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Chiu HY, Wang TW, Hsu MS, Chao HS, Liao CY, Lu CF, Wu YT, Chen YM. Progress in Serial Imaging for Prognostic Stratification of Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis. Cancers (Basel) 2024; 16:615. [PMID: 38339369 PMCID: PMC10854498 DOI: 10.3390/cancers16030615] [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: 11/30/2023] [Revised: 01/23/2024] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
Immunotherapy, particularly with checkpoint inhibitors, has revolutionized non-small cell lung cancer treatment. Enhancing the selection of potential responders is crucial, and researchers are exploring predictive biomarkers. Delta radiomics, a derivative of radiomics, holds promise in this regard. For this study, a meta-analysis was conducted that adhered to PRISMA guidelines, searching PubMed, Embase, Web of Science, and the Cochrane Library for studies on the use of delta radiomics in stratifying lung cancer patients receiving immunotherapy. Out of 223 initially collected studies, 10 were included for qualitative synthesis. Stratifying patients using radiomic models, the pooled analysis reveals a predictive power with an area under the curve of 0.81 (95% CI 0.76-0.86, p < 0.001) for 6-month response, a pooled hazard ratio of 4.77 (95% CI 2.70-8.43, p < 0.001) for progression-free survival, and 2.15 (95% CI 1.73-2.66, p < 0.001) for overall survival at 6 months. Radiomics emerges as a potential prognostic predictor for lung cancer, but further research is needed to compare traditional radiomics and deep-learning radiomics.
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Affiliation(s)
- Hwa-Yen Chiu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (H.-Y.C.); (T.-W.W.); (M.-S.H.); (H.-S.C.)
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Internal Medicine, Taipei Veterans General Hospital, Hsinchu Branch, Chutong 310, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Ting-Wei Wang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (H.-Y.C.); (T.-W.W.); (M.-S.H.); (H.-S.C.)
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Ming-Sheng Hsu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (H.-Y.C.); (T.-W.W.); (M.-S.H.); (H.-S.C.)
| | - Heng-Shen Chao
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (H.-Y.C.); (T.-W.W.); (M.-S.H.); (H.-S.C.)
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (C.-Y.L.); (C.-F.L.)
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (C.-Y.L.); (C.-F.L.)
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Yuh-Ming Chen
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
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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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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: 21] [Impact Index Per Article: 21.0] [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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Multiregional Radiomic Signatures Based on Functional Parametric Maps from DCE-MRI for Preoperative Identification of Estrogen Receptor and Progesterone Receptor Status in Breast Cancer. Diagnostics (Basel) 2022; 12:diagnostics12102558. [PMID: 36292247 PMCID: PMC9601361 DOI: 10.3390/diagnostics12102558] [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: 09/08/2022] [Revised: 10/01/2022] [Accepted: 10/07/2022] [Indexed: 11/16/2022] Open
Abstract
Radiomics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been used for breast estrogen receptor (ER) and progesterone receptor (PR) status evaluation. However, the radiomic features of peritumoral regions were not thoroughly analyzed. This study aimed to establish and validate the multiregional radiomic signatures (RSs) for the preoperative identification of the ER and PR status in breast cancer. A total of 443 patients with breast cancer were divided into training (n = 356) and validation (n = 87) sets. Radiomic features were extracted from intra- and peritumoral regions on six functional parametric maps from DCE-MRI. A two-sample t-test, least absolute shrinkage and selection operator regression, and stepwise were used for feature selections. Three RSs for predicting the ER and PR status were constructed using a logistic regression model based on selected intratumoral, peritumoral, and combined intra- and peritumoral radiomic features. The area under the receiver operator characteristic curve (AUC) was used to assess the discriminative performance of three RSs. The AUCs of intra- and peritumoral RSs for identifying the ER status were 0.828/0.791 and 0.755/0.733 in the training and validation sets, respectively. For predicting the PR status, intra- and peritumoral RSs resulted in AUCs of 0.816/0.749 and 0.806/0.708 in the training and validation sets, respectively. Multiregional RSs achieved the best AUCs among three RSs for evaluating the ER (0.851 and 0.833) and PR (0.848 and 0.763) status. In conclusion, multiregional RSs based on functional parametric maps from DCE-MRI showed promising results for preoperatively evaluating the ER and PR status in breast cancer patients. Further studies using a larger cohort from multiple centers are necessary to confirm the reliability of the established models before clinical application.
