Use of Computation Ecosystems to Analyze the Kidney-Heart Crosstalk.

The identification of mediators for physiologic processes, correlation of molecular processes, or even pathophysiological processes within a single organ such as the kidney or heart has been extensively studied to answer specific research questions using organ-centered approaches in the past 50 years. However, it has become evident that these approaches do not adequately complement each other and display a distorted single-disease progression, lacking holistic multilevel/multidimensional correlations. Holistic approaches have become increasingly significant in understanding and uncovering high dimensional interactions and molecular overlaps between different organ systems in the pathophysiology of multimorbid and systemic diseases like cardiorenal syndrome because of pathological heart-kidney crosstalk. Holistic approaches to unraveling multimorbid diseases are based on the integration, merging, and correlation of extensive, heterogeneous, and multidimensional data from different data sources, both -omics and nonomics databases. These approaches aimed at generating viable and translatable disease models using mathematical, statistical, and computational tools, thereby creating first computational ecosystems. As part of these computational ecosystems, systems medicine solutions focus on the analysis of -omics data in single-organ diseases. However, the data-scientific requirements to address the complexity of multimodality and multimorbidity reach far beyond what is currently available and require multiphased and cross-sectional approaches. These approaches break down complexity into small and comprehensible challenges. Such holistic computational ecosystems encompass data, methods, processes, and interdisciplinary knowledge to manage the complexity of multiorgan crosstalk. Therefore, this review summarizes the current knowledge of kidney-heart crosstalk, along with methods and opportunities that arise from the novel application of computational ecosystems providing a holistic analysis on the example of kidney-heart crosstalk.

Abbreviated Breast MRI: State of the Art.

Abbreviated MRI is an umbrella term, defined as a focused MRI examination tailored to answer a single specific clinical question. For abbreviated breast MRI, this question is: "Is there evidence of breast cancer?" Abbreviated MRI of the breast makes maximum use of the fact that the kinetics of breast cancers and of benign tissue differ most in the very early postcontrast phase; therefore, abbreviated breast MRI focuses on this period. The different published approaches to abbreviated MRI include the following three subtypes: (a) short protocols, consisting of a precontrast and either a single postcontrast acquisition (first postcontrast subtracted [FAST]) or a time-resolved series of postcontrast acquisitions with lower spatial resolution (ultrafast [UF]), obtained during the early postcontrast phase immediately after contrast agent injection; (b) abridged protocols, consisting of FAST or UF acquisitions plus selected additional pulse sequences; and (c) noncontrast protocols, where diffusion-weighted imaging replaces the contrast information. Abbreviated MRI was proposed to increase tolerability of and access to breast MRI as a screening tool. But its widening application now includes follow-up after breast cancer and even diagnostic assessment. This review defines the three subtypes of abbreviated MRI, highlighting the differences between the protocols and their clinical implications and summarizing the respective evidence on diagnostic accuracy and clinical utility.

Intraindividual Comparison of Different Methods for Automated BPE Assessment at Breast MRI: A Call for Standardization.

Background The level of background parenchymal enhancement (BPE) at breast MRI provides predictive and prognostic information and can have diagnostic implications. However, there is a lack of standardization regarding BPE assessment. Purpose To investigate how well results of quantitative BPE assessment methods correlate among themselves and with assessments made by radiologists experienced in breast MRI. Materials and Methods In this pseudoprospective analysis of 5773 breast MRI examinations from 3207 patients (mean age, 60 years ± 10 [SD]), the level of BPE was prospectively categorized according to the Breast Imaging Reporting and Data System by radiologists experienced in breast MRI. For automated extraction of BPE, fibroglandular tissue (FGT) was segmented in an automated pipeline. Four different published methods for automated quantitative BPE extractions were used: two methods (A and B) based on enhancement intensity and two methods (C and D) based on the volume of enhanced FGT. The results from all methods were correlated, and agreement was investigated in comparison with the respective radiologist-based categorization. For surrogate validation of BPE assessment, how accurately the methods distinguished premenopausal women with (n = 50) versus without (n = 896) antihormonal treatment was determined. Results Intensity-based methods (A and B) exhibited a correlation with radiologist-based categorization of 0.56 ± 0.01 and 0.55 ± 0.01, respectively, and volume-based methods (C and D) had a correlation of 0.52 ± 0.01 and 0.50 ± 0.01 (P < .001). There were notable correlation differences (P < .001) between the BPE determined with the four methods. Among the four quantitation methods, method D offered the highest accuracy for distinguishing women with versus without antihormonal therapy (P = .01). Conclusion Results of different methods for quantitative BPE assessment agree only moderately among themselves or with visual categories reported by experienced radiologists; intensity-based methods correlate more closely with radiologists' ratings than volume-based methods. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Mann in this issue.

