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Lacroix M, Aouad T, Feydy J, Biau D, Larousserie F, Fournier L, Feydy A. Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications. Diagn Interv Imaging 2023; 104:18-23. [PMID: 36270953 DOI: 10.1016/j.diii.2022.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 10/05/2022] [Indexed: 01/10/2023]
Abstract
Artificial intelligence (AI) is increasingly being studied in musculoskeletal oncology imaging. AI has been applied to both primary and secondary bone tumors and assessed for various predictive tasks that include detection, segmentation, classification, and prognosis. Still, in the field of clinical research, further efforts are needed to improve AI reproducibility and reach an acceptable level of evidence in musculoskeletal oncology. This review describes the basic principles of the most common AI techniques, including machine learning, deep learning and radiomics. Then, recent developments and current results of AI in the field of musculoskeletal oncology are presented. Finally, limitations and future perspectives of AI in this field are discussed.
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Affiliation(s)
- Maxime Lacroix
- Department of Radiology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, 75015, France; Université Paris Cité, Faculté de Médecine, Paris, 75006, France; PARCC UMRS 970, INSERM, Paris 75015, France
| | - Theodore Aouad
- Université Paris-Saclay, CentraleSupélec, Inria, Centre for Visual Computing, 91190, Gif-sur-Yvette, France
| | - Jean Feydy
- Université Paris Cité, HeKA team, Inria Paris, Inserm, 75006, Paris, France
| | - David Biau
- Université Paris Cité, Faculté de Médecine, Paris, 75006, France; Department of Orthopedic Surgery, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
| | - Frédérique Larousserie
- Université Paris Cité, Faculté de Médecine, Paris, 75006, France; Department of Pathology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
| | - Laure Fournier
- Department of Radiology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, 75015, France; Université Paris Cité, Faculté de Médecine, Paris, 75006, France; PARCC UMRS 970, INSERM, Paris 75015, France
| | - Antoine Feydy
- Université Paris Cité, Faculté de Médecine, Paris, 75006, France; Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
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Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review. Eur Radiol 2023; 33:2105-2117. [PMID: 36307554 PMCID: PMC9935659 DOI: 10.1007/s00330-022-09174-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/20/2022] [Accepted: 09/16/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To provide an overarching evaluation of the value of peritumoral CT radiomics features for predicting the prognosis of non-small cell lung cancer and to assess the quality of the available studies. METHODS The PubMed, Embase, Web of Science, and Cochrane Library databases were searched for studies predicting the prognosis in patients with non-small cell lung cancer (NSCLC) using CT-based peritumoral radiomics features. Information about the patient, CT-scanner, and radiomics analyses were all extracted for the included studies. Study quality was assessed using the Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS Thirteen studies were included with 2942 patients from 2017 to 2022. Only one study was prospective, and the others were all retrospectively designed. Manual segmentation and multicenter studies were performed by 69% and 46% of the included studies, respectively. 3D-Slicer and MATLAB software were most commonly used for the segmentation of lesions and extraction of features. The peritumoral region was most frequently defined as dilated from the tumor boundary of 15 mm, 20 mm, or 30 mm. The median RQS of the studies was 13 (range 4-19), while all of included studies were assessed as having a high risk of bias (ROB) overall. CONCLUSIONS Peritumoral radiomics features based on CT images showed promise in predicting the prognosis of NSCLC, although well-designed studies and further biological validation are still needed. KEY POINTS • Peritumoral radiomics features based on CT images are promising and encouraging for predicting the prognosis of non-small cell lung cancer. • The peritumoral region was often dilated from the tumor boundary of 15 mm or 20 mm because these were considered safe margins. • The median Radiomics Quality Score of the included studies was 13 (range 4-19), and all of studies were considered to have a high risk of bias overall.
