{"title":"纹理和放射学分析参数在预测乳腺癌患者肿瘤组织病理学参数中的作用。","authors":"Rutuja Kote, Mudalsha Ravina, Harish Goyal, Debajyoti Mohanty, Rakesh Gupta, Arvind Kumar Shukla, Moulish Reddy, Pratheek N Prasanth","doi":"10.1097/MNM.0000000000001885","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Texture and radiomic analysis characterizes the tumor's phenotype and evaluates its microenvironment in quantitative terms. This study aims to investigate the role of textural and radiomic analysis parameters in predicting histopathological factors in breast cancer patients.</p><p><strong>Materials and methods: </strong>Two hundred and twelve primary breast cancer patients underwent 18F-FDG PET/computed tomography for staging. The images were processed in a commercially available textural analysis software. ROI was drawn over the primary tumor with a 40% threshold and was processed further to derive textural and radiomic parameters. These parameters were then compared with histopathological factors of tumor. Receiver-operating characteristic analysis was performed with a P-value <0.05 for statistical significance. The significant parameters were subsequently utilized in various machine learning models to assess their predictive accuracy.</p><p><strong>Results: </strong>A retrospective study of 212 primary breast cancer patients was done. Among all the significant parameters, SUVmin, SUVmean, SUVstd, SUVmax, discretized HISTO_Entropy, and gray level co-occurrence matrix_Contrast were found to be significantly associated with ductal carcinoma type. Four parameters (SUVmin, SUVmean, SUVstd, and SUVmax) were significant in differentiating the luminal subtypes of the tumor. Five parameters (SUVmin, SUVmean, SUVstd, SUVmax, and SUV kurtosis) were significant in predicting the grade of the tumor. These parameters showcased robust capabilities in predicting multiple histopathological parameters when tested using machine learning algorithms.</p><p><strong>Conclusion: </strong>Though textural analysis could not predict hormonal receptor status, lymphovascular invasion status, perineural invasion status, microcalcification status of tumor, and all the molecular subtypes of the tumor, it could predict the tumor's histologic type, triple-negative subtype, and score of the tumor noninvasively.</p>","PeriodicalId":19708,"journal":{"name":"Nuclear Medicine Communications","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Role of textural and radiomic analysis parameters in predicting histopathological parameters of the tumor in breast cancer patients.\",\"authors\":\"Rutuja Kote, Mudalsha Ravina, Harish Goyal, Debajyoti Mohanty, Rakesh Gupta, Arvind Kumar Shukla, Moulish Reddy, Pratheek N Prasanth\",\"doi\":\"10.1097/MNM.0000000000001885\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Texture and radiomic analysis characterizes the tumor's phenotype and evaluates its microenvironment in quantitative terms. This study aims to investigate the role of textural and radiomic analysis parameters in predicting histopathological factors in breast cancer patients.</p><p><strong>Materials and methods: </strong>Two hundred and twelve primary breast cancer patients underwent 18F-FDG PET/computed tomography for staging. The images were processed in a commercially available textural analysis software. ROI was drawn over the primary tumor with a 40% threshold and was processed further to derive textural and radiomic parameters. These parameters were then compared with histopathological factors of tumor. Receiver-operating characteristic analysis was performed with a P-value <0.05 for statistical significance. The significant parameters were subsequently utilized in various machine learning models to assess their predictive accuracy.</p><p><strong>Results: </strong>A retrospective study of 212 primary breast cancer patients was done. Among all the significant parameters, SUVmin, SUVmean, SUVstd, SUVmax, discretized HISTO_Entropy, and gray level co-occurrence matrix_Contrast were found to be significantly associated with ductal carcinoma type. Four parameters (SUVmin, SUVmean, SUVstd, and SUVmax) were significant in differentiating the luminal subtypes of the tumor. Five parameters (SUVmin, SUVmean, SUVstd, SUVmax, and SUV kurtosis) were significant in predicting the grade of the tumor. These parameters showcased robust capabilities in predicting multiple histopathological parameters when tested using machine learning algorithms.</p><p><strong>Conclusion: </strong>Though textural analysis could not predict hormonal receptor status, lymphovascular invasion status, perineural invasion status, microcalcification status of tumor, and all the molecular subtypes of the tumor, it could predict the tumor's histologic type, triple-negative subtype, and score of the tumor noninvasively.</p>\",\"PeriodicalId\":19708,\"journal\":{\"name\":\"Nuclear Medicine Communications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-08-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nuclear Medicine Communications\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/MNM.0000000000001885\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nuclear Medicine Communications","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/MNM.0000000000001885","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Role of textural and radiomic analysis parameters in predicting histopathological parameters of the tumor in breast cancer patients.
Introduction: Texture and radiomic analysis characterizes the tumor's phenotype and evaluates its microenvironment in quantitative terms. This study aims to investigate the role of textural and radiomic analysis parameters in predicting histopathological factors in breast cancer patients.
Materials and methods: Two hundred and twelve primary breast cancer patients underwent 18F-FDG PET/computed tomography for staging. The images were processed in a commercially available textural analysis software. ROI was drawn over the primary tumor with a 40% threshold and was processed further to derive textural and radiomic parameters. These parameters were then compared with histopathological factors of tumor. Receiver-operating characteristic analysis was performed with a P-value <0.05 for statistical significance. The significant parameters were subsequently utilized in various machine learning models to assess their predictive accuracy.
Results: A retrospective study of 212 primary breast cancer patients was done. Among all the significant parameters, SUVmin, SUVmean, SUVstd, SUVmax, discretized HISTO_Entropy, and gray level co-occurrence matrix_Contrast were found to be significantly associated with ductal carcinoma type. Four parameters (SUVmin, SUVmean, SUVstd, and SUVmax) were significant in differentiating the luminal subtypes of the tumor. Five parameters (SUVmin, SUVmean, SUVstd, SUVmax, and SUV kurtosis) were significant in predicting the grade of the tumor. These parameters showcased robust capabilities in predicting multiple histopathological parameters when tested using machine learning algorithms.
Conclusion: Though textural analysis could not predict hormonal receptor status, lymphovascular invasion status, perineural invasion status, microcalcification status of tumor, and all the molecular subtypes of the tumor, it could predict the tumor's histologic type, triple-negative subtype, and score of the tumor noninvasively.
期刊介绍:
Nuclear Medicine Communications, the official journal of the British Nuclear Medicine Society, is a rapid communications journal covering nuclear medicine and molecular imaging with radionuclides, and the basic supporting sciences. As well as clinical research and commentary, manuscripts describing research on preclinical and basic sciences (radiochemistry, radiopharmacy, radiobiology, radiopharmacology, medical physics, computing and engineering, and technical and nursing professions involved in delivering nuclear medicine services) are welcomed, as the journal is intended to be of interest internationally to all members of the many medical and non-medical disciplines involved in nuclear medicine. In addition to papers reporting original studies, frankly written editorials and topical reviews are a regular feature of the journal.