1
|
Collarino A, Feudo V, Pasciuto T, Florit A, Pfaehler E, de Summa M, Bizzarri N, Annunziata S, Zannoni GF, de Geus-Oei LF, Ferrandina G, Gambacorta MA, Scambia G, Boellaard R, Sala E, Rufini V, van Velden FH. Is PET Radiomics Useful to Predict Pathologic Tumor Response and Prognosis in Locally Advanced Cervical Cancer? J Nucl Med 2024; 65:962-970. [PMID: 38548352 DOI: 10.2967/jnumed.123.267044] [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: 11/10/2023] [Revised: 03/15/2024] [Indexed: 06/05/2024] Open
Abstract
This study investigated whether radiomic features extracted from pretreatment [18F]FDG PET could improve the prediction of both histopathologic tumor response and survival in patients with locally advanced cervical cancer (LACC) treated with neoadjuvant chemoradiotherapy followed by surgery compared with conventional PET parameters and histopathologic features. Methods: The medical records of all consecutive patients with LACC referred between July 2010 and July 2016 were reviewed. [18F]FDG PET/CT was performed before neoadjuvant chemoradiotherapy. Radiomic features were extracted from the primary tumor volumes delineated semiautomatically on the PET images and reduced by factor analysis. A receiver-operating-characteristic analysis was performed, and conventional and radiomic features were dichotomized with Liu's method according to pathologic response (pR) and cancer-specific death. According to the study protocol, only areas under the curve of more than 0.70 were selected for further analysis, including logistic regression analysis for response prediction and Cox regression analysis for survival prediction. Results: A total of 195 patients fulfilled the inclusion criteria. At pathologic evaluation after surgery, 131 patients (67.2%) had no or microscopic (≤3 mm) residual tumor (pR0 or pR1, respectively); 64 patients (32.8%) had macroscopic residual tumor (>3 mm, pR2). With a median follow-up of 76.0 mo (95% CI, 70.7-78.7 mo), 31.3% of patients had recurrence or progression and 20.0% died of the disease. Among conventional PET parameters, SUVmean significantly differed between pathologic responders and nonresponders. Among radiomic features, 1 shape and 3 textural features significantly differed between pathologic responders and nonresponders. Three radiomic features significantly differed between presence and absence of recurrence or progression and between presence and absence of cancer-specific death. Areas under the curve were less than 0.70 for all parameters; thus, univariate and multivariate regression analyses were not performed. Conclusion: In a large series of patients with LACC treated with neoadjuvant chemoradiotherapy followed by surgery, PET radiomic features could not predict histopathologic tumor response and survival. It is crucial to further explore the biologic mechanism underlying imaging-derived parameters and plan a large, prospective, multicenter study with standardized protocols for all phases of the process of radiomic analysis to validate radiomics before its use in clinical routine.
Collapse
Affiliation(s)
- Angela Collarino
- Nuclear Medicine Unit, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Vanessa Feudo
- Section of Nuclear Medicine, University Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Tina Pasciuto
- Research Core Facility Data Collection G-STeP, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Anita Florit
- Section of Nuclear Medicine, University Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Elisabeth Pfaehler
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Marco de Summa
- PET/CT Center, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Nicolò Bizzarri
- Gynecologic Oncology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Salvatore Annunziata
- Nuclear Medicine Unit, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Gian Franco Zannoni
- Gynecopathology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
- Section of Pathology, Department of Woman and Child Health and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Lioe-Fee de Geus-Oei
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Biomedical Photonic Imaging Group, MIRA Institute, University of Twente, Enschede, The Netherlands
- Department of Radiation Science and Technology, Technical University of Delft, Delft, The Netherlands
| | - Gabriella Ferrandina
- Gynecologic Oncology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
- Section of Obstetrics and Gynecology, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Maria Antonietta Gambacorta
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Roma, Italy
- Section of Radiology, University Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giovanni Scambia
- Gynecologic Oncology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
- Section of Obstetrics and Gynecology, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VU University Medical Center, Amsterdam, The Netherlands; and
| | - Evis Sala
- Section of Radiology, University Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
- Advanced Radiodiagnostics Centre, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
| | - Vittoria Rufini
- Nuclear Medicine Unit, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy;
- Section of Nuclear Medicine, University Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Floris Hp van Velden
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| |
Collapse
|
2
|
Prinzi F, Orlando A, Gaglio S, Vitabile S. Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1038-1053. [PMID: 38351223 PMCID: PMC11169144 DOI: 10.1007/s10278-024-01012-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/20/2023] [Accepted: 12/05/2023] [Indexed: 06/13/2024]
Abstract
Breast microcalcifications are observed in 80% of mammograms, and a notable proportion can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated and error-prone process due to their diverse sizes, shapes, and subtle variations. In this study, we propose a radiomic signature that effectively differentiates between healthy tissue, benign microcalcifications, and malignant microcalcifications. Radiomic features were extracted from a proprietary dataset, composed of 380 healthy tissue, 136 benign, and 242 malignant microcalcifications ROIs. Subsequently, two distinct signatures were selected to differentiate between healthy tissue and microcalcifications (detection task) and between benign and malignant microcalcifications (classification task). Machine learning models, namely Support Vector Machine, Random Forest, and XGBoost, were employed as classifiers. The shared signature selected for both tasks was then used to train a multi-class model capable of simultaneously classifying healthy, benign, and malignant ROIs. A significant overlap was discovered between the detection and classification signatures. The performance of the models was highly promising, with XGBoost exhibiting an AUC-ROC of 0.830, 0.856, and 0.876 for healthy, benign, and malignant microcalcifications classification, respectively. The intrinsic interpretability of radiomic features, and the use of the Mean Score Decrease method for model introspection, enabled models' clinical validation. In fact, the most important features, namely GLCM Contrast, FO Minimum and FO Entropy, were compared and found important in other studies on breast cancer.
Collapse
Affiliation(s)
- Francesco Prinzi
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy.
- Department of Computer Science and Technology, University of Cambridge, CB2 1TN, Cambridge, United Kingdom.
| | - Alessia Orlando
- Section of Radiology - Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University Hospital "Paolo Giaccone", Palermo, Italy
| | - Salvatore Gaglio
- Department of Engineering, University of Palermo, Palermo, Italy
- Institute for High-Performance Computing and Networking, National Research Council (ICAR-CNR), Palermo, Italy
| | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
| |
Collapse
|
3
|
Cheng DO, Khaw CR, McCabe J, Pennycuick A, Nair A, Moore DA, Janes SM, Jacob J. Predicting histopathological features of aggressiveness in lung cancer using CT radiomics: a systematic review. Clin Radiol 2024:S0009-9260(24)00248-4. [PMID: 38853080 DOI: 10.1016/j.crad.2024.04.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE To examine the accuracy of CT radiomics to predict histopathological features of aggressiveness in lung cancer using a systematic review of test accuracy studies. METHODS Data sources searched included Medline, Embase, Web of Science, and Cochrane Library from up to 3 November 2023. Included studies reported test accuracy of CT radiomics models to detect the presence of: spread through air spaces (STAS), predominant adenocarcinoma pattern, adenocarcinoma grade, lymphovascular invasion (LVI), tumour infiltrating lymphocytes (TIL) and tumour necrosis, in patients with lung cancer. The primary outcome was test accuracy. Two reviewers independently assessed articles for inclusion and assessed methodological quality using the QUality Assessment of Diagnostic Accuracy Studies-2 tool. A single reviewer extracted data, which was checked by a second reviewer. Narrative data synthesis was performed. RESULTS Eleven studies were included in the final analysis. 10/11 studies were in East Asian populations. 4/11 studies investigated STAS, 6/11 investigated adenocarcinoma invasiveness or growth pattern, and 1/11 investigated LVI. No studies investigating TIL or tumour necrosis met inclusion criteria. Studies were of generally mixed to poor methodological quality. Reported accuracies for radiomic models ranged from 0.67 to 0.94. CONCLUSION Due to the high risk of bias and concerns regarding applicability, the evidence is inconclusive as to whether radiomic features can accurately predict prognostically important histopathological features of cancer aggressiveness. Many studies were excluded due to lack of external validation. Rigorously conducted prospective studies with sufficient external validity will be required for radiomic models to play a role in improving lung cancer outcomes.
Collapse
Affiliation(s)
- D O Cheng
- University College London, Department of Respiratory Medicine, UK
| | - C R Khaw
- University College London, Department of Respiratory Medicine, UK
| | - J McCabe
- University College London, Department of Respiratory Medicine, UK
| | - A Pennycuick
- University College London, Department of Respiratory Medicine, UK
| | - A Nair
- University College London, Department of Radiology, UK
| | - D A Moore
- University College London, Department of Pathology, UK
| | - S M Janes
- University College London, Department of Respiratory Medicine, UK
| | - J Jacob
- University College London, Department of Respiratory Medicine, UK; University College London, Department of Radiology, UK.
| |
Collapse
|
4
|
Wang M, Peng M, Yang X, Zhang Y, Wu T, Wang Z, Wang K. Preoperative prediction of microsatellite instability status: development and validation of a pan-cancer PET/CT-based radiomics model. Nucl Med Commun 2024; 45:372-380. [PMID: 38312051 DOI: 10.1097/mnm.0000000000001816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
OBJECTIVE The purpose of this study is to verify the feasibility of preoperative prediction of patients' microsatellite instability status by applying a PET/CT-based radiation model. METHODS This retrospective study ultimately included 142 patients. Three prediction models have been developed. The predictive performance of all models was evaluated by the receiver operating characteristic curve and area under the curve values. The PET/CT radiological histology score (Radscore) was calculated to evaluate the microsatellite instability status, and the corresponding nomogram was established. The correlation between clinical factors and radiological characteristics was analyzed to verify the value of radiological characteristics in predicting microsatellite instability status. RESULTS Twelve features were retained to establish a comprehensive prediction model of radiological and clinical features. M phase of the tumor has been proven to be an independent predictor of microsatellite instability status. The receiver operating characteristic results showed that the area under the curve values of the training set and the validation set of the radiomics model were 0.82 and 0.75, respectively. The sensitivity, specificity, positive predictive value and negative predictive value of the training set were 0.72, 0.78, 0.83 and 0.66, respectively. The sensitivity, specificity, positive predictive value and negative predictive value of the validation set were 1.00, 0.50, 0.76 and 1.00, respectively. The risk of patients with microsatellite instability was calculated by Radscore and nomograph, and the cutoff value was -0.4385. The validity of the results was confirmed by the decision and calibration curves. CONCLUSION Radiological models based on PET/CT can provide clinical and practical noninvasive prediction of microsatellite instability status of several different cancer types, reducing or avoiding unnecessary biopsy to a certain extent.
Collapse
Affiliation(s)
- Menglu Wang
- Department of PET-CT, Harbin Medical University Cancer Hospital, Harbin and
| | - Mengye Peng
- Department of PET-CT, Harbin Medical University Cancer Hospital, Harbin and
| | - Xinyue Yang
- Department of PET-CT, Harbin Medical University Cancer Hospital, Harbin and
| | - Ying Zhang
- Department of PET-CT, Harbin Medical University Cancer Hospital, Harbin and
| | - Tingting Wu
- Department of PET-CT, Harbin Medical University Cancer Hospital, Harbin and
| | - Zeyu Wang
- The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Kezheng Wang
- Department of PET-CT, Harbin Medical University Cancer Hospital, Harbin and
| |
Collapse
|
5
|
Chakraborty J, Midya A, Kurland BF, Welch ML, Gonen M, Moskowitz CS, Simpson AL. Use of Response Permutation to Measure an Imaging Dataset's Susceptibility to Overfitting by Selected Standard Analysis Pipelines. Acad Radiol 2024:S1076-6332(24)00097-7. [PMID: 38614825 DOI: 10.1016/j.acra.2024.02.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 02/14/2024] [Accepted: 02/16/2024] [Indexed: 04/15/2024]
Abstract
RATIONALE AND OBJECTIVES This study demonstrates a method for quantifying the impact of overfitting on the receiving operator characteristic curve (AUC) when using standard analysis pipelines to develop imaging biomarkers. We illustrate the approach using two publicly available repositories of radiology and pathology images for breast cancer diagnosis. MATERIALS AND METHODS For each dataset, we permuted the outcome (cancer diagnosis) values to eliminate any true association between imaging features and outcome. Seven types of classification models (logistic regression, linear discriminant analysis, Naïve Bayes, linear support vector machines, nonlinear support vector machine, random forest, and multi-layer perceptron) were fitted to each scrambled dataset and evaluated by each of four techniques (all data, hold-out, 10-fold cross-validation, and bootstrapping). After repeating this process for a total of 50 outcome permutations, we averaged the resulting AUCs. Any increase over a null AUC of 0.5 can be attributed to overfitting. RESULTS Applying this approach and varying sample size and the number of imaging features, we found that failing to control for overfitting could result in near-perfect prediction (AUC near 1.0). Cross-validation offered greater protection against overfitting than the other evaluation techniques, and for most classification algorithms a sample size of at least 200 was required to assess as few as 10 features with less than 0.05 AUC inflation attributable to overfitting. CONCLUSION This approach could be applied to any curated dataset to suggest the number of features and analysis approaches to limit overfitting.
