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Chen Z, Gao S, Ding C, Luo T, Xu J, Xu S, Li S. CT-based non-invasive identification of the most common gene mutation status in patients with non-small cell lung cancer. Med Phys 2024; 51:1872-1882. [PMID: 37706584 DOI: 10.1002/mp.16744] [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: 02/23/2023] [Revised: 09/05/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023] Open
Abstract
BACKGROUND Epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) are mutually exclusive, and they are two important genes that are most prone to mutation in patients with non-small cell lung cancer. PURPOSE This retrospective study investigated the ability of radiomics to predict the mutation status of EGFR and KRAS in patients with non-small cell lung cancer (NSCLC) and guide precision medicine. METHODS Computed tomography images of 1045 NSCLC patients from five different institutions were collected, and 1204 imaging features were extracted. In the training set (EGFR: 678, KRAS: 246), Max-Relevance and Min-Redundancy and least absolute shrinkage and selection operator logistic regression were used to screen radiomics features. The combination of selected radiomics features and clinical factors was used to establish the combined models in identifying EGFR and KRAS mutation status, respectively, through stepwise logistic regression. Then, on two independent external validation sets (EGFR: 203/164, KRAS: 123/95), the performance of each model was evaluated separately, and then the overall performance of predicting the two mutation states was calculated. RESULTS In the EGFR and KRAS groups, radiomics signatures comprised 14 and 10 radiomics features, respectively. They were mutually exclusive between the tumors with positive EGFR mutation and those with positive KRAS mutation in imaging phenotype. For the EGFR group, the area under the curve (AUC) of the combined model in the two validation sets was 0.871 (95% CI: 0.821-0.926) and 0.861 (95% CI: 0.802-0.911), respectively, whereas the AUC of the combined model in the two validation sets was 0.798 (95% CI: 0.739-0.850) and 0.778 (95% CI: 0.735-0.821), respectively, for the KRAS group. Considering both EGFR and KRAS, the overall precision, recall, and F1-score of the combined model in the two validation sets were 0.704, 0.844, and 0.768, as well as 0.754, 0.693, and 0.722, respectively. CONCLUSIONS Our study demonstrates the potential of radiomics in the non-invasive identification of EGFR and KRAS mutation status, which may guide patients with non-small cell lung cancer to choose the most appropriate personalized treatment. This method can be used when biopsy will bring unacceptable risk to patients with NSCLC.
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Affiliation(s)
- Zongjian Chen
- School of Health Management, China Medical University, Shenyang, Liaoning, China
| | - Si Gao
- Department of Radiology, The First Affiliated Hospital of China Medical University Liaoning, Shenyang, China
| | - Changwei Ding
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Ting Luo
- Department of Radiology, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning, China
| | - Jiaqi Xu
- School of Health Management, China Medical University, Shenyang, Liaoning, China
| | - Shuang Xu
- Library of China Medical University, Shenyang, Liaoning, China
| | - Shu Li
- School of Health Management, China Medical University, Shenyang, Liaoning, China
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Nguyen HS, Ho DKN, Nguyen NN, Tran HM, Tam KW, Le NQK. Predicting EGFR Mutation Status in Non-Small Cell Lung Cancer Using Artificial Intelligence: A Systematic Review and Meta-Analysis. Acad Radiol 2024; 31:660-683. [PMID: 37120403 DOI: 10.1016/j.acra.2023.03.040] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/25/2023] [Accepted: 03/28/2023] [Indexed: 05/01/2023]
Abstract
RATIONALE AND OBJECTIVES Recent advancements in artificial intelligence (AI) render a substantial promise for epidermal growth factor receptor (EGFR) mutation status prediction in non-small cell lung cancer (NSCLC). We aimed to evaluate the performance and quality of AI algorithms that use radiomics features in predicting EGFR mutation status in patient with NSCLC. MATERIALS AND METHODS We searched PubMed (Medline), EMBASE, Web of Science, and IEEExplore for studies published up to February 28, 2022. Studies utilizing an AI algorithm (either conventional machine learning [cML] and deep learning [DL]) for predicting EGFR mutations in patients with NSLCL were included. We extracted binary diagnostic accuracy data and constructed a bivariate random-effects model to obtain pooled sensitivity, specificity, and 95% confidence interval. This study is registered with PROSPERO, CRD42021278738. RESULTS Our search identified 460 studies, of which 42 were included. Thirty-five studies were included in the meta-analysis. The AI algorithms exhibited an overall area under the curve (AUC) value of 0.789 and pooled sensitivity and specificity levels of 72.2% and 73.3%, respectively. The DL algorithms outperformed cML in terms of AUC (0.822 vs. 0.775) and sensitivity (80.1% vs. 71.1%), but had lower specificity (70.0% vs. 73.8%, p-value < 0.001) compared to cML. Subgroup analysis revealed that the use of positron-emission tomography/computed tomography, additional clinical information, deep feature extraction, and manual segmentation can improve diagnostic performance. CONCLUSION DL algorithms can serve as a novel method for increasing predictive accuracy and thus have considerable potential for use in predicting EGFR mutation status in patient with NSCLC. We also suggest that guidelines on using AI algorithms in medical image analysis should be developed with a focus on oncologic radiomics.
