The association between clinical, sociodemographic, familial, and environmental factors and treatment resistance in schizophrenia: A machine-learning-based approach.

BACKGROUND: Prediction of treatment resistance in schizophrenia (TRS) would be helpful to reduce the duration of ineffective treatment and avoid delays in clozapine initiation. We applied machine learning to identify clinical, sociodemographic, familial, and environmental variables that are associated with TRS and could potentially predict TRS in the future. STUDY DESIGN: Baseline and follow-up data on trait(-like) variables from the Genetic Risk and Outcome of Psychosis (GROUP) study were used. For the main analysis, we selected patients with non-affective psychotic disorders who met TRS (n = 200) or antipsychotic-responsive criteria (n = 423) throughout the study. For a sensitivity analysis, we only selected patients who met TRS (n = 76) or antipsychotic-responsive criteria (n = 123) at follow-up but not at baseline. Random forest models were trained to predict TRS in both datasets. SHapley Additive exPlanation values were used to examine the variables' contributions to the prediction. STUDY RESULTS: Premorbid functioning, age at onset, and educational degree were most consistently associated with TRS across both analyses. Marital status, current household, intelligence quotient, number of moves, and family loading score for substance abuse also consistently contributed to the prediction of TRS in the main or sensitivity analysis. The diagnostic performance of our models was modest (area under the curve: 0.66-0.69). CONCLUSIONS: We demonstrate that various clinical, sociodemographic, familial, and environmental variables are associated with TRS. Our models only showed modest performance in predicting TRS. Prospective large multi-centre studies are needed to validate our findings and investigate whether the model's performance can be improved by adding data from different modalities.

[A diabetic patient with a fulminant hand infection caused by a domestic rat bite]. / Ernstige handinfectie bij een patiënt met diabetes door een tamme-rattenbeet.

BACKGROUND: Animal bite wounds are common and can cause serious hand infections. Risk factors not only include the oral flora of the animal and the anatomy of the teeth, but also the comorbidities of the bitten patient. CASE DESCRIPTION: In this case report we describe a 46-years old female patient with comorbidities, including diabetes mellitus type 2 and peripheral artery disease, suffering a fulminant hand infection after a domestic rat bite with the newly described bacterium Rodentibacterratti. Despite extensive antibiotic therapy and surgical debridement, a ray amputation was inevitable to maintain adequate hand function. CONCLUSION: Infections after animal bites may be caused by a variety of pathogens. The pathogen Rodentibacterratti has not previously been associated with infections in humans and future research is indicated to assess therapeutic strategies. Patients should be referred to a (plastic) surgeon if there is no clinical improvement within 48 hours of initiating antibiotic treatment.

Sensitivity of an AI method for [18F]FDG PET/CT outcome prediction of diffuse large B-cell lymphoma patients to image reconstruction protocols.

BACKGROUND: Convolutional neural networks (CNNs), applied to baseline [18F]-FDG PET/CT maximum intensity projections (MIPs), show potential for treatment outcome prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study is to investigate the robustness of CNN predictions to different image reconstruction protocols. Baseline [18F]FDG PET/CT scans were collected from 20 DLBCL patients. EARL1, EARL2 and high-resolution (HR) protocols were applied per scan, generating three images with different image qualities. Image-based transformation was applied by blurring EARL2 and HR images to generate EARL1 compliant images using a Gaussian filter of 5 and 7 mm, respectively. MIPs were generated for each of the reconstructions, before and after image transformation. An in-house developed CNN predicted the probability of tumor progression within 2 years for each MIP. The difference in probabilities per patient was then calculated between both EARL2 and HR with respect to EARL1 (delta probabilities or ΔP). We compared these to the probabilities obtained after aligning the data with ComBat using the difference in median and interquartile range (IQR). RESULTS: CNN probabilities were found to be sensitive to different reconstruction protocols (EARL2 ΔP: median = 0.09, interquartile range (IQR) = [0.06, 0.10] and HR ΔP: median = 0.1, IQR = [0.08, 0.16]). Moreover, higher resolution images (EARL2 and HR) led to higher probability values. After image-based and ComBat transformation, an improved agreement of CNN probabilities among reconstructions was found for all patients. This agreement was slightly better after image-based transformation (transformed EARL2 ΔP: median = 0.022, IQR = [0.01, 0.02] and transformed HR ΔP: median = 0.029, IQR = [0.01, 0.03]). CONCLUSION: Our CNN-based outcome predictions are affected by the applied reconstruction protocols, yet in a predictable manner. Image-based harmonization is a suitable approach to harmonize CNN predictions across image reconstruction protocols.

