Sándor Czibor, Zselyke Csatlós, Krisztián Fábián, Márton Piroska, Tamás Györke
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Receiver operating characteristics (ROC) analyses were performed and 24-months progression-free survival (PFS) of low- and high-risk cohorts were compared by log-rank analyses. A machine learning algorithm was used to build a prognostic model from the available clinical, volumetric, and textural data based on logistic regression.</p><p><strong>Results: </strong>The area-under-the-curve (AUC) on ROC analysis was the highest of MTVrate at 0.74, followed by lactate-dehydrogenase, MTV, and skewness, with AUCs of 0.68, 0.63, and 0.55, respectively which parameters were also able to differentiate the PFS. A combined survival analysis including MTV and MTVrate identified a subgroup with particularly low PFS at 38%. In the machine learning-based model had an AUC of 0.83 and the highest relative importance was attributed to three textural features and both MTV and MTVrate as important predictors of PFS.</p><p><strong>Conclusion: </strong>Individual evaluation of different biomarkers yielded only limited prognostic data, whereas a machine learning-based combined analysis had higher effectivity. MTVrate had the highest prognostic ability on individual analysis and, combined with MTV, it identified a patient group with particularly poor prognosis.</p>","PeriodicalId":19708,"journal":{"name":"Nuclear Medicine Communications","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11460743/pdf/","citationCount":"0","resultStr":"{\"title\":\"Volumetric and textural analysis of PET/CT in patients with diffuse large B-cell lymphoma highlights the importance of novel MTVrate feature.\",\"authors\":\"Sándor Czibor, Zselyke Csatlós, Krisztián Fábián, Márton Piroska, Tamás Györke\",\"doi\":\"10.1097/MNM.0000000000001884\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>To investigate the prognostic value of clinical, volumetric, and radiomics-based textural parameters in baseline [ 18 F]FDG-PET/CT scans of diffuse large B-cell lymphoma (DLBCL) patients.</p><p><strong>Methods: </strong>We retrospectively investigated baseline PET/CT scans and collected clinical data of fifty DLBCL patients. PET images were segmented semiautomatically to determine metabolic tumor volume (MTV), then the largest segmented lymphoma volume of interest (VOI) was used to extract first-, second-, and high-order textural features. A novel value, MTVrate was introduced as the quotient of the largest lesion's volume and total body MTV. Receiver operating characteristics (ROC) analyses were performed and 24-months progression-free survival (PFS) of low- and high-risk cohorts were compared by log-rank analyses. A machine learning algorithm was used to build a prognostic model from the available clinical, volumetric, and textural data based on logistic regression.</p><p><strong>Results: </strong>The area-under-the-curve (AUC) on ROC analysis was the highest of MTVrate at 0.74, followed by lactate-dehydrogenase, MTV, and skewness, with AUCs of 0.68, 0.63, and 0.55, respectively which parameters were also able to differentiate the PFS. 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引用次数: 0
摘要
研究目的研究弥漫大B细胞淋巴瘤(DLBCL)患者基线[18F]FDG-PET/CT扫描中临床、容积和基于放射组学的纹理参数的预后价值:我们回顾性地调查了50名DLBCL患者的基线PET/CT扫描结果,并收集了他们的临床数据。我们对 PET 图像进行了半自动分割,以确定代谢肿瘤体积(MTV),然后利用最大的分割淋巴瘤感兴趣体积(VOI)提取一阶、二阶和高阶纹理特征。引入了一个新值,即 MTVrate,作为最大病灶体积与全身 MTV 的商。进行了接收器操作特征(ROC)分析,并通过对数秩分析比较了低风险和高风险组群的24个月无进展生存期(PFS)。在逻辑回归的基础上,使用机器学习算法从可用的临床、体积和纹理数据中建立预后模型:ROC分析中,MTVrate的曲线下面积(AUC)最高,为0.74,其次是乳酸脱氢酶、MTV和偏度,AUC分别为0.68、0.63和0.55,这些参数也能区分PFS。包括 MTV 和 MTVrate 的综合生存分析发现了一个 PFS 特别低的亚组,仅为 38%。基于机器学习的模型的AUC为0.83,三个纹理特征以及MTV和MTVrate的相对重要性最高,是预测PFS的重要指标:结论:单独评估不同的生物标志物只能获得有限的预后数据,而基于机器学习的综合分析则具有更高的有效性。在单独分析中,MTVrate的预后能力最高,与MTV结合后,它能识别出预后特别差的患者群体。
Volumetric and textural analysis of PET/CT in patients with diffuse large B-cell lymphoma highlights the importance of novel MTVrate feature.
Objectives: To investigate the prognostic value of clinical, volumetric, and radiomics-based textural parameters in baseline [ 18 F]FDG-PET/CT scans of diffuse large B-cell lymphoma (DLBCL) patients.
Methods: We retrospectively investigated baseline PET/CT scans and collected clinical data of fifty DLBCL patients. PET images were segmented semiautomatically to determine metabolic tumor volume (MTV), then the largest segmented lymphoma volume of interest (VOI) was used to extract first-, second-, and high-order textural features. A novel value, MTVrate was introduced as the quotient of the largest lesion's volume and total body MTV. Receiver operating characteristics (ROC) analyses were performed and 24-months progression-free survival (PFS) of low- and high-risk cohorts were compared by log-rank analyses. A machine learning algorithm was used to build a prognostic model from the available clinical, volumetric, and textural data based on logistic regression.
Results: The area-under-the-curve (AUC) on ROC analysis was the highest of MTVrate at 0.74, followed by lactate-dehydrogenase, MTV, and skewness, with AUCs of 0.68, 0.63, and 0.55, respectively which parameters were also able to differentiate the PFS. A combined survival analysis including MTV and MTVrate identified a subgroup with particularly low PFS at 38%. In the machine learning-based model had an AUC of 0.83 and the highest relative importance was attributed to three textural features and both MTV and MTVrate as important predictors of PFS.
Conclusion: Individual evaluation of different biomarkers yielded only limited prognostic data, whereas a machine learning-based combined analysis had higher effectivity. MTVrate had the highest prognostic ability on individual analysis and, combined with MTV, it identified a patient group with particularly poor prognosis.
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.