients undergoing contrast-enhanced MRI during pregnancy because of fetal safety concerns, contrast-enhanced MRI is safe during lactation. As more women delay childbearing, the incidence of pregnancy-associated breast cancer (PABC) and breast cancer in lactating women beyond the 1st year after pregnancy is increasing. Thus, MRI is increasingly being performed in lactating women for diagnostic evaluation and screening of patients at high risk. PABC is associated with a worse prognosis than that of non-PABCs, with delays in diagnosis contributing to an increased likelihood of advanced-stage disease at diagnosis. Familiarity with the MRI features of the lactating breast and the appearance of various pathologic conditions is essential to avoid diagnostic pitfalls and prevent delays in cancer diagnosis and treatment. The authors review clinical indications for breast MRI during lactation, describe characteristic features of the lactating breast at MRI, and compare MRI features of a spectrum of benign and malignant breast abnormalities. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Chikarmane in this issue.

Radiological Society of North America (RSNA) 3D Printing Special Interest Group (SIG) clinical situations for which 3D printing is considered an appropriate representation or extension of data contained in a medical imaging examination: breast conditions

The use of medical 3D printing has expanded dramatically for breast diseases. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (SIG) provides updated appropriateness criteria for breast 3D printing in various clinical scenarios. Evidence-based appropriateness criteria are provided for the following clinical scenarios: benign breast lesions and high-risk breast lesions, breast cancer, breast reconstruction, and breast radiation (treatment planning and radiation delivery).

Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer

Background There is increasing interest in noncontrast breast MRI alternatives for tumor visualization to increase the accessibility of breast MRI. Purpose To evaluate the feasibility and accuracy of generating simulated contrast-enhanced T1-weighted breast MRI scans from precontrast MRI sequences in biopsy-proven invasive breast cancer with use of deep learning. Materials and Methods Women with invasive breast cancer and a contrast-enhanced breast MRI examination that was performed for initial evaluation of the extent of disease between January 2015 and December 2019 at a single academic institution were retrospectively identified. A three-dimensional, fully convolutional deep neural network simulated contrast-enhanced T1-weighted breast MRI scans from five precontrast sequences (T1-weighted non-fat-suppressed [FS], T1-weighted FS, T2-weighted FS, apparent diffusion coefficient, and diffusion-weighted imaging). For qualitative assessment, four breast radiologists (with 3-15 years of experience) blinded to whether the method of contrast was real or simulated assessed image quality (excellent, acceptable, good, poor, or unacceptable), presence of tumor enhancement, and maximum index mass size by using 22 pairs of real and simulated contrast-enhanced MRI scans. Quantitative comparison was performed using whole-breast similarity and error metrics and Dice coefficient analysis of enhancing tumor overlap. Results Ninety-six MRI examinations in 96 women (mean age, 52 years ± 12 [SD]) were evaluated. The readers assessed all simulated MRI scans as having the appearance of a real MRI scan with tumor enhancement. Index mass sizes on real and simulated MRI scans demonstrated good to excellent agreement (intraclass correlation coefficient, 0.73-0.86; P < .001) without significant differences (mean differences, -0.8 to 0.8 mm; P = .36-.80). Almost all simulated MRI scans (84 of 88 [95%]) were considered of diagnostic quality (ratings of excellent, acceptable, or good). Quantitative analysis demonstrated strong similarity (structural similarity index, 0.88 ± 0.05), low voxel-wise error (symmetric mean absolute percent error, 3.26%), and Dice coefficient of enhancing tumor overlap of 0.75 ± 0.25. Conclusion It is feasible to generate simulated contrast-enhanced breast MRI scans with use of deep learning. Simulated and real contrast-enhanced MRI scans demonstrated comparable tumor sizes, areas of tumor enhancement, and image quality without significant qualitative or quantitative differences. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Slanetz in this issue. An earlier incorrect version appeared online. This article was corrected on January 17, 2023.

MRI Evaluation of the Lactating Breast

Purpose of Review

To review the MRI appearance of physiologic lactational changes, common benign pathologies, and malignancies in the lactating breast.

