[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122180-en":3,"doc-seo-122180-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122180,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Assessing Preoperative Risk of STR in Skull Meningiomas Using MR Radiomics and Machine Learning","The study predicts whether skull meningiomas can be grossly totally or subtotally resected before surgery using pretreatment T1 post-contrast MRI radiomics and machine learning. Tumor regions were semi-automatically segmented with 3D Slicer, then 107 radiomic features were extracted. Models trained on split data were repeatedly estimated and evaluated on independent test cases to reduce overfitting. Performance remained stable (AUC ~0.90) and high accuracy was achieved, though very small postoperative volumes were harder to classify correctly.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nAssessing preoperative risk of STRin skull meningiomas using MR radiomics and machine learning  \nManfred Musigmann1, Burak Han Akkurt1, Hermann Krähling1, Benjamin Brokinkel2, Dylan J. H. A. Henssen3, Thomas Sartoretti4,5,6, Nabila Gala Nacul1, Walter Stummer2, Walter Heindel1 & Manoj Mannil1*  \nOur aim is to predict possible gross total and subtotal resections of skull meningiomas from pretreatment T1 post contrast MR-images using radiomics and machine learning in a representative patient cohort. We analyse the accuracy of our model predictions depending on the tumor location within the skull and the postoperative tumor volume. In this retrospective, IRB-approved study, image segmentation of the contrast enhancing parts of the tumor was semi-automatically performed using the 3D Slicer open-source software platform. Imaging data were split into training data and independent test data at random. We extracted a total of 107 radiomic features by hand-delineated regions of interest onT1 post contrast MR images. Feature preselection and model construction were performed with eight different machine learning algorithms. Each model was estimated 100 times on new training data and then tested on a previously unknown, independent test data set to avoid possible overfitting. Our cohort included 138 patients. A gross total resection of the meningioma was performed in 107 cases and a subtotal resection in the remaining 31 cases. Using the training data, the mean area under the curve (AUC), mean accuracy, mean kappa, mean sensitivity and mean specificity were 0.901, 0.875, 0.629, 0.675 and 0.933 respectively. We obtained very similar results with the independent test data: mean AUC = 0.900, mean accuracy= 0.881, mean kappa = 0.644, mean sensitivity = 0.692 and mean specificity = 0.936. Thus, our model exposes good and stable predictive performance with both training and test data. Our radiomics approach shows that with machine learning algorithms and comparatively few explanatory factors such as the location of the tumor within the skull as well as its shape, it is possible to make accurate predictions about whether a meningioma can be completely resected by surgery. Complete resections and resections with larger postoperative tumor volumes can be predicted with very high accuracy. However, cases with very small postoperative tumor volumes are comparatively difficult to predict correctly.  \nMeningiomas are mostly benign, extra-axial tumors originating from the arachnoid cap cells. They represent 13–26% of all intracranial tumors1. The annual incidence of meningiomas in the United States is 5.3 per 100,000 people and increases steadily with age2.  \nAccording to international guidelines and current literature, primary therapy of meningiomas consists of surgery and adjuvant radiotherapy3. Further treatment modalities include systemic and targeted therapies. Following the National Comprehensive Cancer Network (NCCN) guidelines for meningiomas ([http://nccn.org/](http://nccn.org/)), chemotherapy is only recommended for recurrent (progressive) disease when radiation therapy or further surgical resection is not feasible4.  \nAn important aspect impacting the further prognosis and therapy planning concerns the extent of resection (EOR) during initial surgical treatment of the tumor. Specifically, subtotal resection is often associated with  \n1University Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, E48149 Muenster, Germany. 2Department of Neurosurgery, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, E48149 Muenster, Germany. 3Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands. 4Faculty of Medicine, University of Zürich, Zürich, Switzerland. 5 The Institute of Diagnostic","cbCaifOe4KcBods0","https://ap.wps.com/l/cbCaifOe4KcBods0","pdf",1875217,1,11,"English","en",105,"# Materials and methods\n## Study design and compliance\n## Patient cohort and imaging workflow\n## Radiomics feature extraction\n## Machine learning model construction and evaluation","[{\"question\":\"What clinical outcome does the study aim to predict before surgery?\",\"answer\":\"It predicts whether skull meningiomas can be resected with gross total resection or only with subtotal resection (STR) based on pretreatment MRI radiomics.\"},{\"question\":\"How were tumor features obtained for machine learning?\",\"answer\":\"Contrast-enhancing tumor parts were semi-automatically segmented using 3D Slicer, then 107 radiomic features were extracted from T1 post-contrast MR images.\"},{\"question\":\"How were the models validated to avoid overfitting?\",\"answer\":\"The imaging data were randomly split into training and independent test sets; models were estimated repeatedly on new training data and then tested on the previously unknown test dataset.\"},{\"question\":\"Which cases were most difficult to classify accurately?\",\"answer\":\"Cases with very small postoperative tumor volumes were comparatively difficult to predict correctly, even though overall performance was stable.\"}]","Assessing Preoperative Risk of STR in Skull Meningiomas Using MR Radiomics and Machine Learning | PDF",1785809218,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"assessing-preoperative-risk-of-str-in-skull-meningiomas-using-mr-radiomics-and-machine-learning","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/assessing-preoperative-risk-of-str-in-skull-meningiomas-using-mr-radiomics-and-machine-learning/122180/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What clinical outcome does the study aim to predict before surgery?","Question",{"text":75,"@type":76},"It predicts whether skull meningiomas can be resected with gross total resection or only with subtotal resection (STR) based on pretreatment MRI radiomics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were tumor features obtained for machine learning?",{"text":80,"@type":76},"Contrast-enhancing tumor parts were semi-automatically segmented using 3D Slicer, then 107 radiomic features were extracted from T1 post-contrast MR images.",{"name":82,"@type":73,"acceptedAnswer":83},"How were the models validated to avoid overfitting?",{"text":84,"@type":76},"The imaging data were randomly split into training and independent test sets; models were estimated repeatedly on new training data and then tested on the previously unknown test dataset.",{"name":86,"@type":73,"acceptedAnswer":87},"Which cases were most difficult to classify accurately?",{"text":88,"@type":76},"Cases with very small postoperative tumor volumes were comparatively difficult to predict correctly, even though overall performance was stable.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]