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Choi BS, Yoo SK, Moon J, Chung SY, Oh J, Baek S, Kim Y, Chang JS, Kim H, Kim JS. Acute coronary event (ACE) prediction following breast radiotherapy by features extracted from 3D CT, dose, and cardiac structures. Med Phys 2023; 50:6409-6420. [PMID: 36974390 DOI: 10.1002/mp.16398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 02/22/2023] [Accepted: 03/21/2023] [Indexed: 03/29/2023] Open
Abstract
PURPOSE Heart toxicity, such as major acute coronary events (ACE), following breast radiation therapy (RT) is of utmost concern. Thus, many studies have been investigating the effect of mean heart dose (MHD) and dose received in heart sub-structures on toxicity. Most studies focused on the dose thresholds in the heart and its sub-structures, while few studies adopted such computational methods as deep neural networks (DNN) and radiomics. This work aims to construct a feature-driven predictive model for ACE after breast RT. METHODS A recently proposed two-step predictive model that extracts a number of features from a deep auto-segmentation network and processes the selected features for prediction was adopted. This work refined the auto-segmenting network and feature processing algorithms to enhance performance in cardiac toxicity prediction. In the predictive model, the deep convolutional neural network (CNN) extracted features from 3D computed tomography (CT) images and dose distributions in three automatically segmented heart sub-structures, including the left anterior descending artery (LAD), right coronary artery (RCA), and left ventricle (LV). The optimal feature processing workflow for the extracted features was explored to enhance the prediction accuracy. The regions associated with toxicity were visualized using a class activation map (CAM)-based technique. Our proposed model was validated against a conventional DNN (convolutional and fully connected layers) and radiomics with a patient cohort of 84 cases, including 29 and 55 patient cases with and without ACE. Of the entire 84 cases, 12 randomly chosen cases (5 toxicity and 7 non-toxicity cases) were set aside for independent test, and the remaining 72 cases were applied to 4-fold stratified cross-validation. RESULTS Our predictive model outperformed the conventional DNN by 38% and 10% and radiomics-based predictive models by 9% and 10% in AUC for 4-fold cross-validations and independent test, respectively. The degree of enhancement was greater when incorporating dose information and heart sub-structures into feature extraction. The model whose inputs were CT, dose, and three sub-structures (LV, LAD, and RCA) reached 96% prediction accuracy on average and 0.94 area under the curve (AUC) on average in the cross-validation, and also achieved prediction accuracy of 83% and AUC of 0.83 in the independent test. On 10 correctly predicted cases out of 12 for the independent test, the activation maps implied that for cases of ACE toxicity, the higher intensity was more likely to be observed inside the LV. CONCLUSIONS The proposed model characterized by modifications in model input with dose distributions and cardiac sub-structures, and serial processing of feature extraction and feature selection techniques can improve the predictive performance in ACE following breast RT.
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Affiliation(s)
- Byong Su Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Sang Kyun Yoo
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Jinyoung Moon
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Seung Yeun Chung
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, South Korea
| | - Jaewon Oh
- Cardiology Division, Severance Cardiovascular Hospital, and Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Stephen Baek
- School of Data Science, University of Virginia, Charlottesville, Virginia, USA
| | - Yusung Kim
- Department of Radiation Physics, The Universiy of Texas MD Anderson Cancer Center, Texas, USA
| | - Jee Suk Chang
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
- Department of Radiation Oncology, Gangnam Severance Hospital, Seoul, South Korea
| | - Hojin Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
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Tavakoli H, Pirzad Jahromi G, Sedaghat A. Investigating the Ability of Radiomics Features for Diagnosis of the Active Plaque of Multiple Sclerosis Patients. J Biomed Phys Eng 2023; 13:421-432. [PMID: 37868943 PMCID: PMC10589693 DOI: 10.31661/jbpe.v0i0.2302-1597] [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: 02/13/2023] [Accepted: 03/05/2023] [Indexed: 10/24/2023]
Abstract
Background Multiple sclerosis (MS) is the most common non-traumatic disabling disease. Objective The aim of this study is to investigate the ability of radiomics features for diagnosing active plaques in patients with MS from T2 Fluid Attenuated Inversion Recovery (FLAIR) images. Material and Methods In this experimental study, images of 82 patients with 122 MS lesions were investigated. Boruta and Relief algorithms were used for feature selection on the train data set (70%). Four different classifier algorithms, including Multi-Layer Perceptron (MLP), Gradient Boosting (GB), Decision Tree (DT), and Extreme Gradient Boosting (XGB) were used as classifiers for modeling. Finally, Performance metrics were obtained on the test data set (30%) with 1000 bootstrap and 95% confidence intervals (95% CIs). Results A total of 107 radiomics features were extracted for each lesion, of which 7 and 8 features were selected by the Relief method and Boruta method, respectively. DT classifier had the best performance in the two feature selection algorithms. The best performance on the test data set was related to Boruta-DT with an average accuracy of 0.86, sensitivity of 1.00, specificity of 0.84, and Area Under the Curve (AUC) of 0.92 (95% CI: 0.92-0.92). Conclusion Radiomics features have the potential for diagnosing MS active plaque by T2 FLAIR image features. Additionally, choosing the feature selection and classifier algorithms plays an important role in the diagnosis of active plaque in MS patients. The radiomics-based predictive models predict active lesions accurately and non-invasively.
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Affiliation(s)
- Hassan Tavakoli
- Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
- Radiation Injuries Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
- Department of Physiology and Biophysics, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Gila Pirzad Jahromi
- Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Abdolrasoul Sedaghat
- Department of Radiology, Karaj Central Medical Imaging Institute, Karaj, Alborz, Iran
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Volpe F, Nappi C, Piscopo L, Zampella E, Mainolfi CG, Ponsiglione A, Imbriaco M, Cuocolo A, Klain M. Emerging Role of Nuclear Medicine in Prostate Cancer: Current State and Future Perspectives. Cancers (Basel) 2023; 15:4746. [PMID: 37835440 PMCID: PMC10571937 DOI: 10.3390/cancers15194746] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023] Open
Abstract
Prostate cancer is the most frequent epithelial neoplasia after skin cancer in men starting from 50 years and prostate-specific antigen (PSA) dosage can be used as an early screening tool. Prostate cancer imaging includes several radiological modalities, ranging from ultrasonography, computed tomography (CT), and magnetic resonance to nuclear medicine hybrid techniques such as single-photon emission computed tomography (SPECT)/CT and positron emission tomography (PET)/CT. Innovation in radiopharmaceutical compounds has introduced specific tracers with diagnostic and therapeutic indications, opening the horizons to targeted and very effective clinical care for patients with prostate cancer. The aim of the present review is to illustrate the current knowledge and future perspectives of nuclear medicine, including stand-alone diagnostic techniques and theragnostic approaches, in the clinical management of patients with prostate cancer from initial staging to advanced disease.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Michele Klain
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80138 Naples, Italy; (F.V.); (C.N.); (L.P.); (E.Z.); (C.G.M.); (A.P.); (M.I.); (A.C.)
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104
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Bartels HC, O'Doherty J, Wolsztynski E, Brophy DP, MacDermott R, Atallah D, Saliba S, Young C, Downey P, Donnelly J, Geoghegan T, Brennan DJ, Curran KM. Radiomics-based prediction of FIGO grade for placenta accreta spectrum. Eur Radiol Exp 2023; 7:54. [PMID: 37726591 PMCID: PMC10509122 DOI: 10.1186/s41747-023-00369-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 06/26/2023] [Indexed: 09/21/2023] Open
Abstract
BACKGROUND Placenta accreta spectrum (PAS) is a rare, life-threatening complication of pregnancy. Predicting PAS severity is critical to individualise care planning for the birth. We aim to explore whether radiomic analysis of T2-weighted magnetic resonance imaging (MRI) can predict severe cases by distinguishing between histopathological subtypes antenatally. METHODS This was a bi-centre retrospective analysis of a prospective cohort study conducted between 2018 and 2022. Women who underwent MRI during pregnancy and had histological confirmation of PAS were included. Radiomic features were extracted from T2-weighted images. Univariate regression and multivariate analyses were performed to build predictive models to differentiate between non-invasive (International Federation of Gynecology and Obstetrics [FIGO] grade 1 or 2) and invasive (FIGO grade 3) PAS using R software. Prediction performance was assessed based on several metrics including sensitivity, specificity, accuracy and area under the curve (AUC) at receiver operating characteristic analysis. RESULTS Forty-one women met the inclusion criteria. At univariate analysis, 0.64 sensitivity (95% confidence interval [CI] 0.0-1.00), specificity 0.93 (0.38-1.0), 0.58 accuracy (0.37-0.78) and 0.77 AUC (0.56-.097) was achieved for predicting severe FIGO grade 3 PAS. Using a multivariate approach, a support vector machine model yielded 0.30 sensitivity (95% CI 0.18-1.0]), 0.74 specificity (0.38-1.00), 0.58 accuracy (0.40-0.82), and 0.53 AUC (0.40-0.85). CONCLUSION Our results demonstrate a predictive potential of this machine learning pipeline for classifying severe PAS cases. RELEVANCE STATEMENT This study demonstrates the potential use of radiomics from MR images to identify severe cases of placenta accreta spectrum antenatally. KEY POINTS • Identifying severe cases of placenta accreta spectrum from imaging is challenging. • We present a methodological approach for radiomics-based prediction of placenta accreta. • We report certain radiomic features are able to predict severe PAS subtypes. • Identifying severe PAS subtypes ensures safe and individualised care planning for birth.
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Affiliation(s)
- Helena C Bartels
- Department of UCD Obstetrics and Gynaecology, School of Medicine, University College Dublin, National Maternity Hospital, Holles Street, Dublin 2, Ireland.
| | - Jim O'Doherty
- Siemens Medical Solutions, Malvern, PA, USA
- Department of Radiology & Radiological Science, Medical University of South Carolina, Charleston, SC, USA
- Radiography & Diagnostic Imaging, University College Dublin, Dublin, Ireland
| | - Eric Wolsztynski
- Statistics Department, University College Cork, Cork, Ireland
- Insight Centre for Data Analytics, Dublin, Ireland
| | - David P Brophy
- Department of Radiology, St. Vincent's University Hospital, Dublin, Ireland
| | - Roisin MacDermott
- Department of Radiology, St. Vincent's University Hospital, Dublin, Ireland
| | - David Atallah
- Department of Gynecology and Obstetrics, Hôtel-Dieu de France University Hospital, Saint Joseph University, Beirut, Lebanon
| | - Souha Saliba
- Department of Radiology: Fetal and Placental Imaging, Hôtel-Dieu de France University Hospital, Saint Joseph University, Beirut, Lebanon
| | - Constance Young
- Department of Histopathology, National Maternity Hospital, Dublin, Ireland
| | - Paul Downey
- Department of Histopathology, National Maternity Hospital, Dublin, Ireland
| | - Jennifer Donnelly
- Department of Obstetrics and Gynaecology, Rotunda Hospital, Dublin, Ireland
| | - Tony Geoghegan
- Department of Radiology, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Donal J Brennan
- Department of UCD Obstetrics and Gynaecology, School of Medicine, University College Dublin, National Maternity Hospital, Holles Street, Dublin 2, Ireland
- University College Dublin Gynaecological Oncology Group (UCD-GOG), Mater Misericordiae University Hospital and St Vincent's University Hospital, Dublin, Ireland
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
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Wang X, Liu Z, Yin X, Yang C, Zhang J. A radiomics model fusing clinical features to predict microsatellite status preoperatively in colorectal cancer liver metastasis. BMC Gastroenterol 2023; 23:308. [PMID: 37700238 PMCID: PMC10498531 DOI: 10.1186/s12876-023-02922-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 08/10/2023] [Indexed: 09/14/2023] Open
Abstract
PURPOSE To study the combined model of radiomic features and clinical features based on enhanced CT images for noninvasive evaluation of microsatellite instability (MSI) status in colorectal liver metastasis (CRLM) before surgery. METHODS The study included 104 patients retrospectively and collected CT images of patients. We adjusted the region of interest to increase the number of MSI-H images. Radiomic features were extracted from these CT images. The logistic models of simple clinical features, simple radiomic features, and radiomic features with clinical features were constructed from the original image data and the expanded data, respectively. The six models were evaluated in the validation set. A nomogram was made to conveniently show the probability of the patient having a high MSI (MSI-H). RESULTS The model including radiomic features and clinical features in the expanded data worked best in the validation group. CONCLUSION A logistic regression prediction model based on enhanced CT images combining clinical features and radiomic features after increasing the number of MSI-H images can effectively identify patients with CRLM with MSI-H and low-frequency microsatellite instability (MSI-L), and provide effective guidance for clinical immunotherapy of CRLM patients with unknown MSI status.
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Affiliation(s)
- Xuehu Wang
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China.
- Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China.
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China.
| | - Ziqi Liu
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China
- Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China
| | - Xiaoping Yin
- Affiliated Hospital of Hebei University, Bao Ding, 071000, China
| | - Chang Yang
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China
- Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China
| | - Jushuo Zhang
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China
- Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China
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De Muzio F, Pellegrino F, Fusco R, Tafuto S, Scaglione M, Ottaiano A, Petrillo A, Izzo F, Granata V. Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics. Diagnostics (Basel) 2023; 13:2877. [PMID: 37761243 PMCID: PMC10529975 DOI: 10.3390/diagnostics13182877] [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: 07/13/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
Neuroendocrine neoplasms (NENs) are a group of lesions originating from cells of the diffuse neuroendocrine system. NENs may involve different sites, including the gastrointestinal tract (GEP-NENs). The incidence and prevalence of GEP-NENs has been constantly rising thanks to the increased diagnostic power of imaging and immuno-histochemistry. Despite the plethora of biochemical markers and imaging techniques, the prognosis and therapeutic choice in GEP-NENs still represents a challenge, mainly due to the great heterogeneity in terms of tumor lesions and clinical behavior. The concept that biomedical images contain information about tissue heterogeneity and pathological processes invisible to the human eye is now well established. From this substrate comes the idea of radiomics. Computational analysis has achieved promising results in several oncological settings, and the use of radiomics in different types of GEP-NENs is growing in the field of research, yet with conflicting results. The aim of this narrative review is to provide a comprehensive update on the role of radiomics on GEP-NEN management, focusing on the main clinical aspects analyzed by most existing reports: predicting tumor grade, distinguishing NET from other tumors, and prognosis assessment.