Recent Findings

The prevalence of pregnancy-associated breast cancer has increased as more women delay childbirth and lactation. There is a transient increase in breast cancer risk after delivery when women may be lactating. MRI is more sensitive than mammography and ultrasound for the evaluation of the extent of disease in lactating women.

Summary

Understanding the key MRI findings of benign and malignant pathologies in the lactating breast is critical for accurate diagnosis and prompt evaluation of pregnancy-associated breast cancer.

Applications of 3D printing in breast cancer management

Three-dimensional (3D) printing is a method by which two-dimensional (2D) virtual data is converted to 3D objects by depositing various raw materials into successive layers. Even though the technology was invented almost 40 years ago, a rapid expansion in medical applications of 3D printing has only been observed in the last few years. 3D printing has been applied in almost every subspecialty of medicine for pre-surgical planning, production of patient-specific surgical devices, simulation, and training. While there are multiple review articles describing utilization of 3D printing in various disciplines, there is paucity of literature addressing applications of 3D printing in breast cancer management. Herein, we review the current applications of 3D printing in breast cancer management and discuss the potential impact on future practices.

Post-lumpectomy breast calcifications: Can original tumor features assist in determining need for biopsy?

OBJECTIVE

The objective of our study was to determine whether, in the digital era, imaging features of a primary breast tumor can be used to influence the decision to biopsy ipsilateral breast calcifications that occur following surgery in women treated with breast conservation surgery (BCS).

MATERIALS AND METHODS

We retrospectively identified women treated with BCS who subsequently developed suspicious calcifications in the treated breast (BI-RADS 4 or 5) from January 2012 - December 2018. Only cases with histopathological diagnosis by stereotactic or surgical biopsy were included. Pathology reports were reviewed, and biopsy results were considered malignant if invasive carcinoma or ductal carcinoma in situ (DCIS) was found. All other results were considered benign. Fisher's exact test was done comparing frequencies of malignancy between those patients whose original tumor had calcifications versus those whose original tumors were not calcified.

RESULTS

Of 90 women with suspicious calcifications on a post-BCS mammogram, 65 (72.2%) were biopsy proven benign and 25 (27.8%) were malignant. The original tumor presented without calcifications in 39 patients (43%), and 51 (57%) had calcifications with or without associated mass, focal asymmetry, or architectural distortion. New calcifications were less likely to be malignant if the original tumor presented without calcifications (5/39; 12.8%) as compared to original tumors with calcifications (20/51; 38.5%) [p-value < 0.05].

CONCLUSION

New calcifications after BCS are significantly less likely to be malignant if the original tumor presented without calcifications. However, with a PPV of 12.8%, even calcifications in a patient with a non-calcified primary tumor require biopsy.

Digital Breast Tomosynthesis: Technique and Common Artifacts

Image optimization at digital breast tomosynthesis (DBT) involves a series of trade-offs between multiple variables. Wider sweep angles provide better separation of overlapping tissues, but they result in decreased in-plane resolution as well as increased scan times that may be prone to patient motion. Techniques to reduce scan time, such as continuous tube motion and pixel binning during detector readout, reduce the chances of patient motion but may degrade the in-plane resolution. Image artifacts are inherent to DBT because of the limited angular range of the acquisition. Iterative reconstruction algorithms have been shown to reduce various DBT artifacts.

An Open-Source, Vender Agnostic Hardware and Software Pipeline for Integration of Artificial Intelligence in Radiology Workflow