The virtual reference radiologist: comprehensive AI assistance for clinical image reading and interpretation.

OBJECTIVES: Large language models (LLMs) have shown potential in radiology, but their ability to aid radiologists in interpreting imaging studies remains unexplored. We investigated the effects of a state-of-the-art LLM (GPT-4) on the radiologists' diagnostic workflow. MATERIALS AND METHODS: In this retrospective study, six radiologists of different experience levels read 40 selected radiographic [n = 10], CT [n = 10], MRI [n = 10], and angiographic [n = 10] studies unassisted (session one) and assisted by GPT-4 (session two). Each imaging study was presented with demographic data, the chief complaint, and associated symptoms, and diagnoses were registered using an online survey tool. The impact of Artificial Intelligence (AI) on diagnostic accuracy, confidence, user experience, input prompts, and generated responses was assessed. False information was registered. Linear mixed-effect models were used to quantify the factors (fixed: experience, modality, AI assistance; random: radiologist) influencing diagnostic accuracy and confidence. RESULTS: When assessing if the correct diagnosis was among the top-3 differential diagnoses, diagnostic accuracy improved slightly from 181/240 (75.4%, unassisted) to 188/240 (78.3%, AI-assisted). Similar improvements were found when only the top differential diagnosis was considered. AI assistance was used in 77.5% of the readings. Three hundred nine prompts were generated, primarily involving differential diagnoses (59.1%) and imaging features of specific conditions (27.5%). Diagnostic confidence was significantly higher when readings were AI-assisted (p > 0.001). Twenty-three responses (7.4%) were classified as hallucinations, while two (0.6%) were misinterpretations. CONCLUSION: Integrating GPT-4 in the diagnostic process improved diagnostic accuracy slightly and diagnostic confidence significantly. Potentially harmful hallucinations and misinterpretations call for caution and highlight the need for further safeguarding measures. CLINICAL RELEVANCE STATEMENT: Using GPT-4 as a virtual assistant when reading images made six radiologists of different experience levels feel more confident and provide more accurate diagnoses; yet, GPT-4 gave factually incorrect and potentially harmful information in 7.4% of its responses.

Efficacy and Safety of Half-Dose Gadopiclenol versus Full-Dose Gadobutrol for Contrast-enhanced Body MRI.

Background Gadopiclenol is a macrocyclic gadolinium-based contrast agent (GBCA) with higher relaxivity compared with standard GBCAs, potentially allowing gadolinium dose reduction without decreasing efficacy. Purpose To investigate whether gadopiclenol at 0.05 mmol/kg is noninferior to gadobutrol at 0.1 mmol/kg for lesion visualization in body MRI. Materials and Methods A randomized, double-blind, crossover, phase 3 study was conducted between August 2019 and December 2020 at 33 centers in 11 countries. Adults with at least one suspected focal lesion in one of three different body regions (head and neck; breast, thorax, abdomen, or pelvis; or musculoskeletal system) underwent two contrast-enhanced MRI examinations, randomized to start with either gadopiclenol or gadobutrol. MRI examinations were read by three blinded expert readers for each respective body region. Readers rated border delineation, internal morphologic characteristics, and visual contrast enhancement. Three additional blinded readers assessed reader preference. For safety analysis, adverse events were recorded. The differences between gadopiclenol- and gadobutrol-enhanced MRI in terms of lesion visualization were analyzed with a generalized linear mixed model using a two-sided paired t test. Results Among 273 participants (mean age, 57 years ± 13 [SD]; 162 women) who underwent both gadopiclenol- and gadobutrol-enhanced MRI and had at least one correlating lesion, 260 participants without major protocol deviations were analyzed for noninferiority. Gadopiclenol was noninferior to gadobutrol for all qualitative visualization parameters and for all readers (lower limit 95% CI of the difference of at least -0.10, which was above the noninferiority margin [-0.35]; P < .001). For most participants (75%-83% [206-228 of 276]), readers reported no preference between gadopiclenol- and gadobutrol-enhanced images. Adverse events did not differ in frequency, intensity, type, or association with GBCA injection (12 of 288 participants receiving gadopiclenol and 16 of 290 receiving gadobutrol). Conclusion Gadopiclenol at 0.05 mmol/kg was comparable with gadobutrol at 0.1 mmol/kg for lesion evaluation at contrast-enhanced body MRI and had a similar safety profile. Clinical trial registration no. NCT03986138 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Bashir and Thomas in this issue.