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Affiliation(s)
- Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | | | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy;
| | - Salvatore Tafuto
- Unit of Sarcomi e Tumori Rari, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Mariano Scaglione
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy
| | - Alessandro Ottaiano
- Unit for Innovative Therapies of Abdominal Metastastes, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Francesco Izzo
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
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107
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Nakajo M, Nagano H, Jinguji M, Kamimura Y, Masuda K, Takumi K, Tani A, Hirahara D, Kariya K, Yamashita M, Yoshiura T. The usefulness of machine-learning-based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer. Br J Radiol 2023; 96:20220772. [PMID: 37393538 PMCID: PMC10461278 DOI: 10.1259/bjr.20220772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 07/03/2023] Open
Abstract
OBJECTIVE To examine whether machine learning (ML) analyses involving clinical and 18F-FDG-PET-based radiomic features are helpful in predicting prognosis in patients with laryngeal cancer. METHODS This retrospective study included 49 patients with laryngeal cancer who underwent18F-FDG-PET/CT before treatment, and these patients were divided into the training (n = 34) and testing (n = 15) cohorts.Seven clinical (age, sex, tumor size, T stage, N stage, Union for International Cancer Control stage, and treatment) and 40 18F-FDG-PET-based radiomic features were used to predict disease progression and survival. Six ML algorithms (random forest, neural network, k-nearest neighbors, naïve Bayes, logistic regression, and support vector machine) were used for predicting disease progression. Two ML algorithms (cox proportional hazard and random survival forest [RSF] model) considering for time-to-event outcomes were used to assess progression-free survival (PFS), and prediction performance was assessed by the concordance index (C-index). RESULTS Tumor size, T stage, N stage, GLZLM_ZLNU, and GLCM_Entropy were the five most important features for predicting disease progression.In both cohorts, the naïve Bayes model constructed by these five features was the best performing classifier (training: AUC = 0.805; testing: AUC = 0.842). The RSF model using the five features (tumor size, GLZLM_ZLNU, GLCM_Entropy, GLRLM_LRHGE and GLRLM_SRHGE) exhibited the highest performance in predicting PFS (training: C-index = 0.840; testing: C-index = 0.808). CONCLUSION ML analyses involving clinical and 18F-FDG-PET-based radiomic features may help predict disease progression and survival in patients with laryngeal cancer. ADVANCES IN KNOWLEDGE ML approach using clinical and 18F-FDG-PET-based radiomic features has the potential to predict prognosis of laryngeal cancer.
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Affiliation(s)
- Masatoyo Nakajo
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Hiromi Nagano
- Department of Otolaryngology Head and Neck Surgery, Kagoshima University, Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Megumi Jinguji
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Yoshiki Kamimura
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Keiko Masuda
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Koji Takumi
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Atsushi Tani
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Daisuke Hirahara
- Department of Management Planning Division, Harada Academy, Kagoshima, Japan
| | - Keisuke Kariya
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Masaru Yamashita
- Department of Otolaryngology Head and Neck Surgery, Kagoshima University, Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Takashi Yoshiura
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, Kagoshima, Japan
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Du Plessis T, Rae WID, Ramkilawon G, Martinson NA, Sathekge MM. Quantitative Chest X-ray Radiomics for Therapy Response Monitoring in Patients with Pulmonary Tuberculosis. Diagnostics (Basel) 2023; 13:2842. [PMID: 37685380 PMCID: PMC10486768 DOI: 10.3390/diagnostics13172842] [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: 07/19/2023] [Revised: 08/16/2023] [Accepted: 08/30/2023] [Indexed: 09/10/2023] Open
Abstract
Tuberculosis (TB) remains the second leading cause of death globally from a single infectious agent, and there is a critical need to develop improved imaging biomarkers and aid rapid assessments of responses to therapy. We aimed to utilize radiomics, a rapidly developing image analysis tool, to develop a scoring system for this purpose. A chest X-ray radiomics score (RadScore) was developed by implementing a unique segmentation method, followed by feature extraction and parameter map construction. Signature parameter maps that showed a high correlation to lung pathology were consolidated into four frequency bins to obtain the RadScore. A clinical score (TBscore) and a radiological score (RLscore) were also developed based on existing scoring algorithms. The correlation between the change in the three scores, calculated from serial X-rays taken while patients received TB therapy, was evaluated using Spearman's correlation. Poor correlations were observed between the changes in the TBscore and the RLscore (0.09 (p-value = 0.36)) and the TBscore and the RadScore (0.02 (p-value = 0.86)). The changes in the RLscore and the RadScore had a much stronger correlation of 0.22, which is statistically significant (p-value = 0.02). This shows that the developed RadScore has the potential to be a quantitative monitoring tool for responses to therapy.
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Affiliation(s)
- Tamarisk Du Plessis
- Department of Nuclear Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria 0001, South Africa
| | | | - Gopika Ramkilawon
- Department of Statistics, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria 0081, South Africa
| | - Neil Alexander Martinson
- Perinatal HIV Research Unit (PHRU), University of the Witwatersrand, Johannesburg 1862, South Africa
- Centre for Tuberculosis Research, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Mike Michael Sathekge
- Department of Nuclear Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria 0001, South Africa
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du Plessis T, Ramkilawon G, Rae WID, Botha T, Martinson NA, Dixon SAP, Kyme A, Sathekge MM. Introducing a secondary segmentation to construct a radiomics model for pulmonary tuberculosis cavities. LA RADIOLOGIA MEDICA 2023; 128:1093-1102. [PMID: 37474665 PMCID: PMC10474191 DOI: 10.1007/s11547-023-01681-y] [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: 01/01/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023]
Abstract
PURPOSE Accurate segmentation (separating diseased portions of the lung from normal appearing lung) is a challenge in radiomic studies of non-neoplastic diseases, such as pulmonary tuberculosis (PTB). In this study, we developed a segmentation method, applicable to chest X-rays (CXR), that can eliminate the need for precise disease delineation, and that is effective for constructing radiomic models for automatic PTB cavity classification. METHODS This retrospective study used a dataset of 266 posteroanterior CXR of patients diagnosed with laboratory confirmed PTB. The lungs were segmented using a U-net-based in-house automatic segmentation model. A secondary segmentation was developed using a sliding window, superimposed on the primary lung segmentation. Pyradiomics was used for feature extraction from every window which increased the dimensionality of the data, but this allowed us to accurately capture the spread of the features across the lung. Two separate measures (standard-deviation and variance) were used to consolidate the features. Pearson's correlation analysis (with a 0.8 cut-off value) was then applied for dimensionality reduction followed by the construction of Random Forest radiomic models. RESULTS Two almost identical radiomic signatures consisting of 10 texture features each (9 were the same plus 1 other feature) were identified using the two separate consolidation measures. Two well performing random forest models were constructed from these signatures. The standard-deviation model (AUC = 0.9444 (95% CI, 0.8762; 0.9814)) performed marginally better than the variance model (AUC = 0.9288 (95% CI, 0.9046; 0.9843)). CONCLUSION The introduction of the secondary sliding window segmentation on CXR could eliminate the need for disease delineation in pulmonary radiomic studies, and it could improve the accuracy of CXR reporting currently regaining prominence as a high-volume screening tool as the developed radiomic models correctly classify cavities from normal CXR.
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Affiliation(s)
- Tamarisk du Plessis
- Department of Nuclear Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa.
| | - Gopika Ramkilawon
- Department of Statistics, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, South Africa
| | | | - Tanita Botha
- Department of Statistics, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, South Africa
| | - Neil Alexander Martinson
- Perinatal HIV Research Unit (PHRU), University of the Witwatersrand, Johannesburg, South Africa
- Johns Hopkins University Centre for TB Research, Baltimore, MD, USA
| | | | - Andre Kyme
- School of Biomedical Engineering, University of Sydney, Sydney, Australia
| | - Mike Michael Sathekge
- Department of Nuclear Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
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Wang C, Zhang Z, Dou Y, Liu Y, Chen B, Liu Q, Wang S. Development of clinical and magnetic resonance imaging-based radiomics nomograms for the differentiation of nodular fasciitis from soft tissue sarcoma. Acta Radiol 2023; 64:2578-2589. [PMID: 37593946 DOI: 10.1177/02841851231188473] [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] [Indexed: 08/19/2023]
Abstract
BACKGROUND Accurate differentiation of nodular fasciitis (NF) from soft tissue sarcoma (STS) before surgery is essential for the subsequent diagnosis and treatment of patients. PURPOSE To develop and evaluate radiomics nomograms based on clinical factors and magnetic resonance imaging (MRI) for the preoperative differentiation of NF from STS. MATERIAL AND METHODS This retrospective study analyzed the MRI data of 27 patients with pathologically diagnosed NF and 58 patients with STS who were randomly divided into training (n = 62) and validation (n = 23) groups. Univariate and multivariate analyses were performed to identify the clinical factors and semantic features of MRI. Radiomics analysis was applied to fat-suppressed T1-weighted (T1W-FS) images, fat-suppressed T2-weighted (T2W-FS) images, and contrast-enhanced T1-weighted (CE-T1W) images. The radiomics nomograms incorporating the radiomics signatures, clinical factors, and semantic features of MRI were developed. ROC curves and AUCs were carried out to compare the performance of the clinical factors, radiomics signatures, and clinical radiomics nomograms. RESULTS Tumor location, size, heterogeneous signal intensity on T2W-FS imaging, heterogeneous signal intensity on CE-T1W imaging, margin definitions on CE-T1W imaging, and septa were independent predictors for differentiating NF from STS (P < 0.05). The performance of the radiomics signatures based on T2W-FS imaging (AUC = 0.961) and CE-T1W imaging (AUC = 0.938) was better than that based on T1W-FS imaging (AUC = 0.833). The radiomics nomograms had AUCs of 0.949, which demonstrated good clinical utility and calibration. CONCLUSION The non-invasive clinical radiomics nomograms exhibited good performance in the differentiation of NF from STS, and they have clinical application in the preoperative diagnosis of diseases.
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Affiliation(s)
- Chunjie Wang
- Department of Radiology, The Second Hospital of Dalian Medical University, Dalian, PR China
| | - Zhengyang Zhang
- Department of Radiology, The First Affiliated Hospital of Hebei North University, Zhangjiakou, PR China
| | - Yanping Dou
- Department of Ultrasound, The First Affiliated Hospital of Dalian Medical University, Dalian, PR China
| | - Yajie Liu
- Department of Radiology, The Second Hospital of Dalian Medical University, Dalian, PR China
| | - Bo Chen
- Department of Nuclear Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, PR China
| | - Qing Liu
- Department of Radiology, The Second Hospital of Dalian Medical University, Dalian, PR China
| | - Shaowu Wang
- Department of Radiology, The Second Hospital of Dalian Medical University, Dalian, PR China
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Vezakis I, Vezakis A, Gourtsoyianni S, Koutoulidis V, Polydorou AA, Matsopoulos GK, Koutsouris DD. An Automated Prognostic Model for Pancreatic Ductal Adenocarcinoma. Genes (Basel) 2023; 14:1742. [PMID: 37761882 PMCID: PMC10530933 DOI: 10.3390/genes14091742] [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: 08/11/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) constitutes a leading cause of cancer-related mortality despite advances in detection and treatment methods. While computed tomography (CT) serves as the current gold standard for initial evaluation of PDAC, its prognostic value remains limited, as it relies on diagnostic stage parameters encompassing tumor size, lymph node involvement, and metastasis. Radiomics have recently shown promise in predicting postoperative survival of PDAC patients; however, they rely on manual pancreas and tumor delineation by clinicians. In this study, we collected a dataset of pre-operative CT scans from a cohort of 40 PDAC patients to evaluate a fully automated pipeline for survival prediction. Employing nnU-Net trained on an external dataset, we generated automated pancreas and tumor segmentations. Subsequently, we extracted 854 radiomic features from each segmentation, which we narrowed down to 29 via feature selection. We then combined these features with the Tumor, Node, Metastasis (TNM) system staging parameters, as well as the patient's age. We trained a random survival forest model to perform an overall survival prediction over time, as well as a random forest classifier for the binary classification of two-year survival, using repeated cross-validation for evaluation. Our results exhibited promise, with a mean C-index of 0.731 for survival modeling and a mean accuracy of 0.76 in two-year survival prediction, providing evidence of the feasibility and potential efficacy of a fully automated pipeline for PDAC prognostication. By eliminating the labor-intensive manual segmentation process, our streamlined pipeline demonstrates an efficient and accurate prognostication process, laying the foundation for future research endeavors.
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Affiliation(s)
- Ioannis Vezakis
- Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, Greece; (I.V.); (D.D.K.)
| | - Antonios Vezakis
- 2nd Department of Surgery, Aretaieion Hospital, School of Medicine, National and Kapodistrian University of Athens, 76 Vas. Sophias Ave., 11528 Athens, Greece; (A.V.); (A.A.P.)
| | - Sofia Gourtsoyianni
- 1st Department of Radiology, Aretaieion Hospital, School of Medicine, National and Kapodistrian University of Athens, 76 Vas. Sophias Ave., 11528 Athens, Greece; (S.G.); (V.K.)
| | - Vassilis Koutoulidis
- 1st Department of Radiology, Aretaieion Hospital, School of Medicine, National and Kapodistrian University of Athens, 76 Vas. Sophias Ave., 11528 Athens, Greece; (S.G.); (V.K.)
| | - Andreas A. Polydorou
- 2nd Department of Surgery, Aretaieion Hospital, School of Medicine, National and Kapodistrian University of Athens, 76 Vas. Sophias Ave., 11528 Athens, Greece; (A.V.); (A.A.P.)
| | - George K. Matsopoulos
- Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, Greece; (I.V.); (D.D.K.)
| | - Dimitrios D. Koutsouris
- Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, Greece; (I.V.); (D.D.K.)
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Huynh BN, Groendahl AR, Tomic O, Liland KH, Knudtsen IS, Hoebers F, van Elmpt W, Malinen E, Dale E, Futsaether CM. Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics. Front Med (Lausanne) 2023; 10:1217037. [PMID: 37711738 PMCID: PMC10498924 DOI: 10.3389/fmed.2023.1217037] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 07/07/2023] [Indexed: 09/16/2023] Open
Abstract
Background Radiomics can provide in-depth characterization of cancers for treatment outcome prediction. Conventional radiomics rely on extraction of image features within a pre-defined image region of interest (ROI) which are typically fed to a classification algorithm for prediction of a clinical endpoint. Deep learning radiomics allows for a simpler workflow where images can be used directly as input to a convolutional neural network (CNN) with or without a pre-defined ROI. Purpose The purpose of this study was to evaluate (i) conventional radiomics and (ii) deep learning radiomics for predicting overall survival (OS) and disease-free survival (DFS) for patients with head and neck squamous cell carcinoma (HNSCC) using pre-treatment 18F-fluorodeoxuglucose positron emission tomography (FDG PET) and computed tomography (CT) images. Materials and methods FDG PET/CT images and clinical data of patients with HNSCC treated with radio(chemo)therapy at Oslo University Hospital (OUS; n = 139) and Maastricht University Medical Center (MAASTRO; n = 99) were collected retrospectively. OUS data was used for model training and initial evaluation. MAASTRO data was used for external testing to assess cross-institutional generalizability. Models trained on clinical and/or conventional radiomics features, with or without feature selection, were compared to CNNs trained on PET/CT images without or with the gross tumor volume (GTV) included. Model performance was measured using accuracy, area under the receiver operating characteristic curve (AUC), Matthew's correlation coefficient (MCC), and the F1 score calculated for both classes separately. Results CNNs trained directly on images achieved the highest performance on external data for both endpoints. Adding both clinical and radiomics features to these image-based models increased performance further. Conventional radiomics including clinical data could achieve competitive performance. However, feature selection on clinical and radiomics data lead to overfitting and poor cross-institutional generalizability. CNNs without tumor and node contours achieved close to on-par performance with CNNs including contours. Conclusion High performance and cross-institutional generalizability can be achieved by combining clinical data, radiomics features and medical images together with deep learning models. However, deep learning models trained on images without contours can achieve competitive performance and could see potential use as an initial screening tool for high-risk patients.
