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Article ; Online: CT-measured body composition radiomics predict lymph node metastasis in localized pancreatic ductal adenocarcinoma.

Gu, Qianbiao / He, Mengqing / He, Yaqiong / Dai, Anqi / Liu, Jianbin / Chen, Xiang / Liu, Peng

Discover. Oncology

2023 Volume 14, Issue 1, Page(s) 16

Abstract: Background: To explored the value of CT-measured body composition radiomics in preoperative evaluation of lymph node metastasis (LNM) in localized pancreatic ductal adenocarcinoma (LPDAC).: Methods: We retrospectively collected patients with LPDAC ... ...

Abstract	Background: To explored the value of CT-measured body composition radiomics in preoperative evaluation of lymph node metastasis (LNM) in localized pancreatic ductal adenocarcinoma (LPDAC). Methods: We retrospectively collected patients with LPDAC who underwent surgical resection from January 2016 to June 2022. According to whether there was LNM after operation, the patients were divided into LNM group and non-LNM group in both male and female patients. The patient's body composition was measured by CT images at the level of the L3 vertebral body before surgery, and the radiomics features of adipose tissue and muscle were extracted. Multivariate logistic regression (forward LR) analyses were used to determine the predictors of LNM from male and female patient, respectively. Sexual dimorphism prediction signature using adipose tissue radiomics features, muscle tissue radiomics features and combined signature of both were developed and compared. The model performance is evaluated on discrimination and validated through a leave-one-out cross-validation method. Results: A total of 196 patients (mean age, 60 years ± 9 [SD]; 117 men) were enrolled, including 59 LNM in male and 36 LNM in female. Both male and female CT-measured body composition radiomics signatures have a certain predictive power on LNM of LPDAC. Among them, the female adipose tissue signature showed the highest performance (area under the ROC curve (AUC), 0.895), and leave one out cross validation (LOOCV) indicated that the signature could accurately classify 83.5% of cases; The prediction efficiency of the signature can be further improved after adding the muscle radiomics features (AUC, 0.924, and the accuracy of the LOOCV was 87.3%); The abilities of male adipose tissue and muscle tissue radiomics signatures in predicting LNM of LPDAC was similar, AUC was 0.735 and 0.773, respectively, and the accuracy of LOOCV was 62.4% and 68.4%, respectively. Conclusions: CT-measured body composition Radiomics strategy showed good performance for predicting LNM in LPDAC, and has sexual dimorphism. It may provide a reference for individual treatment of LPDAC and related research about body composition in the future.
Language	English
Publishing date	2023-02-03
Publishing country	United States
Document type	Journal Article
ISSN	2730-6011
ISSN (online)	2730-6011
DOI	10.1007/s12672-023-00624-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Dual-Energy Computed Tomography for the Diagnosis of Mediastinal Lymph Node Metastasis in Lung Cancer Patients: A Preliminary Study.

Hu, Xiaoli / Gu, Qianbiao / Zhang, Kun / Deng, Dong / Li, Lei / Li, Ping / Shen, Hongrong

Journal of computer assisted tomography

2021 Volume 45, Issue 3, Page(s) 490–494

Abstract: Objective: This study explored the feasibility of dual-energy computed tomography (DECT) for the diagnosis of mediastinal lymph node (LN) metastasis in patients with lung cancer.: Methods: Forty-two consecutive patients with lung cancer, who ... ...

