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OBJECTIVE: Lubricin, also referred to as superficial zone protein and PRG4, is a synovial glycoprotein that supplies a friction-resistant, antiadhesive coating to the surfaces of articular cartilage, thereby protecting against arthritis-associated tissue wear and degradation. This study was undertaken to generate and characterize a novel recombinant lubricin protein construct, LUB:1, and to evaluate its therapeutic efficacy following intraarticular delivery in a rat model of osteoarthritis (OA). METHODS: Binding and localization of LUB:1 to cartilage surfaces was assessed by immunohistochemistry. The cartilage-lubricating properties of LUB:1 were determined using a custom friction testing apparatus. A cell-binding assay was performed to quantify the ability of LUB:1 to prevent cell adhesion. Efficacy studies were conducted in a rat meniscal tear model of OA. One week after the surgical induction of OA, LUB:1 or phosphate buffered saline vehicle was administered by intraarticular injection for 4 weeks, with dosing intervals of either once per week or 3 times per week. OA pathology scores were determined by histologic analysis. RESULTS: LUB:1 was shown to bind effectively to cartilage surfaces, and facilitated both cartilage boundary lubrication and inhibition of synovial cell adhesion. Treatment of rat knee joints with LUB:1 resulted in significant disease-modifying, chondroprotective effects during the progression of OA, by markedly reducing cartilage degeneration and structural damage. CONCLUSION: Our findings demonstrate the potential use of recombinant lubricin molecules in novel biotherapeutic approaches to the treatment of OA and associated cartilage abnormalities. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Protein kinase Czeta (PKCzeta), an atypical PKC, has been found to be transcriptionally up-regulated in human osteoarthritic (OA) articular cartilage. This study was undertaken to examine the role of PKCzeta in interleukin-1beta (IL-1beta)-induced NF-kappaB signaling in human OA chondrocytes, and ultimately to better understand its function in the regulation of downstream mediators of cartilage matrix degradation. METHODS: Pharmacologic inhibitors or genetic knockdown techniques were used to investigate the role of PKCzeta. Western blot analysis was used to evaluate phosphorylation of PKCzeta and NF-kappaB. Quantitative polymerase chain reaction (PCR) and activity assays were used to evaluate ADAMTS-4 expression and aggrecanase activity, respectively. Quantitative PCR, biochemical identification, and Western blot analysis were used to evaluate type 2 nitric oxide synthase (NOS2) and NO production. RESULTS: Phosphorylation of PKCzeta and NF-kappaB was induced by IL-1beta treatment in a time-dependent manner, and was specifically inhibited by inhibitors of atypical PKCs. Inhibition of PKCzeta suppressed IL-1beta-induced up-regulation of ADAMTS-4 messenger RNA (mRNA) and aggrecanase activity. Inhibitors of atypical PKCs also inhibited IL-1beta-induced NO production and NOS2 mRNA expression, demonstrating a novel link between PKCzeta and NO production. Furthermore, small interfering RNA- or short hairpin RNA-mediated knockdown of PKCzeta mRNA resulted in significant repression of both ADAMTS-4 and NOS2 mRNA expression. CONCLUSION: Our results show that PKCzeta is involved in the regulation of IL-1beta-induced NF-kappaB signaling in human OA chondrocytes, which in turn regulates downstream expression of ADAMTS-4 and NOS2. Therefore, inhibition of PKCzeta could potentially regulate the production of matrix-degrading enzymes as well as NO production and have a profound effect on disease progression in OA. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To identify potential molecular mediators and biomarkers for osteoarthritis (OA), through comparative proteomic analysis of articular cartilage tissue obtained from normal donors without OA (n = 7) and patients with OA (n = 7). METHODS: The proteomic analyses comprised extraction of soluble proteins from cartilage, separation of the protein mixtures by sodium dodecyl sulfate-polyacrylamide gel electrophoresis followed by in-gel digestion, and subsequent nano-liquid chromatography-tandem mass spectrometry analysis in conjunction with a database search for protein identification and semiquantitation. RESULTS: A total of 814 distinct proteins were identified with high confidence from 14 samples; 420 of these proteins were detected with > or = 3 unique peptides in at least 4 samples from the same group. Using stringent criteria, 59 proteins were found to be differentially expressed in OA cartilage. Gene Ontology and Ingenuity pathway analysis tools were used to characterize these proteins into functional categories. One of the up-regulated proteins, HtrA1, a serine protease, was detected at high levels in cartilage. CONCLUSION: Altered protein expression in the disease state is associated with many aspects of the pathogenesis of OA, such as increased proteolysis, lipid metabolism, immune response, and decreased signal transduction. To our knowledge, this is the first time that a large portion of these proteins and their expression patterns were identified in cartilage, thus providing new insights for finding novel pathologic mediators and biomarkers of OA. [ABSTRACT FROM AUTHOR]

This article describes the recent experiments undertaken by the William Blake Archive in preparing a digital edition of Vala, or the Four Zoas, one of Blake’s most materially challenging works. The aim is to provide users with multiple tools for analysing Blake’s messy and mysterious manuscript: a high-quality image of the manuscript page paired with a dynamic transcription that allows readers to choose the level of detail they want for understanding what it is, precisely, they are looking at. However, the existing encoding standards and display were revealed to be totally inadequate by the complexity of The Four Zoas, and early models were almost impossible to read and understand. The article traces the journey towards the latest prototype as the team try to strike a balance between detail and accessibility, description and interpretation, readability and reliability, and established conventions and new approaches.

Rationale and Objectives: To develop and evaluate an AI algorithm that detects breast cancer in MRI scans up to one year before radiologists typically identify it, potentially enhancing early detection in high-risk women., Materials and Methods: A convolutional neural network (CNN) AI model, pre-trained on breast MRI data, was fine-tuned using a retrospective dataset of 3029 MRI scans from 910 patients. These contained 115 cancers that were diagnosed within one year of a negative MRI. The model aimed to identify these cancers, with the goal of predicting cancer development up to one year in advance. The network was fine-tuned and tested with 10-fold cross-validation. Mean age of patients was 52 years (range, 18-88 years), with average follow-up of 4.3 years (range 1-12 years)., Results: The AI detected cancers one year earlier with an area under the ROC curve of 0.72 (0.67-0.76). Retrospective analysis by a radiologist of the top 10% highest risk MRIs as ranked by the AI could have increased early detection by up to 30%. (35/115, CI:22.2-39.7%, 30% sensitivity). A radiologist identified a visual correlate to biopsy-proven cancers in 83 of prior-year MRIs (83/115, CI: 62.1-79.4%). The AI algorithm identified the anatomic region where cancer would be detected in 66 cases (66/115, CI:47.8-66.5%); with both agreeing in 54 cases (54/115, CI:%37.5-56.4%)., Conclusion: This novel AI-aided re-evaluation of "benign" breasts shows promise for improving early breast cancer detection with MRI. As datasets grow and image quality improves, this approach is expected to become even more impactful., Competing Interests: Declaration of Competing Interest All authors declare no financial or non-financial competing interests., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Artificial intelligence (AI) has the potential to improve breast cancer screening; however, prospective evidence of the safe implementation of AI into real clinical practice is limited. A commercially available AI system was implemented as an additional reader to standard double reading to flag cases for further arbitration review among screened women. Performance was assessed prospectively in three phases: a single-center pilot rollout, a wider multicenter pilot rollout and a full live rollout. The results showed that, compared to double reading, implementing the AI-assisted additional-reader process could achieve 0.7-1.6 additional cancer detection per 1,000 cases, with 0.16-0.30% additional recalls, 0-0.23% unnecessary recalls and a 0.1-1.9% increase in positive predictive value (PPV) after 7-11% additional human reads of AI-flagged cases (equating to 4-6% additional overall reading workload). The majority of cancerous cases detected by the AI-assisted additional-reader process were invasive (83.3%) and small-sized (≤10 mm, 47.0%). This evaluation suggests that using AI as an additional reader can improve the early detection of breast cancer with relevant prognostic features, with minimal to no unnecessary recalls. Although the AI-assisted additional-reader workflow requires additional reads, the higher PPV suggests that it can increase screening effectiveness., (© 2023. The Author(s).)

