摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-肿瘤学的新研究-胰腺癌是一篇报道的主题。根据NewsRx记者从中国济南发回的新闻报道，研究称，本研究的目的是初步评价F-脱氧葡萄糖正电子发射断层显像(F-FDG PET/ct)代谢参数和放射组学在鉴别胰脏肿块型淋巴瘤和胰腺癌中的能力,并将86例胰脏肿块型淋巴瘤和胰腺癌患者的88个病灶随机分为训练组和验证组。以4比1的比例我们的新闻编辑引用了山东大学齐鲁医院的一篇研究报道:“用ITK-Snap软件进行感兴趣区域的分割,用3Dslicer和PYTHON提取PET代谢参数和放射组学特征,在选择最佳代谢参数和放射组学特征后,采用Logistic回归(LR),支持向量机(S VM),建立了PET代谢参数、CT放射组学、PET放射组学、PET放射组学和PET/CT放射组学的随机森林(RF)模型,从曲线下面积(AUC)、训练集和验证集的准确性、敏感性和特异性等方面评价模型性能,所有模型均具有较强的判别能力。AUC值在0.727~0.978.之间，PET与CT放射组学组合特征表现出最高性能，PET/CT放射组学模型在训练集的AUC值为LR 0.994，SVM 0.994，RF 0.989.，AUC值为LR 0.909，SVM 0.883，利用F-FDG PET/ct代谢参数和放射组学的RF 0.844.机器学习模型在鉴别胰腺癌和肿块型泛发性淋巴瘤方面的应用前景
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Pancreatic Cancer is the subject of a report. According to news reporting originating from Jinan, People's Republic of China, by NewsRx correspondents, research stated, "T he objective of this study was to preliminarily assess the ability of metabolic parameters and radiomics derived from F-fluorodeoxyglucose positron emission tom ography/computed tomography (F-FDG PET/CT) to distinguish mass-forming pancreati c lymphoma from pancreatic carcinoma using machine learning. A total of 88 lesio ns from 86 patients diagnosed as mass-forming pancreatic lymphoma or pancreatic carcinoma were included and randomly divided into a training set and a validatio n set at a 4-to-1 ratio." Our news editors obtained a quote from the research from the Qilu Hospital of Sh andong University, "The segmentation of regions of interest was performed using ITK-SNAP software, PET metabolic parameters and radiomics features were extracte d using 3Dslicer and PYTHON. Following the selection of optimal metabolic parame ters and radiomics features, Logistic regression (LR), support vector machine (S VM), and random forest (RF) models were constructed for PET metabolic parameters , CT radiomics, PET radiomics, and PET/CT radiomics. Model performance was asses sed in terms of area under the curve (AUC), accuracy, sensitivity, and specifici ty in both the training and validation sets. Strong discriminative ability obser ved in all models, with AUC values ranging from 0.727 to 0.978. The highest perf ormance exhibited by the combined PET and CT radiomics features. AUC values for PET/CT radiomics models in the training set were LR 0.994, SVM 0.994, RF 0.989. In the validation set, AUC values were LR 0.909, SVM 0.883, RF 0.844. Machine le arning models utilizing the metabolic parameters and radiomics of F-FDG PET/CT s how promise in distinguishing between pancreatic carcinoma and mass-forming panc reatic lymphoma."
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