Abstract	Objective: This study explored the feasibility of dual-energy computed tomography (DECT) for the diagnosis of mediastinal lymph node (LN) metastasis in patients with lung cancer. Methods: Forty-two consecutive patients with lung cancer, who underwent DECT, were included in this retrospective study. The attenuation value (Hounsfield unit) in virtual monochromatic images and the iodine concentration in the iodine map were measured at mediastinal LNs. The slope of the spectral attenuation curve (K) and normalized iodine concentration (in thoracic aorta) were calculated. The measurement results were statistically compared using 2 independent samples t test. Receiver operating characteristic curve analysis, net reclassification improvement, and integrated discrimination improvement were used to evaluate the diagnostic performance of DECT for mediastinal LN metastasis. Results: A total of 74 mediastinal LNs were obtained, including 33 metastatic LNs and 41 nonmetastatic LNs. The attenuation value at the lower energy levels of virtual monochromatic images (40-90 keV), K, and normalized iodine concentration demonstrated a significant difference between metastatic LNs and nonmetastatic LNs. The attenuation value at 40 keV was the most favorable biomarker for the diagnosis of mediastinal LN metastasis (area under curve, 0.91; sensitivity, 0.94; specificity, 0.81), which showed a much better performance than the LN diameter-based evaluation method (area under curve, 0.72; sensitivity, 0.66; specificity, 0.82; net reclassification improvement, 0.359; integrated discrimination improvement, 0.330). Conclusions: Dual-energy computed tomography is a promising diagnostic approach for the diagnosis of mediastinal LN metastasis in patients with lung cancer, which may help clinicians implement personalized treatment strategies.
MeSH term(s)	Aged ; Feasibility Studies ; Female ; Humans ; Lung Neoplasms/diagnostic imaging ; Lymphatic Metastasis/diagnostic imaging ; Male ; Mediastinal Neoplasms/diagnostic imaging ; Mediastinal Neoplasms/secondary ; Middle Aged ; Precision Medicine ; ROC Curve ; Radiography, Dual-Energy Scanned Projection/methods ; Retrospective Studies ; Tomography, X-Ray Computed
Language	English
Publishing date	2021-07-22
Publishing country	United States
Document type	Journal Article
ZDB-ID	80392-3
ISSN	1532-3145 ; 0363-8715
ISSN (online)	1532-3145
ISSN	0363-8715
DOI	10.1097/RCT.0000000000001157
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Article ; Online: Applying a radiomics-based strategy to preoperatively predict lymph node metastasis in the resectable pancreatic ductal adenocarcinoma.

Liu, Peng / Gu, Qianbiao / Hu, Xiaoli / Tan, Xianzheng / Liu, Jianbin / Xie, An / Huang, Feng

Journal of X-ray science and technology

2020 Volume 28, Issue 6, Page(s) 1113–1121

Abstract: Purpose: This retrospective study is designed to develop a Radiomics-based strategy for preoperatively predicting lymph node (LN) status in the resectable pancreatic ductal adenocarcinoma (PDAC) patients.: Methods: Eighty-five patients with ... ...

Abstract	Purpose: This retrospective study is designed to develop a Radiomics-based strategy for preoperatively predicting lymph node (LN) status in the resectable pancreatic ductal adenocarcinoma (PDAC) patients. Methods: Eighty-five patients with histopathological confirmed PDAC are included, of which 35 are LN metastasis positive and 50 are LN metastasis negative. Initially, 1,124 radiomics features are computed from CT images of each patient. After a series of feature selection, a Radiomics logistic regression (LOG) model is developed. Subsequently, the predictive efficiency of the model is validated using a leave-one-out cross-validation method. The model performance is evaluated on discrimination and compared with the conventional CT evaluation method based on subjective CT image features. Results: Radiomics LOG model is developed based on eight most related radiomics features. Remarkable differences are demonstrated between patients with LN metastasis positive and LN metastasis negative in Radiomics LOG scores namely, 0.535±1.307 (mean±standard deviation) vs. -1.514±1.800 (mean±standard deviation) with p < 0.001. Radiomics LOG model shows significantly higher predictive efficiency compared to the conventional evaluation method of LN status in which areas under ROC curves are AUC = 0.841 with 95% confidence interval (CI: 0.758∼0.925) vs. AUC = 0.682 with (95% CI: 0.566∼0.798). Leave-one-out cross validation indicates that the Radiomics LOG model correctly classifies 70.3% cases, while the conventional CT evaluation method only correctly classifies 57.0% cases. Conclusion: A radiomics-based strategy provides an individualized LN status evaluation in PDAC patients, which may help clinicians implement an optimal personalized patient treatment.
MeSH term(s)	Aged ; Carcinoma, Pancreatic Ductal/diagnostic imaging ; Carcinoma, Pancreatic Ductal/pathology ; Female ; Humans ; Lymph Nodes/diagnostic imaging ; Lymph Nodes/pathology ; Lymphatic Metastasis/diagnostic imaging ; Lymphatic Metastasis/pathology ; Male ; Middle Aged ; Models, Statistical ; Pancreatic Neoplasms/diagnostic imaging ; Pancreatic Neoplasms/pathology ; Radiographic Image Interpretation, Computer-Assisted/methods ; Retrospective Studies ; Tomography, X-Ray Computed/methods
Language	English
Publishing date	2020-10-19
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2012019-9
ISSN	1095-9114 ; 0895-3996
ISSN (online)	1095-9114
ISSN	0895-3996
DOI	10.3233/XST-200730
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Article ; Online: A retrospective study of the initial chest CT imaging findings in 50 COVID-19 patients stratified by gender and age.

