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Choi W, Jia Y, Kwak J, Werner-Wasik M, Dicker AP, Simone NL, Storozynsky E, Jain V, Vinogradskiy Y. Novel Functional Radiomics for Prediction of Cardiac Positron Emission Tomography Avidity in Lung Cancer Radiotherapy. JCO Clin Cancer Inform 2024; 8:e2300241. [PMID: 38452302 PMCID: PMC10939651 DOI: 10.1200/cci.23.00241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/22/2023] [Accepted: 01/26/2024] [Indexed: 03/09/2024] Open
Abstract
PURPOSE Traditional methods of evaluating cardiotoxicity focus on radiation doses to the heart. Functional imaging has the potential to provide improved prediction for cardiotoxicity for patients with lung cancer. Fluorine-18 (18F) fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) imaging is routinely obtained in a standard cancer staging workup. This work aimed to develop a radiomics model predicting clinical cardiac assessment using 18F-FDG PET/CT scans before thoracic radiation therapy. METHODS Pretreatment 18F-FDG PET/CT scans from three study populations (N = 100, N = 39, N = 70) were used, comprising two single-institutional protocols and one publicly available data set. A clinician (V.J.) classified the PET/CT scans per clinical cardiac guidelines as no uptake, diffuse uptake, or focal uptake. The heart was delineated, and 210 novel functional radiomics features were selected to classify cardiac FDG uptake patterns. Training data were divided into training (80%)/validation (20%) sets. Feature reduction was performed using the Wilcoxon test, hierarchical clustering, and recursive feature elimination. Ten-fold cross-validation was carried out for training, and the accuracy of the models to predict clinical cardiac assessment was reported. RESULTS From 202 of 209 scans, cardiac FDG uptake was scored as no uptake (39.6%), diffuse uptake (25.3%), and focal uptake (35.1%), respectively. Sixty-two independent radiomics features were reduced to nine clinically pertinent features. The best model showed 93% predictive accuracy in the training data set and 80% and 92% predictive accuracy in two external validation data sets. CONCLUSION This work used an extensive patient data set to develop a functional cardiac radiomic model from standard-of-care 18F-FDG PET/CT scans, showing good predictive accuracy. The radiomics model has the potential to provide an automated method to predict existing cardiac conditions and provide an early functional biomarker to identify patients at risk of developing cardiac complications after radiotherapy.
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Affiliation(s)
- Wookjin Choi
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Yingcui Jia
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Jennifer Kwak
- Department of Radiology, University of Colorado School of Medicine, Aurora, CO
| | - Maria Werner-Wasik
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Adam P. Dicker
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Nicole L. Simone
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Eugene Storozynsky
- Department of Cardiology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Varsha Jain
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
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Arabi M, Nazari M, Salahshour A, Jenabi E, Hajianfar G, Khateri M, Shayesteh SP. A machine learning-based sonomics for prediction of thyroid nodule malignancies. Endocrine 2023; 82:326-334. [PMID: 37291392 DOI: 10.1007/s12020-023-03407-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 05/20/2023] [Indexed: 06/10/2023]
Abstract
OBJECTIVES This study aims to use ultrasound derived features as biomarkers to assess the malignancy of thyroid nodules in patients who were candidates for FNA according to the ACR TI-RADS guidelines. METHODS Two hundred and ten patients who met the selection criteria were enrolled in the study and subjected to ultrasound-guided FNA of thyroid nodules. Different radiomics features were extracted from sonographic images, including intensity, shape, and texture feature sets. Least Absolute Shrinkage and Selection Operator (LASSO), Minimum Redundancy Maximum Relevance (MRMR), and Random Forests/Extreme Gradient Boosting Machine (XGBoost) algorithms were used for feature selection and classification of the univariate and multivariate modeling, respectively. Evaluation of models performed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS In the univariate analysis, Gray Level Run Length Matrix - Run-Length Non-Uniformity (GLRLM-RLNU) and gray-level zone length matrix - Run-Length Non-Uniformity (GLZLM-GLNU) (both with an AUC of 0.67) were top-performing for predicting nodules malignancy. In the multivariate analysis of the training dataset, the AUC of all combinations of feature selection algorithms and classifiers was 0.99, and the highest sensitivity was for XGBoost classifier and MRMR feature selection algorithms (0.99). Finally, the test dataset was used to evaluate our model in which XGBoost classifier with MRMR and LASSO feature selection algorithms had the highest performance (AUC = 0.95). CONCLUSIONS Ultrasound-extracted features can be used as non-invasive biomarkers for thyroid nodules' malignancy prediction.
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Affiliation(s)
- Mohsen Arabi
- Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran
| | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Salahshour
- Department of Radiology, Alborz University of Medical Sciences, Karaj, Iran
| | - Elnaz Jenabi
- Research Centre for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | - Maziar Khateri
- Research Centre for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Sajad P Shayesteh
- Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran.