Photon-counting versus Dual-Source CT of Congenital Heart Defects in Neonates and Infants: Initial Experience.

Background Photon-counting CT (PCCT) has been shown to improve cardiovascular CT imaging in adults. Data in neonates, infants, and young children under the age of 3 years are missing. Purpose To compare image quality and radiation dose of ultrahigh-pitch PCCT with that of ultrahigh-pitch dual-source CT (DSCT) in children suspected of having congenital heart defects. Materials and Methods This is a prospective analysis of existing clinical CT studies in children suspected of having congenital heart defects who underwent contrast-enhanced PCCT or DSCT in the heart and thoracic aorta between January 2019 and October 2022. CT dose index and dose-length product were used to calculate effective radiation dose. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated by standardized region-of-interest analysis. SNR and CNR dose ratios were calculated. Visual image quality was assessed by four independent readers on a five-point scale: 5, excellent or absent; 4, good or minimal; 3, moderate; 2, limited or substantial; and 1, poor or massive. Results Contrast-enhanced PCCT (n = 30) or DSCT (n = 84) was performed in 113 children (55 female and 58 male participants; median age, 66 days [IQR, 15-270]; median height, 56 cm [IQR, 52-67]; and median weight, 4.5 kg [IQR, 3.4-7.1]). A diagnostic image quality score of at least 3 was obtained in 29 of 30 (97%) with PCCT versus 65 of 84 (77%) with DSCT. Mean overall image quality ratings were higher for PCCT versus DSCT (4.17 vs 3.16, respectively; P < .001). SNR and CNR were higher for PCCT versus DSCT with SNR (46.3 ± 16.3 vs 29.9 ± 15.3, respectively; P = .007) and CNR (62.0 ± 50.3 vs 37.2 ± 20.8, respectively; P = .001). Mean effective radiation doses were similar for PCCT and DSCT (0.50 mSv vs 0.52 mSv; P = .47). Conclusion At a similar radiation dose, PCCT offers a higher SNR and CNR and thus better cardiovascular imaging quality than DSCT in children suspected of having cardiac heart defects. © RSNA, 2023.

An MRI Deep Learning Model Predicts Outcome in Rectal Cancer.

Background Deep learning (DL) models can potentially improve prognostication of rectal cancer but have not been systematically assessed. Purpose To develop and validate an MRI DL model for predicting survival in patients with rectal cancer based on segmented tumor volumes from pretreatment T2-weighted MRI scans. Materials and Methods DL models were trained and validated on retrospectively collected MRI scans of patients with rectal cancer diagnosed between August 2003 and April 2021 at two centers. Patients were excluded from the study if there were concurrent malignant neoplasms, prior anticancer treatment, incomplete course of neoadjuvant therapy, or no radical surgery performed. The Harrell C-index was used to determine the best model, which was applied to internal and external test sets. Patients were stratified into high- and low-risk groups based on a fixed cutoff calculated in the training set. A multimodal model was also assessed, which used DL model-computed risk score and pretreatment carcinoembryonic antigen level as input. Results The training set included 507 patients (median age, 56 years [IQR, 46-64 years]; 355 men). In the validation set (n = 218; median age, 55 years [IQR, 47-63 years]; 144 men), the best algorithm reached a C-index of 0.82 for overall survival. The best model reached hazard ratios of 3.0 (95% CI: 1.0, 9.0) in the high-risk group in the internal test set (n = 112; median age, 60 years [IQR, 52-70 years]; 76 men) and 2.3 (95% CI: 1.0, 5.4) in the external test set (n = 58; median age, 57 years [IQR, 50-67 years]; 38 men). The multimodal model further improved the performance, with a C-index of 0.86 and 0.67 for the validation and external test set, respectively. Conclusion A DL model based on preoperative MRI was able to predict survival of patients with rectal cancer. The model could be used as a preoperative risk stratification tool. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Langs in this issue.

Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images.

