[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128238-en":3,"doc-seo-128238-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128238,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Classification of Patients with Parotid Cancer Using Dynamic Contrast and Diffusion Examinations with Radiomics and Machine Learning Techniques - Doctoral Thesis","This retrospective doctoral study evaluates MRI-based radiomics and machine learning for differentiating pleomorphic adenoma, Warthin’s tumor, and malignant parotid tumors using two MRI strategies: DWI with multiple B values and dynamic contrast-enhanced T1-weighted sequences. Forty-one histopathologically confirmed patients were included, from whom 13 radiomics features were extracted. Feature–class associations were assessed with ROC, ReliefF, and RFE, and optimal ensembles were used to train SVM and neural network classifiers. Results show SVM slightly higher overall accuracy and both methods still present false negatives, highlighting opportunities for further dataset expansion and balance improvements.","CORSO DI DOTTORATO IN RICERCA CLINICADipartimento di Oncologia ed Emato-Oncologia  \nTESI DI DOTTORATO DI RICERCA  \nClassification of patients with parotid cancer using dynamic contrast and diffusion examinations with radiomics and machine learning techniques.  \nSettore MED/36-Diagnostica per Immagini e Radioterapia  \nTUTOR DOTTORANDO  \nProf. Gianpaolo Carrafiello Dott.ssa Silvia Tortora  \nCOORDINATORE DEL DOTTORATO Prof. Massimo Del Fabbro  \nA.A.  \n(2022-2023)  \nClassification of patients with parotid cancer using dynamic contrast and diffusion examinations with radiomics and machine learning techniques.  \nABSTRACT  \nObjectives: The aim of this study is to evaluate the role ofMRI-based radiomics analysis and machine learning using both DWI with multiples B values and dynamic contrastenhanced T1-weighted sequences to differentiate pleomorphic adenoma (A), Warthin’s tumor (W) and malignant (M) tumors.  \nMaterials and Methods: This retrospective study involving 41 patients (19 male, 22 female, age 18–88, median 59.4) with parotid gland lesions (10 W, 15 A and 18 M) who underwent a neck MRI examination at San Paolo Hospital of Milan between April 2020 and July 2023 and histopathologically-confirmed. In total, 13 radiomics features were extracted from DWI with 11 B values and dynamic contrast-enhanced T1-weighted sequence. The intensity of association between features and type of tumor class (A, Wand M) has been evaluated using three different features importance algorithms: Receiver operating characteristic (ROC) analysis, ReliefF, and Recursive Feature Elimination (RFE) that allowed the identification of the association between each considered radiomics predictor and the patient class (A, M, W), thus enabling the selection of an ensemble of optimal features to train the machine learning classification algorithm. Two different classification algorithms have been adopted to model the dataset under study, namely the Support Vector Machine (SVM) and the artificial Neural Networks (NN) .  \nResults: The SVM model provides a slightly higher overall accuracy (80%) compared to the NN one (78%) . However, both models still provide a non-negligible number of false negatives, especially for class A and M, which exhibit lower sensitivity scores (below 80%), thus leaving room for further improvements to make the model more robust by increasing the dataset size and balance (as measured through the Shannon Entropy) to enhance the sensitivity score and therefore decrease the false negative rate. Analogous considerations can be done for the positive predictive value for patients in class W, which is slightly below 70% in the NN model compared to a higher value (75%) in the SVM  \nmodel, thereby indicating that the SVM model could be potentially a more reliable model compared to NN one.  \nConclusions: Radiomics and machine learning allow a good diagnostic performance in differentiating pleomorphic adenoma, Warthin’s tumor and malignant tumor.  \n1. INTRODUCTION  \nSalivary gland tumors are relatively rare, constituting 3–11% of all head and neck neoplasms and about 0.2% of all malignancies [1][2] . The global annual incidence of salivary gland tumors is 0.4–13.5 cases per 100,000 individuals [3] . Between 64 and 80% of all salivary gland tumors occur in the parotid gland, with the peak of incidence in the 6th and 7th decades and a male-female ratio of 1.3:1[1][3][4] . Salivary gland tumors include several histotypes, the most of them are benign (80%) [5] . The most common tumor subtype is pleomorphic adenoma (A) followed by Warthin’s tumor (W) both benign and mucoepidermoid carcinoma is the most common malignant tumor (M)  \n[3][4][5] .  \nAccurate preoperative differentiation has a crucial role in the planning of surgical strategy in fact total parotidectomy is necessary in case of malignant tumors instead large free resection margins [7] or total parotidectomy are necessary in pleomorphic adenoma for the risk of recurrence rate of 45–50%[8] [9], and s","cbCaiopXsfxZ4SCw","https://ap.wps.com/l/cbCaiopXsfxZ4SCw","pdf",1112031,4,1,29,"English","en",105,"# Abstract\n# Introduction\n# Methods\n## Materials and Methods\n## Feature Extraction and Selection\n## Classification Algorithms\n# Results\n# Conclusions","[{\"question\":\"What are the main tumor types differentiated in this study?\",\"answer\":\"The study differentiates pleomorphic adenoma, Warthin’s tumor, and malignant parotid tumors using MRI-based radiomics and machine learning.\"},{\"question\":\"Which MRI techniques and radiomics features were used?\",\"answer\":\"It uses DWI with multiple B values and dynamic contrast-enhanced T1-weighted sequences, extracting 13 radiomics features in total.\"},{\"question\":\"How did the SVM model perform compared with the neural network?\",\"answer\":\"The SVM model achieved slightly higher overall accuracy (80%) than the neural network (78%), while both models still showed notable false negatives, especially in classes A and M.\"}]","Classification of Patients with Parotid Cancer Using Dynamic Contrast and Diffusion Examinations with Radiomics and Machine Learning Techniques - Doctoral Thesis | PDF",1785946009,73,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"classification-of-patients-with-parotid-cancer-using-dynamic-contrast-and-diffusion-examinations-with-radiomics-and-machine-learning-techniques-doctoral-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/classification-of-patients-with-parotid-cancer-using-dynamic-contrast-and-diffusion-examinations-with-radiomics-and-machine-learning-techniques-doctoral-thesis/128238/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What are the main tumor types differentiated in this study?","Question",{"text":76,"@type":77},"The study differentiates pleomorphic adenoma, Warthin’s tumor, and malignant parotid tumors using MRI-based radiomics and machine learning.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which MRI techniques and radiomics features were used?",{"text":81,"@type":77},"It uses DWI with multiple B values and dynamic contrast-enhanced T1-weighted sequences, extracting 13 radiomics features in total.",{"name":83,"@type":74,"acceptedAnswer":84},"How did the SVM model perform compared with the neural network?",{"text":85,"@type":77},"The SVM model achieved slightly higher overall accuracy (80%) than the neural network (78%), while both models still showed notable false negatives, especially in classes A and M.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]