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Khodabakhshi Z, Amini M, Hajianfar G, Oveisi M, Shiri I, Zaidi H. Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and Machine Learning Algorithms for Non-small Cell Lung Cancer Histopathological Subtype Phenotype Decoding. Clin Oncol (R Coll Radiol) 2023; 35:713-725. [PMID: 37599160 DOI: 10.1016/j.clon.2023.08.003] [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: 09/27/2022] [Revised: 06/10/2023] [Accepted: 08/05/2023] [Indexed: 08/22/2023]
Abstract
AIMS We aimed to build radiomic models for classifying non-small cell lung cancer (NSCLC) histopathological subtypes through a dual-centre dataset and comprehensively evaluate the effect of ComBat harmonisation on the performance of single- and multimodality radiomic models. MATERIALS AND METHODS A public dataset of NSCLC patients from two independent centres was used. Two image fusion methods, namely guided filtering-based fusion and image fusion based on visual saliency map and weighted least square optimisation, were used. Radiomic features were extracted from each scan, including first-order, texture and moment-invariant features. Subsequently, ComBat harmonisation was applied to the extracted features from computed tomography (CT), positron emission tomography (PET) and fused images to correct the centre effect. For feature selection, least absolute shrinkage and selection operator (Lasso) and recursive feature elimination (RFE) were investigated. For machine learning, logistic regression (LR), support vector machine (SVM) and AdaBoost were evaluated for classifying NSCLC subtypes. Training and evaluation of the models were carried out in a robust framework to offset plausible errors and performance was reported using area under the curve, balanced accuracy, sensitivity and specificity before and after harmonisation. N-way ANOVA was used to assess the effect of different factors on the performance of the models. RESULTS Support vector machine fed with selected features by recursive feature elimination from a harmonised PET feature set achieved the highest performance (area under the curve = 0.82) in classifying NSCLC histopathological subtypes. Although the performance of the models did not significantly improve for CT images after harmonisation, the performance of PET and guided filtering-based fusion feature signatures significantly improved for almost all models. Although the selection of the image modality and feature selection methods was effective on the performance of the model (ANOVA P-values <0.001), machine learning and harmonisation did not change the performance significantly (ANOVA P-values = 0.839 and 0.292, respectively). CONCLUSION This study confirmed the potential of radiomic analysis on PET, CT and hybrid images for histopathological classification of NSCLC subtypes.
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Affiliation(s)
- Z Khodabakhshi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - M Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - G Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - M Oveisi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran; Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, Kings College London, London, UK; Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - I Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - H Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Ma T, Wang H, Ye Z. Artificial intelligence applications in computed tomography in gastric cancer: a narrative review. Transl Cancer Res 2023; 12:2379-2392. [PMID: 37859746 PMCID: PMC10583011 DOI: 10.21037/tcr-23-201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 08/01/2023] [Indexed: 10/21/2023]
Abstract
Background and Objective Artificial intelligence (AI) is a revolutionary technique which is deeply impacting and reshaping clinical practice in oncology. This review aims to summarize the current status of the clinical application of AI-based computed tomography (CT) for gastric cancer (GC), focusing on diagnosis, genetic status detection and risk prediction of metastasis, prognosis and treatment efficacy. The challenges and prospects for future research will also be discussed. Methods We searched the PubMed/MEDLINE database to identify clinical studies published between 1990 and November 2022 that investigated AI applications in CT in GC. The major findings of the verified studies were summarized. Key Content and Findings AI applications in CT images have attracted considerable attention in various fields such as diagnosis, prediction of metastasis risk, survival, and treatment response. These emerging techniques have shown a high potential to outperform clinicians in diagnostic accuracy and time-saving. Conclusions AI-powered tools showed great potential to increase diagnostic accuracy and reduce radiologists' workload. However, the goal of AI is not to replace human ability but to help oncologists make decisions in their practice. Therefore, radiologists should play a predominant role in AI applications and decide the best ways to integrate these complementary techniques within clinical practice.