Background Reducing the amount of contrast agent needed for contrast-enhanced breast MRI is desirable. Purpose To investigate if generative adversarial networks (GANs) can recover contrast-enhanced breast MRI scans from unenhanced images and virtual low-contrast-enhanced images. Materials and Methods In this retrospective study of breast MRI performed from January 2010 to December 2019, simulated low-contrast images were produced by adding virtual noise to the existing contrast-enhanced images. GANs were then trained to recover the contrast-enhanced images from the simulated low-contrast images (approach A) or from the unenhanced T1- and T2-weighted images (approach B). Two experienced radiologists were tasked with distinguishing between real and synthesized contrast-enhanced images using both approaches. Image appearance and conspicuity of enhancing lesions on the real versus synthesized contrast-enhanced images were independently compared and rated on a five-point Likert scale. P values were calculated by using bootstrapping. Results A total of 9751 breast MRI examinations from 5086 patients (mean age, 56 years ± 10 [SD]) were included. Readers who were blinded to the nature of the images could not distinguish real from synthetic contrast-enhanced images (average accuracy of differentiation: approach A, 52 of 100; approach B, 61 of 100). The test set included images with and without enhancing lesions (29 enhancing masses and 21 nonmass enhancement; 50 total). When readers who were not blinded compared the appearance of the real versus synthetic contrast-enhanced images side by side, approach A image ratings were significantly higher than those of approach B (mean rating, 4.6 ± 0.1 vs 3.0 ± 0.2; P < .001), with the noninferiority margin met by synthetic images from approach A (P < .001) but not B (P > .99). Conclusion Generative adversarial networks may be useful to enable breast MRI with reduced contrast agent dose. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bahl in this issue.

Multimodal Deep Learning for Integrating Chest Radiographs and Clinical Parameters: A Case for Transformers.

Background Clinicians consider both imaging and nonimaging data when diagnosing diseases; however, current machine learning approaches primarily consider data from a single modality. Purpose To develop a neural network architecture capable of integrating multimodal patient data and compare its performance to models incorporating a single modality for diagnosing up to 25 pathologic conditions. Materials and Methods In this retrospective study, imaging and nonimaging patient data were extracted from the Medical Information Mart for Intensive Care (MIMIC) database and an internal database comprised of chest radiographs and clinical parameters inpatients in the intensive care unit (ICU) (January 2008 to December 2020). The MIMIC and internal data sets were each split into training (n = 33 893, n = 28 809), validation (n = 740, n = 7203), and test (n = 1909, n = 9004) sets. A novel transformer-based neural network architecture was trained to diagnose up to 25 conditions using nonimaging data alone, imaging data alone, or multimodal data. Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUC) analysis. Results The MIMIC and internal data sets included 36 542 patients (mean age, 63 years ± 17 [SD]; 20 567 male patients) and 45 016 patients (mean age, 66 years ± 16; 27 577 male patients), respectively. The multimodal model showed improved diagnostic performance for all pathologic conditions. For the MIMIC data set, the mean AUC was 0.77 (95% CI: 0.77, 0.78) when both chest radiographs and clinical parameters were used, compared with 0.70 (95% CI: 0.69, 0.71; P < .001) for only chest radiographs and 0.72 (95% CI: 0.72, 0.73; P < .001) for only clinical parameters. These findings were confirmed on the internal data set. Conclusion A model trained on imaging and nonimaging data outperformed models trained on only one type of data for diagnosing multiple diseases in patients in an ICU setting. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Kitamura and Topol in this issue.

Artificial Intelligence for Clinical Interpretation of Bedside Chest Radiographs.

Background Supine chest radiography for bedridden patients in intensive care units (ICUs) is one of the most frequently ordered imaging studies worldwide. Purpose To evaluate the diagnostic performance of a neural network-based model that is trained on structured semiquantitative radiologic reports of bedside chest radiographs. Materials and Methods For this retrospective single-center study, children and adults in the ICU of a university hospital who had been imaged using bedside chest radiography from January 2009 to December 2020 were reported by using a structured and itemized template. Ninety-eight radiologists rated the radiographs semiquantitatively for the severity of disease patterns. These data were used to train a neural network to identify cardiomegaly, pulmonary congestion, pleural effusion, pulmonary opacities, and atelectasis. A held-out internal test set (100 radiographs from 100 patients) that was assessed independently by an expert panel of six radiologists provided the ground truth. Individual assessments by each of these six radiologists, by two nonradiologist physicians in the ICU, and by the neural network were compared with the ground truth. Separately, the nonradiologist physicians assessed the images without and with preliminary readings provided by the neural network. The weighted Cohen κ coefficient was used to measure agreement between the readers and the ground truth. Results A total of 193 566 radiographs in 45 016 patients (mean age, 66 years ± 16 [SD]; 61% men) were included and divided into training (n = 122 294; 64%), validation (n = 31 243; 16%), and test (n = 40 029; 20%) sets. The neural network exhibited higher agreement with a majority vote of the expert panel (κ = 0.86) than each individual radiologist compared with the majority vote of the expert panel (κ = 0.81 to ≤0.84). When the neural network provided preliminary readings, the reports of the nonradiologist physicians improved considerably (aided vs unaided, κ = 0.87 vs 0.79, respectively; P < .001). Conclusion A neural network trained with structured semiquantitative bedside chest radiography reports allowed nonradiologist physicians improved interpretations compared with the consensus reading of expert radiologists. © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Wielpütz in this issue.