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Affiliation(s)
- Bao Ngoc Huynh
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | | | - Oliver Tomic
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Kristian Hovde Liland
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Ingerid Skjei Knudtsen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Medical Physics, Oslo University Hospital, Oslo, Norway
| | - Frank Hoebers
- Department of Radiation Oncology (MAASTRO), Maastricht University Medical Center, Maastricht, Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), Maastricht University Medical Center, Maastricht, Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, Netherlands
| | - Eirik Malinen
- Department of Medical Physics, Oslo University Hospital, Oslo, Norway
- Department of Physics, University of Oslo, Oslo, Norway
| | - Einar Dale
- Department of Oncology, Oslo University Hospital, Oslo, Norway
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Lu WJ, Mao L, Li J, OuYang LY, Chen JY, Chen SY, Lin YY, Wu YW, Chen SN, Qiu SD, Chen F. Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer. Front Oncol 2023; 13:1046951. [PMID: 37681026 PMCID: PMC10482087 DOI: 10.3389/fonc.2023.1046951] [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: 09/17/2022] [Accepted: 07/21/2023] [Indexed: 09/09/2023] Open
Abstract
Purpose To develop and validate a three-dimensional ultrasound (3D US) radiomics nomogram for the preoperative prediction of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC). Methods This retrospective study included 168 patients with surgically proven PTC (non-ETE, n = 90; ETE, n = 78) who were divided into training (n = 117) and validation (n = 51) cohorts by a random stratified sampling strategy. The regions of interest (ROIs) were obtained manually from 3D US images. A larger number of radiomic features were automatically extracted. Finally, a nomogram was built, incorporating the radiomics scores and selected clinical predictors. Receiver operating characteristic (ROC) curves were performed to validate the capability of the nomogram on both the training and validation sets. The nomogram models were compared with conventional US models. The DeLong test was adopted to compare different ROC curves. Results The area under the receiver operating characteristic curve (AUC) of the radiologist was 0.67 [95% confidence interval (CI), 0.580-0.757] in the training cohort and 0.62 (95% CI, 0.467-0.746) in the validation cohort. Sixteen features from 3D US images were used to build the radiomics signature. The radiomics nomogram, which incorporated the radiomics signature, tumor location, and tumor size showed good calibration and discrimination in the training cohort (AUC, 0.810; 95% CI, 0.727-0.876) and the validation cohort (AUC, 0.798; 95% CI, 0.662-0.897). The result suggested that the diagnostic efficiency of the 3D US-based radiomics nomogram was better than that of the radiologist and it had a favorable discriminate performance with a higher AUC (DeLong test: p < 0.05). Conclusions The 3D US-based radiomics signature nomogram, a noninvasive preoperative prediction method that incorporates tumor location and tumor size, presented more advantages over radiologist-reported ETE statuses for PTC.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Fei Chen
- *Correspondence: Shao-Dong Qiu, ; Fei Chen,
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Liu N, Wan Y, Tong Y, He J, Xu S, Hu X, Luo C, Xu L, Guo F, Shen B, Yu H. A Clinic-Radiomics Model for Predicting the Incidence of Persistent Organ Failure in Patients with Acute Necrotizing Pancreatitis. Gastroenterol Res Pract 2023; 2023:2831024. [PMID: 37637352 PMCID: PMC10449595 DOI: 10.1155/2023/2831024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 04/25/2023] [Accepted: 06/08/2023] [Indexed: 08/29/2023] Open
Abstract
Background Persistent organ failure (POF) is the leading cause of death in patients with acute necrotizing pancreatitis (ANP). Although several risk factors have been identified, there remains a lack of efficient instruments to accurately predict the incidence of POF in ANP. Methods Retrospectively, the clinical and imaging data of 178 patients with ANP were collected from our database, and the patients were divided into training (n = 125) and validation (n = 53) cohorts. Through computed tomography image acquisition, the volume of interest segmentation, and feature extraction and selection, a pure radiomics model in terms of POF prediction was established. Then, a clinic-radiomics model integrating the pure radiomics model and clinical risk factors was constructed. Both primary and secondary endpoints were compared between the high- and low-risk groups stratified by the clinic-radiomics model. Results According to the 547 selected radiomics features, four models were derived from features. A clinic-radiomics model in the training and validation sets showed better predictive performance than pure radiomics and clinical models. The clinic-radiomics model was evaluated by the ratios of intervention and mechanical ventilation, intensive care unit (ICU) stays, and hospital stays. The results showed that the high-risk group had significantly higher intervention rates, ICU stays, and hospital stays than the low-risk group, with the confidence interval of 90% (p < 0.1 for all). Conclusions This clinic-radiomics model is a useful instrument for clinicians to evaluate the incidence of POF, facilitating patients' and their families' understanding of the ANP prognosis.
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Affiliation(s)
- Nan Liu
- Department of Critical Care Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Center of Severe Pancreatitis, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yidong Wan
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China
| | - Yifan Tong
- Center of Severe Pancreatitis, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jie He
- Department of Radiology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Shufeng Xu
- Department of Radiology, People's Hospital of Quzhou, Quzhou, China
| | - Xi Hu
- Department of Radiology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chen Luo
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China
| | - Lei Xu
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China
| | - Feng Guo
- Department of Critical Care Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Center of Severe Pancreatitis, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Bo Shen
- Center of Severe Pancreatitis, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Hong Yu
- Center of Severe Pancreatitis, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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Fanni SC, Febi M, Francischello R, Caputo FP, Ambrosini I, Sica G, Faggioni L, Masala S, Tonerini M, Scaglione M, Cioni D, Neri E. Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment. Diagnostics (Basel) 2023; 13:2623. [PMID: 37627882 PMCID: PMC10453085 DOI: 10.3390/diagnostics13162623] [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: 06/30/2023] [Revised: 07/25/2023] [Accepted: 08/05/2023] [Indexed: 08/27/2023] Open
Abstract
The spleen, often referred to as the "forgotten organ", plays numerous important roles in various diseases. Recently, there has been an increased interest in the application of radiomics in different areas of medical imaging. This systematic review aims to assess the current state of the art and evaluate the methodological quality of radiomics applications in spleen imaging. A systematic search was conducted on PubMed, Scopus, and Web of Science. All the studies were analyzed, and several characteristics, such as year of publication, research objectives, and number of patients, were collected. The methodological quality was evaluated using the radiomics quality score (RQS). Fourteen articles were ultimately included in this review. The majority of these articles were published in non-radiological journals (78%), utilized computed tomography (CT) for extracting radiomic features (71%), and involved not only the spleen but also other organs for feature extraction (71%). Overall, the included papers achieved an average RQS total score of 9.71 ± 6.37, corresponding to an RQS percentage of 27.77 ± 16.04. In conclusion, radiomics applications in spleen imaging demonstrate promising results in various clinical scenarios. However, despite all the included papers reporting positive outcomes, there is a lack of consistency in the methodological approaches employed.
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Affiliation(s)
- Salvatore Claudio Fanni
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Maria Febi
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Roberto Francischello
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Francesca Pia Caputo
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Ilaria Ambrosini
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Giacomo Sica
- Radiology Unit, Monaldi Hospital, 80131 Napoli, Italy
| | - Lorenzo Faggioni
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Salvatore Masala
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Michele Tonerini
- Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, 56124 Pisa, Italy
| | - Mariano Scaglione
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Dania Cioni
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Emanuele Neri
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
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Hongwei S, Xinzhong H, Huiqin X, Shuqin X, Ruonan W, Li L, Jianzhong C, Sijin L. Standard deviation of CT radiomic features among malignancies in each individual: prognostic ability in lung cancer patients. J Cancer Res Clin Oncol 2023; 149:7165-7173. [PMID: 36884114 DOI: 10.1007/s00432-023-04649-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/12/2023] [Indexed: 03/09/2023]
Abstract
PURPOSE It was reported that individual heterogeneity among malignancies (IHAM) might correlate well to the prognosis of lung cancer; however, seldom radiomic study is on this field. Standard deviation (SD) in statistics could scale average amount of variability of a variable; therefore, we used SD of CT feature (FeatureSD) among primary tumor and malignant lymph nodes (LNs) in an individual to represent IHAM, and its prognostic ability was explored. METHODS The enrolled patients who had accepted PET/CT scans were selected from our previous study (ClinicalTrials.gov, NCT03648151). The patients had primary tumor and at least one LN, and standardized uptake value of LN higher than 2.0 and 2.5 were enrolled as the cohort 1 (n = 94) and 2 (n = 88), respectively. FeatureSD from the combined or thin-section CT were calculated among primary tumor and malignant LNs in each patient, and were separately selected by the survival XGBoost method. Finally, their prognostic ability was compared to the significant patient characteristics identified by the Cox regression. RESULTS In the univariate and multi-variate Cox analysis, surgery, target therapy, and TNM stage were significantly against OS in the both cohorts. In the survival XGBoost analysis of the thin-section CT dataset, none FeatureSD could be repeatably ranked on the top list of the both cohorts. For the combined CT dataset, only one FeatureSD ranked in the top three of both cohorts, but the three significant factors in the Cox regression were not even on the list. Both in the cohort 1 and 2, C-index of the model composed of the three factors could be improved by integrating the continuous FeatureSD; furthermore, that of each factor was obviously lower than FeatureSD. CONCLUSION Standard deviation of CT features among malignant foci within an individual was a powerful prognostic factor in vivo for lung cancer patients.
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Affiliation(s)
- Si Hongwei
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, China.
- Department of Nuclear Medicine, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China.
| | - Hao Xinzhong
- Department of Nuclear Medicine, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Xu Huiqin
- Department of Medical imaging, Shenzhen Second People's Hospital/the First Affiliated Hospital of Shenzhen University Health Science Center, 518035, Shenzhen, China
| | - Xue Shuqin
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, China
| | - Wang Ruonan
- Department of Nuclear Medicine, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Li Li
- Department of Nuclear Medicine, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Cao Jianzhong
- Department of Radiation Oncology, The Cancer Hospital of Shanxi Province, Taiyuan, 030013, Shanxi Province, China
| | - Li Sijin
- Department of Nuclear Medicine, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
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Zheng YM, Che JY, Yuan MG, Wu ZJ, Pang J, Zhou RZ, Li XL, Dong C. A CT-Based Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma. Acad Radiol 2023; 30:1591-1599. [PMID: 36460582 DOI: 10.1016/j.acra.2022.11.007] [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: 10/03/2022] [Revised: 11/01/2022] [Accepted: 11/10/2022] [Indexed: 12/03/2022]
Abstract
RATIONALE AND OBJECTIVES Accurate pretreatment assessment of histological differentiation grade of head and neck squamous cell carcinoma (HNSCC) is crucial for prognosis evaluation. This study aimed to construct and validate a contrast-enhanced computed tomography (CECT)-based deep learning radiomics nomogram (DLRN) to predict histological differentiation grades of HNSCC. MATERIALS AND METHODS A total of 204 patients with HNSCC who underwent CECT scans were enrolled in this study. The participants recruited from two hospitals were split into a training set (n=124, 74 well/moderately differentiated and 50 poorly differentiated) of patients from one hospital and an external test set of patients from the other hospital (n=80, 49 well/moderately differentiated and 31 poorly differentiated). CECT-based manually-extracted radiomics (MER) features and deep learning (DL) features were extracted and selected. The selected MER features and DL features were then combined to construct a DLRN via multivariate logistic regression. The predictive performance of the DLRN was assessed using ROCs and decision curve analysis (DCA). RESULTS Three MER features and seven DL features were finally selected. The DLRN incorporating the selected MER and DL features showed good predictive value for the histological differentiation grades of HNSCC (well/moderately differentiated vs. poorly differentiated) in both the training (AUC, 0.878) and test (AUC, 0.822) sets. DCA demonstrated that the DLRN was clinically useful for predicting histological differentiation grades of HNSCC. CONCLUSION A CECT-based DLRN was constructed to predict histological differentiation grades of HNSCC. The DLRN showed good predictive efficacy and might be useful for prognostic evaluation of patients with HNSCC.
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Affiliation(s)
- Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jun-Yi Che
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
| | - Ming-Gang Yuan
- Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Zeng-Jie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jing Pang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Rui-Zhi Zhou
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiao-Li Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
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Lim CH, Choi JY, Choi JH, Lee JH, Lee J, Lim CW, Kim Z, Woo SK, Park SB, Park JM. Development and External Validation of 18F-FDG PET-Based Radiomic Model for Predicting Pathologic Complete Response after Neoadjuvant Chemotherapy in Breast Cancer. Cancers (Basel) 2023; 15:3842. [PMID: 37568658 PMCID: PMC10417050 DOI: 10.3390/cancers15153842] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/21/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
The aim of our retrospective study is to develop and externally validate an 18F-FDG PET-derived radiomics model for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients. A total of 87 breast cancer patients underwent curative surgery after NAC at Soonchunhyang University Seoul Hospital and were randomly assigned to a training cohort and an internal validation cohort. Radiomic features were extracted from pretreatment PET images. A radiomic-score model was generated using the LASSO method. A combination model incorporating significant clinical variables was constructed. These models were externally validated in a separate cohort of 28 patients from Soonchunhyang University Buscheon Hospital. The model performances were assessed using area under the receiver operating characteristic (AUC). Seven radiomic features were selected to calculate the radiomic-score. Among clinical variables, human epidermal growth factor receptor 2 status was an independent predictor of pCR. The radiomic-score model achieved good discriminability, with AUCs of 0.963, 0.731, and 0.729 for the training, internal validation, and external validation cohorts, respectively. The combination model showed improved predictive performance compared to the radiomic-score model alone, with AUCs of 0.993, 0.772, and 0.906 in three cohorts, respectively. The 18F-FDG PET-derived radiomic-based model is useful for predicting pCR after NAC in breast cancer.
