Interval Cancers in Understanding Screening Outcomes.

Interval breast cancers are not detected at routine screening and are diagnosed in the interval between screening examinations. A variety of factors contribute to interval cancers, including patient and tumor characteristics as well as the screening technique and frequency. The interval cancer rate is an important metric by which the effectiveness of screening may be assessed and may serve as a surrogate for mortality benefit.

brainlife.io: a decentralized and open-source cloud platform to support neuroscience research.

Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.

18F-FDG Dedicated Breast PET Complementary to Breast MRI for Evaluating Early Response to Neoadjuvant Chemotherapy.

Purpose To compare quantitative measures of tumor metabolism and perfusion using fluorine 18 (18F) fluorodeoxyglucose (FDG) dedicated breast PET (dbPET) and breast dynamic contrast-enhanced (DCE) MRI during early treatment with neoadjuvant chemotherapy (NAC). Materials and Methods Prospectively collected DCE MRI and 18F-FDG dbPET examinations were analyzed at baseline (T0) and after 3 weeks (T1) of NAC in 20 participants with 22 invasive breast cancers. FDG dbPET-derived standardized uptake value (SUV), metabolic tumor volume, and total lesion glycolysis (TLG) and MRI-derived percent enhancement (PE), signal enhancement ratio (SER), and functional tumor volume (FTV) were calculated at both time points. Differences between FDG dbPET and MRI parameters were evaluated after stratifying by receptor status, Ki-67 index, and residual cancer burden. Parameters were compared using Wilcoxon signed rank and Mann-Whitney U tests. Results High Ki-67 tumors had higher baseline SUVmean (difference, 5.1; P = .01) and SUVpeak (difference, 5.5; P = .04). At T1, decreases were observed in FDG dbPET measures (pseudo-median difference T0 minus T1 value [95% CI]) of SUVmax (-6.2 [-10.2, -2.6]; P < .001), SUVmean (-2.6 [-4.9, -1.3]; P < .001), SUVpeak (-4.2 [-6.9, -2.3]; P < .001), and TLG (-29.1 mL3 [-71.4, -6.8]; P = .005) and MRI measures of SERpeak (-1.0 [-1.3, -0.2]; P = .02) and FTV (-11.6 mL3 [-22.2, -1.7]; P = .009). Relative to nonresponsive tumors, responsive tumors showed a difference (95% CI) in percent change in SUVmax of -34.3% (-55.9%, 1.5%; P = .06) and in PEpeak of -42.4% (95% CI: -110.5%, 8.5%; P = .08). Conclusion 18F-FDG dbPET was sensitive to early changes during NAC and provided complementary information to DCE MRI that may be useful for treatment response evaluation. Keywords: Breast, PET, Dynamic Contrast-enhanced MRI Clinical trial registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2024.

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.

Prevalent vs Incident Screen: Why Does It Matter?

There are important differences in the performance and outcomes of breast cancer screening in the prevalent compared to the incident screening rounds. The prevalent screen is the first screening examination using a particular imaging technique and identifies pre-existing, undiagnosed cancers in the population. The incident screen is any subsequent screening examination using that technique. It is expected to identify fewer cancers than the prevalent screen because it captures only those cancers that have become detectable since the prior screening examination. The higher cancer detection rate at prevalent relative to incident screening should be taken into account when analyzing the medical audit and effectiveness of new screening technologies.

The role of digital mammographic surveillance for detection of asymptomatic recurrence in autologous flap reconstructions.

OBJECTIVE: To evaluate the utility of digital mammography in detecting asymptomatic malignancy in autologous flap reconstructions after mastectomy. METHODS: A retrospective database review identified all mammograms performed on asymptomatic patients with flap reconstructions over a 9-year period (1/1/2009 to 12/31/2017). A negative examination was defined as BI-RADS 1 or 2 and a positive examination was defined as BI-RADS 0, 4, or 5 assigned to the mastectomy side. Malignant outcomes were determined by pathology results. Interval cancers, or false negatives, were defined as locoregional malignant diagnosis within one year of a negative mammogram. Sensitivity, specificity, predictive values, abnormal interpretation rate, and cancer detection rate were calculated. RESULTS: 626 mammograms of asymptomatic flap reconstructions were performed in 183 patients. The most common flap type was TRAM (83.5 %, 523/626) and DIEP (13.4 %, 84/626). Most exams (98.2 %, 615/626) were negative, assessed as BI-RADS 1 or 2, with no interval cancers at follow-up. Eleven exams (1.8 %, 11/626) were positive, assessed as BI-RADS 0, 4, or 5. After diagnostic work-up of all BI-RADS 0 exams, 9 cases had a final recommendation for biopsy of which 3 were malignant. Mammography yielded a cancer detection rate of 0.5 % (3/626), abnormal interpretation rate of 1.8 % (11/626), NPV of 100 % (615/615), overall PPV of 27.3 % (3/11), PPV2 (positive predictive value of a biopsy recommendation) of 33.3 % (3/9), sensitivity of 100 % (3/3), and specificity of 98.7 % (615/623). CONCLUSION: Digital mammography of asymptomatic autologous flap reconstructions after mastectomy demonstrated high sensitivity and low abnormal interpretation rate. Cancer detection rate was comparable to current national benchmarks for mammographic screening in the general U.S. population without mastectomy.