Background parenchymal enhancement in contrast-enhanced MR imaging suggests systemic effects of intrauterine contraceptive devices.

OBJECTIVES: Levonorgestrel-releasing intrauterine contraceptive devices (LNG-IUDs) are designed to exhibit only local hormonal effects. There is an ongoing debate on whether LNG-IUDs can have side effects similar to systemic hormonal medication. Benign background parenchymal enhancement (BPE) in dynamic contrast-enhanced (DCE) MRI has been established as a sensitive marker of hormonal stimulation of the breast. We investigated the association between LNG-IUD use and BPE in breast MRI to further explore possible systemic effects of LNG-IUDs. METHODS: Our hospital database was searched to identify premenopausal women without personal history of breast cancer, oophorectomy, and hormone replacement or antihormone therapy, who had undergone standardized DCE breast MRI at least twice, once with and without an LNG-IUD in place. To avoid confounding aging-related effects on BPE, half of included women had their first MRI without, the other half with, LNG-IUD in place. Degree of BPE was analyzed according to the ACR categories. Wilcoxon-matched-pairs signed-rank test was used to compare the distribution of ACR categories with vs. without LNG-IUD. RESULTS: Forty-eight women (mean age, 46 years) were included. In 24/48 women (50% [95% CI: 35.9-64.1%]), ACR categories did not change with vs. without LNG-IUDs. In 23/48 women (48% [33.9-62.1%]), the ACR category was higher with vs. without LNG-IUDs; in 1/48 (2% [0-6%]), the ACR category was lower with vs. without LNG-IUDs. The change of ACR category depending on the presence or absence of an LNG-IUD proved highly significant (p < 0.001). CONCLUSION: The use of an LNG-IUD can be associated with increased BPE in breast MRI, providing further evidence that LNG-IUDs do have systemic effects. KEY POINTS: • The use of levonorgestrel-releasing intrauterine contraceptive devices is associated with increased background parenchymal enhancement in breast MRI. • This suggests that hormonal effects of these devices are not only confined to the uterine cavity, but may be systemic. • Potential systemic effects of levonorgestrel-releasing intrauterine contraceptive devices should therefore be considered.

Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI).

Breast density is an independent risk factor for the development of breast cancer and also decreases the sensitivity of mammography for screening. Consequently, women with extremely dense breasts face an increased risk of late diagnosis of breast cancer. These women are, therefore, underserved with current mammographic screening programs. The results of recent studies reporting on contrast-enhanced breast MRI as a screening method in women with extremely dense breasts provide compelling evidence that this approach can enable an important reduction in breast cancer mortality for these women and is cost-effective. Because there is now a valid option to improve breast cancer screening, the European Society of Breast Imaging (EUSOBI) recommends that women should be informed about their breast density. EUSOBI thus calls on all providers of mammography screening to share density information with the women being screened. In light of the available evidence, in women aged 50 to 70 years with extremely dense breasts, the EUSOBI now recommends offering screening breast MRI every 2 to 4 years. The EUSOBI acknowledges that it may currently not be possible to offer breast MRI immediately and everywhere and underscores that quality assurance procedures need to be established, but urges radiological societies and policymakers to act on this now. Since the wishes and values of individual women differ, in screening the principles of shared decision-making should be embraced. In particular, women should be counselled on the benefits and risks of mammography and MRI-based screening, so that they are capable of making an informed choice about their preferred screening method. KEY POINTS: • The recommendations in Figure 1 summarize the key points of the manuscript.

Safety and efficacy of right portal vein embolization in patients with prior left lateral liver resection.