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Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography. Cancers (Basel) 2022; 14:cancers14153798. [PMID: 35954461 PMCID: PMC9367244 DOI: 10.3390/cancers14153798] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 12/22/2022] Open
Abstract
Simple Summary The five-year survival rate of non-small-cell lung cancer (NSCLC), which accounts for 85% of all lung cancer cases, is only 10–20%. A reliable prediction model of overall survival (OS) that integrates imaging and clinical data is required. Overall, 492 patients with NSCLC from two hospitals were enrolled in this study. The compensation method was applied to reduce the variation of imaging features among different hospitals. We constructed a deep learning prediction model, DeepSurv, based on computed tomography radiomics and key clinical features to generate a personalized survival curve for each patient. The results of DeepSurv showed a good performance in discriminating high and low risk of survival. Furthermore, the generated personalized survival curves could be intuitively applied for individual OS prediction in clinical practice. We concluded that the proposed prediction model could benefit physicians, patients, and caregivers in managing NSCLC and facilitate personalized medicine. Abstract Patient outcomes of non-small-cell lung cancer (NSCLC) vary because of tumor heterogeneity and treatment strategies. This study aimed to construct a deep learning model combining both radiomic and clinical features to predict the overall survival of patients with NSCLC. To improve the reliability of the proposed model, radiomic analysis complying with the Image Biomarker Standardization Initiative and the compensation approach to integrate multicenter datasets were performed on contrast-enhanced computed tomography (CECT) images. Pretreatment CECT images and the clinical data of 492 patients with NSCLC from two hospitals were collected. The deep neural network architecture, DeepSurv, with the input of radiomic and clinical features was employed. The performance of survival prediction model was assessed using the C-index and area under the curve (AUC) 8, 12, and 24 months after diagnosis. The performance of survival prediction that combined eight radiomic features and five clinical features outperformed that solely based on radiomic or clinical features. The C-index values of the combined model achieved 0.74, 0.75, and 0.75, respectively, and AUC values of 0.76, 0.74, and 0.73, respectively, 8, 12, and 24 months after diagnosis. In conclusion, combining the traits of pretreatment CECT images, lesion characteristics, and treatment strategies could effectively predict the survival of patients with NSCLC using a deep learning model.
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Jiang Y, Wang Y, Fu S, Chen T, Zhou Y, Zhang X, Chen C, He LN, Du W, Li H, Lin Z, Zhao Y, Yang Y, Zhao H, Fang W, Huang Y, Hong S, Zhang L. A CT-based radiomics model to predict subsequent brain metastasis in patients with ALK-rearranged non-small cell lung cancer undergoing crizotinib treatment. Thorac Cancer 2022; 13:1558-1569. [PMID: 35437945 PMCID: PMC9161316 DOI: 10.1111/1759-7714.14386] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/26/2022] [Accepted: 02/28/2022] [Indexed: 11/27/2022] Open
Abstract
Background Brain metastasis (BM) comprises the most common reason for crizotinib failure in patients with anaplastic lymphoma kinase (ALK)‐rearranged non–small cell lung cancer (NSCLC). We hypothesize that its occurrence could be predicted by a computed tomography (CT)‐based radiomics model, therefore, allowing for selection of enriched patient populations for prevention therapies. Methods A total of 75 eligible patients were enrolled from Sun Yat‐sen University Cancer Center between June 2014 and September 2019. The primary endpoint was brain metastasis‐free survival (BMFS), estimated from the initiation of crizotinib to the date of the occurrence of BM. Patients were randomly divided into two cohorts for model training (n = 51) and validation (n = 24), respectively. A radiomics signature was constructed based on features extracted from chest CT before crizotinib treatment. Clinical model was developed using the Cox proportional hazards model. Log‐rank test was performed to describe the difference of BMFS risk. Results Patients with low radiomics score had significantly longer BMFS than those with higher, both in the training cohort (p = 0.019) and validation cohort (p = 0.048). The nomogram combining smoking history and the radiomics signature showed good performance for the estimation of BMFS, both in the training (concordance index [C‐index], 0.762; 95% confidence interval [CI], 0.663–0.861) and validation cohort (C‐index, 0.724; 95% CI, 0.601–0.847). Conclusion We have developed a CT‐based radiomics model to predict subsequent BM in patients with non‐brain metastatic NSCLC undergoing crizotinib treatment. Selection of an enriched patient population at high BM risk will facilitate the design of clinical trials or strategies to prevent BM.
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Affiliation(s)
- Yongluo Jiang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yixing Wang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Sha Fu
- Cellular & Molecular Diagnostics Center, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tao Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yixin Zhou
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of VIP region, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xuanye Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chen Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Li-Na He
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wei Du
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Haifeng Li
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zuan Lin
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yuanyuan Zhao
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yunpeng Yang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Hongyun Zhao
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wenfeng Fang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yan Huang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Shaodong Hong
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Li Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
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Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction. Diagnostics (Basel) 2022; 12:diagnostics12020247. [PMID: 35204338 PMCID: PMC8871487 DOI: 10.3390/diagnostics12020247] [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: 12/06/2021] [Revised: 01/12/2022] [Accepted: 01/18/2022] [Indexed: 11/23/2022] Open
Abstract
Machine learning results based on radiomic analysis are often not transferrable. A potential reason for this is the variability of radiomic features due to varying human made segmentations. Therefore, the aim of this study was to provide comprehensive inter-reader reliability analysis of radiomic features in five clinical image datasets and to assess the association of inter-reader reliability and survival prediction. In this study, we analyzed 4598 tumor segmentations in both computed tomography and magnetic resonance imaging data. We used a neural network to generate 100 additional segmentation outlines for each tumor and performed a reliability analysis of radiomic features. To prove clinical utility, we predicted patient survival based on all features and on the most reliable features. Survival prediction models for both computed tomography and magnetic resonance imaging datasets demonstrated less statistical spread and superior survival prediction when based on the most reliable features. Mean concordance indices were Cmean = 0.58 [most reliable] vs. Cmean = 0.56 [all] (p < 0.001, CT) and Cmean = 0.58 vs. Cmean = 0.57 (p = 0.23, MRI). Thus, preceding reliability analyses and selection of the most reliable radiomic features improves the underlying model’s ability to predict patient survival across clinical imaging modalities and tumor entities.