Gu, Qianbiao / Ouyang, Xin / Xie, An / Tan, Xianzheng / Liu, Jianbin / Huang, Feng / Liu, Peng

Journal of X-ray science and technology

2020 Volume 28, Issue 5, Page(s) 875–884

Abstract: Objective: To retrospectively analyze and stratify the initial clinical features and chest CT imaging findings of patients with COVID-19 by gender and age.: Methods: Data of 50 COVID-19 patients were collected in two hospitals. The clinical ... ...

Abstract	Objective: To retrospectively analyze and stratify the initial clinical features and chest CT imaging findings of patients with COVID-19 by gender and age. Methods: Data of 50 COVID-19 patients were collected in two hospitals. The clinical manifestations, laboratory examination and chest CT imaging features were analyzed, and a stratification analysis was performed according to gender and age [younger group: <50 years old, elderly group ≥50 years old]. Results: Most patients had a history of epidemic exposure within 2 weeks (96%). The main clinical complaints are fever (54%) and cough (46%). In chest CT images, ground-glass opacity (GGO) is the most common feature (37/38, 97%) in abnormal CT findings, with the remaining 12 patients (12/50, 24%) presenting normal CT images. Other concomitant abnormalities include dilatation of vessels in lesion (76%), interlobular thickening (47%), adjacent pleural thickening (37%), focal consolidation (26%), nodules (16%) and honeycomb pattern (13%). The lesions were distributed in the periphery (50%) or mixed (50%). Subgroup analysis showed that there was no difference in the gender distribution of all the clinical and imaging features. Laboratory findings, interlobular thickening, honeycomb pattern and nodules demonstrated remarkable difference between younger group and elderly group. The average CT score for pulmonary involvement degree was 5.0±4.7. Correlation analysis revealed that CT score was significantly correlated with age, body temperature and days from illness onset (p < 0.05). Conclusions: COVID-19 has various clinical and imaging appearances. However, it has certain characteristics that can be stratified. CT plays an important role in disease diagnosis and early intervention.
MeSH term(s)	Adolescent ; Adult ; Age Factors ; Aged ; Aged, 80 and over ; Betacoronavirus ; COVID-19 ; Child ; Coronavirus Infections/diagnostic imaging ; Coronavirus Infections/epidemiology ; Coronavirus Infections/pathology ; Coronavirus Infections/physiopathology ; Female ; Humans ; Lung/diagnostic imaging ; Lung/pathology ; Male ; Middle Aged ; Pandemics ; Pneumonia, Viral/diagnostic imaging ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/pathology ; Pneumonia, Viral/physiopathology ; Retrospective Studies ; SARS-CoV-2 ; Tomography, X-Ray Computed ; Young Adult
Keywords	covid19
Language	English
Publishing date	2020-08-17
Publishing country	Netherlands
Document type	Comparative Study ; Journal Article
ZDB-ID	2012019-9
ISSN	1095-9114 ; 0895-3996
ISSN (online)	1095-9114
ISSN	0895-3996
DOI	10.3233/XST-200709
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Article ; Online: Inflammatory myofibroblastic tumor of the pancreatic neck misdiagnosed as neuroendocrine tumor: A case report.