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Affiliation(s)
- Hung Song Nguyen
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan (H.S.N., N.N.N.); Department of Pediatrics, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Viet Nam (H.S.N.); Intensive Care Unit Department, Children's Hospital 1, Ho Chi Minh City, Viet Nam (H.S.N.)
| | - Dang Khanh Ngan Ho
- School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan (D.K.N.H.)
| | - Nam Nhat Nguyen
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan (H.S.N., N.N.N.)
| | - Huy Minh Tran
- Department of Neurosurgery, Faculty of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Viet Nam (H.M.T.)
| | - Ka-Wai Tam
- Center for Evidence-based Health Care, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan (K.-W.T.); Cochrane Taiwan, Taipei Medical University, Taipei City, Taiwan (K.-W.T.); Division of General Surgery, Department of Surgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan (K.-W.T.); Division of General Surgery, Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan (K.-W.T.)
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan (N.Q.K.L.); Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110, Taiwan (N.Q.K.L.); AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan (N.Q.K.L.); Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan (N.Q.K.L.).
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Egashira M, Arimura H, Kobayashi K, Moriyama K, Kodama T, Tokuda T, Ninomiya K, Okamoto H, Igaki H. Magnetic resonance-based imaging biopsy with signatures including topological Betti number features for prediction of primary brain metastatic sites. Phys Eng Sci Med 2023; 46:1411-1426. [PMID: 37603131 DOI: 10.1007/s13246-023-01308-6] [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: 02/02/2023] [Accepted: 07/20/2023] [Indexed: 08/22/2023]
Abstract
This study incorporated topology Betti number (BN) features into the prediction of primary sites of brain metastases and the construction of magnetic resonance-based imaging biopsy (MRB) models. The significant features of the MRB model were selected from those obtained from gray-scale and three-dimensional wavelet-filtered images, BN and inverted BN (iBN) maps, and clinical variables (age and gender). The primary sites were predicted as either lung cancer or other cancers using MRB models, which were built using seven machine learning methods with significant features chosen by three feature selection methods followed by a combination strategy. Our study dealt with a dataset with relatively smaller brain metastases, which included effective diameters greater than 2 mm, with metastases ranging from 2 to 9 mm accounting for 17% of the dataset. The MRB models were trained by T1-weighted contrast-enhanced images of 494 metastases chosen from 247 patients and applied to 115 metastases from 62 test patients. The most feasible model attained an area under the receiver operating characteristic curve (AUC) of 0.763 for the test patients when using a signature including features of BN and iBN maps, gray-scale and wavelet-filtered images, and clinical variables. The AUCs of the model were 0.744 for non-small cell lung cancer and 0.861 for small cell lung cancer. The results suggest that the BN signature boosted the performance of MRB for the identification of primary sites of brain metastases including small tumors.