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Tomaszewski MR, Fan S, Garcia A, Qi J, Kim Y, Gatenby RA, Schabath MB, Tap WD, Reinke DK, Makanji RJ, Reed DR, Gillies RJ. AI-Radiomics Can Improve Inclusion Criteria and Clinical Trial Performance. Tomography 2022; 8:341-355. [PMID: 35202193 PMCID: PMC8880510 DOI: 10.3390/tomography8010028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 12/03/2021] [Accepted: 12/16/2021] [Indexed: 12/29/2022] Open
Abstract
Purpose: Success of clinical trials increasingly relies on effective selection of the target patient populations. We hypothesize that computational analysis of pre-accrual imaging data can be used for patient enrichment to better identify patients who can potentially benefit from investigational agents. Methods: This was tested retrospectively in soft-tissue sarcoma (STS) patients accrued into a randomized clinical trial (SARC021) that evaluated the efficacy of evofosfamide (Evo), a hypoxia activated prodrug, in combination with doxorubicin (Dox). Notably, SARC021 failed to meet its overall survival (OS) objective. We tested whether a radiomic biomarker-driven inclusion/exclusion criterion could have been used to improve the difference between the two arms (Evo + Dox vs. Dox) of the study. 164 radiomics features were extracted from 296 SARC021 patients with lung metastases, divided into training and test sets. Results: A single radiomics feature, Short Run Emphasis (SRE), was representative of a group of correlated features that were the most informative. The SRE feature value was combined into a model along with histological classification and smoking history. This model as able to identify an enriched subset (52%) of patients who had a significantly longer OS in Evo + Dox vs. Dox groups [p = 0.036, Hazard Ratio (HR) = 0.64 (0.42-0.97)]. Applying the same model and threshold value in an independent test set confirmed the significant survival difference [p = 0.016, HR = 0.42 (0.20-0.85)]. Notably, this model was best at identifying exclusion criteria for patients most likely to benefit from doxorubicin alone. Conclusions: The study presents a first of its kind clinical-radiomic approach for patient enrichment in clinical trials. We show that, had an appropriate model been used for selective patient inclusion, SARC021 trial could have met its primary survival objective for patients with metastatic STS.
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Affiliation(s)
- Michal R. Tomaszewski
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (M.R.T.); (S.F.); (A.G.); (J.Q.)
| | - Shuxuan Fan
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (M.R.T.); (S.F.); (A.G.); (J.Q.)
- Department of Radiology, Tianjin Medical University, Tianjin 301700, China
| | - Alberto Garcia
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (M.R.T.); (S.F.); (A.G.); (J.Q.)
| | - Jin Qi
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (M.R.T.); (S.F.); (A.G.); (J.Q.)
| | - Youngchul Kim
- Department of Biostatistics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - Robert A. Gatenby
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - Matthew B. Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - William D. Tap
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
- Department of Medicine, Weil Cornell Medical College, New York, NY 10021, USA
| | - Denise K. Reinke
- Sarcoma Alliance for Research through Collaboration, Ann Arbor, MI 48106, USA;
| | - Rikesh J. Makanji
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - Damon R. Reed
- Sarcoma Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Robert J. Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (M.R.T.); (S.F.); (A.G.); (J.Q.)
