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Magnetic resonance imaging (MRI) and prostate specific membrane antigen (PSMA)- positron emission tomography (PET)/computed tomography (CT)-imaging of prostate cancer (PCa) are emerging techniques to assess the presence of significant disease and tumor progression. It is not known, however, whether and to what extent lesions detected by these imaging techniques correlate with genomic features of PCa. The aim of this study was therefore to define a genomic index lesion based on chromosomal copy number alterations (CNAs) as marker for tumor aggressiveness in prostate biopsies in direct correlation to multiparametric (mp) MRI and 68Ga-PSMA-PET/CT imaging features. CNA profiles of 46 biopsies from five consecutive patients with clinically high-risk PCa were obtained from radiologically suspicious and unsuspicious areas. All patients underwent mpMRI, MRI/TRUS-fusion biopsy, 68Ga-PSMA-PET/CT and a radical prostatectomy. CNAs were directly correlated to imaging features and radiogenomic analyses were performed. Highly significant CNAs (≥10 Mbp) were found in 22 of 46 biopsies. Chromosome 8p, 13q and 5q losses were the most common findings. There was an strong correspondence between the radiologic and the genomic index lesions. The radiogenomic analyses suggest the feasibility of developing radiologic signatures that can distinguish between genomically more or less aggressive lesions. In conclusion, imaging features of mpMRI and 68Ga-PSMA-PET/CT can guide to the genomically most aggressive lesion of a PCa. Radiogenomics may help to better differentiate between indolent and aggressive PCa in the future.

Background Multiparametric magnetic resonance imaging (mpMRI) facilitates the detection of significant prostate cancer. Therefore, addition of mpMRI to clinical parameters might improve the prediction of extraprostatic extension (EPE) in radical prostatectomy (RP) specimens. Objective To investigate the accuracy of a novel risk model (RM) combining clinical and mpMRI parameters to predict EPE in RP specimens. Design, setting, and participants We added prebiopsy mpMRI to clinical parameters and developed an RM to predict individual side-specific EPE (EPE-RM). Clinical parameters of 264 consecutive men with mpMRI prior to MRI/transrectal ultrasound fusion biopsy and subsequent RP between 2012 and 2015 were retrospectively analysed. Outcome measurements and statistical analysis Multivariate regression analyses were used to determine significant EPE predictors for RM development. The prediction performance of the novel EPE-RM was compared with clinical T stage (cT), MR–European Society of Urogenital Radiology (ESUR) classification for EPE, two established nomograms (by Steuber et al and Ohori et al) and a clinical nomogram based on the coefficients of the established nomograms, and was constructed based on the data of the present cohort, using receiver operating characteristics (ROCs). For comparison, models’ likelihood ratio (LR) tests and Vuong tests were used. Discrimination and calibration of the EPE-RM were validated based on resampling methods using bootstrapping. Results and limitations International society of Urogenital Pathology grade on biopsy, ESUR criteria, prostate-specific antigen, cT, prostate volume, and capsule contact length were included in the EPE-RM. Calibration of the EPE-RM was good (error 0.018). The ROC area under the curve for the EPE-RM was larger (0.87) compared with cT (0.66), Memorial Sloan Kettering Cancer Center nomogram (0.73), Steuber nomogram (0.70), novel clinical nomogram (0.79), and ESUR classification (0.81). Based on LR and Vuong tests, the EPE-RM’s model fit was significantly better than that of cT, all clinical models, and ESUR classification alone (p Conclusions This novel EPE-RM, incorporating clinical and MRI parameters, performed better than contemporary clinical RMs and MRI predictors, therefore providing an accurate patient-tailored preoperative risk stratification of side-specific EPE. Patient summary Extraprostatic extension of prostate cancer can be predicted accurately using a combination of magnetic resonance imaging and clinical parameters. This novel risk model outperforms magnetic resonance imaging and clinical predictors alone and can be useful when planning nerve-sparing radical prostatectomy.