Background Background parenchymal enhancement (BPE) at breast MRI has been associated with increased breast cancer risk in several independent studies. However, variability of subjective BPE assessments have precluded its use in clinical practice. Purpose To examine the association between fully objective measures of BPE at MRI and odds of breast cancer. Materials and Methods This prospective case-control study included patients who underwent a bilateral breast MRI examination and were receiving care at one of three centers in the United States from November 2010 to July 2017. Breast volume, fibroglandular tissue (FGT) volume, and BPE were quantified using fully automated software. Fat volume was defined as breast volume minus FGT volume. BPE extent was defined as the proportion of FGT voxels with enhancement of 20% or more. Spearman rank correlation between quantitative BPE extent and Breast Imaging Reporting and Data System (BI-RADS) BPE categories assigned by an experienced board-certified breast radiologist was estimated. With use of multivariable logistic regression, breast cancer case-control status was regressed on tertiles (low, moderate, and high) of BPE, FGT volume, and fat volume, with adjustment for covariates. Results In total, 536 case participants with breast cancer (median age, 48 years [IQR, 43-55 years]) and 940 cancer-free controls (median age, 46 years [IQR, 38-55 years]) were included. BPE extent was positively associated with BI-RADS BPE ( r s = 0.54; P < .001). Compared with low BPE extent (range, 2.9%-34.2%), high BPE extent (range, 50.7%-97.3%) was associated with increased odds of breast cancer (odds ratio [OR], 1.74 [95% CI: 1.23, 2.46]; P for trend = .002) in a multivariable model also including FGT volume (OR, 1.39 [95% CI: 0.97, 1.98]) and fat volume (OR, 1.46 [95% CI: 1.04, 2.06]). The association of high BPE extent with increased odds of breast cancer was similar for premenopausal and postmenopausal women (ORs, 1.75 and 1.83, respectively; interaction P = .73). Conclusion Objectively measured BPE at breast MRI is associated with increased breast cancer odds for both premenopausal and postmenopausal women. Clinical trial registration no. NCT02301767 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bokacheva in this issue.

Congenital hypothyroidism rarely causes a clinically significant neck mass in newborns. We present the case of a newborn with congenital hypothyroidism and significantly enlarged goiter and discuss imaging considerations and medical and surgical management. This infant was prenatally discovered to have a midline neck mass on 28 week ultrasound measuring 6.0 cm × 3.4 cm × 5.8 cm. Diagnostic cordocentesis demonstrated elevated thyroid-stimulating hormone (TSH, 361 µIU/mL). Maternal evaluation for thyroid disease and antithyroid antibodies was negative. A Cesarean section at 38 weeks gestation was recommended due to hyperextension of the fetal neck. The infant was intubated for respiratory distress. Postnatal magnetic resonance imaging revealed a 5.5 cm × 4.4 cm × 7.6 cm goiter and laboratory studies confirmed the diagnosis of primary hypothyroidism (TSH 16.7 µIU/mL). Treatment was initiated with intravenous levothyroxine and transitioned to oral supplementation. Serial ultrasounds showed decreased goiter volume over several weeks, with recent volume per lobe being 22% and 44% of original volume. This case demonstrates the importance of prompt diagnosis and initiation of thyroid hormone replacement, allowing for significant goiter regression without surgical intervention and ensuring normal growth and neurodevelopmental outcome. Surgical management should be considered for those with persistent compressive symptoms despite optimal medical management., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background There is considerable interest in the potential use of artificial intelligence (AI) systems in mammographic screening. However, it is essential to critically evaluate the performance of AI before it can become a modality used for independent mammographic interpretation. Purpose To evaluate the reported standalone performances of AI for interpretation of digital mammography and digital breast tomosynthesis (DBT). Materials and Methods A systematic search was conducted in PubMed, Google Scholar, Embase (Ovid), and Web of Science databases for studies published from January 2017 to June 2022. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) values were reviewed. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Comparative (QUADAS-2 and QUADAS-C, respectively). A random effects meta-analysis and meta-regression analysis were performed for overall studies and for different study types (reader studies vs historic cohort studies) and imaging techniques (digital mammography vs DBT). Results In total, 16 studies that include 1 108 328 examinations in 497 091 women were analyzed (six reader studies, seven historic cohort studies on digital mammography, and four studies on DBT). Pooled AUCs were significantly higher for standalone AI than radiologists in the six reader studies on digital mammography (0.87 vs 0.81, P = .002), but not for historic cohort studies (0.89 vs 0.96, P = .152). Four studies on DBT showed significantly higher AUCs in AI compared with radiologists (0.90 vs 0.79, P < .001). Higher sensitivity and lower specificity were seen for standalone AI compared with radiologists. Conclusion Standalone AI for screening digital mammography performed as well as or better than radiologists. Compared with digital mammography, there is an insufficient number of studies to assess the performance of AI systems in the interpretation of DBT screening examinations. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Scaranelo in this issue.

Response to immunotherapy across multiple cancer types is approximately 25%, with some tumor types showing increased response rates compared to others (i.e. response rates in melanoma and non-small cell lung cancer (NSCLC) are typically 30-60%). Patients whose tumors are resistant to immunotherapy often lack high levels of pre-existing inflammation in the tumor microenvironment. Increased tumor glycolysis, acting through glucose deprivation and lactic acid accumulation, has been shown to have pleiotropic immune suppressive effects using in-vitro and in-vivo models of disease. To determine whether the immune suppressive effect of tumor glycolysis is observed across human solid tumors, we analyzed glycolytic and immune gene expression patterns in multiple solid malignancies. We found that increased expression of a glycolytic signature was associated with decreased immune infiltration and a more aggressive disease across multiple tumor types. Radiologic and pathologic analysis of untreated estrogen receptor (ER)-negative breast cancers corroborated these observations, and demonstrated that protein expression of glycolytic enzymes correlates positively with glucose uptake and negatively with infiltration of CD3 + and CD8 + lymphocytes. This study reveals an inverse relationship between tumor glycolysis and immune infiltration in a large cohort of multiple solid tumor types., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Cohen, Pareja, Socci, Shen, Doane, Schwartz, Khanin, Morris, Sutton and Blasberg.)

Breast tissue enhances on contrast MRI and is called background parenchymal enhancement (BPE). Having high BPE has been associated with an increased risk of breast cancer. We examined the relationship between BPE and the amount of fibroglandular tissue on MRI (MRI-FGT) and breast cancer risk factors. This was a cross-sectional study of 415 women without breast cancer undergoing contrast-enhanced breast MRI at Memorial Sloan Kettering Cancer Center. All women completed a questionnaire assessing exposures at the time of MRI. Prevalence ratios (PR) and 95% confidence intervals (CI) describing the relationship between breast cancer risk factors and BPE and MRI-FGT were generated using modified Poisson regression. In multivariable-adjusted models a positive association between body mass index (BMI) and BPE was observed, with a 5-unit increase in BMI associated with a 14% and 44% increase in prevalence of high BPE in pre- and post-menopausal women, respectively. Conversely, a strong inverse relationship between BMI and MRI-FGT was observed in both pre- (PR = 0.66, 95% CI 0.57, 0.76) and post-menopausal (PR = 0.66, 95% CI 0.56, 0.78) women. Use of preventive medication (e.g., tamoxifen) was associated with having low BPE, while no association was observed for MRI-FGT. BPE is an imaging marker available from standard contrast-enhanced MRI, that is influenced by endogenous and exogenous hormonal exposures in both pre- and post-menopausal women., (© 2022. The Author(s).)