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Tunali I, Tan Y, Gray JE, Katsoulakis E, Eschrich SA, Saller J, Aerts HJWL, Boyle T, Qi J, Guvenis A, Gillies RJ, Schabath MB. Hypoxia-Related Radiomics and Immunotherapy Response: A Multicohort Study of Non-Small Cell Lung Cancer. JNCI Cancer Spectr 2021; 5:pkab048. [PMID: 34409252 PMCID: PMC8363765 DOI: 10.1093/jncics/pkab048] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 03/30/2021] [Accepted: 04/16/2021] [Indexed: 12/26/2022] Open
Abstract
Background Immunotherapy yields survival benefit for some advanced stage non-small cell lung cancer (NSCLC) patients. Because highly predictive biomarkers of immunotherapy response are an unmet clinical need, we used pretreatment radiomics and clinical data to train and validate a parsimonious model associated with survival outcomes among NSCLC patients treated with immunotherapy. Methods Three cohorts of NSCLC patients treated with immunotherapy were analyzed: training (n = 180), validation 1 (n = 90), and validation 2 (n = 62). The most informative clinical and radiomic features were subjected to decision tree analysis, which stratified patients into risk groups of low, moderate, high, and very high risk of death after initiation of immunotherapy. All statistical tests were 2-sided. Results The very high-risk group was associated with extremely poor overall survival (OS) in validation cohorts 1 (hazard ratio [HR] = 5.35, 95% confidence interval [CI] = 2.14 to 13.36; 1-year OS = 11.1%, 95% CI = 1.9% to 29.8%; 3-year OS = 0%) and 2 (HR = 13.81, 95% CI = 2.58 to 73.93; 1-year OS = 47.6%, 95% CI = 18.2% to 72.4%; 3-year OS = 0%) when compared with the low-risk group (HR = 1.00) in validation cohorts 1 (1-year OS = 85.0%, 95% CI = 60.4% to 94.9%; 3-year OS = 38.9%, 95% CI = 17.1% to 60.3%) and 2 (1-year OS = 80.2%, 95% CI = 40.3% to 94.8%; 3-year OS = 40.1%, 95% CI = 1.3% to 83.5%). The most informative radiomic feature, gray-level co-occurrence matrix (GLCM) inverse difference, was positively associated with hypoxia-related carbonic anhydrase 9 using gene-expression profiling and immunohistochemistry. Conclusion Utilizing standard-of-care imaging and clinical data, we identified and validated a novel parsimonious model associated with survival outcomes among NSCLC patients treated with immunotherapy. Based on this model, clinicians can identify patients who are unlikely to respond to immunotherapy.
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Affiliation(s)
- Ilke Tunali
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Yan Tan
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, China
| | - Jhanelle E Gray
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | - Steven A Eschrich
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - James Saller
- Department of Molecular Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Hugo J W L Aerts
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Theresa Boyle
- Department of Molecular Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jin Qi
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Albert Guvenis
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
| | - Robert J Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Matthew B Schabath
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer. Diagnostics (Basel) 2021; 11:diagnostics11081491. [PMID: 34441425 PMCID: PMC8395031 DOI: 10.3390/diagnostics11081491] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/06/2021] [Accepted: 08/11/2021] [Indexed: 12/22/2022] Open
Abstract
This study aimed to establish and validate a radiomics nomogram using the radiomics score (rad-score) based on multiregional diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) features combined with clinical factors for evaluating HER-2 2+ status of breast cancer. A total of 223 patients were retrospectively included. Radiomic features were extracted from multiregional DWI and ADC images. Based on the intratumoral, peritumoral, and combined regions, three rad-scores were calculated using the logistic regression model. Independent parameters were selected among clinical factors and combined rad-score (com-rad-score) using multivariate logistic analysis and used to construct a radiomics nomogram. The performance of the nomogram was evaluated using calibration, discrimination, and clinical usefulness. The areas under the receiver operator characteristic curve (AUCs) of intratumoral and peritumoral rad-scores were 0.824/0.763 and 0.794/0.731 in the training and validation cohorts, respectively. Com-rad-score achieved the highest AUC (0.860/0.790) among three rad-scores. ER status and com-rad-score were selected to establish the nomogram, which yielded good discrimination (AUC: 0.883/0.848) and calibration. Decision curve analysis demonstrated the clinical value of the nomogram in the validation cohort. In conclusion, radiomics nomogram, including clinical factors and com-rad-score, showed favorable performance for evaluating HER-2 2+ status in breast cancer.
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Tunali I, Gillies RJ, Schabath MB. Application of Radiomics and Artificial Intelligence for Lung Cancer Precision Medicine. Cold Spring Harb Perspect Med 2021; 11:cshperspect.a039537. [PMID: 33431509 PMCID: PMC8288444 DOI: 10.1101/cshperspect.a039537] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Medical imaging is the standard-of-care for early detection, diagnosis, treatment planning, monitoring, and image-guided interventions of lung cancer patients. Most medical images are stored digitally in a standardized Digital Imaging and Communications in Medicine format that can be readily accessed and used for qualitative and quantitative analysis. Over the several last decades, medical images have been shown to contain complementary and interchangeable data orthogonal to other sources such as pathology, hematology, genomics, and/or proteomics. As such, "radiomics" has emerged as a field of research that involves the process of converting standard-of-care images into quantitative image-based data that can be merged with other data sources and subsequently analyzed using conventional biostatistics or artificial intelligence (AI) methods. As radiomic features capture biological and pathophysiological information, these quantitative radiomic features have shown to provide rapid and accurate noninvasive biomarkers for lung cancer risk prediction, diagnostics, prognosis, treatment response monitoring, and tumor biology. In this review, radiomics and emerging AI methods in lung cancer research are highlighted and discussed including advantages, challenges, and pitfalls.