MRI has limited ability to detect multifocal disease or the full extent of prostate involvement with clinically significant prostate cancer (sPC). We compare the spatial co-localization at sextant resolution of MRI lesions and histopathological mapping by combined targeted and extended systematic biopsies. Sextants were mapped for sPC (ISUP group ≥ 2) by 24-core transperineal systematic biopsies in 316 patients with suspicion for sPC and by MR lesions of PI-RADS score of ≥ 3. The gold standard is combined systematic (median 23 cores) and targeted biopsies. Of 316 men, 121 (38%) harbored sPC. Of these 121 patients, 4 (3%) had a negative MRI. MRI correctly identified 117/121 (97%) patients with sPC. In these patients, mpMRI missed no additional sPC in 96 (82%), while MRI-negative sPC lesions were present in 21 patients (18%). Of 1896 sextants, 379 (20%) harbored sPC. MR-positive sextants contained sPC in 26% (337/1275), compared to 7% (42/621) in MR-negative sextants. On a patient basis, sensitivity was 0.97, specificity 0.22, positive predictive value 0.43, and negative predictive value 0.91. On a sextant basis, sensitivity was 0.73, specificity 0.38, positive predictive value 0.26, and negative predictive value 0.93. MpMRI mapping agreed well with histopathology with, at the observed sPC prevalence and on a patient basis, excellent sensitivity and negative predictive value, and acceptable specificity and positive predictive value for sPC. However, 18% of sPC was outside the mpMRI mapped region, quantifying limitations of MRI for complete localization of disease extent. • Currently, exclusive MRI mapping of the prostate for focal treatment planning cannot be recommended, as significant prostate cancer may remain untreated in a substantial number of cases. • At the observed sPC prevalence and on a patient basis, mpMRI has excellent sensitivity and NPV, and acceptable specificity and PPV for detection of prostate cancer, supporting its use to detect suspicious lesions before biopsy. • Despite the excellent global performance, 18% of sPC was outside the mpMRI mapped region even when a security margin of 10 mm was considered, indicating that prostate MRI has limited ability to completely map all cancer foci within the prostate.

Surgical data science is a new research field that aims to observe all aspects of the patient treatment process in order to provide the right assistance at the right time. Due to the breakthrough successes of deep learning-based solutions for automatic image annotation, the availability of reference annotations for algorithm training is becoming a major bottleneck in the field. The purpose of this paper was to investigate the concept of self-supervised learning to address this issue. Our approach is guided by the hypothesis that unlabeled video data can be used to learn a representation of the target domain that boosts the performance of state-of-the-art machine learning algorithms when used for pre-training. Core of the method is an auxiliary task based on raw endoscopic video data of the target domain that is used to initialize the convolutional neural network (CNN) for the target task. In this paper, we propose the re-colorization of medical images with a conditional generative adversarial network (cGAN)-based architecture as auxiliary task. A variant of the method involves a second pre-training step based on labeled data for the target task from a related domain. We validate both variants using medical instrument segmentation as target task. The proposed approach can be used to radically reduce the manual annotation effort involved in training CNNs. Compared to the baseline approach of generating annotated data from scratch, our method decreases exploratively the number of labeled images by up to 75% without sacrificing performance. Our method also outperforms alternative methods for CNN pre-training, such as pre-training on publicly available non-medical (COCO) or medical data (MICCAI EndoVis2017 challenge) using the target task (in this instance: segmentation). As it makes efficient use of available (non-)public and (un-)labeled data, the approach has the potential to become a valuable tool for CNN (pre-)training.

A multistage single arm phase II trial with binary endpoint is considered. Bayesian posterior probabilities are used to monitor futility in interim analyses and efficacy in the final analysis. For a beta-binomial model, decision rules based on Bayesian posterior probabilities are converted to "traditional" decision rules in terms of number of responders among patients observed so far. Analytical derivations are given for the probability of stopping for futility and for the probability to declare efficacy. A workflow is presented on how to select the parameters specifying the Bayesian design, and the operating characteristics of the design are investigated. It is outlined how the presented approach can be transferred to statistical models other than the beta-binomial model.