BACKGROUND: In patients with bilobar metastatic liver disease, surgical clearance of both liver lobes may be achieved through multiple-stage liver resections. For patients with extensive disease, a major two-staged hepatectomy consisting of resection of liver segments II and III before right-sided portal vein embolization (PVE) and resection of segments V-VIII may be performed, leaving only segments IV ± I as the liver remnant. PURPOSE: To describe the outcome following right-sided PVE after prior complete resection of liver segments II and III. MATERIAL AND METHODS: In this retrospective study, 15 patients (mean age = 60.4 ± 9.3 years) with liver metastases from colorectal cancer (n = 14) and uveal melanoma (n = 1) who were scheduled to undergo a major two-stage hepatectomy, were included. Total liver volume (TLV) and volume of the future liver remnant (FLR) were measured on pre- and postinterventional computed tomography (CT) scans, and standardized FLR volumes (ratio FLR/TLV) were calculated. Patient data were retrospectively analyzed regarding peri- and postinterventional complications, with special emphasis on liver function tests. RESULTS: The mean standardized post-PVE FLR volume was 26.9% ± 6.4% and no patient developed hepatic insufficiency after the PVE. Based on FLR hypertrophy and liver function tests, all but one patient were considered eligible for the subsequent right-sided hepatectomy. However, due to local tumor progression, only 9/15 patients eventually proceeded to the second stage of surgery.  . CONCLUSION: Right-sided PVE was safe and efficacious in this cohort of patients who had previously undergone a complete resection of liver segments II and III as part of a major staged hepatectomy pathway leaving only segments IV(±I) as the FLR. .

An accelerometer-based guidance device for CT-guided procedures: an improved wireless prototype.

INTRODUCTION: The aim of the study was to demonstrate the feasibility of a prototype for accelerometer-based guidance for percutaneous CT-guided punctures and compare it with free-hand punctures. MATERIAL AND METHODS: The prototype enabled alignment with the CT coordinate system and a wireless connectivity. Its feasibility was tested in a swine cadaver model: 20 out-of-plane device-assisted punctures performed without intermittent control scans (one-step punctures) were evaluated regarding deviation to target and difference between planned and obtained angle. Thereafter, 22 device-assisted punctures were compared with 20 free-hand punctures regarding distance to target, deviation from the planned angle, number of control scans and procedure time. Differences were compared with the Mann-Whitney U-test (p < .05). RESULTS: The one-step punctures revealed a deviation to target of 0.26 ± 0.37 cm (axial plane) and 0.21 ± 0.19 cm (sagittal plane) and differences between planned and performed puncture angles of 0.9 ± 1.09° (axial plane) and 1.15 ± 0.91° (sagittal planes). In the comparative study, device-assisted punctures showed a significantly higher accuracy, 0.20 ± 0.17 cm vs. 0.30 ± 0.21 cm (p < .05) and lower number of required control scans, 1.3 ± 1.1 vs. 3.7 ± 0.9 (p < .05) compared with free-hand punctures. CONCLUSION: The accelerometer-based device proved to be feasible and demonstrated significantly higher accuracy and required significantly less control scans compared to free-hand puncture.

Magnetic resonance imaging and ultrasound for prediction of residual tumor size in early breast cancer within the ADAPT subtrials.

BACKGROUND: Prediction of histological tumor size by post-neoadjuvant therapy (NAT) ultrasound and magnetic resonance imaging (MRI) was evaluated in different breast cancer subtypes. METHODS: Imaging was performed after 12-week NAT in patients enrolled into three neoadjuvant WSG ADAPT subtrials. Imaging performance was analyzed for prediction of residual tumor measuring ≤10 mm and summarized using positive (PPV) and negative (NPV) predictive values. RESULTS: A total of 248 and 588 patients had MRI and ultrasound, respectively. Tumor size was over- or underestimated by < 10 mm in 4.4% and 21.8% of patients by MRI and in 10.2% and 15.8% by ultrasound. Overall, NPV (proportion of correctly predicted tumor size ≤10 mm) of MRI and ultrasound was 0.92 and 0.83; PPV (correctly predicted tumor size > 10 mm) was 0.52 and 0.61. MRI demonstrated a higher NPV and lower PPV than ultrasound in hormone receptor (HR)-positive/human epidermal growth factor receptor 2 (HER2)-positive and in HR-/HER2+ tumors. Both methods had a comparable NPV and PPV in HR-/HER2- tumors. CONCLUSIONS: In HR+/HER2+ and HR-/HER2+ breast cancer, MRI is less likely than ultrasound to underestimate while ultrasound is associated with a lower risk to overestimate tumor size. These findings may help to select the most optimal imaging approach for planning surgery after NAT. TRIAL REGISTRATION: Clinicaltrials.gov , NCT01815242 (registered on March 21, 2013), NCT01817452 (registered on March 25, 2013), and NCT01779206 (registered on January 30, 2013).

