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Wang XY, Wu SH, Ren J, Zeng Y, Guo LL. Predicting Gene Comutation of EGFR and TP53 by Radiomics and Deep Learning in Patients With Lung Adenocarcinomas. J Thorac Imaging 2024:00005382-990000000-00158. [PMID: 39319553 DOI: 10.1097/rti.0000000000000817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2024]
Abstract
PURPOSE This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of epidermal growth factor receptor (EGFR) and TP53 mutations and to assess the models' capacities to identify patients who are suitable for TKI-targeted therapy and those with poor prognoses. MATERIALS AND METHODS A total of 267 patients with lung adenocarcinomas who underwent genetic testing and noncontrast chest computed tomography from our hospital were retrospectively included. Clinical information and imaging characteristics were gathered, and high-throughput feature acquisition on all defined regions of interest (ROIs) was carried out. We selected features and constructed clinical models, radiomics models, deep learning models, and ensemble models to predict EGFR status with all patients and TP53 status with EGFR-positive patients, respectively. The validity and reliability of each model were expressed as the area under the curve (AUC), sensitivity, specificity, accuracy, precision, and F1 score. RESULTS We constructed 7 kinds of models for 2 different dichotomies, namely, the clinical model, the radiomics model, the DL model, the rad-clin model, the DL-clin model, the DL-rad model, and the DL-rad-clin model. For EGFR- and EGFR+, the DL-rad-clin model got the highest AUC value of 0.783 (95% CI: 0.677-0.889), followed by the rad-clin model, the DL-clin model, and the DL-rad model. In the group with an EGFR mutation, for TP53- and TP53+, the rad-clin model got the highest AUC value of 0.811 (95% CI: 0.651-0.972), followed by the DL-rad-clin model and the DL-rad model. CONCLUSION Our progressive binary classification models based on radiomics and deep learning may provide a good reference and complement for the clinical identification of TKI responders and those with poor prognoses.
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Affiliation(s)
- Xiao-Yan Wang
- Department of Radiology, the Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian
| | - Shao-Hong Wu
- Department of Radiology, the Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian
| | - Jiao Ren
- Department of Radiology, the Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian
| | - Yan Zeng
- Department of Research Center, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Li-Li Guo
- Department of Radiology, the Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian
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He W, Huang W, Zhang L, Wu X, Zhang S, Zhang B. Radiogenomics: bridging the gap between imaging and genomics for precision oncology. MedComm (Beijing) 2024; 5:e722. [PMID: 39252824 PMCID: PMC11381657 DOI: 10.1002/mco2.722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 08/06/2024] [Accepted: 08/18/2024] [Indexed: 09/11/2024] Open
Abstract
Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker-guided clinical decision-making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost. In recent years, radiomics has overcomes the limitations of human visual evaluation by high-throughput quantitative analysis, enabling the comprehensive utilization of the vast amount of information underlying radiological images. The cross-scale integration of radiomics and genomics (hereafter radiogenomics) has the enormous potential to enhance cancer decoding and act as a catalyst for digital precision medicine. Herein, we provide a comprehensive overview of the current framework and potential clinical applications of radiogenomics in patient care. We also highlight recent research advances to illustrate how radiogenomics can address common clinical problems in solid tumors such as breast cancer, lung cancer, and glioma. Finally, we analyze existing literature to outline challenges and propose solutions, while also identifying future research pathways. We believe that the perspectives shared in this survey will provide a valuable guide for researchers in the realm of radiogenomics aiming to advance precision oncology.
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Affiliation(s)
- Wenle He
- Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou Guangdong China
| | - Wenhui Huang
- Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou Guangdong China
| | - Lu Zhang
- Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou Guangdong China
| | - Xuewei Wu
- Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou Guangdong China
| | - Shuixing Zhang
- Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou Guangdong China
| | - Bin Zhang
- Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou Guangdong China
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Yao X, Zhu Y, Huang Z, Wang Y, Cong S, Wan L, Wu R, Chen L, Hu Z. Fusion of shallow and deep features from 18F-FDG PET/CT for predicting EGFR-sensitizing mutations in non-small cell lung cancer. Quant Imaging Med Surg 2024; 14:5460-5472. [PMID: 39144023 PMCID: PMC11320501 DOI: 10.21037/qims-23-1028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/20/2023] [Indexed: 08/16/2024]
Abstract
Background Non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor-sensitizing (EGFR-sensitizing) mutations exhibit a positive response to tyrosine kinase inhibitors (TKIs). Given the limitations of current clinical predictive methods, it is critical to explore radiomics-based approaches. In this study, we leveraged deep-learning technology with multimodal radiomics data to more accurately predict EGFR-sensitizing mutations. Methods A total of 202 patients who underwent both flourine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scans and EGFR sequencing prior to treatment were included in this study. Deep and shallow features were extracted by a residual neural network and the Python package PyRadiomics, respectively. We used least absolute shrinkage and selection operator (LASSO) regression to select predictive features and applied a support vector machine (SVM) to classify the EGFR-sensitive patients. Moreover, we compared predictive performance across different deep models and imaging modalities. Results In the classification of EGFR-sensitive mutations, the areas under the curve (AUCs) of ResNet-based deep-shallow features and only shallow features from different multidata were as follows: RES_TRAD, PET/CT vs. CT-only vs. PET-only: 0.94 vs. 0.89 vs. 0.92; and ONLY_TRAD, PET/CT vs. CT-only vs. PET-only: 0.68 vs. 0.50 vs. 0.38. Additionally, the receiver operating characteristic (ROC) curves of the model using both deep and shallow features were significantly different from those of the model built using only shallow features (P<0.05). Conclusions Our findings suggest that deep features significantly enhance the detection of EGFR-sensitizing mutations, especially those extracted with ResNet. Moreover, PET/CT images are more effective than CT-only and PET-only images in producing EGFR-sensitizing mutation-related signatures.