An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients.

Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL 18F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume and Dmaxbulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (< 0.4, generally). These findings suggest that MIP-based CNNs are able to predict treatment outcome in DLBCL.

Peristomal adenocarcinoma 16 years after colorectal adenocarcinoma resection with curative intent.

Metachronous colorectal cancer is relatively rare, occurring in 0.7-3.6% of patients diagnosed with colorectal adenocarcinoma. Cutaneous metastases are similarly a rare presentation, occurring in <6% of metastatic colorectal cancer patients. Even more rare are the cutaneous recurrences at the peristomal site. Clinically, it is difficult to distinguish between metachronous cancer and cutaneous metastases. This paper reports a case of an elderly woman presenting with a slowly progressing peristomal cutaneous lesion 16 years after surgical resection for colorectal cancer. Core punch biopsy revealed a cutaneous localization of an intestinal type of adenocarcinoma. A surgical resection of the peristomal area was carried out whereby a new colostomy was created on the contralateral side. Definite histopathological examination showed a superficially located intestinal type adenocarcinoma with extensive pagetoid spread in the epidermal surface. In conclusion, it is important to remain alert and strive for early detection for cutaneous abnormalities following colorectal cancer.

Impact of analysis of the sentinel lymph node by one-step nucleic acid amplification (OSNA) compared to conventional histopathology on axillary and systemic treatment: data from the Dutch nationwide cohort of breast cancer patients.

PURPOSE: The outcome of the sentinel lymph node in breast cancer patients affects adjuvant treatment. Compared to conventional histopathology, analysis by one-step nucleic acid amplification (OSNA) harvests more micrometastasis, potentially inducing overtreatment. In this study we investigated the impact of OSNA analysis on adjuvant treatment, compared to histopathological analysis. METHODS: Data from T1-3 breast cancer patients with sentinel nodes analysed between January 2016 and December 2019 by OSNA (OSNA group, n = 1086) from Zuyderland Medical Centre, the Netherlands, were compared to concurrent data from the Netherlands Cancer Registry (NKR) where sentinel nodes were examined by histology (histology group, n = 35,143). Primary outcomes were micro- or macrometastasis, axillary treatments (axillary lymph node dissection (ALND) or axillary radiotherapy (ART)), chemotherapy, and endocrine therapy. Statistics with Pearson Chi-square. RESULTS: In the OSNA group more micrometastasis (14.9%) were detected compared to the histology group (7.9%, p < 0.001). No difference in axillary treatment between groups was detected (14.3 vs. 14.4%). In case of mastectomy and macrometastasis, ALND was preferred over ART in the OSNA group (14.9%) compared to the histology group (4.4%, p < 0.001). In cases of micrometastasis, no difference was seen. There was no difference in administration of adjuvant chemotherapy between groups. Endocrine treatment was administrated less often in the OSNA group compared to the histology group (45.8% vs. 50.8%, p < 0.002). CONCLUSION: More micrometastasis were detected by OSNA compared to histopathology, but no subsequent increase in adjuvant axillary and systematic treatment was noticed. When performing mastectomy and OSNA, there was a preference for ALND compared to ART.

Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review.