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Affiliation(s)
- Tingting Ma
- Department of Radiology, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Hua Wang
- Department of Radiology, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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Cilla S, Pistilli D, Romano C, Macchia G, Pierro A, Arcelli A, Buwenge M, Morganti AG, Deodato F. CT-based radiomics prediction of complete response after stereotactic body radiation therapy for patients with lung metastases. Strahlenther Onkol 2023:10.1007/s00066-023-02086-6. [PMID: 37256303 DOI: 10.1007/s00066-023-02086-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 04/11/2023] [Indexed: 06/01/2023]
Abstract
PURPOSE Stereotactic body radiotherapy (SBRT) is a key treatment modality for lung cancer patients. This study aims to develop a machine learning-based prediction model of complete response for lung oligometastatic cancer patients undergoing SBRT. MATERIALS AND METHODS CT images of 80 pulmonary oligometastases from 56 patients treated with SBRT were analyzed. The gross tumor volumes (GTV) were contoured on CT images. Patients that achieved complete response (CR) at 4 months were defined as responders. For each GTV, 107 radiomic features were extracted using the Pyradiomics software. The concordance correlation coefficients (CCC) between the region of interest (ROI)-based radiomics features obtained by the two segmentations were calculated. Pairwise feature interdependencies were evaluated using the Spearman rank correlation coefficient. The association of clinical variables and radiomics features with CR was evaluated with univariate logistic regression. Two supervised machine learning models, the logistic regression (LR) and the classification and regression tree analysis (CART), were trained to predict CR. The models were cross-validated using a five-fold cross-validation. The performance of models was assessed by receiver operating characteristic curve (ROC) and class-specific accuracy, precision, recall, and F1-measure evaluation metrics. RESULTS Complete response was associated with four radiomics features, namely the surface to volume ratio (SVR; p = 0.003), the skewness (Skew; p = 0.027), the correlation (Corr; p = 0.024), and the grey normalized level uniformity (GNLU; p = 0.015). No significant relationship between clinical parameters and CR was found. In the validation set, the developed LR and CART machine learning models had an accuracy, precision, and recall of 0.644 and 0.750, 0.644 and 0.651, and 0.635 and 0.754, respectively. The area under the curve for CR prediction was 0.707 and 0.753 for the LR and CART models, respectively. CONCLUSION This analysis demonstrates that radiomics features obtained from pretreatment CT could predict complete response of lung oligometastases following SBRT.
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Affiliation(s)
- Savino Cilla
- Gemelli Molise Hospital, Medical Physics Unit, Largo Gemelli 1, 86100, Campobasso, Italy.
| | - Domenico Pistilli
- Gemelli Molise Hospital, Medical Physics Unit, Largo Gemelli 1, 86100, Campobasso, Italy
| | - Carmela Romano
- Gemelli Molise Hospital, Medical Physics Unit, Largo Gemelli 1, 86100, Campobasso, Italy
| | | | - Antonio Pierro
- Radiology Unit, Gemelli Molise Hospital, Campobasso, Italy
| | - Alessandra Arcelli
- Radiation Oncology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Milly Buwenge
- Radiation Oncology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Alessio Giuseppe Morganti
- Radiation Oncology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Experimental, Diagnostic, and Specialty Medicine-DIMES, Alma Mater Studiorum, Università di Bologna, Diagnostic, Italy
| | - Francesco Deodato
- Radiation Oncology Unit, Gemelli Molise Hospital, Campobasso, Italy
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, Roma, Italy
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Cai J, Guo L, Zhu L, Xia L, Qian L, Lure YMF, Yin X. Impact of localized fine tuning in the performance of segmentation and classification of lung nodules from computed tomography scans using deep learning. Front Oncol 2023; 13:1140635. [PMID: 37056345 PMCID: PMC10088514 DOI: 10.3389/fonc.2023.1140635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
BackgroundAlgorithm malfunction may occur when there is a performance mismatch between the dataset with which it was developed and the dataset on which it was deployed.MethodsA baseline segmentation algorithm and a baseline classification algorithm were developed using public dataset of Lung Image Database Consortium to detect benign and malignant nodules, and two additional external datasets (i.e., HB and XZ) including 542 cases and 486 cases were involved for the independent validation of these two algorithms. To explore the impact of localized fine tuning on the individual segmentation and classification process, the baseline algorithms were fine tuned with CT scans of HB and XZ datasets, respectively, and the performance of the fine tuned algorithms was tested to compare with the baseline algorithms.ResultsThe proposed baseline algorithms of both segmentation and classification experienced a drop when directly deployed in external HB and XZ datasets. Comparing with the baseline validation results in nodule segmentation, the fine tuned segmentation algorithm obtained better performance in Dice coefficient, Intersection over Union, and Average Surface Distance in HB dataset (0.593 vs. 0.444; 0.450 vs. 0.348; 0.283 vs. 0.304) and XZ dataset (0.601 vs. 0.486; 0.482 vs. 0.378; 0.225 vs. 0.358). Similarly, comparing with the baseline validation results in benign and malignant nodule classification, the fine tuned classification algorithm had improved area under the receiver operating characteristic curve value, accuracy, and F1 score in HB dataset (0.851 vs. 0.812; 0.813 vs. 0.769; 0.852 vs. 0.822) and XZ dataset (0.724 vs. 0.668; 0.696 vs. 0.617; 0.737 vs. 0.668).ConclusionsThe external validation performance of localized fine tuned algorithms outperformed the baseline algorithms in both segmentation process and classification process, which showed that localized fine tuning may be an effective way to enable a baseline algorithm generalize to site-specific use.
