[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100656207":3},{"organization":4,"outcomesModule":7,"designInfo":39,"detailedDescription":38,"studyPopulation":41,"armGroups":42,"interventions":38,"overallOfficials":38,"centralContacts":49,"locations":55,"responsibleParty":72,"collaborators":76,"id":86,"slug":87,"hasResults":88,"nctId":89,"briefTitle":90,"officialTitle":91,"acronym":38,"eligibilityCriteria":92,"healthyVolunteers":88,"sex":93,"minAge":38,"maxAge":38,"enrollmentInfo":94,"targetDuration":38,"studyType":97,"phases":38,"briefSummary":98,"conditions":99,"keywords":101,"overallStatus":58,"whyStopped":38,"lastUpdateSubmitDate":106,"lastUpdatePostDateStruct":107,"startDateStruct":110,"completionDateStruct":112,"leadSponsor":114,"locationsCount":115},{"fullName":5,"class":6},"Karolinska Institutet","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":38},[9],{"measure":10,"description":11,"timeFrame":12},"Discordance between molecularly defined class and the original histopathological diagnosis","Proportion of tumors in the molecularly profiled cohort whose class assignment from integrated genomic, transcriptomic and methylation data, made without reference to the diagnostic label, differs from the diagnosis based on the World Health Organization (WHO) classification in the original pathology report. Reported with a 95 percent confidence interval and accompanied by the adjusted Rand index for the full cross-classification. Only assignments above a pre-specified confidence threshold are counted as discordant; low-confidence assignments are reported separately. Discordant cases undergo blinded review by two sarcoma pathologists to separate reclassification from diagnostic error.","Through completion of the primary analysis of the molecular cohort, up to [5] years after study start.",[14,18,21,25,28,31,35],{"measure":15,"description":16,"timeFrame":17},"Number of stable molecular classes identified","Number of classes recovered by consensus clustering of the integrated data that meet pre-specified stability criteria, reported with the stability metrics used (consensus matrix concordance, proportion of ambiguous clustering, cophenetic correlation) across the tested range of cluster numbers.","Through completion of the primary analysis, up to [5] years.",{"measure":19,"description":20,"timeFrame":17},"Proportion of tumors in residual diagnostic categories that receive a defined molecular class","Among tumors originally reported as undifferentiated pleomorphic sarcoma, spindle cell sarcoma not otherwise specified, or an equivalent unclassified bone lesion, the proportion assigned above the confidence threshold to a molecular class that has a defined biological identity.",{"measure":22,"description":23,"timeFrame":24},"Accuracy of molecular class prediction from digitized slides","Performance of machine learning models predicting molecular class membership from whole slide images, evaluated in held-out data split at the level of individual participants. Reported as balanced accuracy and macro-averaged area under the receiver operating characteristic curve, with 95 percent confidence intervals, plus per-class sensitivity and specificity.","Through completion of model development and evaluation, up to [5] years.",{"measure":26,"description":27,"timeFrame":17},"Frequency of clinically actionable somatic alterations","Proportion of tumors carrying at least one somatic alteration classified as clinically actionable according to a pre-specified evidence framework, reported by molecular class and by original World Health Organization (WHO) diagnosis.",{"measure":29,"description":30,"timeFrame":17},"Frequency of pathogenic germline variants in cancer predisposition genes","Proportion of participants with a matched germline sample carrying a pathogenic or likely pathogenic variant in a pre-specified panel of cancer predisposition genes, classified according to current guidelines.",{"measure":32,"description":33,"timeFrame":34},"Overall survival by molecular class","Time from date of diagnosis to death from any cause, with participants alive at last contact censored at that date. Estimated by the Kaplan-Meier method for each molecular class with a sufficient number of participants, and reported with the number of participants per class.","From date of diagnosis to death from any cause, assessed up to [30] years.",{"measure":36,"description":37,"timeFrame":34},"Prognostic information added by molecular class beyond current diagnosis and grade","Comparison of Cox proportional hazards models for overall survival that include molecular class against models that include the original diagnosis and grade, using the concordance index and likelihood-based comparison, adjusted for age, anatomical site, tumor size and treatment.",null,{"allocation":38,"interventionModel":38,"interventionModelDescription":38,"primaryPurpose":38,"observationalModel":40,"timePerspective":6,"maskingInfo":38},"COHORT","People of any age with a tumor or tumor-like lesion of bone or soft tissue, benign, intermediate or malignant, diagnosed or reviewed at the Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden. The retrospective cohort is identified from the pathology archive by a predefined search based on the Systematized Nomenclature of Medicine (SNOMED) covering bone and soft tissue tumor codes, with an addendum capturing non-mesenchymal tumors that enter the differential diagnosis, such as melanoma, sarcomatoid carcinoma and lymphoma. The prospective cohort comprises people evaluated at the center during the study period. The center is a national referral center for sarcoma, so the population includes both locally diagnosed and referred cases.",[43,46],{"label":44,"type":38,"description":45,"interventionNames":38},"Group\u002FCohort 1 - Retrospective archival cohort","People of any age with a bone or soft tissue tumor sampled or resected at the study center before the start of the study, identified from the pathology archive using a predefined search based on the Systematized Nomenclature of Medicine (SNOMED). Diagnostic glass slides are scanned for the full archival cohort. A nested subset with tissue of sufficient quality also undergoes whole genome sequencing, RNA sequencing and DNA methylation profiling. Clinical and outcome data are taken from pathology reports and medical records. No study procedure is performed on participants.