Liu, Jia-Bei / Gu, Qian-Biao / Liu, Peng

World journal of gastroenterology

2023 Volume 29, Issue 20, Page(s) 3216–3221

Abstract: Background: Inflammatory myofibroblastic tumor (IMT) is a relatively rare tumor. The global incidence of IMT is less than 1%. There is no specific clinical manifestation. It usually occurs in the lungs, but the pancreas is not the predilection site.: ... ...

Abstract	Background: Inflammatory myofibroblastic tumor (IMT) is a relatively rare tumor. The global incidence of IMT is less than 1%. There is no specific clinical manifestation. It usually occurs in the lungs, but the pancreas is not the predilection site. Case summary: We present a case of a male patient, 51 years old, who was diagnosed with a pancreatic neck small mass on ultrasound one year ago during a physical examination. As he had no clinical symptoms and the mass was relatively small, he did not undergo treatment. However, the mass was found to be larger on review, and he was referred to our hospital. Since the primal clinical diagnosis was pancreatic neuroendocrine tumor, the patient underwent surgical treatment. However, the case was confirmed as pancreatic IMT by postoperative pathology. Conclusion: Pancreatic IMT is relatively rare and easily misdiagnosed. We can better under-stand and correctly diagnose this disease by this case report.
MeSH term(s)	Humans ; Male ; Middle Aged ; Neuroendocrine Tumors/diagnostic imaging ; Neuroendocrine Tumors/surgery ; Pancreatic Neoplasms/diagnostic imaging ; Pancreatic Neoplasms/surgery ; Pancreas ; Diagnostic Errors
Language	English
Publishing date	2023-06-06
Publishing country	United States
Document type	Case Reports
ZDB-ID	2185929-2
ISSN	2219-2840 ; 1007-9327
ISSN (online)	2219-2840
ISSN	1007-9327
DOI	10.3748/wjg.v29.i20.3216
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Article: [Development of a radiomics signature to predict Ki-67 expression level in non-small cell lung cancer].

Gu, Qianbiao / Feng, Zhichao / Liang, Qi / Li, Meijiao / Wang, Wei / Rong, Pengfei

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences

2019 Volume 43, Issue 11, Page(s) 1216–1222

Abstract: Objective: To develop a radiomics signature based on CT image features to estimate the expression level of Ki-67 in non-small cell lung cancer (NSCLC).  Methods: A total of 108 NSCLC patients, who underwent non-enhanced and contrast-enhanced CT scan in ... ...