Treatment of patients with neck lymph node metastasis of squamous cell carcinoma (SCC) from unknown primary tumor (NSCCUP) is challenging due to the risk of missing occult tumors or inducing toxicity to unaffected sites. Human papillomavirus (HPV) is a promising biomarker given its causal link to oropharyngeal SCC and superior survival of patients with HPV-driven oropharyngeal SCC and NSCCUP. Identification of HPV-driven NSCCUP could focus diagnostic work-up and treatment on the oropharynx. For the first time, we assessed HPV antibodies and their prognostic value in NSCCUP patients. Antibodies against E6 and E7 (HPV16/18/31/33/35), E1 and E2 (HPV16/18) were assessed in 46 NSCCUP patients in sera collected at diagnosis, and in follow-up sera from five patients. In 28 patients, HPV tumor status was determined using molecular markers (HPV DNA, mRNA and cellular p16

Patients with neck squamous cell carcinomas of unknown primary tumour (NSCCUP) present with lymph node metastasis without evidence for a primary tumour. Most patients undergo an aggressive multimodal treatment, which induces severe, potentially unnecessary toxicity. Primary tumours of NSCCUP can be hidden in the oropharynx. Human papillomavirus (HPV) is causally involved in a subgroup of oropharyngeal squamous cell carcinomas (OPSCC) associated with early lymph node metastasis and good prognosis. Detection of markers for HPV transformation in NSCCUP could allow focussing on the oropharynx in primary tumour search and could be of value for choice and extent of treatment. In a retrospective multicentre study (Germany, Italy and Spain), we analysed metastatic lymph nodes from 180 NSCCUP patients for the presence of HPV DNA, HPV E6*I mRNA and cellular p16(INK4a) overexpression, a surrogate marker for HPV-induced transformation. HPV status, defined as positivity for viral mRNA with at least one additional marker, was correlated with clinical parameters and survival outcome. A substantial proportion (16%) of NSCCUP were HPV-driven, mainly by HPV16 (89%). HPV prevalence increased with-year of diagnosis from 9% during 1998-2004 to 23% during 2005-2014 (p = 0.007). HPV-driven NSCCUP had significantly better overall and progression-free survival rates (p < 0.008). Based on this survival benefit, it is contended that HPV RNA status should be included in NSCCUP diagnosis and in therapeutic decision-making. Deintensification of radiation in patients with HPV-driven NSCCUP, while concurrently concentrating on the oropharynx appears to be a promising therapeutic strategy, the efficacy of which should be assessed in prospective trials. To our knowledge, this is the largest study on HPV in NSCCUP. (C) 2017 Elsevier Ltd. All rights reserved.

Juvenile myelomonocytic leukemia (JMML) is an aggressive myeloproliferative disorder of early childhood characterized by mutations activating RAS signaling. Established clinical and genetic markers fail to fully recapitulate the clinical and biological heterogeneity of this disease. Here we report DNA methylome analysis and mutation profiling of 167 JMML samples. We identify three JMML subgroups with unique molecular and clinical characteristics. The high methylation group (HM) is characterized by somatic PTPN11 mutations and poor clinical outcome. The low methylation group is enriched for somatic NRAS and CBL mutations, as well as for Noonan patients, and has a good prognosis. The intermediate methylation group (IM) shows enrichment for monosomy 7 and somatic KRAS mutations. Hypermethylation is associated with repressed chromatin, genes regulated by RAS signaling, frequent co-occurrence of RAS pathway mutations and upregulation of DNMT1 and DNMT3B, suggesting a link between activation of the DNA methylation machinery and mutational patterns in JMML., Juvenile myelomonocytic leukemia (JMML) is an aggressive disease with limited options for treatment. Here, the authors analyse the DNA methylome and mutational profile of JMML to define three subgroups with unique molecular and clinical characteristics.

