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Beltrão FEDL, Beltrão DCDA, Carvalhal G, Beltrão FLDL, Oliveira JDB, Silva HDS, Teixeira HMP, Rodrigues JL, de Figueiredo CAV, Costa RDS, Hecht F, Vieira GC, Gonçalves MDCR, Bianco AC, Ramos HE. Thr92Ala-DIO2 heterozygosity is associated with skeletal muscle mass and myosteatosis in patients with COVID-19. Eur Thyroid J 2024; 13:e240068. [PMID: 38869458 PMCID: PMC11301567 DOI: 10.1530/etj-24-0068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 06/13/2024] [Indexed: 06/14/2024] Open
Abstract
Introduction The type 2 deiodinase and its Thr92Ala-DIO2 polymorphism have been linked to clinical outcomes in acute lung injury and coronavirus disease 2019 (COVID-19). Objective The objective was to identify a potential association between Thr92Ala-DIO2 polymorphism and body composition (appendicular muscle mass, myosteatosis, and fat distribution) and to determine whether they reflect the severity or mortality associated with the disease. Methods In this prospective cohort study (June-August 2020), 181 patients hospitalized with moderate-to-severe COVID-19 underwent a non-contrast-enhanced computed tomography (CT) of the thorax to assess body composition, laboratory tests, and genotyping for the Thr92Ala-DIO2 polymorphism. Results In total, 181 consecutive patients were stratified into three subgroups according to the genotype: Thr/Thr (n = 64), Thr/Ala (n = 96), and Ala/Ala (n = 21). The prevalence of low muscle area (MA) (< 92 cm²) was 52.5%. Low MA was less frequent in Ala/Thr patients (44.8%) than in Thr/Thr (60.9%) or Ala/Ala patients (61.9%) (P = 0.027). Multivariate logistic regression analysis confirmed that the Thr/Ala allele was associated with a reduced risk of low MA (41% to 69%) and myosteatosis (62% to 72%) compared with Thr/Thr + Ala/Ala (overdominant model). Kaplan-Meier curves showed that patients with low muscle mass and homozygosity had lower survival rates than the other groups. Notably, the heterozygotes with MA ≥92 cm² exhibited the best survival rate. Conclusion Thr92Ala-DIO2 heterozygosity is associated with increased skeletal MA and less myosteatosis in patients with COVID-19. The protective effect of Thr92Ala-DIO2 heterozygosity on COVID-19 mortality is restricted to patients with reduced MA.
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Affiliation(s)
- Fabyan Esberard de Lima Beltrão
- Lauro Wanderley University Hospital, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
- University Center of João Pessoa – UNIPE, João Pessoa, PB, Brazil
| | - Daniele Carvalhal de Almeida Beltrão
- University Center of João Pessoa – UNIPE, João Pessoa, PB, Brazil
- Post-Graduation Program in Cognitive Neuroscience and Behavior, Psychology Department of the Center of Human Sciences, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
| | - Giulia Carvalhal
- Center for Biological and Health Sciences, Federal University of Campina Grande, Campina Grande, Paraíba, Brazil
| | | | - Jocyel de Brito Oliveira
- Bioregulation Department, Health and Science Institut, Federal University of Bahia, Salvador, Bahia, Brazil
| | - Hatilla dos Santos Silva
- Bioregulation Department, Health and Science Institut, Federal University of Bahia, Salvador, Bahia, Brazil
| | | | - Juliana Lopes Rodrigues
- Laboratory of Immunopharmacology and Molecular Biology, Health Sciences Institute, Federal University of Bahia, Brazil
| | | | - Ryan dos Santos Costa
- Laboratory of Immunopharmacology and Molecular Biology, Health Sciences Institute, Federal University of Bahia, Brazil
| | - Fabio Hecht
- The Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Giciane Carvalho Vieira
- Post-Graduation Program in Cognitive Neuroscience and Behavior, Psychology Department of the Center of Human Sciences, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
| | | | - Antonio C. Bianco
- Section of Endocrinology and Metabolism, Division of the Biological Sciences, University of Chicago, Chicago, Illinois, USA
| | - Helton Estrela Ramos
- Post-Graduate Program in Medicine and Health, Medical School of Medicine, Federal University of Bahia, Salvador, Brazil
- Postgraduate Program in Interactive Processes of Organs and Systems, Health & Science Institute, Federal University of Bahia, Salvador, BA, Brazil
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2
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He S, Zhang G, Huang N, Chen S, Ruan L, Liu X, Zeng Y. Utilizing the T12 skeletal muscle index on computed tomography images for sarcopenia diagnosis in lung cancer patients. Asia Pac J Oncol Nurs 2024; 11:100512. [PMID: 38975610 PMCID: PMC11225817 DOI: 10.1016/j.apjon.2024.100512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Accepted: 05/11/2024] [Indexed: 07/09/2024] Open
Affiliation(s)
- Shi He
- School of Nursing, Guangzhou Medical University, Guangzhou, China
| | - Guolong Zhang
- Respiratory Intervention Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Ningbin Huang
- School of Nursing, Guangzhou Medical University, Guangzhou, China
| | - Siting Chen
- School of Nursing, Guangzhou Medical University, Guangzhou, China
| | - Liang Ruan
- Department of Nursing Management, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xuanhui Liu
- Department of Industrial Design, Hangzhou City University, Hangzhou, China
| | - Yingchun Zeng
- School of Medicine, Hangzhou City University, Hangzhou, China
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Leng Y, Kan A, Wang X, Li X, Xiao X, Wang Y, Liu L, Gong L. Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study. BMC Cancer 2024; 24:307. [PMID: 38448945 PMCID: PMC10916071 DOI: 10.1186/s12885-024-12037-8] [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: 08/14/2023] [Accepted: 02/21/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Preoperative prediction of International Federation of Gynecology and Obstetrics (FIGO) stage in patients with epithelial ovarian cancer (EOC) is crucial for determining appropriate treatment strategy. This study aimed to explore the value of contrast-enhanced CT (CECT) radiomics in predicting preoperative FIGO staging of EOC, and to validate the stability of the model through an independent external dataset. METHODS A total of 201 EOC patients from three centers, divided into a training cohort (n = 106), internal (n = 46) and external (n = 49) validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening radiomics features. Five machine learning algorithms, namely logistic regression, support vector machine, random forest, light