Abstract	Objective: To develop a radiomics signature based on CT image features to estimate the expression level of Ki-67 in non-small cell lung cancer (NSCLC).  Methods: A total of 108 NSCLC patients, who underwent non-enhanced and contrast-enhanced CT scan in our hospital from January 2014 to November 2017, were retrospectively analyzed. They were confirmed by histopathological examination and undergone Ki-67 expression level test within 2 weeks after CT examination. The non-enhanced and contrast-enhanced CT three-dimensional structural images of the lesions were manually delineated by MaZda software, and the texture features of the region of interest were extracted. Combination of feature selection and classification methods were used to build radiomics signatures, and the classification were assessed using misclassification rates. The MaZda software provides texture feature selection methods including mutual information (MI), Fisher coefficients (Fisher), classification error probability combined with average correlation coefficients (POE+ACC), and Fisher+POE+ACC+MI (FPM), and texture feature analysis including raw data analysis (RDA), principal component analysis (PCA), linear classification analysis (LDA) and nonlinear classification analysis (NDA).  Results: Among the 108 patients, 50 cases were at high levels of Ki-67 expression and 58 cases were at low levels of Ki-67 expression, respectively. The differences of gender, age and pathological type between the two groups were statistically significant (P<0.05). The radiomics signature built by FPM feature selection combined with NDA feature analysis based on non-enhanced CT images achieved the best performance for predicting the level of Ki-67 with a misclassification rate of 14.81%. However, radiomics signature based on contrast-enhanced CT images did not reduce the misclassification rate.  Conclusion: The radiomics signature based on conventional CT image texture features is helpful to predict the expression of Ki-67 in NSCLC lesions, which can provide a non-invasive technique for assessing the invasiveness and prognosis for NSCLC.
MeSH term(s)	Carcinoma, Non-Small-Cell Lung/diagnostic imaging ; Gene Expression Regulation, Neoplastic ; Humans ; Ki-67 Antigen/genetics ; Lung Neoplasms/diagnostic imaging ; Prognosis ; Retrospective Studies ; Tomography, X-Ray Computed
Chemical Substances	Ki-67 Antigen
Language	Chinese
Publishing date	2019-01-14
Publishing country	China
Document type	Journal Article
ZDB-ID	2168533-2
ISSN	1672-7347
ISSN	1672-7347
DOI	10.11817/j.issn.1672-7347.2018.11.008
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Article: [Application of CT-based radiomics in differentiating primary gastric lymphoma from Borrmann type IV gastric cancer].

Deng, Jiao / Tan, Yixiong / Gu, Qianbiao / Rong, Pengfei / Wang, Wei / Liu, Sheng

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences

2019 Volume 44, Issue 3, Page(s) 257–263

Abstract: Objective: To explore the feasibility of CT-based image radiomics signature in identification of primary gastric lymphoma and Borrmann type IV gastric cancer.  Methods: A retrospective analysis of 71 patients with primary gastric lymphoma or Borrmann ... ...

Abstract	Objective: To explore the feasibility of CT-based image radiomics signature in identification of primary gastric lymphoma and Borrmann type IV gastric cancer.  Methods: A retrospective analysis of 71 patients with primary gastric lymphoma or Borrmann type IV gastric cancer confirmed by pathology in our Hospital from January 2009 to April 2017 was performed. There were 28 patients with primary gastric lymphoma and 43 patients with Borrmann type IV gastric cancer. The feature extraction algorithm based on Matlab 2017a software was used to extract the features of image, and the logistic regression model was used to screen the features to establish radiomics signature. The CT sign diagnosis model was established, which included the periplasmic fat infiltration, softness of the stomach wall, abdominal lymph node and peripheral organ metastasis, ascites, mucosal white line sign and lesion thickness. The classification of the two models was evaluated by receiver operating characteristic curve.  Results: A total of 32 3D features were extracted from CT image for each patients. Two features were found to be the most important differential diagnosis factors, and the radiomics signature was established. The CT sign diagnosis model consisted of ascites, periplasmic fat infiltration, stomach wall softness and mucosal white line. For the radiomics signature and the CT subjective finding model, the AUCs were 0.964 and 0.867 with the accuracy at 94.4% and 80.2%, the sensitivity at 93.0% and 74.4%, the specificity at 96.4% and 89.3%, respectively. After Delong test, the diagnostic efficacy of the radiomics signature was higher than the CT sign diagnosis model (P<0.001).  Conclusion: CT-based image radiomics signature can accurately identify primary gastric lymphoma and Borrmann type IV gastric cancer, and can potentially provide important assistance in clinical diagnosis for the two diseases.
MeSH term(s)	Humans ; Lymphoma, Non-Hodgkin ; Neoplasm Staging ; Retrospective Studies ; Stomach Neoplasms ; Tomography, X-Ray Computed
Language	Chinese
Publishing date	2019-04-10
Publishing country	China
Document type	Journal Article
ZDB-ID	2168533-2
ISSN	1672-7347
ISSN	1672-7347
DOI	10.11817/j.issn.1672-7347.2019.03.005
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Article: A retrospective study of the initial chest CT imaging findings in 50 COVID-19 patients stratified by gender and age