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Affiliation(s)
- Xiaohui Yao
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao, China
| | - Yuan Zhu
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao, China
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhenxing Huang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yue Wang
- Department of PET/CT Center and Department of Thoracic Cancer I, Cancer Center of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shan Cong
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao, China
| | - Liwen Wan
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Ruodai Wu
- Department of Radiology, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen, China
| | - Long Chen
- Department of PET/CT Center and Department of Thoracic Cancer I, Cancer Center of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Kohan A, Hinzpeter R, Kulanthaivelu R, Mirshahvalad SA, Avery L, Tsao M, Li Q, Ortega C, Metser U, Hope A, Veit-Haibach P. Contrast Enhanced CT Radiogenomics in a Retrospective NSCLC Cohort: Models, Attempted Validation of a Published Model and the Relevance of the Clinical Context. Acad Radiol 2024; 31:2953-2961. [PMID: 38383258 DOI: 10.1016/j.acra.2024.01.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 02/23/2024]
Abstract
RATIONALE AND OBJECTIVE To develop a radiogenomic predictive model for non-small cell lung cancer (NSCLC) patients studied through contrast enhanced chest computed tomography (CE-CT) targeting the most frequent gene alterations. M&M: A retrospective study of patients with NSCLC imaged with CE-CT before treatment and had their tumor genomics sequenced at our institution was performed. Data was gathered from their imaging studies, their electronic medical records and a web-based database search (cBioPortal.ca). All of the patient data was tabulated for analysis. Two predictive models (M1 & M2) were created using different approaches and a third model was extracted from the literature to also be tested in our population. RESULTS Out of 157 patients, eighty were male (51%) and 124 (79%) had a history of smoking. The three most prevalent genes were KRAS, TP53 and EGFR. The M1 radiomics-only model median AUC were 0.61 (TP53), 0.53 (KRAS) and 0.64 (EGFR) and for M1 radiomics + clinical were 0.61 (TP53), 0.61 (KRAS) and 0.80 (EGFR). The M2 radiomics-only model median AUC were 0.63 (TP53), 0.60 (KRAS) and 0.65 (EGFR) and for M2 radiomics + clinical were 0.64 (TP53), 0.62 (KRAS) and 0.81 (EGFR). The external EGFR radiomic model showed an AUC of 0.69 and 0.86 for the radiomics-only and combined radiomics + clinical respectively. CONCLUSION Our study was able to provide robust predictive radiomics model evaluation for the detection of TP53, KRAS and EGFR. We also compared our performance with an already published model and observed how impactful clinical variables can be on models' performance. CLINICAL RELEVANCE STATEMENT Identifying tumor mutations in patients that can't undergo biopsy is critical for their outcomes. KEYPOINTS • Tumor genomic profiling is critical for treatment selection • CE-CT radiomics produce robust predictive models comparable to those already published • Clinical variables should be considered/included in predictive models.
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Affiliation(s)
- A Kohan
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada.
| | - R Hinzpeter
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - R Kulanthaivelu
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - S A Mirshahvalad
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - L Avery
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - M Tsao
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Q Li
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - C Ortega
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - U Metser
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
| | - A Hope
- Department of Radiation Oncology, University Health Network, University of Toronto, ON, Canada
| | - P Veit-Haibach
- Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada
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Zhang G, Man Q, Shang L, Zhang J, Cao Y, Li S, Qian R, Ren J, Pu H, Zhou J, Zhang Z, Kong W. Using Multi-phase CT Radiomics Features to Predict EGFR Mutation Status in Lung Adenocarcinoma Patients. Acad Radiol 2024; 31:2591-2600. [PMID: 38290884 DOI: 10.1016/j.acra.2023.12.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 02/01/2024]
Abstract
RATIONALE AND OBJECTIVES This study aimed to non-invasively predict epidermal growth factor receptor (EGFR) mutation status in patients with lung adenocarcinoma using multi-phase computed tomography (CT) radiomics features. MATERIALS AND METHODS A total of 424 patients with lung adenocarcinoma were recruited from two hospitals who underwent preoperative non-enhanced CT (NE-CT) and enhanced CT (including arterial phase CT [AP-CT], and venous phase CT [VP-CT]). Patients were divided into training (n = 297) and external validation (n = 127) cohorts according to hospital. Radiomics features were extracted from the NE-CT, AP-CT, and VP-CT images, respectively. The Wilcoxon test, correlation analysis, and simulated annealing were used for feature screening. A clinical model and eight radiomics models were established. Furthermore, a clinical-radiomics model was constructed by incorporating multi-phase CT features and clinical risk factors. Receiver operating characteristic curves were used to evaluate the predictive performance of the models. RESULTS The predictive performance of multi-phase CT radiomics model (AUC of 0.925 [95% CI, 0.879-0.971] in the validation cohort) was higher than that of NE-CT, AP-CT, VP-CT, and clinical models (AUCs of 0.860 [95% CI,0.794-0.927], 0.792 [95% CI, 0.713-0.871], 0.753 [95% CI, 0.669-0.838], and 0.706 [95% CI, 0.620-0.791] in the validation cohort, respectively) (all P < 0.05). The predictive performance of the clinical-radiomics model (AUC of 0.927 [95% CI, 0.882-0.971] in the validation cohort) was comparable to that of multi-phase CT radiomics model (P > 0.05). CONCLUSION Our multi-phase CT radiomics model showed good performance in identifying the EGFR mutation status in patients with lung adenocarcinoma, which may assist personalized treatment decisions.
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Affiliation(s)
- Guojin Zhang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China (G.Z., L.S., R.Q., H.P., W.K.)
| | - Qiong Man
- School of Pharmacy, Chengdu Medical College, Chengdu, China (Q.M.)
| | - Lan Shang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China (G.Z., L.S., R.Q., H.P., W.K.)
| | - Jing Zhang
- Department of Radiology, Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China (J.Z.)
| | - Yuntai Cao
- Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China (Y.C.)
| | - Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China (S.L., J.Z.)
| | - Rong Qian
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China (G.Z., L.S., R.Q., H.P., W.K.)
| | - Jialiang Ren
- ŌGE Healthcare China, Department of Radiology, China (J.R.)
| | - Hong Pu
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China (G.Z., L.S., R.Q., H.P., W.K.)
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China (S.L., J.Z.)
| | - Zhuoli Zhang
- Department of Radiology and BME, University of California Irvine, Irvine, California, USA (Z.Z.)
| | - Weifang Kong
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China (G.Z., L.S., R.Q., H.P., W.K.).
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Xu X, Zhu M, Wang Z, Li J, Ouyang T, Chen C, Huang K, Zhang Y, Gao YL. Prognostic and predictive value of interstitial lung abnormalities and EGFR mutation status in patients with non-small cell lung cancer. Cancer Imaging 2024; 24:66. [PMID: 38783331 PMCID: PMC11119023 DOI: 10.1186/s40644-024-00712-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 05/11/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND To determine the predictive value of interstitial lung abnormalities (ILA) for epidermal growth factor receptor (EGFR) mutation status and assess the prognostic significance of EGFR and ILA in patients with non-small cell lung cancer (NSCLC). METHODS We reviewed 797 consecutive patients with a histologically proven diagnosis of primary NSCLC from January 2013 to October 2018. Of these, 109 patients with NSCLC were found to have concomitant ILA. Multivariate logistic regression analysis was used to identify the significant clinical and computed tomography (CT) findings in predicting EGFR mutations. Cox proportional hazard models were used to identify significant prognostic factors. RESULTS EGFR mutations were identified in 22 of 109 tumors (20.2%). Multivariate analysis showed that the models incorporating clinical, tumor CT and ILA CT features yielded areas under the receiver operating characteristic curve (AUC) values of 0.749, 0.838, and 0.849, respectively. When combining the three models, the independent predictive factors for EGFR mutations were non-fibrotic ILA, female sex, and small tumor size, with an AUC value of 0.920 (95% confidence interval[CI]: 0.861-0.978, p < 0.001). In the multivariate Cox model, EGFR mutations (hazard ratio = 0.169, 95% CI = 0.042-0.675, p = 0.012; 692 days vs. 301 days) were independently associated with extended overall survival compared to the wild-type. CONCLUSION Non-fibrotic ILA independently predicts the presence of EGFR mutations, and the presence of EGFR mutations rather than non-fibrotic ILA serves as an independent good prognostic factor for patients with NSCLC.