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Affiliation(s)
- Ilke Tunali
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA
| | - Robert J Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA
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Li C, Song L, Yin J. Intratumoral and Peritumoral Radiomics Based on Functional Parametric Maps from Breast DCE-MRI for Prediction of HER-2 and Ki-67 Status. J Magn Reson Imaging 2021; 54:703-714. [PMID: 33955619 DOI: 10.1002/jmri.27651] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/04/2021] [Accepted: 04/05/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Radiomics has been applied to breast magnetic resonance imaging (MRI) for gene status prediction. However, the features of peritumoral regions were not thoroughly investigated. PURPOSE To evaluate the use of intratumoral and peritumoral regions from functional parametric maps based on breast dynamic contrast-enhanced MRI (DCE-MRI) for prediction of HER-2 and Ki-67 status. STUDY TYPE Retrospective. POPULATION A total of 351 female patients (average age, 51 years) with pathologically confirmed breast cancer were assigned to the training (n = 243) and validation (n = 108) cohorts. FIELD STRENGTH/SEQUENCE 3.0T, T1 gradient echo. ASSESSMENT Radiomic features were extracted from intratumoral and peritumoral regions on six functional parametric maps calculated using time-intensity curves of DCE-MRI. The intraclass correlation coefficients (ICCs) were used to determine the reproducibility of feature extraction. Based on the intratumoral, peritumoral, and combined intra- and peritumoral regions, three radiomics signatures (RSs) were built using the least absolute shrinkage and selection operator (LASSO) logistic regression model, respectively. STATISTICAL TESTS Wilcoxon rank-sum test, minimum redundancy maximum relevance, LASSO, receiver operating characteristic curve (ROC) analysis, and DeLong test. RESULTS The intratumoral and peritumoral RSs for prediction of HER-2 and Ki-67 status achieved areas under the ROC (AUCs) of 0.683 (95% confidence interval [CI], 0.574-0.793) and 0.690 (95% CI, 0.577-0.804), and 0.714 (95% CI, 0.616-0.812) and 0.692 (95% CI, 0.590-0.794) in the validation cohort, respectively. The combined RSs yielded AUCs of 0.713 (95% CI, 0.604-0.823) and 0.749 (95% CI, 0.656-0.841), respectively. There were no significant differences in prediction performance among intratumoral, peritumoral, and combined RSs. Most (69.7%) of the features had good agreement (ICCs >0.8). DATA CONCLUSION Radiomic features of intratumoral and peritumoral regions on functional parametric maps based on breast DCE-MRI had the potential to identify HER-2 and Ki-67 status. LEVEL OF EVIDENCE 3 Technical Efficacy Stage: 2.
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Affiliation(s)
- Chunli Li
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China.,Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Lirong Song
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Tomaszewski MR, Gillies RJ. The Biological Meaning of Radiomic Features. Radiology 2021; 298:505-516. [PMID: 33399513 PMCID: PMC7924519 DOI: 10.1148/radiol.2021202553] [Citation(s) in RCA: 238] [Impact Index Per Article: 79.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 07/30/2020] [Accepted: 08/17/2020] [Indexed: 02/06/2023]
Abstract
An earlier incorrect version appeared online. This article was corrected on February 10, 2021.
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Affiliation(s)
- Michal R. Tomaszewski
- From the Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612
| | - Robert J. Gillies
- From the Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612
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Hagiwara A, Fujita S, Ohno Y, Aoki S. Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence. Invest Radiol 2020; 55:601-616. [PMID: 32209816 PMCID: PMC7413678 DOI: 10.1097/rli.0000000000000666] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 01/28/2020] [Indexed: 12/19/2022]
Abstract
Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.