Collapse
Affiliation(s)
- Jayasree Chakraborty
- Department of Surgery, Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Abhishek Midya
- Department of Surgery, Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | | | - Mattea L Welch
- Princess Margaret Data Science Program, University Health Network, Toronto, Ontario, Canada
| | - Mithat Gonen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Chaya S Moskowitz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Amber L Simpson
- School of Computing, Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada.
| |
Collapse
|
6
|
Hameed M, Yeung J, Boone D, Mallett S, Halligan S. Meta-research: How many diagnostic or prognostic models published in radiological journals are evaluated externally? Eur Radiol 2024; 34:2524-2533. [PMID: 37696974 PMCID: PMC10957714 DOI: 10.1007/s00330-023-10168-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 07/03/2023] [Accepted: 07/11/2023] [Indexed: 09/13/2023]
Abstract
OBJECTIVES Prognostic and diagnostic models must work in their intended clinical setting, proven via "external evaluation", preferably by authors uninvolved with model development. By systematic review, we determined the proportion of models published in high-impact radiological journals that are evaluated subsequently. METHODS We hand-searched three radiological journals for multivariable diagnostic/prognostic models 2013-2015 inclusive, developed using regression. We assessed completeness of data presentation to allow subsequent external evaluation. We then searched literature to August 2022 to identify external evaluations of these index models. RESULTS We identified 98 index studies (73 prognostic; 25 diagnostic) describing 145 models. Only 15 (15%) index studies presented an evaluation (two external). No model was updated. Only 20 (20%) studies presented a model equation. Just 7 (15%) studies developing Cox models presented a risk table, and just 4 (9%) presented the baseline hazard. Two (4%) studies developing non-Cox models presented the intercept. Just 20 (20%) articles presented a Kaplan-Meier curve of the final model. The 98 index studies attracted 4224 citations (including 559 self-citations), median 28 per study. We identified just six (6%) subsequent external evaluations of an index model, five of which were external evaluations by researchers uninvolved with model development, and from a different institution. CONCLUSIONS Very few prognostic or diagnostic models published in radiological literature are evaluated externally, suggesting wasted research effort and resources. Authors' published models should present data sufficient to allow external evaluation by others. To achieve clinical utility, researchers should concentrate on model evaluation and updating rather than continual redevelopment. CLINICAL RELEVANCE STATEMENT The large majority of prognostic and diagnostic models published in high-impact radiological journals are never evaluated. It would be more efficient for researchers to evaluate existing models rather than practice continual redevelopment. KEY POINTS • Systematic review of highly cited radiological literature identified few diagnostic or prognostic models that were evaluated subsequently by researchers uninvolved with the original model. • Published radiological models frequently omit important information necessary for others to perform an external evaluation: Only 20% of studies presented a model equation or nomogram. • A large proportion of research citing published models focuses on redevelopment and ignores evaluation and updating, which would be a more efficient use of research resources.
Collapse
Affiliation(s)
- Maira Hameed
- Centre for Medical Imaging, University College London UCL, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Jason Yeung
- Centre for Medical Imaging, University College London UCL, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Darren Boone
- Centre for Medical Imaging, University College London UCL, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Sue Mallett
- Centre for Medical Imaging, University College London UCL, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Steve Halligan
- Centre for Medical Imaging, University College London UCL, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
| |
Collapse
|
7
|
Prinzi F, Currieri T, Gaglio S, Vitabile S. Shallow and deep learning classifiers in medical image analysis. Eur Radiol Exp 2024; 8:26. [PMID: 38438821 PMCID: PMC10912073 DOI: 10.1186/s41747-024-00428-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 01/03/2024] [Indexed: 03/06/2024] Open
Abstract
An increasingly strong connection between artificial intelligence and medicine has enabled the development of predictive models capable of supporting physicians' decision-making. Artificial intelligence encompasses much more than machine learning, which nevertheless is its most cited and used sub-branch in the last decade. Since most clinical problems can be modeled through machine learning classifiers, it is essential to discuss their main elements. This review aims to give primary educational insights on the most accessible and widely employed classifiers in radiology field, distinguishing between "shallow" learning (i.e., traditional machine learning) algorithms, including support vector machines, random forest and XGBoost, and "deep" learning architectures including convolutional neural networks and vision transformers. In addition, the paper outlines the key steps for classifiers training and highlights the differences between the most common algorithms and architectures. Although the choice of an algorithm depends on the task and dataset dealing with, general guidelines for classifier selection are proposed in relation to task analysis, dataset size, explainability requirements, and available computing resources. Considering the enormous interest in these innovative models and architectures, the problem of machine learning algorithms interpretability is finally discussed, providing a future perspective on trustworthy artificial intelligence.Relevance statement The growing synergy between artificial intelligence and medicine fosters predictive models aiding physicians. Machine learning classifiers, from shallow learning to deep learning, are offering crucial insights for the development of clinical decision support systems in healthcare. Explainability is a key feature of models that leads systems toward integration into clinical practice. Key points • Training a shallow classifier requires extracting disease-related features from region of interests (e.g., radiomics).• Deep classifiers implement automatic feature extraction and classification.• The classifier selection is based on data and computational resources availability, task, and explanation needs.
Collapse
Affiliation(s)
- Francesco Prinzi
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
- Department of Computer Science and Technology, University of Cambridge, Cambridge, CB2 1TN, UK
| | - Tiziana Currieri
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
| | - Salvatore Gaglio
- Department of Engineering, University of Palermo, Palermo, Italy
- Institute for High-Performance Computing and Networking, National Research Council (ICAR-CNR), Palermo, Italy
| | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy.
| |
Collapse
|
8
|
An S, Huang G, Yu X, Liu J, Chen Y. The added diagnostic value of 18 F-FDG PET/CT radiomic analysis in multiple myeloma patients with negative visual analysis. Nucl Med Commun 2024; 45:244-252. [PMID: 38165165 DOI: 10.1097/mnm.0000000000001809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
PURPOSE A small number of patients diagnosed with multiple myeloma (MM) by bone marrow aspiration reported as being disease-free on 18 F-FDG PET/CT. We aim to evaluate the diagnostic value of radiomics approach in patients with MM who were negative by visual analysis. MATERIALS AND METHODS Thirty-three patients judged negative by visual analysis were assigned to the MM group. Contemporaneous 31 disease-free patients served as the control group. 70% of the whole data set was used as training set (23 from MM group and 22 from control group) and 30% as testing set (10 from MM group and 9 from control group). Axial skeleton volumes were automatically segmented and high-dimensional imaging features were extracted from PET and CT. The unsupervised machine learning method was used to filter and reduce the dimensions of the extracted features. Random forest was used to construct the prediction model and then validated with 10-fold cross-validation and evaluated on the independent testing set. RESULTS One thousand seven hundred two quantitative features were extracted from PET and CT. Of those, three first-order and one high-order imaging features were uncorrelated. With the cross-validation on the training group, the sensitivity, specificity, accuracy and area under the curve of random forest were 0.850, 0.792, 0.818 and 0.894, respectively. On the independent testing set, the accuracy of the model was 0.850 and the area under the curve was 0.909. CONCLUSION Radiomic analysis based on 18 F-FDG PET/CT using machine learning model provides a quantitative, objective and efficient mechanism for diagnosing patients with MM who were negative by visual analysis.
Collapse
Affiliation(s)
- Shuxian An
- Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | | | | | | | | |
Collapse
|
9
|
Cheung BMF. Radiomics in stereotactic body radiotherapy for non-small cell lung cancer: a systematic review and radiomic quality score study. Radiat Oncol J 2024; 42:4-16. [PMID: 38549380 PMCID: PMC10982060 DOI: 10.3857/roj.2023.00612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/22/2023] [Accepted: 10/10/2023] [Indexed: 04/04/2024] Open
Abstract
PURPOSE Stereotactic body radiotherapy (SBRT) has been widely utilized for curative treatment of early-stage non-small cell lung cancer (NSCLC). It has achieved good local control rate comparable to surgery. Currently, no standard risk model exists for SBRT outcome or complication prediction. Radiomics has the potential to improve clinical outcome prognostication. Here, we reviewed the current literature on the radiomic analyses of thoracic SBRT through the use of radiomic quality score (RQS). MATERIALS AND METHODS Literature search was conducted on PubMed and Embase to retrieve radiomics studies on SBRT for early NSCLC. The literature search included studies up to June 2021. Only full papers published in peer reviewed journals were included. Studies that included metastatic lung cancers or non-lung cancers were excluded. Two independent investigators evaluated each study using the RQS and resolved discrepancies through discussion. RESULTS A total number of 25 studies were analysed. The mean RQS was 7.76 of a maximum score of 36. This corresponds to 21.56% of the maximum score. Lack of feature reduction strategies, external validation and open data sharing were identified as key limitations of the reviewed studies. Meanwhile, various common radiomic signatures across different studies such as gray level co-occurrence matrix Homogeneity and energy have been identified. Multiple robust radiomic models have also been reviewed that may improve outcome or complication prediction. CONCLUSION Radiomics in thoracic SBRT has a very promising future as a prognostication tool. However, larger multicenter prospective studies are required to confirm radiomic signatures. Improvement in future study methodologies can also facilitate its wider application.
Collapse
|
10
|
Hassan J, Saeed SM, Deka L, Uddin MJ, Das DB. Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges. Pharmaceutics 2024; 16:260. [PMID: 38399314 PMCID: PMC10892549 DOI: 10.3390/pharmaceutics16020260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.
Collapse
Affiliation(s)
- Jasmin Hassan
- Drug Delivery & Therapeutics Lab, Dhaka 1212, Bangladesh; (J.H.); (S.M.S.)
| | | | - Lipika Deka
- Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK;
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Diganta B. Das
- Department of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, UK
| |
Collapse
|
11
|
Bomhals B, Cossement L, Maes A, Sathekge M, Mokoala KMG, Sathekge C, Ghysen K, Van de Wiele C. Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules. J Clin Med 2023; 12:7731. [PMID: 38137800 PMCID: PMC10743692 DOI: 10.3390/jcm12247731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/11/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
Here, we report on the added value of principal component analysis applied to a dataset of texture features derived from 39 solitary pulmonary lung nodule (SPN) lesions for the purpose of differentiating benign from malignant lesions, as compared to the use of SUVmax alone. Texture features were derived using the LIFEx software. The eight best-performing first-, second-, and higher-order features for separating benign from malignant nodules, in addition to SUVmax (MaximumGreyLevelSUVbwIBSI184IY), were included for PCA. Two principal components (PCs) were retained, of which the contributions to the total variance were, respectively, 87.6% and 10.8%. When included in a logistic binomial regression analysis, including age and gender as covariates, both PCs proved to be significant predictors for the underlying benign or malignant character of the lesions under study (p = 0.009 for the first PC and 0.020 for the second PC). As opposed to SUVmax alone, which allowed for the accurate classification of 69% of the lesions, the regression model including both PCs allowed for the accurate classification of 77% of the lesions. PCs derived from PCA applied on selected texture features may allow for more accurate characterization of SPN when compared to SUVmax alone.
Collapse
Affiliation(s)
- Birte Bomhals
- Department of Diagnostic Sciences, University Ghent, 9000 Ghent, Belgium; (B.B.); (L.C.)
| | - Lara Cossement
- Department of Diagnostic Sciences, University Ghent, 9000 Ghent, Belgium; (B.B.); (L.C.)
| | - Alex Maes
- Department of Morphology and Functional Imaging, University Hospital Leuven, 3000 Leuven, Belgium;
- Department of Nuclear Medicine, Katholieke University Leuven, AZ Groeninge, President Kennedylaan 4, 8500 Kortrijk, Belgium
| | - Mike Sathekge
- Department of Nuclear Medicine, Steve Biko Academic Hospital and Nuclear Medicine Research Infrastructure (NuMeRi), University of Pretoria, Pretoria 0002, South Africa
| | - Kgomotso M. G. Mokoala
- Department of Nuclear Medicine, Steve Biko Academic Hospital and Nuclear Medicine Research Infrastructure (NuMeRi), University of Pretoria, Pretoria 0002, South Africa
| | - Chabi Sathekge
- Department of Nuclear Medicine, Steve Biko Academic Hospital and Nuclear Medicine Research Infrastructure (NuMeRi), University of Pretoria, Pretoria 0002, South Africa
| | - Katrien Ghysen
- Department of Pneumology, AZ Groeninge, 8500 Kortrijk, Belgium
| | - Christophe Van de Wiele
- Department of Diagnostic Sciences, University Ghent, 9000 Ghent, Belgium; (B.B.); (L.C.)
- Department of Nuclear Medicine, Katholieke University Leuven, AZ Groeninge, President Kennedylaan 4, 8500 Kortrijk, Belgium
| |
Collapse
|
12
|
Huang W, Tao Z, Younis MH, Cai W, Kang L. Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives. VIEW 2023; 4:20230032. [PMID: 38179181 PMCID: PMC10766416 DOI: 10.1002/viw.20230032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/20/2023] [Indexed: 01/06/2024] Open
Abstract
Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.