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Affiliation(s)
- Mai Egashira
- Division of Medical Quantum Science, Department of Health Science, Graduate School of Medical Science, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Hidetaka Arimura
- Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
| | - Kazuma Kobayashi
- Department of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Kazutoshi Moriyama
- Division of Medical Quantum Science, Department of Health Science, Graduate School of Medical Science, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Takumi Kodama
- Division of Medical Quantum Science, Department of Health Science, Graduate School of Medical Science, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Tomoki Tokuda
- Joint Graduate School of Mathematics for Innovation, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Kenta Ninomiya
- Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA, USA
| | - Hiroyuki Okamoto
- Radiation Safety and Quality Assurance Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Hiroshi Igaki
- Department of Radiation Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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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: 0] [Impact Index Per Article: 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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Ninomiya K, Arimura H, Tanaka K, Chan WY, Kabata Y, Mizuno S, Gowdh NFM, Yaakup NA, Liam CK, Chai CS, Ng KH. Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 236:107544. [PMID: 37148668 DOI: 10.1016/j.cmpb.2023.107544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 02/16/2023] [Accepted: 04/07/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVES To elucidate a novel radiogenomics approach using three-dimensional (3D) topologically invariant Betti numbers (BNs) for topological characterization of epidermal growth factor receptor (EGFR) Del19 and L858R mutation subtypes. METHODS In total, 154 patients (wild-type EGFR, 72 patients; Del19 mutation, 45 patients; and L858R mutation, 37 patients) were retrospectively enrolled and randomly divided into 92 training and 62 test cases. Two support vector machine (SVM) models to distinguish between wild-type and mutant EGFR (mutation [M] classification) as well as between the Del19 and L858R subtypes (subtype [S] classification) were trained using 3DBN features. These features were computed from 3DBN maps by using histogram and texture analyses. The 3DBN maps were generated using computed tomography (CT) images based on the Čech complex constructed on sets of points in the images. These points were defined by coordinates of voxels with CT values higher than several threshold values. The M classification model was built using image features and demographic parameters of sex and smoking status. The SVM models were evaluated by determining their classification accuracies. The feasibility of the 3DBN model was compared with those of conventional radiomic models based on pseudo-3D BN (p3DBN), two-dimensional BN (2DBN), and CT and wavelet-decomposition (WD) images. The validation of the model was repeated with 100 times random sampling. RESULTS The mean test accuracies for M classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.810, 0.733, 0.838, 0.782, and 0.799, respectively. The mean test accuracies for S classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.773, 0.694, 0.657, 0.581, and 0.696, respectively. CONCLUSION 3DBN features, which showed a radiogenomic association with the characteristics of the EGFR Del19/L858R mutation subtypes, yielded higher accuracy for subtype classifications in comparison with conventional features.
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Affiliation(s)
- Kenta Ninomiya
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA.
| | - Hidetaka Arimura
- Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kentaro Tanaka
- Department of Respiratory Medicine, Kyushu University Hospital, Fukuoka, Japan; Department of Respiratory Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia; Radiology Department, Gleneagles Hospital Kuala Lumpur, Jalan Ampang, 50450 Kuala Lumpur, Malaysia
| | - Yutaro Kabata
- School of Information and Data Sciences, Nagasaki University, Nagasaki, Japan
| | - Shinichi Mizuno
- Division of Medical Sciences and Technology, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | | | - Nur Adura Yaakup
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Chong-Kin Liam
- Department of Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Chee-Shee Chai
- Department of Medicine, Faculty of Medicine and Health Science, University Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia; Faculty of Medicine and Health Sciences, UCSI University, Springhill, Negeri Sembilan, Malaysia
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Jin Y, Arimura H, Cui Y, Kodama T, Mizuno S, Ansai S. CT Image-Based Biopsy to Aid Prediction of HOPX Expression Status and Prognosis for Non-Small Cell Lung Cancer Patients. Cancers (Basel) 2023; 15:cancers15082220. [PMID: 37190150 DOI: 10.3390/cancers15082220] [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: 02/06/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 05/17/2023] Open
Abstract
This study aimed to elucidate a computed tomography (CT) image-based biopsy with a radiogenomic signature to predict homeodomain-only protein homeobox (HOPX) gene expression status and prognosis in patients with non-small cell lung cancer (NSCLC). Patients were labeled as HOPX-negative or positive based on HOPX expression and were separated into training (n = 92) and testing (n = 24) datasets. In correlation analysis between genes and image features extracted by Pyradiomics for 116 patients, eight significant features associated with HOPX expression were selected as radiogenomic signature candidates from the 1218 image features. The final signature was constructed from eight candidates using the least absolute shrinkage and selection operator. An imaging biopsy model with radiogenomic signature was built by a stacking ensemble learning model to predict HOPX expression status and prognosis. The model exhibited predictive power for HOPX expression with an area under the receiver operating characteristic curve of 0.873 and prognostic power in Kaplan-Meier curves (p = 0.0066) in the test dataset. This study's findings implied that the CT image-based biopsy with a radiogenomic signature could aid physicians in predicting HOPX expression status and prognosis in NSCLC.
