[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124987-en":3,"doc-seo-124987-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},124987,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",7,"Healthcare","Distinguishing Renal Cell Carcinoma From Normal Kidney Tissue Using Mass Spectrometry Imaging Combined With Machine Learning","Accurate discrimination between renal cell carcinoma (RCC) and normal kidney tissue is essential for determining positive surgical margins during partial and radical nephrectomy and for reducing reoperation risk, patient anxiety, and overall costs. This work extends combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) with machine learning to identify metabolite and lipid signatures from tissue surfaces, distinguishing normal tissue from clear cell, papillary, and chromophobe RCC. A multinomial lasso classifier selects 281 analytes and achieves 84.5% accuracy overall, with robust performance across independent institutional test sets.","UCSF  \nUC San Francisco Previously Published Works  \nTitle  \nDistinguishing Renal Cell Carcinoma From Normal Kidney Tissue Using Mass Spectrometry Imaging Combined With Machine Learning.  \nPermalink  \n[https://escholarship.org/uc/item/7hs6z8dm](https://escholarship.org/uc/item/7hs6z8dm)  \nAuthors  \nShankar, Vishnu  \nVijayalakshmi, Kanchustambham Nolley, Rosie  \net al.  \nPublication Date  \n2023-06-01  \nDOI  \n10.1200/PO.22.00668  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nor ig ina l report s abstract  \nDIAGNOSTICS  \nDistinguishing Renal Cell Carcinoma From Normal Kidney Tissue Using Mass Spectrometry Imaging Combined With Machine Learning  \nVishnu Shankar, MS1; Kanchustambham Vijayalakshmi, PhD2; Rosalie Nolley, BS3; Geoffrey A. Sonn, MD3; Chia-Sui Kao, MD4; Hongjuan Zhao, MD3; Ru Wen, PhD3; Livia S. Eberlin, PhD5; Robert Tibshirani, PhD6; Richard N. Zare, PhD2; and James D. Brooks, MD3  \nPURPOSE Accurately distinguishing renal cell carcinoma (RCC) from normal kidney tissue is critical for identifying positive surgical margins (PSMs) during partial and radical nephrectomy, which remains the primary intervention for localized RCC. Techniques that detect PSM with higher accuracy and faster turnaround time than intraoperative frozen section (IFS) analysis can help decrease reoperation rates, relieve patient anxiety and costs, and potentially improve patient outcomes.  \nMATERIALS AND METHODS Here, we extended our combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and machine learning methodology to identify metabolite and lipid species from tissue surfaces that can distinguish normal tissues from clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) tissues.  \nRESULTS From 24 normal and 40 renal cancer (23 ccRCC, 13 pRCC, and 4 chRCC) tissues, we developed a multinomial lasso classifier that selects 281 total analytes from over 27,000 detected molecular species that distinguishes all histological subtypes of RCC from normal kidney tissues with 84 .5% accuracy. On the basis of independent test data reflecting distinct patient populations, the classifier achieves 85.4% and 91.2% accuracy on a Stanford test set (20 normal and 28 RCC) and a Baylor-UT Austin test set (16 normal and 41 RCC), respectively. The majority of the model’s selected features show consistent trends across data sets affirming its stable performance, where the suppression of arachidonic acid metabolism is identified as a shared molecular feature of ccRCC and pRCC.  \nCONCLUSION Together, these results indicate that signatures derived from DESI-MSI combined with machine learning may be used to rapidly determine surgical margin status with accuracies that meet or exceed those reported for IFS.  \nJCO Precis Oncol 7:e2200668 . © 2023 by American Society of Clinical Oncology  \nASSOCIATED CONTENT  \nData Supplement  \nAuthor affiliationsand support information (if applicable) appear atthe end of this article.  \nAccepted on April 10, 2023 and published at[ascopubs.org/journal/](ascopubs.org/journal/)[ ](ascopubs.org/journal/)po on June 7, 2023: DOI [https://doi.org/10](https://doi.org/10) . 1200/PO.22.00668  \nINTRODUCTION  \nThe standard treatment for localized renal cell carcinoma (RCC) is surgical resection including partial and radical nephrectomy. However, 30%-60% of the patients will experience tumor recurrence.1 The finding of positive surgical margins (PSMs) on surgical pathological analysis, ranging from 0.1% to 18% for patients with small renal masses and 18% to 32% for patients with advanced RCC,2 has been associated with increased rate of local relapse3 and worse overall survival independent of other predictors.4 Patients with PSM either undergo an immediate second surgery or are actively monitored for tumor progression by imaging.5 ,6 Avoiding PSM will help decrease reoperation rates, relieve patient anxiety and costs, and improve patient o","cbCaicGRtus0f3cF","https://ap.wps.com/l/cbCaicGRtus0f3cF","pdf",923074,1,11,"English","en",105,"# Purpose\n# Materials and Methods\n# Results\n# Conclusion\n# Introduction\n# Context","[{\"question\":\"Why is distinguishing RCC from normal kidney tissue important clinically?\",\"answer\":\"It supports identification of positive surgical margins during partial and radical nephrectomy, helping reduce reoperation rates and improve patient outcomes.\"},{\"question\":\"What approach does the study use to differentiate RCC from normal tissue?\",\"answer\":\"It combines DESI-MSI with machine learning, selecting metabolite and lipid species from tissue surfaces to train a multinomial lasso classifier.\"},{\"question\":\"How accurate is the developed classifier across tissue subtypes and datasets?\",\"answer\":\"It reaches 84.5% accuracy in the training dataset and shows 85.4% and 91.2% accuracy on independent Stanford and Baylor-UT Austin test sets, respectively.\"}]","Distinguishing Renal Cell Carcinoma From Normal Kidney Tissue Using Mass Spectrometry Imaging Combined With Machine Learning | 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is distinguishing RCC from normal kidney tissue important clinically?","Question",{"text":75,"@type":76},"It supports identification of positive surgical margins during partial and radical nephrectomy, helping reduce reoperation rates and improve patient outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What approach does the study use to differentiate RCC from normal tissue?",{"text":80,"@type":76},"It combines DESI-MSI with machine learning, selecting metabolite and lipid species from tissue surfaces to train a multinomial lasso classifier.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate is the developed classifier across tissue subtypes and datasets?",{"text":84,"@type":76},"It reaches 84.5% accuracy in the training dataset and shows 85.4% and 91.2% accuracy on independent Stanford and Baylor-UT Austin test sets, 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