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Affiliation(s)
- Chae Hong Lim
- Department of Nuclear Medicine, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea;
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea;
| | - Joon Ho Choi
- Department of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Jun-Hee Lee
- Department of Surgery, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea
| | - Jihyoun Lee
- Department of Surgery, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea
| | - Cheol Wan Lim
- Department of Surgery, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Zisun Kim
- Department of Surgery, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Sang-Keun Woo
- Division of Applied RI, Korea Institutes of Radiological and Medical Sciences, Seoul 01812, Republic of Korea
| | - Soo Bin Park
- Department of Nuclear Medicine, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea;
| | - Jung Mi Park
- Department of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
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Tabassum M, Suman AA, Suero Molina E, Pan E, Di Ieva A, Liu S. Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review. Cancers (Basel) 2023; 15:3845. [PMID: 37568660 PMCID: PMC10417709 DOI: 10.3390/cancers15153845] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Radiomics is a rapidly evolving field that involves extracting and analysing quantitative features from medical images, such as computed tomography or magnetic resonance images. Radiomics has shown promise in brain tumor diagnosis and patient-prognosis prediction by providing more detailed and objective information about tumors' features than can be obtained from the visual inspection of the images alone. Radiomics data can be analyzed to determine their correlation with a tumor's genetic status and grade, as well as in the assessment of its recurrence vs. therapeutic response, among other features. In consideration of the multi-parametric and high-dimensional space of features extracted by radiomics, machine learning can further improve tumor diagnosis, treatment response, and patients' prognoses. There is a growing recognition that tumors and their microenvironments (habitats) mutually influence each other-tumor cells can alter the microenvironment to increase their growth and survival. At the same time, habitats can also influence the behavior of tumor cells. In this systematic review, we investigate the current limitations and future developments in radiomics and machine learning in analysing brain tumors and their habitats.
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Affiliation(s)
- Mehnaz Tabassum
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia;
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
| | - Abdulla Al Suman
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
| | - Eric Suero Molina
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
- Department of Neurosurgery, University Hospital of Münster, 48149 Münster, Germany
| | - Elizabeth Pan
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
| | - Sidong Liu
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia;
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW 2109, Australia; (A.A.S.); (E.S.M.); (E.P.)
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Luo Y, Chen H, Gui M. Radiomics and Hybrid Models Based on Machine Learning to Predict Levodopa-Induced Dyskinesia of Parkinson's Disease in the First 6 Years of Levodopa Treatment. Diagnostics (Basel) 2023; 13:2511. [PMID: 37568874 PMCID: PMC10417024 DOI: 10.3390/diagnostics13152511] [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: 06/21/2023] [Revised: 07/23/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Current research on the prediction of movement complications associated with levodopa therapy in Parkinson's disease (PD) is limited. levodopa-induced dyskinesia (LID) is a movement complication that seriously affects the life quality of PD patients. One-third of PD patients develop LID within 1 to 6 years of levodopa treatment. This study aimed to construct models based on radiomics and machine learning to predict early LID in PD. METHODS We extracted radiomics features from the T1-weighted MRI obtained in the baseline of 49 PD control and 54 PD with LID in the first 6 years of levodopa therapy. Six brain regions related to the onset of PD were segmented as regions of interest (ROIs). The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Using the machine learning methods of support vector machine (SVM), random forest (RF), and AdaBoost, we constructed radiomics models and hybrid models. The hybrid models combined the radiomics features and the Unified Parkinson's Disease Rating Scale part III (UPDRS III) total score. The five-fold cross-validation was performed and repeated 20 times to validate the stability of the classifiers. We used sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curves, and area under the ROC curve (AUC) for model validation. RESULTS We selected 33 out of 6138 radiomics features. In the testing set of the radiomics model, the AUC values of the SVM, RF, and AdaBoost classifiers were 0.905, 0.808, and 0.778, respectively, and the accuracies were 0.839, 0.742, and 0.710. The hybrid models had better prediction performance. In the testing set, the AUC values of SVM, RF, and AdaBoost classifiers were 0.958, 0.861, and 0.832, respectively, and the accuracies were 0.903, 0.806, and 0.774. CONCLUSIONS Our results indicate that T1-weighted MRI is valuable in predicting early LID in PD. This work demonstrates that the combination of radiomics features and clinical features has good potential and value for identifying early LID in PD.
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Affiliation(s)
- Yang Luo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410083, China;
| | - Huiqin Chen
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410083, China;
| | - Mingzhen Gui
- School of Automation, Central South University, Changsha 410083, China
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121
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Samimi R, Shiri I, Ahmadyar Y, van den Hoff J, Kamali-Asl A, Rezaee A, Yousefirizi F, Geramifar P, Rahmim A. Radiomics predictive modeling from dual-time-point FDG PET K i parametric maps: application to chemotherapy response in lymphoma. EJNMMI Res 2023; 13:70. [PMID: 37493872 PMCID: PMC10371962 DOI: 10.1186/s13550-023-01022-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 07/18/2023] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND To investigate the use of dynamic radiomics features derived from dual-time-point (DTP-feature) [18F]FDG PET metabolic uptake rate Ki parametric maps to develop a predictive model for response to chemotherapy in lymphoma patients. METHODS We analyzed 126 lesions from 45 lymphoma patients (responding n = 75 and non-responding n = 51) treated with chemotherapy from two different centers. Static and DTP radiomics features were extracted from baseline static PET images and DTP Ki parametric maps. Spearman's rank correlations were calculated between static and DTP features to identify features with potential additional information. We first employed univariate analysis to determine correlations between individual features, and subsequently utilized multivariate analysis to derive predictive models utilizing DTP and static radiomics features before and after ComBat harmonization. For multivariate modeling, we utilized both the minimum redundancy maximum relevance feature selection technique and the XGBoost classifier. To evaluate our model, we partitioned the patient datasets into training/validation and testing sets using an 80/20% split. Different metrics for classification including area under the curve (AUC), sensitivity (SEN), specificity (SPE), and accuracy (ACC) were reported in test sets. RESULTS Via Spearman's rank correlations, there was negligible to moderate correlation between 32 out of 65 DTP features and some static features (ρ < 0.7); all the other 33 features showed high correlations (ρ ≥ 0.7). In univariate modeling, no significant difference between AUC of DTP and static features was observed. GLRLM_RLNU from static features demonstrated a strong correlation (AUC = 0.75, p value = 0.0001, q value = 0.0007) with therapy response. The most predictive DTP features were GLCM_Energy, GLCM_Entropy, and Uniformity, each with AUC = 0.73, p value = 0.0001, and q value < 0.0005. In multivariate analysis, the mean ranges of AUCs increased following harmonization. Use of harmonization plus combining DTP and static features was shown to provide significantly improved predictions (AUC = 0.97 ± 0.02, accuracy = 0.89 ± 0.05, sensitivity = 0.92 ± 0.09, and specificity = 0.88 ± 0.05). All models depicted significant performance in terms of AUC, ACC, SEN, and SPE (p < 0.05, Mann-Whitney test). CONCLUSIONS Our results demonstrate significant value in harmonization of radiomics features as well as combining DTP and static radiomics models for predicting response to chemotherapy in lymphoma patients.
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Affiliation(s)
- Rezvan Samimi
- Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Yashar Ahmadyar
- Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran
| | - Jörg van den Hoff
- PET Center, Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, 01328, Dresden, Germany
- Department of Nuclear Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307, Dresden, Germany
| | - Alireza Kamali-Asl
- Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran.
| | | | - Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, 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
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Waldenberg C, Brisby H, Hebelka H, Lagerstrand KM. Associations between Vertebral Localized Contrast Changes and Adjacent Annular Fissures in Patients with Low Back Pain: A Radiomics Approach. J Clin Med 2023; 12:4891. [PMID: 37568293 PMCID: PMC10420134 DOI: 10.3390/jcm12154891] [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: 07/06/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 08/13/2023] Open
Abstract
Low back pain (LBP) is multifactorial and associated with various spinal tissue changes, including intervertebral disc fissures, vertebral pathology, and damaged endplates. However, current radiological markers lack specificity and individualized diagnostic capability, and the interactions between the various markers are not fully clear. Radiomics, a data-driven analysis of radiological images, offers a promising approach to improve evaluation and deepen the understanding of spinal changes related to LBP. This study investigated possible associations between vertebral changes and annular fissures using radiomics. A dataset of 61 LBP patients who underwent conventional magnetic resonance imaging followed by discography was analyzed. Radiomics features were extracted from segmented vertebrae and carefully reduced to identify the most relevant features associated with annular fissures. The results revealed three important texture features that display concentrated high-intensity gray levels, extensive regions with elevated gray levels, and localized areas with reduced gray levels within the vertebrae. These features highlight patterns within vertebrae that conventional classification systems cannot reflect on distinguishing between vertebrae adjacent to an intervertebral disc with or without an annular fissure. As such, the present study reveals associations that contribute to the understanding of pathophysiology and may provide improved diagnostics of LBP.
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Affiliation(s)
- Christian Waldenberg
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden;
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Helena Brisby
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Orthopaedics, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Hanna Hebelka
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Radiology, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Kerstin Magdalena Lagerstrand
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden;
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
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Liu X, Chen H, Xue P, Xi H, He S, Sun G, Du B. [Visual and quantitative assessment of the effectiveness of non-vascularized bone grafting in osteonecrosis of the femoral head via CT-based radiomics and clinical data]. ZHONGGUO XIU FU CHONG JIAN WAI KE ZA ZHI = ZHONGGUO XIUFU CHONGJIAN WAIKE ZAZHI = CHINESE JOURNAL OF REPARATIVE AND RECONSTRUCTIVE SURGERY 2023; 37:846-855. [PMID: 37460182 DOI: 10.7507/1002-1892.202303072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Objective To investigate the value of CT-based radiomics and clinical data in predicting the efficacy of non-vascularized bone grafting (NVBG) in hip preservation, and to construct a visual, quantifiable, and effective method for decision-making of hip preservation. Methods Between June 2009 and June 2019, 153 patients (182 hips) with osteonecrosis of the femoral head (ONFH) who underwent NVBG for hip preservation were included, and the training and testing sets were divided in a 7∶3 ratio to define hip preservation success or failure according to the 3-year postoperative follow-up. The radiomic features of the region of interest in the CT images were extracted, and the radiomics-scores were calculated by the linear weighting and coefficients of the radiomic features after dimensionality reduction. The clinical predictors were screened using univariate and multivariate Cox regression analysis. The radiomics model, clinical model, and clinical-radiomics (C-R) model were constructed respectively. Their predictive performance for the efficacy of hip preservation was compared in the training and testing sets, with evaluation indexes including area under the curve, C-Index, sensitivity, specificity, and calibration curve, etc. The best model was visualised using nomogram, and its clinical utility was assessed by decision curves. Results At the 3-year postoperative follow-up, the cumulative survival rate of hip preservation was 70.33%. Continued exposure to risk factors postoperative and Japanese Investigation Committee (JIC) staging were clinical predictors of the efficacy of hip preservation, and 13 radiomic features derived from least absolute shrinkage and selection operator downscaling were used to calculate Rad-scores. The C-R model outperformed both the clinical and radiomics models in predicting the efficacy of hip preservation 1, 2, 3 years postoperative in both the training and testing sets ( P<0.05), with good agreement between the predicted and observed values. A nomogram constructed based on the C-R model showed that patients with lower Rad-scores, no further postoperative exposure to risk factors, and B or C1 types of JIC staging had a higher probability of femoral survival at 1, 2, 3 years postoperatively. The decision curve analysis showed that the C-R model had a higher total net benefit than both the clinical and radiomics models with a single predictor, and it could bring more net benefit to patients within a larger probability threshold. Conclusion The prediction model and nomogram constructed by CT-based radiomics combined with clinical data is a visual, quantifiable, and effective method for decision-making of hip preservation, which can predict the efficacy of NVBG before surgery and has a high value of clinical application.
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Affiliation(s)
- Xin Liu
- Department of Orthopedics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing Jiangsu, 210029, P. R. China
| | - Hao Chen
- Department of Orthopedics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing Jiangsu, 210029, P. R. China
| | - Peng Xue
- Department of Orthopedics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing Jiangsu, 210029, P. R. China
| | - Hongzhong Xi
- Department of Orthopedics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing Jiangsu, 210029, P. R. China
| | - Shuai He
- Department of Orthopedics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing Jiangsu, 210029, P. R. China
| | - Guangquan Sun
- Department of Orthopedics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing Jiangsu, 210029, P. R. China
| | - Bin Du
- Department of Orthopedics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing Jiangsu, 210029, P. R. China
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Ho NH, Jeong YH, Kim J. Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay. Sci Rep 2023; 13:11243. [PMID: 37433809 PMCID: PMC10336016 DOI: 10.1038/s41598-023-37500-7] [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: 11/02/2022] [Accepted: 06/22/2023] [Indexed: 07/13/2023] Open
Abstract
Early identification and treatment of moderate cognitive impairment (MCI) can halt or postpone Alzheimer's disease (AD) and preserve brain function. For prompt diagnosis and AD reversal, precise prediction in the early and late phases of MCI is essential. This research investigates multimodal framework-based multitask learning in the following situations: (1) Differentiating early mild cognitive impairment (eMCI) from late MCI and (2) predicting when an MCI patient would acquire AD. Clinical data and two radiomics features on three brain areas deduced from magnetic resonance imaging were investigated (MRI). We proposed an attention-based module, Stack Polynomial Attention Network (SPAN), to firmly encode clinical and radiomics data input characteristics for successful representation from a small dataset. To improve multimodal data learning, we computed a potent factor using adaptive exponential decay (AED). We used experiments from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort study, which included 249 eMCI and 427 lMCI participants at baseline visits. The proposed multimodal strategy yielded the best c-index score in time prediction of MCI to AD conversion (0.85) and the best accuracy in MCI-stage categorization ([Formula: see text]). Moreover, our performance was equivalent to that of contemporary research.
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Affiliation(s)
- Ngoc-Huynh Ho
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, South Korea
| | - Yang-Hyung Jeong
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, South Korea.
| | - Jahae Kim
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, South Korea
- Department of Nuclear Medicine, Chonnam National University Hospital, Gwangju, 61469, South Korea
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Wang TW, Hsu MS, Lin YH, Chiu HY, Chao HS, Liao CY, Lu CF, Wu YT, Huang JW, Chen YM. Application of Radiomics in Prognosing Lung Cancer Treated with Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors: A Systematic Review and Meta-Analysis. Cancers (Basel) 2023; 15:3542. [PMID: 37509204 PMCID: PMC10377421 DOI: 10.3390/cancers15143542] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/01/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
In the context of non-small cell lung cancer (NSCLC) patients treated with EGFR tyrosine kinase inhibitors (TKIs), this research evaluated the prognostic value of CT-based radiomics. A comprehensive systematic review and meta-analysis of studies up to April 2023, which included 3111 patients, was conducted. We utilized the Quality in Prognosis Studies (QUIPS) tool and radiomics quality scoring (RQS) system to assess the quality of the included studies. Our analysis revealed a pooled hazard ratio for progression-free survival of 2.80 (95% confidence interval: 1.87-4.19), suggesting that patients with certain radiomics features had a significantly higher risk of disease progression. Additionally, we calculated the pooled Harrell's concordance index and area under the curve (AUC) values of 0.71 and 0.73, respectively, indicating good predictive performance of radiomics. Despite these promising results, further studies with consistent and robust protocols are needed to confirm the prognostic role of radiomics in NSCLC.