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Affiliation(s)
- Akifumi Hagiwara
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
| | | | - Yoshiharu Ohno
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Shigeki Aoki
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
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Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening. Sci Rep 2020; 10:10528. [PMID: 32601340 PMCID: PMC7324394 DOI: 10.1038/s41598-020-67378-8] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 06/07/2020] [Indexed: 12/13/2022] Open
Abstract
The National Lung Screening Trial (NLST) demonstrated that screening with low-dose computed tomography (LDCT) is associated with a 20% reduction in lung cancer mortality. One potential limitation of LDCT screening is overdiagnosis of slow growing and indolent cancers. In this study, peritumoral and intratumoral radiomics was used to identify a vulnerable subset of lung patients associated with poor survival outcomes. Incident lung cancer patients from the NLST were split into training and test cohorts and an external cohort of non-screen detected adenocarcinomas was used for further validation. After removing redundant and non-reproducible radiomics features, backward elimination analyses identified a single model which was subjected to Classification and Regression Tree to stratify patients into three risk-groups based on two radiomics features (NGTDM Busyness and Statistical Root Mean Square [RMS]). The final model was validated in the test cohort and the cohort of non-screen detected adenocarcinomas. Using a radio-genomics dataset, Statistical RMS was significantly associated with FOXF2 gene by both correlation and two-group analyses. Our rigorous approach generated a novel radiomics model that identified a vulnerable high-risk group of early stage patients associated with poor outcomes. These patients may require aggressive follow-up and/or adjuvant therapy to mitigate their poor outcomes.
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Tunali I, Hall LO, Napel S, Cherezov D, Guvenis A, Gillies RJ, Schabath MB. Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions. Med Phys 2019; 46:5075-5085. [PMID: 31494946 DOI: 10.1002/mp.13808] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 07/02/2019] [Accepted: 08/27/2019] [Indexed: 12/31/2022] Open
Abstract
PURPOSE Recent efforts have demonstrated that radiomic features extracted from the peritumoral region, the area surrounding the tumor parenchyma, have clinical utility in various cancer types. However, as like any radiomic features, peritumoral features could also be unstable and/or nonreproducible. Hence, the purpose of this study was to assess the stability and reproducibility of computed tomography (CT) radiomic features extracted from the peritumoral regions of lung lesions where stability was defined as the consistency of a feature by different segmentations, and reproducibility was defined as the consistency of a feature to different image acquisitions. METHODS Stability was measured utilizing the "moist run" dataset and reproducibility was measured utilizing the Reference Image Database to Evaluate Therapy Response test-retest dataset. Peritumoral radiomic features were extracted from incremental distances of 3-12 mm outside the tumor segmentation. A total of 264 statistical, histogram, and texture radiomic features were assessed from the selected peritumoral region-of-interests (ROIs). All features (except wavelet texture features) were extracted using standardized algorithms defined by the Image Biomarker Standardisation Initiative. Stability and reproducibility of features were assessed using the concordance correlation coefficient. The clinical utility of stable and reproducible peritumoral features was tested in three previously published lung cancer datasets using overall survival as the endpoint. RESULTS Features found to be stable and reproducible, regardless of the peritumoral distances, included statistical, histogram, and a subset of texture features suggesting that these features are less affected by changes (e.g., size or shape) of the peritumoral region due to different segmentations and image acquisitions. The stability and reproducibility of Laws and wavelet texture features were inconsistent across all peritumoral distances. The analyses also revealed that a subset of features were consistently stable irrespective of the initial parameters (e.g., seed point) for a given segmentation algorithm. No significant differences were found in stability for features that were extracted from ROIs bounded by a lung parenchyma mask versus ROIs that were not bounded by a lung parenchyma mask (i.e., peritumoral regions that extended outside of lung parenchyma). After testing the clinical utility of peritumoral features, stable and reproducible features were shown to be more likely to create repeatable models than unstable and nonreproducible features. CONCLUSIONS This study identified a subset of stable and reproducible CT radiomic features extracted from the peritumoral region of lung lesions. The stable and reproducible features identified in this study could be applied to a feature selection pipeline for CT radiomic analyses. According to our findings, top performing features in survival models were more likely to be stable and reproducible hence, it may be best practice to utilize them to achieve repeatable studies and reduce the chance of overfitting.