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Affiliation(s)
- Xiaoli Xu
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, No. 8 Gongtinan Road, Beijing, 100020, China
| | - Min Zhu
- Department of Pulmonary and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, No. 8 Gongtinan Road, Beijing, 100020, China
| | - Zixing Wang
- Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Jialu Li
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, No. 8 Gongtinan Road, Beijing, 100020, China
| | - Tao Ouyang
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, No. 8 Gongtinan Road, Beijing, 100020, China
| | - Cen Chen
- Department of Radiology, Beijing Nuclear Industry Hospital, Beijing, China
| | - Kewu Huang
- Department of Pulmonary and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, No. 8 Gongtinan Road, Beijing, 100020, China
| | - Yuhui Zhang
- Department of Pulmonary and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, No. 8 Gongtinan Road, Beijing, 100020, China.
| | - Yanli L Gao
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, No. 8 Gongtinan Road, Beijing, 100020, China.
- Department of Pulmonary and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, No. 8 Gongtinan Road, Beijing, 100020, China.
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Li F, Qi L, Cheng S, Liu J, Chen J, Cui S, Dong S, Wang J. Predicting epidermal growth factor receptor mutations in non-small cell lung cancer through dual-layer spectral CT: a prospective study. Insights Imaging 2024; 15:109. [PMID: 38679659 PMCID: PMC11056350 DOI: 10.1186/s13244-024-01678-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 03/22/2024] [Indexed: 05/01/2024] Open
Abstract
OBJECTIVE To determine whether quantitative parameters of detector-derived dual-layer spectral computed tomography (DLCT) can reliably identify epidermal growth factor receptor (EGFR) mutation status in patients with non-small cell lung cancer (NSCLC). METHODS Patients with NSCLC who underwent arterial phase (AP) and venous phase (VP) DLCT between December 2021 and November 2022 were subdivided into the mutated and wild-type EGFR groups following EGFR mutation testing. Their baseline clinical data, conventional CT images, and spectral images were obtained. Iodine concentration (IC), iodine no water (INW), effective atomic number (Zeff), virtual monoenergetic images, the slope of the spectral attenuation curve (λHU), enhancement degree (ED), arterial enhancement fraction (AEF), and normalized AEF (NAEF) were measured for each lesion. RESULTS Ninety-two patients (median age, 61 years, interquartile range [51, 67]; 33 men) were evaluated. The univariate analysis indicated that IC, normalized IC (NIC), INW and ED for the AP and VP, as well as Zeff and λHU for the VP were significantly associated with EGFR mutation status (all p < 0.05). INW(VP) showed the best diagnostic performance (AUC, 0.892 [95% confidence interval {CI}: 0.823, 0.960]). However, neither AEF (p = 0.156) nor NAEF (p = 0.567) showed significant differences between the two groups. The multivariate analysis showed that INW(AP) and NIC(VP) were significant predictors of EGFR mutation status, with the latter showing better performance (p = 0.029; AUC, 0.897 [95% CI: 0.816, 0.951] vs. 0.774 [95% CI: 0.675, 0.855]). CONCLUSION Quantitative parameters of DLCT can help predict EGFR mutation status in patients with NSCLC. CRITICAL RELEVANCE STATEMENT Quantitative parameters of DLCT, especially NIC(VP), can help predict EGFR mutation status in patients with NSCLC, facilitating appropriate and individualized treatment for them. KEY POINTS Determining EGFR mutation status in patients with NSCLC before starting therapy is essential. Quantitative parameters of DLCT can predict EGFR mutation status in NSCLC patients. NIC in venous phase is an important parameter to guide individualized treatment selection for NSCLC patients.
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Affiliation(s)
- Fenglan Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Beijing, Chaoyang District, 100021, China
| | - Linlin Qi
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Beijing, Chaoyang District, 100021, China
| | - Sainan Cheng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Beijing, Chaoyang District, 100021, China
| | - Jianing Liu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Beijing, Chaoyang District, 100021, China
| | - Jiaqi Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Beijing, Chaoyang District, 100021, China
| | - Shulei Cui
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Beijing, Chaoyang District, 100021, China
| | - Shushan Dong
- Clinical Science, Philips Healthcare, Beijing, China
| | - Jianwei Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Beijing, Chaoyang District, 100021, China.
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Xie Z, Suo S, Zhang W, Zhang Q, Dai Y, Song Y, Li X, Zhou Y. Prediction of high Ki-67 proliferation index of gastrointestinal stromal tumors based on CT at non-contrast-enhanced and different contrast-enhanced phases. Eur Radiol 2024; 34:2223-2232. [PMID: 37773213 PMCID: PMC10957607 DOI: 10.1007/s00330-023-10249-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 07/12/2023] [Accepted: 07/23/2023] [Indexed: 10/01/2023]
Abstract
OBJECTIVES To evaluate and analyze radiomics models based on non-contrast-enhanced computed tomography (CT) and different phases of contrast-enhanced CT in predicting Ki-67 proliferation index (PI) among patients with pathologically confirmed gastrointestinal stromal tumors (GISTs). METHODS A total of 383 patients with pathologically proven GIST were divided into a training set (n = 218, vendor 1) and 2 validation sets (n = 96, vendor 2; n = 69, vendors 3-5). Radiomics features extracted from the most recent non-contrast-enhanced and three contrast-enhanced CT scan prior to pathological examination. Random forest models were trained for each phase to predict tumors with high Ki-67 proliferation index (Ki-67>10%) and were evaluated using the area under the receiver operating characteristic curve (AUC) and other metrics on the validation sets. RESULTS Out of 107 radiomics features extracted from each phase of CT images, four were selected for analysis. The model trained using the non-contrast-enhanced phase achieved an AUC of 0.792 in the training set and 0.822 and 0.711 in the two validation sets, similar to models trained on different contrast-enhanced phases (p > 0.05). Several relevant features, including NGTDM Busyness and tumor size, remained predictive in non-contrast-enhanced and different contrast-enhanced images. CONCLUSION The results of this study indicate that a radiomics model based on non-contrast-enhanced CT matches that of models based on different phases of contrast-enhanced CT in predicting the Ki-67 PI of GIST. GIST may exhibit similar radiological patterns irrespective of the use of contrast agent, and such radiomics features may help quantify these patterns to predict Ki-67 PI of GISTs. CLINICAL RELEVANCE STATEMENT GIST may exhibit similar radiomics patterns irrespective of contrast agent; thus, radiomics models based on non-contrast-enhanced CT could be an alternative for risk stratification in GIST patients with contraindication to contrast agent. KEY POINTS • Performance of radiomics models in predicting Ki-67 proliferation based on different CT phases is evaluated. • Non-contrast-enhanced CT-based radiomics models performed similarly to contrast-enhanced CT in risk stratification in GIST patients. • NGTDM Busyness remains stable to contrast agents in GISTs in radiomics models.