To assess radiomics as a tool to determine how well lesions found suspicious on breast cancer screening X-ray mammography can be categorized into malignant and benign with unenhanced magnetic resonance (MR) mammography with diffusion-weighted imaging and TFrom an asymptomatic screening cohort, 50 women with mammographically suspicious findings were examined with contrast-enhanced breast MRI (ceMRI) at 1.5T. Out of this protocol an unenhanced, abbreviated diffusion-weighted imaging protocol (ueMRI) including TThe unconstrained and constrained radiomic classifiers consisted of 11 parameters each and achieved differentiation of malignant from benign lesions with a .632 + bootstrap receiver operating characteristics (ROC) area under the curve (AUC) of 84.2%/85.1%, compared to 77.4% for mean ADC and 95.9%/95.9% for the experienced radiologist using ceMRI/ueMRI.In this pilot study we identified two ueMRI radiomics classifiers that performed well in the differentiation of malignant from benign lesions and achieved higher performance than the mean ADC parameter alone. Classification was lower than the almost perfect performance of a highly experienced breast radiologist. The potential of radiomics to provide a training-independent diagnostic decision tool is indicated. A performance reaching the human expert would be highly desirable and based on our results is considered possible when the concept is extended in larger cohorts with further development and validation of the technique.1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:604-616.

Juvenile myelomonocytic leukemia (JMML) is a rare myeloproliferative neoplasm of early childhood characterized by Ras pathway mutations and aberrant DNA methylation. Despite allogeneic hematopoietic stem cell transplantation (HSCT) approx. 30% of patients (pts) succumb to resistant leukemia. Slowly regressing disease in the absence of HSCT is, however, noted in some pts. Mutational patterns and known clinical risk factors are insufficient to fully explain this heterogeneity. Three independent recent studies demonstrated the potential of DNA methylation profiling to identify clinically meaningful risk groups (Lipka et al., Nat Commun 2017; Stieglitz et al., Nat Commun 2017; Murakami et al., Blood 2018). The aim of this work was to establish an international consensus for the classification of JMML based on DNA methylation, to systematically describe the biological and clinical features of the subgroups identified and to develop a molecular classifier that enables the prospective assignment of DNA methylation categories. In a collaborative effort, we re-analyzed three well-annotated Illumina 450k methylation array data sets. In total, data from 292 pts with JMML or Noonan syndrome-associated myeloproliferative disorder (NS/MPD; EWOG-MDS, Europe: 147 pts, Nagoya, Japan: 106 pts, UCSF, USA: 39 pts) were available. Methylation data from 285 pts passed our quality control and were included in the present analysis. Of these, 256 (89.8%) pts had been diagnosed with JMML and 29 (10.2%) with NS/MPD. Mean age at diagnosis was 1.9 yrs. Levels of fetal hemoglobin (HbF) were available in 228/285 pts and elevated for age in 61%. Driver mutations affecting the Ras pathway were identified in 92% of JMML pts (PTPN11: 36%, NF1: 11%, NRAS: 15%, KRAS: 15%, CBL: 14%), and monosomy 7 in 14%. Initial exploratory analysis revealed batch effects that could, at least in part, be explained by the different ethnical backgrounds of the pts but also included a technical component. Therefore, we stringently filtered probes containing SNPs and performed batch correction using an empirical Bayes framework (Combat). CpG dinucleotides (CpGs) showing variable methylation across normal hematopoietic cell types were excluded from the analysis to avoid confounding with differential cell type composition between the samples. Unsupervised consensus clustering using the 5000 most variable CpGs identified three stable methylation clusters in this inter-group patient cohort. According to their DNA methylation levels, the clusters were designated "high methylation" (HM), "intermediate methylation" (IM) and "low methylation" (LM). Overall, 29.5%, 23.5%, and 47.0% of the cohort of JMML and NS/MPD pts were assigned to the HM, IM, and LM clusters, respectively. The methylation clusters showed a highly significant association with specific Ras-pathway mutations and with known clinical risk factors. All NS/MPD pts clustered with the LM subgroup and showed the lowest methylation levels. The HM cluster was enriched for somatic PTPN11 mutations, elevated HbF and older age, while the LM cluster was enriched for NRAS and CBL mutations, normal HbF and age assignments made in the present study to those from the original publications found only 3.9% (11/285) discordant cases. This encouraged us to establish a low-dimensional DNA methylation classifier for JMML that could be applied in a prospective setting. Therefore, we split the intergroup cohort randomly into a training (N=202) and a validation cohort (N=83) and trained an extreme gradient boosting machine (xgboost). When using as few as 10 CpGs, the resulting model predicted methylation clusters with 94% accuracy in the validation cohort. This inter-group meta-analysis is the single largest dataset of JMML patients to date and demonstrates the robustness of the JMML methylation signature across continents of origin and its power to identify biologically and clinically distinct JMML subgroups. Our ongoing efforts include developing a sequencing-based assay to predict DNA methylation clusters and to validate this assay using an independent cohort of 60 pts from the three participating research groups. This assay would allow for inclusion of DNA methylation as a biomarker into prospective clinical trials to assist in molecularly driven risk-stratification of JMML. Disclosures Niemeyer: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees.