gradient boosting machine (LightGBM), and decision tree, were utilized in developing the radiomics model. The optimal performing algorithm was selected to establish the radiomics model, clinical model, and the combined model. The diagnostic performances of the models were evaluated through receiver operating characteristic analysis, and the comparison of the area under curves (AUCs) were conducted using the Delong test or F-test. RESULTS Seven optimal radiomics features were retained by the LASSO algorithm. The five radiomics models demonstrate that the LightGBM model exhibits notable prediction efficiency and robustness, as evidenced by AUCs of 0.83 in the training cohort, 0.80 in the internal validation cohort, and 0.68 in the external validation cohort. The multivariate logistic regression analysis indicated that carcinoma antigen 125 and tumor location were identified as independent predictors for the FIGO staging of EOC. The combined model exhibited best diagnostic efficiency, with AUCs of 0.95 in the training cohort, 0.83 in the internal validation cohort, and 0.79 in the external validation cohort. The F-test indicated that the combined model exhibited a significantly superior AUC value compared to the radiomics model in the training cohort (P < 0.001). CONCLUSIONS The combined model integrating clinical characteristics and radiomics features shows potential as a non-invasive adjunctive diagnostic modality for preoperative evaluation of the FIGO staging status of EOC, thereby facilitating clinical decision-making and enhancing patient outcomes.
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Affiliation(s)
- Yinping Leng
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Minde Road No. 1, 330006, Nanchang, Jiangxi Province, China
| | - Ao Kan
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Minde Road No. 1, 330006, Nanchang, Jiangxi Province, China
| | - Xiwen Wang
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Minde Road No. 1, 330006, Nanchang, Jiangxi Province, China
| | - Xiaofen Li
- Department of Radiology, Jiangxi Provincial People's Hospital, Nanchang, Jiangxi, China
| | - Xuan Xiao
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Minde Road No. 1, 330006, Nanchang, Jiangxi Province, China
| | - Yu Wang
- Clinical and Technical Support, Philips Healthcare, Shanghai, China
| | - Lan Liu
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, Jiangxi, China.
| | - Lianggeng Gong
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Minde Road No. 1, 330006, Nanchang, Jiangxi Province, China.
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Vogele D, Mueller T, Wolf D, Otto S, Manoj S, Goetz M, Ettrich TJ, Beer M. Applicability of the CT Radiomics of Skeletal Muscle and Machine Learning for the Detection of Sarcopenia and Prognostic Assessment of Disease Progression in Patients with Gastric and Esophageal Tumors. Diagnostics (Basel) 2024; 14:198. [PMID: 38248074 PMCID: PMC10814393 DOI: 10.3390/diagnostics14020198] [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: 09/12/2023] [Revised: 01/10/2024] [Accepted: 01/10/2024] [Indexed: 01/23/2024] Open
Abstract
PURPOSE Sarcopenia is considered a negative prognostic factor in patients with malignant tumors. Among other diagnostic options, computed tomography (CT), which is repeatedly performed on tumor patients, can be of further benefit. The present study aims to establish a framework for classifying the impact of sarcopenia on the prognosis of patients diagnosed with esophageal or gastric cancer. Additionally, it explores the significance of CT radiomics in both diagnostic and prognostic methodologies. MATERIALS AND METHODS CT scans of 83 patients with esophageal or gastric cancer taken at the time of diagnosis and during a follow-up period of one year were evaluated retrospectively. A total of 330 CT scans were analyzed. Seventy three of these patients received operative tumor resection after neoadjuvant chemotherapy, and 74% of the patients were male. The mean age was 64 years (31-83 years). Three time points (t) were defined as a basis for the statistical analysis in order to structure the course of the disease: t1 = initial diagnosis, t2 = following (neoadjuvant) chemotherapy and t3 = end of the first year after surgery in the "surgery" group or end of the first year after chemotherapy. Sarcopenia was determined using the psoas muscle index (PMI). The additional analysis included the analysis of selected radiomic features of the psoas major, quadratus lumborum, and erector spinae muscles at the L3 level. Disease progression was monitored according to the response evaluation criteria in solid tumors (RECIST 1.1). CT scans and radiomics were used to assess the likelihood of tumor progression and their correlation to sarcopenia. For machine learning, the established algorithms decision tree (DT), K-nearest neighbor (KNN), and random forest (RF) were applied. To evaluate the performance of each model, a 10-fold cross-validation as well as a calculation of Accuracy and Area Under the Curve (AUC) was used. RESULTS During the observation period of the study, there was a significant decrease in PMI. This was most evident in patients with surgical therapy in the comparison between diagnosis and after both neoadjuvant therapy and surgery (each p < 0.001). Tumor progression (PD) was not observed significantly more often in the patients with sarcopenia compared to those without sarcopenia at any time point (p = 0.277 to p = 0.465). On average, PD occurred after 271.69 ± 104.20 days. The time from initial diagnosis to PD in patients "with sarcopenia" was not significantly shorter than in patients "without sarcopenia" at any of the time points (p = 0.521 to p = 0.817). The CT radiomics of skeletal muscle could predict both sarcopenia and tumor progression, with the best results for the psoas major muscle using the RF algorithm. For the detection of sarcopenia, the Accuracy was 0.90 ± 0.03 and AUC was 0.96 ± 0.02. For the prediction of PD, the Accuracy was 0.88 ± 0.04 and the AUC was 0.93 ± 0.04. CONCLUSIONS In the present study, the CT radiomics of skeletal muscle together with machine learning correlated with the presence of sarcopenia, and this can additionally assist in predicting disease progression. These features can be classified as promising alternatives to conventional methods, with great potential for further research and future clinical application. However, when sarcopenia was diagnosed with PMI, no significant correlation between sarcopenia and PD could be observed.