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Affiliation(s)
- Yu Jin
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - Hidetaka Arimura
- Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - YunHao Cui
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - Takumi Kodama
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - Shinichi Mizuno
- Division of Medical Technology, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - Satoshi Ansai
- Laboratory of Genome Editing Breeding, Graduate School of Agriculture, Kyoto University, Kyoto 606-8520, Japan
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Ge G, Zhang J. Feature selection methods and predictive models in CT lung cancer radiomics. J Appl Clin Med Phys 2023; 24:e13869. [PMID: 36527376 PMCID: PMC9860004 DOI: 10.1002/acm2.13869] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/31/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.
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Affiliation(s)
- Gary Ge
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| | - Jie Zhang
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
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Kodama T, Arimura H, Shirakawa Y, Ninomiya K, Yoshitake T, Shioyama Y. Relapse predictability of topological signature on pretreatment planning CT images of stage I non-small cell lung cancer patients before treatment with stereotactic ablative radiotherapy. Thorac Cancer 2022; 13:2117-2126. [PMID: 35711108 PMCID: PMC9346172 DOI: 10.1111/1759-7714.14483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 05/04/2022] [Indexed: 12/25/2022] Open
Abstract
Background This study aimed to explore the predictability of topological signatures linked to the locoregional relapse (LRR) and distant metastasis (DM) on pretreatment planning computed tomography images of stage I non‐small cell lung cancer (NSCLC) patients before treatment with stereotactic ablative radiotherapy (SABR). Methods We divided 125 primary stage I NSCLC patients (LRR: 34, DM: 22) into training (n = 60) and test datasets (n = 65), and the training dataset was augmented to 260 cases using a synthetic minority oversampling technique. The relapse predictabilities of the conventional wavelet‐based features (WF), topology‐based features [BF, Betti number (BN) map features; iBF, inverted BN map features], and their combined features (BWF, iBWF) were compared. The patients were stratified into high‐risk and low‐risk groups using the medians of the radiomics scores in the training dataset. Results For the LRR in the test, the iBF, iBWF, and WF showed statistically significant differences (p < 0.05), and the highest nLPC was obtained for the iBF. For the DM in the test, the iBWF showed a significant difference and the highest nLPC. Conclusion The iBF indicated the potential of improving the LRR and DM prediction of stage I NSCLC patients prior to undergoing SABR.
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Affiliation(s)
- Takumi Kodama
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
| | - Hidetaka Arimura
- Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical SciencesKyushu UniversityFukuokaJapan
| | - Yuko Shirakawa
- National Hospital Organization Kyushu Cancer CenterFukuokaJapan
| | - Kenta Ninomiya
- Sanford Burnham Prebys Medical Discovery InstituteLa JollaCaliforniaUSA
| | - Tadamasa Yoshitake
- Department of Clinical Radiology, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
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Urakami A, Arimura H, Takayama Y, Kinoshita F, Ninomiya K, Imada K, Watanabe S, Nishie A, Oda Y, Ishigami K. Stratification of prostate cancer patients into low- and high-grade groups using multiparametric magnetic resonance radiomics with dynamic contrast-enhanced image joint histograms. Prostate 2022; 82:330-344. [PMID: 35014713 DOI: 10.1002/pros.24278] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 11/09/2021] [Accepted: 11/23/2021] [Indexed: 01/04/2023]
Abstract
PURPOSE This study aimed to investigate the potential of stratification of prostate cancer patients into low- and high-grade groups (GGs) using multiparametric magnetic resonance (mpMR) radiomics in conjunction with two-dimensional (2D) joint histograms computed with dynamic contrast-enhanced (DCE) images. METHODS A total of 101 prostate cancer regions extracted from the MR images of 44 patients were identified and divided into training (n = 31 with 72 cancer regions) and test datasets (n = 13 with 29 cancer regions). Each dataset included low-grade tumors (International Society of Urological Pathology [ISUP] GG ≤ 2) and high-grade tumors (ISUP GG ≥ 3). A total of 137,970 features consisted of mpMR image (16 types of images in four sequences)-based and joint histogram (DCE images at 10 phases)-based features for each cancer region. Joint histogram features can visualize temporally changing perfusion patterns in prostate cancer based on the joint histograms between different phases or subtraction phases of DCE images. Nine signatures (a set of significant features related to GGs) were determined using the best combinations of features selected using the least absolute shrinkage and selection operator. Further, support vector machine models with the nine signatures were built based on a leave-one-out cross-validation for the training dataset and evaluated with receiver operating characteristic (ROC) curve analysis. RESULTS The signature showing the best performance was constructed using six features derived from the joint histograms, DCE original images, and apparent diffusion coefficient maps. The areas under the ROC curves for the training and test datasets were 1.00 and 0.985, respectively. CONCLUSION This study suggests that the proposed approach with mpMR radiomics in conjunction with 2D joint histogram computed with DCE images could have the potential to stratify prostate cancer patients into low- and high-GGs.