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Affiliation(s)
- Zhenhui Xie
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shiteng Suo
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wang Zhang
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qingwei Zhang
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China
| | - Xiaobo Li
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China.
| | - Yan Zhou
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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Zhang X, Zhang G, Qiu X, Yin J, Tan W, Yin X, Yang H, Wang H, Zhang Y. Non-invasive decision support for clinical treatment of non-small cell lung cancer using a multiscale radiomics approach. Radiother Oncol 2024; 191:110082. [PMID: 38195018 DOI: 10.1016/j.radonc.2024.110082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 12/01/2023] [Accepted: 01/02/2024] [Indexed: 01/11/2024]
Abstract
BACKGROUND Selecting therapeutic strategies for cancer patients is typically based on key target-molecule biomarkers that play an important role in cancer onset, progression, and prognosis. Thus, there is a pressing need for novel biomarkers that can be utilized longitudinally to guide treatment selection. METHODS Using data from 508 non-small cell lung cancer (NSCLC) patients across three institutions, we developed and validated a comprehensive predictive biomarker that distinguishes six genotypes and infiltrative immune phenotypes. These features were analyzed to establish the association between radiological phenotypes and tumor genotypes/immune phenotypes and to create a radiological interpretation of molecular features. In addition, we assessed the sensitivity of the models by evaluating their performance at five different voxel intervals, resulting in improved generalizability of the proposed approach. FINDINGS The radiomics model we developed, which integrates clinical factors and multi-regional features, outperformed the conventional model that only uses clinical and intratumoral features. Our combined model showed significant performance for EGFR, KRAS, ALK, TP53, PIK3CA, and ROS1 mutation status with AUCs of 0.866, 0.874, 0.902, 0.850, 0.860, and 0.900, respectively. Additionally, the predictive performance for PD-1/PD-L1 was 0.852. Although the performance of all models decreased to different degrees at five different voxel space resolutions, the performance advantage of the combined model did not change. CONCLUSIONS We validated multiscale radiomic signatures across tumor genotypes and immunophenotypes in a multi-institutional cohort. This imaging-based biomarker offers a non-invasive approach to select patients with NSCLC who are sensitive to targeted therapies or immunotherapy, which is promising for developing personalized treatment strategies during therapy.
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Affiliation(s)
- Xingping Zhang
- School of Medical Information Engineering, Gannan Medical University, 341000, Ganzhou, China; Cyberspace Institute of Advanced Technology, Guangzhou University, 510006 Guangzhou, China; Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia; Department of New Networks, Peng Cheng Laboratory, 518000, Shenzhen, China
| | - Guijuan Zhang
- Department of Respiratory and Critical Care, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Jiao Yin
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, 110189, Shenyang, China
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006 Guangzhou, China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006 Guangzhou, China
| | - Hua Wang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia.
| | - Yanchun Zhang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia; School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China; Department of New Networks, Peng Cheng Laboratory, 518000, Shenzhen, China.
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10
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Zheng YM, Pang J, Liu ZJ, Yuan MG, Li J, Wu ZJ, Jiang Y, Dong C. A CT-based Deep Learning Radiomics Nomogram for the Prediction of EGFR Mutation Status in Head and Neck Squamous Cell Carcinoma. Acad Radiol 2024; 31:628-638. [PMID: 37481418 DOI: 10.1016/j.acra.2023.06.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/18/2023] [Accepted: 06/24/2023] [Indexed: 07/24/2023]
Abstract
RATIONALE AND OBJECTIVES Accurately assessing epidermal growth factor receptor (EGFR) mutation status in head and neck squamous cell carcinoma (HNSCC) patients is crucial for prognosis and treatment selection. This study aimed to construct and validate a contrast-enhanced computed tomography (CECT)-based deep learning radiomics nomogram (DLRN) to predict EGFR mutation status of HNSCC. MATERIALS AND METHODS A total of 300 HNSCC patients who underwent CECT scans were enrolled in this study. Participants from two hospitals were separated into a training set (n = 200, 56 EGFR-negative and 144 EGFR-positive) from one hospital and an external test set from the other hospital (n = 100, 37 EGFR-negative and 63 EGFR-positive). The least absolute shrinkage and selection operator method was used to select the key features from CECT-based manually extracted radiomics (MER) features and features automatically extracted using a deep learning model (DL, extracted using a GoogLeNet model). The selected independent clinical factors, MER features, and DL features were then combined to construct a DLRN. The DLRN's performance was evaluated using receiver operating characteristics curves. RESULTS Five MER and six DL features were finally chosen. The DLRN, which includes "gender" and "necrotic areas," along with the selected features, predicted EGFR mutation status of HNSCC (EGFR-negative vs. positive) well in both the training (area under the curve [AUC], 0.901) and test (AUC, 0.875) sets. CONCLUSION A DLRN using CECT was built to predict EGFR mutation in HNSCC. The model showed high predictive ability and may aid in treatment selection and patient prognosis.
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Affiliation(s)
- Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China (Y.-m.Z.)
| | - Jing Pang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (J.P., J.L., Z.-j.W., C.D.)
| | - Zong-Jing Liu
- Department of Pediatric Hematology, The Affiliated Hospital of Qingdao University, Qingdao, China (Z.-j.L.)
| | - Ming-Gang Yuan
- Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China (M.-g.Y.)
| | - Jie Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (J.P., J.L., Z.-j.W., C.D.)
| | - Zeng-Jie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (J.P., J.L., Z.-j.W., C.D.)
| | - Yan Jiang
- Department of Otolaryngology - Head and Neck Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China (Y.J.)
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (J.P., J.L., Z.-j.W., C.D.).
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11
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Hou S, Wang H, Wang X, Chen H, Zhou B, Meng R, Sha X, Chang S, Wang H, Jiang W. Tumor-liver interface in MRI of liver metastasis enables prediction of EGFR mutation in patients with lung cancer: A proof-of-concept study. Med Phys 2024; 51:1083-1091. [PMID: 37408393 DOI: 10.1002/mp.16581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 04/19/2023] [Accepted: 06/05/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND Preoperative prediction of the epidermal growth factor receptor (EGFR) status in non-small-cell lung cancer (NSCLC) patients with liver metastasis (LM) may have potential clinical values for assisting in treatment decision-making. PURPOSE To explore the value of tumor-liver interface (TLI)-based magnetic resonance imaging (MRI) radiomics for detecting the EGFR mutation in NSCLC patients with LM. METHODS This retrospective study included 123 and 44 patients from hospital 1 (between Feb. 2018 and Dec. 2021) and hospital 2 (between Nov. 2015 and Aug. 2022), respectively. The patients received contrast-enhanced T1-weighted (CET1) and T2-weighted (T2W) liver MRI scans before treatment. Radiomics features were extracted from MRI images of TLI and the whole tumor region, separately. The least absolute shrinkage and selection operator (LASSO) regression was used to screen the features and establish radiomics signatures (RSs) based on TLI (RS-TLI) and the whole tumor (RS-W). The RSs were evaluated by the receiver operating characteristic (ROC) curve analysis. RESULTS A total of 5 and 6 features were identified highly correlated with the EGFR mutation status from TLI and the whole tumor, respectively. The RS-TLI showed better prediction performance than RS-W in the training (AUCs, RS-TLI vs. RS-W, 0.842 vs. 0.797), internal validation (AUCs, RS-TLI vs. RS-W, 0.771 vs. 0.676) and external validation (AUCs, RS-TLI vs. RS-W, 0.733 vs. 0.679) cohort. CONCLUSION Our study demonstrated that TLI-based radiomics can improve prediction performance of the EGFR mutation in lung cancer patients with LM. The established multi-parametric MRI radiomics models may be used as new markers that can potentially assist in personalized treatment planning.