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Affiliation(s)
- Jingwei Cai
- Radiology Department, Affiliated Hospital of Hebei University, Baoding, Hebei, China
- Clinical Medical College, Hebei University, Baoding, Hebei, China
| | - Lin Guo
- Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China
| | - Litong Zhu
- Department of Medicine, Queen Mary Hospital, University of Hong, Hong Kong, Hong Kong SAR, China
| | - Li Xia
- Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China
| | - Lingjun Qian
- Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China
| | | | - Xiaoping Yin
- Radiology Department, Affiliated Hospital of Hebei University, Baoding, Hebei, China
- *Correspondence: Xiaoping Yin,
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Voigt W, Prosch H, Silva M. Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges? Cancers (Basel) 2023; 15:cancers15041218. [PMID: 36831559 PMCID: PMC9954060 DOI: 10.3390/cancers15041218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 02/05/2023] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
As most lung cancer (LC) cases are still detected at advanced and incurable stages, there are increasing efforts to foster detection at earlier stages by low dose computed tomography (LDCT) based LC screening. In this scoping review, we describe current advances in candidate selection for screening (selection phase), technical aspects (screening), and probability evaluation of malignancy of CT-detected pulmonary nodules (PN management). Literature was non-systematically assessed and reviewed for suitability by the authors. For the selection phase, we describe current eligibility criteria for screening, along with their limitations and potential refinements through advanced clinical scores and biomarker assessments. For LC screening, we discuss how the accuracy of computerized tomography (CT) scan reading might be augmented by IT tools, helping radiologists to cope with increasing workloads. For PN management, we evaluate the precision of follow-up scans by semi-automatic volume measurements of CT-detected PN. Moreover, we present an integrative approach to evaluate the probability of PN malignancy to enable safe decisions on further management. As a clear limitation, additional validation studies are required for most innovative diagnostic approaches presented in this article, but the integration of clinical risk models, current imaging techniques, and advancing biomarker research has the potential to improve the LC screening performance generally.
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Affiliation(s)
- Wieland Voigt
- Medical Innovation and Management, Steinbeis University Berlin, Ernst-Augustin-Strasse 15, 12489 Berlin, Germany
- Correspondence:
| | - Helmut Prosch
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, General Hospital, 1090 Vienna, Austria
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, 43121 Parma, Italy
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Gao Y, Hua M, Lv J, Ma Y, Liu Y, Ren M, Tian Y, Li X, Zhang H. Reproducibility of radiomic features of pulmonary nodules between low-dose CT and conventional-dose CT. Quant Imaging Med Surg 2022; 12:2368-2377. [PMID: 35371962 PMCID: PMC8923849 DOI: 10.21037/qims-21-609] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 12/30/2021] [Indexed: 09/25/2023]
Abstract
BACKGROUND The reproducibility of radiomic features is essential to lung cancer detection. This study aimed to investigate the reproducibility of radiomic features of pulmonary nodules between low-dose computed tomography (LDCT) and conventional-dose computed tomography (CDCT). METHODS A total of 105 patients with 119 pulmonary nodules [39 ground-glass nodules (GGNs) and 80 solid nodules] who underwent LDCT and CDCT were retrospectively studied between September 2019 and November 2020. Pulmonary nodules were manually segmented and 1,125 radiomic features (shape, first-order intensity, texture, wavelet, and Laplacian of the Gaussian features) were extracted from both LDCT and CDCT images. The concordance correlation coefficient (CCC) was used to evaluate the reproducibility of these radiomic features. RESULTS Of the 1,125 radiomic features considered, 35.5% (399 of 1,125) and 41.5% (467 of 1,125) were reproducible (CCC ≥0.85) for GGNs and solid nodules, respectively. The intensity, texture, and wavelet features of solid nodules were more reproducible than those of GGNs. The mean CCC values for intensity and texture features of solid nodules were of 0.85 and above, whereas the mean values for those of GGNs were of less than 0.85. After Gaussian kernel (σ =2) preprocessing, the CCC of intensity and texture features of GGNs improved from 0.77 to 0.90, and 84.9% (79 of 93) of the radiomic features were reproducible (mean CCC increase from 0.84±0.13 to 0.92±0.08 for intensity features, and from 0.75±0.15 to 0.89±0.11 for texture features). Wavelet features had the lowest CCCs for both GGNs and solid nodules. CONCLUSIONS The majority of the radiomic feature classes of solid pulmonary nodules have a high level of reproducibility between LDCT and CDCT. However, LDCT should not be used as an alternative to CDCT in the radiomic study of GGNs.
