PDF(Pubmed)
Abstract：
UNASSIGNED: In situations where pathological acquisition is difficult, there is a lack of consensus on distinguishing between adenocarcinoma and squamous cell carcinoma from imaging images, and each doctor can only make judgments based on their own experience. This study aims to extract imaging features of chest CT, extract sensitive factors through logistic univariate and multivariate analysis, and model to distinguish between lung squamous cell carcinoma and lung adenocarcinoma.
UNASSIGNED: We downloaded chest CT scans with clear diagnosis of adenocarcinoma and squamous cell carcinoma from The Cancer Imaging Archive (TCIA), extracted 19 imaging features by a radiologist and a thoracic surgeon, including location, spicule, lobulation, cavity, vacuolar sign, necrosis, pleural traction sign, vascular bundle sign, air bronchogram sign, calcification, enhancement degree, distance from pulmonary hilum, atelectasis, pulmonary hilum and bronchial lymph nodes, mediastinal lymph nodes, interlobular septal thickening, pulmonary metastasis, adjacent structures invasion, pleural effusion. Firstly, we apply the glm function of R language to perform logistic univariate analysis on all variables to select variables with P < 0.1. Then, perform logistic multivariate analysis on the selected variables to obtain a predictive model. Next, use the roc function in R language to calculate the AUC value and draw the ROC curve, use the val.prob function in R language to draw the Calibrat curve, and use the rmda package in R language to draw the DCA curve and clinical impact curve. At the same time, 45 patients diagnosed with lung squamous cell carcinoma and lung adenocarcinoma through surgery or biopsy in the Radiotherapy Department and Thoracic Surgery Department of our hospital from 2023 to 2024 were included in the validation group. The chest CT features were jointly determined and recorded by the two doctors mentioned above and included in the validation group. The included image feature data are complete and does not require preprocessing, so directly entering statistical calculations. Perform ROC curves, calibration curves, DCA, and clinical impact curves in the validation group to further validate the predictive model. If the predictive model performs well in the validation group, further draw a nomogram to demonstrate.
UNASSIGNED: This study extracted 19 imaging features from the chest CT scans of 75 patients downloaded from TCIA and finally selected 18 complete data for analysis. First, univariate analysis and multivariate analysis were performed, and a total of 5 variables were obtained: spicule, necrosis, air bronchogram Sign, atelectasis, pulmonary hilum and bronchial lymph nodes. After conducting modeling analysis with AUC = 0.887, a validation group was established using clinical cases from our hospital, Draw ROC curve with AUC = 0.865 in the validation group, evaluate the accuracy of the model through Calibrate calibration curve, evaluate the reliability of the model in clinical practice through DCA curve, and further evaluate the practicality of the model in clinical practice through clinical impact curve.
UNASSIGNED: It is possible to extract influential features from ordinary chest CT scans to determine lung adenocarcinoma and squamous cell carcinoma. The model we have set up performs well in terms of discrimination, accuracy, reliability, and practicality.
UNASSIGNED: We downloaded chest CT scans with clear diagnosis of adenocarcinoma and squamous cell carcinoma from The Cancer Imaging Archive (TCIA), extracted 19 imaging features by a radiologist and a thoracic surgeon, including location, spicule, lobulation, cavity, vacuolar sign, necrosis, pleural traction sign, vascular bundle sign, air bronchogram sign, calcification, enhancement degree, distance from pulmonary hilum, atelectasis, pulmonary hilum and bronchial lymph nodes, mediastinal lymph nodes, interlobular septal thickening, pulmonary metastasis, adjacent structures invasion, pleural effusion. Firstly, we apply the glm function of R language to perform logistic univariate analysis on all variables to select variables with P < 0.1. Then, perform logistic multivariate analysis on the selected variables to obtain a predictive model. Next, use the roc function in R language to calculate the AUC value and draw the ROC curve, use the val.prob function in R language to draw the Calibrat curve, and use the rmda package in R language to draw the DCA curve and clinical impact curve. At the same time, 45 patients diagnosed with lung squamous cell carcinoma and lung adenocarcinoma through surgery or biopsy in the Radiotherapy Department and Thoracic Surgery Department of our hospital from 2023 to 2024 were included in the validation group. The chest CT features were jointly determined and recorded by the two doctors mentioned above and included in the validation group. The included image feature data are complete and does not require preprocessing, so directly entering statistical calculations. Perform ROC curves, calibration curves, DCA, and clinical impact curves in the validation group to further validate the predictive model. If the predictive model performs well in the validation group, further draw a nomogram to demonstrate.
UNASSIGNED: This study extracted 19 imaging features from the chest CT scans of 75 patients downloaded from TCIA and finally selected 18 complete data for analysis. First, univariate analysis and multivariate analysis were performed, and a total of 5 variables were obtained: spicule, necrosis, air bronchogram Sign, atelectasis, pulmonary hilum and bronchial lymph nodes. After conducting modeling analysis with AUC = 0.887, a validation group was established using clinical cases from our hospital, Draw ROC curve with AUC = 0.865 in the validation group, evaluate the accuracy of the model through Calibrate calibration curve, evaluate the reliability of the model in clinical practice through DCA curve, and further evaluate the practicality of the model in clinical practice through clinical impact curve.