Rational: Deep learning (DL) has demonstrated a remarkable performance in diagnostic imaging for various diseases and modalities and therefore has a high potential to be used as a clinical tool. However, current practice shows low deployment of these algorithms in clinical practice, because DL algorithms lack transparency and trust due to their underlying black-box mechanism. For successful employment, explainable artificial intelligence (XAI) could be introduced to close the gap between the medical professionals and the DL algorithms. In this literature review, XAI methods available for magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET) imaging are discussed and future suggestions are made. Methods: PubMed, Embase.com and Clarivate Analytics/Web of Science Core Collection were screened. Articles were considered eligible for inclusion if XAI was used (and well described) to describe the behavior of a DL model used in MR, CT and PET imaging. Results: A total of 75 articles were included of which 54 and 17 articles described post and ad hoc XAI methods, respectively, and 4 articles described both XAI methods. Major variations in performance is seen between the methods. Overall, post hoc XAI lacks the ability to provide class-discriminative and target-specific explanation. Ad hoc XAI seems to tackle this because of its intrinsic ability to explain. However, quality control of the XAI methods is rarely applied and therefore systematic comparison between the methods is difficult. Conclusion: There is currently no clear consensus on how XAI should be deployed in order to close the gap between medical professionals and DL algorithms for clinical implementation. We advocate for systematic technical and clinical quality assessment of XAI methods. Also, to ensure end-to-end unbiased and safe integration of XAI in clinical workflow, (anatomical) data minimization and quality control methods should be included.

3D Convolutional Neural Network-Based Denoising of Low-Count Whole-Body 18F-Fluorodeoxyglucose and 89Zr-Rituximab PET Scans.

Acquisition time and injected activity of 18F-fluorodeoxyglucose (18F-FDG) PET should ideally be reduced. However, this decreases the signal-to-noise ratio (SNR), which impairs the diagnostic value of these PET scans. In addition, 89Zr-antibody PET is known to have a low SNR. To improve the diagnostic value of these scans, a Convolutional Neural Network (CNN) denoising method is proposed. The aim of this study was therefore to develop CNNs to increase SNR for low-count 18F-FDG and 89Zr-antibody PET. Super-low-count, low-count and full-count 18F-FDG PET scans from 60 primary lung cancer patients and full-count 89Zr-rituximab PET scans from five patients with non-Hodgkin lymphoma were acquired. CNNs were built to capture the features and to denoise the PET scans. Additionally, Gaussian smoothing (GS) and Bilateral filtering (BF) were evaluated. The performance of the denoising approaches was assessed based on the tumour recovery coefficient (TRC), coefficient of variance (COV; level of noise), and a qualitative assessment by two nuclear medicine physicians. The CNNs had a higher TRC and comparable or lower COV to GS and BF and was also the preferred method of the two observers for both 18F-FDG and 89Zr-rituximab PET. The CNNs improved the SNR of low-count 18F-FDG and 89Zr-rituximab PET, with almost similar or better clinical performance than the full-count PET, respectively. Additionally, the CNNs showed better performance than GS and BF.

Iatrogenic caecal perforation two days after a caesarean section, a case report.

INTRODUCTION: and importance: The caesarean section is a widely spread procedure and 29.7 million times performed every year inn 169 countries in the world. Overall, complications are seen in 6% for elective caesarean to 15% for emergency caesarean. CASE PRESENTATION: We here report a case which was initially diagnosed as a postoperative paralytic ileus. After a complicated caesarean section caused by bleeding and problems with haemostasis, a healthy child was born with full mother recovery for the first 24 hours after surgery. Unfortunately, her condition deteriorated between 24 and 48 hours and she reported progressive nausea and painful bloating. Laboratory tests and CT imaging showed progressive signs of inflammation and distention of the caecum and colon. A second CT scan the next day revealed signs of perforation. An ileocecal resection was performed with a primary anastomosis. Full recovery occurred two weeks later. CLINICAL DISCUSSION: With an estimated incidence of only 0,08%, bowel perforations due to caesarean section, are rare. Moreover, is the clinical presentation diverse and computed topography is essential during the diagnostic process. To avoid potential morbidity and mortality, the surgeon must consider performing a laparotomy in case of a deteriorating patient in non-invasive treatment fails. CONCLUSION: Caecal perforation must be considered as complication after a caesarean section. An ileocecal resection is necessary in this situation. This case report shows that a primary anastomosis is a possible option in a healthy patient that is hemodynamically stable during the operation. In case of an unhealthy or hemodynamic unstable patient, the safest option is a temporary ileostomy.