Collapse
Affiliation(s)
- Wenpeng Huang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Zihao Tao
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Muhsin H. Younis
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Weibo Cai
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lei Kang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| |
Collapse
|
13
|
Schurink NW, van Kranen SR, van Griethuysen JJM, Roberti S, Snaebjornsson P, Bakers FCH, de Bie SH, Bosma GPT, Cappendijk VC, Geenen RWF, Neijenhuis PA, Peterson GM, Veeken CJ, Vliegen RFA, Peters FP, Bogveradze N, El Khababi N, Lahaye MJ, Maas M, Beets GL, Beets-Tan RGH, Lambregts DMJ. Development and multicenter validation of a multiparametric imaging model to predict treatment response in rectal cancer. Eur Radiol 2023; 33:8889-8898. [PMID: 37452176 PMCID: PMC10667134 DOI: 10.1007/s00330-023-09920-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
OBJECTIVES To develop and validate a multiparametric model to predict neoadjuvant treatment response in rectal cancer at baseline using a heterogeneous multicenter MRI dataset. METHODS Baseline staging MRIs (T2W (T2-weighted)-MRI, diffusion-weighted imaging (DWI) / apparent diffusion coefficient (ADC)) of 509 patients (9 centres) treated with neoadjuvant chemoradiotherapy (CRT) were collected. Response was defined as (1) complete versus incomplete response, or (2) good (Mandard tumor regression grade (TRG) 1-2) versus poor response (TRG3-5). Prediction models were developed using combinations of the following variable groups: (1) Non-imaging: age/sex/tumor-location/tumor-morphology/CRT-surgery interval (2) Basic staging: cT-stage/cN-stage/mesorectal fascia involvement, derived from (2a) original staging reports, or (2b) expert re-evaluation (3) Advanced staging: variables from 2b combined with cTN-substaging/invasion depth/extramural vascular invasion/tumor length (4) Quantitative imaging: tumour volume + first-order histogram features (from T2W-MRI and DWI/ADC) Models were developed with data from 6 centers (n = 412) using logistic regression with the Least Absolute Shrinkage and Selector Operator (LASSO) feature selection, internally validated using repeated (n = 100) random hold-out validation, and externally validated using data from 3 centers (n = 97). RESULTS After external validation, the best model (including non-imaging and advanced staging variables) achieved an area under the curve of 0.60 (95%CI=0.48-0.72) to predict complete response and 0.65 (95%CI=0.53-0.76) to predict a good response. Quantitative variables did not improve model performance. Basic staging variables consistently achieved lower performance compared to advanced staging variables. CONCLUSIONS Overall model performance was moderate. Best results were obtained using advanced staging variables, highlighting the importance of good-quality staging according to current guidelines. Quantitative imaging features had no added value (in this heterogeneous dataset). CLINICAL RELEVANCE STATEMENT Predicting tumour response at baseline could aid in tailoring neoadjuvant therapies for rectal cancer. This study shows that image-based prediction models are promising, though are negatively affected by variations in staging quality and MRI acquisition, urging the need for harmonization. KEY POINTS This multicenter study combining clinical information and features derived from MRI rendered disappointing performance to predict response to neoadjuvant treatment in rectal cancer. Best results were obtained with the combination of clinical baseline information and state-of-the-art image-based staging variables, highlighting the importance of good quality staging according to current guidelines and staging templates. No added value was found for quantitative imaging features in this multicenter retrospective study. This is likely related to acquisition variations, which is a major problem for feature reproducibility and thus model generalizability.
Collapse
Affiliation(s)
- Niels W Schurink
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Simon R van Kranen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Joost J M van Griethuysen
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Sander Roberti
- Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Petur Snaebjornsson
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Frans C H Bakers
- Department of Radiology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Shira H de Bie
- Department of Radiology, Deventer Ziekenhuis, Schalkhaar, The Netherlands
| | - Gerlof P T Bosma
- Department of Interventional Radiology, Elisabeth Tweesteden Hospital, Tilburg, The Netherlands
| | - Vincent C Cappendijk
- Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands
| | - Remy W F Geenen
- Department of Radiology, Northwest Clinics, Alkmaar, The Netherlands
| | | | | | - Cornelis J Veeken
- Department of Radiology, IJsselland Hospital, Capelle aan den IJssel, The Netherlands
| | - Roy F A Vliegen
- Department of Radiology, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Femke P Peters
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Nino Bogveradze
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Department of Radiology, Acad. F. Todua Medical Center, Research Institute of Clinical Medicine, Tbilisi, Georgia
| | - Najim El Khababi
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Max J Lahaye
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Geerard L Beets
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Department of Surgery, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Institute of Regional Health Research, University of Southern Denmark, Vejle, Denmark
| | - Doenja M J Lambregts
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands.
| |
Collapse
|
14
|
Small C, Prior P, Nasief H, Zeitlin R, Saeed H, Paulson E, Morrow N, Rownd J, Erickson B, Bedi M. A general framework to develop a radiomic fingerprint for progression-free survival in cervical cancer. Brachytherapy 2023; 22:728-735. [PMID: 37574352 DOI: 10.1016/j.brachy.2023.06.004] [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: 02/27/2023] [Revised: 05/11/2023] [Accepted: 06/06/2023] [Indexed: 08/15/2023]
Abstract
PURPOSE Treatment of locally advanced cervical cancer patients includes chemoradiation followed by brachytherapy. Our aim is to develop a delta radiomics (DRF) model from MRI-based brachytherapy treatment and assess its association with progression free survival (PFS). MATERIALS AND METHODS A retrospective analysis of FIGO stage IB- IV cervical cancer patients between 2012 and 2018 who were treated with definitive chemoradiation followed by MRI-based intracavitary brachytherapy was performed. Clinical factors together with 18 radiomic features extracted from different radiomics matrices were analyzed. The delta radiomic features (DRFs) were extracted from MRI on the first and last brachytherapy fractions. Support Vector Machine (SVM) models were fitted to combinations of 2-3 DRFs found significant after Spearman correlation and Wilcoxon rank sum test statistics. Additional models were tested that included clinical factors together with DRFs. RESULTS A total of 39 patients were included in the analysis with a median patient age of 52 years. Progression occurred in 20% of patients (8/39). The significant DRFs using two DRF feature combinations was a model using auto correlation (AC) and sum variance (SV). The best performing three feature model combined mean, AC & SV. Additionally, the inclusion of FIGO stages with the 2- and 3 DRF combination model(s) improved performance compared to models with only DRFs. However, all the clinical factor + DRF models were not significantly different from one another (all AUCs were 0.77). CONCLUSIONS Our study shows promising evidence that radiomics metrics are associated with progression free survival in cervical cancer.
Collapse
Affiliation(s)
- Christina Small
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI.
| | - Phillip Prior
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Haidy Nasief
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Ross Zeitlin
- Department of Radiation Oncology, John H Stroger, Jr. Hospital of Cook County, Chicago, IL
| | - Hina Saeed
- Department of Radiation Oncology, Lynn Cancer Institute, Baptist Health South Florida, Boynton Beach, FL
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Natalya Morrow
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Jason Rownd
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Beth Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Meena Bedi
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| |
Collapse
|
15
|
Han PL, Jiang ZK, Gu R, Huang S, Jiang Y, Yang ZG, Li K. Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics. Quant Imaging Med Surg 2023; 13:6468-6481. [PMID: 37869344 PMCID: PMC10585548 DOI: 10.21037/qims-23-372] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/21/2023] [Indexed: 10/24/2023]
Abstract
Background Although there are many studies on the prognostic factors of left ventricular myocardial noncompaction (LVNC), the determinants are varied and not entirely consistent. This study aimed to build predictive models using radiomics features and machine learning to predict major adverse cardiovascular events (MACEs) in patients with LVNC. Methods In total, 96 patients with LVNC were included and randomly divided into training and test cohorts. A total of 105 cine cardiac magnetic resonance (CMR)-derived radiomics features and 35 clinical characteristics were extracted. Five different oversampling algorithms were compared for selection of the optimal imbalanced processing. Feature importance was assessed with extreme gradient boosting (XGBoost). We compared the performance of 5 machine learning classification methods with different sample:feature ratios to determine the optimal hybrid classification strategy. Subsequently, radiomics, clinical, and combined radiomics-clinical models were developed and compared. Results The machine learning pipeline included an adaptive synthetic (ADASYN) algorithm for imbalanced processing, XGBoost feature selection with a sample:feature ratio of 10, and support vector machine (SVM) modeling. The areas under the receiver operating characteristic curves (AUCs) of the radiomics model, clinical model, and combined model in the validation cohort were 0.87 (sensitivity 83.33%, specificity 64.29%), 0.65 (sensitivity 16.67%, specificity 78.57%), and 0.92 (specificity 33.33%, sensitivity 100.00%), respectively. The radiomics model performed similarly to the clinical and combined models (P=0.124 and P=0.621, respectively). The performance of the combined model was significantly better than that of the clinical model (P=0.003). Conclusions The machine learning-based cine CMR radiomics model performed well at predicting MACEs in patients with LVNC. Adding radiomics features offered incremental prognostic value over clinical factors alone.
Collapse
Affiliation(s)
- Pei-Lun Han
- Department of Radiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Ze-Kun Jiang
- Department of Radiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Ran Gu
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Shan Huang
- Department of Radiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Jiang
- Department of Radiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhi-Gang Yang
- Department of Radiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Kang Li
- Department of Radiology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| |
Collapse
|
16
|
Salmanpour MR, Hosseinzadeh M, Rezaeijo SM, Rahmim A. Fusion-based tensor radiomics using reproducible features: Application to survival prediction in head and neck cancer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107714. [PMID: 37473589 DOI: 10.1016/j.cmpb.2023.107714] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 05/19/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Numerous features are commonly generated in radiomics applications as applied to medical imaging, and identification of robust radiomics features (RFs) can be an important step to derivation of reliable, reproducible solutions. In this work, we utilize a tensor radiomics (TR) framework, where numerous fusions are explored, to generate different flavours of RFs, and we aimed to identify RFs that are robust to fusion techniques in head and neck cancer. Overall, we aimed to predict progression-free survival (PFS) using Hybrid Machine Learning Systems (HMLS) and reproducible RFs. METHODS The study was performed on 408 patients with head and neck cancer from The Cancer Imaging Archive. After image preprocessing, 15 fusion techniques were employed to combine Positron Emission Tomography (PET) and Computed Tomography (CT) images. Subsequently, 215 RFs were extracted through a standardized radiomics software, with 17 'flavours' generated using PET-only, CT-only, and 15 fused PET&CT images. The variability of RFs across flavours was studied using the Intraclass Correlation Coefficient (ICC). Furthermore, the features were categorized into seven reliability groups, 106 reproducible RFs with ICC>0.75 were selected, highly correlated flavours were removed, Principal Component Analysis was used to convert 17 flavours to 1 attribute, the polynomial function was utilized to increase RFs, and Analysis of variance (ANOVA) was used to select the relevant attributes. Finally, 3 classifiers including Random Forest (RFC), Logistic regression (LR), and Multi-layer perceptron were applied to the preselected relevant attributes to predict binary PFS. In 5-fold cross-validation, 80% of 4 divisions were utilized to train the model, and the remaining 20% was utilized to evaluate the model. Further, the remaining fold was used for external nested testing. RESULTS Reliability analysis indicated that most morphological features belong to the high-reliability category. By contrast, local intensity and statistical features extracted from images belong to the low-reliability category. In the tensor framework, the highest 5-fold cross-validation accuracy of 76.7%±3.3% with an external nested testing of 70.6%±6.7% resulted from the reproducible TR+polynomial function+ANOVA+LR algorithm while the accuracy of 70.0%±4.2% with the external nested testing of 67.7%±4.9% was achieved through the PCA fusion+RFC (non-tensor paradigm). CONCLUSIONS This study demonstrated that using reproducible RFs as utilized within a tensor fusion radiomics framework, linked with ANOVA and LR, added value to prediction of progression-free survival outcome in head and neck cancer patients.
Collapse
Affiliation(s)
- Mohammad R Salmanpour
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada; Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC, Canada.
| | - Mahdi Hosseinzadeh
- Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC, Canada; Department of Electrical & Computer Engineering, University of Tarbiat Modares, Tehran, Iran
| | - Seyed Masoud Rezaeijo
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada; Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| |
Collapse
|
17
|
Lu N, Guan X, Zhu J, Li Y, Zhang J. A Contrast-Enhanced CT-Based Deep Learning System for Preoperative Prediction of Colorectal Cancer Staging and RAS Mutation. Cancers (Basel) 2023; 15:4497. [PMID: 37760468 PMCID: PMC10526233 DOI: 10.3390/cancers15184497] [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: 08/23/2023] [Revised: 09/04/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
PURPOSE This study aimed to build a deep learning system using enhanced computed tomography (CT) portal-phase images for predicting colorectal cancer patients' preoperative staging and RAS gene mutation status. METHODS The contrast-enhanced CT image dataset comprises the CT portal-phase images from a retrospective cohort of 231 colorectal cancer patients. The deep learning system was developed via migration learning for colorectal cancer detection, staging, and RAS gene mutation status prediction. This study used pre-trained Yolov7, vision transformer (VIT), swin transformer (SWT), EfficientNetV2, and ConvNeXt. 4620, and contrast-enhanced CT images and annotated tumor bounding boxes were included in the tumor identification and staging dataset. A total of 19,700 contrast-enhanced CT images comprise the RAS gene mutation status prediction dataset. RESULTS In the validation cohort, the Yolov7-based detection model detected and staged tumors with a mean accuracy precision (IoU = 0.5) (mAP_0.5) of 0.98. The area under the receiver operating characteristic curve (AUC) in the test set and validation set for the VIT-based prediction model in predicting the mutation status of the RAS genes was 0.9591 and 0.9554, respectively. The detection network and prediction network of the deep learning system demonstrated great performance in explaining contrast-enhanced CT images. CONCLUSION In this study, a deep learning system was created based on the foundation of contrast-enhanced CT portal-phase imaging to preoperatively predict the stage and RAS mutation status of colorectal cancer patients. This system will help clinicians choose the best treatment option to increase colorectal cancer patients' chances of survival and quality of life.