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Affiliation(s)
- Yufan Gao
- Department of Radiology, Chest Hospital, Tianjin University, Tianjin, China
| | - Minghui Hua
- Department of Radiology, Chest Hospital, Tianjin University, Tianjin, China
| | - Jun Lv
- Department of Radiology, Chest Hospital, Tianjin University, Tianjin, China
| | - Yanhe Ma
- Department of Radiology, Chest Hospital, Tianjin University, Tianjin, China
| | - Yanzhen Liu
- Department of Radiology, Chest Hospital, Tianjin University, Tianjin, China
| | - Min Ren
- Tianjin Cardiovascular Institute, Chest Hospital, Tianjin University, Tianjin, China
| | - Yaohua Tian
- Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ximing Li
- Department of Cardiovascular Medicine, Chest Hospital, Tianjin University, Tianjin, China
| | - Hong Zhang
- Department of Radiology, Chest Hospital, Tianjin University, Tianjin, China
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Garau N, Orro A, Summers P, De Maria L, Bertolotti R, Bassis D, Minotti M, De Fiori E, Baroni G, Paganelli C, Rampinelli C. Integrating Biological and Radiological Data in a Structured Repository: a Data Model Applied to the COSMOS Case Study. J Digit Imaging 2022; 35:970-982. [PMID: 35296941 PMCID: PMC9485502 DOI: 10.1007/s10278-022-00615-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 02/17/2022] [Accepted: 02/28/2022] [Indexed: 11/29/2022] Open
Abstract
Integrating the information coming from biological samples with digital data, such as medical images, has gained prominence with the advent of precision medicine. Research in this field faces an ever-increasing amount of data to manage and, as a consequence, the need to structure these data in a functional and standardized fashion to promote and facilitate cooperation among institutions. Inspired by the Minimum Information About BIobank data Sharing (MIABIS), we propose an extended data model which aims to standardize data collections where both biological and digital samples are involved. In the proposed model, strong emphasis is given to the cause-effect relationships among factors as these are frequently encountered in clinical workflows. To test the data model in a realistic context, we consider the Continuous Observation of SMOking Subjects (COSMOS) dataset as case study, consisting of 10 consecutive years of lung cancer screening and follow-up on more than 5000 subjects. The structure of the COSMOS database, implemented to facilitate the process of data retrieval, is therefore presented along with a description of data that we hope to share in a public repository for lung cancer screening research.
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Affiliation(s)
- Noemi Garau
- Dipartimento Di Elettronica, Informazione E Bioingegneria, Politecnico Di Milano, Milano, Italy. .,Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy.
| | - Alessandro Orro
- Institute for Biomedical Technologies, National Research Council (ITB-CNR), Segrate, Italy
| | - Paul Summers
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Lorenza De Maria
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Raffaella Bertolotti
- Division of Data Management, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Danny Bassis
- School of Medicine, University of Milan, Milan, Italy
| | - Marta Minotti
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Elvio De Fiori
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Guido Baroni
- Dipartimento Di Elettronica, Informazione E Bioingegneria, Politecnico Di Milano, Milano, Italy.,Bioengineering Unit, CNAO Foundation, Pavia, Italy
| | - Chiara Paganelli
- Dipartimento Di Elettronica, Informazione E Bioingegneria, Politecnico Di Milano, Milano, Italy
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Radiomic Phenotypes for Improving Early Prediction of Survival in Stage III Non-Small Cell Lung Cancer Adenocarcinoma after Chemoradiation. Cancers (Basel) 2022; 14:cancers14030700. [PMID: 35158971 PMCID: PMC8833400 DOI: 10.3390/cancers14030700] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 01/23/2022] [Accepted: 01/28/2022] [Indexed: 02/04/2023] Open
Abstract
We evaluate radiomic phenotypes derived from CT scans as early predictors of overall survival (OS) after chemoradiation in stage III primary lung adenocarcinoma. We retrospectively analyzed 110 thoracic CT scans acquired between April 2012-October 2018. Patients received a median radiation dose of 66.6 Gy at 1.8 Gy/fraction delivered with proton (55.5%) and photon (44.5%) beam treatment, as well as concurrent chemotherapy (89%) with carboplatin-based (55.5%) and cisplatin-based (36.4%) doublets. A total of 56 death events were recorded. Using manual tumor segmentations, 107 radiomic features were extracted. Feature harmonization using ComBat was performed to mitigate image heterogeneity due to the presence or lack of intravenous contrast material and variability in CT scanner vendors. A binary radiomic phenotype to predict OS was derived through the unsupervised hierarchical clustering of the first principal components explaining 85% of the variance of the radiomic features. C-scores and likelihood ratio tests (LRT) were used to compare the performance of a baseline Cox model based on ECOG status and age, with a model integrating the radiomic phenotype with such clinical predictors. The model integrating the radiomic phenotype (C-score = 0.69, 95% CI = (0.62, 0.77)) significantly improved (p<0.005) upon the baseline model (C-score = 0.65, CI = (0.57, 0.73)). Our results suggest that harmonized radiomic phenotypes can significantly improve OS prediction in stage III NSCLC after chemoradiation.