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Affiliation(s)
- Daniel Vogele
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
| | - Teresa Mueller
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
| | - Daniel Wolf
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
- Visual Computing Group, Institute for Media Informatics, Ulm University, 89081 Ulm, Germany
- XAIRAD—Artificial Intelligence in Experimental Radiology, University Hospital of Ulm, 89081 Ulm, Germany
| | - Stephanie Otto
- Comprehensive Cancer Center Ulm (CCCU), Ulm University Medical Center, 89081 Ulm, Germany;
| | - Sabitha Manoj
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
- XAIRAD—Artificial Intelligence in Experimental Radiology, University Hospital of Ulm, 89081 Ulm, Germany
| | - Michael Goetz
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
- XAIRAD—Artificial Intelligence in Experimental Radiology, University Hospital of Ulm, 89081 Ulm, Germany
| | - Thomas J. Ettrich
- Department of Internal Medicine I, Ulm University Medical Center, 89081 Ulm, Germany;
- i2SouI—Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, 89081 Ulm, Germany
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
- i2SouI—Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, 89081 Ulm, Germany
- MoMan—Center for Translational Imaging, Department of Internal Medicine II, University Hospital of Ulm, 89081 Ulm, Germany
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Seok M, Kim W, Kim J. Machine Learning for Sarcopenia Prediction in the Elderly Using Socioeconomic, Infrastructure, and Quality-of-Life Data. Healthcare (Basel) 2023; 11:2881. [PMID: 37958025 PMCID: PMC10649858 DOI: 10.3390/healthcare11212881] [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: 10/02/2023] [Revised: 10/30/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023] Open
Abstract
Since the WHO's 2021 aging redefinition emphasizes "healthy aging" by focusing on the elderly's ability to perform daily activities, sarcopenia, which is defined as the loss of skeletal muscle mass, is now becoming a critical health concern, especially in South Korea with a rapidly aging population. Therefore, we develop a prediction model for sarcopenia by using machine learning (ML) techniques based on the Korea National Health and Nutrition Examination Survey (KNHANES) data 2008-2011, in which we focus on the role of socioeconomic status (SES), social infrastructure, and quality of life (QoL) in the prevalence of sarcopenia. We successfully identify sarcopenia with approximately 80% accuracy by using random forest (RF) and LightGBM (LGB), CatBoost (CAT), and a deep neural network (DNN). For prediction reliability, we achieve area under curve (AUC) values of 0.831, 0.868, and 0.773 for both genders, males, and females, respectively. Especially when using only male data, all the models consistently exhibit better performance overall. Furthermore, using the SHapley Additive exPlanations (SHAP) analysis, we find several common key features, which mainly contribute to model building. These include SES features, such as monthly household income, housing type, marriage status, and social infrastructure accessibility. Furthermore, the causal relationships of household income, per capita neighborhood sports facility area, and life satisfaction are analyzed to establish an effective prediction model for sarcopenia management in an aging population.
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Affiliation(s)
- Minje Seok
- Computer Engineering Department, Gachon University, Seongnam 13120, Republic of Korea;
| | - Wooseong Kim
- Computer Engineering Department, Gachon University, Seongnam 13120, Republic of Korea;
| | - Jiyoun Kim
- Convergence Health Science, Gachon University, Incheon 21936, Republic of Korea;
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Giraudo C, Modugno A, Negro G, Dell'Amore A, Cecchin D, Motta R, Balestro E, Boscolo A, Calabrese F, Faccioli E, Navalesi P, Vianello A, Rea F, Stramare R. Radiomics of spinal muscles: toward a radiological biomarker for allograft rejection in lung transplant. LA RADIOLOGIA MEDICA 2023; 128:1070-1078. [PMID: 37458906 PMCID: PMC10474186 DOI: 10.1007/s11547-023-01674-x] [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: 11/09/2022] [Accepted: 06/29/2023] [Indexed: 09/02/2023]
Abstract
PURPOSE To assess the role of muscle composition and radiomics in predicting allograft rejection in lung transplant. MATERIAL AND METHODS The last available HRCT before surgery of lung transplant candidates referring to our tertiary center from January 2010 to February 2020 was retrospectively examined. Only scans with B30 kernel reconstructions and 1 mm slice thickness were included. One radiologist segmented the spinal muscles of each patient at the level of the 11th dorsal vertebra by an open-source software. The same software was used to extract Hu values and 72 radiomic features of first and second order. Factor analysis was applied to select highly correlating features and then their prognostic value for allograft rejection was investigated by logistic regression analysis (level of significance p < 0.05). In case of significant results, the diagnostic value of the model was computed by ROC curves. RESULTS Overall 200 patients had a HRCT prior to the transplant but only 97 matched the inclusion criteria (29 women; mean age 50.4 ± 13 years old). Twenty-one patients showed allograft rejection. The following features were selected by the factor analysis: cluster prominence, Imc2, gray level non-uniformity normalized, median, kurtosis, gray level non-uniformity, and inverse variance. The radiomic-based model including also Hu demonstrated that only the feature Imc2 acts as a predictor of allograft rejection (p = 0.021). The model showed 76.6% accuracy and the Imc2 value of 0.19 demonstrated 81% sensitivity and 64.5% specificity in predicting lung transplant rejection. CONCLUSION The radiomic feature Imc2 demonstrated to be a predictor of allograft rejection in lung transplant.
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Affiliation(s)
- Chiara Giraudo
- Unit of Advanced Clinical and Translational Imaging, Department of Medicine - DIMED, University of Padova, Padua, Italy.
| | - Antonella Modugno
- Unit of Advanced Clinical and Translational Imaging, Department of Medicine - DIMED, University of Padova, Padua, Italy
| | - Giacomo Negro
- Unit of Advanced Clinical and Translational Imaging, Department of Medicine - DIMED, University of Padova, Padua, Italy
| | - Andrea Dell'Amore
- Thoracic Surgery Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy
| | - Diego Cecchin
- Nuclear Medicine Unit, Department of Medicine - DIMED, University of Padova, Padua, Italy
| | - Raffaella Motta
- Unit of Advanced Clinical and Translational Imaging, Department of Medicine - DIMED, University of Padova, Padua, Italy
| | - Elisabetta Balestro
- Respiratory Disease Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy
| | - Annalisa Boscolo
- Anesthesiology and Intensive Care Unit, Department of Medicine-DIMED, University of Padova, Padua, Italy
| | - Fiorella Calabrese
- Pathological Anatomy Section, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy
| | - Eleonora Faccioli
- Thoracic Surgery Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy
| | - Paolo Navalesi
- Anesthesiology and Intensive Care Unit, Department of Medicine-DIMED, University of Padova, Padua, Italy
| | - Andrea Vianello
- Respiratory Pathophysiology Division, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy
| | - Federico Rea
- Thoracic Surgery Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy
| | - Roberto Stramare
- Unit of Advanced Clinical and Translational Imaging, Department of Medicine - DIMED, University of Padova, Padua, Italy
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Fischer M, Küstner T, Pappa S, Niendorf T, Pischon T, Kröncke T, Bette S, Schramm S, Schmidt B, Haubold J, Nensa F, Nonnenmacher T, Palm V, Bamberg F, Kiefer L, Schick F, Yang B. Identification of radiomic biomarkers in a set of four skeletal muscle groups on Dixon MRI of the NAKO MR study. BMC Med Imaging 2023; 23:104. [PMID: 37553619 PMCID: PMC10408104 DOI: 10.1186/s12880-023-01056-9] [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: 02/15/2023] [Accepted: 07/18/2023] [Indexed: 08/10/2023] Open
Abstract
In this work, we propose a processing pipeline for the extraction and identification of meaningful radiomics biomarkers in skeletal muscle tissue as displayed using Dixon-weighted MRI. Diverse and robust radiomics features can be identified that may be of aid in the accurate quantification e.g. varying degrees of sarcopenia in respective muscles of large cohorts. As such, the approach comprises the texture feature extraction from raw data based on well established approaches, such as a nnU-Net neural network and the Pyradiomics toolbox, a subsequent selection according to adequate conditions for the muscle tissue of the general population, and an importance-based ranking to further narrow the amount of meaningful features with respect to auxiliary targets. The performance was investigated with respect to the included auxiliary targets, namely age, body mass index (BMI), and fat fraction (FF). Four skeletal muscles with different fiber architecture were included: the mm. glutaei, m. psoas, as well as the extensors and adductors of the thigh. The selection allowed for a reduction from 1015 available texture features to 65 for age, 53 for BMI, and 36 for FF from the available fat/water contrast images considering all muscles jointly. Further, the dependence of the importance rankings calculated for the auxiliary targets on validation sets (in a cross-validation scheme) was investigated by boxplots. In addition, significant differences between subgroups of respective auxiliary targets as well as between both sexes were shown to be present within the ten lowest ranked features by means of Kruskal-Wallis H-tests and Mann-Whitney U-tests. The prediction performance for the selected features and the ranking scheme were verified on validation sets by a random forest based multi-class classification, with strong area under the curve (AUC) values of the receiver operator characteristic (ROC) of 73.03 ± 0.70 % and 73.63 ± 0.70 % for the water and fat images in age, 80.68 ± 0.30 % and 88.03 ± 0.89 % in BMI, as well as 98.36 ± 0.03 % and 98.52 ± 0.09 % in FF.
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Affiliation(s)
- Marc Fischer
- Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany
| | - Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), University Hospital Tübingen, Tübingen, Germany.
| | - Sofia Pappa
- Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
| | - Tobias Pischon
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
| | - Thomas Kröncke
- Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Augsburg, Germany
- Centre for Advanced Analytics and Predictive Sciences (CAAPS), University Augsburg, Augsburg, Germany
| | - Stefanie Bette
- Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Augsburg, Germany
| | - Sara Schramm
- Institute for Medical Informatics, Biometry and Epidemiology, Essen University Hospital, Essen, Germany
| | - Börge Schmidt
- Institute for Medical Informatics, Biometry and Epidemiology, Essen University Hospital, Essen, Germany
| | | | | | | | | | | | - Lena Kiefer
- Department of Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Fritz Schick
- Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Bin Yang
- Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany
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8
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Bologna M, Corino V, Cavalieri S, Calareso G, Gazzani SE, Poli T, Ravanelli M, Mattavelli D, de Graaf P, Nauta I, Scheckenbach K, Licitra L, Mainardi L. Prognostic radiomic signature for head and neck cancer: development and validation on a multi-centric MRI dataset. Radiother Oncol 2023; 183:109638. [PMID: 37004837 DOI: 10.1016/j.radonc.2023.109638] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 03/10/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023]
Abstract
BACKGROUND AND PURPOSE Prognosis in locally advanced head and neck cancer (HNC) is currently based on TNM staging system and tumor subsite. However, quantitative imaging features (i.e., radiomic features) from magnetic resonance imaging (MRI) may provide additional prognostic info. The aim of this work is to develop and validate an MRI-based prognostic radiomic signature for locally advanced HNC. MATERIALS AND METHODS Radiomic features were extracted from T1- and T2-weighted MRI (T1w and T2w) using the segmentation of the primary tumor as mask. In total 1072 features (536 per image type) were extracted for each tumor. A retrospective multi-centric dataset (n=285) was used for features selection and model training. The selected features were used to fit a Cox proportional hazard regression model for overall survival (OS) that outputs the radiomic signature. The signature was then validated on a prospective multi-centric dataset (n=234). Prognostic performance for OS and disease-free survival (DFS) was evaluated using C-index. Additional prognostic value of the radiomic signature was explored. RESULTS The radiomic signature had C-index=0.64 for OS and C-index=0.60 for DFS in the validation set. The addition of the radiomic signature to other clinical features (TNM staging and tumor subsite) increased prognostic ability for both OS (HPV- C-index 0.63 to 0.65; HPV+ C-index 0.75 to 0.80) and DFS (HPV- C-index 0.58 to 0.61; HPV+ C-index 0.64 to 0.65). CONCLUSION An MRI-based prognostic radiomic signature was developed and prospectively validated. Such signature can successfully integrate clinical factors in both HPV+ and HPV- tumors. Grant support: European Union Horizon 2020 Framework Programme, Grant/Award, Number: 689715.
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Affiliation(s)
- Marco Bologna
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy.
| | - Valentina Corino
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Stefano Cavalieri
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano and Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Silvia Eleonora Gazzani
- Unit of Diagnostic and Interventional Radiology, Department of Surgical Sciences, University of Parma, Parma, Italy
| | - Tito Poli
- Unit of Maxillo-Facial Surgery, Department of Biomedical, Biotechnological and Translational Sciences (S.Bi.Bi.T.), University of Parma, Parma, Italy
| | - Marco Ravanelli
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Davide Mattavelli
- Department of Otorhinolaryngology Head and Neck Surgery, Spedali Civili di Brescia and University of Brescia, Brescia, Italy
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Irene Nauta
- Department of Otolaryngology/Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, The Netherlands
| | - Kathrin Scheckenbach
- Department of Otolaryngology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano and Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan. Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
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9
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Vogele D, Otto S, Sollmann N, Haggenmüller B, Wolf D, Beer M, Schmidt SA. Sarcopenia - Definition, Radiological Diagnosis, Clinical Significance. ROFO-FORTSCHR RONTG 2023; 195:393-405. [PMID: 36630983 DOI: 10.1055/a-1990-0201] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
BACKGROUND Sarcopenia is an age-related syndrome characterized by a loss of muscle mass and strength. As a result, the independence of the elderly is reduced and the hospitalization rate and mortality increase. The onset of sarcopenia often begins in middle age due to an unbalanced diet or malnutrition in association with a lack of physical activity. This effect is intensified by concomitant diseases such as obesity or metabolic diseases including diabetes mellitus. METHOD With effective preventative diagnostic procedures and specific therapeutic treatment of sarcopenia, the negative effects on the individual can be reduced and the negative impact on health as well as socioeconomic effects can be prevented. Various diagnostic options are available for this purpose. In addition to basic clinical methods such as measuring muscle strength, sarcopenia can also be detected using imaging techniques like dual X-ray absorptiometry (DXA), computed tomography (CT), magnetic resonance imaging (MRI), and sonography. DXA, as a simple and cost-effective method, offers a low-dose option for assessing body composition. With cross-sectional imaging techniques such as CT and MRI, further diagnostic possibilities are available, including MR spectroscopy (MRS) for noninvasive molecular analysis of muscle tissue. CT can also be used in the context of examinations performed for other indications to acquire additional parameters of the skeletal muscles (opportunistic secondary use of CT data), such as abdominal muscle mass (total abdominal muscle area - TAMA) or the psoas as well as the pectoralis muscle index. The importance of sarcopenia is already well studied for patients with various tumor entities and also infections such as SARS-COV2. RESULTS AND CONCLUSION Sarcopenia will become increasingly important, not least due to demographic changes in the population. In this review, the possibilities for the diagnosis of sarcopenia, the clinical significance, and therapeutic options are described. In particular, CT examinations, which are repeatedly performed on tumor patients, can be used for diagnostics. This opportunistic use can be supported by the use of artificial intelligence. KEY POINTS · Sarcopenia is an age-related syndrome with loss of muscle mass and strength.. · Early detection and therapy can prevent negative effects of sarcopenia.. · In addition to DEXA, cross-sectional imaging techniques (CT, MRI) are available for diagnostic purposes.. · The use of artificial intelligence (AI) offers further possibilities in sarcopenia diagnostics.. CITATION FORMAT · Vogele D, Otto S, Sollmann N et al. Sarcopenia - Definition, Radiological Diagnosis, Clinical Significance. Fortschr Röntgenstr 2023; DOI: 10.1055/a-1990-0201.
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Affiliation(s)
- Daniel Vogele
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Germany
| | - Stephanie Otto
- Comprehensive Cancer Center (CCCU), University Hospital Ulm, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Germany
| | - Benedikt Haggenmüller
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Germany
| | - Daniel Wolf
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Germany
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Germany
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10
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Hu X, Yin S, Zhang X, Menon C, Fang C, Chen Z, Elgendi M, Liang Y. Blood pressure stratification using photoplethysmography and light gradient boosting machine. Front Physiol 2023; 14:1072273. [PMID: 36891146 PMCID: PMC9986584 DOI: 10.3389/fphys.2023.1072273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 02/06/2023] [Indexed: 02/22/2023] Open
Abstract
Introduction: Globally, hypertension (HT) is a substantial risk factor for cardiovascular disease and mortality; hence, rapid identification and treatment of HT is crucial. In this study, we tested the light gradient boosting machine (LightGBM) machine learning method for blood pressure stratification based on photoplethysmography (PPG), which is used in most wearable devices. Methods: We used 121 records of PPG and arterial blood pressure (ABP) signals from the Medical Information Mart for Intensive Care III public database. PPG, velocity plethysmography, and acceleration plethysmography were used to estimate blood pressure; the ABP signals were used to determine the blood pressure stratification categories. Seven feature sets were established and used to train the Optuna-tuned LightGBM model. Three trials compared normotension (NT) vs. prehypertension (PHT), NT vs. HT, and NT + PHT vs. HT. Results: The F1 scores for these three classification trials were 90.18%, 97.51%, and 92.77%, respectively. The results showed that combining multiple features from PPG and its derivative led to a more accurate classification of HT classes than using features from only the PPG signal. Discussion: The proposed method showed high accuracy in stratifying HT risks, providing a noninvasive, rapid, and robust method for the early detection of HT, with promising applications in the field of wearable cuffless blood pressure measurement.
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Affiliation(s)
- Xudong Hu
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
| | - Shimin Yin
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
| | - Xizhuang Zhang
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China
| | - Carlo Menon
- Biomedical and Mobile Health Technology Lab, ETH Zurich, Zurich, Switzerland
| | - Cheng Fang
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
| | - Zhencheng Chen
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China.,Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin, China.,Guangxi Engineering Technology Research Center of Human Physiological Information Noninvasive Detection, Guilin, China
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Lab, ETH Zurich, Zurich, Switzerland
| | - Yongbo Liang
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China.,Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin, China.,Guangxi Engineering Technology Research Center of Human Physiological Information Noninvasive Detection, Guilin, China
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11
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Miao S, Jia H, Cheng K, Hu X, Li J, Huang W, Wang R. Deep learning radiomics under multimodality explore association between muscle/fat and metastasis and survival in breast cancer patients. Brief Bioinform 2022; 23:6748489. [PMID: 36198668 DOI: 10.1093/bib/bbac432] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 09/03/2022] [Accepted: 09/06/2022] [Indexed: 12/14/2022] Open
Abstract
Sarcopenia is correlated with poor clinical outcomes in breast cancer (BC) patients. However, there is no precise quantitative study on the correlation between body composition changes and BC metastasis and survival. The present study proposed a deep learning radiomics (DLR) approach to investigate the effects of muscle and fat on distant metastasis and death outcomes in BC patients. Image feature extraction was performed on 4th thoracic vertebra (T4) and 11th thoracic vertebra (T11) on computed tomography (CT) image levels by DLR, and image features were combined with clinical information to predict distant metastasis in BC patients. Clinical information combined with DLR significantly predicted distant metastasis in BC patients. In the test cohort, the area under the curve of model performance on clinical information combined with DLR was 0.960 (95% CI: 0.942-0.979, P < 0.001). The patients with distant metastases had a lower pectoral muscle index in T4 (PMI/T4) than in patients without metastases. PMI/T4 and visceral fat tissue area in T11 (VFA/T11) were independent prognostic factors for the overall survival in BC patients. The pectoralis muscle area in T4 (PMA/T4) and PMI/T4 is an independent prognostic factor for distant metastasis-free survival in BC patients. The current study further confirmed that muscle/fat of T4 and T11 levels have a significant effect on the distant metastasis of BC. Appending the network features of T4 and T11 to the model significantly enhances the prediction performance of distant metastasis of BC, providing a valuable biomarker for the early treatment of BC patients.
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Affiliation(s)
- Shidi Miao
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Haobo Jia
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Ke Cheng
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Xiaohui Hu
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Jing Li
- Department of Geriatrics, the Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Wenjuan Huang
- Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China
| | - Ruitao Wang
- Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China
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12
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Wang J, Chu H, Pan Y. Prediction of renal damage in children with IgA vasculitis based on machine learning. Medicine (Baltimore) 2022; 101:e31135. [PMID: 36281102 PMCID: PMC9592501 DOI: 10.1097/md.0000000000031135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
This article is objected to explore the value of machine learning algorithm in predicting the risk of renal damage in children with IgA vasculitis by constructing a predictive model and analyzing the related risk factors of IgA vasculitis Nephritis in children. Case data of 288 hospitalized children with IgA vasculitis from November 2018 to October 2021 were collected. The data included 42 indicators such as demographic characteristics, clinical symptoms and laboratory tests, etc. Univariate feature selection was used for feature extraction, and logistic regression, support vector machine (SVM), decision tree and random forest (RF) algorithms were used separately for classification prediction. Lastly, the performance of four algorithms is compared using accuracy rate, recall rate and AUC. The accuracy rate, recall rate and AUC of the established RF model were 0.83, 0.86 and 0.91 respectively, which were higher than 0.74, 0.80 and 0.89 of the logistic regression model; higher than 0.70, 0.80 and 0.89 of SVM model; higher than 0.74, 0.80 and 0.81 of the decision tree model. The top 10 important features provided by RF model are: Persistent purpura ≥4 weeks, Cr, Clinic time, ALB, WBC, TC, Relapse, TG, Recurrent purpura and EB-DNA. The model based on RF algorithm has better performance in the prediction of children with IgA vasculitis renal damage, indicated by better classification accuracy, better classification effect and better generalization performance.
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Affiliation(s)
- Jinjuan Wang
- Shandong University of Traditional Chinese Medicine, Shandong, PR China
| | - Huimin Chu
- Shandong University of Traditional Chinese Medicine, Shandong, PR China
| | - Yueli Pan
- Shandong University of Traditional Chinese Medicine, Shandong, PR China
- * Correspondence: Yueli Pan, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250014, PR China (e-mail: )
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13
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Dynamic forecasting of the Shanghai Stock Exchange index movement using multiple types of investor sentiment. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109132] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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14
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Bedrikovetski S, Seow W, Kroon HM, Traeger L, Moore JW, Sammour T. Artificial intelligence for body composition and sarcopenia evaluation on computed tomography: A systematic review and meta-analysis. Eur J Radiol 2022; 149:110218. [DOI: 10.1016/j.ejrad.2022.110218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/30/2021] [Accepted: 02/10/2022] [Indexed: 12/13/2022]
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15
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Manzano W, Lenchik L, Chaudhari AS, Yao L, Gupta S, Boutin RD. Sarcopenia in rheumatic disorders: what the radiologist and rheumatologist should know. Skeletal Radiol 2022; 51:513-524. [PMID: 34268590 DOI: 10.1007/s00256-021-03863-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 07/03/2021] [Accepted: 07/04/2021] [Indexed: 02/02/2023]
Abstract
Sarcopenia is defined as the loss of muscle mass, strength, and function. Increasing evidence shows that sarcopenia is common in patients with rheumatic disorders. Although sarcopenia can be diagnosed using bioelectrical impedance analysis or DXA, increasingly it is diagnosed using CT, MRI, and ultrasound. In rheumatic patients, CT and MRI allow "opportunistic" measurement of body composition, including surrogate markers of sarcopenia, from studies obtained during routine patient care. Recognition of sarcopenia is important in rheumatic patients because sarcopenia can be associated with disease progression and poor outcomes. This article reviews how opportunistic evaluation of sarcopenia in rheumatic patients can be accomplished and potentially contribute to improved patient care.
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Affiliation(s)
- Wilfred Manzano
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305-5105, USA.
| | - Leon Lenchik
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Akshay S Chaudhari
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305-5105, USA
| | - Lawrence Yao
- Department of Radiology, National Institute of Health, Bethesda, MD, 20892, USA
| | - Sarthak Gupta
- Department of Medicine, National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), National Institutes of Health, Bethesda, MD, 20892, USA
| | - Robert D Boutin
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305-5105, USA
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16
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Chianca V, Albano D, Messina C, Gitto S, Ruffo G, Guarino S, Del Grande F, Sconfienza LM. Sarcopenia: imaging assessment and clinical application. Abdom Radiol (NY) 2022; 47:3205-3216. [PMID: 34687326 PMCID: PMC8536908 DOI: 10.1007/s00261-021-03294-3] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 09/21/2021] [Accepted: 09/22/2021] [Indexed: 02/06/2023]
Abstract
Sarcopenia is a progressive, generalized skeletal muscle disorder characterized by reduction of muscle mass and strength. It is associated with increased adverse outcomes including falls, fractures, physical disability, and mortality, particularly, in elderly patients. Nowadays, sarcopenia has become a specific imaging biomarker able to predict clinical outcomes of patients. Muscle fibre reduction has shown to be an unfavourable pre-operative predictive factor in patients with cancer, and is associated with worse clinical outcomes in terms of postoperative complications, morbidity, mortality, and lower tolerance of chemoradiation therapy. Several imaging modalities, including dual-energy X-ray absorptiometry, CT, MRI, and US can be used to estimate muscle mass and quality to reach the diagnosis of sarcopenia. This article reviews the clinical implications of sarcopenia, how this condition can be assessed through different imaging modalities, and future perspectives of imaging of sarcopenia.
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Affiliation(s)
- Vito Chianca
- Clinica di Radiologia EOC IIMSI, Lugano, Switzerland ,Ospedale Evangelico Betania, Napoli, Italy
| | - Domenico Albano
- grid.417776.4IRCCS Istituto Ortopedico Galeazzi, Milano, Italy ,grid.10776.370000 0004 1762 5517Sezione di Scienze Radiologiche, Dipartimento di Biomedicina, Neuroscienze e Diagnostica Avanzata, Università degli Studi di Palermo, Palermo, Italy
| | - Carmelo Messina
- grid.417776.4IRCCS Istituto Ortopedico Galeazzi, Milano, Italy
| | - Salvatore Gitto
- grid.4708.b0000 0004 1757 2822Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milano, Italy
| | - Gaetano Ruffo
- grid.417776.4IRCCS Istituto Ortopedico Galeazzi, Milano, Italy
| | | | | | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milano, Italy. .,Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milano, Italy.
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17
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Scapicchio C, Gabelloni M, Barucci A, Cioni D, Saba L, Neri E. A deep look into radiomics. LA RADIOLOGIA MEDICA 2021; 126:1296-1311. [PMID: 34213702 PMCID: PMC8520512 DOI: 10.1007/s11547-021-01389-x] [Citation(s) in RCA: 170] [Impact Index Per Article: 56.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/15/2021] [Indexed: 11/29/2022]
Abstract
Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It is an evolving field of research with many potential applications in medical imaging. The purpose of this review is to offer a deep look into radiomics, from the basis, deeply discussed from a technical point of view, through the main applications, to the challenges that have to be addressed to translate this process in clinical practice. A detailed description of the main techniques used in the various steps of radiomics workflow, which includes image acquisition, reconstruction, pre-processing, segmentation, features extraction and analysis, is here proposed, as well as an overview of the main promising results achieved in various applications, focusing on the limitations and possible solutions for clinical implementation. Only an in-depth and comprehensive description of current methods and applications can suggest the potential power of radiomics in fostering precision medicine and thus the care of patients, especially in cancer detection, diagnosis, prognosis and treatment evaluation.
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Affiliation(s)
- Camilla Scapicchio
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Andrea Barucci
- CNR-IFAC Institute of Applied Physics "N. Carrara", 50019, Sesto Fiorentino, Italy
| | - Dania Cioni
- Academic Radiology, Department of Surgical, Medical, Molecular Pathology and Emergency Medicine, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Monserrato (Cagliari),Cagliari, Italy
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122, Milano, Italy
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18
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Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation. Phys Eng Sci Med 2021; 44:745-754. [PMID: 34075559 DOI: 10.1007/s13246-021-01022-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 05/26/2021] [Indexed: 12/28/2022]
Abstract
The purpose of this study was to develop Bi-parametric Magnetic Resonance Imaging (BP-MRI) based radiomics models for differentiation between benign and malignant prostate lesions, and to cross-vendor validate the generalization ability of the models. The prebiopsy BP-MRI data (T2-Weighted Image [T2WI] and the Apparent Diffusion Coefficient [ADC]) of 459 patients with clinical suspicion of prostate cancer were acquired using two scanners from different vendors. The prostate biopsies are the reference standard for diagnosing benign and malignant prostate lesions. The training set was 168 patients' data from Siemens (Vendor 1), and the inner test set was 70 patients' data from the same vendor. The external test set was 221 patients' data from GE (Vendor 2). The lesion Region of Interest (ROI) was manually delineated by experienced radiologists. A total of 851 radiomics features including shape, first-order statistical, texture, and wavelet features were extracted from ROI in T2WI and ADC, respectively. Two feature-ranking methods (Minimum Redundancy Maximum Relevance [MRMR] and Wilcoxon Rank-Sum Test [WRST]) and three classifiers (Random Forest [RF], Support Vector Machine [SVM], and the Least Absolute Shrinkage and Selection Operator [LASSO] regression) were investigated for their efficacy in building single-parametric radiomics signatures. A biparametric radiomics model was built by combining the optimal single-parametric radiomics signatures. A comprehensive diagnosis model was built by combining the biparametric radiomics model with age and Prostate Specific Antigen (PSA) value using multivariable logistic regression. All models were built in the training set and independently validated in the inner and external test sets, and the performances of models in the diagnosis of benign and malignant prostate lesions were quantified by the Area Under the Receiver Operating Characteristic Curve (AUC). The mean AUCs of the inner and external test sets were calculated for each model. The non-inferiority test was used to test if the AUC of model in external test was not inferior to the AUC of model in inner test. Combining MRMR and LASSO produced the best-performing single-parametric radiomics signatures with the highest mean AUC of 0.673 for T2WI (inner test AUC = 0.729 vs. external test AUC = 0.616, p = 0.569) and the highest mean AUC of 0.810 for ADC (inner test AUC = 0.822 vs. external test AUC = 0.797, p = 0.102). The biparametric radiomics model produced a mean AUC of 0.833 (inner test AUC = 0.867 vs. external test AUC = 0.798, p = 0.051). The comprehensive diagnosis model had an improved mean AUC of 0.911 (inner test AUC = 0.935 vs. external test AUC = 0.886, p = 0.010). The comprehensive diagnosis model for differentiating benign from malignant prostate lesions was accurate and generalizable.
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