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Affiliation(s)
- Ilke Tunali
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA.,Institute of Biomedical Engineering, Bogazici University, Istanbul, 34684, Turkey
| | - Lawrence O Hall
- Department of Computer Science and Engineering, University of South Florida, Tampa, FL, 33620, USA
| | - Sandy Napel
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Dmitry Cherezov
- Department of Computer Science and Engineering, University of South Florida, Tampa, FL, 33620, USA
| | - Albert Guvenis
- Institute of Biomedical Engineering, Bogazici University, Istanbul, 34684, Turkey
| | - Robert J Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
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Mattonen SA, Davidzon GA, Benson J, Leung ANC, Vasanawala M, Horng G, Shrager JB, Napel S, Nair VS. Bone Marrow and Tumor Radiomics at 18F-FDG PET/CT: Impact on Outcome Prediction in Non-Small Cell Lung Cancer. Radiology 2019; 293:451-459. [PMID: 31526257 DOI: 10.1148/radiol.2019190357] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Primary tumor maximum standardized uptake value is a prognostic marker for non-small cell lung cancer. In the setting of malignancy, bone marrow activity from fluorine 18-fluorodeoxyglucose (FDG) PET may be informative for clinical risk stratification. Purpose To determine whether integrating FDG PET radiomic features of the primary tumor, tumor penumbra, and bone marrow identifies lung cancer disease-free survival more accurately than clinical features alone. Materials and Methods Patients were retrospectively analyzed from two distinct cohorts collected between 2008 and 2016. Each tumor, its surrounding penumbra, and bone marrow from the L3-L5 vertebral bodies was contoured on pretreatment FDG PET/CT images. There were 156 bone marrow and 512 tumor and penumbra radiomic features computed from the PET series. Randomized sparse Cox regression by least absolute shrinkage and selection operator identified features that predicted disease-free survival in the training cohort. Cox proportional hazards models were built and locked in the training cohort, then evaluated in an independent cohort for temporal validation. Results There were 227 patients analyzed; 136 for training (mean age, 69 years ± 9 [standard deviation]; 101 men) and 91 for temporal validation (mean age, 72 years ± 10; 91 men). The top clinical model included stage; adding tumor region features alone improved outcome prediction (log likelihood, -158 vs -152; P = .007). Adding bone marrow features continued to improve performance (log likelihood, -158 vs -145; P = .001). The top model integrated stage, two bone marrow texture features, one tumor with penumbra texture feature, and two penumbra texture features (concordance, 0.78; 95% confidence interval: 0.70, 0.85; P < .001). This fully integrated model was a predictor of poor outcome in the independent cohort (concordance, 0.72; 95% confidence interval: 0.64, 0.80; P < .001) and a binary score stratified patients into high and low risk of poor outcome (P < .001). Conclusion A model that includes pretreatment fluorine 18-fluorodeoxyglucose PET texture features from the primary tumor, tumor penumbra, and bone marrow predicts disease-free survival of patients with non-small cell lung cancer more accurately than clinical features alone. © RSNA, 2019 Online supplemental material is available for this article.
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Affiliation(s)
- Sarah A Mattonen
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Guido A Davidzon
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Jalen Benson
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Ann N C Leung
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Minal Vasanawala
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - George Horng
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Joseph B Shrager
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Sandy Napel
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Viswam S Nair
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
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Bogowicz M, Vuong D, Huellner MW, Pavic M, Andratschke N, Gabrys HS, Guckenberger M, Tanadini-Lang S. CT radiomics and PET radiomics: ready for clinical implementation? THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2019; 63:355-370. [PMID: 31527578 DOI: 10.23736/s1824-4785.19.03192-3] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Today, rapid technical and clinical developments result in an increasing number of treatment options for oncological diseases. Thus, decision support systems are needed to offer the right treatment to the right patient. Imaging biomarkers hold great promise in patient-individual treatment guidance. Routinely performed for diagnosis and staging, imaging datasets are expected to hold more information than used in the clinical practice. Radiomics describes the extraction of a large number of meaningful quantitative features from medical images, such as computed tomography (CT) and positron emission tomography (PET). Due to the non-invasive nature and ability to capture 3D image-based heterogeneity, radiomic features are potential surrogate markers of the cancer phenotype. Several radiomic studies are published per day, owing to encouraging results of many radiomics-based patient outcome models. Despite this comparably large number of studies, radiomics is mainly studied in proof of principle concept. Hence, a translation of radiomics from a hot topic research field into an essential clinical decision-making tool is lacking, but of high clinical interest. EVIDENCE ACQUISITION Herein, we present a literature review addressing the clinical evidence of CT and PET radiomics. An extensive literature review was conducted in PubMed, including papers on robustness and clinical applications. EVIDENCE SYNTHESIS We summarize image-modality related influences on the robustness of radiomic features and provide an overview of clinical evidence reported in the literature. Today, more evidence has been provided for CT imaging, however, PET imaging offers the promise of direct imaging of biological processes and functions. We provide a summary of future research directions, which needs to be addressed in order to successfully introduce radiomics into clinical medicine. In comparison to CT, more focus should be directed towards harmonization of PET acquisition and reconstruction protocols, which is important for transferable modelling. CONCLUSIONS Both CT and PET radiomics are promising pre-treatment and intra-treatment biomarkers for outcome prediction. Most studies are performed in retrospective setting, however their validation in prospective data collections is ongoing.