Background: Technology acceptability and usage surveys (TAUS) are brief questionnaires that measure technology comfort, typical daily use, and access in a population. However, current measures are not adapted to low- and middle-income country (LMIC) contexts., Objective: The objective of this pilot study was to develop a TAUS that could be used to inform the implementation of a mobile health (mHealth) intervention in Nigeria., Methods: A literature review of validated technology comfort and usage scales was conducted to identify candidate items. The draft measure was reviewed for face validity by an expert panel comprised of clinicians and researchers with cultural, methodological, and clinical expertise. The measure was piloted by radiologists at an oncology symposium in Nigeria., Results: After expert review, the final measure included 18 items organized into 3 domains: (1) comfort with using mobile applications, (2) reliability of internet or electricity, and (3) attitudes toward using computers or mobile applications in clinical practice. The pilot sample (n=16) reported high levels of comfort and acceptability toward using mHealth applications in the clinical setting but faced numerous infrastructure challenges., Conclusions: Pilot results indicate that the TAUS may be a feasible and appropriate measure for assessing technology usage and acceptability in LMIC clinical contexts. Dedicating a domain to technology infrastructure and access yielded valuable insights for program implementation., (©Kathleen A Lynch, Thomas M Atkinson, Adeleye D Omisore, Olusola Famurewa, Olalekan Olasehinde, Oluwole Odujoko, Olusegun I Alatise, Adedeji Egberongbe, T Peter Kingham, Elizabeth A Morris, Elizabeth Sutton. Originally published in JMIR Formative Research (https://formative.jmir.org), 20.04.2022.)

Purpose: To develop a deep network architecture that would achieve fully automated radiologist-level segmentation of cancers at breast MRI., Materials and Methods: In this retrospective study, 38 229 examinations (composed of 64 063 individual breast scans from 14 475 patients) were performed in female patients (age range, 12-94 years; mean age, 52 years ± 10 [standard deviation]) who presented between 2002 and 2014 at a single clinical site. A total of 2555 breast cancers were selected that had been segmented on two-dimensional (2D) images by radiologists, as well as 60 108 benign breasts that served as examples of noncancerous tissue; all these were used for model training. For testing, an additional 250 breast cancers were segmented independently on 2D images by four radiologists. Authors selected among several three-dimensional (3D) deep convolutional neural network architectures, input modalities, and harmonization methods. The outcome measure was the Dice score for 2D segmentation, which was compared between the network and radiologists by using the Wilcoxon signed rank test and the two one-sided test procedure., Results: The highest-performing network on the training set was a 3D U-Net with dynamic contrast-enhanced MRI as input and with intensity normalized for each examination. In the test set, the median Dice score of this network was 0.77 (interquartile range, 0.26). The performance of the network was equivalent to that of the radiologists (two one-sided test procedures with radiologist performance of 0.69-0.84 as equivalence bounds, P < .001 for both; n = 250)., Conclusion: When trained on a sufficiently large dataset, the developed 3D U-Net performed as well as fellowship-trained radiologists in detailed 2D segmentation of breast cancers at routine clinical MRI. Keywords: MRI, Breast, Segmentation, Supervised Learning, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning AlgorithmsPublished under a CC BY 4.0 license. Supplemental material is available for this article., Competing Interests: Disclosures of Conflicts of Interest: L.H. Grant from NIBIB and NIMH through the NIH BRAIN Initiative (R01 EB02157); consulting fee from City College of New York. Y.H. Consulting fee from City College of New York. S.L. No relevant relationships. C.R.S. No relevant relationships. R.L.G. No relevant relationships. I.D.N. Grant from Alfonso Martín Escudero Foundation. A.G.V.B. No relevant relationships. N.O. No relevant relationships. E.S.K. No relevant relationships. D.L. No relevant relationships. D.A. Consulting fee AEAD850301RG4. S.E.W. RSNA Research and Education Foundation grant no. RF1905 (content is solely the responsibility of the authors and does not necessarily represent the official views of the RSNA R&E Foundation). M.H. No relevant relationships. D.F.M. No relevant relationships. K.P. Funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748 and the Breast Cancer Research Foundation; ongoing research grants include Digital Hybrid Breast PET/MRI for Enhanced Diagnosis of Breast Cancer (HYPMED) H2020 - Research and Innovation Framework Programme PHC-11-2015 #667211-2, A Body Scan for Cancer Detection using Quantum Technology (CANCERSCAN) H2020-FETOPEN-2018-2019-2020-01 # 828978, Multiparametric 18F-Fluoroestradiol PET/MRI coupled with Radiomics Analysis and Machine Learning for Prediction and Assessment of Response to Neoadjuvant Endocrine Therapy in Patients with Hormone Receptor+/HER2− Invasive Breast Cancer Jubiläumsfonds of the Austrian National Bank # Nr: 18207, Deciphering breast cancer heterogeneity and tackling the hypoxic tumor microenvironment challenge with PET/MRI, MSI and radiomics The Vienna Science and Technology Fund LS19-046, MSKCC 2020 Molecularly Targeted Intra-Operative Imaging Award 07/2020-06/2021, Breast Cancer Research Foundation 06/2019 - 05/2021 PI Mark Robson Co-I, NIH R01 Breast Cancer Intravoxel-Incoherent-Motion MRI Multisite (BRIMM) 09/01/2020-08/30/2025 PI Eric Sigmund Co-I, NIH R01 subaward: Abbreviated Non-Contrast-Enhanced MRI for Breast Cancer Screening 09/01/2023-08/31/2025 PI Brian Hargreaves; payment for lectures, service on speakers bureaus and for travel/accommodations/meeting expenses from the European Society of Breast Imaging (MRI educational course, annual scientific meeting) and Siemens Healthcare (lectures). K.J. No relevant relationships. A.E.E. No relevant relationships. P.E. No relevant relationships. E.A.M. No relevant relationships. H.A.M. Grant from NIBIB and NIMH through the NIH BRAIN Initiative (R01 EB028157). L.C.P. Grant from NIH (R01CA247910). E.J.S. Grant from National Institutes of Health/National Cancer Institute (P30 CA008748)., (2022 by the Radiological Society of North America, Inc.)

The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018-March 2020; Medical University Vienna, from January 2011-August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7-99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70-0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75-0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77-0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0-88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies.