Non-invasive Standardised Uptake Value for Verification of the Use of Previously Validated Reference Region for [18F]Flortaucipir and [18F]Florbetapir Brain PET Studies.

PURPOSE: The simplified reference tissue model (SRTM) is commonly applied for the quantification of brain positron emission tomography (PET) studies, particularly because it avoids arterial cannulation. SRTM requires a validated reference region which is obtained by baseline-blocking or displacement studies. Once a reference region is validated, the use should be verified for each new subject. This verification normally requires volume of distribution (VT) of a reference region. However, performing dynamic scanning and arterial sampling is not always possible, specifically in elderly subjects and in advanced disease stages. The aim of this study was to investigate the use of non-invasive standardised uptake value (SUV) approaches, in comparison to VT, as a verification of the previously validated grey matter cerebellum reference region for [18F]flortaucipir and [18F]florbetapir PET imaging in Alzheimer's disease (AD) patients and controls. PROCEDURES: Dynamic 130-min [18F]flortaucipir PET scans obtained from nineteen subjects (10 AD patients) and 90-min [18F]florbetapir dynamic scans obtained from fourteen subjects (8 AD patients) were included. Regional VT's were estimated for both tracers and were considered the standard verification of the previously validated reference region. Non-invasive SUVs corrected for body weight (SUVBW), lean body mass (SUL), and body surface area (SUVBSA) were obtained by using later time intervals of the dynamic scans. Simulations were also performed to assess the effect of flow and specific binding (BPND) on the SUVs. RESULTS: A low SUV corresponded well with a low VT for both [18F]flortaucipir and [18F]florbetapir. Simulation confirmed that SUVs were only slightly affected by flow changes and that increases in SUV were predominantly determined by the presence of specific binding. CONCLUSIONS: In situations where dynamic scanning and arterial sampling is not possible, a low SUV(80-100 min) for [18F]flortaucipir and a low SUV(50-70 min) for [18F]florbetapir may be used as indication for absence of specific binding in the grey matter cerebellum reference region.

Classification of negative and positive 18F-florbetapir brain PET studies in subjective cognitive decline patients using a convolutional neural network.

PURPOSE: Visual reading of 18F-florbetapir positron emission tomography (PET) scans is used in the diagnostic process of patients with cognitive disorders for assessment of amyloid-ß (Aß) depositions. However, this can be time-consuming, and difficult in case of borderline amyloid pathology. Computer-aided pattern recognition can be helpful in this process but needs to be validated. The aim of this work was to develop, train, validate and test a convolutional neural network (CNN) for discriminating between Aß negative and positive 18F-florbetapir PET scans in patients with subjective cognitive decline (SCD). METHODS: 18F-florbetapir PET images were acquired and visually assessed. The SCD cohort consisted of 133 patients from the SCIENCe cohort and 22 patients from the ADNI database. From the SCIENCe cohort, standardized uptake value ratio (SUVR) images were computed. From the ADNI database, SUVR images were extracted. 2D CNNs (axial, coronal and sagittal) were built to capture features of the scans. The SCIENCe scans were randomly divided into training and validation set (5-fold cross-validation), and the ADNI scans were used as test set. Performance was evaluated based on average accuracy, sensitivity and specificity from the cross-validation. Next, the best performing CNN was evaluated on the test set. RESULTS: The sagittal 2D-CNN classified the SCIENCe scans with the highest average accuracy of 99% ± 2 (SD), sensitivity of 97% ± 7 and specificity of 100%. The ADNI scans were classified with a 95% accuracy, 100% sensitivity and 92.3% specificity. CONCLUSION: The 2D-CNN algorithm can classify Aß negative and positive 18F-florbetapir PET scans with high performance in SCD patients.