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Affiliation(s)
- Shaoping Hou
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China
| | - Hongbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, P.R. China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, P.R. China
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital, Shenyang, Liaoning, P.R. China
| | - Boyu Zhou
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China
| | - Ruiqing Meng
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China
| | - Xianzheng Sha
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China
| | - Shijie Chang
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China
| | - Huan Wang
- Radiation Oncology Department of Thoracic Cancer, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, P.R. China
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, P.R. China
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12
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Çalışkan M, Tazaki K. AI/ML advances in non-small cell lung cancer biomarker discovery. Front Oncol 2023; 13:1260374. [PMID: 38148837 PMCID: PMC10750392 DOI: 10.3389/fonc.2023.1260374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/16/2023] [Indexed: 12/28/2023] Open
Abstract
Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.
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Affiliation(s)
- Minal Çalışkan
- Translational Science Department, Precision Medicine Function, Daiichi Sankyo, Inc., Basking Ridge, NJ, United States
| | - Koichi Tazaki
- Translational Science Department I, Precision Medicine Function, Daiichi Sankyo, Tokyo, Japan
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13
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Hunter B, Argyros C, Inglese M, Linton-Reid K, Pulzato I, Nicholson AG, Kemp SV, L Shah P, Molyneaux PL, McNamara C, Burn T, Guilhem E, Mestas Nuñez M, Hine J, Choraria A, Ratnakumar P, Bloch S, Jordan S, Padley S, Ridge CA, Robinson G, Robbie H, Barnett J, Silva M, Desai S, Lee RW, Aboagye EO, Devaraj A. Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis. Br J Cancer 2023; 129:1949-1955. [PMID: 37932513 PMCID: PMC10703918 DOI: 10.1038/s41416-023-02480-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 09/21/2023] [Accepted: 10/23/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Methods to improve stratification of small (≤15 mm) lung nodules are needed. We aimed to develop a radiomics model to assist lung cancer diagnosis. METHODS Patients were retrospectively identified using health records from January 2007 to December 2018. The external test set was obtained from the national LIBRA study and a prospective Lung Cancer Screening programme. Radiomics features were extracted from multi-region CT segmentations using TexLab2.0. LASSO regression generated the 5-feature small nodule radiomics-predictive-vector (SN-RPV). K-means clustering was used to split patients into risk groups according to SN-RPV. Model performance was compared to 6 thoracic radiologists. SN-RPV and radiologist risk groups were combined to generate "Safety-Net" and "Early Diagnosis" decision-support tools. RESULTS In total, 810 patients with 990 nodules were included. The AUC for malignancy prediction was 0.85 (95% CI: 0.82-0.87), 0.78 (95% CI: 0.70-0.85) and 0.78 (95% CI: 0.59-0.92) for the training, test and external test datasets, respectively. The test set accuracy was 73% (95% CI: 65-81%) and resulted in 66.67% improvements in potentially missed [8/12] or delayed [6/9] cancers, compared to the radiologist with performance closest to the mean of six readers. CONCLUSIONS SN-RPV may provide net-benefit in terms of earlier cancer diagnosis.
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Affiliation(s)
- Benjamin Hunter
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Christos Argyros
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Marianna Inglese
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
- Department of Biomedicine and Prevention, University of Rome, Tor Vergata, Italy
| | - Kristofer Linton-Reid
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Ilaria Pulzato
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Andrew G Nicholson
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Histopathology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
| | - Samuel V Kemp
- Nottingham University Hospitals NHS Trust, Department of Respiratory Medicine, Nottingham, UK
| | - Pallav L Shah
- Imperial College London, National Heart and Lung Institute, London, UK
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Respiratory Medicine, London, UK
| | - Philip L Molyneaux
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Respiratory Medicine, London, UK
| | - Cillian McNamara
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Toby Burn
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Emily Guilhem
- King's College Hospital, Department of Radiology, London, UK
| | | | - Julia Hine
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Anika Choraria
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Prashanthi Ratnakumar
- Imperial College London, National Heart and Lung Institute, London, UK
- St Mary's Hospital, Imperial College Healthcare Trust, Department of Respiratory Medicine, London, UK
| | - Susannah Bloch
- Imperial College London, National Heart and Lung Institute, London, UK
- St Mary's Hospital, Imperial College Healthcare Trust, Department of Respiratory Medicine, London, UK
| | - Simon Jordan
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Thoracic Surgery, London, UK
| | - Simon Padley
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
| | - Carole A Ridge
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
| | - Graham Robinson
- The Royal United Hospital, Bath, Department of Radiology, Bath, UK
| | - Hasti Robbie
- King's College Hospital, Department of Radiology, London, UK
| | - Joseph Barnett
- Department of Radiology, Royal Free Hospital, London, UK
| | - Mario Silva
- Section of "Scienze Radiologiche", Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Sujal Desai
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
- Imperial College London, Margaret Turner-Warwick Centre for Fibrosing Lung Disease, London, UK
| | - Richard W Lee
- Imperial College London, National Heart and Lung Institute, London, UK
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
- Early Diagnosis and Detection, Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
| | - Eric O Aboagye
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Anand Devaraj
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK.
- Imperial College London, National Heart and Lung Institute, London, UK.
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14
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Zhang Y, Yi J, Sun D, Su Y, Zuo Y, Zhu M, Zhang S, Huang K, Guo X, Zhang Y. The association of chest computed tomography-defined visual emphysema and prognosis in patients with nonsmall cell lung cancer. ERJ Open Res 2023; 9:00195-2023. [PMID: 38020560 PMCID: PMC10658642 DOI: 10.1183/23120541.00195-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 08/25/2023] [Indexed: 12/01/2023] Open
Abstract
Background Although computed tomography (CT)-defined emphysema is considered a predictor of lung cancer risk, it is not fully clear whether CT-defined emphysema is associated with the prognosis of lung cancer. We aimed to assess the clinical impact of CT-defined emphysema on the survival of lung cancer. Methods In the prospective cohort study of nonsmall cell lung cancer (NSCLC), the correlation between CT-defined emphysema and clinical variables was analysed. A multivariable Cox regression model was built to assess the association between CT-defined emphysema and overall survival (OS) for up to 8.8 years. The differences in survival analyses were derived by Kaplan-Meier analysis and log-rank testing. Low attenuation area (LAA%) was defined as the per cent of voxels below -950 HU. Results 854 patients were included and CT-defined emphysema was present in 300 (35.1%) at diagnosis. Epidermal growth factor receptor (EGFR) wild-type (OR 1.998; p<0.001) and anaplastic lymphoma kinase (ALK) wild-type (OR 2.277; p=0.004) were associated with CT-defined emphysema. CT-defined emphysema remained a significant predictor of prognosis adjusting for age, sex, smoking history, tumour histology and Eastern Cooperative Oncology Group Performance Status (ECOG PS), whether in I-IIIA stage (adjusted hazard ratio (HR) 1.745; p=0.017) or in IIIB-IV stage (adjusted HR 1.291; p=0.022). Stratified analyses showed that OS rate among the driver oncogene groups with different CT-defined emphysema status differed significantly (log-rank p<0.001). Furthermore, patients with centrilobular emphysema (CLE) with LAA% >17% displayed poorer survival than those with LAA% ≤17% (median 432 versus 670 days; HR 1.564; p=0.020). Conclusions CT-defined emphysema, especially CLE with LAA%>17%, is an independent predictor of NSCLC prognosis. Moreover, prospective studies are needed to further explore this association.