Gu, Qianbiao / Ouyang, Xin / Xie, An / Tan, Xianzheng / Liu, Jianbin / Huang, Feng / Liu, Peng

J Xray Sci Technol

Abstract: OBJECTIVE: To retrospectively analyze and stratify the initial clinical features and chest CT imaging findings of patients with COVID-19 by gender and age. METHODS: Data of 50 COVID-19 patients were collected in two hospitals. The clinical manifestations, ...

Abstract	OBJECTIVE: To retrospectively analyze and stratify the initial clinical features and chest CT imaging findings of patients with COVID-19 by gender and age. METHODS: Data of 50 COVID-19 patients were collected in two hospitals. The clinical manifestations, laboratory examination and chest CT imaging features were analyzed, and a stratification analysis was performed according to gender and age [younger group: <50 years old, elderly group ≥50 years old]. RESULTS: Most patients had a history of epidemic exposure within 2 weeks (96%). The main clinical complaints are fever (54%) and cough (46%). In chest CT images, ground-glass opacity (GGO) is the most common feature (37/38, 97%) in abnormal CT findings, with the remaining 12 patients (12/50, 24%) presenting normal CT images. Other concomitant abnormalities include dilatation of vessels in lesion (76%), interlobular thickening (47%), adjacent pleural thickening (37%), focal consolidation (26%), nodules (16%) and honeycomb pattern (13%). The lesions were distributed in the periphery (50%) or mixed (50%). Subgroup analysis showed that there was no difference in the gender distribution of all the clinical and imaging features. Laboratory findings, interlobular thickening, honeycomb pattern and nodules demonstrated remarkable difference between younger group and elderly group. The average CT score for pulmonary involvement degree was 5.0±4.7. Correlation analysis revealed that CT score was significantly correlated with age, body temperature and days from illness onset (pâ<â0.05). CONCLUSIONS: COVID-19 has various clinical and imaging appearances. However, it has certain characteristics that can be stratified. CT plays an important role in disease diagnosis and early intervention.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #721454
Database	COVID19

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Article ; Online: Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer.

Gu, Qianbiao / Feng, Zhichao / Liang, Qi / Li, Meijiao / Deng, Jiao / Ma, Mengtian / Wang, Wei / Liu, Jianbin / Liu, Peng / Rong, Pengfei

European journal of radiology

2019 Volume 118, Page(s) 32–37

Abstract: Purpose: To explore the feasibility and performance of machine learning-based radiomics classifier to predict the cell proliferation(Ki-67)in non-small cell lung cancer (NSCLC).: Methods: 245 histopathological confirmed NSCLC patients who underwent ... ...