Machine learning-based analysis of [18F]DCFPyL PET radiomics for risk stratification in primary prostate cancer.

PURPOSE: Quantitative prostate-specific membrane antigen (PSMA) PET analysis may provide for non-invasive and objective risk stratification of primary prostate cancer (PCa) patients. We determined the ability of machine learning-based analysis of quantitative [18F]DCFPyL PET metrics to predict metastatic disease or high-risk pathological tumor features. METHODS: In a prospective cohort study, 76 patients with intermediate- to high-risk PCa scheduled for robot-assisted radical prostatectomy with extended pelvic lymph node dissection underwent pre-operative [18F]DCFPyL PET-CT. Primary tumors were delineated using 50-70% peak isocontour thresholds on images with and without partial-volume correction (PVC). Four hundred and eighty standardized radiomic features were extracted per tumor. Random forest models were trained to predict lymph node involvement (LNI), presence of any metastasis, Gleason score ≥ 8, and presence of extracapsular extension (ECE). For comparison, models were also trained using standard PET features (SUVs, volume, total PSMA uptake). Model performance was validated using 50 times repeated 5-fold cross-validation yielding the mean receiver-operator characteristic curve AUC. RESULTS: The radiomics-based machine learning models predicted LNI (AUC 0.86 ± 0.15, p < 0.01), nodal or distant metastasis (AUC 0.86 ± 0.14, p < 0.01), Gleason score (0.81 ± 0.16, p < 0.01), and ECE (0.76 ± 0.12, p < 0.01). The highest AUCs reached using standard PET metrics were lower than those of radiomics-based models. For LNI and metastasis prediction, PVC and a higher delineation threshold improved model stability. Machine learning pre-processing methods had a minor impact on model performance. CONCLUSION: Machine learning-based analysis of quantitative [18F]DCFPyL PET metrics can predict LNI and high-risk pathological tumor features in primary PCa patients. These findings indicate that PSMA expression detected on PET is related to both primary tumor histopathology and metastatic tendency. Multicenter external validation is needed to determine the benefits of using radiomics versus standard PET metrics in clinical practice.

The Noninvasive Fetal Electrocardiogram During Labor: A Review of the Literature.

IMPORTANCE: The introduction of the cardiotocogram (CTG) during labor has not been found to improve neonatal outcome. The search for a more reliable, less invasive, and patient-friendly technique is ongoing. The noninvasive fetal electrocardiogram (NI-fECG) has been proposed as one such alternative. OBJECTIVES: The aim of this study was to review the literature on the performance of NI-fECG for fetal monitoring during labor. Following the PRISMA guidelines, a systematic search in MEDLINE, EMBASE, and Cochrane Library was performed. Studies involving original research investigating the performance of NI-fECG during labor were included. Animal studies and articles in languages other than English, Dutch, or German were excluded. The QUADAS-2 checklist was used for quality assessment. A descriptive analysis of the results is provided. RESULTS: Eight articles were included. Pooled analysis of the results of the separate studies was not possible due to heterogeneity. All studies demonstrate that it is possible to apply NI-fECG during labor. Compared with Doppler ultrasound, NI-fECG performs equal or better in most studies. CONCLUSIONS AND RELEVANCE: NI-fECG for fetal monitoring is a promising noninvasive and patient-friendly technique that provides accurate information. Future studies should focus on signal quality throughout labor, with the aim to further optimize technical development of NI-fECG.

The trans-DATA study: aims and design of a translational breast cancer prognostic marker identification study.