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Affiliation(s)
- Yixiao Zhang
- Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Respiratory Medicine, Beijing, China
- These authors contributed equally to this work
| | - Jiawen Yi
- Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Respiratory Medicine, Beijing, China
- These authors contributed equally to this work
| | - Dan Sun
- Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Respiratory Medicine, Beijing, China
- These authors contributed equally to this work
| | - Yanping Su
- Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Respiratory Medicine, Beijing, China
| | - Yingting Zuo
- Beijing Institute of Respiratory Medicine, Beijing, China
- Department of Clinical Epidemiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Min Zhu
- Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Respiratory Medicine, Beijing, China
| | - Shu Zhang
- Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Respiratory Medicine, Beijing, China
| | - Kewu Huang
- Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Respiratory Medicine, Beijing, China
| | - Xiaojuan Guo
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Yuhui Zhang
- Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Respiratory Medicine, Beijing, China
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15
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Deng M, Liu A, Kang H, Xi L, Yu P, Xu W, Yang H, Xie W, Liu M, Zhang R. Development and validation of a lung graph-based machine learning model to predict acute pulmonary thromboembolism on chest noncontrast computed tomography. Quant Imaging Med Surg 2023; 13:6710-6723. [PMID: 37869274 PMCID: PMC10585544 DOI: 10.21037/qims-22-1059] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 07/30/2023] [Indexed: 10/24/2023]
Abstract
Background Computed tomography pulmonary angiography (CTPA) is a first-line noninvasive method to diagnose acute pulmonary thromboembolism (APE); however, whether chest noncontrast CT (NC-CT) could aid in the diagnosis of APE remains unknown. The aim of this study was to build and evaluate a holistic lung graph-based machine learning (HLG-ML) using NC-CT for the diagnosis of APE and to compare its performance with that of radiologists and the YEARS algorithm. Methods This study enrolled 178 cases (77 males; age 63.9±16.7 years) who underwent NC-CT and CTPA in the same day from January 2019 to December 2020. Of these patients, 133 (75% of cases; 58 males; age 65.4±15.6 years) were placed into a training group and 45 (25% of cases; 19 males; age 59.6±19.2 years) into a testing group. The other 43 cases (18 males; age 62.8±20.0 years) were used to externally validate the model between January 2021 and March 2022. A HLG was developed with a pulmonary radiomics descriptor derived from NC-CT images. The approach extracted local radiomics features and encoded these local features into a radiomics descriptor as a characterization of global radiomics feature distribution. Subsequently, 8 ML models were trained and compared based on the radiomics descriptor. In the validation group, area under the curves (AUCs) of the HLG-ML model in the diagnosis of APE were compared with those of the 3 radiologists and the YEARS algorithm. Results Among the 8 ML models, gradient boosting decision tree demonstrated the best classification performance (AUC =0.772) on the training set. In the testing set, the AUC of gradient boosting decision trees was 0.857 [95% confidence intervals (CIs): 0.699-0.951]. In the validation set, the performance of gradient boosting decision tree (AUC =0.810; 95% CI: 0.669-0.952; Youden index =0.621) outperformed 3 radiologists (AUC =0.508, 95% CI: 0.335-0.681, Youden index =0.016; AUC =0.504, 95% CI: 0.354-0.654, Youden index =0.008; AUC =0.527, 95% CI: 0.363-0.691, Youden index =0.050) and the YEARS algorithm (AUC =0.618; 95% CI: 0.469-0.767; Youden index =0.237). Conclusions Compared to all 3 radiologists and the YEARS algorithm, the proposed HLG-based gradient boosting decision tree model achieved a superior performance in the diagnosis of APE on the NC-CT and may thus serve as a valuable tool for physicians in the diagnosis of APE.
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Affiliation(s)
- Mei Deng
- Department of Radiology, China-Japan Friendship Hospital of Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Anqi Liu
- Department of Radiology, China-Japan Friendship Hospital of Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Han Kang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, China
| | - Linfeng Xi
- Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, China
| | - Pengxin Yu
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, China
| | - Wenqing Xu
- Department of Radiology, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Haoyu Yang
- Department of Radiology, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Wanmu Xie
- Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, China
| | - Min Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Rongguo Zhang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, China
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16
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Felfli M, Liu Y, Zerka F, Voyton C, Thinnes A, Jacques S, Iannessi A, Bodard S. Systematic Review, Meta-Analysis and Radiomics Quality Score Assessment of CT Radiomics-Based Models Predicting Tumor EGFR Mutation Status in Patients with Non-Small-Cell Lung Cancer. Int J Mol Sci 2023; 24:11433. [PMID: 37511192 PMCID: PMC10380456 DOI: 10.3390/ijms241411433] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023] Open
Abstract
Assessment of the quality and current performance of computed tomography (CT) radiomics-based models in predicting epidermal growth factor receptor (EGFR) mutation status in patients with non-small-cell lung carcinoma (NSCLC). Two medical literature databases were systematically searched, and articles presenting original studies on CT radiomics-based models for predicting EGFR mutation status were retrieved. Forest plots and related statistical tests were performed to summarize the model performance and inter-study heterogeneity. The methodological quality of the selected studies was assessed via the Radiomics Quality Score (RQS). The performance of the models was evaluated using the area under the curve (ROC AUC). The range of the Risk RQS across the selected articles varied from 11 to 24, indicating a notable heterogeneity in the quality and methodology of the included studies. The average score was 15.25, which accounted for 42.34% of the maximum possible score. The pooled Area Under the Curve (AUC) value was 0.801, indicating the accuracy of CT radiomics-based models in predicting the EGFR mutation status. CT radiomics-based models show promising results as non-invasive alternatives for predicting EGFR mutation status in NSCLC patients. However, the quality of the studies using CT radiomics-based models varies widely, and further harmonization and prospective validation are needed before the generalization of these models.
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Affiliation(s)
- Mehdi Felfli
- Median Technologies, F-06560 Valbonne, France; (M.F.); (Y.L.); (F.Z.); (C.V.); (A.T.); (S.J.); (A.I.)
| | - Yan Liu
- Median Technologies, F-06560 Valbonne, France; (M.F.); (Y.L.); (F.Z.); (C.V.); (A.T.); (S.J.); (A.I.)
| | - Fadila Zerka
- Median Technologies, F-06560 Valbonne, France; (M.F.); (Y.L.); (F.Z.); (C.V.); (A.T.); (S.J.); (A.I.)
| | - Charles Voyton
- Median Technologies, F-06560 Valbonne, France; (M.F.); (Y.L.); (F.Z.); (C.V.); (A.T.); (S.J.); (A.I.)
| | - Alexandre Thinnes
- Median Technologies, F-06560 Valbonne, France; (M.F.); (Y.L.); (F.Z.); (C.V.); (A.T.); (S.J.); (A.I.)
| | - Sebastien Jacques
- Median Technologies, F-06560 Valbonne, France; (M.F.); (Y.L.); (F.Z.); (C.V.); (A.T.); (S.J.); (A.I.)
| | - Antoine Iannessi
- Median Technologies, F-06560 Valbonne, France; (M.F.); (Y.L.); (F.Z.); (C.V.); (A.T.); (S.J.); (A.I.)