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Affiliation(s)
- Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland -
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matea Pavic
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Hubert S Gabrys
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
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Abstract
PURPOSE OF REVIEW To briefly review the radiomics concept, its applications, and challenges in oncology in the era of precision medicine. RECENT FINDINGS Over the last 5 years, more than 500 studies have evaluated the role of radiomics to predict tumor diagnosis, genetic pattern, tumor response to therapy, and survival in multiple cancers. This new post-processing method is aimed at extracting multiple quantitative features from the image and converting them into mineable data. Radiomics models developed have shown promising results and may play a role in the near future in the daily patient management especially to assess tumor heterogeneity acting as a whole tumor virtual biopsy. For now, radiomics is limited by its lack of standardization; future challenges will be to provide robust and reproducible metrics extracted from large multicenter databases.
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Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin 2019; 69:127-157. [PMID: 30720861 PMCID: PMC6403009 DOI: 10.3322/caac.21552] [Citation(s) in RCA: 635] [Impact Index Per Article: 127.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.
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Affiliation(s)
- Wenya Linda Bi
- Assistant Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Ahmed Hosny
- Research Scientist, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Matthew B. Schabath
- Associate Member, Department of Cancer EpidemiologyH. Lee Moffitt Cancer Center and Research InstituteTampaFL
| | - Maryellen L. Giger
- Professor of Radiology, Department of RadiologyUniversity of ChicagoChicagoIL
| | - Nicolai J. Birkbak
- Research Associate, The Francis Crick InstituteLondonUnited Kingdom
- Research Associate, University College London Cancer InstituteLondonUnited Kingdom
| | - Alireza Mehrtash
- Research Assistant, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
- Research Assistant, Department of Electrical and Computer EngineeringUniversity of British ColumbiaVancouverBCCanada
| | - Tavis Allison
- Research Assistant, Department of RadiologyColumbia University College of Physicians and SurgeonsNew YorkNY
- Research Assistant, Department of RadiologyNew York Presbyterian HospitalNew YorkNY
| | - Omar Arnaout
- Assistant Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Christopher Abbosh
- Research Fellow, The Francis Crick InstituteLondonUnited Kingdom
- Research Fellow, University College London Cancer InstituteLondonUnited Kingdom
| | - Ian F. Dunn
- Associate Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Raymond H. Mak
- Associate Professor, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Rulla M. Tamimi
- Associate Professor, Department of MedicineBrigham and Women’s Hospital, Dana‐Farber Cancer Institute, Harvard Medical SchoolBostonMA
| | - Clare M. Tempany
- Professor of Radiology, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Charles Swanton
- Professor, The Francis Crick InstituteLondonUnited Kingdom
- Professor, University College London Cancer InstituteLondonUnited Kingdom
| | - Udo Hoffmann
- Professor of Radiology, Department of RadiologyMassachusetts General Hospital and Harvard Medical SchoolBostonMA
| | - Lawrence H. Schwartz
- Professor of Radiology, Department of RadiologyColumbia University College of Physicians and SurgeonsNew YorkNY
- Chair, Department of RadiologyNew York Presbyterian HospitalNew YorkNY
| | - Robert J. Gillies
- Professor of Radiology, Department of Cancer PhysiologyH. Lee Moffitt Cancer Center and Research InstituteTampaFL
| | - Raymond Y. Huang
- Assistant Professor, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Hugo J. W. L. Aerts
- Associate Professor, Departments of Radiation Oncology and Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
- Professor in AI in Medicine, Radiology and Nuclear Medicine, GROWMaastricht University Medical Centre (MUMC+)MaastrichtThe Netherlands
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Tunali I, Gray JE, Qi J, Abdalah M, Jeong DK, Guvenis A, Gillies RJ, Schabath MB. Novel clinical and radiomic predictors of rapid disease progression phenotypes among lung cancer patients treated with immunotherapy: An early report. Lung Cancer 2019; 129:75-79. [PMID: 30797495 DOI: 10.1016/j.lungcan.2019.01.010] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Revised: 01/17/2019] [Accepted: 01/23/2019] [Indexed: 12/26/2022]
Abstract