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11
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Yan Q, Yi Y, Shen J, Shan F, Zhang Z, Yang G, Shi Y. Preliminary study of 3 T-MRI native T1-mapping radiomics in differential diagnosis of non-calcified solid pulmonary nodules/masses. Cancer Cell Int 2021; 21:539. [PMID: 34663307 PMCID: PMC8522214 DOI: 10.1186/s12935-021-02195-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 09/04/2021] [Indexed: 12/30/2022] Open
Abstract
Background Cumulative CT radiation damage was positively correlated with increased tumor risks. Although it has recently been known that non-radiation MRI is alternative for pulmonary imaging. There is little known about the value of MRI T1-mapping in the diagnosis of pulmonary nodules. This article aimed to investigate the value of native T1-mapping-based radiomics features in differential diagnosis of pulmonary lesions. Methods 73 patients underwent 3 T-MRI examination in this prospective study. The 99 pulmonary lesions on native T1-mapping images were segmented twice by one radiologist at indicated time points utilizing the in-house semi-automated software, followed by extraction of radiomics features. The inter-class correlation coefficient (ICC) was used for analyzing intra-observer’s agreement. Dimensionality reduction and feature selection were performed via univariate analysis, and least absolute shrinkage and selection operator (LASSO) analysis. Then, the binary logical regression (LR), support vector machine (SVM) and decision tree classifiers with the input of optimal features were selected for differentiating malignant from benign lesions. The receiver operative characteristics (ROC) curve, area under the curve (AUC), sensitivity, specificity and accuracy were calculated. Z-test was used to compare differences among AUCs. Results 107 features were obtained, of them, 19.5% (n = 21) had relatively good reliability (ICC ≥ 0.6). The remained 5 features (3 GLCM, 1 GLSZM and 1 shape features) by dimensionality reduction were useful. The AUC of LR was 0.82(95%CI: 0.67–0.98), with sensitivity, specificity and accuracy of 70%, 85% and 80%. The AUC of SVM was 0.82(95%CI: 0.67–0.98), with sensitivity, specificity and accuracy of 70, 85 and 80%. The AUC of decision tree was 0.69(95%CI: 0.49–0.87), with sensitivity, specificity and accuracy of 50, 85 and 73.3%. Conclusions The LR and SVM models using native T1-mapping-based radiomics features can differentiate pulmonary malignant from benign lesions, especially for uncertain nodules requiring long-term follow-ups.
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Affiliation(s)
- Qinqin Yan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China
| | - Yinqiao Yi
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Jie Shen
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China
| | - Zhiyong Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China.
| | - Yuxin Shi
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China.
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12
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A segmentation tool for pulmonary nodules in lung cancer screening: Testing and clinical usage. Phys Med 2021; 90:23-29. [PMID: 34530212 DOI: 10.1016/j.ejmp.2021.08.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/28/2021] [Accepted: 08/21/2021] [Indexed: 12/17/2022] Open
Abstract
PURPOSE With the future goal of defining a large dataset based on low-dose CT with labelled pulmonary lesions for lung cancer screening (LCS) research, the aim of this work is to propose and evaluate into a clinical context a tool for semi-automatic segmentation able to facilitate the process of labels collection from a LCS study (COSMOS, Continuous Observation of SMOking Subjects). METHODS Considering a preliminary set of manual annotations, a segmentation model based on a 2D-Unet was trained from scratch. Contour quality of the final 2D-Unet was assessed on an internal test set of manual annotations and on a subset of the public available LIDC dataset used as external test set. The tool for semi-automatic segmentation was then designed integrating the tested model into a Graphical User Interface. According to the opinion of two clinical users, the percentage of lesions properly contoured through the tool was quantified (Acceptance Rate, AR). The variability between segmentations derived by the two readers was estimated as mean percentage of difference (MPD) between the two sets of volumes and comparing the likelihood of malignancy derived from Volume Doubling Time (VDT). RESULTS Performance in test sets were found similar (DICE ~ 0.75(0.15)). Accordingly, a good mean AR (80.1%) resulted from the two readers. Variability in terms of MPD was equal to 23.6% while 2.7% was the VDTs percentage of disagreement. CONCLUSIONS A semi-automatic segmentation tool was developed and its applicability evaluated into a clinical context demonstrating the efficacy of the tool in facilitating the collection of labelled data.
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Rundo L, Ledda RE, di Noia C, Sala E, Mauri G, Milanese G, Sverzellati N, Apolone G, Gilardi MC, Messa MC, Castiglioni I, Pastorino U. A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules. Diagnostics (Basel) 2021; 11:1610. [PMID: 34573951 PMCID: PMC8471292 DOI: 10.3390/diagnostics11091610] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 08/25/2021] [Accepted: 08/30/2021] [Indexed: 12/25/2022] Open
Abstract
Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs.
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Affiliation(s)
- Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK;
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Roberta Eufrasia Ledda
- Unit of Radiological Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, 43126 Parma, Italy; (R.E.L.); (G.M.); (N.S.)
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (G.A.); (U.P.)
| | - Christian di Noia
- Department of Physics “Giuseppe Occhialini”, University of Milano-Bicocca, 20126 Milan, Italy;
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK;
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy;
| | - Gianluca Milanese
- Unit of Radiological Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, 43126 Parma, Italy; (R.E.L.); (G.M.); (N.S.)
| | - Nicola Sverzellati
- Unit of Radiological Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, 43126 Parma, Italy; (R.E.L.); (G.M.); (N.S.)
| | - Giovanni Apolone
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (G.A.); (U.P.)
| | - Maria Carla Gilardi
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (M.C.G.); (M.C.M.)
| | - Maria Cristina Messa
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (M.C.G.); (M.C.M.)