Collapse
Affiliation(s)
- Na Lu
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing 210011, China (X.G.)
| | - Xiao Guan
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing 210011, China (X.G.)
| | - Jianguo Zhu
- Department of Radiology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210011, China;
| | - Yuan Li
- Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China;
| | - Jianping Zhang
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing 210011, China (X.G.)
| |
Collapse
|
18
|
van der Kroft G, Wee L, Rensen SS, Brecheisen R, van Dijk DPJ, Eickhoff R, Roeth AA, Ulmer FT, Dekker A, Neumann UP, Olde Damink SWM. Identifying radiomics signatures in body composition imaging for the prediction of outcome following pancreatic cancer resection. Front Oncol 2023; 13:1062937. [PMID: 37637046 PMCID: PMC10449585 DOI: 10.3389/fonc.2023.1062937] [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: 10/06/2022] [Accepted: 06/26/2023] [Indexed: 08/29/2023] Open
Abstract
Background Computerized radiological image analysis (radiomics) enables the investigation of image-derived phenotypes by extracting large numbers of quantitative features. We hypothesized that radiomics features may contain prognostic information that enhances conventional body composition analysis. We aimed to investigate whether body composition-associated radiomics features hold additional value over conventional body composition analysis and clinical patient characteristics used to predict survival of pancreatic ductal adenocarcinoma (PDAC) patients. Methods Computed tomography images of 304 patients undergoing elective pancreatic cancer resection were analysed. 2D radiomics features were extracted from skeletal muscle and subcutaneous and visceral adipose tissue (SAT and VAT) compartments from a single slice at the third lumbar vertebra. The study population was randomly split (80:20) into training and holdout subsets. Feature ranking with Least Absolute Shrinkage Selection Operator (LASSO) followed by multivariable stepwise Cox regression in 1000 bootstrapped re-samples of the training data was performed and tested on the holdout data. The fitted regression predictors were used as "scores" for a clinical (C-Score), body composition (B-Score), and radiomics (R-Score) model. To stratify patients into the highest 25% and lowest 25% risk of mortality compared to the middle 50%, the Harrell Concordance Index was used. Results Based on LASSO and stepwise cox regression for overall survival, ASA ≥3 and age were the most important clinical variables and constituted the C-score, and VAT-index (VATI) was the most important body composition variable and constituted the B-score. Three radiomics features (SATI_original_shape2D_Perimeter, VATI_original_glszm_SmallAreaEmphasis, and VATI_original_firstorder_Maximum) emerged as the most frequent set of features and yielded an R-Score. Of the mean concordance indices of C-, B-, and R-scores, R-score performed best (0.61, 95% CI 0.56-0.65, p<0.001), followed by the C-score (0.59, 95% CI 0.55-0.63, p<0.001) and B-score (0.55, 95% CI 0.50-0.60, p=0.03). Kaplan-Meier projection revealed that C-, B, and R-scores showed a clear split in the survival curves in the training set, although none remained significant in the holdout set. Conclusion It is feasible to implement a data-driven radiomics approach to body composition imaging. Radiomics features provided improved predictive performance compared to conventional body composition variables for the prediction of overall survival of PDAC patients undergoing primary resection.
Collapse
Affiliation(s)
- Gregory van der Kroft
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Sander S. Rensen
- Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
- NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
| | - Ralph Brecheisen
- Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
| | - David P. J. van Dijk
- Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
- NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
| | - Roman Eickhoff
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
| | - Anjali A. Roeth
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
| | - Florian T. Ulmer
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Ulf P. Neumann
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
- Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
| | - Steven W. M. Olde Damink
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
- Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
- NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
| |
Collapse
|
19
|
Fang X, Zhang Q, Liu F, Li J, Wang T, Cao K, Zhang H, Li Q, Yu J, Zhou J, Zhu M, Li N, Jiang H, Shao C, Lu J, Wang L, Bian Y. T2-Weighted Image Radiomics Nomogram to Predict Pancreatic Serous and Mucinous Cystic Neoplasms. Acad Radiol 2023; 30:1562-1571. [PMID: 36379815 DOI: 10.1016/j.acra.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/23/2022] [Accepted: 10/06/2022] [Indexed: 11/13/2022]
Affiliation(s)
- Xu Fang
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Qianru Zhang
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Tiegong Wang
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Kai Cao
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Hao Zhang
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Qi Li
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Jian Zhou
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Mengmeng Zhu
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Na Li
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Hui Jiang
- Department of Pathology, Changhai Hospital, Shanghai, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Li Wang
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Yun Bian
- Department of Radiology, Changhai Hospital, Shanghai, China.
| |
Collapse
|
20
|
Das D, Das T. The "P"-Value: The Primary Alphabet of Research Revisited. Int J Prev Med 2023; 14:41. [PMID: 37351025 PMCID: PMC10284198 DOI: 10.4103/ijpvm.ijpvm_200_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 01/09/2023] [Indexed: 06/24/2023] Open
Abstract
Each research roves around the P value. A value less than 0.05 is considered to be statistically significant. Very few researchers are aware of the history, real-world significance, statistical insight, and in-depth criticism about this monumental alphabet of research. This article will provide detailed insight into the most common molecule of research which will be rewarding for the young students and researchers in the primary world of research. It is not a simple value; it is the longest and broadest description of research squeezed to a number for the ground level worker to the principal investigator. The present review will provide a detailed and unique insight into the P value which would be rewarding for the primary care physicians toward translating research into their clinical practice.
Collapse
Affiliation(s)
- Debasish Das
- Department of Cardiology, All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha, India
| | - Tutan Das
- Department of Cardiology, All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha, India
| |
Collapse
|
21
|
Liu Y, Wei X, Feng X, Liu Y, Feng G, Du Y. Repeatability of radiomics studies in colorectal cancer: a systematic review. BMC Gastroenterol 2023; 23:125. [PMID: 37059990 PMCID: PMC10105401 DOI: 10.1186/s12876-023-02743-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 03/22/2023] [Indexed: 04/16/2023] Open
Abstract
BACKGROUND Recently, radiomics has been widely used in colorectal cancer, but many variable factors affect the repeatability of radiomics research. This review aims to analyze the repeatability of radiomics studies in colorectal cancer and to evaluate the current status of radiomics in the field of colorectal cancer. METHODS The included studies in this review by searching from the PubMed and Embase databases. Then each study in our review was evaluated using the Radiomics Quality Score (RQS). We analyzed the factors that may affect the repeatability in the radiomics workflow and discussed the repeatability of the included studies. RESULTS A total of 188 studies was included in this review, of which only two (2/188, 1.06%) studies controlled the influence of individual factors. In addition, the median score of RQS was 11 (out of 36), range-1 to 27. CONCLUSIONS The RQS score was moderately low, and most studies did not consider the repeatability of radiomics features, especially in terms of Intra-individual, scanners, and scanning parameters. To improve the generalization of the radiomics model, it is necessary to further control the variable factors of repeatability.
Collapse
Affiliation(s)
- Ying Liu
- School of Medical Imaging, North Sichuan Medical College, Sichuan Province, Nanchong City, 637000, China
| | - Xiaoqin Wei
- School of Medical Imaging, North Sichuan Medical College, Sichuan Province, Nanchong City, 637000, China
| | | | - Yan Liu
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China
| | - Guiling Feng
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China
| | - Yong Du
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China.
| |
Collapse
|
22
|
Mireștean CC, Iancu RI, Iancu DPT. Simultaneous Integrated Boost (SIB) vs. Sequential Boost in Head and Neck Cancer (HNC) Radiotherapy: A Radiomics-Based Decision Proof of Concept. J Clin Med 2023; 12:jcm12062413. [PMID: 36983413 PMCID: PMC10057404 DOI: 10.3390/jcm12062413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 03/07/2023] [Accepted: 03/14/2023] [Indexed: 03/30/2023] Open
Abstract
Artificial intelligence (AI) and in particular radiomics has opened new horizons by extracting data from medical imaging that could be used not only to improve diagnostic accuracy, but also to be included in predictive models contributing to treatment stratification of cancer. Head and neck cancers (HNC) are associated with higher recurrence rates, especially in advanced stages of disease. It is considered that approximately 50% of cases will evolve with loco-regional recurrence, even if they will benefit from a current standard treatment consisting of definitive chemo-radiotherapy. Radiotherapy, the cornerstone treatment in locally advanced HNC, could be delivered either by the simultaneous integrated boost (SIB) technique or by the sequential boost technique, the decision often being a subjective one. The principles of radiobiology could be the basis of an optimal decision between the two methods of radiation dose delivery, but the heterogeneity of HNC radio-sensitivity makes this approach difficult. Radiomics has demonstrated the ability to non-invasively predict radio-sensitivity and the risk of relapse in HNC. Tumor heterogeneity evaluated with radiomics, the inclusion of coarseness, entropy and other first order features extracted from gross tumor volume (GTV) in multivariate models could identify pre-treatment cases that will benefit from one of the approaches (SIB or sequential boost radio-chemotherapy) considered the current standard of care for locally advanced HNC. Computer tomography (CT) simulation and daily cone beam CT (CBCT) could be chosen as imaging source for radiomic analysis.
Collapse
Affiliation(s)
- Camil Ciprian Mireștean
- Department of Oncology and Radiotherapy, University of Medicine and Pharmacy Craiova, 200349 Craiova, Romania
- Department of Surgery, Railways Clinical Hospital Iasi, 700506 Iași, Romania
| | - Roxana Irina Iancu
- Oral Pathology Department, Faculty of Dental Medicine, "Gr. T. Popa" University of Medicine and Pharmacy, 700115 Iași, Romania
- Department of Clinical Laboratory, "St. Spiridon" Emergency Universitary Hospital, 700111 Iași, Romania
| | - Dragoș Petru Teodor Iancu
- Oncology and Radiotherapy Department, Faculty of Medicine, "Gr. T. Popa" University of Medicine and Pharmacy, 700115 Iași, Romania
- Department of Radiation Oncology, Regional Institute of Oncology, 700483 Iași, Romania
| |
Collapse
|
23
|
Zhang J, Zhu H, Wang J, Chen Y, Li Y, Chen X, Chen M, Cai Z, Liu W. Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis. Front Oncol 2023; 13:1082423. [PMID: 37025583 PMCID: PMC10072228 DOI: 10.3389/fonc.2023.1082423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 02/20/2023] [Indexed: 03/19/2023] Open
Abstract
BackgroundMachine learning is now well-developed in non-small cell lung cancer (NSCLC) radiotherapy. But the research trend and hotspots are still unclear. To investigate the progress in machine learning in radiotherapy NSCLC, we performed a bibliometric analysis of associated research and discuss the current research hotspots and potential hot areas in the future.MethodsThe involved researches were obtained from the Web of Science Core Collection database (WoSCC). We used R-studio software, the Bibliometrix package and VOSviewer (Version 1.6.18) software to perform bibliometric analysis.ResultsWe found 197 publications about machine learning in radiotherapy for NSCLC in the WoSCC, and the journal Medical Physics contributed the most articles. The University of Texas MD Anderson Cancer Center was the most frequent publishing institution, and the United States contributed most of the publications. In our bibliometric analysis, “radiomics” was the most frequent keyword, and we found that machine learning is mainly applied to analyze medical images in the radiotherapy of NSCLC.ResultsThe research we identified about machine learning in NSCLC radiotherapy was mainly related to the radiotherapy planning of NSCLC and the prediction of treatment effects and adverse events in NSCLC patients who were under radiotherapy. Our research has added new insights into machine learning in NSCLC radiotherapy and could help researchers better identify hot research areas in the future.
Collapse
Affiliation(s)
- Jiaming Zhang
- Department of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Huijun Zhu
- Department of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jue Wang
- Department of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yulu Chen
- Department of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yihe Li
- Department of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xinyu Chen
- Department of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Menghua Chen
- Department of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zhengwen Cai
- Department of Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
- *Correspondence: Zhengwen Cai, ; Wenqi Liu,
| | - Wenqi Liu
- Department of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
- *Correspondence: Zhengwen Cai, ; Wenqi Liu,
| |
Collapse
|
24
|
Li Q, Li X, Liu W, Yu J, Chen Y, Zhu M, Li N, Liu F, Wang T, Fang X, Li J, Lu J, Shao C, Bian Y. Non-enhanced magnetic resonance imaging-based radiomics model for the differentiation of pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma. Front Oncol 2023; 13:1108545. [PMID: 36756153 PMCID: PMC9900003 DOI: 10.3389/fonc.2023.1108545] [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: 11/26/2022] [Accepted: 01/11/2023] [Indexed: 01/24/2023] Open
Abstract
Purpose To evaluate the diagnostic performance of radiomics model based on fully automatic segmentation of pancreatic tumors from non-enhanced magnetic resonance imaging (MRI) for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). Materials and methods In this retrospective study, patients with surgically resected histopathologically confirmed PASC and PDAC who underwent MRI scans between January 2011 and December 2020 were included in the study. Multivariable logistic regression analysis was conducted to develop a clinical and radiomics model based on non-enhanced T1-weighted and T2-weighted images. The model performances were determined based on their discrimination and clinical utility. Kaplan-Meier and log-rank tests were used for survival analysis. Results A total of 510 consecutive patients including 387 patients (age: 61 ± 9 years; range: 28-86 years; 250 males) with PDAC and 123 patients (age: 62 ± 10 years; range: 36-84 years; 78 males) with PASC were included in the study. All patients were split into training (n=382) and validation (n=128) sets according to time. The radiomics model showed good discrimination in the validation (AUC, 0.87) set and outperformed the MRI model (validation set AUC, 0.80) and the ring-enhancement (validation set AUC, 0.74). Conclusions The radiomics model based on non-enhanced MRI outperformed the MRI model and ring-enhancement to differentiate PASC from PDAC; it can, thus, provide important information for decision-making towards precise management and treatment of PASC.