Early response by MR imaging and ultrasound as predictor of pathologic complete response to 12-week neoadjuvant therapy for different early breast cancer subtypes: Combined analysis from the WSG ADAPT subtrials.

We evaluated the role of early response after 3 weeks of neoadjuvant treatment (NAT) assessed by ultrasound (US), magnetic resonance imaging (MRI) and Ki-67 dynamics for prediction of pathologic complete response (pCR) in different early breast cancer subtypes. Patients with HR+/HER2+, HR-/HER2- and HR-/HER2+ tumors enrolled into three neoadjuvant WSG ADAPT subtrials underwent US, MRI and Ki-67 assessment at diagnosis and after 3 weeks of NAT. Early response was defined as complete or partial response (US, MRI) and ≥30% proliferation decrease or <500 invasive tumor cells (Ki-67). Predictive values and area under the receiver operating characteristic (AUC) curves for prediction of pCR (ypT0/is ypN0) after 12-week NAT were calculated. Two hundred twenty-six had MRI and 401 US; 107 underwent both MRI and US. All three methods yielded a similar AUC in HR+/HER2+ (0.66-0.67) and HR-/HER2- tumors (0.53-0.63), while MRI and Ki-67 performed better than US in HR-/HER2+ tumors (0.83 and 0.79 vs 0.56). Adding MRI+/-Ki-67 increased AUC of US in HR-/HER2+ tumors to 0.64 to 0.75. MRI and Ki-67 demonstrated highest sensitivity in HR-/HER2- (0.8-1) and HR-/HER2+ tumors (1, both). Negative predictive value was similar for all methods in HR+/HER2+ (0.71-0.74) and HR-/HER2- tumors (0.85-1), while it was higher for MRI and Ki-67 compared to US in HR-/HER2+ subtype (1 vs 0.5). Early response assessed by US, MRI and Ki-67 is a strong predictor for pCR after 12-week NAT. Strength of pCR prediction varies according to tumor subtype. Adding MRI+/-Ki-67 to US did not improve pCR prediction in majority of our patients.

Abbreviated Magnetic Resonance Imaging (MRI) for Breast Cancer Screening: Rationale, Concept, and Transfer to Clinical Practice.

Given the increasing understanding of cancer as a heterogeneous group of diseases, detection methods should offer a sensitivity profile that ensures perfect sensitivity for biologically important cancers while screening out self-limiting pseudocancers. However, mammographic screening is biased toward detection of ductal carcinoma in situ and slowly growing cancers-and thus frequently fails to detect biologically aggressive cancers. This explains the persistently high rates of interval cancers and high rates of breast cancer mortality observed in spite of decades of mammographic screening. Magnetic resonance imaging (MRI), in contrast, has a sensitivity profile that matches clinical needs. Conventional MRI is not suitable for population-wide screening due to high cost, limited tolerability, and lack of availability. We introduced abbreviated MRI in 2014. Abbreviated MRI will change the way MRI is used in clinical medicine. This article describes the rationale to use MRI in general, and abbreviated MRI in particular, for breast cancer screening.

Percutaneous transhepatic biliary drainage (PTBD) in patients with dilated vs. nondilated bile ducts: technical considerations and complications.

OBJECTIVES: The aim of this study was to compare success, technical complexity, and complication rates of percutaneous transhepatic biliary drainage (PTBD) in patients with dilated vs. nondilated bile ducts. METHODS: In a retrospective analysis, we evaluated all consecutive PTBD performed in our department over a period of 5 years. Technical success, technical data (side, fluoroscopy time, radiation dose, amount of contrast media, use of disposable equipment), procedure-related complications and peri-interventional mortality were compared for patients with dilated vs. non-dilated bile ducts. Independent t test and χ2 test were used to evaluate the statistical significance. RESULTS: A total of 253 procedures were performed on 187 patients, of whom 101/253 had dilated bile ducts and 152/253 not. In total, 243/253 procedures were successful. PTBD was significantly more often successful in patients with dilated vs. nondilated bile ducts (150/153 vs. 93/101; p 0.02). Overall complication rate (13%) did not differ significantly between patients with dilated vs. nondilated bile ducts. Procedures in patients with normal, nondilated bile ducts were associated with a significantly higher rate of post-interventional bleeding (5/101 vs. 0/152). Mean fluoroscopy time (42:36 ± 35:39 h vs. 30:28 ± 25:10 h; p 0.002) and amount of contrast media (66 ± 40 ml vs. 52 ± 24 ml; p 0.07) or use of disposables were significantly higher in patients with nondilated ducts. A significantly lower fluoroscopy time and amount of contrast medium were used in left hepatic PTBD. CONCLUSION: Despite the higher technical complexity, PTBD with nondilated bile ducts was associated with similar overall complication rates but higher bleeding complications compared with PTBD with dilated bile ducts. KEY POINTS: • PTBD was associated with similar overall complication rates in patients with dilated vs. nondilated bile ducts. • Although overall complication rates were low, PTBD in patients with nondilated bile ducts was associated with a higher incidence of post-interventional bleeding. • PTBD in patients with nondilated bile ducts is technically more complex.