Purpose: To assess the feasibility and diagnostic accuracy of multispectral MRI (MSI) in the detection and localization of biopsy markers during MRI-guided breast biopsy., Methods: This prospective study included 20 patients undergoing MR-guided breast biopsy. In 10 patients (Group 1), MSI was acquired following tissue sampling and biopsy marker deployment. In the other 10 patients (Group 2), MSI was acquired following tissue sampling but before biopsy marker deployment (to simulate deployment failure). All patients received post-procedure mammograms. Group 1 and Group 2 designations, in combination with the post-procedure mammogram, served as the reference standard. The diagnostic performance of MSI for biopsy marker identification was independently evaluated by two readers using two-spectral-bin MR and one-spectral-bin MR. The κ statistic was used to assess inter-rater agreement for biopsy marker identification., Results: The sensitivity, specificity, and accuracy of biopsy marker detection for readers 1 and 2 using 2-bin MSI were 90.0% (9/10) and 90.0% (9/10), 100.0% (10/10) and 100.0% (10/10), 95.0% (19/20) and 95.0% (19/20); and using 1-bin MSI were 70.0% (7/10) and 80.0% (8/10), 100.0% (8/8) and 100.0% (10/10), 85.0% (17/20) and 90.0% (18/20). Positive predictive value was 100% for both readers for all numbers of bins. Inter-rater agreement was excellent: κ was 1.0 for 2-bin MSI and 0.81 for 1-bin MSI., Conclusion: MSI is a feasible, diagnostically accurate technique for identifying metallic biopsy markers during MRI-guided breast biopsy and may eliminate the need for a post-procedure mammogram., Competing Interests: YW and PS are inventors on QSM-related patents issued to Cornell University and hold equity in Medimagemetric LLC. KP received payment for activities not related to the present article including lectures including service on speakers bureaus and for travel/accommodations/meeting expenses unrelated to activities listed from the European Society of Breast Imaging (MRI educational course, annual scientific meeting), and the IDKD 2019 (educational course). EM has received a grant from GRAIL, Inc. for research not related to the present article. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Eskreis-Winkler, Simon, Reichman, Spincemaille, Nguyen, Christos, Drotman, Prince, Pinker, Sutton, Morris and Wang.)

Altered metabolism including lipids is an emerging hallmark of breast cancer. The purpose of this study was to investigate if breast cancers exhibit different magnetic resonance spectroscopy (MRS)-based lipid composition than normal fibroglandular tissue (FGT). MRS spectra, using the stimulated echo acquisition mode sequence, were collected with a 3T scanner from patients with suspicious lesions and contralateral normal tissue. Fat peaks at 1.3 + 1.6 ppm (L13 + L16), 2.1 + 2.3 ppm (L21 + L23), 2.8 ppm (L28), 4.1 + 4.3 ppm (L41 + L43), and 5.2 + 5.3 ppm (L52 + L53) were quantified using LCModel software. The saturation index (SI), number of double bods (NBD), mono and polyunsaturated fatty acids (MUFA and PUFA), and mean chain length (MCL) were also computed. Results showed that mean concentrations of all lipid metabolites and PUFA were significantly lower in tumors compared with that of normal FGT ( p ≤ 0.002 and 0.04, respectively). The measure best separating normal and tumor tissues after adjusting with multivariable analysis was L21 + L23, which yielded an area under the curve of 0.87 (95% CI: 0.75-0.98). Similar results were obtained between HER2 positive versus HER2 negative tumors. Hence, MRS-based lipid measurements may serve as independent variables in a multivariate approach to increase the specificity of breast cancer characterization.

Purpose: Breast MRI background parenchymal enhancement (BPE) can potentially serve as a prognostic marker, by possible correlation with molecular subtype. Oncotype Dx, a gene assay, is a prognostic and predictive surrogate for tumor aggressiveness and treatment response. The purpose of this study was to investigate the association between contralateral non-tumor breast magnetic resonance imaging (MRI) background parenchymal enhancement and tumor oncotype score., Methods: In this retrospective study, patients with ER+ and HER2- early stage invasive ductal carcinoma who underwent preoperative breast MRI, oncotype risk scoring, and breast conservation surgery from 2008-2010 were identified. After registration, BPE from the pre and three post-contrast phases was automatically extracted using a k-means clustering algorithm. Four metrics were calculated: initial enhancement (IE) relative to the pre-contrast signal, late enhancement, overall enhancement (OE), and area under the enhancement curve (AUC). Histogram analysis was performed to determine first order metrics which were compared to oncotype risk score groups using Mann-Whitney tests and Spearman rank correlation analysis., Results: This study included 80 women (mean age = 51.1 ± 10.3 years); 46 women were categorized as low risk (≤17) and 34 women were categorized as intermediate/high risk (≥18) according to Oncotype Dx. For the mean of the top 10% pixels, significant differences were noted for IE (p = 0.032), OE (p = 0.049), and AUC (p = 0.044). Using the risk score as a continuous variable, correlation analysis revealed a weak but significant correlation with the mean of the top 10% pixels for IE (r = 0.26, p = 0.02), OE (r = 0.25, p = 0.02), and AUC (r = 0.27, p = 0.02)., Conclusion: BPE metrics of enhancement in the non-tumor breast are associated with tumor Oncotype Dx recurrence score, suggesting that the breast microenvironment may relate to likelihood of recurrence and magnitude of chemotherapy benefit., Competing Interests: EM received a grant from GRAIL Inc. for research not related to the present article. MS is currently employed by Promaxo in San Francisco, CA. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Zhang, Sadinski, Haddad, Bae, Martinez, Morris, Gibbs and Sutton.)

Importance: After neoadjuvant chemotherapy (NAC), pathologic complete response (pCR) is an optimal outcome and a surrogate end point for improved disease-free and overall survival. To date, surgical resection remains the only reliable method for diagnosing pCR., Objective: To evaluate the accuracy of magnetic resonance imaging (MRI)-guided biopsy for diagnosing a pCR after NAC compared with reference-standard surgical resection., Design, Setting, and Participants: Single-arm, phase 1, nonrandomized controlled trial in a single tertiary care cancer center from September 26, 2017, to July 29, 2019. The median follow-up was 1.26 years (interquartile range, 0.85-1.59 years). Data analysis was performed in November 2019. Eligible patients had (1) stage IA to IIIC biopsy-proven operable invasive breast cancer; (2) standard-of-care NAC; (3) MRI before and after NAC, with imaging complete response defined as no residual enhancement on post-NAC MRI; and (4) definitive surgery. Patients were excluded if they were younger than 18 years, had a medical reason precluding study participation, or had a prior history of breast cancer., Interventions: Post-NAC MRI-guided biopsy without the use of intravenous contrast of the tumor bed before definitive surgery., Main Outcomes and Measures: The primary end point was the negative predictive value of MRI-guided biopsy, with true-negative defined as negative results of the biopsy (ie, no residual cancer) corresponding to a surgical pCR. Accuracy, sensitivity, positive predictive value, and specificity were also calculated. Two clinical definitions of pCR were independently evaluated: definition 1 was no residual invasive cancer; definition 2, no residual invasive or in situ cancer., Results: Twenty of 23 patients (87%) had evaluable data (median [interquartile range] age, 51.5 [39.0-57.5] years; 20 women [100%]; 13 White patients [65%]). Of the 20 patients, pre-NAC median tumor size on MRI was 3.0 cm (interquartile range, 2.0-5.0 cm). Nineteen of 20 patients (95%) had invasive ductal carcinoma; 15 of 20 (75%) had stage II cancer; 11 of 20 (55%) had ERBB2 (formerly HER2 or HER2/neu)-positive cancer; and 6 of 20 (30%) had triple-negative cancer. Surgical pathology demonstrated a pCR in 13 of 20 (65%) patients and no pCR in 7 of 20 patients (35%) when pCR definition 1 was used. Results of MRI-guided biopsy had a negative predictive value of 92.8% (95% CI, 66.2%-99.8%), with accuracy of 95% (95% CI, 75.1%-99.9%), sensitivity of 85.8% (95% CI, 42.0%-99.6%), positive predictive value of 100%, and specificity of 100% for pCR definition 1. Only 1 patient had a false-negative MRI-guided biopsy result (surgical pathology showed <0.02 cm of residual invasive cancer)., Conclusions and Relevance: This study's results suggest that the accuracy of MRI-guided biopsy to diagnose a post-NAC pCR approaches that of reference-standard surgical resection. MRI-guided biopsy may be a viable alternative to surgical resection for this population after NAC, which supports the need for further investigation., Trial Registration: ClinicalTrials.gov Identifier: NCT03289195.