BACKGROUND: The effect of extended adjuvant aromatase inhibition in hormone-positive breast cancer after sequential tamoxifen, aromatase inhibitor treatment of 5 years was recently investigated by the DATA study. This study found no statistically significant effect of prolonged aromatase therapy. However, subgroup analysis showed post hoc statistically significant benefits in certain sub-populations. The trans-DATA study is a translational sub-study aiming to identify DNA methylation markers prognostic of patient outcome. METHODS: Patients from the DATA study are included in the trans-DATA study. Primary breast tumour tissue will be collected, subtyped and used for DNA isolation. A genome-wide DNA methylation discovery assay will be performed on 60 patients that had a distant recurrence and 60 patients that did not have a distant recurrence using the Infinium Methylation EPIC Bead Chip platform. Differentially methylated regions of interest will be selected based on Akaike's Information Criterion, Gene Ontology Analysis and correlation between methylation and expression levels. Selected candidate genes will subsequently be validated in the remaining patients using qMSP. DISCUSSION: The trans-DATA study uses a cohort derived from a clinical randomised trial. This study was designed to avoid common pitfalls in marker discovery studies such as selection bias, confounding and lack of reproducibility. In addition to the usual clinical risk factors, the results of this study may identify predictors of high recurrence risk in hormone receptor-positive breast cancer patients treated with sequential tamoxifen and aromatase inhibitor therapy.

Phase-locked and non-phase-locked EEG responses to pinprick stimulation before and after experimentally-induced secondary hyperalgesia.

OBJECTIVE: Pinprick-evoked brain potentials (PEPs) have been proposed as a technique to investigate secondary hyperalgesia and central sensitization in humans. However, the signal-to-noise (SNR) of PEPs is low. Here, using time-frequency analysis, we characterize the phase-locked and non-phase-locked EEG responses to pinprick stimulation, before and after secondary hyperalgesia. METHODS: Secondary hyperalgesia was induced using high-frequency electrical stimulation (HFS) of the left/right forearm skin in 16 volunteers. EEG responses to 64 and 96mN pinprick stimuli were elicited from both arms, before and 20min after HFS. RESULTS: Pinprick stimulation applied to normal skin elicited a phase-locked low-frequency (<5Hz) response followed by a reduction of alpha-band oscillations (7-10Hz). The low-frequency response was significantly increased when pinprick stimuli were delivered to the area of secondary hyperalgesia. There was no change in the reduction of alpha-band oscillations. Whereas the low-frequency response was enhanced for both 64 and 96mN intensities, PEPs analyzed in the time domain were only significantly enhanced for the 64mN intensity. CONCLUSIONS: Time-frequency analysis may be more sensitive than conventional time-domain analysis in revealing EEG changes associated to secondary hyperalgesia. SIGNIFICANCE: Time-frequency analysis of PEPs can be used to investigate central sensitization in humans.

Risk of regional recurrence in triple-negative breast cancer patients: a Dutch cohort study.

Triple-negative breast cancer is associated with early recurrence and low survival rates. Several trials investigate the safety of a more conservative approach of axillary treatment in clinically T1-2N0 breast cancer. Triple-negative breast cancer comprises only 15 % of newly diagnosed breast cancers, which might result in insufficient power for representative results for this subgroup. We aimed to provide a nationwide overview on the occurrence of (regional) recurrences in triple-negative breast cancer patients with a clinically T1-2N0 status. For this cohort study, 2548 women diagnosed between 2005 and 2008 with clinically T1-2N0 triple-negative breast cancer were selected from the Netherlands Cancer Registry. Follow-up data until 2014 were analyzed using Kaplan-Meier. Sentinel lymph node biopsy was performed in 2486 patients, and (completion) axillary lymph node dissection in 562 patients. Final pathologic nodal status was pN0 in 78.5 %, pN1mi in 4.5 %, pN1 in 12.3 %, pN2-3 in 3.6 %, and pNx in 1.1 %. During a follow-up of 5 years, regional recurrence occurred in 2.9 %, local recurrence in 4.2 % and distant recurrence in 12.2 %. Five-year disease-free survival was 78.7 %, distant disease-free survival 80.5 %, and 5-year overall survival 82.3 %. Triple-negative clinically T1-2N0 breast cancer patients rarely develop a regional recurrence. Their disease-free survival is more threatened by distant recurrence, affecting their overall survival. Consequently, it seems justified to include triple-negative breast cancer patients in randomized controlled trials investigating the safety of minimizing axillary staging and treatment.