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Affiliation(s)
- Ting-Wei Wang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Ming-Sheng Hsu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Yi-Hui Lin
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Hwa-Yen Chiu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Heng-Sheng Chao
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Jing-Wen Huang
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
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Bagher-Ebadian H, Brown SL, Ghassemi MM, Nagaraja TN, Movsas B, Ewing JR, Chetty IJ. Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI. Sci Rep 2023; 13:10693. [PMID: 37394559 DOI: 10.1038/s41598-023-37723-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/27/2023] [Indexed: 07/04/2023] Open
Abstract
Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC), k-fold Nested-Cross-Validation (k-fold-NCV), and feature engineering analyses were performed on the K-SOMs' feature spaces to quantify the distinction power of radiomics features compared to raw-DCE-MRI for classification of different Nested Models. Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875% ± 12.922%, p < 0.001. This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA.
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Osteopathic Medicine, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Physics, Oakland University, Rochester, MI, 48309, USA.
| | - Stephen L Brown
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Mohammad M Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Tavarekere N Nagaraja
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - James R Ewing
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Wayne State University, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
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Hu Z, Jiang D, Zhao X, Yang J, Liang D, Wang H, Zhao C, Liao J. Predicting Drug Treatment Outcomes in Childrens with Tuberous Sclerosis Complex-Related Epilepsy: A Clinical Radiomics Study. AJNR Am J Neuroradiol 2023; 44:853-860. [PMID: 37348968 PMCID: PMC10337615 DOI: 10.3174/ajnr.a7911] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 05/22/2023] [Indexed: 06/24/2023]
Abstract
BACKGROUND AND PURPOSE Highly predictive markers of drug treatment outcomes of tuberous sclerosis complex-related epilepsy are a key unmet clinical need. The objective of this study was to identify meaningful clinical and radiomic predictors of outcomes of epilepsy drug treatment in patients with tuberous sclerosis complex. MATERIALS AND METHODS A total of 105 children with tuberous sclerosis complex-related epilepsy were enrolled in this retrospective study. The pretreatment baseline predictors that were used to predict drug treatment outcomes included patient demographic and clinical information, gene data, electroencephalogram data, and radiomic features that were extracted from pretreatment MR imaging scans. The Spearman correlation coefficient and least absolute shrinkage and selection operator were calculated to select the most relevant features for the drug treatment outcome to build a comprehensive model with radiomic and clinical features for clinical application. RESULTS Four MR imaging-based radiomic features and 5 key clinical features were selected to predict the drug treatment outcome. Good discriminative performances were achieved in testing cohorts (area under the curve = 0.85, accuracy = 80.0%, sensitivity = 0.75, and specificity = 0.83) for the epilepsy drug treatment outcome. The model of radiomic and clinical features resulted in favorable calibration curves in all cohorts. CONCLUSIONS Our results suggested that the radiomic and clinical features model may predict the epilepsy drug treatment outcome. Age of onset, infantile spasms, antiseizure medication numbers, epileptiform discharge in left parieto-occipital area of electroencephalography, and gene mutation type are the key clinical factors to predict the epilepsy drug treatment outcome. The texture and first-order statistic features are the most valuable radiomic features for predicting drug treatment outcomes.
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Affiliation(s)
- Z Hu
- From the Departments of Neurology (Z.H., X.Z., J.L.)
| | - D Jiang
- Research Centre for Medical AI (D.J., J.Y., D.L.)
- Shenzhen College of Advanced Technology (D.J., J.Y., D.L.), University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - X Zhao
- From the Departments of Neurology (Z.H., X.Z., J.L.)
| | - J Yang
- Research Centre for Medical AI (D.J., J.Y., D.L.)
- Shenzhen College of Advanced Technology (D.J., J.Y., D.L.), University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - D Liang
- Research Centre for Medical AI (D.J., J.Y., D.L.)
- Paul C. Lauterbur Research Center for Biomedical Imaging (D.L., H.W.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen College of Advanced Technology (D.J., J.Y., D.L.), University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - H Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging (D.L., H.W.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - C Zhao
- Radiology (C.Z.), Shenzhen Children's Hospital, Shenzhen, China
| | - J Liao
- From the Departments of Neurology (Z.H., X.Z., J.L.)
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Zsarnóczay E, Varga-Szemes A, Emrich T, Szilveszter B, van der Werf NR, Mastrodicasa D, Maurovich-Horvat P, Willemink MJ. Characterizing the Heart and the Myocardium With Photon-Counting CT. Invest Radiol 2023; 58:505-514. [PMID: 36822653 DOI: 10.1097/rli.0000000000000956] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
ABSTRACT Noninvasive cardiac imaging has rapidly evolved during the last decade owing to improvements in computed tomography (CT)-based technologies, among which we highlight the recent introduction of the first clinical photon-counting detector CT (PCD-CT) system. Multiple advantages of PCD-CT have been demonstrated, including increased spatial resolution, decreased electronic noise, and reduced radiation exposure, which may further improve diagnostics and may potentially impact existing management pathways. The benefits that can be obtained from the initial experiences with PCD-CT are promising. The implementation of this technology in cardiovascular imaging allows for the quantification of coronary calcium, myocardial extracellular volume, myocardial radiomics features, epicardial and pericoronary adipose tissue, and the qualitative assessment of coronary plaques and stents. This review aims to discuss these major applications of PCD-CT with a focus on cardiac and myocardial characterization.
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Affiliation(s)
| | - Akos Varga-Szemes
- From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston
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129
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Liu H, Hou CJ, Tang JL, Sun LT, Lu KF, Liu Y, Du P. Deep learning and ultrasound feature fusion model predicts the malignancy of complex cystic and solid breast nodules with color Doppler images. Sci Rep 2023; 13:10500. [PMID: 37380667 DOI: 10.1038/s41598-023-37319-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 06/20/2023] [Indexed: 06/30/2023] Open
Abstract
This study aimed to evaluate the performance of traditional-deep learning combination model based on Doppler ultrasound for diagnosing malignant complex cystic and solid breast nodules. A conventional statistical prediction model based on the ultrasound features and basic clinical information was established. A deep learning prediction model was used to train the training group images and derive the deep learning prediction model. The two models were validated, and their accuracy rates were compared using the data and images of the test group, respectively. A logistic regression method was used to combine the two models to derive a combination diagnostic model and validate it in the test group. The diagnostic performance of each model was represented by the receiver operating characteristic curve and the area under the curve. In the test cohort, the diagnostic efficacy of the deep learning model was better than traditional statistical model, and the combined diagnostic model was better and outperformed the other two models (combination model vs traditional statistical model: AUC: 0.95 > 0.70, P = 0.001; combination model vs deep learning model: AUC: 0.95 > 0.87, P = 0.04). A combination model based on deep learning and ultrasound features has good diagnostic value.
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Affiliation(s)
- Han Liu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Chun-Jie Hou
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Jing-Lan Tang
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China.
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China.
| | - Li-Tao Sun
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Ke-Feng Lu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Ying Liu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Pei Du
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
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130
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Li Q, Chi L, Zhao W, Wu L, Jiao C, Zheng X, Zhang K, Li X. Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis. Front Neurol 2023; 14:1039794. [PMID: 37388543 PMCID: PMC10299899 DOI: 10.3389/fneur.2023.1039794] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 05/25/2023] [Indexed: 07/01/2023] Open
Abstract
Background Recent studies have reported that machine learning (ML), with a relatively strong capacity for processing non-linear data and adaptive ability, could improve the accuracy and efficiency of prediction. The article summarizes the published studies on ML models that predict motor function 3-6 months post-stroke. Methods A systematic literature search was conducted in PubMed, Embase, Cochorane and Web of Science as of April 3, 2023 for studies on ML prediction of motor function in stroke patients. The quality of the literature was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects model was preferred for meta-analysis using R4.2.0 because of the different variables and parameters. Results A total of 44 studies were included in this meta-analysis, involving 72,368 patients and 136 models. Models were categorized into subgroups according to the predicted outcome Modified Rankin Scale cut-off value and whether they were constructed based on radiomics. C-statistics, sensitivity, and specificity were calculated. The random-effects model showed that the C-statistics of all models were 0.81 (95% CI: 0.79; 0.83) in the training set and 0.82 (95% CI: 0.80; 0.85) in the validation set. According to different Modified Rankin Scale cut-off values, C-statistics of ML models predicting Modified Rankin Scale>2(used most widely) in stroke patients were 0.81 (95% CI: 0.78; 0.84) in the training set, and 0.84 (95% CI: 0.81; 0.87) in the validation set. C-statistics of radiomics-based ML models in the training set and validation set were 0.81 (95% CI: 0.78; 0.84) and 0.87 (95% CI: 0.83; 0.90), respectively. Conclusion ML can be used as an assessment tool for predicting the motor function in patients with 3-6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022335260, identifier: CRD42022335260.
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Affiliation(s)
- Qinglin Li
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Lei Chi
- Department of Acupuncture, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Weiying Zhao
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Lei Wu
- Department of Acupuncture, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Chuanxu Jiao
- Department of Neurorehabilitation, Taizhou Enze Medical Center Luqiao Hospital, Taizhou, Zhejiang, China
| | - Xue Zheng
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Kaiyue Zhang
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Xiaoning Li
- Department of Acupuncture, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
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131
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Franco D, Granata V, Fusco R, Grassi R, Nardone V, Lombardi L, Cappabianca S, Conforti R, Briganti F, Grassi R, Caranci F. Artificial intelligence and radiation effects on brain tissue in glioblastoma patient: preliminary data using a quantitative tool. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01655-0. [PMID: 37289266 DOI: 10.1007/s11547-023-01655-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/26/2023] [Indexed: 06/09/2023]
Abstract
PURPOSE The quantification of radiotherapy (RT)-induced functional and morphological brain alterations is fundamental to guide therapeutic decisions in patients with brain tumors. The magnetic resonance imaging (MRI) allows to define structural RT-brain changes, but it is unable to evaluate early injuries and to objectively quantify the volume tissue loss. Artificial intelligence (AI) tools extract accurate measurements that permit an objective brain different region quantification. In this study, we assessed the consistency between an AI software (Quibim Precision® 2.9) and qualitative neruroradiologist evaluation, and its ability to quantify the brain tissue changes during RT treatment in patients with glioblastoma multiforme (GBM). METHODS GBM patients treated with RT and subjected to MRI assessment were enrolled. Each patient, pre- and post-RT, undergoes to a qualitative evaluation with global cerebral atrophy (GCA) and medial temporal lobe atrophy (MTA) and a quantitative assessment with Quibim Brain screening and hippocampal atrophy and asymmetry modules on 19 extracted brain structures features. RESULTS A statistically significant strong negative association between the percentage value of the left temporal lobe and the GCA score and the left temporal lobe and the MTA score was found, while a moderate negative association between the percentage value of the right hippocampus and the GCA score and the right hippocampus and the MTA score was assessed. A statistically significant strong positive association between the CSF percentage value and the GCA score and a moderate positive association between the CSF percentage value and the MTA score was found. Finally, quantitative feature values showed that the percentage value of the cerebro-spinal fluid (CSF) statistically differences between pre- and post-RT. CONCLUSIONS AI tools can support a correct evaluation of RT-induced brain injuries, allowing an objective and earlier assessment of the brain tissue modifications.
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Affiliation(s)
- Donatella Franco
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy.
| | - Roberta Fusco
- Research & Development and Medical Oncology Division, Igea SpA, Naples, Italy
| | - Roberta Grassi
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122, Milan, Italy
| | - Valerio Nardone
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Laura Lombardi
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Salvatore Cappabianca
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Renata Conforti
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Francesco Briganti
- Advanced Biomedical Sciences Department, Federico II University, Naples, Italy
| | - Roberto Grassi
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Ferdinando Caranci
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
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Doyle JP, Patel PH, Petrou N, Shur J, Orton M, Kumar S, Bhogal RH. Radiomic applications in upper gastrointestinal cancer surgery. Langenbecks Arch Surg 2023; 408:226. [PMID: 37278924 DOI: 10.1007/s00423-023-02951-z] [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: 02/19/2023] [Accepted: 05/21/2023] [Indexed: 06/07/2023]
Abstract
INTRODUCTION Cross-sectional imaging plays an integral role in the management of upper gastrointestinal (UGI) cancer, from initial diagnosis and staging to determining appropriate treatment strategies. Subjective imaging interpretation has known limitations. The field of radiomics has evolved to extract quantitative data from medical imaging and relate these to biological processes. The key concept behind radiomics is that the high-throughput analysis of quantitative imaging features can provide predictive or prognostic information, with the goal of providing individualised care. OBJECTIVE Radiomic studies have shown promising utility in upper gastrointestinal oncology, highlighting a potential role in determining stage of disease and degree of tumour differentiation and predicting recurrence-free survival. This narrative review aims to provide an insight into the concepts underpinning radiomics, as well as its potential applications for guiding treatment and surgical decision-making in upper gastrointestinal malignancy. CONCLUSION Outcomes from studies to date have been promising; however, further standardisation and collaboration are required. Large prospective studies with external validation and evaluation of radiomic integration into clinical pathways are needed. Future research should now focus on translating the promising utility of radiomics into meaningful patient outcomes.
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Affiliation(s)
- Joseph P Doyle
- Department of Surgery, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK
| | - Pranav H Patel
- Department of Surgery, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK
| | - Nikoletta Petrou
- Department of Surgery, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK
| | - Joshua Shur
- Department of Radiology, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK
| | - Matthew Orton
- Department of Radiology, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK
| | - Sacheen Kumar
- Department of Surgery, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK
- Upper GI Surgical Oncology Research Group, The Institute for Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
| | - Ricky H Bhogal
- Department of Surgery, The Royal Marsden Hospital NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK.
- Upper GI Surgical Oncology Research Group, The Institute for Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK.