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Chai H, Xia L, Zhang L, Yang J, Zhang Z, Qian X, Yang Y, Pan W. An Adaptive Transfer-Learning-Based Deep Cox Neural Network for Hepatocellular Carcinoma Prognosis Prediction. Front Oncol 2021; 11:692774. [PMID: 34646759 PMCID: PMC8504135 DOI: 10.3389/fonc.2021.692774] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 09/01/2021] [Indexed: 12/24/2022] Open
Abstract
Background Predicting hepatocellular carcinoma (HCC) prognosis is important for treatment selection, and it is increasingly interesting to predict prognosis through gene expression data. Currently, the prognosis remains of low accuracy due to the high dimension but small sample size of liver cancer omics data. In previous studies, a transfer learning strategy has been developed by pre-training models on similar cancer types and then fine-tuning the pre-trained models on the target dataset. However, transfer learning has limited performance since other cancer types are similar at different levels, and it is not trivial to balance the relations with different cancer types. Methods Here, we propose an adaptive transfer-learning-based deep Cox neural network (ATRCN), where cancers are represented by 12 phenotype and 10 genotype features, and suitable cancers were adaptively selected for model pre-training. In this way, the pre-trained model can learn valuable prior knowledge from other cancer types while reducing the biases. Results ATRCN chose pancreatic and stomach adenocarcinomas as the pre-training cancers, and the experiments indicated that our method improved the C-index of 3.8% by comparing with traditional transfer learning methods. The independent tests on three additional HCC datasets proved the robustness of our model. Based on the divided risk subgroups, we identified 10 HCC prognostic markers, including one new prognostic marker, TTC36. Further wet experiments indicated that TTC36 is associated with the progression of liver cancer cells. Conclusion These results proved that our proposed deep-learning-based method for HCC prognosis prediction is robust, accurate, and biologically meaningful.
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Affiliation(s)
- Hua Chai
- School of Mathematics and Big Data, Foshan University, Foshan, China.,Department of Pancreatic-Hepato-Biliary-Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Long Xia
- Department of Hepatobiliary-Pancreatic-Splenic Surgery, Inner Mongolia Autonomous Region People's Hospital, Hohhot, China
| | - Lei Zhang
- Department of Pancreatic-Hepato-Biliary-Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiarui Yang
- Department of Pancreatic-Hepato-Biliary-Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhongyue Zhang
- School of Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Xiangjun Qian
- Department of Pancreatic-Hepato-Biliary-Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yuedong Yang
- School of Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Weidong Pan
- Department of Pancreatic-Hepato-Biliary-Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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Xue C, Yuan J, Lo GG, Chang ATY, Poon DMC, Wong OL, Zhou Y, Chu WCW. Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review. Quant Imaging Med Surg 2021; 11:4431-4460. [PMID: 34603997 DOI: 10.21037/qims-21-86] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 05/17/2021] [Indexed: 12/13/2022]
Abstract
Radiomics research is rapidly growing in recent years, but more concerns on radiomics reliability are also raised. This review attempts to update and overview the current status of radiomics reliability research in the ever expanding medical literature from the perspective of a single reliability metric of intraclass correlation coefficient (ICC). To conduct this systematic review, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. After literature search and selection, a total of 481 radiomics studies using CT, PET, or MRI, covering a wide range of subject and disease types, were included for review. In these highly heterogeneous studies, feature reliability to image segmentation was much more investigated than reliability to other factors, such as image acquisition, reconstruction, post-processing, and feature quantification. The reported ICCs also suggested high radiomics feature reliability to image segmentation. Image acquisition was found to introduce much more feature variability than image segmentation, in particular for MRI, based on the reported ICC values. Image post-processing and feature quantification yielded different levels of radiomics reliability and might be used to mitigate image acquisition-induced variability. Some common flaws and pitfalls in ICC use were identified, and suggestions on better ICC use were given. Due to the extremely high study heterogeneities and possible risks of bias, the degree of radiomics feature reliability that has been achieved could not yet be safely synthesized or derived in this review. More future researches on radiomics reliability are warranted.