Ultrasound is at least as good as magnetic resonance imaging in predicting tumour size post-neoadjuvant chemotherapy in breast cancer.

BACKGROUND: The aim of this study was to evaluate the accuracy of clinical imaging of the primary breast tumour post-neoadjuvant chemotherapy (NAC) related to the post-neoadjuvant histological tumour size (gold standard) and whether this varies with breast cancer subtype. In this study, results of both magnetic resonance imaging (MRI) and ultrasound (US) were reported. METHODS: Patients with invasive breast cancer were enrolled in the INTENS study between 2006 and 2009. We included 182 patients, of whom data were available for post-NAC MRI (n=155), US (n=123), and histopathological tumour size. RESULTS: MRI estimated residual tumour size with <10-mm discordance in 54% of patients, overestimated size in 28% and underestimated size in 18% of patients. With US, this was 63%, 20% and 17%, respectively. The negative predictive value in hormone receptor-positive tumours for both MRI and US was low, 26% and 33%, respectively. The median deviation in clinical tumour size as percentage of pathological tumour was 63% (P25=26, P75=100) and 49% (P25=22, P75=100) for MRI and US, respectively (P=0.06). CONCLUSIONS: In this study, US was at least as good as breast MRI in providing information on residual tumour size post-neoadjuvant chemotherapy. However, both modalities suffered from a substantial percentage of over- and underestimation of tumour size and in addition both showed a low negative predictive value of pathologic complete remission (Gov nr: NCT00314977).

Prognostic Relevance of Gene-Environment Interactions in Patients With Dilated Cardiomyopathy: Applying the MOGE(S) Classification.

BACKGROUND: The multifactorial pathogenesis leading to dilated cardiomyopathy (DCM) makes stratification difficult. The recent MOGE(S) (morphofunctional, organ involvement, genetic or familial, etiology, stage) classification addresses this issue. OBJECTIVES: The purpose of this study was to investigate the applicability and prognostic relevance of the MOGE(S) classification in patients with DCM. METHODS: This study used patients from the Maastricht Cardiomyopathy Registry in the Netherlands and excluded patients with ischemic, valvular, hypertensive, and congenital heart disease. All other patients underwent a complete diagnostic work-up, including genetic evaluation and endomyocardial biopsy. RESULTS: A total of 213 consecutive patients with DCM were included: organ involvement was demonstrated in 35 (16%) and genetic or familial DCM in 70 (33%) patients, including 16 (8%) patients with a pathogenic mutation. At least 1 cause was found in 155 (73%) patients, of whom 48 (23%) had more than 1 possible cause. Left ventricular reverse remodeling was more common in patients with nongenetic or nonfamilial DCM than in patients with genetic or familial DCM (40% vs. 25%; p = 0.04). After a median follow-up of 47 months, organ involvement and higher New York Heart Association functional class were associated with adverse outcome (p < 0.001 and p = 0.02, respectively). Genetic or familial DCM per se was of no prognostic significance, but when it was accompanied by additional etiologic-environmental factors such as significant viral load, immune-mediated factors, rhythm disturbances, or toxic triggers, a worse outcome was revealed (p = 0.03). A higher presence of MOGE(S) attributes (≥2 vs. ≤1 attributes) showed an adverse outcome (p = 0.007). CONCLUSIONS: The MOGE(S) classification in DCM is applicable, and each attribute or the gene-environment interaction is associated with outcome. Importantly, the presence of multiple attributes was a strong predictor of adverse outcome. Finally, adaptation of the MOGE(S) involving multiple possible etiologies is recommended.