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133
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Le VH, Kha QH, Minh TNT, Nguyen VH, Le VL, Le NQK. Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer. J Digit Imaging 2023; 36:911-922. [PMID: 36717518 PMCID: PMC10287593 DOI: 10.1007/s10278-023-00778-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 02/01/2023] Open
Abstract
The malignant tumors in nature share some common morphological characteristics. Radiomics is not only images but also data; we think that a probability exists in a set of radiomics signatures extracted from CT scan images of one cancer tumor in one specific organ also be utilized for overall survival prediction in different types of cancers in different organs. The retrospective study enrolled four data sets of cancer patients in three different organs (420, 157, 137, and 191 patients for lung 1 training, lung 2 testing, and two external validation set: kidney and head and neck, respectively). In the training set, radiomics features were obtained from CT scan images, and essential features were chosen by LASSO algorithm. Univariable and multivariable analyses were then conducted to find a radiomics signature via Cox proportional hazard regression. The Kaplan-Meier curve was performed based on the risk score. The integrated time-dependent area under the ROC curve (iAUC) was calculated for each predictive model. In the training set, Kaplan-Meier curve classified patients as high or low-risk groups (p-value < 0.001; log-rank test). The risk score of radiomics signature was locked and independently evaluated in the testing set, and two external validation sets showed significant differences (p-value < 0.05; log-rank test). A combined model (radiomics + clinical) showed improved iAUC in lung 1, lung 2, head and neck, and kidney data set are 0.621 (95% CI 0.588, 0.654), 0.736 (95% CI 0.654, 0.819), 0.732 (95% CI 0.655, 0.809), and 0.834 (95% CI 0.722, 0.946), respectively. We believe that CT-based radiomics signatures for predicting overall survival in various cancer sites may exist.
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Affiliation(s)
- Viet Huan Le
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
- Department of Thoracic Surgery, Khanh Hoa General Hospital, Nha Trang, 65000, Vietnam
| | - Quang Hien Kha
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
| | - Tran Nguyen Tuan Minh
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
| | - Van Hiep Nguyen
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
- Oncology Center, Bai Chay Hospital, Quang Ninh, 20000, Vietnam
| | - Van Long Le
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
- Department of Anesthesiology and Critical Care, Hue University of Medicine and Pharmacy, Hue University, Hue City, 52000, Vietnam
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, 106, Taiwan.
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, 106, Taiwan.
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, 110, Taiwan.
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134
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Chiacchiaretta P, Mastrodicasa D, Chiarelli AM, Luberti R, Croce P, Sguera M, Torrione C, Marinelli C, Marchetti C, Domenico A, Cocco G, Di Credico A, Russo A, D’Eramo C, Corvino A, Colasurdo M, Sensi SL, Muzi M, Caulo M, Delli Pizzi A. MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 - Early Breast Cancer Patients. J Digit Imaging 2023; 36:1071-1080. [PMID: 36698037 PMCID: PMC10287859 DOI: 10.1007/s10278-023-00781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 01/11/2023] [Accepted: 01/13/2023] [Indexed: 01/26/2023] Open
Abstract
Oncotype Dx Recurrence Score (RS) has been validated in patients with ER + /HER2 - invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissues to predict the risk of tumor recurrence. A total of 62 patients with biopsy-proved ER + /HER2 - breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS > 25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine learning algorithm. PLS β-weights of radiomics features included the 5% features with the largest β-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The exploratory analysis for the complete dataset revealed an average absolute correlation among features of 0.51. The nCV framework delivered an AUC of 0.76 (p = 1.1∙10-3). When combining "early" and "peak" DCE images of only T or TST, a tendency toward statistical significance was obtained for TST with an AUC of 0.61 (p = 0.05). The 47 features included in the top 5% were balanced between T and TST (23 and 24, respectively). Moreover, 33/47 (70%) were texture-related, and 25/47 (53%) were derived from high-resolution images (1 mm). A radiomics-based machine learning approach shows the potential to accurately predict the recurrence risk in early ER + /HER2 - breast cancer patients.
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Affiliation(s)
- Piero Chiacchiaretta
- Advanced Computing Core, Center of Advanced Studies and Technology (CAST), “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
- Department of Innovative Technologies in Medicine and Odonoiatry, “G. d’Annunzio” University, Chieti, Italy
| | | | - Antonio Maria Chiarelli
- Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University, Chieti, Italy
| | - Riccardo Luberti
- Unit of Radiology, “Santissima Annunziata” Hospital, Chieti, Italy
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University, Chieti, Italy
| | - Mario Sguera
- Unit of Radiology, “Santissima Annunziata” Hospital, Chieti, Italy
| | | | | | - Chiara Marchetti
- Unit of Radiology, “Santissima Annunziata” Hospital, Chieti, Italy
| | | | - Giulio Cocco
- Unit of Ultrasound in Internal Medicine, Department of Medicine and Science of Aging, “G. D’Annunzio” University, Chieti, Italy
| | | | | | | | - Antonio Corvino
- Motor Science and Wellness Department, University of Naples “Parthenope”, 80133 Naples, Italy
| | - Marco Colasurdo
- Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University, Chieti, Italy
| | - Stefano L. Sensi
- Advanced Computing Core, Center of Advanced Studies and Technology (CAST), “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
- Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University, Chieti, Italy
| | - Marzia Muzi
- Breast Unit, “Gaetano Bernabeo” Hospital, Ortona, Italy
| | - Massimo Caulo
- Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University, Chieti, Italy
| | - Andrea Delli Pizzi
- Department of Innovative Technologies in Medicine and Odonoiatry, “G. d’Annunzio” University, Chieti, Italy
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Palani D, Ganesh KM, Karunagaran L, Govindaraj K, Shanmugam S. Statistical Analysis on Impact of Image Preprocessing of CT Texture Patterns and Its CT Radiomic Feature Stability: A Phantom Study. Asian Pac J Cancer Prev 2023; 24:2061-2072. [PMID: 37378937 PMCID: PMC10505874 DOI: 10.31557/apjcp.2023.24.6.2061] [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: 02/22/2023] [Accepted: 06/23/2023] [Indexed: 06/29/2023] Open
Abstract
AIM To examine computed tomography (CT) radiomic feature stability on various texture patterns during pre-processing utilizing the Credence Cartridge Radiomics (CCR) phantom textures. MATERIALS AND METHODS Imaging Biomarker Explorer (IBEX) expansion for the abbreviation IBEX extracted 51 radiomic features of 4 categories from 11 textures image regions of interest (ROI) of the phantom. 19 software pre-processing algorithms processed each CCR phantom ROI. All ROI texture processed image features were retrieved. Pre-processed CT image radiomic features were compared to non-processed features to measure its textural influence. Wilcoxon T-tests measured the pre-processing relevance of CT radiomic features on various textures. Hierarchical cluster analysis (HCA) was performed to cluster processer potency and texture impression likeness. RESULTS The pre-processing filter, CT texture Cartridge, and feature category affect the CCR phantom CT image's radiomic properties. Pre-processing is statistically unaltered by Gray Level Run Length Matrix (GLRLM ) expansion for the abbreviation GLRLM and Neighborhood Intensity Difference matrix (NID) expansion for the abbreviation NID feature categories. The 30%, 40%, and 50% honeycomb are regular directional textures and smooth 3D-printed plaster resin, most of the image pre-processing feature alterations exhibited significant p-values in the histogram feature category. The Laplacian Filter, Log Filter, Resample, and Bit Depth Rescale Range pre-processing algorithms hugely influenced histogram and Gray Level Co-occurrence Matrix (GLCM) image features. CONCLUSION We found that homogenous intensity phantom inserts, CT radiomic feature, are less sensitive to feature swaps during pre-processing than normal directed honeycomb and regular projected smooth 3D-printed plaster resin CT image textures. Because they lose fewer information during image enhancement, This feature concentration empowerment of the images also enhances texture pattern recognition.
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Affiliation(s)
- Dharmendran Palani
- Research and Development Centre, Bharathiar University, Coimbatore, India.
| | - Kadirampatti M. Ganesh
- Department of Radiation Physics, Kidwai Memorial Institute of Oncology, Bengaluru, India.
| | - Lavanya Karunagaran
- Department of Oral and Maxillofacial Pathology, Asan Memorial Dental College and Hospital, Chennai, India.
| | - Kesavan Govindaraj
- Department of Radiotherapy, Vadamalayan Hospitals Integrated Cancer Centre, Madurai, India.
| | - Senthilkumar Shanmugam
- Department of Radiotherapy Government Rajaji Hospital & Madurai Medical College, Madurai, India.
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Chiu PF, Chang RCH, Lai YC, Wu KC, Wang KP, Chiu YP, Ji HR, Kao CH, Chiu CD. Machine Learning Assisting the Prediction of Clinical Outcomes following Nucleoplasty for Lumbar Degenerative Disc Disease. Diagnostics (Basel) 2023; 13:diagnostics13111863. [PMID: 37296715 DOI: 10.3390/diagnostics13111863] [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: 05/12/2023] [Revised: 05/23/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND Lumbar degenerative disc disease (LDDD) is a leading cause of chronic lower back pain; however, a lack of clear diagnostic criteria and solid LDDD interventional therapies have made predicting the benefits of therapeutic strategies challenging. Our goal is to develop machine learning (ML)-based radiomic models based on pre-treatment imaging for predicting the outcomes of lumbar nucleoplasty (LNP), which is one of the interventional therapies for LDDD. METHODS The input data included general patient characteristics, perioperative medical and surgical details, and pre-operative magnetic resonance imaging (MRI) results from 181 LDDD patients receiving lumbar nucleoplasty. Post-treatment pain improvements were categorized as clinically significant (defined as a ≥80% decrease in the visual analog scale) or non-significant. To develop the ML models, T2-weighted MRI images were subjected to radiomic feature extraction, which was combined with physiological clinical parameters. After data processing, we developed five ML models: support vector machine, light gradient boosting machine, extreme gradient boosting, extreme gradient boosting random forest, and improved random forest. Model performance was measured by evaluating indicators, such as the confusion matrix, accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC), which were acquired using an 8:2 allocation of training to testing sequences. RESULTS Among the five ML models, the improved random forest algorithm had the best performance, with an accuracy of 0.76, a sensitivity of 0.69, a specificity of 0.83, an F1 score of 0.73, and an AUC of 0.77. The most influential clinical features included in the ML models were pre-operative VAS and age. In contrast, the most influential radiomic features had the correlation coefficient and gray-scale co-occurrence matrix. CONCLUSIONS We developed an ML-based model for predicting pain improvement after LNP for patients with LDDD. We hope this tool will provide both doctors and patients with better information for therapeutic planning and decision-making.
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Affiliation(s)
- Po-Fan Chiu
- Spine Center, China Medical University Hospital, Taichung 404327, Taiwan
- Department of Neurosurgery, China Medical University Hospital, Taichung 404327, Taiwan
| | - Robert Chen-Hao Chang
- Department of Electrical Engineering, National Chung Hsing University, Taichung 40227, Taiwan
| | - Yung-Chi Lai
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 404327, Taiwan
| | - Kuo-Chen Wu
- Center of Artificial Intelligence, China Medical University Hospital, Taichung 404327, Taiwan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan
| | - Kuan-Pin Wang
- Center of Artificial Intelligence, China Medical University Hospital, Taichung 404327, Taiwan
- Department of Computer Science and Engineering, National Chung Hsing University, Taichung 40227, Taiwan
| | - You-Pen Chiu
- Spine Center, China Medical University Hospital, Taichung 404327, Taiwan
- School of Medicine, China Medical University, Taichung 404327, Taiwan
- Graduate Institute of Biomedical Science, China Medical University, Taichung 404327, Taiwan
| | - Hui-Ru Ji
- Spine Center, China Medical University Hospital, Taichung 404327, Taiwan
- School of Medicine, China Medical University, Taichung 404327, Taiwan
- Graduate Institute of Biomedical Science, China Medical University, Taichung 404327, Taiwan
| | - Chia-Hung Kao
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 404327, Taiwan
- Center of Artificial Intelligence, China Medical University Hospital, Taichung 404327, Taiwan
- Graduate Institute of Biomedical Science, China Medical University, Taichung 404327, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Cheng-Di Chiu
- Spine Center, China Medical University Hospital, Taichung 404327, Taiwan
- Department of Neurosurgery, China Medical University Hospital, Taichung 404327, Taiwan
- School of Medicine, China Medical University, Taichung 404327, Taiwan
- Graduate Institute of Biomedical Science, China Medical University, Taichung 404327, Taiwan
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei 11490, Taiwan
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Heitkamp A, Madesta F, Amberg S, Wahaj S, Schröder T, Bechstein M, Meyer L, Broocks G, Hanning U, Gauer T, Werner R, Fiehler J, Gellißen S, Kniep HC. Discordant and Converting Receptor Expressions in Brain Metastases from Breast Cancer: MRI-Based Non-Invasive Receptor Status Tracking. Cancers (Basel) 2023; 15:cancers15112880. [PMID: 37296843 DOI: 10.3390/cancers15112880] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/15/2023] [Accepted: 05/18/2023] [Indexed: 06/12/2023] Open
Abstract
Discordance and conversion of receptor expressions in metastatic lesions and primary tumors is often observed in patients with brain metastases from breast cancer. Therefore, personalized therapy requires continuous monitoring of receptor expressions and dynamic adaptation of applied targeted treatment options. Radiological in vivo techniques may allow receptor status tracking at high frequencies at low risk and cost. The present study aims to investigate the potential of receptor status prediction through machine-learning-based analysis of radiomic MR image features. The analysis is based on 412 brain metastases samples from 106 patients acquired between 09/2007 and 09/2021. Inclusion criteria were as follows: diagnosed cerebral metastases from breast cancer; histopathology reports on progesterone (PR), estrogen (ER), and human epidermal growth factor 2 (HER2) receptor status; and availability of MR imaging data. In total, 3367 quantitative features of T1 contrast-enhanced, T1 non-enhanced, and FLAIR images and corresponding patient age were evaluated utilizing random forest algorithms. Feature importance was assessed using Gini impurity measures. Predictive performance was tested using 10 permuted 5-fold cross-validation sets employing the 30 most important features of each training set. Receiver operating characteristic areas under the curves of the validation sets were 0.82 (95% confidence interval [0.78; 0.85]) for ER+, 0.73 [0.69; 0.77] for PR+, and 0.74 [0.70; 0.78] for HER2+. Observations indicate that MR image features employed in a machine learning classifier could provide high discriminatory accuracy in predicting the receptor status of brain metastases from breast cancer.