Collapse
Affiliation(s)
- Qi Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China,Department of Radiology, 96601 Military Hospital of PLA, Huangshan, Anhui, China
| | - Xuezhou Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Wenbin Liu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Yukun Chen
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Mengmeng Zhu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Na Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Tiegong Wang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China,*Correspondence: Yun Bian, ; Chengwei Shao,
| | - Yun Bian
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China,*Correspondence: Yun Bian, ; Chengwei Shao,
| |
Collapse
|
25
|
Ma XH, Yang J, Jia X, Zhou HC, Liang JW, Ding YS, Shu Q, Niu T. Preoperative radiomic signature based on CT images for noninvasive evaluation of localized nephroblastoma in pediatric patients. Front Oncol 2023; 13:1122210. [PMID: 37152031 PMCID: PMC10157206 DOI: 10.3389/fonc.2023.1122210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 04/10/2023] [Indexed: 05/09/2023] Open
Abstract
Background Nephron sparing nephrectomy may not reduce the prognosis of nephroblastoma in the absence of involvement of the renal capsule, sinus vessels, and lymph nodes, However, there is no accurate preoperative noninvasive evaluation method at present. Materials and methods 105 nephroblastoma patients underwent contrast-enhanced CT scan between 2013 and 2020 in our hospital were retrospectively collected, including 59 cases with localized stage and 46 cases with non-localized stage, and then were divided into training cohort (n= 73) and validation cohort (n= 32) according to the order of CT scanning time. After lesion segmentation and data preprocessing, radiomic features were extracted from each volume of interest. The multi-step procedure including Pearson correlation analysis and sequential forward floating selection was performed to produce radiomic signature. Prediction model was constructed using the radiomic signature and Logistic Regression classifier for predicting the localized nephroblastoma in the training cohort. Finally, the model performance was validated in the validation cohort. Results A total of 1652 radiomic features have been extracted, from which TOP 10 features were selected as the radiomic signature. The area under the receiver operating characteristic curve, accuracy, sensitivity and specificity of the prediction model were 0.796, 0.795, 0.732 and 0.875 for the training cohort respectively, and 0.710, 0.719, 0.611 and 0.857 for the validation cohort respectively. The result comparison with prediction models composed of different machine learning classifiers and different parameters also manifest the effectiveness of our radiomic model. Conclusion A logistic regression model based on radiomic features extracted from preoperative CT images had good ability to noninvasively predict nephroblastoma without renal capsule, sinus vessel, and lymph node involvement.
Collapse
Affiliation(s)
- Xiao-Hui Ma
- The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China
| | - Jing Yang
- Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xuan Jia
- The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China
| | - Hai-Chun Zhou
- The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China
| | - Jia-Wei Liang
- The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China
| | - Yu-Shuang Ding
- The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China
| | - Qiang Shu
- The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China
- *Correspondence: Tianye Niu, ; Qiang Shu,
| | - Tianye Niu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, China
- *Correspondence: Tianye Niu, ; Qiang Shu,
| |
Collapse
|
26
|
Virtual Image-based Biopsy of Lung Metastases: The Promise of Radiomics. Acad Radiol 2023; 30:47-48. [PMID: 36371374 DOI: 10.1016/j.acra.2022.10.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 10/31/2022] [Indexed: 11/12/2022]
|
27
|
Hatt M, Krizsan AK, Rahmim A, Bradshaw TJ, Costa PF, Forgacs A, Seifert R, Zwanenburg A, El Naqa I, Kinahan PE, Tixier F, Jha AK, Visvikis D. Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. Eur J Nucl Med Mol Imaging 2023; 50:352-375. [PMID: 36326868 PMCID: PMC9816255 DOI: 10.1007/s00259-022-06001-6] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. METHODS In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. CONCLUSION Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.
Collapse
Affiliation(s)
- M Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - A Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - T J Bradshaw
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - P F Costa
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | | | - R Seifert
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
- Department of Nuclear Medicine, Münster University Hospital, Münster, Germany.
| | - A Zwanenburg
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - I El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33626, USA
| | - P E Kinahan
- Imaging Research Laboratory, PET/CT Physics, Department of Radiology, UW Medical Center, University of Washington, Seattle, WA, USA
| | - F Tixier
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - A K Jha
- McKelvey School of Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, MO, USA
| | - D Visvikis
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| |
Collapse
|
28
|
Santos GNM, da Silva HEC, Ossege FEL, Figueiredo PTDS, Melo NDS, Stefani CM, Leite AF. Radiomics in bone pathology of the jaws. Dentomaxillofac Radiol 2023; 52:20220225. [PMID: 36416666 PMCID: PMC9793454 DOI: 10.1259/dmfr.20220225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 09/02/2022] [Accepted: 10/02/2022] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVE To define which are and how the radiomics features of jawbone pathologies are extracted for diagnosis, predicting prognosis and therapeutic response. METHODS A comprehensive literature search was conducted using eight databases and gray literature. Two independent observers rated these articles according to exclusion and inclusion criteria. 23 papers were included to assess the radiomics features related to jawbone pathologies. Included studies were evaluated by using JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies. RESULTS Agnostic features were mined from periapical, dental panoramic radiographs, cone beam CT, CT and MRI images of six different jawbone alterations. The most frequent features mined were texture-, shape- and intensity-based features. Only 13 studies described the machine learning step, and the best results were obtained with Support Vector Machine and random forest classifier. For osteoporosis diagnosis and classification, filtering, shape-based and Tamura texture features showed the best performance. For temporomandibular joint pathology, gray-level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), first-order statistics analysis and shape-based analysis showed the best results. Considering odontogenic and non-odontogenic cysts and tumors, contourlet and SPHARM features, first-order statistical features, GLRLM, GLCM had better indexes. For odontogenic cysts and granulomas, first-order statistical analysis showed better classification results. CONCLUSIONS GLCM was the most frequent feature, followed by first-order statistics, and GLRLM features. No study reported predicting response, prognosis or therapeutic response, but instead diseases diagnosis or classification. Although the lack of standardization in the radiomics workflow of the included studies, texture analysis showed potential to contribute to radiologists' reports, decreasing the subjectivity and leading to personalized healthcare.
Collapse
Affiliation(s)
| | | | | | | | - Nilce de Santos Melo
- Dentistry Department, Faculty of Health Science, University of Brasília, Brasilia, Brazil
| | - Cristine Miron Stefani
- Dentistry Department, Faculty of Health Science, University of Brasília, Brasilia, Brazil
| | - André Ferreira Leite
- Dentistry Department, Faculty of Health Science, University of Brasília, Brasilia, Brazil
| |
Collapse
|
29
|
Duff L, Scarsbrook AF, Mackie SL, Frood R, Bailey M, Morgan AW, Tsoumpas C. A methodological framework for AI-assisted diagnosis of active aortitis using radiomic analysis of FDG PET-CT images: Initial analysis. J Nucl Cardiol 2022; 29:3315-3331. [PMID: 35322380 PMCID: PMC9834376 DOI: 10.1007/s12350-022-02927-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 01/05/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND The aim of this study was to explore the feasibility of assisted diagnosis of active (peri-)aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. METHODS The aorta was manually segmented on FDG PET-CT in 50 patients with aortitis and 25 controls. Radiomic features (RF) (n = 107), including SUV (Standardized Uptake Value) metrics, were extracted from the segmented data and harmonized using the ComBat technique. Individual RFs and groups of RFs (i.e., signatures) were used as input in Machine Learning classifiers. The diagnostic utility of these classifiers was evaluated with area under the receiver operating characteristic curve (AUC) and accuracy using the clinical diagnosis as the ground truth. RESULTS Several RFs had high accuracy, 84% to 86%, and AUC scores 0.83 to 0.97 when used individually. Radiomic signatures performed similarly, AUC 0.80 to 1.00. CONCLUSION A methodological framework for a radiomic-based approach to support diagnosis of aortitis was outlined. Selected RFs, individually or in combination, showed similar performance to the current standard of qualitative assessment in terms of AUC for identifying active aortitis. This framework could support development of a clinical decision-making tool for a more objective and standardized assessment of aortitis.
Collapse
Affiliation(s)
- Lisa Duff
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, 8.49b Worsley Building, Clarendon Way, Leeds, LS2 9JT, UK.
- Institute of Medical and Biological Engineering, University of Leeds, Leeds, UK.
| | - Andrew F Scarsbrook
- Leeds Institute of Medical Research - St James's, University of Leeds, Leeds, UK
- Department of Radiology, St. James University Hospital, Leeds, UK
| | - Sarah L Mackie
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Biomedical Research Centre, NIHR Leeds, Leeds, UK
| | - Russell Frood
- Leeds Institute of Medical Research - St James's, University of Leeds, Leeds, UK
- Department of Radiology, St. James University Hospital, Leeds, UK
| | - Marc Bailey
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, 8.49b Worsley Building, Clarendon Way, Leeds, LS2 9JT, UK
- The Leeds Vascular Institute, Leeds General Infirmary, Leeds, UK
| | - Ann W Morgan
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, 8.49b Worsley Building, Clarendon Way, Leeds, LS2 9JT, UK
- Leeds Teaching Hospitals NHS Trust, Biomedical Research Centre, NIHR Leeds, Leeds, UK
| | - Charalampos Tsoumpas
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, 8.49b Worsley Building, Clarendon Way, Leeds, LS2 9JT, UK
- Icahn School of Medicine at Mount Sinai, Biomedical Engineering and Imaging Institute, New York, USA
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center of Groningen, University of Groningen, 9700 RB, Groningen, Netherlands
| |
Collapse
|
30
|
Radiomics for Discriminating Benign and Malignant Salivary Gland Tumors; Which Radiomic Feature Categories and MRI Sequences Should Be Used? Cancers (Basel) 2022; 14:cancers14235804. [PMID: 36497285 PMCID: PMC9740105 DOI: 10.3390/cancers14235804] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 11/12/2022] [Accepted: 11/22/2022] [Indexed: 11/26/2022] Open
Abstract
The lack of a consistent MRI radiomic signature, partly due to the multitude of initial feature analyses, limits the widespread clinical application of radiomics for the discrimination of salivary gland tumors (SGTs). This study aimed to identify the optimal radiomics feature category and MRI sequence for characterizing SGTs, which could serve as a step towards obtaining a consensus on a radiomics signature. Preliminary radiomics models were built to discriminate malignant SGTs (n = 34) from benign SGTs (n = 57) on T1-weighted (T1WI), fat-suppressed (FS)-T2WI and contrast-enhanced (CE)-T1WI images using six feature categories. The discrimination performances of these preliminary models were evaluated using 5-fold-cross-validation with 100 repetitions and the area under the receiver operating characteristic curve (AUC). The differences between models’ performances were identified using one-way ANOVA. Results show that the best feature categories were logarithm for T1WI and CE-T1WI and exponential for FS-T2WI, with AUCs of 0.828, 0.754 and 0.819, respectively. These AUCs were higher than the AUCs obtained using all feature categories combined, which were 0.750, 0.707 and 0.774, respectively (p < 0.001). The highest AUC (0.846) was obtained using a combination of T1WI + logarithm and FS-T2WI + exponential features, which reduced the initial features by 94.0% (from 1015 × 3 to 91 × 2). CE-T1WI did not improve performance. Using one feature category rather than all feature categories combined reduced the number of initial features without compromising radiomic performance.
Collapse
|
31
|
Guan X, Lu N, Zhang J. Accurate preoperative staging and HER2 status prediction of gastric cancer by the deep learning system based on enhanced computed tomography. Front Oncol 2022; 12:950185. [PMID: 36452488 PMCID: PMC9702985 DOI: 10.3389/fonc.2022.950185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/24/2022] [Indexed: 10/24/2023] Open
Abstract
PURPOSE To construct the deep learning system (DLS) based on enhanced computed tomography (CT) images for preoperative prediction of staging and human epidermal growth factor receptor 2 (HER2) status in gastric cancer patients. METHODS The raw enhanced CT image dataset consisted of CT images of 389 patients in the retrospective cohort, The Cancer Imaging Archive (TCIA) cohort, and the prospective cohort. DLS was developed by transfer learning for tumor detection, staging, and HER2 status prediction. The pre-trained Yolov5, EfficientNet, EfficientNetV2, Vision Transformer (VIT), and Swin Transformer (SWT) were studied. The tumor detection and staging dataset consisted of 4860 enhanced CT images and annotated tumor bounding boxes. The HER2 state prediction dataset consisted of 38900 enhanced CT images. RESULTS The DetectionNet based on Yolov5 realized tumor detection and staging and achieved a mean Average Precision (IoU=0.5) (mAP_0.5) of 0.909 in the external validation cohort. The VIT-based PredictionNet performed optimally in HER2 status prediction with the area under the receiver operating characteristics curve (AUC) of 0.9721 and 0.9995 in the TCIA cohort and prospective cohort, respectively. DLS included DetectionNet and PredictionNet had shown excellent performance in CT image interpretation. CONCLUSION This study developed the enhanced CT-based DLS to preoperatively predict the stage and HER2 status of gastric cancer patients, which will help in choosing the appropriate treatment to improve the survival of gastric cancer patients.
Collapse
Affiliation(s)
| | | | - Jianping Zhang
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| |
Collapse
|
32
|
Artificial intelligence and machine learning in cancer imaging. COMMUNICATIONS MEDICINE 2022; 2:133. [PMID: 36310650 PMCID: PMC9613681 DOI: 10.1038/s43856-022-00199-0] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 10/06/2022] [Indexed: 11/16/2022] Open
Abstract
An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.