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Affiliation(s)
- Akimasa Urakami
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hidetaka Arimura
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yukihisa Takayama
- Department of Radiology, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Fumio Kinoshita
- Department of Anatomic Pathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kenta Ninomiya
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kenjiro Imada
- Department of Urology, Prostate, Kidney, Adrenal Surgery, Kyushu University Hospital, Fukuoka, Japan
| | - Sumiko Watanabe
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Akihiro Nishie
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoshinao Oda
- Department of Anatomic Pathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kousei Ishigami
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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Ninomiya K, Arimura H, Yoshitake T, Hirose TA, Shioyama Y. Synergistic combination of a topologically invariant imaging signature and a biomarker for the accurate prediction of symptomatic radiation pneumonitis before stereotactic ablative radiotherapy for lung cancer: A retrospective analysis. PLoS One 2022; 17:e0263292. [PMID: 35100322 PMCID: PMC8803154 DOI: 10.1371/journal.pone.0263292] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 01/18/2022] [Indexed: 12/25/2022] Open
Abstract
Objectives We aimed to explore the synergistic combination of a topologically invariant Betti number (BN)-based signature and a biomarker for the accurate prediction of symptomatic (grade ≥2) radiation-induced pneumonitis (RP+) before stereotactic ablative radiotherapy (SABR) for lung cancer. Methods A total of 272 SABR cases with early-stage non-small cell lung cancer were chosen for this study. The occurrence of RP+ was predicted using a support vector machine (SVM) model trained with the combined features of the BN-based signature extracted from planning computed tomography (pCT) images and a pretreatment biomarker, serum Krebs von den Lungen-6 (BN+KL-6 model). In all, 242 (20 RP+ and 222 RP–(grade 1)) and 30 cases (8 RP+ and 22 RP–) were used for training and testing the model, respectively. The BN-based features were extracted from BN maps that characterize topologically invariant heterogeneous traits of potential RP+ lung regions on pCT images by applying histogram- and texture-based feature calculations to the maps. The SVM models were built to predict RP+ patients with a BN signature that was constructed based on the least absolute shrinkage and selection operator logistic regression model. The evaluation of the prediction models was performed based on the area under the receiver operating characteristic curves (AUCs) and accuracy in the test. The performance of the BN+KL-6 model was compared to the performance based on the BN, conventional original pCT, and wavelet decomposition (WD) models. Results The test AUCs obtained for the BN+KL-6, BN, pCT, and WD models were 0.825, 0.807, 0.642, and 0.545, respectively. The accuracies of the BN+KL-6, BN, pCT, and WD models were found to be 0.724, 0.708, 0.591, and 0.534, respectively. Conclusion This study demonstrated the comprehensive performance of the BN+KL-6 model for the prediction of potential RP+ patients before SABR for lung cancer.