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Affiliation(s)
- Alexander Heitkamp
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Frederic Madesta
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Sophia Amberg
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Schohla Wahaj
- Department of Radiotherapy and Radiation Oncology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Tanja Schröder
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Matthias Bechstein
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Lukas Meyer
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Gabriel Broocks
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Uta Hanning
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Tobias Gauer
- Department of Radiotherapy and Radiation Oncology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - René Werner
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
- Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Susanne Gellißen
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Helge C Kniep
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
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Gong X, Li Q, Gu L, Chen C, Liu X, Zhang X, Wang B, Sun C, Yang D, Li L, Wang Y. Conventional ultrasound and contrast-enhanced ultrasound radiomics in breast cancer and molecular subtype diagnosis. Front Oncol 2023; 13:1158736. [PMID: 37287927 PMCID: PMC10242104 DOI: 10.3389/fonc.2023.1158736] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 05/11/2023] [Indexed: 06/09/2023] Open
Abstract
Objectives This study aimed to explore the value of conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) radiomics to diagnose breast cancer and predict its molecular subtype. Method A total of 170 lesions (121 malignant, 49 benign) were selected from March 2019 to January 2022. Malignant lesions were further divided into six categories of molecular subtype: (non-)Luminal A, (non-)Luminal B, (non-)human epidermal growth factor receptor 2 (HER2) overexpression, (non-)triple-negative breast cancer (TNBC), hormone receptor (HR) positivity/negativity, and HER2 positivity/negativity. Participants were examined using CUS and CEUS before surgery. Regions of interest images were manually segmented. The pyradiomics toolkit and the maximum relevance minimum redundancy algorithm were utilized to extract and select features, multivariate logistic regression models of CUS, CEUS, and CUS combined with CEUS radiomics were then constructed and evaluated by fivefold cross-validation. Results The accuracy of the CUS combined with CEUS model was superior to CUS model (85.4% vs. 81.3%, p<0.01). The accuracy of the CUS radiomics model in predicting the six categories of breast cancer is 68.2% (82/120), 69.3% (83/120), 83.7% (100/120), 86.7% (104/120), 73.5% (88/120), and 70.8% (85/120), respectively. In predicting breast cancer of Luminal A, HER2 overexpression, HR-positivity, and HER2 positivity, CEUS video improved the predictive performance of CUS radiomics model [accuracy=70.2% (84/120), 84.0% (101/120), 74.5% (89/120), and 72.5% (87/120), p<0.01]. Conclusion CUS radiomics has the potential to diagnose breast cancer and predict its molecular subtype. Moreover, CEUS video has auxiliary predictive value for CUS radiomics.
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Affiliation(s)
- Xuantong Gong
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qingfeng Li
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Lishuang Gu
- Department of Ultrasound, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China
| | - Chen Chen
- Hangzhou Innovation Institute, Beihang University, Hangzhou, China
| | - Xuefeng Liu
- State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), Beihang University, Beijing, China
| | - Xuan Zhang
- Department of Ultrasound, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Bo Wang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chao Sun
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Di Yang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lin Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Wang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Zhang YP, Zhang XY, Cheng YT, Li B, Teng XZ, Zhang J, Lam S, Zhou T, Ma ZR, Sheng JB, Tam VCW, Lee SWY, Ge H, Cai J. Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling. Mil Med Res 2023; 10:22. [PMID: 37189155 DOI: 10.1186/s40779-023-00458-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/04/2023] [Indexed: 05/17/2023] Open
Abstract
Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients' anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research.
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Affiliation(s)
- Yuan-Peng Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, Guangdong, China
| | - Xin-Yun Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
| | - Yu-Ting Cheng
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
| | - Bing Li
- Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Xin-Zhi Teng
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Saikit Lam
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Ta Zhou
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Zong-Rui Ma
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Jia-Bao Sheng
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Victor C W Tam
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Shara W Y Lee
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Hong Ge
- Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Jing Cai
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China.
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, Guangdong, China.
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Qi X, Wang K, Feng B, Sun X, Yang J, Hu Z, Zhang M, Lv C, Jin L, Zhou L, Wang Z, Yao J. Comparison of machine learning models based on multi-parametric magnetic resonance imaging and ultrasound videos for the prediction of prostate cancer. Front Oncol 2023; 13:1157949. [PMID: 37260984 PMCID: PMC10227569 DOI: 10.3389/fonc.2023.1157949] [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: 02/08/2023] [Accepted: 05/04/2023] [Indexed: 06/02/2023] Open
Abstract
Objective To establish machine learning (ML) prediction models for prostate cancer (PCa) using transrectal ultrasound videos and multi-parametric magnetic resonance imaging (mpMRI) and compare their diagnostic performance. Materials and methods We systematically collated the data of 383 patients, including 187 with PCa and 196 with benign lesions. Of them, 307 patients (150 with PCa and 157 with benign lesions) were randomly selected to train and validate the ML models, 76 patients were used as test set. B-Ultrasound videos (BUS), mpMRI T2 sequence (T2), and ADC sequence (ADC) were obtained from all patients. We extracted 851 features of each patient in the BUS, T2, and ADC groups and used a t-test, the Mann-Whitney U test, and LASSO regression to screen the features. Support vector machine (SVM), random forest (RF), adaptive boosting (ADB), and gradient boosting machine (GBM) models were used to establish radiomics models. In addition, we fused the features screened via LASSO regression from three groups as new features and rebuilt ML models. The performance of the ML models in diagnosing PCa in the BUS, T2, ADC, and fusion groups was compared using the area under the ROC curve (AUC), sensitivity, specificity, and accuracy. Results In the test cohort, the AUC of each model in the ADC group was higher than that of in the.BUS and T2 groups. Among the models, the RF model had the best diagnostic performance, with an AUC of 0.85, sensitivity of 0.78 (0.61-0.89), specificity of 0.84 (0.69-0.94), and accuracy of 0.83 (0.66-0.93). The SVM model in both the BUS and T2 groups performed best. Based on the features screened in the BUS, T2, and ADC groups fused to construct the models, the SVM model was found to perform best, with an AUC of 0.87, sensitivity of 0.73 (0.56-0.86), specificity of 0.79 (0.63-0.90), and accuracy of 0.77 (0.59-0.89). The difference in the results was statistically significant (p<0.05). Conclusion The ML prediction models had a good diagnostic ability for PCa. Among them, the SVM model in the fusion group showed the best performance in diagnosing PCa. These prediction models can help radiologists make better diagnoses.
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Affiliation(s)
- Xiaoyang Qi
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Kai Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Bojian Feng
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, Zhejiang, China
| | - Xingbo Sun
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Jie Yang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Zhengbiao Hu
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Maoliang Zhang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Cheng Lv
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Liyuan Jin
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Lingyan Zhou
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, Zhejiang, China
| | - Zhengping Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Jincao Yao
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, Zhejiang, China
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Kimura Y, Kadoya N, Oku Y, Jingu K. Development of a deep learning-based error detection system without error dose maps in the patient-specific quality assurance of volumetric modulated arc therapy. JOURNAL OF RADIATION RESEARCH 2023:7160591. [PMID: 37177789 DOI: 10.1093/jrr/rrad028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/15/2022] [Indexed: 05/15/2023]
Abstract
To detect errors in patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), we proposed an error detection method based on dose distribution analysis using unsupervised deep learning approach and analyzed 161 prostate VMAT beams measured with a cylindrical detector. For performing error simulation, in addition to error-free dose distribution, dose distributions containing nine types of error, including multileaf collimator (MLC) positional errors, gantry rotation errors, radiation output errors and phantom setup errors, were generated. Only error-free data were employed for the model training, and error-free and error data were employed for the tests. As a deep learning model, the variational autoencoder (VAE) was adopted. The anomaly of test data was quantified by calculating Mahalanobis distance based on the feature vectors acquired from a trained encoder. Based on this anomaly, test data were classified as 'error-free' or 'any-error.' For comparison with conventional approaches, gamma (γ)-analysis was performed, and supervised learning convolutional neural network (S-CNN) was constructed. Receiver operating characteristic curves were obtained to evaluate their performance with the area under the curve (AUC). For all error types, except systematic MLC positional and radiation output errors, the performance of the methods was in the order of S-CNN ˃ VAE-based ˃ γ-analysis (only S-CNN required error data for model training). For example, in random MLC positional error simulation, the AUC of our method, S-CNN and γ-analysis were 0.699, 0.921 and 0.669, respectively. Our results showed that the VAE-based method has the potential to detect errors in patient-specific VMAT QA.
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Affiliation(s)
- Yuto Kimura
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan
- Radiation Oncology Center, Ofuna Chuo Hospital, 6-2-24 Ofuna, Kamakura, 247-0056, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan
| | - Yohei Oku
- Radiation Oncology Center, Ofuna Chuo Hospital, 6-2-24 Ofuna, Kamakura, 247-0056, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan
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O'Sullivan NJ, Kelly ME. Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions. Curr Oncol 2023; 30:4936-4945. [PMID: 37232830 DOI: 10.3390/curroncol30050372] [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: 03/08/2023] [Revised: 04/19/2023] [Accepted: 05/08/2023] [Indexed: 05/27/2023] Open
Abstract
Radiomics refers to the conversion of medical imaging into high-throughput, quantifiable data in order to analyse disease patterns, guide prognosis and aid decision making. Radiogenomics is an extension of radiomics that combines conventional radiomics techniques with molecular analysis in the form of genomic and transcriptomic data, serving as an alternative to costly, labour-intensive genetic testing. Data on radiomics and radiogenomics in the field of pelvic oncology remain novel concepts in the literature. We aim to perform an up-to-date analysis of current applications of radiomics and radiogenomics in the field of pelvic oncology, particularly focusing on the prediction of survival, recurrence and treatment response. Several studies have applied these concepts to colorectal, urological, gynaecological and sarcomatous diseases, with individual efficacy yet poor reproducibility. This article highlights the current applications of radiomics and radiogenomics in pelvic oncology, as well as the current limitations and future directions. Despite a rapid increase in publications investigating the use of radiomics and radiogenomics in pelvic oncology, the current evidence is limited by poor reproducibility and small datasets. In the era of personalised medicine, this novel field of research has significant potential, particularly for predicting prognosis and guiding therapeutic decisions. Future research may provide fundamental data on how we treat this cohort of patients, with the aim of reducing the exposure of high-risk patients to highly morbid procedures.
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Affiliation(s)
- Niall J O'Sullivan
- The Trinity St. James's Cancer Institute, D08 NHY1 Dublin, Ireland
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Michael E Kelly
- The Trinity St. James's Cancer Institute, D08 NHY1 Dublin, Ireland
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
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Brown KH, Illyuk J, Ghita M, Walls GM, McGarry CK, Butterworth KT. Assessment of Variabilities in Lung-Contouring Methods on CBCT Preclinical Radiomics Outputs. Cancers (Basel) 2023; 15:2677. [PMCID: PMC10216427 DOI: 10.3390/cancers15102677] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 04/28/2023] [Accepted: 05/08/2023] [Indexed: 06/01/2023] Open
Abstract
Simple Summary This study is the first to evaluate the impact of contouring differences on radiomics analysis in preclinical CBCT scans. We found that the variation in quantitative image readouts was greater between segmentation tools than between observers. Abstract Radiomics image analysis has the potential to uncover disease characteristics for the development of predictive signatures and personalised radiotherapy treatment. Inter-observer and inter-software delineation variabilities are known to have downstream effects on radiomics features, reducing the reliability of the analysis. The purpose of this study was to investigate the impact of these variabilities on radiomics outputs from preclinical cone-beam computed tomography (CBCT) scans. Inter-observer variabilities were assessed using manual and semi-automated contours of mouse lungs (n = 16). Inter-software variabilities were determined between two tools (3D Slicer and ITK-SNAP). The contours were compared using Dice similarity coefficient (DSC) scores and the 95th percentile of the Hausdorff distance (HD95p) metrics. The good reliability of the radiomics outputs was defined using intraclass correlation coefficients (ICC) and their 95% confidence intervals. The median DSC scores were high (0.82–0.94), and the HD95p metrics were within the submillimetre range for all comparisons. the shape and NGTDM features were impacted the most. Manual contours had the most reliable features (73%), followed by semi-automated (66%) and inter-software (51%) variabilities. From a total of 842 features, 314 robust features overlapped across all contouring methodologies. In addition, our results have a 70% overlap with features identified from clinical inter-observer studies.
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Affiliation(s)
- Kathryn H. Brown
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
| | - Jacob Illyuk
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
| | - Mihaela Ghita
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
| | - Gerard M. Walls
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
- Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Belfast BT9 7JL, UK
| | - Conor K. McGarry
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
- Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Belfast BT9 7JL, UK
| | - Karl T. Butterworth
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
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Sun Y, Salerno S, He X, Pan Z, Yang E, Sujimongkol C, Song J, Wang X, Han P, Kang J, Sjoding MW, Jolly S, Christiani DC, Li Y. Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality. Sci Rep 2023; 13:7318. [PMID: 37147440 PMCID: PMC10161188 DOI: 10.1038/s41598-023-34559-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 05/03/2023] [Indexed: 05/07/2023] Open
Abstract
As portable chest X-rays are an efficient means of triaging emergent cases, their use has raised the question as to whether imaging carries additional prognostic utility for survival among patients with COVID-19. This study assessed the importance of known risk factors on in-hospital mortality and investigated the predictive utility of radiomic texture features using various machine learning approaches. We detected incremental improvements in survival prognostication utilizing texture features derived from emergent chest X-rays, particularly among older patients or those with a higher comorbidity burden. Important features included age, oxygen saturation, blood pressure, and certain comorbid conditions, as well as image features related to the intensity and variability of pixel distribution. Thus, widely available chest X-rays, in conjunction with clinical information, may be predictive of survival outcomes of patients with COVID-19, especially older, sicker patients, and can aid in disease management by providing additional information.
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Affiliation(s)
- Yuming Sun
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Stephen Salerno
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Xinwei He
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Ziyang Pan
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Eileen Yang
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Chinakorn Sujimongkol
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Jiyeon Song
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Xinan Wang
- Department of Environmental Health and Epidemiology, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Peisong Han
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Michael W Sjoding
- Division of Pulmonary and Critical Care, Department of Internal Medicine, University of Michigan Medical School, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan Rogel Cancer Center, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - David C Christiani
- Department of Environmental Health and Epidemiology, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care, Department of Internal Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Yi Li
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.
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145
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Derlin T, Riethdorf S, Schumacher U, Lafos M, Peine S, Coith C, Ross TL, Pantel K, Bengel FM. PSMA-heterogeneity in metastatic castration-resistant prostate cancer: Circulating tumor cells, metastatic tumor burden, and response to targeted radioligand therapy. Prostate 2023. [PMID: 37147881 DOI: 10.1002/pros.24549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/11/2023] [Accepted: 04/24/2023] [Indexed: 05/07/2023]
Abstract
BACKGROUND We explored the interrelation between prostate-specific membrane antigen (PSMA) expression on circulating tumor cells (CTCs) and that of solid metastatic lesions as determined by whole-body PSMA-targeted positron emission tomography (PET) to refine the prediction of response to subsequent PSMA-targeted radioligand therapy (RLT). METHODS A prospective study was performed in 20 patients with advanced mCRPC. Of these, 16 underwent subsequent RLT with [177 Lu]Lu-PSMA-617 at a dose of 7.4 GBq every 6-8 weeks. PSMA expression on CTCs using the CellSearch system was compared to clinical and serological results, and to marker expression in targeted imaging and available histological sections of prostatectomy specimens (19% of RLT patients). Clinical outcome was obtained after two cycles of RLT. RESULTS Marked heterogeneity of PSMA expression was observed already at first diagnosis in available histological specimens. Targeted whole-body imaging also showed heterogeneous inter- and intra-patient PSMA expression between metastases. Heterogeneity of CTC PSMA expression was partially paralleled by heterogeneity of whole-body tumor burden PSMA expression. Twenty percent of CTC samples showed no PSMA expression, despite unequivocal PSMA expression of solid metastases at PET. A high fraction of PSMA-negative CTCs emerged as the sole predictor of poor RLT response (odds ratio [OR]: 0.9379 [95% confidence interval, CI, 0.8558-0.9902]; p = 0.0160), and was prognostic for both shorter progression-free survival (OR: 1.236 [95% CI, 1.035-2.587]; p = 0.0043) and overall survival (OR: 1.056 [95% CI, 1.008-1.141]; p = 0.0182). CONCLUSION This proof-of-principle study suggests that liquid biopsy for CTC PSMA expression is complementary to PET for individual PSMA phenotyping of mCRPC.