Induction of Contralateral Hepatic Hypertrophy by Unilobar Yttrium-90 Transarterial Radioembolization versus Portal Vein Embolization: An Animal Study.

PURPOSE: To compare hepatic hypertrophy in the contralateral lobe achieved by unilobar transarterial radioembolization (TARE) versus portal vein embolization (PVE) in a swine model. METHODS: After an escalation study to determine the optimum dose to achieve hypertrophy after unilobar TARE in 4 animals, 16 pigs were treated by TARE (yttrium-90 resin microspheres) or PVE (lipiodol/n-butyl cyanoacrylate). Liver volume was calculated based on CT before treatment and during 6 months of follow-up. Independent t-test (P < .05) was used to compare hypertrophy. The relationship between hypertrophy after TARE and absorbed dose was calculated using the Pearson correlation. RESULTS: At 2 and 4 weeks after treatment, a significantly higher degree of future liver remnant hypertrophy was observed in the PVE group versus the TARE group, with a median volume gain of 31% (interquartile range [IQR]: 16%-66%) for PVE versus 23% (IQR: 6%-36%) for TARE after 2 weeks and 51% (IQR: 47%-69%) for PVE versus 29% (IQR: 20%-50%) for TARE after 4 weeks. After 3 and 6 months, hypertrophy converged without a statistically significant difference, with a volume gain of 103% (IQR: 86%-119%) for PVE versus 82% (IQR: 70%-96%) for TARE after 3 months and 115% (IQR: 70%-46%) for PVE versus 86% (IQR: 58%-111%) for TARE after 6 months. A strong correlation was observed between radiation dose (median 162 Gy, IQR: 139-175) and hypertrophy. CONCLUSIONS: PVE resulted in rapid hypertrophy within 1 month of the procedure, followed by a plateau, whereas TARE resulted in comparable hypertrophy by 3-6 months. TARE-induced hypertrophy correlated with radiation absorbed dose.

Volumetric measurements of target lesions: does it improve inter-reader variability for oncological response assessment according to RECIST 1.1 guidelines compared to standard unidimensional measurements?

PURPOSE: Target lesion selection is known to be a major factor for inter-reader discordance in RECIST 1.1. The purpose of this study was to assess whether volumetric measurements of target lesions result in different response categorization, as opposed to standard unidimensional measurements, and to evaluate the impact on inter-reader agreement for response categorization when different readers select different sets of target lesions. MATERIAL AND METHODS: Fifty patients with measurable disease from solid tumours, in which 3 readers had blindly and independently selected different sets of target lesions and subsequently reached clinically significant discordant response categorizations (progressive disease [PD] vs. non-progressive disease [non-PD]) based on RECIST 1.1 analyses were included in this study. Additional volumetric measurements of all target lesions were performed by the same readers in a second read. Intra-reader agreement between standard unidimensional measurements (uRECIST) and volumetric measurements (vRECIST) was assessed using Cohen's k statistics. Fleiss k statistics was used to analyse the inter-reader agreement for uRECIST and vRECIST results. RESULTS: The 3 readers assigned the same response classifications based on uRECIST and vRECIST in 33/50 (66%), 42/50 patients (84%), and 44/50 patients (88%), respectively. Inter-reader agreement improved from 0% when using uRECIST to 36% when using vRECIST. CONCLUSIONS: Volumetric measurement of target lesions may improve inter-reader variability for response assessment as opposed to standard unidimensional measurements. However, in about two-thirds of patients, readers disagreed regardless of the measurement method, indicating that a limited set of target lesions may not be sufficiently representative of the whole-body tumour burden.