\n\nAssigned Interventions:\n\nDiagnostic Test: Digital pathology and computational image analysis Genetic: Whole genome and whole transcriptome sequencing (nested subset) Genetic: Genome-wide DNA methylation profiling (nested subset)",{"label":47,"type":38,"description":48,"interventionNames":38},"Group\u002FCohort 2 - Prospective cohort","People of any age newly diagnosed with, or referred for evaluation of, a bone or soft tissue tumor at the study center during the study period, who consent to the use of their tissue and data. Diagnostic tissue undergoes whole genome and transcriptome sequencing with a matched germline reference sample, DNA methylation profiling and slide digitization. Diagnosis and treatment follow routine practice and are not altered by the study.\n\nAssigned Interventions:\n\nDiagnostic Test: Digital pathology and computational image analysis Genetic: Whole genome and whole transcriptome sequencing Genetic: Genome-wide DNA methylation profiling",[50],{"name":51,"role":52,"phone":53,"phoneExt":38,"email":54},"Felix Haglund de Flon, MD PhD, Associate Professor","CONTACT","+46-8-524 800 00","felix.haglund@ki.se",[56],{"facility":57,"status":58,"city":59,"state":38,"zip":60,"country":61,"countryCode":62,"cosmosGeoPoint":63,"geoPoint":68,"contacts":69},"Karolinska University Hospital","RECRUITING","Stockholm","17176","Sweden","SE",{"type":64,"coordinates":65},"Point",[66,67],18.06871,59.32938,{"lat":67,"lon":66},[70],{"name":51,"role":52,"phone":71,"phoneExt":38,"email":54},"0046-8-524 800 00",{"type":73,"investigatorFullName":74,"investigatorTitle":75,"investigatorAffiliation":5,"oldNameTitle":38,"oldOrganization":38},"PRINCIPAL_INVESTIGATOR","Felix Haglund de Flon","Associate Professor, Research group leader",[77,80,82,84],{"name":78,"class":79},"Region Stockholm","OTHER_GOV",{"name":81,"class":6},"Lund University",{"name":83,"class":6},"KTH Royal Institute of Technology",{"name":85,"class":6},"Uppsala University","100656207","molecular-and-clinical-studies-of-bone-and-soft-tissue-tumors-100656207",false,"NCT07822204","Molecular and Clinical Studies of Bone and Soft Tissue Tumors","Molekylära Och Kliniska Studier av Ben- Och mjukdelstumörer","Inclusion Criteria:\n\n* Histopathologically diagnosed tumor or tumor-like lesion of bone or soft tissue, including benign, intermediate and malignant lesions\n* Tissue sampled, resected or reviewed at the study center\n* Archived diagnostic material available and adequate for the analyses of the relevant cohort: formalin-fixed paraffin-embedded tissue, fresh-frozen tissue, or diagnostic glass slides\n* For the prospective cohort, documented informed consent\n\nExclusion Criteria:\n\n* No retrievable diagnostic material or diagnostic report\n* Documented objection by the participant to the research use of their material or data","ALL",{"count":95,"type":96},50000,"ESTIMATED","OBSERVATIONAL","The goal of this observational study is to learn whether tumors of bone and soft tissue can be sorted into groups by their genes. Today these tumors are named mainly by how they look under a microscope. There are more than 150 names in use. The same tumor can be given different names by different doctors.\n\nThe study includes people of any age with a bone or soft tissue tumor. All of them had tissue sampled at Karolinska University Hospital in Stockholm, Sweden.\n\nThe main questions it aims to answer are:\n\nCan tests of a tumor's genes sort these tumors into clearer groups than the names used today? How often does a tumor's gene group differ from the name it was given at diagnosis? Can a computer program tell these groups apart from scanned pictures of the tissue?\n\nResearchers will compare the groups found by the gene tests with the diagnoses given at the time. They will also look at how each group did over the years that followed. This shows which way of sorting tumors better matches what happened to participants.\n\nParticipants will not have extra visits, tests, or treatment for this study. The study does not change anyone's care.\n\nResearchers will:\n\nUse tumor tissue that was already taken as part of regular care Read the deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) in the tumor, which carry its genetic instructions Read the chemical marks on the tumor's DNA that switch genes on and off Scan the glass slides of the tissue into digital pictures Collect facts about treatment and health from medical records\n\nParticipants who are newly diagnosed will also be asked for a blood sample. Blood shows which gene changes a person was born with and which ones started in the tumor.\n\nIf the study finds a gene change that matters for a participant's care, the study team tells the treating doctors through normal hospital routines.",[100],"Bone and Soft Tissue Tumors",[102,100,103,104,105],"Sarcoma","Methylation","Whole Transcriptome Sequencing (WTS)","Whole Genome Sequencing (WGS)","2026-09-29",{"date":108,"type":109},"2026-10-02","ACTUAL",{"date":111,"type":109},"2026-03-01",{"date":113,"type":96},"2046-03-01",{"name":5,"class":6},1]