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.

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.

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.

Utility of Diagnostic Mammography as the Primary Imaging Modality for Palpable Lumps in Women With Almost Entirely Fatty Breasts.

OBJECTIVE. The purpose of this study was to assess the performance of diagnostic mammography alone for evaluation of palpable symptoms in women with almost entirely fatty breast composition. MATERIALS AND METHODS. All diagnostic mammograms performed for palpable symptoms in women who had been assigned a breast density of "almost entirely fatty" over an 8-year period (2009-2017) at an academic breast center were retrospectively identified. Each symptomatic breast was considered a separate case and analyses were performed at the case level. Clinical, imaging, and pathologic results were reviewed. Descriptive statistics and 2 × 2 contingency table analyses were performed. RESULTS. The study cohort included 323 cases evaluated with mammography. Of these, 294 (91%) had undergone targeted ultrasound. At mammography, 240 (74%) had no correlate to the palpable lump; 38 (12%), a benign correlate; and 45 (14%), a suspicious correlate. Three cases had incidental suspicious mammographic findings, for a total of 48 positive mammography cases. Twenty-seven (8%) cases were malignant. Mammography alone detected all but one cancer, which was detected by ultrasound. In retrospect, the woman from whom this single false-negative mammogram was obtained did not have almost entirely fatty breast density. Mammography alone yielded a negative predictive value of 99.6%, percentage of diagnostic examinations recommended for biopsy that resulted in a tissue diagnosis of malignancy within 1 year of 54%, sensitivity of 96%, and specificity of 93%. Adjunct ultrasound contributed to 11 false-positives but also identified benign correlates in eight cases with no mammographic finding. CONCLUSION. In patients with almost entirely fatty breast tissue presenting with palpable symptoms, mammography alone had a high sensitivity and specificity. Our results support that mammography alone may be sufficient for evaluation of palpable symptoms in these women as long as density criteria are strictly applied.

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.

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.

Use of a 3D-Printed Abdominal Compression Device to Facilitate CT Fluoroscopy-Guided Percutaneous Interventions.

OBJECTIVE: The purpose of this article is to describe a handheld external compression device used to facilitate CT fluoroscopy-guided percutaneous interventions in the abdomen. CONCLUSION: The device was designed with computer-aided design software to modify an existing gastrointestinal fluoroscopy compression device and was constructed by 3D printing. This abdominal compression device facilitates access to interventional targets, and its use minimizes radiation exposure of radiologists. Twenty-one procedures, including biopsies, drainage procedures, and an ablation, were performed with the device. Radiation dosimetry data were collected during two procedures.

Three-Dimensional Printing Facilitates Successful Endovascular Closure of a Type II Abernethy Malformation Using an Amplatzer Atrial Septal Occluder Device.

Type II Abernethy malformations, characterized by side-to-side portosystemic shunting with preserved intrahepatic portal venous system, have been treated with shunt closure surgically and endovascularly. Three-dimensional printing has been used to develop highly accurate patient-specific representations for surgical and endovascular planning and intervention. This innovation describes 3-dimensional printing to successfully close a flush-oriented type II Abernethy malformation with discrepant dimensions on computed tomography, conventional venography, and intravascular ultrasound, using a 12-mm Amplatzer atrial septal occluder device.

Case Report: Three-Dimensional Printed Model for Deep Infiltrating Endometriosis.

The combination of a thorough physical examination and imaging with either magnetic resonance imaging (MRI) or pelvic ultrasound are important in the preoperative planning for deep infiltrating endometriosis (DIE). A 2-dimensional (2D) rendering of the pathology by imaging does not always accurately represent intraoperative findings. The detailed topographical relationship and extent of surrounding tissue invasion can be better appreciated by 3-dimensional (3D) modeling. A 49-year-old patient with history of endometriosis and persistent pain underwent preoperative MRI that showed features consistent with DIE endometriosis. Surgery was performed, and the findings were documented. A 3D printed model of the DIE was generated from the MRI and retrospectively compared with intraoperative findings. The 3D model demonstrated both the laterality and spatial relationship of the endometriotic nodule to the posterior uterine wall and rectum. Three-dimensional printing of DIE may be a beneficial adjunct to 2D imaging and can identify further structural relationships to support surgical planning.

Computer-Based Vertebral Tumor Cryoablation Planning and Procedure Simulation Involving Two Cases Using MRI-Visible 3D Printing and Advanced Visualization.

OBJECTIVE: We report the development and use of MRI-compatible and MRI-visible 3D printed models in conjunction with advanced visualization software models to plan and simulate safe access routes to achieve a theoretic zone of cryoablation for percutaneous image-guided treatment of a C7 pedicle osteoid osteoma and an L1 lamina osteoblastoma. Both models altered procedural planning and patient care. CONCLUSION: Patient-specific MRI-visible models can be helpful in planning complex percutaneous image-guided cryoablation procedures.

Current Concepts in the Molecular Genetics and Management of Thyroid Cancer: An Update for Radiologists.

Substantial improvement in the understanding of the oncogenic pathways in thyroid cancer has led to identification of specific molecular alterations, including mutations of BRAF and RET in papillary thyroid cancer, mutation of RAS and rearrangement of PPARG in follicular thyroid cancer, mutation of RET in medullary thyroid cancer, and mutations of TP53 and in the phosphatidylinositol 3'-kinase (PI3K)/AKT1 pathway in anaplastic thyroid cancer. Ultrasonography (US) and US-guided biopsy remain cornerstones in the initial workup of thyroid cancer. Surgery is the mainstay of treatment, with radioactive iodine (RAI) therapy reserved for differentiated subtypes. Posttreatment surveillance of thyroid cancer is done with US of the thyroid bed as well as monitoring of tumor markers such as serum thyroglobulin and serum calcitonin. Computed tomography (CT), magnetic resonance imaging, and fluorine 18 fluorodeoxyglucose positron emission tomography/CT are used in the follow-up of patients with negative iodine 131 imaging and elevated tumor markers. Certain mutations, such as mutations of BRAF in papillary thyroid carcinoma and mutations in RET codons 883, 918, and 928, are associated with an aggressive course in medullary thyroid carcinoma, and affected patients need close surveillance. Treatment options for metastatic RAI-refractory thyroid cancer are limited. Currently, Food and Drug Administration-approved molecularly targeted therapies for metastatic RAI-refractory thyroid cancer, including sorafenib, lenvatinib, vandetanib, and cabozantinib, target the vascular endothelial growth factor receptor and RET kinases. Imaging plays an important role in assessment of response to these therapies, which can be atypical owing to antiangiogenic effects. A wide spectrum of toxic effects is associated with the molecularly targeted therapies used in thyroid cancer and can be detected at restaging scans. (©)RSNA, 2016.

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.

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.

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).

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.

Outcomes of screening mammography performed prior to fertility treatment in women ages 40-49.

OBJECTIVE: There are currently various conflicting recommendations for breast cancer screening with mammography in women between ages 40-49. There are no specific guidelines for breast cancer screening in women of this age group prior to assisted reproductive technology (ART) for the treatment of infertility. The purpose of our study was to evaluate outcomes of screening mammography, specifically ordered for the purpose of pre-fertility treatment clearance in women aged 40-49 years old. MATERIALS AND METHODS: This was an IRB approved retrospective study of women aged 40-49 presenting for screening mammography prior to ART between January 2010 and October 2018. Clinical history, imaging, and pathology results were gathered from the electronic medical record. Descriptive statistics were performed. RESULTS: Our study cohort consisted of 118 women with a mean age of 42 years (range 40-49). Sixteen of 118 (14%) women were recalled from screening for additional diagnostic work-up. Five of the 16 (31%) were recommended for biopsy (BI-RADS 4 or 5). One of 5 biopsies yielded a malignant result (PPV 20%). Overall cancer detection rate was 0.85% or 8.5 women per 1000 women screened. The single cancer in this cohort was an ER+ PR+ HER2- invasive ductal carcinoma. CONCLUSION: Screening mammography in women 40-49 performed prior to initiation of ART may identify asymptomatic breast malignancy. In accordance with ACR and SBI guidelines to screen women of this age group, women of this age group should undergo screening mammography prior to ART.

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.