OBJECTIVES Immune-checkpoint blockades have exhibited durable responses and improved long-term survival in a subset of advanced non-small cell lung cancer (NSCLC) patients. However, highly predictive markers of positive and negative responses to immunotherapy are a significant unmet clinical need. The objective of this study was to identify clinical and computational image-based predictors of rapid disease progression phenotypes in NSCLC patients treated with immune-checkpoint blockades. MATERIALS AND METHODS Using time-to-progression (TTP) and/or tumor growth rates, rapid disease progression phenotypes were developed including hyperprogressive disease. The pre-treatment baseline predictors that were used to identify these phenotypes included patient demographics, clinical data, driver mutations, hematology data, and computational image-based features (radiomics) that were extracted from pre-treatment computed tomography scans. Synthetic Minority Oversampling Technique (SMOTE) was used to subsample minority groups to eliminate classification bias. Patient-level probabilities were calculated from the final clinical-radiomic models to subgroup patients by progression-free survival (PFS). RESULTS Among 228 NSCLC patients treated with single agent or double agent immunotherapy, we identified parsimonious clinical-radiomic models with modest to high ability to predict rapid disease progression phenotypes with area under the receiver-operator characteristics ranging from 0.804 to 0.865. Patients who had TTP < 2 months or hyperprogressive disease were classified with 73.41% and 82.28% accuracy after SMOTE subsampling, respectively. When the patient subgroups based on patient-level probabilities were analyzed for survival outcomes, patients with higher probability scores had significantly worse PFS. CONCLUSIONS The models found in this study have potential important translational implications to identify highly vulnerable NSCLC patients treated with immunotherapy that experience rapid disease progression and poor survival outcomes.
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Affiliation(s)
- Ilke Tunali
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
| | - Jhanelle E Gray
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jin Qi
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Mahmoud Abdalah
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Daniel K Jeong
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Albert Guvenis
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
| | - Robert J Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Matthew B Schabath
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
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Brown D, Zingone A, Yu Y, Zhu B, Candia J, Cao L, Ryan BM. Relationship between Circulating Inflammation Proteins and Lung Cancer Diagnosis in the National Lung Screening Trial. Cancer Epidemiol Biomarkers Prev 2018; 28:110-118. [PMID: 30297515 DOI: 10.1158/1055-9965.epi-18-0598] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 08/09/2018] [Accepted: 09/27/2018] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Previously, we observed a strong relationship between circulating serum inflammation proteins in relation to lung cancer diagnosis and risk, both in case-control and prospective cohorts. Low-dose computed tomography (LDCT) screening has a high prevalence of false-positive nodules, thus companion noninvasive biomarkers that can distinguish between benign and malignant nodules could have clinical utility and positive impact on patient outcomes. METHODS We conducted a nested case-control study within the National Lung Screening Trial. Concentrations of 30 inflammation proteins were measured on plasma samples of 262 cases and 528 controls using a highly sensitive and analytically validated electrochemiluminescence V-PLEX immunoassay. RESULTS Comparing the fourth quartile with the first quartile, we found increased IFNγ and IL12/IL23p40 associated with increased odds of a lung cancer diagnosis [OR 1.89, 95% confidence intervals (CI), 1.16-3.09; OR 2.49, 95% CI, 1.46-4.23, respectively]. Confirming our previous observations, we also detected a relationship between increased IL6, IL8, and C-reactive protein (CRP) with lung cancer diagnosis. These relationships were significant after adjustment for age, gender, race, smoking, body mass index (BMI), family history of lung cancer, and previous diagnoses of inflammatory conditions. However, none of these proteins could distinguish between a benign and malignant lung nodule (IL6: OR 1.25, 95% CI, 0.59-2.64; IL8: OR 1.40, 95% CI, 0.70-2.81; CRP: OR 0.98, 95% CI, 0.45-2.12). CONCLUSIONS We have discovered new associations for IFNγ and IL12/IL23p40 with lung cancer but have no evidence that these proteins can distinguish between benign and malignant lung nodules. IMPACT Circulating inflammation proteins are unlikely to have utility as companion LDCT biomarkers.
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Affiliation(s)
- Derek Brown
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
| | - Adriana Zingone
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
| | - Yunkai Yu
- Genetics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
| | - Bin Zhu
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Julián Candia
- Trans-NIH Center for Human Immunology (CHI), National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland
| | - Liang Cao
- Genetics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
| | - Bríd M Ryan
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland.
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