Social scientists have advocated for the use of participatory research methods for Global Health project design and planning. However, community-engaged approaches can be time and resource-intensive. This article proposes a feasible framework for conducting a participatory needs assessment in time-limited settings using multiple, triangulated qualitative methods. This framework is outlined through a case study: a participatory needs assessment to inform the design of an ultrasound-guided biopsy training program in Nigeria. Breast cancer is the leading cause of death for Nigerian women and most cases in Nigeria are diagnosed at an advanced stage; timely diagnosis is impeded by fractious referral pathways, costly imaging equipment, and limited access outside urban centers. The project involved participant observation, surveys, and focus groups at the African Research Group for Oncology (ARGO) in Ile-Ife, Nigeria. Through this timely research and engagement, participants spoke about diagnostic challenges, institutional power dynamics, and infrastructure considerations for program implementation.

The purpose of this study was to investigate whether ultra-high-field dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast at 7T using quantitative pharmacokinetic (PK) analysis can differentiate between benign and malignant breast tumors for improved breast cancer diagnosis and to predict molecular subtypes, histologic grade, and proliferation rate in breast cancer. In this prospective study, 37 patients with 43 lesions suspicious on mammography or ultrasound underwent bilateral DCE-MRI of the breast at 7T. PK parameters (K Trans , k ep , V e ) were evaluated with two region of interest (ROI) approaches (2D whole-tumor ROI or 2D 10 mm standardized ROI) manually drawn by two readers (senior reader, R1, and R2) independently. Histopathology served as the reference standard. PK parameters differentiated benign and malignant lesions (n = 16, 27, respectively) with good accuracy (AUCs = 0.655-0.762). The addition of quantitative PK analysis to subjective BI-RADS classification improved breast cancer detection from 88.4% to 97.7% for R1 and 86.04% to 97.67% for R2. Different ROI approaches did not influence diagnostic accuracy for both readers. Except for K Trans for whole-tumor ROI for R2, none of the PK parameters were valuable to predict molecular subtypes, histologic grade, or proliferation rate in breast cancer. In conclusion, PK-enhanced BI-RADS is promising for the noninvasive differentiation of benign and malignant breast tumors.

Background: Background parenchymal enhancement (BPE) on breast magnetic resonance imaging (MRI) may be associated with breast cancer risk, but previous studies of the association are equivocal and limited by incomplete blinding of BPE assessment. In this study, we evaluated the association between BPE and breast cancer based on fully blinded assessments of BPE in the unaffected breast., Methods: The Imaging and Epidemiology (IMAGINE) study is a multicenter breast cancer case-control study of women receiving diagnostic, screening, or follow-up breast MRI, recruited from three comprehensive cancer centers in the USA. Cases had a first diagnosis of unilateral breast cancer and controls had no history of or current breast cancer. A single board-certified breast radiologist with 12 years' experience, blinded to case-control status and clinical information, assessed the unaffected breast for BPE without view of the affected breast of cases (or the corresponding breast laterality of controls). The association between BPE and breast cancer was estimated by multivariable logistic regression separately for premenopausal and postmenopausal women., Results: The analytic dataset included 835 cases and 963 controls. Adjusting for fibroglandular tissue (breast density), age, race/ethnicity, BMI, parity, family history of breast cancer, BRCA1/BRCA2 mutations, and other confounders, moderate/marked BPE (vs minimal/mild BPE) was associated with breast cancer among premenopausal women [odds ratio (OR) 1.49, 95% CI 1.05-2.11; p = 0.02]. Among postmenopausal women, mild/moderate/marked vs minimal BPE had a similar, but statistically non-significant, association with breast cancer (OR 1.45, 95% CI 0.92-2.27; p = 0.1)., Conclusions: BPE is associated with breast cancer in premenopausal women, and possibly postmenopausal women, after adjustment for breast density and confounders. Our results suggest that BPE should be evaluated alongside breast density for inclusion in models predicting breast cancer risk.

Purpose: The incidence of breast cancer is rising in Nigeria, and one major barrier to care is the lack of affordable and appropriate breast cancer diagnosis by ultrasound (US)-guided biopsy. The prohibitive cost of US devices limits their availability in low- and middle-income countries. The emergence of mobile health (mHealth) imaging devices may offer an acceptable low-cost alternative. The purpose of this research was to perform a comprehensive needs assessment to understand knowledge, use, training needs, and attitudes as regards image-guided biopsy in Nigeria to inform the development of an mHealth-based US-guided biopsy training program., Methods: A multistakeholder needs assessment was conducted at the Sixth Annual African Research Group for Oncology Symposium. Voluntary anonymous surveys were administered to all attendees. A subset of attendees (ie, surgeons, radiologists, pathologists, and nurses) participated in six focus groups. Survey items and interview guides were developed collaboratively with local and international input., Results: Surveys focusing on use, training needs, and attitudes regarding US-guided biopsies were completed with a 55% response rate (n = 54 of 98) among participants from 22 hospitals across Nigeria. Respondents expressed dissatisfaction with the way breast biopsies were currently performed at their hospitals and high interest in having their institution participate in a US-guided biopsy training program. Focus group participants (n = 37) identified challenges to performing US-guided procedures, including equipment functionality and cost, staff training, and access to consumables. Groups brainstormed the design of an mHealth US-guided biopsy training program, preferring a train-the-trainer format combining in-person teaching with independent modules., Conclusion: A multidisciplinary needs assessment of local stakeholders identified a need for and acceptability of an mHealth-based US-guided biopsy training program in Nigeria.