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Affiliation(s)
- Thorsten Derlin
- Department of Nuclear Medicine, Hannover Medical School, Hannover, Germany
| | - Sabine Riethdorf
- University Medical Center Hamburg-Eppendorf, Institute of Tumor Biology, Hamburg, Germany
| | - Udo Schumacher
- Department of Anatomy and Experimental Morphology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Medical School Berlin, Berlin, Germany
| | - Marcel Lafos
- Hannover Medical School, Institute of Pathology, Hannover, Germany
| | - Sven Peine
- University Medical Center Hamburg-Eppendorf, Institute of Transfusion Medicine, Hamburg, Germany
| | - Cornelia Coith
- University Medical Center Hamburg-Eppendorf, Institute of Tumor Biology, Hamburg, Germany
| | - Tobias L Ross
- Department of Nuclear Medicine, Hannover Medical School, Hannover, Germany
| | - Klaus Pantel
- University Medical Center Hamburg-Eppendorf, Institute of Tumor Biology, Hamburg, Germany
| | - Frank M Bengel
- Department of Nuclear Medicine, Hannover Medical School, Hannover, Germany
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Agnese M, Toia P, Sollami G, Militello C, Rundo L, Vitabile S, Maffei E, Agnello F, Gagliardo C, Grassedonio E, Galia M, Cademartiri F, Midiri M, La Grutta L. Epicardial and thoracic subcutaneous fat texture analysis in patients undergoing cardiac CT. Heliyon 2023; 9:e15984. [PMID: 37215845 PMCID: PMC10196784 DOI: 10.1016/j.heliyon.2023.e15984] [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: 01/09/2023] [Revised: 04/09/2023] [Accepted: 04/28/2023] [Indexed: 05/24/2023] Open
Abstract
Introduction The aim of our study was to evaluate the feasibility of texture analysis of epicardial fat (EF) and thoracic subcutaneous fat (TSF) in patients undergoing cardiac CT (CCT). Materials and methods We compared a consecutive population of 30 patients with BMI ≤25 kg/m2 (Group A, 60.6 ± 13.7 years) with a control population of 30 patients with BMI >25 kg/m2 (Group B, 63.3 ± 11 years). A dedicated computer application for quantification of EF and a texture analysis application for the study of EF and TSF were employed. Results The volume of EF was higher in group B (mean 116.1 cm3 vs. 86.3 cm3, p = 0.014), despite no differences were found neither in terms of mean density (-69.5 ± 5 HU vs. -68 ± 5 HU, p = 0.28), nor in terms of quartiles distribution (Q1, p = 0.83; Q2, p = 0.22, Q3, p = 0.83, Q4, p = 0.34). The discriminating parameters of the histogram class were mean (p = 0.02), 0,1st (p = 0.001), 10th (p = 0.002), and 50th percentiles (p = 0.02). DifVarnc was the discriminating parameter of the co-occurrence matrix class (p = 0.007).The TSF thickness was 15 ± 6 mm in group A and 19.5 ± 5 mm in group B (p = 0.003). The TSF had a mean density of -97 ± 19 HU in group A and -95.8 ± 19 HU in group B (p = 0.75). The discriminating parameters of texture analysis were 10th (p = 0.03), 50th (p = 0.01), 90th percentiles (p = 0.04), S(0,1)SumAverg (p = 0.02), S(1,-1)SumOfSqs (p = 0.02), S(3,0)Contrast (p = 0.03), S(3,0)SumAverg (p = 0.02), S(4,0)SumAverg (p = 0.04), Horzl_RLNonUni (p = 0.02), and Vertl_LngREmph (p = 0.0005). Conclusions Texture analysis provides distinctive radiomic parameters of EF and TSF. EF and TSF had different radiomic features as the BMI varies.
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Affiliation(s)
- Manfredi Agnese
- Department of Biomedicine, Neurosciences and Advanced Diagnostics - BIND, University of Palermo, Via del Vespro 127, 90100, Palermo, Italy
| | - Patrizia Toia
- Department of Biomedicine, Neurosciences and Advanced Diagnostics - BIND, University of Palermo, Via del Vespro 127, 90100, Palermo, Italy
| | - Giulia Sollami
- Department of Biomedicine, Neurosciences and Advanced Diagnostics - BIND, University of Palermo, Via del Vespro 127, 90100, Palermo, Italy
| | - Carmelo Militello
- Institute for High-Performance Computing and Networking, National Research Council (ICAR-CNR), Palermo, Italy
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Salerno, Italy
| | - Salvatore Vitabile
- Department of Biomedicine, Neurosciences and Advanced Diagnostics - BIND, University of Palermo, Via del Vespro 127, 90100, Palermo, Italy
| | - Erica Maffei
- Department of Radiology, Fondazione Monasterio, Pisa, Italy
| | - Francesco Agnello
- Department of Biomedicine, Neurosciences and Advanced Diagnostics - BIND, University of Palermo, Via del Vespro 127, 90100, Palermo, Italy
| | - Cesare Gagliardo
- Department of Biomedicine, Neurosciences and Advanced Diagnostics - BIND, University of Palermo, Via del Vespro 127, 90100, Palermo, Italy
| | - Emanuele Grassedonio
- Department of Biomedicine, Neurosciences and Advanced Diagnostics - BIND, University of Palermo, Via del Vespro 127, 90100, Palermo, Italy
| | - Massimo Galia
- Department of Biomedicine, Neurosciences and Advanced Diagnostics - BIND, University of Palermo, Via del Vespro 127, 90100, Palermo, Italy
| | | | - Massimo Midiri
- Department of Biomedicine, Neurosciences and Advanced Diagnostics - BIND, University of Palermo, Via del Vespro 127, 90100, Palermo, Italy
| | - Ludovico La Grutta
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties - ProMISE, University of Palermo, Palermo, Italy
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Brocki L, Chung NC. Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models. Cancers (Basel) 2023; 15:cancers15092459. [PMID: 37173930 PMCID: PMC10177141 DOI: 10.3390/cancers15092459] [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: 03/16/2023] [Revised: 04/13/2023] [Accepted: 04/19/2023] [Indexed: 05/15/2023] Open
Abstract
Despite the unprecedented performance of deep neural networks (DNNs) in computer vision, their clinical application in the diagnosis and prognosis of cancer using medical imaging has been limited. One of the critical challenges for integrating diagnostic DNNs into radiological and oncological applications is their lack of interpretability, preventing clinicians from understanding the model predictions. Therefore, we studied and propose the integration of expert-derived radiomics and DNN-predicted biomarkers in interpretable classifiers, which we refer to as ConRad, for computerized tomography (CT) scans of lung cancer. Importantly, the tumor biomarkers can be predicted from a concept bottleneck model (CBM) such that once trained, our ConRad models do not require labor-intensive and time-consuming biomarkers. In our evaluation and practical application, the only input to ConRad is a segmented CT scan. The proposed model was compared to convolutional neural networks (CNNs) which act as a black box classifier. We further investigated and evaluated all combinations of radiomics, predicted biomarkers and CNN features in five different classifiers. We found the ConRad models using nonlinear SVM and the logistic regression with the Lasso outperformed the others in five-fold cross-validation, with the interpretability of ConRad being its primary advantage. The Lasso is used for feature selection, which substantially reduces the number of nonzero weights while increasing the accuracy. Overall, the proposed ConRad model combines CBM-derived biomarkers and radiomics features in an interpretable ML model which demonstrates excellent performance for lung nodule malignancy classification.
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Affiliation(s)
- Lennart Brocki
- Institute of Informatics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland
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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.
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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.
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Huang Y, Zhu T, Zhang X, Li W, Zheng X, Cheng M, Ji F, Zhang L, Yang C, Wu Z, Ye G, Lin Y, Wang K. Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study. EClinicalMedicine 2023; 58:101899. [PMID: 37007742 PMCID: PMC10050775 DOI: 10.1016/j.eclinm.2023.101899] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 02/17/2023] [Accepted: 02/21/2023] [Indexed: 04/04/2023] Open
Abstract
Background Accurate identification of pCR to neoadjuvant chemotherapy (NAC) is essential for determining appropriate surgery strategy and guiding resection extent in breast cancer. However, a non-invasive tool to predict pCR accurately is lacking. Our study aims to develop ensemble learning models using longitudinal multiparametric MRI to predict pCR in breast cancer. Methods From July 2015 to December 2021, we collected pre-NAC and post-NAC multiparametric MRI sequences per patient. We then extracted 14,676 radiomics and 4096 deep learning features and calculated additional delta-value features. In the primary cohort (n = 409), the inter-class correlation coefficient test, U-test, Boruta and the least absolute shrinkage and selection operator regression were used to select the most significant features for each subtype of breast cancer. Five machine learning classifiers were then developed to predict pCR accurately for each subtype. The ensemble learning strategy was used to integrate the single-modality models. The diagnostic performances of models were evaluated in the three external cohorts (n = 343, 170 and 340, respectively). Findings A total of 1262 patients with breast cancer from four centers were enrolled in this study, and pCR rates were 10.6% (52/491), 54.3% (323/595) and 37.5% (66/176) in HR+/HER2-, HER2+ and TNBC subtype, respectively. Finally, 20, 15 and 13 features were selected to construct the machine learning models in HR+/HER2-, HER2+ and TNBC subtypes, respectively. The multi-Layer Perception (MLP) yields the best diagnostic performances in all subtypes. For the three subtypes, the stacking model integrating pre-, post- and delta-models yielded the highest AUCs of 0.959, 0.974 and 0.958 in the primary cohort, and AUCs of 0.882-0.908, 0.896-0.929 and 0.837-0.901 in the external validation cohorts, respectively. The stacking model had accuracies of 85.0%-88.9%, sensitivities of 80.0%-86.3%, and specificities of 87.4%-91.5% in the external validation cohorts. Interpretation Our study established a novel tool to predict the responses of breast cancer to NAC and achieve excellent performance. The models could help to determine post-NAC surgery strategy for breast cancer. Funding This study is supported by grants from the National Natural Science Foundation of China (82171898, 82103093), the Deng Feng project of high-level hospital construction (DFJHBF202109), the Guangdong Basic and Applied Basic Research Foundation (grant number, 2020A1515010346, 2022A1515012277), the Science and Technology Planning Project of Guangzhou City (202002030236), the Beijing Medical Award Foundation (YXJL-2020-0941-0758), and the Beijing Science and Technology Innovation Medical Development Foundation (KC2022-ZZ-0091-5). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication.
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Affiliation(s)
- YuHong Huang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - Teng Zhu
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - XiaoLing Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wei Li
- Department of Breast Cancer, The First People's Hospital of Foshan, Foshan, Guangdong, China
| | - XingXing Zheng
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - MinYi Cheng
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - Fei Ji
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - LiuLu Zhang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - CiQiu Yang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - ZhiYong Wu
- Diagnosis and Treatment Center of Breast Diseases, Shantou Central Hospital, Shantou, China
- Corresponding author. Diagnosis and Treatment Center of Breast Diseases, Shantou Central Hospital, Shantou, China
| | - GuoLin Ye
- Department of Breast Cancer, The First People's Hospital of Foshan, Foshan, Guangdong, China
- Corresponding author. Department of Breast Cancer, The First People's Hospital of Foshan, Foshan, 528000, China.
| | - Ying Lin
- Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Corresponding author. Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Kun Wang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
- Corresponding author. Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
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Brown KH, Payan N, Osman S, Ghita M, Walls GM, Patallo IS, Schettino G, Prise KM, McGarry CK, Butterworth KT. Development and optimisation of a preclinical cone beam computed tomography-based radiomics workflow for radiation oncology research. Phys Imaging Radiat Oncol 2023; 26:100446. [PMID: 37252250 PMCID: PMC10213103 DOI: 10.1016/j.phro.2023.100446] [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: 02/24/2023] [Revised: 05/03/2023] [Accepted: 05/08/2023] [Indexed: 05/31/2023] Open
Abstract
Background and purpose Radiomics features derived from medical images have the potential to act as imaging biomarkers to improve diagnosis and predict treatment response in oncology. However, the complex relationships between radiomics features and the biological characteristics of tumours are yet to be fully determined. In this study, we developed a preclinical cone beam computed tomography (CBCT) radiomics workflow with the aim to use in vivo models to further develop radiomics signatures. Materials and methods CBCT scans of a mouse phantom were acquired using onboard imaging from a small animal radiotherapy research platform (SARRP, Xstrahl). The repeatability and reproducibility of radiomics outputs were compared across different imaging protocols, segmentation sizes, pre-processing parameters and materials. Robust features were identified and used to compare scans of two xenograft mouse tumour models (A549 and H460). Results Changes to the radiomics workflow significantly impact feature robustness. Preclinical CBCT radiomics analysis is feasible with 119 stable features identified from scans imaged at 60 kV, 25 bin width and 0.26 mm slice thickness. Large variation in segmentation volumes reduced the number of reliable radiomics features for analysis. Standardization in imaging and analysis parameters is essential in preclinical radiomics analysis to improve accuracy of outputs, leading to more consistent and reproducible findings. Conclusions We present the first optimised workflow for preclinical CBCT radiomics to identify imaging biomarkers. Preclinical radiomics has the potential to maximise the quantity of data captured in in vivo experiments and could provide key information supporting the wider application of radiomics.
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Affiliation(s)
- Kathryn H. Brown
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Neree Payan
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Sarah Osman
- University College London Hospitals NHS Foundation Trust Department of Radiotherapy, London, UK
| | - Mihaela Ghita
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Gerard M. Walls
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
- Cancer Centre, Belfast Health & Social Care Trust, Lisburn Road, Belfast BT9 7AB, Northern Ireland, UK
| | | | | | - Kevin M. Prise
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Conor K. McGarry
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
- Cancer Centre, Belfast Health & Social Care Trust, Lisburn Road, Belfast BT9 7AB, Northern Ireland, UK
| | - Karl T. Butterworth
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
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