UNASSIGNED: It is possible to extract influential features from ordinary chest CT scans to determine lung adenocarcinoma and squamous cell carcinoma. The model we have set up performs well in terms of discrimination, accuracy, reliability, and practicality.
摘要：
■在病理获取困难的情况下，从影像学图像中区分腺癌和鳞状细胞癌缺乏共识，每个医生只能根据自己的经验做出判断。本研究旨在提取胸部CT的影像学特征,通过Logistic单变量和多变量分析提取敏感因素，和模型来区分肺鳞癌和肺腺癌。
■我们从癌症影像档案（TCIA）下载了明确诊断为腺癌和鳞状细胞癌的胸部CT扫描，放射科医生和胸外科医生提取了19个成像特征，包括位置,针状,分叶,空腔,空泡征,坏死,胸膜牵引征,维管束征,空气支气管图征象,钙化,增强程度,与肺门的距离，肺不张,肺门和支气管淋巴结，纵隔淋巴结，小叶间隔增厚，肺转移,相邻结构侵入，胸腔积液.首先,我们应用R语言的glm函数对所有变量进行Logistic单变量分析，以选择P<0.1的变量。然后，对选定的变量进行逻辑多变量分析,得到预测模型。接下来,使用R语言中的roc函数计算AUC值并绘制ROC曲线，使用val。prob函数在R语言中绘制Calibrat曲线，并使用R语言的rmda软件包绘制DCA曲线和临床影响曲线。同时,将2023年至2024年在我院放疗科和胸外科经手术或活检确诊为肺鳞癌和肺腺癌的45例患者纳入验证组。胸部CT特征由上述两位医生共同确定并记录，并纳入验证组。包含的图像特征数据完整，不需要预处理，所以直接进入统计计算。执行ROC曲线,校正曲线,DCA,和验证组的临床影响曲线，进一步验证预测模型。如果预测模型在验证组中表现良好，进一步绘制列线图进行演示。
■这项研究从TCIA下载的75名患者的胸部CT扫描中提取了19个成像特征，最终选择了18个完整数据进行分析。首先,进行了单因素分析和多因素分析，总共获得了5个变量：针尖，坏死,空气支气管图征象,肺不张,肺门和支气管淋巴结。在AUC=0.887进行建模分析后，使用我院的临床病例建立验证组，在验证组中绘制ROC曲线，AUC=0.865，通过校准曲线评估模型的准确性，通过DCA曲线评估模型在临床实践中的可靠性，并通过临床影响曲线进一步评价模型在临床实践中的实用性。
■可以从普通的胸部CT扫描中提取有影响力的特征，以确定肺腺癌和鳞状细胞癌。我们建立的模型在辨别方面表现得很好，准确度,可靠性,和实用性。
■我们从癌症影像档案（TCIA）下载了明确诊断为腺癌和鳞状细胞癌的胸部CT扫描，放射科医生和胸外科医生提取了19个成像特征，包括位置,针状,分叶,空腔,空泡征,坏死,胸膜牵引征,维管束征,空气支气管图征象,钙化,增强程度,与肺门的距离，肺不张,肺门和支气管淋巴结，纵隔淋巴结，小叶间隔增厚，肺转移,相邻结构侵入，胸腔积液.首先,我们应用R语言的glm函数对所有变量进行Logistic单变量分析，以选择P<0.1的变量。然后，对选定的变量进行逻辑多变量分析,得到预测模型。接下来,使用R语言中的roc函数计算AUC值并绘制ROC曲线，使用val。prob函数在R语言中绘制Calibrat曲线，并使用R语言的rmda软件包绘制DCA曲线和临床影响曲线。同时,将2023年至2024年在我院放疗科和胸外科经手术或活检确诊为肺鳞癌和肺腺癌的45例患者纳入验证组。胸部CT特征由上述两位医生共同确定并记录，并纳入验证组。包含的图像特征数据完整，不需要预处理，所以直接进入统计计算。执行ROC曲线,校正曲线,DCA,和验证组的临床影响曲线，进一步验证预测模型。如果预测模型在验证组中表现良好，进一步绘制列线图进行演示。
■这项研究从TCIA下载的75名患者的胸部CT扫描中提取了19个成像特征，最终选择了18个完整数据进行分析。首先,进行了单因素分析和多因素分析，总共获得了5个变量：针尖，坏死,空气支气管图征象,肺不张,肺门和支气管淋巴结。在AUC=0.887进行建模分析后，使用我院的临床病例建立验证组，在验证组中绘制ROC曲线，AUC=0.865，通过校准曲线评估模型的准确性，通过DCA曲线评估模型在临床实践中的可靠性，并通过临床影响曲线进一步评价模型在临床实践中的实用性。
■可以从普通的胸部CT扫描中提取有影响力的特征，以确定肺腺癌和鳞状细胞癌。我们建立的模型在辨别方面表现得很好，准确度,可靠性,和实用性。