The broad clinical spectrum of neuroblastoma ranges from spontaneous regression to rapid progression despite intensive multimodal therapy. This diversity is not fully explained by known genetic aberrations, suggesting the possibility of epigenetic involvement in pathogenesis. In pursuit of this hypothesis, we took an integrative approach to analyze the methylomes, transcriptomes, and copy number variations in 105 cases of neuroblastoma, complemented by primary tumor- and cell line–derived global histone modification analyses and epigenetic drug treatment in vitro. We found that DNA methylation patterns identify divergent patient subgroups with respect to survival and clinicobiologic variables, including amplified MYCN. Transcriptome integration and histone modification–based definition of enhancer elements revealed intragenic enhancer methylation as a mechanism for high-risk–associated transcriptional deregulation. Furthermore, in high-risk neuroblastomas, we obtained evidence for cooperation between PRC2 activity and DNA methylation in blocking tumor-suppressive differentiation programs. Notably, these programs could be re-activated by combination treatments, which targeted both PRC2 and DNA methylation. Overall, our results illuminate how epigenetic deregulation contributes to neuroblastoma pathogenesis, with novel implications for its diagnosis and therapy. Cancer Res; 76(18); 5523–37. ©2016 AACR.

Recently, the World Bank re-estimated the international poverty line used for global poverty measurement and the first Millennium Development Goal based on an updated country sample of national poverty lines and new results for PPP exchange rates. This generated the new international poverty line of $1.25 per capita per day in 2005PPP$. In this paper we show, using the same data, that the best statistical estimation of the relationship between mean consumption and national poverty lines generates an international poverty line that is substantially higher than $1.25 a day.

This article proposes a simple and fast approach to build simultaneous confidence bands and perform specification tests for smooth curves in additive models. The method allows for handling of spatially heterogeneous functions and its derivatives as well as heteroscedasticity in the data. It is applied to study the determinants of chronic undernutrition of Kenyan children, with a particular focus on the highly nonlinear age pattern in undernutrition. Model estimation using the mixed model representation of penalized splines in combination with simultaneous probability calculations based on the volume-of-tube formula enable the simultaneous inference directly, that is, without resampling methods. Finite sample properties of simultaneous confidence bands and specification tests are investigated in simulations. To facilitate and enhance its application, the method has been implemented in the R package AdaptFitOS.