- Centre Antoine Lacassagne, F-06100 Nice, France
| | - Sylvain Bodard
- AP-HP, Service d’Imagerie Adulte, Hôpital Necker Enfants Malades, Université de Paris Cité, F-75015 Paris, France
- CNRS UMR 7371, INSERM U 1146, Laboratoire d’Imagerie Biomédicale, Sorbonne Université, F-75006 Paris, France
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CT-Based Radiomics Can Predict the Efficacy of Anlotinib in Advanced Non-Small-Cell Lung Cancer. JOURNAL OF ONCOLOGY 2022; 2022:4182540. [PMID: 36600966 PMCID: PMC9807313 DOI: 10.1155/2022/4182540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 11/25/2022] [Accepted: 11/29/2022] [Indexed: 12/27/2022]
Abstract
Anlotinib is a small-molecule RTK inhibitor that has achieved certain results in further-line treatment, but many patients do not respond to this drug and lack effective methods for identification. Although radiomics has been widely used in lung cancer, very few studies have been conducted in the field of antiangiogenic drugs. This study aims to develop a new model to predict the efficacy of patients receiving anlotinib by combining pretreatment computed tomography (CT) radiomic characters with clinical characters, in order to assist precision medicine of pulmonary cancer. 254 patients from seven institutions were involved in the study. Lesions were selected according to the RECIST 1.1 criteria, and the corresponding radiomic features were obtained. We constructed prediction models based on clinical, NCE-CT, and CE-CT radiomic features, respectively, and evaluated the prediction performance of the models for training sets, internal validation sets, and external validation sets. In the RAD score only model, the area under curve(AUC) of the NCE-CT cohort was 0.740 (95% CI: 0.622, 0.857) for the training set, 0.711 (95% CI: 0.480, 0.942) for the internal validation set, and 0.633(95% CI: 0.479, 0.787) for the external validation set, while that of the CE-CT cohort was 0.815 (95% CI: 0.705, 0.926) for the training set, 0.771 (95% CI: 0.539, 1.000) for the internal validation set, and 0.701 (95% CI: 0.489, 0.913) for the external validation set. In the RAD score-combined model, the AUC of the NCE-CT cohort was 0.796 (95% CI: 0.691, 0.901) for the training set, 0.579 (95% CI: 0.309, 0.848) for the internal validation set, and 0.590 (95% CI: 0.427, 0.753) for the external validation set, while that of the CE-CT cohort was 0.902 (95% CI: 0.828, 0.977) for the training set, 0.865 (95% CI: 0.696, 1.000) for the internal validation set, and 0.837 (95% CI: 0.682, 0.992) for the external validation set. In conclusion, radiomics has accurate predictions for the efficacy of anlotinib. CE-CT-based radiomic models have the best predictive potential in predicting the efficacy of anlotinib, and model predictions become better when they are combined with clinical characteristics.
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Yamazaki M, Yagi T, Tominaga M, Minato K, Ishikawa H. Role of intratumoral and peritumoral CT radiomics for the prediction of EGFR gene mutation in primary lung cancer. Br J Radiol 2022; 95:20220374. [DOI: 10.1259/bjr.20220374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Objectives To determine the added value of combining intratumoral and peritumoral CT radiomics for the prediction of epidermal growth factor receptor (EGFR) gene mutations in primary lung cancer (PLC). Methods This study included 478 patients with PLC (348 adenocarcinomas and 130 other histological types) who underwent surgical resection and EGFR gene testing. Two radiologists performed segmentation of tumors and peritumoral regions using precontrast high-resolution CT images, and 398 radiomic features (212 intra- and 186 peritumoral features) were extracted. The peritumoral region was defined as the lung parenchyma within a distance of 3 mm from the tumor border. Model performance was estimated using Random Forest, a machine-learning algorithm. Results EGFR mutations were found in 162 tumors; 161 adenocarcinomas, and one pleomorphic carcinoma. After exclusion of poorly reproducible and redundant features, 32 radiomic features remained (14 intra- and 18 peritumoral features) and were included in the model building. For predicting EGFR mutations, combining intra- and peritumoral radiomics significantly improved the performance compared to intratumoral radiomics alone (AUC [area under the receiver operating characteristic curve], 0.774 vs 0.730; p < 0.001). Even in adenocarcinomas only, adding peritumoral radiomics significantly increased performance (AUC, 0.687 vs 0.630; p < 0.001). The predictive performance using radiomics and clinical features was significantly higher than that of clinical features alone (AUC, 0.826 vs 0.777; p = 0.005). Conclusions Combining intra- and peritumoral radiomics improves the predictive accuracy of EGFR mutations and could be used to aid in decision-making of whether to perform biopsy for gene tests. Advances in knowledge Adding peritumoral to intratumoral radiomics yields greater accuracy than intratumoral radiomics alone in predicting EGFR mutations and may serve as a non-invasive method of predicting of the gene status in PLC.
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Affiliation(s)
- Motohiko Yamazaki
- Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Takuya Yagi
- Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Masaki Tominaga
- Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Kojiro Minato
- Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Hiroyuki Ishikawa
- Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
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Yi L, Peng Z, Chen Z, Tao Y, Lin Z, He A, Jin M, Peng Y, Zhong Y, Yan H, Zuo M. Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features. Front Oncol 2022; 12:924055. [PMID: 36147924 PMCID: PMC9485677 DOI: 10.3389/fonc.2022.924055] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/22/2022] [Indexed: 11/17/2022] Open
Abstract
To develop and validate a predictive model based on clinical radiology and radiomics to enhance the ability to distinguish between benign and malignant solitary solid pulmonary nodules. In this study, we retrospectively collected computed tomography (CT) images and clinical data of 286 patients with isolated solid pulmonary nodules diagnosed by surgical pathology, including 155 peripheral adenocarcinomas and 131 benign nodules. They were randomly divided into a training set and verification set at a 7:3 ratio, and 851 radiomic features were extracted from thin-layer enhanced venous phase CT images by outlining intranodal and perinodal regions of interest. We conducted preprocessing measures of image resampling and eigenvalue normalization. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (lasso) methods were used to downscale and select features. At the same time, univariate and multifactorial analyses were performed to screen clinical radiology features. Finally, we constructed a nomogram based on clinical radiology, intranodular, and perinodular radiomics features. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC), and the clinical decision curve (DCA) was used to evaluate the clinical practicability of the models. Univariate and multivariate analyses showed that the two clinical factors of sex and age were statistically significant. Lasso screened four intranodal and four perinodal radiomic features. The nomogram based on clinical radiology, intranodular, and perinodular radiomics features showed the best predictive performance (AUC=0.95, accuracy=0.89, sensitivity=0.83, specificity=0.96), which was superior to other independent models. A nomogram based on clinical radiology, intranodular, and perinodular radiomics features is helpful to improve the ability to predict benign and malignant solitary pulmonary nodules.