Collapse
|
33
|
Xia X, Li D, Du W, Wang Y, Nie S, Tan Q, Gou Q. Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical Cancer. Diagnostics (Basel) 2022; 12:diagnostics12102446. [PMID: 36292135 PMCID: PMC9600299 DOI: 10.3390/diagnostics12102446] [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: 09/01/2022] [Revised: 09/23/2022] [Accepted: 10/06/2022] [Indexed: 12/09/2022] Open
Abstract
The accurate prediction of the status of PLNM preoperatively plays a key role in treatment strategy decisions in early-stage cervical cancer. The aim of this study was to develop and validate a radiomics-based nomogram for the preoperative prediction of pelvic lymph node metastatic status in early-stage cervical cancer. One hundred fifty patients were enrolled in this study. Radiomics features were extracted from T2-weighted MRI imaging (T2WI). Based on the selected features, a support vector machine (SVM) algorithm was used to build the radiomics signature. The radiomics-based nomogram was developed incorporating radiomics signature and clinical risk factors. In the training cohort (AUC = 0.925, accuracy = 81.6%, sensitivity = 70.3%, and specificity = 92.0%) and the testing cohort (AUC = 0.839, accuracy = 74.2%, sensitivity = 65.7%, and specificity = 82.8%), clinical models that combine stromal invasion depth, FIGO stage, and MTD perform poorly. The combined model had the highest AUC in the training cohort (AUC = 0.988, accuracy = 95.9%, sensitivity = 92.0%, and specificity = 100.0%) and the testing cohort (AUC = 0.922, accuracy = 87.1%, sensitivity = 85.7%, and specificity = 88.6%) when compared to the radiomics and clinical models. The study may provide valuable guidance for clinical physicians regarding the treatment strategies for early-stage cervical cancer patients.
Collapse
Affiliation(s)
- Xueming Xia
- Department of Head and Neck Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Dongdong Li
- Department of Network Engineering, School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China
| | - Wei Du
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yu Wang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 402103, China
| | - Shihong Nie
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Qiaoyue Tan
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Qiheng Gou
- Department of Head and Neck Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- Correspondence:
| |
Collapse
|
34
|
Horvat N, Miranda J, El Homsi M, Peoples JJ, Long NM, Simpson AL, Do RKG. A primer on texture analysis in abdominal radiology. Abdom Radiol (NY) 2022; 47:2972-2985. [PMID: 34825946 DOI: 10.1007/s00261-021-03359-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 11/16/2021] [Accepted: 11/17/2021] [Indexed: 01/18/2023]
Abstract
The number of publications on texture analysis (TA), radiomics, and radiogenomics has been growing exponentially, with abdominal radiologists aiming to build new prognostic or predictive biomarkers for a wide range of clinical applications including the use of oncological imaging to advance the field of precision medicine. TA is specifically concerned with the study of the variation of pixel intensity values in radiological images. Radiologists aim to capture pixel variation in radiological images to deliver new insights into tumor biology that cannot be derived from visual inspection alone. TA remains an active area of investigation and requires further standardization prior to its clinical acceptance and applicability. This review is for radiologists interested in this rapidly evolving field, who are thinking of performing research or want to better interpret results in this arena. We will review the main concepts in TA, workflow processes, and existing challenges and steps to overcome them, as well as look at publications in body imaging with external validation.
Collapse
Affiliation(s)
- Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo, SP, Brazil
| | - Maria El Homsi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Niamh M Long
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Amber L Simpson
- School of Computing, Queen's University, Kingston, ON, Canada.,Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA.
| |
Collapse
|
35
|
Paquier Z, Chao SL, Acquisto A, Fenton C, Guiot T, Dhont J, Levillain H, Gulyban A, Bali MA, Reynaert N. Radiomics software comparison using digital phantom and patient data: IBSI-compliance does not guarantee concordance of feature values. Biomed Phys Eng Express 2022; 8. [PMID: 36049399 DOI: 10.1088/2057-1976/ac8e6f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 09/01/2022] [Indexed: 11/12/2022]
Abstract
Introduction Radiomics is a promising imaging-based tool which could enhance clinical observation and identify representative features. To avoid different interpretations, the Image Biomarker Standardisation Initiative (IBSI) imposed conditions for harmonisation. This study evaluates IBSI-compliant radiomics applications against a known benchmark and clinical datasets for agreements. Materials and methods The applications compared were RadiomiX Research Toolbox, LIFEx v7.0.0, and syngo.via Frontier Radiomics v1.2.5 (based on PyRadiomics v2.1). Basic assessment included comparing feature names and their formulas. The IBSI digital phantom was used for evaluation against reference values. For agreement evaluation (including same software but different versions), two clinical datasets were used: 27 contrast-enhanced computed tomography (CECT) of colorectal liver metastases and 39 magnetic resonance imaging (MRI) of breast cancer, including intravoxel incoherent motion and dynamic contrast-enhanced (DCE) MRI. The lower 95% confidence interval of intraclass correlation coefficient was used to define excellent agreement (>0.9). Results The three radiomics applications share 41 (3 shape, 8 intensity, 30 texture) out of 172, 84 and 110 features for RadiomiX, LIFEx and syngo.via, respectively, as well as wavelet filtering. The naming convention is, however, different between them. Syngo.via had excellent agreement with the IBSI benchmark, while LIFEx and RadiomiX showed slightly worse agreement. Excellent reproducibility was achieved for shape features only, while intensity and texture features varied considerably with the imaging type. For intensity, excellent agreement ranged from 46% for the DCE maps to 100% for CECT, while this lowered to 44% and 73% for texture features, respectively. Wavelet features produced the greatest variation between applications, with an excellent agreement for only 3% to 11% features. Conclusion Even with IBSI-compliance, the reproducibility of features between radiomics applications is not guaranteed. To evaluate variation, quality assurance of radiomics applications should be performed and repeated when updating to a new version or adding a new modality.
Collapse
Affiliation(s)
- Zelda Paquier
- Medical Physics, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| | - Shih-Li Chao
- Medical Physics, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| | - Anaïs Acquisto
- Radiology, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| | - Chifra Fenton
- Radiology, Erasmus Hospital, Rte de Lennik 808, Bruxelles, 1070, BELGIUM
| | - Thomas Guiot
- Medical Physics, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| | - Jennifer Dhont
- Medical Physics, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| | - Hugo Levillain
- Medical Physics, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| | - Akos Gulyban
- Medical Physics, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| | | | - Nick Reynaert
- Medical Physics, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| |
Collapse
|
36
|
Zhang H, Meng Y, Li Q, Yu J, Liu F, Fang X, Li J, Feng X, Zhou J, Zhu M, Li N, Lu J, Shao C, Bian Y. Two nomograms for differentiating mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis. Eur Radiol 2022; 32:6336-6347. [PMID: 35394185 DOI: 10.1007/s00330-022-08698-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 02/16/2022] [Accepted: 02/25/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To develop and validate a CT nomogram and a radiomics nomogram to differentiate mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC) in patients with chronic pancreatitis (CP). METHODS In this retrospective study, the data of 138 patients with histopathologically diagnosed MFCP or PDAC treated at our institution were retrospectively analyzed. Two radiologists analyzed the original cross-sectional CT images based on predefined criteria. Image segmentation, feature extraction, and feature reduction and selection were used to create the radiomics model. The CT and radiomics models were developed using data from a training cohort of 103 consecutive patients. The models were validated in 35 consecutive patients. Multivariable logistic regression analysis was conducted to develop a model for the differential diagnosis of MFCP and PDAC and visualized as a nomogram. The nomograms' performances were determined based on their differentiating ability and clinical utility. RESULTS The mean age of patients was 53.7 years, 75.4% were male. The CT nomogram showed good differentiation between the two entities in the training (area under the curve [AUC], 0.87) and validation (AUC, 0.94) cohorts. The radiomics nomogram showed good differentiation in the training (AUC, 0.91) and validation (AUC, 0.93) cohorts. Decision curve analysis showed that patients could benefit from the CT and radiomics nomograms, if the threshold probability was 0.05-0.85 and > 0.05, respectively. CONCLUSIONS The two nomograms reasonably accurately differentiated MFCP from PDAC in patients with CP and hold potential for refining the management of pancreatic masses in CP patients. KEY POINTS • A CT nomogram and a computed tomography-based radiomics nomogram reasonably accurately differentiated mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis (CP). • The two nomograms can monitor the cancer risk in patients with CP and hold promise to optimize the management of pancreatic masses in patients with CP.
Collapse
Affiliation(s)
- Hao Zhang
- Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Yinghao Meng
- Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China
- Department of Radiology, No. 971 Hospital of Navy, Qingdao, 266071, Shandong, China
| | - Qi Li
- Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Xiaochen Feng
- Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Jian Zhou
- Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Mengmeng Zhu
- Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Na Li
- Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China.
| | - Yun Bian
- Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China.
| |
Collapse
|
37
|
Kim G, Moon S, Choi JH. Deep Learning with Multimodal Integration for Predicting Recurrence in Patients with Non-Small Cell Lung Cancer. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22176594. [PMID: 36081053 PMCID: PMC9459700 DOI: 10.3390/s22176594] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 08/27/2022] [Accepted: 08/30/2022] [Indexed: 05/20/2023]
Abstract
Due to high recurrence rates in patients with non-small cell lung cancer (NSCLC), medical professionals need extremely accurate diagnostic methods to prevent bleak prognoses. However, even the most commonly used diagnostic method, the TNM staging system, which describes the tumor-size, nodal-involvement, and presence of metastasis, is often inaccurate in predicting NSCLC recurrence. These limitations make it difficult for clinicians to tailor treatments to individual patients. Here, we propose a novel approach, which applies deep learning to an ensemble-based method that exploits patient-derived, multi-modal data. This will aid clinicians in successfully identifying patients at high risk of recurrence and improve treatment planning.
Collapse
Affiliation(s)
- Gihyeon Kim
- Computational Medicine, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul 03760, Korea
| | - Sehwa Moon
- Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul 03760, Korea
| | - Jang-Hwan Choi
- Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul 03760, Korea
- Department of Artificial Intelligence, Ewha Womans University, Seoul 03760, Korea
- Correspondence:
| |
Collapse
|
38
|
Jiang C, Zhao L, Xin B, Ma G, Wang X, Song S. 18F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Quant Imaging Med Surg 2022; 12:4135-4150. [PMID: 35919043 PMCID: PMC9338369 DOI: 10.21037/qims-21-1167] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 06/06/2022] [Indexed: 12/24/2022]
Abstract
Background Microvascular invasion (MVI) is a critical risk factor for early recurrence of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The aim of this study was to explore the contribution of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomic features for the preoperative prediction of HCC and ICC classification and MVI. Methods In this retrospective study, 127 (HCC: ICC =76:51) patients with suspected MVI accompanied by either HCC or ICC were included (In HCC group, MVI positive: negative =46:30 in ICC group, MVI positive: negative =31:20). Results-driven feature engineering workflow was used to select the most predictive feature combinations. The prediction model was based on supervised machine learning classifier. Ten-fold cross validation on training cohort and independent test cohort were constructed to ensure stability and generalization ability of models. Results For HCC and ICC classification, radiomics predictors composed of two PET and one CT feature achieved area under the curve (AUC) of 0.86 (accuracy, sensitivity, specificity was 0.82, 0.78, 0.88, respectively) on test cohort. For MVI prediction, in HCC group, our MVI prediction model achieved AUC of 0.88 (accuracy, sensitivity, specificity was 0.78, 0.88, 0.60 respectively) with three PET features associated with tumor stage on test cohort. In ICC group, the phenotype composed of two PET features and carbohydrate antigen 19-9 (CA19-9) achieved AUC of 0.90 (accuracy, sensitivity, specificity was 0.77, 0.75, 0.80, respectively). Conclusions 18F-FDG PET/CT radiomic features integrating clinical factors have potential in HCC and ICC classification and MVI prediction, while PET features have dominant predictive power in model performance. The prediction model has value in providing a non-invasive biomarker for an earlier indication and comprehensive quantification of primary liver cancers.
Collapse
Affiliation(s)
- Chunjuan Jiang
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
| | - Liwei Zhao
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Bowen Xin
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Guang Ma
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Shaoli Song
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
| |
Collapse
|
39
|
Guan X, Lu N, Zhang J. Computed Tomography-Based Deep Learning Nomogram Can Accurately Predict Lymph Node Metastasis in Gastric Cancer. Dig Dis Sci 2022; 68:1473-1481. [PMID: 35909203 PMCID: PMC10102043 DOI: 10.1007/s10620-022-07640-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 07/18/2022] [Indexed: 12/18/2022]
Abstract
BACKGROUND Computed tomography is the most commonly used imaging modality for preoperative assessment of lymph node status, but the reported accuracy is unsatisfactory. AIMS To evaluate and verify the predictive performance of computed tomography deep learning on the presurgical evaluation of lymph node metastasis in patients with gastric cancer. METHODS 347 patients were retrospectively selected (training cohort: 242, test cohort: 105). The enhanced computed tomography arterial phase images of gastric cancer were used for lesion segmentation, radiomics and deep learning feature extraction. Three methods were used for feature selection. Support vector machine (SVM) or random forest (RF) was used to build models. The classification performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC). We also established a nomogram that included clinical predictors. RESULTS The model based on ResNet50-RF showed favorable classification performance and was verified in the test cohort (AUC = 0.9803). The nomogram based on deep learning feature scores and the lymph node status reported by computed tomography showed excellent discrimination. AUC of 0.9978 was achieved in the training cohort and verified in the test cohort (AUC = 0.9914). Decision analysis curve showed the value of nomogram in clinical application. CONCLUSION The computed tomography-based deep learning nomogram can accurately and effectively evaluate lymph node metastasis in patients with gastric cancer before surgery.