Methods for decoding cortical gradients of functional connectivity.

Macroscale gradients have emerged as a central principle for understanding functional brain organization. Previous studies have demonstrated that a principal gradient of connectivity in the human brain exists, with unimodal primary sensorimotor regions situated at one end and transmodal regions associated with the default mode network and representative of abstract functioning at the other. The functional significance and interpretation of macroscale gradients remains a central topic of discussion in the neuroimaging community, with some studies demonstrating that gradients may be described using meta-analytic functional decoding techniques. However, additional methodological development is necessary to fully leverage available meta-analytic methods and resources and quantitatively evaluate their relative performance. Here, we conducted a comprehensive series of analyses to investigate and improve the framework of data-driven, meta-analytic methods, thereby establishing a principled approach for gradient segmentation and functional decoding. We found that a two-segment solution determined by a k-means segmentation approach and an LDA-based meta-analysis combined with the NeuroQuery database was the optimal combination of methods for decoding functional connectivity gradients. Finally, we proposed a method for decoding additional components of the gradient decomposition. The current work aims to provide recommendations on best practices and flexible methods for gradient-based functional decoding of fMRI data.

brainlife.io: A decentralized and open source cloud platform to support neuroscience research.

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

Altered electroencephalography resting state network coherence in remitted MDD.

Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent depression.

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.

Neural regions associated with gain-loss frequency and average reward in older and younger adults.

Research on the biological basis of reinforcement-learning has focused on how brain regions track expected value based on average reward. However, recent work suggests that humans are more attuned to reward frequency. Furthermore, older adults are less likely to use expected values to guide choice than younger adults. This raises the question of whether brain regions assumed to be sensitive to average reward, like the medial and lateral PFC, also track reward frequency, and whether there are age-based differences. Older (60-81 years) and younger (18-30 years) adults performed the Soochow Gambling task, which separates reward frequency from average reward, while undergoing fMRI. Overall, participants preferred options that provided negative net payoffs, but frequent gains. Older adults improved less over time, were more reactive to recent negative outcomes, and showed greater frequency-related activation in several regions, including DLPFC. We also found broader recruitment of prefrontal and parietal regions associated with frequency value and reward prediction errors in older adults, which may indicate compensation. The results suggest greater reliance on average reward for younger adults than older adults.

Symmetric activation and modulation of the human calcium-sensing receptor.

The human extracellular calcium-sensing (CaS) receptor controls plasma Ca2+ levels and contributes to nutrient-dependent maintenance and metabolism of diverse organs. Allosteric modulation of the CaS receptor corrects disorders of calcium homeostasis. Here, we report the cryogenic-electron microscopy reconstructions of a near-full-length CaS receptor in the absence and presence of allosteric modulators. Activation of the homodimeric CaS receptor requires a break in the transmembrane 6 (TM6) helix of each subunit, which facilitates the formation of a TM6-mediated homodimer interface and expansion of homodimer interactions. This transformation in TM6 occurs without a positive allosteric modulator. Two modulators with opposite functional roles bind to overlapping sites within the transmembrane domain through common interactions, acting to stabilize distinct rotamer conformations of key residues on the TM6 helix. The positive modulator reinforces TM6 distortion and maximizes subunit contact to enhance receptor activity, while the negative modulator strengthens an intact TM6 to dampen receptor function. In both active and inactive states, the receptor displays symmetrical transmembrane conformations that are consistent with its homodimeric assembly.

Meta-analytic connectivity modelling of deception-related brain regions.

Brain-based deception research began only two decades ago and has since included a wide variety of contexts and response modalities for deception paradigms. Investigations of this sort serve to better our neuroscientific and legal knowledge of the ways in which individuals deceive others. To this end, we conducted activation likelihood estimation (ALE) and meta-analytic connectivity modelling (MACM) using BrainMap software to examine 45 task-based fMRI brain activation studies on deception. An activation likelihood estimation comparing activations during deceptive versus honest behavior revealed 7 significant peak activation clusters (bilateral insula, left superior frontal gyrus, bilateral supramarginal gyrus, and bilateral medial frontal gyrus). Meta-analytic connectivity modelling revealed an interconnected network amongst the 7 regions comprising both unidirectional and bidirectional connections. Together with subsequent behavioral and paradigm decoding, these findings implicate the supramarginal gyrus as a key component for the sociocognitive process of deception.