Abstract	Purpose: To explore the feasibility and performance of machine learning-based radiomics classifier to predict the cell proliferation(Ki-67)in non-small cell lung cancer (NSCLC). Methods: 245 histopathological confirmed NSCLC patients who underwent CT scans were retrospectively included. The Ki-67 proliferation index (Ki-67 PI) were measured within 2 weeks after CT scans. A lesion volume of interest (VOI) was manually delineated and radiomics features were extracted by MaZda software from CT images. A random forest feature selection algorithm (RFFS) was used to reduce features. Six kinds of machine learning methods were used to establish radiomics classifiers, subjective imaging feature classifiers and combined classifiers, respectively. The performance of these classifiers was evaluated by the receiver operating characteristic curve (ROC) and compared with Delong test. Results: 103 radiomics features were extracted and 20 optimal features were selected using RFFS. Among the radiomics classifiers established by six machine learning methods, random forest-based radiomics classifier achieved the best performance (AUC = 0.776) in predicting the Ki-67 expression level with sensitivity and specificity of 0.726 and 0.661, which was better than that of subjective imaging classifiers (AUC = 0.625, P < 0.05). However, the combined classifiers did not improve the predictive performance (AUC = 0.780, P > 0.05), with sensitivity and specificity of 0.752 and 0.633. Conclusions: The machine learning-based CT radiomics classifier in NSCLC can facilitate the prediction of the expression level of Ki-67 and provide a novel non-invasive strategy for assessing the cell proliferation.
MeSH term(s)	Adult ; Aged ; Aged, 80 and over ; Algorithms ; Carcinoma, Non-Small-Cell Lung/diagnostic imaging ; Carcinoma, Non-Small-Cell Lung/pathology ; Cell Proliferation ; Female ; Humans ; Lung/diagnostic imaging ; Lung/pathology ; Lung Neoplasms/diagnostic imaging ; Lung Neoplasms/pathology ; Machine Learning ; Male ; Middle Aged ; ROC Curve ; Radiographic Image Interpretation, Computer-Assisted/methods ; Retrospective Studies ; Sensitivity and Specificity ; Tomography, X-Ray Computed/methods
Language	English
Publishing date	2019-06-28
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	138815-0
ISSN	1872-7727 ; 0720-048X
ISSN (online)	1872-7727
ISSN	0720-048X
DOI	10.1016/j.ejrad.2019.06.025
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Article: Radiomics in predicting tumor molecular marker P63 for non-small cell lung cancer.

Gu, Qianbiao / Feng, Zhichao / Hu, Xiaoli / Ma, Mengtian / Mustafa Jumbe, Mwajuma / Yan, Haixiong / Liu, Peng / Rong, Pengfei

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences

2019 Volume 44, Issue 9, Page(s) 1055–1062

Abstract: Objective: To establish a radiomics signature based on CT images of non-small cell lung cancer (NSCLC) to predict the expression of molecular marker P63.  Methods: A total of 245 NSCLC patients who underwent CT scans were retrospectively included. All ... ...

Abstract	Objective: To establish a radiomics signature based on CT images of non-small cell lung cancer (NSCLC) to predict the expression of molecular marker P63.  Methods: A total of 245 NSCLC patients who underwent CT scans were retrospectively included. All patients were confirmed by histopathological examinations and P63 expression were examined within 2 weeks after CT examination. Radiomics features were extracted by MaZda software and subjective image features were defined from original non-enhanced CT images. The Lasso-logistic regression model was used to select features and develop radiomics signature, subjective image features model, and combined diagnostic model. The predictive performance of each model was evaluated by the receiver operating characteristic (ROC) curve, and compared with Delong test.  Results: Of the 245 patients, 96 were P63 positive and 149 were P63 negative. The subjective image feature model consisted of 6 image features. Through feature selection, the radiomics signature consisted of 8 radiomics features. The area under the ROC curves of the subjective image feature model and the radiomics signature in predicting P63 expression statue were 0.700 and 0.755, respectively, without a significant difference (P>0.05). The combined diagnostic model showed the best predictive power (AUC=0.817, P<0.01).  Conclusion: The radiomics-based CT scan images can predict the expression status of NSCLC molecular marker P63. The combination of the radiomics features and subjective image features can significantly improve the predictive performance of the predictive model, which may be helpful to provide a non-invasive way for understanding the molecular information for lung cancer cells.
MeSH term(s)	Biomarkers, Tumor ; Carcinoma, Non-Small-Cell Lung ; Humans ; Lung Neoplasms ; Retrospective Studies ; Tomography, X-Ray Computed
Chemical Substances	Biomarkers, Tumor
Language	English
Publishing date	2019-10-23
Publishing country	China
Document type	Journal Article
ZDB-ID	2168533-2
ISSN	1672-7347
ISSN	1672-7347
DOI	10.11817/j.issn.1672-7347.2019.180752
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