Although machine learning (ML) has made significant improvements in radiology, few algorithms have been integrated into clinical radiology workflow. Complex radiology IT environments and Picture Archiving and Communication System (PACS) pose unique challenges in creating a practical ML schema. However, clinical integration and testing are critical to ensuring the safety and accuracy of ML algorithms. This study aims to propose, develop, and demonstrate a simple, efficient, and understandable hardware and software system for integrating ML models into the standard radiology workflow and PACS that can serve as a framework for testing ML algorithms. A Digital Imaging and Communications in Medicine/Graphics Processing Unit (DICOM/GPU) server and software pipeline was established at a metropolitan county hospital intranet to demonstrate clinical integration of ML algorithms in radiology. A clinical ML integration schema, agnostic to the hospital IT system and specific ML models/frameworks, was implemented and tested with a breast density classification algorithm and prospectively evaluated for time delays using 100 digital 2D mammograms. An open-source clinical ML integration schema was successfully implemented and demonstrated. This schema allows for simple uploading of custom ML models. With the proposed setup, the ML pipeline took an average of 26.52 s per second to process a batch of 100 studies. The most significant processing time delays were noted in model load and study stability times. The code is made available at " http://bit.ly/2Z121hX ". We demonstrated the feasibility to deploy and utilize ML models in radiology without disrupting existing radiology workflow.

Role of Breast MRI in the Evaluation and Detection of DCIS: Opportunities and Challenges

Historically, breast magnetic resonance imaging (MRI) was not considered an effective modality in the evaluation of ductal carcinoma in situ (DCIS). Over the past decade this has changed, with studies demonstrating that MRI is the most sensitive imaging tool for detection of all grades of DCIS. It has been suggested that not only is breast MRI the most sensitive imaging tool for detection but it may also detect the most clinically relevant DCIS lesions. The role and outcomes of MRI in the preoperative setting for patients with DCIS remains controversial; however, several studies have shown benefit in the preoperative evaluation of extent of disease as well as predicting an underlying invasive component. The most common presentation of DCIS on MRI is nonmass enhancement (NME) in a linear or segmental distribution pattern. Maximizing breast MRI spatial resolution is therefore beneficial, given the frequent presentation of DCIS as NME on MRI. Emerging MRI techniques, such as diffusion-weighted imaging (DWI), have shown promising potential to discriminate DCIS from benign and invasive lesions. Future opportunities including advanced imaging visual techniques, radiomics/radiogenomics, and machine learning / artificial intelligence may also be applicable to the detection and treatment of DCIS. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:697-709.

Transcatheter Mustard Revision Using Endovascular Graft Prostheses

We describe the use of percutaneously inserted, transcatheter endovascular graft prostheses to exclude large Mustard baffle leaks in a high-surgical-risk patient. We used 3-dimensional-printed models to determine feasibility and to plan the procedure. Telescoping thoracic and abdominal graft extensions were placed in the inferior and superior limbs of the systemic venous pathways. An atrial septal defect occluder device was also used to close a separate leak not covered by the endovascular graft prostheses. This approach may be useful in patients with complex, large intraatrial baffles that require repair of baffle leaks not amenable to device closure.

3D printing from MRI Data: Harnessing strengths and minimizing weaknesses

3D printing facilitates the creation of accurate physical models of patient-specific anatomy from medical imaging datasets. While the majority of models to date are created from computed tomography (CT) data, there is increasing interest in creating models from other datasets, such as ultrasound and magnetic resonance imaging (MRI). MRI, in particular, holds great potential for 3D printing, given its excellent tissue characterization and lack of ionizing radiation. There are, however, challenges to 3D printing from MRI data as well. Here we review the basics of 3D printing, explore the current strengths and weaknesses of printing from MRI data as they pertain to model accuracy, and discuss considerations in the design of MRI sequences for 3D printing. Finally, we explore the future of 3D printing and MRI, including creative applications and new materials.

LEVEL OF EVIDENCE

5 J. Magn. Reson. Imaging 2017;45:635-645.

Natural Language Processing Technologies in Radiology Research and Clinical Applications

The migration of imaging reports to electronic medical record systems holds great potential in terms of advancing radiology research and practice by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to the heterogeneity of how these data are formatted. Indeed, although there is movement toward structured reporting in radiology (ie, hierarchically itemized reporting with use of standardized terminology), the majority of radiology reports remain unstructured and use free-form language. To effectively "mine" these large datasets for hypothesis testing, a robust strategy for extracting the necessary information is needed. Manual extraction of information is a time-consuming and often unmanageable task. "Intelligent" search engines that instead rely on natural language processing (NLP), a computer-based approach to analyzing free-form text or speech, can be used to automate this data mining task. The overall goal of NLP is to translate natural human language into a structured format (ie, a fixed collection of elements), each with a standardized set of choices for its value, that is easily manipulated by computer programs to (among other things) order into subcategories or query for the presence or absence of a finding. The authors review the fundamentals of NLP and describe various techniques that constitute NLP in radiology, along with some key applications.