- Institute of Biomedical Imaging and Physiology, Italian National Research Council (IBFM-CNR), Segrate, 20090 Milan, Italy
- Fondazione Tecnomed, University of Milano-Bicocca, 20900 Monza, Italy
| | - Isabella Castiglioni
- Department of Physics “Giuseppe Occhialini”, University of Milano-Bicocca, 20126 Milan, Italy;
- Institute of Biomedical Imaging and Physiology, Italian National Research Council (IBFM-CNR), Segrate, 20090 Milan, Italy
| | - Ugo Pastorino
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (G.A.); (U.P.)
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Avanzo M, Gagliardi V, Stancanello J, Blanck O, Pirrone G, El Naqa I, Revelant A, Sartor G. Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy. Med Phys 2021; 48:6257-6269. [PMID: 34415574 DOI: 10.1002/mp.15178] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 07/20/2021] [Accepted: 08/02/2021] [Indexed: 02/06/2023] Open
Abstract
PURPOSE The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non-small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre-treatment computed tomography (CT) with features from the biologically effective dose (BED) distribution. MATERIALS AND METHODS Image features, consisting of crafted radiomic features or machine-learned features extracted using a convolutional neural network, were calculated from pre-treatment CT data and from dose distributions converted into BED for 80 NSCLC lesions over 76 patients treated with robotic guided SBRT. ML models using different combinations of features were trained to predict complete or partial response according to response criteria in solid tumors, including radiomics CT (RadCT ), radiomics CT and BED (RadCT,BED ), deep learning (DL) CT (DLCT ), and DL CT and BED (DLCT,BED ). Training of ML included feature selection by neighborhood component analysis followed by ensemble ML using robust boosting. A model was considered as acceptable when the sum of average sensitivity and specificity on test data in repeated cross validations was at least 1.5. RESULTS Complete or partial response occurred in 58 out of 80 lesions. The best models to predict the tumor response were those using BED variables, achieving significantly better area under curve (AUC) and accuracy than those using only features from CT, including a RadCT,BED model using three radiomic features from BED, which scored an accuracy of 0.799 (95% confidence intervals (0.75-0.85)) and AUC of 0.773 (0.688-0.846), and a DLCT,BED model also using three variables with an accuracy of 0.798 (0.649-0.829) and AUC of 0.812 (0.755-0.867). CONCLUSION According to our results, the inclusion of BED features improves the response prediction of ML models for lung cancer patients undergoing SBRT, regardless of the use of radiomic or DL features.
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Affiliation(s)
- Michele Avanzo
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, Italy
| | - Vito Gagliardi
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, Italy
| | | | - Oliver Blanck
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Giovanni Pirrone
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, Italy
| | - Issam El Naqa
- Department of Machine Learning, Moffitt University, Tampa, Florida, USA
| | - Alberto Revelant
- Radiation Oncology Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, Italy
| | - Giovanna Sartor
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, Italy
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15
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Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix AN, Lambin P, Bottari F, Tsoutzidis N, Miraglio B, Walsh S, Vos W, Hustinx R, Ferreira M, Lovinfosse P, Leijenaar RTH. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev 2021; 42:426-440. [PMID: 34309893 DOI: 10.1002/med.21846] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 07/05/2021] [Accepted: 07/07/2021] [Indexed: 12/14/2022]
Abstract
Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.
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Affiliation(s)
- Julien Guiot
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Akshayaa Vaidyanathan
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Louis Deprez
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Fadila Zerka
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Denis Danthine
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Anne-Noelle Frix
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | | | | | | | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Roland Hustinx
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium.,GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Marta Ferreira
- GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium
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16
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Wu G, Jochems A, Refaee T, Ibrahim A, Yan C, Sanduleanu S, Woodruff HC, Lambin P. Structural and functional radiomics for lung cancer. Eur J Nucl Med Mol Imaging 2021; 48:3961-3974. [PMID: 33693966 PMCID: PMC8484174 DOI: 10.1007/s00259-021-05242-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 02/03/2021] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. METHODS Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. CONCLUSION The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form "Medomics."