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Yin X, Liao H, Yun H, Lin N, Li S, Xiang Y, Ma X. Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer. Semin Cancer Biol 2022; 86:146-159. [PMID: 35963564 DOI: 10.1016/j.semcancer.2022.08.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/06/2022] [Accepted: 08/08/2022] [Indexed: 11/26/2022]
Abstract
Lung cancer accounts for the main proportion of malignancy-related deaths and most patients are diagnosed at an advanced stage. Immunotherapy and targeted therapy have great advances in application in clinics to treat lung cancer patients, yet the efficacy is unstable. The response rate of these therapies varies among patients. Some biomarkers have been proposed to predict the outcomes of immunotherapy and targeted therapy, including programmed cell death-ligand 1 (PD-L1) expression and oncogene mutations. Nevertheless, the detection tests are invasive, time-consuming, and have high demands on tumor tissue. The predictive performance of conventional biomarkers is also unsatisfactory. Therefore, novel biomarkers are needed to effectively predict the outcomes of immunotherapy and targeted therapy. The application of artificial intelligence (AI) can be a possible solution, as it has several advantages. AI can help identify features that are unable to be used by humans and perform repetitive tasks. By combining AI methods with radiomics, pathology, genomics, transcriptomics, proteomics, and clinical data, the integrated model has shown predictive value in immunotherapy and targeted therapy, which significantly improves the precision treatment of lung cancer patients. Herein, we reviewed the application of AI in predicting the outcomes of immunotherapy and targeted therapy in lung cancer patients, and discussed the challenges and future directions in this field.
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Affiliation(s)
- Xiaomeng Yin
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Hu Liao
- Department of Thoracic Surgery, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Hong Yun
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Nan Lin
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Shen Li
- West China School of Medicine, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Yu Xiang
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xuelei Ma
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
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Radiomics based on enhanced CT for differentiating between pulmonary tuberculosis and pulmonary adenocarcinoma presenting as solid nodules or masses. J Cancer Res Clin Oncol 2022:10.1007/s00432-022-04256-y. [PMID: 35939114 DOI: 10.1007/s00432-022-04256-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/02/2022] [Indexed: 10/15/2022]
Abstract
PURPOSE To investigate the incremental value of enhanced CT-based radiomics in discriminating between pulmonary tuberculosis (PTB) and pulmonary adenocarcinoma (PAC) presenting as solid nodules or masses and to develop an optimal radiomics model. METHODS A total of 128 lesions (from 123 patients) from three hospitals were retrospectively analyzed and were randomly divided into training and test datasets at a ratio of 7:3. Independent predictors in subjective image features were used to develop the subjective image model (SIM). The plain CT-based and enhanced CT-based radiomics features were screened by the correlation coefficient method, univariate analysis, and the least absolute shrinkage and selection operator, then used to build the plain CT radiomics model (PRM) and enhanced CT radiomics model (ERM), respectively. Finally, the combined model (CM) combining PRM and ERM was established. In addition, the performance of three radiologists and one respiratory physician was evaluated. The areas under the receiver operating characteristic curve (AUCs) were used to assess the performance of each model. RESULTS The differential diagnostic capability of the ERM (training: AUC = 0.933; test: AUC = 0.881) was better than that of the PRM (training: AUC = 0.861; test: AUC = 0.756) and the SIM (training: AUC = 0.760; test: AUC = 0.611). The CM was optimal (training: AUC = 0.948; test: AUC = 0.917) and outperformed the respiratory physician and most radiologists. CONCLUSIONS The ERM was more helpful than the PRM for identifying PTB and PAC that present as solid nodules or masses, and the CM was the best.
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Yang L, Xu P, Li M, Wang M, Peng M, Zhang Y, Wu T, Chu W, Wang K, Meng H, Zhang L. PET/CT Radiomic Features: A Potential Biomarker for EGFR Mutation Status and Survival Outcome Prediction in NSCLC Patients Treated With TKIs. Front Oncol 2022; 12:894323. [PMID: 35800046 PMCID: PMC9253544 DOI: 10.3389/fonc.2022.894323] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 05/16/2022] [Indexed: 11/14/2022] Open
Abstract
Backgrounds Epidermal growth factor receptor (EGFR) mutation profiles play a vital role in treatment strategy decisions for non–small cell lung cancer (NSCLC). The purpose of this study was to evaluate the predictive efficacy of baseline 18F-FDG PET/CT-based radiomics analysis for EGFR mutation status, mutation site, and the survival benefit of targeted therapy. Methods A sum of 313 NSCLC patients with pre-treatment 18F-FDG PET/CT scans and genetic mutations detection were retrospectively studied. Clinical and PET metabolic parameters were incorporated into independent predictors of determining mutation status and mutation site. The dataset was randomly allocated into the training and the validation sets in a 7:3 ratio. Three-dimensional (3D) radiomics features were extracted from each PET- and CT-volume of interests (VOI) singularly, and then a radiomics signature (RS) associated with EGFR mutation profiles is built by feature selection. Three different prediction models based on support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers were established. Furthermore, nomograms for estimation of overall survival (OS) and progression-free survival (PFS) were established by integrating PET/CT radiomics score (Rad-score), metabolic parameters, and clinical factors. Predictive performance was assessed by the receiver operating characteristic (ROC) analysis and the calibration curve analysis. The decision curve analysis (DCA) was applied to estimate and compare the clinical usefulness of nomograms. Results Three hundred thirteen NSCLC patients were classified into a training set (n=218) and a validation set (n=95). Multivariate analysis demonstrated that SUVmax and sex were independent indicators of EGFR mutation status and mutation site. Eight CT-derived RS, six PET-derived RS, and two clinical factors were retained to develop integrated models, which exhibited excellent ability to distinguish between EGFR wild type (EGFR-WT), EGFR 19 mutation type (EGFR-19-MT), and EGFR 21 mutation type (EGFR-21-MT). The SVM model outperformed the RF model and the DT model, yielding training area under the curves (AUC) of EGFR-WT, EGFR-19-WT, and EGFR-21-WT, with 0.881, 0.851, and 0.849, respectively, and validation AUCs of 0.926, 0.805 and 0.859, respectively. For prediction of OS, the integrated nomogram is superior to the clinical nomogram and the radiomics nomogram, with C-indexes of 0.80 in the training set and 0.83 in the validation set, respectively. Conclusions The PET/CT-based radiomics analysis might provide a novel approach to predict EGFR mutation status and mutation site in NSCLC patients and could serve as useful predictors for the patients’ survival outcome of targeted therapy in clinical practice.
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Affiliation(s)
- Liping Yang
- Positron Emission Tomography/Computed Tomography (PET-CT)/MR Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Panpan Xu
- Positron Emission Tomography/Computed Tomography (PET-CT)/MR Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Mengyue Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Menglu Wang
- Positron Emission Tomography/Computed Tomography (PET-CT)/MR Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Mengye Peng
- Positron Emission Tomography/Computed Tomography (PET-CT)/MR Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Ying Zhang
- Positron Emission Tomography/Computed Tomography (PET-CT)/MR Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Tingting Wu
- Positron Emission Tomography/Computed Tomography (PET-CT)/MR Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Wenjie Chu
- Positron Emission Tomography/Computed Tomography (PET-CT)/MR Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Kezheng Wang
- Positron Emission Tomography/Computed Tomography (PET-CT)/MR Department, Harbin Medical University Cancer Hospital, Harbin, China
- *Correspondence: Lingbo Zhang, ; Kezheng Wang, ; Hongxue Meng,
| | - Hongxue Meng
- Department of Pathology, Harbin Medical University Cancer Hospital, Harbin, China
- *Correspondence: Lingbo Zhang, ; Kezheng Wang, ; Hongxue Meng,
| | - Lingbo Zhang
- Oral Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
- *Correspondence: Lingbo Zhang, ; Kezheng Wang, ; Hongxue Meng,
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