Background: To use clinical and MRI radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC)., Methods: This retrospective study included 311 patients. pCR was defined as no residual invasive carcinoma in the breast or axillary lymph nodes (ypT0/isN0). Radiomics/statistical analysis was performed using MATLAB and CERR software. After ROC and correlation analysis, selected radiomics parameters were advanced to machine learning modelling alongside clinical MRI-based parameters (lesion type, multifocality, size, nodal status). For predicting pCR, the data was split into a training and test set (80:20)., Findings: The overall pCR rate was 60.5% (188/311). The final model to predict HER2 heterogeneity utilised three MRI parameters (two clinical, one radiomic) for a sensitivity of 99.3% (277/279), specificity of 81.3% (26/32), and diagnostic accuracy of 97.4% (303/311). The final model to predict pCR included six MRI parameters (two clinical, four radiomic) for a sensitivity of 86.5% (32/37), specificity of 80.0% (20/25), and diagnostic accuracy of 83.9% (52/62) (test set); these results were independent of age and ER status, and outperformed the best model developed using clinical parameters only (p=0.029, comparison of proportion Chi-squared test)., Interpretation: The machine learning models, including both clinical and radiomics MRI features, can be used to assess HER2 expression level and can predict pCR after NAC in HER2 overexpressing breast cancer patients., Funding: NIH/NCI (P30CA008748), Susan G. Komen Foundation, Breast Cancer Research Foundation, Spanish Foundation Alfonso Martin Escudero, European School of Radiology., Competing Interests: Declaration of Interests Katja Pinker received payment for activities not related to the present article including lectures including service on speakers bureaus and for travel/accommodations/meeting expenses unrelated to activities listed from the European Society of Breast Imaging (MRI educational course, annual scientific meeting), IDKD educational course 2019, and Siemens Healthineers. Maxine S Jochelson has received honoraria not related to the present article including an honorarium from GE for speaking, and an honorarium for speaking at the Lynn Sage Breast Cancer Symposium and at MD Anderson. Elizabeth A Morris has received a grant from GRAIL Inc. for research not related to the present article. Monica Morrow has received honoraria from Genomic Health. The rest of the authors report no potential conflicts of interest., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Importance: Regional nodal irradiation (RNI) for node-positive breast cancer reduces distant metastases and improves survival, albeit with limited reduction in regional nodal recurrences. The mechanism by which RNI robustly reduces distant metastases while modestly influencing nodal recurrences (ie, the presumed target of RNI) remains unclear., Objective: To determine whether some distant metastases putatively arise from occult regional nodal disease and whether regional recurrences otherwise remain largely undetected until an advanced cancer presentation., Design, Setting, and Participants: This cohort study examined patients presenting with de novo stage IV breast cancer to the Memorial Sloan Kettering Cancer Center in New York, New York, from 2006 to 2018. Medical records were reviewed to ascertain clinicopathological parameters, including estrogen receptor status and survival. Pretreatment positron emission tomography-computed tomography (PET-CT) imaging was reviewed to ascertain the extent of regional nodal involvement at metastatic diagnosis using standard nodal assessment criteria. A subset underwent regional lymph node biopsy for diagnostic confirmation and served to validate the radiographic nodal assessment. Data analysis was performed from October 2019 to February 2020., Exposures: Untreated metastatic breast cancer., Main Outcome and Measures: The primary outcome was the likelihood of regional nodal involvement at the time of metastatic breast cancer presentation and was determined by reviewing pretreatment PET-CT imaging and lymph node biopsy findings., Results: Among 597 women (median [interquartile range] age, 53 [44-65] years) with untreated metastatic breast cancer, 512 (85.8%) exhibited regional lymph node involvement by PET-CT or nodal biopsy, 509 (85%) had involvement of axillary level I, 328 (55%) had involvement in axillary level II, 136 (23%) had involvement in axillary level III, 101 (17%) had involvement in the supraclavicular fossa, and 96 (16%) had involvement in the internal mammary chain. Lymph node involvement was more prevalent among estrogen receptor-negative tumors (92.4%) than estrogen receptor-positive tumors (83.6%). Nodal involvement at the time of metastatic diagnosis was not associated with overall survival., Conclusions and Relevance: These findings suggest that a majority of patients with de novo metastatic breast cancer harbor regional lymph node disease at presentation, consistent with the hypothesis that regional involvement may precede metastatic dissemination. This is in alignment with the findings of landmark trials suggesting that RNI reduces distant recurrences. It is possible that this distant effect of RNI may act via eradication of occult regional disease prior to systemic seeding. The challenges inherent in detecting isolated nodal disease (which is typically asymptomatic) may account for the more modest observed benefit of RNI on regional recurrences. Alternative explanations of nodal involvement that arises concurrently or after metastatic dissemination remain possible, but do not otherwise explain the association of RNI with distant recurrence.

Background: To investigate if baseline and/or changes in contralateral background parenchymal enhancement (BPE) and fibroglandular tissue (FGT) measured on magnetic resonance imaging (MRI) and mammographic breast density (MD) can be used as imaging biomarkers for overall and recurrence-free survival in patients with invasive lobular carcinomas (ILCs) undergoing adjuvant endocrine treatment., Methods: Women who fulfilled the following inclusion criteria were included in this retrospective HIPAA-compliant IRB-approved study: unilateral ILC, pre-treatment breast MRI and/or mammography from 2000 to 2010, adjuvant endocrine treatment, follow-up MRI, and/or mammography 1-2 years after treatment onset. BPE, FGT, and mammographic MD of the contralateral breast were independently graded by four dedicated breast radiologists according to BI-RADS. Associations between the baseline levels and change in levels of BPE, FGT, and MD with overall survival and recurrence-free survival were assessed using Kaplan-Meier survival curves and Cox regression analysis., Results: Two hundred ninety-eight patients (average age = 54.1 years, range = 31-79) fulfilled the inclusion criteria. The average follow-up duration was 11.8 years (range = 2-19). Baseline and change in levels of BPE, FGT, and MD were not significantly associated with recurrence-free or overall survival. Recurrence-free and overall survival were affected by histological subtype (p < 0.0001), number of metastatic axillary lymph nodes (p < 0.0001), age (p = 0.01), and adjuvant endocrine treatment duration (p < 0.001)., Conclusions: Qualitative evaluation of BPE, FGT, and mammographic MD changes cannot predict which patients are more likely to benefit from adjuvant endocrine treatment.

The aim of our intra-individual comparison study was to investigate and compare the potential of radiomics analysis of contrast-enhanced mammography (CEM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status, and tumor grade in patients with primary breast cancer. This retrospective study included 48 female patients with 49 biopsy-proven breast cancers who underwent pretreatment breast CEM and MRI. Radiomics analysis was performed by using MaZda software. Radiomics parameters were correlated with tumor histology (invasive vs. non-invasive), hormonal status (HR+ vs. HR-), and grading (low grade G1 + G2 vs. high grade G3). CEM radiomics analysis yielded classification accuracies of up to 92% for invasive vs. non-invasive breast cancers, 95.6% for HR+ vs. HR- breast cancers, and 77.8% for G1 + G2 vs. G3 invasive cancers. MRI radiomics analysis yielded classification accuracies of up to 90% for invasive vs. non-invasive breast cancers, 82.6% for HR+ vs. HR- breast cancers, and 77.8% for G1+G2 vs. G3 cancers. Preliminary results indicate a potential of both radiomics analysis of DCE-MRI and CEM for non-invasive assessment of tumor-invasiveness, hormone receptor status, and tumor grade. CEM may serve as an alternative to MRI if MRI is not available or contraindicated.

We evaluated the performance of radiomics and artificial intelligence (AI) from multiparametric magnetic resonance imaging (MRI) for the assessment of breast cancer molecular subtypes. Ninety-one breast cancer patients who underwent 3T dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping were included retrospectively. Radiomic features were extracted from manually drawn regions of interest (n = 704 features per lesion) on initial DCE-MRI and ADC maps. The ten best features for subtype separation were selected using probability of error and average correlation coefficients. For pairwise comparisons with >20 patients in each group, a multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used (70% of cases for training, 30%, for validation, five times each). For all other separations, linear discriminant analysis (LDA) and leave-one-out cross-validation were applied. Histopathology served as the reference standard. MLP-ANN yielded an overall median area under the receiver-operating-characteristic curve (AUC) of 0.86 (0.77-0.92) for the separation of triple negative (TN) from other cancers. The separation of luminal A and TN cancers yielded an overall median AUC of 0.8 (0.75-0.83). Radiomics and AI from multiparametric MRI may aid in the non-invasive differentiation of TN and luminal A breast cancers from other subtypes.