Real-Life Use and Effectiveness of Adjuvant Trastuzumab in Early Breast Cancer Patients: A Study of the Southeast Netherlands Breast Cancer Consortium.

BACKGROUND: The impact of drug prescriptions in real life as opposed to strict clinical trial prescription is only rarely assessed, although it is well recognized that incorrect use may harm patients and may have a significant impact on health care resources. We investigated the use and effectiveness of adjuvant trastuzumab in daily practice compared with the effectiveness in clinical trials. METHODS: We included all patients with stage I-III invasive breast cancer, irrespective of human epidermal growth factor receptor 2 (HER2) status, diagnosed in five hospitals in the southeast of The Netherlands in 2005-2007. We aimed to assess the actual use of adjuvant trastuzumab in early HER2-positive breast and its efficacy in daily practice. RESULTS: Of 2,684 patients included, 476 (17.7%) had a HER2-positive tumor. Of these, 251 (52.7%) patients had an indication for trastuzumab treatment of which 196 (78.1%) patients actually received it. Of the 225 patients without an indication, 34 (15.1%) received trastuzumab. Five-year disease-free survival was 80.7% for (n = 230) patients treated with versus 68.2% for (n = 246) patients not treated with trastuzumab (p = .0023), and 5-year overall survival rates were 90.7% and 77.4%, respectively (p = .0002). The hazard ratio for disease recurrence was 0.63 (95% confidence interval, 0.37-1.06) for trastuzumab when adjusting for potential confounders. CONCLUSION: This study shows that in real life, patients treated with trastuzumab in early-stage HER2-positive breast cancer had a 5-year disease-free and overall survival comparable to prior randomized trials. For informative decision making, real-life data are of additional value, providing insight on outcome of patients considered ineligible for treatment.

Formalin-fixed, paraffin-embedded (FFPE) tissue epigenomics using Infinium HumanMethylation450 BeadChip assays.

Current genome-wide methods to detect DNA-methylation in healthy and diseased tissue require high-quality DNA from fresh-frozen (FF) samples. However, well-annotated clinical samples are mostly available as formalin-fixed, paraffin-embedded (FFPE) tissues containing poor-quality DNA. To overcome this limitation, we here aimed to evaluate a DNA restoration protocol for usage with the genome-wide Infinium HumanMethylation450 BeadChip assay (HM-450K). Sixty-six DNA samples from normal colon (n=9) and breast cancer (n=11) were interrogated separately using HM-450K. Analyses included matched FF/FFPE samples and technical duplicates. FFPE DNA was processed with (FFPEr) or without a DNA restoration protocol (Illumina). Differentially methylated genes were finally validated in 24 additional FFPE tissues using nested methylation-specific PCR (MSP). In summary, ß-values correlation between FFPEr duplicates was high (ρ=0.9927 (s.d. ±0.0015)). Matched FF/FFPEr correlation was also high (ρ=0.9590 (s.d. ±0.0184)) compared with matched FF/FFPE (ρ=0.8051 (s.d. ±0.1028). Probe detection rate in FFPEr samples (98.37%, s.d. ±0.66) was comparable to FF samples (99.98%, s.d. ±0.019) and substantially lower in FFPE samples (82.31%, s.d. ±18.65). Assay robustness was not decreased by sample archival age up to 10 years. We could also demonstrate no decrease in assay robustness when using 100 ng of DNA input only. Four out of the five selected differentially methylated genes could be validated by MSP. The gene failing validation by PCR showed high variation of CpG ß-values in primer-binding sites. In conclusion, by using the FFPE DNA restoration protocol, HM-450K assays provide robust, accurate and reproducible results with FFPE tissue-derived DNA, which are comparable to those obtained with FF tissue. Most importantly, differentially methylated genes can be validated using more sensitive techniques, such as nested MSP, altogether providing an epigenomics platform for molecular pathological epidemiology research on archived samples with limited tissue amount.