Reporting and Assessing the Quality of Diagnostic Accuracy Studies for Cervical Cancer Screening and Management.

ObjectiveWe adapted the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool for studies of cervical cancer screening and management and used the adapted tool to evaluate the quality of studies included in a systematic review supporting the 2019 Risk-Based Management Consensus Guidelines.MethodsWe evaluated the quality of all studies included in our systematic review for postcolposcopy (n = 5) and posttreatment (n = 23) surveillance using QUADAS-2 criteria. Subsequently, we adapted signaling questions to indications of cervical cancer screening and management. An iterative process was carried out to evaluate interrater agreement between 2 study authors (M.A.C. and N.W.). Discrepant ratings were discussed, and criteria were adapted accordingly. We also evaluated the influence of study quality on risk estimates and between study variation using stratified subgroup meta-analyses.ResultsTwelve signaling questions for bias assessment that were adapted to or newly developed for cervical cancer screening and management are described here. Interrater agreement on bias assessment increased from 70% to 83% during the adaptation process. Detailed assessment of bias and applicability showed that all studies on postcolposcopy management and 90% of studies on posttreatment management had high risk of bias in at least 1 domain. Most commonly, high risk of bias was observed for the patient selection domain, indicating the heterogeneity of study designs and clinical practice in reported studies.ConclusionsThe adapted QUADAS-2 will have broad application for researchers, evidence evaluators, and journals who are interested in designing, conducting, evaluating, and publishing studies for cervical cancer screening and management

Convolutional Neural Network Based Localized Classification of Uterine Cervical Cancer Digital Histology Images

In previous research, we introduced an automated localized, fusion-based algorithm to classify squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN). The approach partitioned the epithelium into 10 segments. Image processing and machine vision algorithms were used to extract features from each segment. The features were then used to classify the segment and the result was fused to classify the whole epithelium. This research extends the previous research by dividing each of the 10 segments into 3 parts and uses a convolutional neural network to classify the 3 parts. The result is then fused to classify the segments and the whole epithelium. The experimental data consists of 65 images. The proposed method accuracy is 77.25% compared to 75.75% using the previous method for the same dataset

Evaluation of TypeSeq, a Novel High-Throughput, Low-Cost, Next-Generation Sequencing-Based Assay for Detection of 51 Human Papillomavirus Genotypes.

BackgroundHuman papillomaviruses (HPV) cause over 500 000 cervical cancers each year, most of which occur in low-resource settings. Human papillomavirus genotyping is important to study natural history and vaccine efficacy. We evaluated TypeSeq, a novel, next-generation, sequencing-based assay that detects 51 HPV genotypes, in 2 large international epidemiologic studies.MethodsTypeSeq was evaluated in 2804 cervical specimens from the Study to Understand Cervical Cancer Endpoints and Early Determinants (SUCCEED) and in 2357 specimens from the Costa Rica Vaccine Trial (CVT). Positive agreement and risks of precancer for individual genotypes were calculated for TypeSeq in comparison to Linear Array (SUCCEED). In CVT, positive agreement and vaccine efficacy were calculated for TypeSeq and SPF10-LiPA.ResultsWe observed high overall and positive agreement for most genotypes between TypeSeq and Linear Array in SUCCEED and SPF10-LiPA in CVT. There was no significant difference in risk of precancer between TypeSeq and Linear Array in SUCCEED or in estimates of vaccine efficacy between TypeSeq and SPF10-LiPA in CVT.ConclusionsThe agreement of TypeSeq with Linear Array and SPF10-LiPA, 2 well established standards for HPV genotyping, demonstrates its high accuracy. TypeSeq provides high-throughput, affordable HPV genotyping for world-wide studies of cervical precancer risk and of HPV vaccine efficacy

RISK ANALYSIS IN DEVELOPING HORIZONTAL TO VERTICAL HOUSING USING SEVERITY INDEX METHOD (CASE STUDY: RUMAH SUSUN PALDAM, BANDUNG)