Multishot multiplexed sensitivity-encoding diffusion-weighted imaging is a feasible and easily implementable routine breast MRI protocol that yields high-quality diffusion-weighted breast images. Purpose: To compare multiplexed sensitivity-encoding (MUSE) diffusion-weighted imaging (DWI) and single-shot DWI for lesion visibility and differentiation of malignant and benign lesions within the breast. Materials and Methods: In this prospective institutional review board-approved study, both MUSE DWI and single-shot DWI sequences were first optimized in breast phantoms and then performed in a group of patients. Thirty women (mean age, 51.1 years ± 10.1 [standard deviation]; age range, 27-70 years) with 37 lesions were included in this study and underwent scanning using both techniques. Visual qualitative analysis of diffusion-weighted images was accomplished by two independent readers; images were assessed for lesion visibility, adequate fat suppression, and the presence of artifacts. Quantitative analysis was performed by calculating apparent diffusion coefficient (ADC) values and image quality parameters (signal-to-noise ratio [SNR] for lesions and fibroglandular tissue; contrast-to-noise ratio) by manually drawing regions of interest within the phantoms and breast tumor tissue. Interreader variability was determined using the Cohen κ coefficient, and quantitative differences between MUSE DWI and single-shot DWI were assessed using the Mann-Whitney U test; significance was defined at P < .05. Results: MUSE DWI yielded significantly improved image quality compared with single-shot DWI in phantoms (SNR, P = .001) and participants (lesion SNR, P = .009; fibroglandular tissue SNR, P = .05; contrast-to-noise ratio, P = .008). MUSE DWI ADC values showed a significant difference between malignant and benign lesions ( P < .001). No significant differences were found between MUSE DWI and single-shot DWI in the mean, maximum, and minimum ADC values ( P = .96, P = .28, and P = .49, respectively). Visual qualitative analysis resulted in better lesion visibility for MUSE DWI over single-shot DWI (κ = 0.70). Conclusion: MUSE DWI is a promising high-spatial-resolution technique that may enhance breast MRI protocols without the need for contrast material administration in breast screening. Keywords: Breast, MR-Diffusion Weighted Imaging, Oncology Supplemental material is available for this article. © RSNA, 2020., Competing Interests: Disclosures of Conflicts of Interest: I.D.N. disclosed no relevant relationships. R.L.G. disclosed no relevant relationships. E.A.M. disclosed no relevant relationships. T.L. disclosed no relevant relationships. M.M.F. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: is and employee of and holds stock in GE Healthcare. Other relationships: disclosed no relevant relationships. A.G. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: is an employee of GE Healthcare. Other relationships: disclosed no relevant relationships. K.P. editorial board member for Radiology: Imaging Cancer. S.B.T. disclosed no relevant relationships., (2020 by the Radiological Society of North America, Inc.)

Background: Ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived kinetic parameters have demonstrated at least equivalent accuracy to standard DCE-MRI in differentiating malignant from benign breast lesions. However, it is unclear if they have any efficacy as prognostic imaging markers. The aim of this study was to investigate the relationship between ultrafast DCE-MRI-derived kinetic parameters and breast cancer characteristics., Methods: Consecutive breast MRI examinations between February 2017 and January 2018 were retrospectively reviewed to determine those examinations that meet the following inclusion criteria: (1) BI-RADS 4-6 MRI performed on a 3T scanner with a 16-channel breast coil and (2) a hybrid clinical protocol with 15 phases of ultrafast DCE-MRI (temporal resolution of 2.7-4.6 s) followed by early and delayed phases of standard DCE-MRI. The study included 125 examinations with 142 biopsy-proven breast cancer lesions. Ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS] and bolus arrival time [BAT]) were calculated for the entire volume of each lesion. Comparisons of these parameters between different cancer characteristics were made using generalized estimating equations, accounting for the presence of multiple lesions per patient. All comparisons were exploratory and adjustment for multiple comparisons was not performed; P values < 0.05 were considered statistically significant., Results: Significantly larger MS and shorter BAT were observed for invasive carcinoma than ductal carcinoma in situ (DCIS) (P < 0.001 and P = 0.008, respectively). Significantly shorter BAT was observed for invasive carcinomas with more aggressive characteristics than those with less aggressive characteristics: grade 3 vs. grades 1-2 (P = 0.025), invasive ductal carcinoma vs. invasive lobular carcinoma (P = 0.002), and triple negative or HER2 type vs. luminal type (P < 0.001)., Conclusions: Ultrafast DCE-MRI-derived parameters showed a strong relationship with some breast cancer characteristics, especially histopathology and molecular subtype.

Background: For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), pathologic complete response (pCR; no invasive or in situ) cannot be assessed non-invasively so all patients undergo surgery. The aim of our study was to develop and validate a radiomics classifier that classifies breast cancer pCR post-NAC on MRI prior to surgery., Methods: This retrospective study included women treated with NAC for breast cancer from 2014 to 2016 with (1) pre- and post-NAC breast MRI and (2) post-NAC surgical pathology report assessing response. Automated radiomics analysis of pre- and post-NAC breast MRI involved image segmentation, radiomics feature extraction, feature pre-filtering, and classifier building through recursive feature elimination random forest (RFE-RF) machine learning. The RFE-RF classifier was trained with nested five-fold cross-validation using (a) radiomics only (model 1) and (b) radiomics and molecular subtype (model 2). Class imbalance was addressed using the synthetic minority oversampling technique., Results: Two hundred seventy-three women with 278 invasive breast cancers were included; the training set consisted of 222 cancers (61 pCR, 161 no-pCR; mean age 51.8 years, SD 11.8), and the independent test set consisted of 56 cancers (13 pCR, 43 no-pCR; mean age 51.3 years, SD 11.8). There was no significant difference in pCR or molecular subtype between the training and test sets. Model 1 achieved a cross-validation AUROC of 0.72 (95% CI 0.64, 0.79) and a similarly accurate (P = 0.1) AUROC of 0.83 (95% CI 0.71, 0.94) in both the training and test sets. Model 2 achieved a cross-validation AUROC of 0.80 (95% CI 0.72, 0.87) and a similar (P = 0.9) AUROC of 0.78 (95% CI 0.62, 0.94) in both the training and test sets., Conclusions: This study validated a radiomics classifier combining radiomics with molecular subtypes that accurately classifies pCR on MRI post-NAC.

Background: To assess the incidence of benign and malignant peri-implant fluid collections and/or masses on magnetic resonance imaging (MRI) in women with silicone implants who are being screened for silent implant rupture., Methods: The institutional review board approved this HIPAA-compliant retrospective study and waived informed consent. Women who underwent silicone implant oncoplastic and/or cosmetic surgery and postoperative implant-protocol MRI from 2000 to 2014 were included. Peri-implant fluid collections and/or masses were measured volumetrically. A benign peri-implant fluid collection and/or mass was pathologically proven or defined as showing 2 years of imaging and/or clinical stability. A malignant peri-implant fluid collection was pathologically proven. Incidence of peri-implant fluid collections and/or masses and positive predictive value (PPV) were calculated on a per-patient level using proportions and exact 95% confidence intervals (CIs). Fisher's exact test was used in the analysis to test statistical significance pre-defined as P-value < 0.05., Results: A total of 1070 women with silicone implants were included (mean age, 50.7 years; range, 40.4-53.8). Median time between reconstructive surgery and first MRI was 88.9 months (range, 0.8-1363.3). Eighteen women (1.7%) had a peri-implant fluid collection and/or mass: 15/18 (83.3%) had adequate follow-up; and only 1/15 was malignant implant associated anaplastic large cell lymphoma, with a PPV of 6.7% (95% CI: 0.003-0.0005). The median peri-implant fluid collection size was 89 mL (range, 18-450 mL)., Conclusion: Peri-implant fluid collections and/or masses identified at silicone implant protocol breast MR imaging are rarely seen 24 months after reconstructive surgery. Image-guided fine-needle aspiration with flow cytometry may be warranted to evaluate for implant-associated lymphoma., (© 2019 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

In patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC), some patients achieve a complete pathologic response (pCR), some achieve a partial response, and some do not respond at all or even progress. Accurate prediction of treatment response has the potential to improve patient care by improving prognostication, enabling de-escalation of toxic treatment that has little benefit, facilitating upfront use of novel targeted therapies, and avoiding delays to surgery. Visual inspection of a patient's tumor on multiparametric MRI is insufficient to predict that patient's response to NAC. However, machine learning and deep learning approaches using a mix of qualitative and quantitative MRI features have recently been applied to predict treatment response early in the course of or even before the start of NAC. This is a novel field but the data published so far has shown promising results. We provide an overview of the machine learning and deep learning models developed to date, as well as discuss some of the challenges to clinical implementation., (Copyright © 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Radiogenomics is the extension of radiomics through the combination of genetic and radiomic data. Because genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients, radiogenomics may play an important role in providing accurate imaging surrogates which are correlated with genetic expression, thereby serving as a substitute for genetic testing., Main Body: In this article, we define the meaning of radiogenomics and the difference between radiomics and radiogenomics. We provide an up-to-date review of the radiomics and radiogenomics literature in oncology, focusing on breast, brain, gynecological, liver, kidney, prostate and lung malignancies. We also discuss the current challenges to radiogenomics analysis., Conclusion: Radiomics and radiogenomics are promising to increase precision in diagnosis, assessment of prognosis, and prediction of treatment response, providing valuable information for patient care throughout the course of the disease, given that this information is easily obtainable with imaging. Larger prospective studies and standardization will be needed to define relevant imaging biomarkers before they can be implemented into the clinical workflow.

The rationale was to assess whether there are differences in multiparametric 18 F-FDG PET/MRI biomarkers of contralateral healthy breast tissue in patients with benign and malignant breast tumors. Methods: In this institutional review board-approved prospective single-institution study, 141 women with imaging abnormalities on mammography or sonography (BI-RADS 4/5) underwent combined 18 F-FDG PET/MRI of the breast at 3T with dynamic contrast-enhanced MRI, diffusion-weighted imaging, and the radiotracer 18 F-FDG. In all patients, the following imaging biomarkers were recorded for the contralateral (tumor-free) breast: breast parenchymal uptake (BPU) (from 18 F-FDG PET), mean apparent diffusion coefficient (from diffusion-weighted imaging), background parenchymal enhancement (BPE), and amount of fibroglandular tissue (FGT) (from MRI). Appropriate statistical tests were used to assess differences in 18 F-FDG PET/MRI biomarkers between patients with benign and malignant lesions. Results: There were 100 malignant and 41 benign lesions. BPE was minimal in 61 patients, mild in 56, moderate in 19, and marked in 5. BPE differed significantly ( P < 0.001) between patients with benign and malignant lesions, with patients with cancer demonstrating decreased BPE in the contralateral tumor-free breast. FGT approached but did not reach significance ( P = 0.055). BPU was 1.5 for patients with minimal BPE, 1.9 for mild BPE, 2.2 for moderate BPE, and 1.9 for marked BPE. BPU differed significantly between patients with benign lesions (mean, 1.9) and patients with malignant lesions (mean, 1.8) ( P < 0.001). Mean apparent diffusion coefficient did not differ between groups ( P = 0.19). Conclusion: Differences in multiparametric 18 F-FDG PET/MRI biomarkers, obtained from contralateral tumor-free breast tissue, exist between patients with benign and patients with malignant breast tumors. Contralateral BPE, BPU, and FGT are decreased in breast cancer patients and may potentially serve as imaging biomarkers for the presence of malignancy., (© 2020 by the Society of Nuclear Medicine and Molecular Imaging.)

Background: Available data proving the value of DWI for breast cancer diagnosis is mainly for enhancing masses; DWI may be less sensitive and specific in non-mass enhancement (NME) lesions. The objective of this study was to assess the diagnostic accuracy of DWI using different ROI measurement approaches and ADC metrics in breast lesions presenting as NME lesions on dynamic contrast-enhanced (DCE) MRI., Methods: In this retrospective study, 95 patients who underwent multiparametric MRI with DCE and DWI from September 2007 to July 2013 and who were diagnosed with a suspicious NME (BI-RADS 4/5) were included. Twenty-nine patients were excluded for lesion non-visibility on DWI (n = 24: 12 benign and 12 malignant) and poor DWI quality (n = 5: 1 benign and 4 malignant). Two readers independently assessed DWI and DCE-MRI findings in two separate randomized readings using different ADC metrics and ROI approaches. NME lesions were classified as either benign (> 1.3 × 10 -3  mm 2 /s) or malignant (≤ 1.3 × 10 -3  mm 2 /s). Histopathology was the standard of reference. ROC curves were plotted, and AUCs were determined. Concordance correlation coefficient (CCC) was measured., Results: There were 39 malignant (59%) and 27 benign (41%) lesions in 66 (65 women, 1 man) patients (mean age, 51.8 years). The mean ADC value of the darkest part of the tumor (Dptu) achieved the highest diagnostic accuracy, with AUCs of up to 0.71. Inter-reader agreement was highest with Dptu ADC max (CCC 0.42) and lowest with the point tumor (Ptu) ADC min (CCC = - 0.01). Intra-reader agreement was highest with Wtu ADC mean (CCC = 0.44 for reader 1, 0.41 for reader 2), but this was not associated with the highest diagnostic accuracy., Conclusions: Diagnostic accuracy of DWI with ADC mapping is limited in NME lesions. Thirty-one percent of lesions presenting as NME on DCE-MRI could not be evaluated with DWI, and therefore, DCE-MRI remains indispensable. Best results were achieved using Dptu 2D ROI measurement and ADC mean.

Objective: To investigate if histogram analysis and visually assessed heterogeneity of diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping can predict molecular subtypes of invasive breast cancers., Materials and Methods: In this retrospective study, 91 patients with invasive breast carcinoma who underwent preoperative magnetic resonance imaging (MRI) with DWI at our institution were included. Two radiologists delineated a 2-D region of interest (ROI) on ADC maps in consensus. Tumors were also independently classified into low and high heterogeneity based on visual assessment of DWI. First-order statistics extracted through histogram analysis within the ROI of the ADC maps (mean, 10th percentile, 50th percentile, 90th percentile, standard deviation, kurtosis, and skewness) and visually assessed heterogeneity were evaluated for associations with tumor receptor status (ER, PR, and HER2 status) as well as molecular subtype., Results: HER2-positive lesions demonstrated significantly higher mean ( p =0.034), Perc50 ( p =0.046), and Perc90 ( p =0.040), with AUCs of 0.605, 0.592, and 0.652, respectively, than HER2-negative lesions. No significant differences were found in the histogram values for ER and PR statuses. Neither quantitative histogram analysis based on ADC maps nor qualitative visual heterogeneity assessment of DWI images was able to significantly differentiate between molecular subtypes, i.e., luminal A versus all other subtypes (luminal B, HER2-enriched, and triple negative) combined, luminal A and B combined versus HER2-enriched and triple negative combined, and triple negative versus all other types combined., Conclusion: Histogram analysis and visual heterogeneity assessment cannot be used to differentiate molecular subtypes of invasive breast cancer., Competing Interests: Katja Pinker received payment for activities not related to the present article including lectures including service on speakers bureaus and for travel/accommodations/meeting expenses unrelated to activities listed from the European Society of Breast Imaging (MRI educational course, annual scientific meeting) and the IDKD 2019 (educational course). Elizabeth A. Morris received a grant from Grail, Inc. for research not related to the present article. All other authors declare no conflicts of interest., (Copyright © 2019 Joao V. Horvat et al.)