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Affiliation(s)
- Cindy Xue
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China.,Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Jing Yuan
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Gladys G Lo
- Department of Diagnostic & Interventional Radiology, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Amy T Y Chang
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Darren M C Poon
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Oi Lei Wong
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Yihang Zhou
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Winnie C W Chu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
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Yoshioka T, Uchiyama Y, Shiraishi J. [Radiomics for Estimating Recurrence Risk of Patients with Lung Cancer by Using Survival Analysis]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2021; 77:153-159. [PMID: 33612693 DOI: 10.6009/jjrt.2021_jsrt_77.2.153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE Because of the promotion of cancer screening, the number of patients with lung cancer detected at the early stage has increased. However, it was reported that 30-40% of the lung cancer patients at stage I relapsed. If the recurrence risk can be accurately predicted, it is possible to give medical care for improving the prognosis of lung cancer patients. The purpose of this study was to develop a method for the prediction of recurrence risk of patients with lung cancer by using survival analysis of radiomics approach. METHOD A public database was used in this study. Fifty patients (25 recurrences and 25 censored cases) classified as stage I or II were selected and their pretreatment computed tomography (CT) images were obtained. First, we selected one slice containing the largest tumor area and manually segmented the tumor regions. We subsequently calculated 367 radiomic features such as tumor size, shape, CT values, and texture. Radiomic features were selected by using least absolute shrinkage and selection (Lasso). Cox regression model and random survival forest (RSF) with the selected radiomic features were used for estimating the recurrence functions of fifty patients. RESULT The experimental result showed that average area under the curve (AUC) values of Cox regression model and RSF for the prediction accuracy were 0.81 and 0.93, respectively. CONCLUSION Since our scheme can predict recurrence risk of patients with lung cancer by using non-invasive image examinations, it would be useful for the selection of treatment and the follow-up after the treatment.
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Affiliation(s)
- Takuya Yoshioka
- Graduate School of Health Sciences, Kumamoto University(Current address: Nagoya City University Hospital)
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Lung Cancer and Granuloma Identification Using a Deep Learning Model to Extract 3-Dimensional Radiomics Features in CT Imaging. Clin Lung Cancer 2021; 22:e756-e766. [PMID: 33678583 DOI: 10.1016/j.cllc.2021.02.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 02/02/2021] [Accepted: 02/02/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND We aimed to evaluate a deep learning (DL) model combining perinodular and intranodular radiomics features and clinical features for preoperative differentiation of solitary granuloma nodules (GNs) from solid lung cancer nodules in patients with spiculation, lobulation, or pleural indentation on CT. PATIENTS AND METHODS We retrospectively recruited 915 patients with solitary solid pulmonary nodules and suspicious signs of malignancy. Data including clinical characteristics and subjective CT findings were obtained. A 3-dimensional U-Net-based DL model was used for tumor segmentation and extraction of 3-dimensional radiomics features. We used the Maximum Relevance and Minimum Redundancy (mRMR) algorithm and the eXtreme Gradient Boosting (XGBoost) algorithm to select the intranodular, perinodular, and gross nodular radiomics features. We propose a medical image DL (IDL) model, a clinical image DL (CIDL) model, a radiomics DL (RDL) model, and a clinical image radiomics DL (CIRDL) model to preoperatively differentiate GNs from solid lung cancer. Five-fold cross-validation was used to select and evaluate the models. The prediction performance of the models was evaluated using receiver operating characteristic and calibration curves. RESULTS The CIRDL model achieved the best performance in differentiating between GNs and solid lung cancer (area under the curve [AUC] = 0.9069), which was significantly higher compared with the IDL (AUC = 0.8322), CIDL (AUC = 0.8652), intra-RDL (AUC = 0.8583), peri-RDL (AUC = 0.8259), and gross-RDL (AUC = 0.8705) models. CONCLUSION The proposed CIRDL model is a noninvasive diagnostic tool to differentiate between granuloma nodules and solid lung cancer nodules and reduce the need for invasive diagnostic and surgical procedures.