Collapse
Affiliation(s)
- Xiao Guan
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing, 210011, Jiangsu, China
| | - Na Lu
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing, 210011, Jiangsu, China
| | - Jianping Zhang
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing, 210011, Jiangsu, China.
| |
Collapse
|
40
|
Zheng X, He B, Hu Y, Ren M, Chen Z, Zhang Z, Ma J, Ouyang L, Chu H, Gao H, He W, Liu T, Li G. Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis. Front Public Health 2022; 10:938113. [PMID: 35923964 PMCID: PMC9339706 DOI: 10.3389/fpubh.2022.938113] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 06/15/2022] [Indexed: 12/24/2022] Open
Abstract
BackgroundArtificial intelligence has far surpassed previous related technologies in image recognition and is increasingly used in medical image analysis. We aimed to explore the diagnostic accuracy of the models based on deep learning or radiomics for lung cancer staging.MethodsStudies were systematically reviewed using literature searches from PubMed, EMBASE, Web of Science, and Wanfang Database, according to PRISMA guidelines. Studies about the diagnostic accuracy of radiomics and deep learning, including the identifications of lung cancer, tumor types, malignant lung nodules and lymph node metastase, were included. After identifying the articles, the methodological quality was assessed using the QUADAS-2 checklist. We extracted the characteristic of each study; the sensitivity, specificity, and AUROC for lung cancer diagnosis were summarized for subgroup analysis.ResultsThe systematic review identified 19 eligible studies, of which 14 used radiomics models and 5 used deep learning models. The pooled AUROC of 7 studies to determine whether patients had lung cancer was 0.83 (95% CI 0.78–0.88). The pooled AUROC of 9 studies to determine whether patients had NSCLC was 0.78 (95% CI 0.73–0.83). The pooled AUROC of the 6 studies that determined patients had malignant lung nodules was 0.79 (95% CI 0.77–0.82). The pooled AUROC of the other 6 studies that determined whether patients had lymph node metastases was 0.74 (95% CI 0.66–0.82).ConclusionThe models based on deep learning or radiomics have the potential to improve diagnostic accuracy for lung cancer staging.Systematic Review Registrationhttps://inplasy.com/inplasy-2022-3-0167/, identifier: INPLASY202230167.
Collapse
Affiliation(s)
- Xiushan Zheng
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Bo He
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Yunhai Hu
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Min Ren
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Zhiyuan Chen
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Zhiguang Zhang
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Jun Ma
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Lanwei Ouyang
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Hongmei Chu
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Huan Gao
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Wenjing He
- School of Electronic Engineering, Chengdu University of Technology, Chengdu, China
| | - Tianhu Liu
- Department of Cardiology, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
- *Correspondence: Tianhu Liu
| | - Gang Li
- Department of Cardiology, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
- Gang Li
| |
Collapse
|
41
|
Luo B, Yang M, Han Z, Que Z, Luo T, Tian J. Establishment of a Nomogram-Based Prognostic Model (LASSO-COX Regression) for Predicting Progression-Free Survival of Primary Non-Small Cell Lung Cancer Patients Treated with Adjuvant Chinese Herbal Medicines Therapy: A Retrospective Study of Case Series. Front Oncol 2022; 12:882278. [PMID: 35875082 PMCID: PMC9304868 DOI: 10.3389/fonc.2022.882278] [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: 02/23/2022] [Accepted: 06/03/2022] [Indexed: 11/13/2022] Open
Abstract
Nowadays, Jin-Fu-Kang oral liquid (JFK), one of Chinese herbal medicines (CHMs) preparations, has been widely used as an adjuvant therapy for primary non-small cell lung cancer (PNSCLC) patients with the syndrome of deficiency of both Qi and Yin (Qi–Yin deficiency pattern) based on Traditional Chinese Medicine (TCM) theory. However, we found insufficient evidence of how long-term CHM treatment influence PNSCLC patients’ progression-free survival (PFS). Thus, using electronic medical records, we established a nomograph-based prognostic model for predicting PNSCLC patients’ PFS involved with JFK supplementary formulas (JFK-SFs) over 6 months, in order to preliminarily investigate potential predictors highly related to adjuvant CHMs therapies in theoretical epidemiology. In our retrospective study, a series of 197 PNSCLC cases from Long Hua Hospital were enrolled by non-probability sampling and divided into 2 datasets at the ratio of 5:4 by Kennard–Stone algorithm, as a result of 109 in training dataset and 88 in validation dataset. Besides, TNM stage, operation history, sIL-2R, and CA724 were considered as 4 highly correlated predictors for modeling based on LASSO-Cox regression. Additionally, we respectively used training dataset and validation dataset for establishment including internal validation and external validation, and the prediction performance of model was measured by concordance index (C-index), integrated discrimination improvement, and net reclassification indices (NRI). Moreover, we found that the model containing clinical characteristics and bio-features presented the best performance by pairwise comparison. Next, the result of sensitivity analysis proved its stability. Then, for preliminarily examination of its discriminative power, all eligible cases were divided into high-risk or low-risk progression by the cut-off value of 57, in the light of predicted nomogram scores. Ultimately, a completed TRIPOD checklist was used for self-assessment of normativity and integrity in modeling. In conclusion, our model might offer crude probability of uncertainly individualized PFS with long-term CHMs therapy in the real-world setting, which could discern the individuals implicated with worse prognosis from the better ones. Nevertheless, our findings were prone to unmeasured bias caused by confounding factors, owing to retrospective cases series.
Collapse
Affiliation(s)
- Bin Luo
- Department of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ming Yang
- Department of Good Practice Criterion, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zixin Han
- School of Pharmacy, Jiangxi University of Traditional Chinese Medicine, Nanchang, China
| | - Zujun Que
- Cancer Institute of Traditional Chinese Medicine, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Tianle Luo
- Department of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jianhui Tian
- Department of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Cancer Institute of Traditional Chinese Medicine, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Jianhui Tian,
| |
Collapse
|
42
|
O'Shea RJ, Rookyard C, Withey S, Cook GJR, Tsoka S, Goh V. Radiomic assessment of oesophageal adenocarcinoma: a critical review of 18F-FDG PET/CT, PET/MRI and CT. Insights Imaging 2022; 13:104. [PMID: 35715706 PMCID: PMC9206060 DOI: 10.1186/s13244-022-01245-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/28/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives Radiomic models present an avenue to improve oesophageal adenocarcinoma assessment through quantitative medical image analysis. However, model selection is complicated by the abundance of available predictors and the uncertainty of their relevance and reproducibility. This analysis reviews recent research to facilitate precedent-based model selection for prospective validation studies.
Methods This analysis reviews research on 18F-FDG PET/CT, PET/MRI and CT radiomics in oesophageal adenocarcinoma between 2016 and 2021. Model design, testing and reporting are evaluated according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score and Radiomics Quality Score (RQS). Key results and limitations are analysed to identify opportunities for future research in the area. Results Radiomic models of stage and therapeutic response demonstrated discriminative capacity, though clinical applications require greater sensitivity. Although radiomic models predict survival within institutions, generalisability is limited. Few radiomic features have been recommended independently by multiple studies. Conclusions Future research must prioritise prospective validation of previously proposed models to further clinical translation. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01245-0.
Collapse
Affiliation(s)
- Robert J O'Shea
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK.
| | - Chris Rookyard
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK
| | - Sam Withey
- Department of Radiology, The Royal Marsden NHS Foundation Trust, London, UK
| | - Gary J R Cook
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK.,King's College London & Guy's and St Thomas' PET Centre, St Thomas' Hospital, London, UK
| | - Sophia Tsoka
- Department of Informatics, School of Natural and Mathematical Sciences, King's College London, London, UK
| | - Vicky Goh
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK.,Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| |
Collapse
|
43
|
Katsuta Y, Kadoya N, Sugai Y, Katagiri Y, Yamamoto T, Takeda K, Tanaka S, Jingu K. Feasibility of Differential Dose-Volume Histogram Features in Multivariate Prediction Model for Radiation Pneumonitis Occurrence. Diagnostics (Basel) 2022; 12:diagnostics12061354. [PMID: 35741164 PMCID: PMC9221601 DOI: 10.3390/diagnostics12061354] [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: 04/01/2022] [Revised: 05/16/2022] [Accepted: 05/20/2022] [Indexed: 11/18/2022] Open
Abstract
The purpose of this study is to introduce differential dose−volume histogram (dDVH) features into machine learning for radiation pneumonitis (RP) prediction and to demonstrate the predictive performance of the developed model based on integrated cumulative dose−volume histogram (cDVH) and dDVH features. Materials and methods: cDVH and dDVH features were calculated for 153 patients treated for non-small-cell lung cancer with 60−66 Gy and dose bins ranging from 2 to 8 Gy in 2 Gy increments. RP prediction models were developed with the least absolute shrinkage and selection operator (LASSO) through fivefold cross-validation. Results: Among the 152 patients in the patient cohort, 41 presented ≥grade 2 RP. The interdependencies between cDVH features evaluated by Spearman’s correlation were significantly resolved by the inclusion of dDVH features. The average area under curve for the RP prediction model using cDVH and dDVH model was 0.73, which was higher than the average area under curve using cDVH model for 0.62 with statistically significance (p < 0.01). An analysis using the entire set of regression coefficients determined by LASSO demonstrated that dDVH features represented four of the top five frequently selected features in the model fitting, regardless of dose bin. Conclusions: We successfully developed an RP prediction model that integrated cDVH and dDVH features. The best RP prediction model was achieved using dDVH (dose bin = 4 Gy) features in the machine learning process.
Collapse
Affiliation(s)
- Yoshiyuki Katsuta
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (N.K.); (Y.S.); (T.Y.); (K.T.); (S.T.); (K.J.)
- Correspondence: ; Tel.: +81-22-717-7312; Fax: +81-22-717-7316
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (N.K.); (Y.S.); (T.Y.); (K.T.); (S.T.); (K.J.)
| | - Yuto Sugai
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (N.K.); (Y.S.); (T.Y.); (K.T.); (S.T.); (K.J.)
| | - Yu Katagiri
- Department of Radiation Oncology, Japan Red Cross Ishinomaki Hospital, Ishinomaki 986-8522, Japan;
| | - Takaya Yamamoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (N.K.); (Y.S.); (T.Y.); (K.T.); (S.T.); (K.J.)
| | - Kazuya Takeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (N.K.); (Y.S.); (T.Y.); (K.T.); (S.T.); (K.J.)
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (N.K.); (Y.S.); (T.Y.); (K.T.); (S.T.); (K.J.)
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (N.K.); (Y.S.); (T.Y.); (K.T.); (S.T.); (K.J.)
| |
Collapse
|
44
|
Moskowitz CS, Welch ML, Jacobs MA, Kurland BF, Simpson AL. Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies. Radiology 2022; 304:265-273. [PMID: 35579522 PMCID: PMC9340236 DOI: 10.1148/radiol.211597] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Rapid advances in automated methods for extracting large numbers of quantitative features from medical images have led to tremendous growth of publications reporting on radiomic analyses. Translation of these research studies into clinical practice can be hindered by biases introduced during the design, analysis, or reporting of the studies. Herein, the authors review biases, sources of variability, and pitfalls that frequently arise in radiomic research, with an emphasis on study design and statistical analysis considerations. Drawing on existing work in the statistical, radiologic, and machine learning literature, approaches for avoiding these pitfalls are described.
Collapse
Affiliation(s)
- Chaya S Moskowitz
- From the Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Ave, 2nd Floor, New York, NY, NY 10017 (C.S.M.); Cancer Digital Intelligence Program, University Health Network, Toronto, ON, Canada (M.L.W.); The Russell H. Morgan Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md (M.A.J.); ERT, Pittsburgh, Pa (B.F.K.); and School of Computing, Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada (A.L.S.)
| | - Mattea L Welch
- From the Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Ave, 2nd Floor, New York, NY, NY 10017 (C.S.M.); Cancer Digital Intelligence Program, University Health Network, Toronto, ON, Canada (M.L.W.); The Russell H. Morgan Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md (M.A.J.); ERT, Pittsburgh, Pa (B.F.K.); and School of Computing, Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada (A.L.S.)
| | - Michael A Jacobs
- From the Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Ave, 2nd Floor, New York, NY, NY 10017 (C.S.M.); Cancer Digital Intelligence Program, University Health Network, Toronto, ON, Canada (M.L.W.); The Russell H. Morgan Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md (M.A.J.); ERT, Pittsburgh, Pa (B.F.K.); and School of Computing, Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada (A.L.S.)
| | - Brenda F Kurland
- From the Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Ave, 2nd Floor, New York, NY, NY 10017 (C.S.M.); Cancer Digital Intelligence Program, University Health Network, Toronto, ON, Canada (M.L.W.); The Russell H. Morgan Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md (M.A.J.); ERT, Pittsburgh, Pa (B.F.K.); and School of Computing, Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada (A.L.S.)
| | - Amber L Simpson
- From the Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Ave, 2nd Floor, New York, NY, NY 10017 (C.S.M.); Cancer Digital Intelligence Program, University Health Network, Toronto, ON, Canada (M.L.W.); The Russell H. Morgan Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md (M.A.J.); ERT, Pittsburgh, Pa (B.F.K.); and School of Computing, Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada (A.L.S.)
| |
Collapse
|
45
|
Comte V, Schmutz H, Chardin D, Orlhac F, Darcourt J, Humbert O. Development and validation of a radiomic model for the diagnosis of dopaminergic denervation on [18F]FDOPA PET/CT. Eur J Nucl Med Mol Imaging 2022; 49:3787-3796. [PMID: 35567626 PMCID: PMC9399031 DOI: 10.1007/s00259-022-05816-7] [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: 01/28/2022] [Accepted: 04/23/2022] [Indexed: 11/30/2022]
Abstract
Purpose FDOPA PET shows good performance for the diagnosis of striatal dopaminergic denervation, making it a valuable tool for the differential diagnosis of Parkinsonism. Textural features are image biomarkers that could potentially improve the early diagnosis and monitoring of neurodegenerative parkinsonian syndromes. We explored the performances of textural features for binary classification of FDOPA scans. Methods We used two FDOPA PET datasets: 443 scans for feature selection, and 100 scans from a different PET/CT system for model testing. Scans were labelled according to expert interpretation (dopaminergic denervation versus no dopaminergic denervation). We built LASSO logistic regression models using 43 biomarkers including 32 textural features. Clinical data were also collected using a shortened UPDRS scale. Results The model built from the clinical data alone had a mean area under the receiver operating characteristics (AUROC) of 63.91. Conventional imaging features reached a maximum score of 93.47 but the addition of textural features significantly improved the AUROC to 95.73 (p < 0.001), and 96.10 (p < 0.001) when limiting the model to the top three features: GLCM_Correlation, Skewness and Compacity. Testing the model on the external dataset yielded an AUROC of 96.00, with 95% sensitivity and 97% specificity. GLCM_Correlation was one of the most independent features on correlation analysis, and systematically had the heaviest weight in the classification model. Conclusion A simple model with three radiomic features can identify pathologic FDOPA PET scans with excellent sensitivity and specificity. Textural features show promise for the diagnosis of parkinsonian syndromes. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-05816-7.