3D printing based on cardiac CT assists anatomic visualization prior to transcatheter aortic valve replacement

BACKGROUND

3D printing is a promising technique that may have applications in medicine, and there is expanding interest in the use of patient-specific 3D models to guide surgical interventions.

OBJECTIVE

To determine the feasibility of using cardiac CT to print individual models of the aortic root complex for transcatheter aortic valve replacement (TAVR) planning as well as to determine the ability to predict paravalvular aortic regurgitation (PAR).

METHODS

This retrospective study included 16 patients (9 with PAR identified on blinded interpretation of post-procedure trans-thoracic echocardiography and 7 age, sex, and valve size-matched controls with no PAR). 3D printed models of the aortic root were created from pre-TAVR cardiac computed tomography data. These models were fitted with printed valves and predictions regarding post-implant PAR were made using a light transmission test.

RESULTS

Aortic root 3D models were highly accurate, with excellent agreement between annulus measurements made on 3D models and those made on corresponding 2D data (mean difference of -0.34 mm, 95% limits of agreement: ± 1.3 mm). The 3D printed valve models were within 0.1 mm of their designed dimensions. Examination of the fit of valves within patient-specific aortic root models correctly predicted PAR in 6 of 9 patients (6 true positive, 3 false negative) and absence of PAR in 5 of 7 patients (5 true negative, 2 false positive).

CONCLUSIONS

Pre-TAVR 3D-printing based on cardiac CT provides a unique patient-specific method to assess the physical interplay of the aortic root and implanted valves. With additional optimization, 3D models may complement traditional techniques used for predicting which patients are more likely to develop PAR.

Prognostic Value of Diagnostic Sonography in Patients With Plantar Fasciitis

OBJECTIVES

The primary objective of this study was to determine whether the sonographic appearance of the plantar fascia is predictive of the treatment (ie, pain) response in patients receiving supportive therapy for proximal plantar fasciitis. This study was a secondary analysis of data obtained from a randomized controlled trial of ambulatory adults, which examined the efficacy of 3 different foot supports for plantar fasciitis.

METHODS

Participants underwent diagnostic sonographic examinations of their heel at baseline and again at 3 months by a single experienced foot and ankle surgeon. Quantitative (eg, thickness) and qualitative (eg, biconvexity) characteristics of the fascia were recorded according to a standard protocol. Logistic regression models were used to identify predictors of the pain response.

RESULTS

Seventy patients completed a baseline evaluation, and 63 patients completed a 3-month follow-up assessment. The pain response was not associated with the type of foot support (P> .05). The only significant indicator of an unfavorable response in the univariate and multivariate analyses was biconvexity of the plantar fascia on sonography at presentation (multivariate odds ratio, 4.76 [95% confidence interval, 1.16-19.5; P= .030). Furthermore, changes in self-reported pain over the 3-month study period were not accompanied by alterations in plantar fascia thickness over this time (r = .056; P = .671).

CONCLUSIONS

We conclude that patients who present with biconvexity of the plantar fascia may be less responsive to tier 1 treatment regimens that center around mechanical support of the plantar fascia. Furthermore, follow-up measurements of the fascia in this population should not weigh heavily in decisions such as return to play.

Hermansky-pudlak syndrome complicated by pulmonary fibrosis: radiologic-pathologic correlation and review of pulmonary complications

Hermansky–Pudlak syndrome (HPS) is a rare autosomal recessive disorder characterized by oculocutaneous hypopigmentation, platelet dysfunction, and in many cases, life-threatening pulmonary fibrosis. We report the clinical course, imaging, and postmortem findings of a 38-year-old female with HPS-related progressive pulmonary fibrosis, highlighting the role of imaging in assessment of disease severity and prognosis.