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Zhao S, Hou D, Zheng X, Song W, Liu X, Wang S, Zhou L, Tao X, Lv L, Sun Q, Jin Y, Ding L, Mao L, Wu N. MRI radiomic signature predicts intracranial progression-free survival in patients with brain metastases of ALK-positive non-small cell lung cancer. Transl Lung Cancer Res 2021; 10:368-380. [PMID: 33569319 PMCID: PMC7867779 DOI: 10.21037/tlcr-20-361] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Background Intracranial progression is considered an important cause of treatment failure in anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC) patients. Recent advances in targeted therapy and radiomics have generated considerable interest for the exploration of prognostic imaging biomarkers to predict the clinical course. Here, we developed a magnetic resonance imaging (MRI) radiomic signature that can stratify survival and intracranial progression. Methods We analyzed 87 brain metastatic lesions in 24 ALK-positive NSCLC patients undergoing ALK-inhibitor ensartinib therapy and divided them into training (n=61) and validation (n=26) sets. Radiomic features were extracted and screened from contrast-enhanced MR images. Combined with these selected features, the Rad-score was calculated with multivariate logistic regression. The predictive model and Rad-score performance were assessed in the training set and validated in the validation set; decision curve analysis was performed with the combined training and validation sets to estimate Rad-score’s patient-stratification ability. Results The prediction model constructed with nine selected radiomic features could predict intracranial progression within 51 weeks (AUC =0.84 and 0.85 in the training and validation sets, respectively), while clinical and regular MRI characteristics were independent of progression (P>0.05). The decision-curve analysis showed that the radiomic prediction model was clinically useful. The Kaplan-Meier analysis showed that the progression-free survival (PFS) difference between the high- and low-risk groups distinguished by the Rad-score was significant (P=0.017). Conclusions Radiomics may provide prognostic information and improve pretreatment risk stratification in ALK-positive NSCLC patients with brain metastases undergoing ensartinib treatment, allowing follow-up and treatment to be tailored to the patient’s individual risk profile.
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Affiliation(s)
- Shijun Zhao
- 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, Beijing, China
| | - Donghui Hou
- 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, Beijing, China
| | - Xiaomin Zheng
- Department of Endocrinology, Chui Yang Liu Hospital affiliated to Tsinghua University, Beijing, China
| | - Wei Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Xiaoqing Liu
- Department of Pulmonary Oncology, the Fifth Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Sicong Wang
- GE Healthcare, Life Sciences, Beijing, China
| | - Lina Zhou
- 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, Beijing, China
| | - Xiuli Tao
- PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lv Lv
- PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qi Sun
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yujing Jin
- PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lieming Ding
- Betta Pharmaceuticals Co., Ltd., Hangzhou, China
| | - Li Mao
- Betta Pharmaceuticals Co., Ltd., Hangzhou, China
| | - Ning Wu
- 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, Beijing, China.,PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Bortolotto C, Lancia A, Stelitano C, Montesano M, Merizzoli E, Agustoni F, Stella G, Preda L, Filippi AR. Radiomics features as predictive and prognostic biomarkers in NSCLC. Expert Rev Anticancer Ther 2020; 21:257-266. [PMID: 33216651 DOI: 10.1080/14737140.2021.1852935] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Introduction: Radiomics extracts a large amount of quantitative information from medical images using specific data characterization algorithms. This information, called radiomic features, can be combined with clinical data to build prediction models for prognostic evaluation and treatment selection.Areas covered: We outlined a series of studies investigating the correlation between radiomics features and outcome (prognostic) as well as response to therapy (predictive) in non-small cell lung cancer (NSCLC). We performed our analysis both in the setting of early and advanced stage of disease, with a focus on the different therapies and imaging modalities adopted.Expert opinion: The prognostic and predictive potential of the radiomic approach, combined with clinical models, could help decision-making process and guide toward the creation of an optimal and 'tailored' therapeutic strategy for lung cancer patients. However, due to the low reproducibility of most of the conducted studies and the lack of validated results, such a desirable scenario has not yet been translated to routine clinical practice.
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Affiliation(s)
| | - Andrea Lancia
- Radiation Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Chiara Stelitano
- Radiology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Marianna Montesano
- Radiation Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Elisa Merizzoli
- Radiation Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | | | - Giulia Stella
- Respiratory Disease Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Lorenzo Preda
- Radiology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
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Fornacon-Wood I, Faivre-Finn C, O'Connor JPB, Price GJ. Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype. Lung Cancer 2020; 146:197-208. [PMID: 32563015 PMCID: PMC7383235 DOI: 10.1016/j.lungcan.2020.05.028] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 05/18/2020] [Accepted: 05/23/2020] [Indexed: 12/24/2022]
Abstract
Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.
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Affiliation(s)
| | - Corinne Faivre-Finn
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Department of Radiation Oncology, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | - James P B O'Connor
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Department of Radiology, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | - Gareth J Price
- Division of Cancer Sciences, University of Manchester, Manchester, UK
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