Collapse
Affiliation(s)
- Victor Comte
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.
| | - Hugo Schmutz
- Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
| | - David Chardin
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.,Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
| | - Fanny Orlhac
- Laboratoire d'Imagerie Translationnelle en Oncologie (LITO) U1288, Institut Curie, Inserm, Université Paris-Saclay, Orsay, France
| | - Jacques Darcourt
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.,Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
| | - Olivier Humbert
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.,Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
| |
Collapse
|
46
|
Ristow I, Madesta F, Well L, Shenas F, Wright F, Molwitz I, Farschtschi S, Bannas P, Adam G, Mautner VF, Werner R, Salamon J. Evaluation of magnetic resonance imaging-based radiomics characteristics for differentiation of benign and malignant peripheral nerve sheath tumors in neurofibromatosis type 1. Neuro Oncol 2022; 24:1790-1798. [PMID: 35426432 PMCID: PMC9527508 DOI: 10.1093/neuonc/noac100] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Patients with neurofibromatosis type 1 (NF1) develop benign (BPNST), premalignant atypical (ANF), and malignant (MPNST) peripheral nerve sheath tumors. Radiological differentiation of these entities is challenging. Therefore, we aimed to evaluate the value of a magnetic resonance imaging (MRI)-based radiomics machine-learning (ML) classifier for differentiation of these three entities of internal peripheral nerve sheath tumors in NF1 patients. METHODS MRI was performed at 3T in 36 NF1 patients (20 male; age: 31 ± 11 years). Segmentation of 117 BPNSTs, 17 MPNSTs, and 8 ANFs was manually performed using T2w spectral attenuated inversion recovery sequences. One hundred seven features per lesion were extracted using PyRadiomics and applied for BPNST versus MPNST differentiation. A 5-feature radiomics signature was defined based on the most important features and tested for signature-based BPNST versus MPNST classification (random forest [RF] classification, leave-one-patient-out evaluation). In a second step, signature feature expressions for BPNSTs, ANFs, and MPNSTs were evaluated for radiomics-based classification for these three entities. RESULTS The mean area under the receiver operator characteristic curve (AUC) for the radiomics-based BPNST versus MPNST differentiation was 0.94, corresponding to correct classification of on average 16/17 MPNSTs and 114/117 BPNSTs (sensitivity: 94%, specificity: 97%). Exploratory analysis with the eight ANFs revealed intermediate radiomic feature characteristics in-between BPNST and MPNST tumor feature expression. CONCLUSION In this proof-of-principle study, ML using MRI-based radiomics characteristics allows sensitive and specific differentiation of BPNSTs and MPNSTs in NF1 patients. Feature expression of premalignant atypical tumors was distributed in-between benign and malignant tumor feature expressions, which illustrates biological plausibility of the considered radiomics characteristics.
Collapse
Affiliation(s)
- Inka Ristow
- Corresponding Author: Inka Ristow, MD, Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg 20246, Germany ()
| | - Frederic Madesta
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Lennart Well
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Farzad Shenas
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Felicia Wright
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Isabel Molwitz
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Said Farschtschi
- Department of Neurology, University Medical Center Hamburg-Eppendorf
, Hamburg, Germany
| | - Peter Bannas
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Gerhard Adam
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Victor F Mautner
- Department of Neurology, University Medical Center Hamburg-Eppendorf
, Hamburg, Germany
| | | | | |
Collapse
|
47
|
Bradic J, Fan J, Zhu Y. Testability of high-dimensional linear models with nonsparse structures. Ann Stat 2022; 50:615-639. [DOI: 10.1214/19-aos1932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Jelena Bradic
- Department of Mathematics and Halicioğlu Data Science Institute, University of California, San Diego
| | - Jianqing Fan
- Department of Operations Research and Financial Engineering, Princeton University
| | - Yinchu Zhu
- Department of Economics and International Business School, Brandeis University
| |
Collapse
|
48
|
Reinert CP, Perl RM, Faul C, Lengerke C, Nikolaou K, Dittmann H, Bethge WA, Horger M. Value of CT-Textural Features and Volume-Based PET Parameters in Comparison to Serologic Markers for Response Prediction in Patients with Diffuse Large B-Cell Lymphoma Undergoing CD19-CAR-T Cell Therapy. J Clin Med 2022; 11:jcm11061522. [PMID: 35329846 PMCID: PMC8951429 DOI: 10.3390/jcm11061522] [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: 01/23/2022] [Revised: 03/07/2022] [Accepted: 03/08/2022] [Indexed: 11/16/2022] Open
Abstract
The goal of this study was to investigate the value of CT-textural features and volume-based PET parameters in comparison to serologic markers for response prediction in patients with diffuse large B-cell lymphoma (DLBCL) undergoing cluster of differentiation (CD19)-chimeric antigen receptor (CAR)-T cell therapy. We retrospectively analyzed the whole-body (WB)-metabolic tumor volume (MTV), the WB-total lesion glycolysis (TLG) and first order textural features derived from 18F-FDG-PET/CT, as well as serologic parameters (C-reactive protein [CRP] and lactate dehydrogenase [LDH], leucocytes) prior and after CAR-T cell therapy in 21 patients with DLBCL (57.7 ± 14.7 year; 7 female). Interleukin 6 (IL-6) and IL-2 receptor peaks were monitored after treatment onset and compared with patient outcome judged by follow-up 18F-FDG-PET/CT. In 12/21 patients (57%), complete remission (CR) was observed, whereas 9/21 patients (43%) showed partial remission (PR). At baseline, WB-MTV and WB-TLG were lower in patients achieving CR (35 ± 38 mL and 319 ± 362) compared to patients achieving PR (88 ± 110 mL and 1487 ± 2254; p < 0.05). The “entropy” proved lower (1.81 ± 0.09) and “uniformity” higher (0.33 ± 0.02) in patients with CR compared to PR (2.08 ± 0.22 and 0.28 ± 0.47; p < 0.05). Patients achieving CR had lower levels of CRP, LDH and leucocytes at baseline compared to patients achieving PR (p < 0.05). In the entire cohort, WB-MTV and WB-TLG decreased after therapy onset (p < 0.01) becoming not measurable in the CR-group. Leucocytes and CRP significantly dropped after therapy (p < 0.01). The IL-6 and IL-2R peaks after therapy were lower in patients with CR compared to PR (p > 0.05). In conclusion, volume-based PET parameters derived from PET/CT and CT-textural features have the potential to predict therapy response in patients with DLBCL undergoing CAR-T cell therapy.
Collapse
Affiliation(s)
- Christian Philipp Reinert
- Department of Radiology, Diagnostic and Interventional Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany; (R.M.P.); (K.N.); (M.H.)
- Correspondence: ; Tel.: +49-7071-298-7212; Fax: +49-7071-295-845
| | - Regine Mariette Perl
- Department of Radiology, Diagnostic and Interventional Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany; (R.M.P.); (K.N.); (M.H.)
| | - Christoph Faul
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany; (C.F.); (C.L.); (W.A.B.)
| | - Claudia Lengerke
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany; (C.F.); (C.L.); (W.A.B.)
| | - Konstantin Nikolaou
- Department of Radiology, Diagnostic and Interventional Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany; (R.M.P.); (K.N.); (M.H.)
- Cluster of Excellence iFIT (EXC 2180) Image Guided and Functionally Instructed Tumor Therapies, University of Tuebingen, 72074 Tuebingen, Germany
| | - Helmut Dittmann
- Department of Radiology, Nuclear Medicine, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany;
| | - Wolfgang A. Bethge
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany; (C.F.); (C.L.); (W.A.B.)
| | - Marius Horger
- Department of Radiology, Diagnostic and Interventional Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany; (R.M.P.); (K.N.); (M.H.)
| |
Collapse
|
49
|
CT Radiomics Features in Differentiation of Focal-Type Autoimmune Pancreatitis from Pancreatic Ductal Adenocarcinoma: A Propensity Score Analysis. Acad Radiol 2022; 29:358-366. [PMID: 34108115 DOI: 10.1016/j.acra.2021.04.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 03/31/2021] [Accepted: 04/01/2021] [Indexed: 12/11/2022]
Abstract
PURPOSE To evaluate the diagnostic performance of the radiomics score (rad-score) for differentiating focal-type autoimmune pancreatitis (fAIP) from pancreatic ductal adenocarcinoma (PDAC). METHODS This retrospective review included 42 consecutive patients with fAIP diagnosed according to the International Consensus Diagnostic Criteria between January 2011 and December 2018. Furthermore, 334 consecutive patients with PDAC confirmed by pathology were also reviewed during the same period. Patients with PDAC and fAIP were matched via propensity score matching (PSM). All patients underwent multidetector computed tomography (MDCT). For each patient, 1409 radiomics features of the portal phase were extracted and reduced using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. The portal rad-score performance was assessed based on its discriminative ability. RESULTS After PSM, we matched 55 patients with PDAC to 42 patients with fAIP, based on clinical and CT characteristics (e.g., patient age, sex, body mass index, location, size, enhanced mode). A rad-score for discriminating fAIP from PDAC, which contained four CT derived radiomic features, was developed (area under the curve = 0.97). The sensitivity, specificity, and accuracy of the radiomics model were 95.24%, 92.73% and 0.94, respectively. CONCLUSION The portal rad-score can accurately and noninvasively differentiate fAIP from PDAC.
Collapse
|
50
|
Hinzpeter R, Baumann L, Guggenberger R, Huellner M, Alkadhi H, Baessler B. Radiomics for detecting prostate cancer bone metastases invisible in CT: a proof-of-concept study. Eur Radiol 2022; 32:1823-1832. [PMID: 34559264 PMCID: PMC8831270 DOI: 10.1007/s00330-021-08245-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/13/2021] [Accepted: 08/03/2021] [Indexed: 01/02/2023]
Abstract
OBJECTIVES To investigate, in patients with metastatic prostate cancer, whether radiomics of computed tomography (CT) image data enables the differentiation of bone metastases not visible on CT from unaffected bone using 68 Ga-PSMA PET imaging as reference standard. METHODS In this IRB-approved retrospective study, 67 patients (mean age 71 ± 7 years; range: 55-84 years) showing a total of 205 68 Ga-PSMA-positive prostate cancer bone metastases in the thoraco-lumbar spine and pelvic bone being invisible in CT were included. Metastases and 86 68 Ga-PSMA-negative bone volumes in the same body region were segmented and further post-processed. Intra- and inter-reader reproducibility was assessed, with ICCs < 0.90 being considered non-reproducible. To account for imbalances in the dataset, data augmentation was performed to achieve improved class balance and to avoid model overfitting. The dataset was split into training, test, and validation set. After a multi-step dimension reduction process and feature selection process, the 11 most important and independent features were selected for statistical analyses. RESULTS A gradient-boosted tree was trained on the selected 11 radiomic features in order to classify patients' bones into bone metastasis and normal bone using the training dataset. This trained model achieved a classification accuracy of 0.85 (95% confidence interval [CI]: 0.76-0.92, p < .001) with 78% sensitivity and 93% specificity. The tuned model was applied on the original, non-augmented dataset resulting in a classification accuracy of 0.90 (95% CI: 0.82-0.98) with 91% sensitivity and 88% specificity. CONCLUSION Our proof-of-concept study indicates that radiomics may accurately differentiate unaffected bone from metastatic bone, being invisible by the human eye on CT. KEY POINTS • This proof-of-concept study showed that radiomics applied on CT images may accurately differentiate between bone metastases and metastatic-free bone in patients with prostate cancer. • Future promising applications include automatic bone segmentation, followed by a radiomics classifier, allowing for a screening-like approach in the detection of bone metastases.
Collapse
Affiliation(s)
- Ricarda Hinzpeter
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistr. 100, CH-8091, Zurich, Switzerland.
| | - Livia Baumann
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistr. 100, CH-8091, Zurich, Switzerland
| | - Roman Guggenberger
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistr. 100, CH-8091, Zurich, Switzerland
| | - Martin Huellner
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistr. 100, CH-8091, Zurich, Switzerland
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistr. 100, CH-8091, Zurich, Switzerland
| |
Collapse
|