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Affiliation(s)
- Kenta Ninomiya
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Hidetaka Arimura
- Faculty of Medical Sciences, Division of Medical Quantum Science, Department of Health Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
- * E-mail: (HA); (TY)
| | - Tadamasa Yoshitake
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
- * E-mail: (HA); (TY)
| | - Taka-aki Hirose
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Higashi-ku, Fukuoka, Japan
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Arimura H, Kodama T, Urakami A, Kamezawa H, Hirose TA, Ninomiya K. [6. Imaging Biopsy for Assisting Cancer Precision Therapy -Information Extracted from Radiomics]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2022; 78:219-224. [PMID: 35185102 DOI: 10.6009/jjrt.780213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Hidetaka Arimura
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University
| | - Takumi Kodama
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University
| | - Akimasa Urakami
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University
| | - Hidemi Kamezawa
- Department of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University
| | - Taka-Aki Hirose
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital
| | - Kenta Ninomiya
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University
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Kang CY, Duarte SE, Kim HS, Kim E, Park J, Lee AD, Kim Y, Kim L, Cho S, Oh Y, Gim G, Park I, Lee D, Abazeed M, Velichko YS, Chae YK. OUP accepted manuscript. Oncologist 2022; 27:e471-e483. [PMID: 35348765 PMCID: PMC9177100 DOI: 10.1093/oncolo/oyac036] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 01/14/2022] [Indexed: 11/17/2022] Open
Abstract
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.
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Affiliation(s)
| | | | - Hye Sung Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Eugene Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Alice Daeun Lee
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yeseul Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Leeseul Kim
- Department of Internal Medicine, AMITA Health Saint Francis Hospital, Evanston, IL, USA
| | - Sukjoo Cho
- Department of Pediatrics, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Yoojin Oh
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gahyun Gim
- Department of Hematology and Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Inae Park
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Dongyup Lee
- Department of Physical Medicine and Rehabilitation, Geisinger Health System, Danville, PA, USA
| | - Mohamed Abazeed
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Yury S Velichko
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Young Kwang Chae
- Corresponding author: Young Kwang Chae, Department of Hematology and Oncology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
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Abdurixiti M, Nijiati M, Shen R, Ya Q, Abuduxiku N, Nijiati M. Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review. Br J Radiol 2021; 94:20201272. [PMID: 33882244 DOI: 10.1259/bjr.20201272] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVES To assess the methodological quality of radiomic studies based on positron emission tomography/computed tomography (PET/CT) images predicting epidermal growth factor receptor (EGFR) mutation status in patients with non-small cell lung cancer (NSCLC). METHODS We systematically searched for eligible studies in the PubMed and Web of Science datasets using the terms "radiomics", "PET/CT", "NSCLC", and "EGFR". The included studies were screened by two reviewers independently. The quality of the radiomic workflow of studies was assessed using the Radiomics Quality Score (RQS). Interclass correlation coefficient (ICC) was used to determine inter rater agreement for the RQS. An overview of the methodologies used in steps of the radiomics workflow and current results are presented. RESULTS Six studies were included with sample sizes of 973 ranging from 115 to 248 patients. Methodologies in the radiomic workflow varied greatly. The first-order statistics were the most reproducible features. The RQS scores varied from 13.9 to 47.2%. All studies were scored below 50% due to defects on multiple segmentations, phantom study on all scanners, imaging at multiple time points, cut-off analyses, calibration statistics, prospective study, potential clinical utility, and cost-effectiveness analysis. The ICC results for majority of RQS items were excellent. The ICC for summed RQS was 0.986 [95% confidence interval (CI): 0.898-0.998]. CONCLUSIONS The PET/CT-based radiomics signature could serve as a diagnostic indicator of EGFR mutation status in NSCLC patients. However, the current conclusions should be interpreted with care due to the suboptimal quality of the studies. Consensus for standardization of PET/CT-based radiomic workflow for EGFR mutation status in NSCLC patients is warranted to further improve research. ADVANCES IN KNOWLEDGE Radiomics can offer clinicians better insight into the prediction of EGFR mutation status in NSCLC patients, whereas the quality of relative studies should be improved before application to the clinical setting.
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Affiliation(s)
- Meilinuer Abdurixiti
- Department of Nuclear Medicine, The First People's Hospital of Kashi Area, Kashi, Xinjiang, China
| | - Mayila Nijiati
- Department of Otolaryngology, The First People's Hospital of Kashi Area, Kashi, Xinjiang, China
| | - Rongfang Shen
- Department of Nuclear Medicine, The First People's Hospital of Kashi Area, Kashi, Xinjiang, China
| | - Qiu Ya
- Department of Radiology, The First People's Hospital of Kashi Area, Kashi, Xinjiang, China
| | - Naibijiang Abuduxiku
- Department of Nuclear Medicine, The First People's Hospital of Kashi Area, Kashi, Xinjiang, China
| | - Mayidili Nijiati
- Department of Radiology, The First People's Hospital of Kashi Area, Kashi, Xinjiang, China
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