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Affiliation(s)
- Guangyao Wu
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands. .,Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. .,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
| | - Arthur Jochems
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands
| | - Turkey Refaee
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Abdalla Ibrahim
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium.,Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - Chenggong Yan
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Sebastian Sanduleanu
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands
| | - Henry C Woodruff
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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17
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Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy. Phys Med 2021; 83:221-241. [DOI: 10.1016/j.ejmp.2021.04.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 03/31/2021] [Accepted: 04/03/2021] [Indexed: 02/06/2023] Open
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18
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Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization. Phys Med 2021; 83:108-121. [DOI: 10.1016/j.ejmp.2021.03.009] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/01/2021] [Accepted: 03/03/2021] [Indexed: 02/06/2023] Open
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19
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Rodríguez M, Ajona D, Seijo LM, Sanz J, Valencia K, Corral J, Mesa-Guzmán M, Pío R, Calvo A, Lozano MD, Zulueta JJ, Montuenga LM. Molecular biomarkers in early stage lung cancer. Transl Lung Cancer Res 2021; 10:1165-1185. [PMID: 33718054 PMCID: PMC7947407 DOI: 10.21037/tlcr-20-750] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival. Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs. Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians’ capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease’s mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.
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Affiliation(s)
- María Rodríguez
- Department of Thoracic Surgery, Clínica Universidad de Navarra, Madrid, Spain
| | - Daniel Ajona
- Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain
| | - Luis M Seijo
- Department of Pulmonology, Clínica Universidad de Navarra, Madrid, Spain.,Centro de Investigación Biomédica en Red Enfermedades Respiratorias (CIBERES), Madrid, Spain
| | - Julián Sanz
- Department of Pathology, Clínica Universidad de Navarra, Madrid, Spain
| | - Karmele Valencia
- Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain
| | - Jesús Corral
- Department of Oncology, Clínica Universidad de Navarra, Madrid, Spain
| | - Miguel Mesa-Guzmán
- Department of Thoracic Surgery, Clínica Universidad de Navarra, Pamplona, Spain
| | - Rubén Pío
- Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain
| | - Alfonso Calvo
- Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Pathology, Anatomy and Physiology, Schools of Medicine and Sciences, University of Navarra, Pamplona, Spain
| | - María D Lozano
- Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Pathology, Anatomy and Physiology, Schools of Medicine and Sciences, University of Navarra, Pamplona, Spain.,Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Javier J Zulueta
- Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Pulmonology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Luis M Montuenga
- Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Pathology, Anatomy and Physiology, Schools of Medicine and Sciences, University of Navarra, Pamplona, Spain
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Buizza G, Paganelli C, D’Ippolito E, Fontana G, Molinelli S, Preda L, Riva G, Iannalfi A, Valvo F, Orlandi E, Baroni G. Radiomics and Dosiomics for Predicting Local Control after Carbon-Ion Radiotherapy in Skull-Base Chordoma. Cancers (Basel) 2021; 13:339. [PMID: 33477723 PMCID: PMC7832399 DOI: 10.3390/cancers13020339] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 01/05/2021] [Accepted: 01/14/2021] [Indexed: 02/08/2023] Open
Abstract
Skull-base chordoma (SBC) can be treated with carbon ion radiotherapy (CIRT) to improve local control (LC). The study aimed to explore the role of multi-parametric radiomic, dosiomic and clinical features as prognostic factors for LC in SBC patients undergoing CIRT. Before CIRT, 57 patients underwent MR and CT imaging, from which tumour contours and dose maps were obtained. MRI and CT-based radiomic, and dosiomic features were selected and fed to two survival models, singularly or by combining them with clinical factors. Adverse LC was given by in-field recurrence or tumour progression. The dataset was split in development and test sets and the models' performance evaluated using the concordance index (C-index). Patients were then assigned a low- or high-risk score. Survival curves were estimated, and risk groups compared through log-rank tests (after Bonferroni correction α = 0.0083). The best performing models were built on features describing tumour shape and dosiomic heterogeneity (median/interquartile range validation C-index: 0.80/024 and 0.79/0.26), followed by combined (0.73/0.30 and 0.75/0.27) and CT-based models (0.77/0.24 and 0.64/0.28). Dosiomic and combined models could consistently stratify patients in two significantly different groups. Dosiomic and multi-parametric radiomic features showed to be promising prognostic factors for LC in SBC treated with CIRT.
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Affiliation(s)
- Giulia Buizza
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; (C.P.); (G.B.)
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; (C.P.); (G.B.)
| | - Emma D’Ippolito
- Radiotherapists Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy; (E.D.); (G.R.); (A.I.); (F.V.); (E.O.)
| | - Giulia Fontana
- Clinical Bioengineering Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy;
| | - Silvia Molinelli
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy;
| | - Lorenzo Preda
- Radiology Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy;
- Unit of Radiology, Department of Intensive Medicine, IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Giulia Riva
- Radiotherapists Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy; (E.D.); (G.R.); (A.I.); (F.V.); (E.O.)
| | - Alberto Iannalfi
- Radiotherapists Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy; (E.D.); (G.R.); (A.I.); (F.V.); (E.O.)
| | - Francesca Valvo
- Radiotherapists Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy; (E.D.); (G.R.); (A.I.); (F.V.); (E.O.)
| | - Ester Orlandi
- Radiotherapists Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy; (E.D.); (G.R.); (A.I.); (F.V.); (E.O.)
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; (C.P.); (G.B.)
- Clinical Bioengineering Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy;
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