Currently the gap between the demands with the supply of livable homes is relatively large. From various national figures, it is noted that this challenge requires a structured, systematic and massive handling / management effort, with no exception in the Capital of West Java Province, Bandung City. To fulfill the community's need for housing, the government has financial constraints due to the limited State Budget, so the government needs the help from business entities to meet the housing needs, as know as PPP. In this case, the construction is on land that is inhabited by the retired TNI community, so it is a separate and risky challenge. In general, any infrastructure development has a risk that needs to be mitigated. Therefore, it is necessary to carry out further and in-depth studies on what risks are affected and the allocation of risks so that management can be prepared. This paper aims to determine the risks posed in the construction of the Paldam Flats in Bandung. The analytical method used is the Severity Index Method. The analysis phase is carried out by starting with a preliminary survey of the parties concerned, then identifying risks. From the main survey results, this data will be processed with a risk probability matrix and risk impact, so it can be seen that the variables are included in the high category. From the results of data analysis, the risk that gives the greatest influence is the increase in construction costs and commercial revenues

The role of liquid based cytology and ancillary techniques in the peritoneal washing analysis: our institutional experience

Background The cytological analysis of peritoneal effusions serves as a diagnostic and prognostic aid for either primary or metastatic diseases. Among the different cytological preparations, liquid based cytology (LBC) represents a feasible and reliable method ensuring also the application of ancillary techniques (i.e immunocytochemistry-ICC and molecular testing). Methods We recorded 10348 LBC peritoneal effusions between January 2000 and December 2014. They were classified as non-diagnostic (ND), negative for malignancy-NM, atypical-suspicious for malignancy-SM and positive for malignancy-PM. Results The cytological diagnosis included 218 ND, 9.035 NM, 213 SM and 882 PM. A total of 8048 (7228 NM, 115SM, 705 PM) cases with histological follow-up were included. Our NM included 21 malignant and 7207 benign histological diagnoses. Our 820 SMs+PMs were diagnosed as 107 unknown malignancies (30SM and 77PM), 691 metastatic lesions (81SM and 610PM), 9 lymphomas (2SM and 7PM), 9 mesotheliomas (1SM and 8SM), 4 sarcomas (1SM and 3PM). Primary gynecological cancers contributed with 64% of the cases. We documented 97.4% sensitivity, 99.9% specificity, 98% diagnostic accuracy, 99.7% negative predictive value (NPV) and 99.7% positive predictive value (PPV). Furthermore, the morphological diagnoses were supported by either 173 conclusive ICC results or 50 molecular analyses. Specifically the molecular testing was performed for the EGFR and KRAS mutational analysis based on the previous or contemporary diagnoses of Non Small Cell Lung Cancer (NSCLC) and colon carcinomas. We identified 10 EGFR in NSCCL and 7 KRAS mutations on LBC stored material. Conclusions Peritoneal cytology is an adjunctive tool in the surgical management of tumors mostly gynecological cancers. LBC maximizes the application of ancillary techniques such as ICC and molecular analysis with feasible diagnostic and predictive yields also in controversial cases.info:eu-repo/semantics/publishedVersio

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Human MLL/KMT2A gene exhibits a second breakpoint cluster region for recurrent MLL–USP2 fusions

Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq: PQ-2017#305529/2017-0Deutsche Forschungsgemeinschaft, DFG: MA 1876/12-1Alexander von Humboldt-Stiftung: 88881.136091/2017-01RVO-VFN64165, 26/203.214/20172018.070.1Associazione Italiana per la Ricerca sul Cancro, AIRC: IG2015, 17593Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPESCancer Australia: PdCCRS1128727CancerfondenBarncancerfondenVetenskapsrÃ¥det, VRCrafoordska StiftelsenKnut och Alice Wallenbergs StiftelseLund University Medical Faculty FoundationXiamen University, XMU2014S0617-74-30019C7838/A15733Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNSF: 31003A_140913CNIBInstitut National Du Cancer, INCaR01 NCI CA167824National Institutes of Health, NIH: S10OD0185222016/2017, 02R/2016AU 525/1-1Deutschen Konsortium für Translationale Krebsforschung, DKTK70112951Smithsonian Institution, SIIsrael Science Foundation, ISFAustrian Science Fund, FWF: W1212SFB-F06107, SFB-F06105Acknowledgements BAL received a fellowship provided by CAPES and the Alexander von Humboldt Foundation (#88881.136091/2017-01). ME is supported by CNPq (PQ-2017#305529/2017-0) and FAPERJ-JCNE (#26/203.214/2017) research scholarships, and ZZ by grant RVO-VFN64165. GC is supported by the AIRC Investigator grant IG2015 grant no. 17593 and RS by Cancer Australia grant PdCCRS1128727. This work was supported by grants to RM from the “Georg und Franziska Speyer’sche Hochsschulstiftung”, the “Wilhelm Sander foundation” (grant 2018.070.1) and DFG grant MA 1876/12-1.Acknowledgements This work was supported by The Swedish Childhood Cancer Foundation, The Swedish Cancer Society, The Swedish Research Council, The Knut and Alice Wallenberg Foundation, BioCARE, The Crafoord Foundation, The Per-Eric and Ulla Schyberg Foundation, The Nilsson-Ehle Donations, The Wiberg Foundation, and Governmental Funding of Clinical Research within the National Health Service. Work performed at the Center for Translational Genomics, Lund University has been funded by Medical Faculty Lund University, Region Skåne and Science for Life Laboratory, Sweden.Acknowledgements This work was supported by the Fujian Provincial Natural Science Foundation 2016S016 China and Putian city Natural Science Foundation 2014S06(2), Fujian Province, China. Alexey Ste-panov and Alexander Gabibov were supported by Russian Scientific Foundation project No. 17-74-30019. Jinqi Huang was supported by a doctoral fellowship from Xiamen University, China.Acknowledgments This work was supported by the Swiss National Science Foundation (grant 31003A_140913; OH) and the Cancer Research UK Experimental Cancer Medicine Centre Network, Cardiff ECMCI, grant C7838/A15733. We thank N. Carpino for the Sts-1/2 double-KO mice.Acknowledgements This work was supported by the French National Cancer Institute (INCA) and the Fondation Française pour la Recherche contre le Myélome et les Gammapathies (FFMRG), the Intergroupe Francophone du Myélome (IFM), NCI R01 NCI CA167824 and a generous donation from Matthew Bell. This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. Research reported in this paper was supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD018522. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors thank the Association des Malades du Myélome Multiple (AF3M) for their continued support and participation. Where authors are identified as personnel of the International Agency for Research on Cancer / World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer / World Health Organization.We are indebted to all members of our groups for useful discussions and for their critical reading of the manuscript. Special thanks go to Silke Furlan, Friederike Opitz and Bianca Killing. F.A. is supported by the Deutsche For-schungsgemeinschaft (DFG, AU 525/1-1). J.H. has been supported by the German Children’s Cancer Foundation (Translational Oncology Program 70112951), the German Carreras Foundation (DJCLS 02R/2016), Kinderkrebsstiftung (2016/2017) and ERA PerMed GEPARD. Support by Israel Science Foundation, ERA-NET and Science Ministry (SI). A. B. is supported by the German Consortium of Translational Cancer Research, DKTK. We are grateful to the Jülich Supercomputing Centre at the Forschungszemtrum Jülich for granting computing time on the supercomputer JURECA (NIC project ID HKF7) and to the “Zentrum für Informations-und Medientechnologie” (ZIM) at the Heinrich Heine University Düsseldorf for providing computational support to H. G. The study was performed in the framework of COST action CA16223 “LEGEND”.Funding The work was supported by the Austrian Science Fund FWF grant SFB-F06105 to RM and SFB-F06107 to VS and FWF grant W1212 to VS

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts