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Stern NM, Mikalsen LTG, Dueland S, Schulz A, Line PD, Stokke C, Grut H. The prognostic value of [ 18F]FDG PET/CT texture analysis prior to transplantation for unresectable colorectal liver metastases. Clin Physiol Funct Imaging 2024. [PMID: 39358976 DOI: 10.1111/cpf.12908] [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: 01/05/2024] [Revised: 08/21/2024] [Accepted: 09/18/2024] [Indexed: 10/04/2024]
Abstract
INTRODUCTION To determine whether heterogeneity in colorectal liver metastases (CRLM) 18F fluorodeoxyglucose [18F]FDG distribution is predictive of disease-free survival (DFS) and overall survival (OS) following liver transplantation (LT) for unresectable CRLM. METHODS The preoperative [18F]FDG positron emission tomography/computed tomography examinations of all patients in the secondary cancer 1 and 2 studies were retrospectively assessed. Maximum standardized uptake value (SUVmax), metabolic tumour volume (MTV), and six texture heterogeneity parameters (joint entropyGLCM, dissimilarityGLCM, grey level varianceSZM, size zone varianceSZM, and zone percentageSZM, and morphological feature convex deficiency) were obtained. DFS and OS for patients over and under the median value for each of these parameters were compared by using the Kaplan Meier method and log rank test. RESULTS Twenty-eight out of 40 patients who underwent LT for unresectable CRLM had liver metastases with uptake above liver background and were eligible for inclusion. Low MTV (p < 0.001) and dissimilarityGLCM (p = 0.016) were correlated to longer DFS. Low MTV (p < 0.001) and low values of the texture parameters dissimilarityGLCM (p = 0.038), joint entropyGLCM (p = 0.015) and zone percentageSZM (p = 0.037) were significantly correlated to longer OS. SUVmax was not correlated to DFS and OS. CONCLUSION Although some texture parameters were able to significantly predict DFS and OS, MTV seems to be superior to predict both DFS and OS following LT for unresectable CRLM.
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Affiliation(s)
- Nadide Mutlukoca Stern
- Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Svein Dueland
- Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
| | - A Schulz
- Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Pål-Dag Line
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
| | - Caroline Stokke
- Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
- Department of Physics, University of Oslo, Oslo, Norway
| | - Harald Grut
- Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway
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Ma Y, Shi Z, Wei Y, Shi F, Qin G, Zhou Z. Exploring the value of multiple preprocessors and classifiers in constructing models for predicting microsatellite instability status in colorectal cancer. Sci Rep 2024; 14:20305. [PMID: 39218940 PMCID: PMC11366760 DOI: 10.1038/s41598-024-71420-4] [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: 02/20/2024] [Accepted: 08/28/2024] [Indexed: 09/04/2024] Open
Abstract
Approximately 15% of patients with colorectal cancer (CRC) exhibit a distinct molecular phenotype known as microsatellite instability (MSI). Accurate and non-invasive prediction of MSI status is crucial for cost savings and guiding clinical treatment strategies. The retrospective study enrolled 307 CRC patients between January 2020 and October 2022. Preoperative images of computed tomography and postoperative status of MSI information were available for analysis. The stratified fivefold cross-validation was used to avoid sample bias in grouping. Feature extraction and model construction were performed as follows: first, inter-/intra-correlation coefficients and the least absolute shrinkage and selection operator algorithm were used to identify the most predictive feature subset. Subsequently, multiple discriminant models were constructed to explore and optimize the combination of six feature preprocessors (Box-Cox, Yeo-Johnson, Max-Abs, Min-Max, Z-score, and Quantile) and three classifiers (logistic regression, support vector machine, and random forest). Selecting the one with the highest average value of the area under the curve (AUC) in the test set as the radiomics model, and the clinical screening model and combined model were also established using the same processing steps as the radiomics model. Finally, the performances of the three models were evaluated and analyzed using decision and correction curves.We observed that the logistic regression model based on the quantile preprocessor had the highest average AUC value in the discriminant models. Additionally, tumor location, the clinical of N stage, and hypertension were identified as independent clinical predictors of MSI status. In the test set, the clinical screening model demonstrated good predictive performance, with the average AUC of 0.762 (95% confidence interval, 0.635-0.890). Furthermore, the combined model showed excellent predictive performance (AUC, 0.958; accuracy, 0.899; sensitivity, 0.929) and favorable clinical applicability and correction effects. The logistic regression model based on the quantile preprocessor exhibited excellent performance and repeatability, which may further reduce the variability of input data and improve the model performance for predicting MSI status in CRC.
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Affiliation(s)
- Yi Ma
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China
| | - Zhihao Shi
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., 701 Yunjin Rd, Xuhui District, Shanghai, 200232, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., 701 Yunjin Rd, Xuhui District, Shanghai, 200232, China
| | - Guochu Qin
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China.
| | - Zhengyang Zhou
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China.
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Wang B, Hu T, Shen R, Liu L, Qiao J, Zhang R, Zhang Z. A 18F-FDG PET/CT based radiomics nomogram for predicting disease-free survival in stage II/III colorectal adenocarcinoma. Abdom Radiol (NY) 2024:10.1007/s00261-024-04515-1. [PMID: 39096393 DOI: 10.1007/s00261-024-04515-1] [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: 06/15/2024] [Revised: 07/23/2024] [Accepted: 07/30/2024] [Indexed: 08/05/2024]
Abstract
OBJECTIVES This study aimed to establish a clinical nomogram model based on a radiomics signatures derived from 18F-fluorodeoxyglucose positron-emission tomography (18F-FDG PET/CT) and clinical parameters to predict disease-free survival (DFS) in patients with stage II/III colorectal adenocarcinoma. Understanding and predicting DFS in these patients is key to optimizing treatment strategies. METHODS A retrospective analysis included 332 cases from July 2011 to July 2021 at The Sixth Affiliated Hospital, Sun Yat-sen University, with PET/CT assessing radiomics features and clinicopathological features. Univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox, and multivariable Cox regression identified recurrence-related radiomics features. We used a weighted radiomics score (Rad-score) and independent risk factors to construct a nomogram. Evaluation involved time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS The nomogram, incorporating Rad-score, pN, and pT demonstrated robust predictive ability for DFS in stage II/III colorectal adenocarcinoma. Training cohort areas under the curve (AUCs) were 0.78, 0.80, and 0.86 at 1, 2, and 3 years, respectively, and validation cohort AUCs were 0.79, 0.75, and 0.73. DCA and calibration curves affirmed the nomogram's clinical relevance. CONCLUSION The 18F-FDG PET/CT based radiomics nomogram, including Rad-score, pN, and pT, effectively predicted tumor recurrence in stage II/III colorectal adenocarcinoma, significantly enhancing prognostic stratification. Our findings highlight the potential of this nomogram as a guide for clinical decision making to improve patient outcomes.
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Affiliation(s)
- Bing Wang
- The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tianyuan Hu
- The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Rongfang Shen
- The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- The First People's Hospital of Xinjiang Kashgar Area, Kashgar, Xinjiang, China
| | - Lian Liu
- The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Junwei Qiao
- The First People's Hospital of Xinjiang Kashgar Area, Kashgar, Xinjiang, China
| | - Rongqin Zhang
- The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
| | - Zhanwen Zhang
- The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
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Ramlee S, Manavaki R, Aloj L, Escudero Sanchez L. Mitigating the impact of image processing variations on tumour [ 18F]-FDG-PET radiomic feature robustness. Sci Rep 2024; 14:16294. [PMID: 39009706 PMCID: PMC11251269 DOI: 10.1038/s41598-024-67239-8] [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: 01/30/2024] [Accepted: 07/09/2024] [Indexed: 07/17/2024] Open
Abstract
Radiomics analysis of [18F]-fluorodeoxyglucose ([18F]-FDG) PET images could be leveraged for personalised cancer medicine. However, the inherent sensitivity of radiomic features to intensity discretisation and voxel interpolation complicates its clinical translation. In this work, we evaluated the robustness of tumour [18F]-FDG-PET radiomic features to 174 different variations in intensity resolution or voxel size, and determined whether implementing parameter range conditions or dependency corrections could improve their robustness. Using 485 patient images spanning three cancer types: non-small cell lung cancer (NSCLC), melanoma, and lymphoma, we observed features were more sensitive to intensity discretisation than voxel interpolation, especially texture features. In most of our investigations, the majority of non-robust features could be made robust by applying parameter range conditions. Correctable features, which were generally fewer than conditionally robust, showed systematic dependence on bin configuration or voxel size that could be minimised by applying corrections based on simple mathematical equations. Melanoma images exhibited limited robustness and correctability relative to NSCLC and lymphoma. Our study provides an in-depth characterisation of the sensitivity of [18F]-FDG-PET features to image processing variations and reinforces the need for careful selection of imaging biomarkers prior to any clinical application.
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Affiliation(s)
- Syafiq Ramlee
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK.
| | - Roido Manavaki
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Luigi Aloj
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Lorena Escudero Sanchez
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
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Amrane K, Meur CL, Thuillier P, Berthou C, Uguen A, Deandreis D, Bourhis D, Bourbonne V, Abgral R. Review on radiomic analysis in 18F-fluorodeoxyglucose positron emission tomography for prediction of melanoma outcomes. Cancer Imaging 2024; 24:87. [PMID: 38970050 PMCID: PMC11225300 DOI: 10.1186/s40644-024-00732-5] [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: 11/02/2023] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
Over the past decade, several strategies have revolutionized the clinical management of patients with cutaneous melanoma (CM), including immunotherapy and targeted tyrosine kinase inhibitor (TKI)-based therapies. Indeed, immune checkpoint inhibitors (ICIs), alone or in combination, represent the standard of care for patients with advanced disease without an actionable mutation. Notably BRAF combined with MEK inhibitors represent the therapeutic standard for disease disclosing BRAF mutation. At the same time, FDG PET/CT has become part of the routine staging and evaluation of patients with cutaneous melanoma. There is growing interest in using FDG PET/CT measurements to predict response to ICI therapy and/or target therapy. While semiquantitative values such as standardized uptake value (SUV) are limited for predicting outcome, new measures including tumor metabolic volume, total lesion glycolysis and radiomics seem promising as potential imaging biomarkers for nuclear medicine. The aim of this review, prepared by an interdisciplinary group of experts, is to take stock of the current literature on radiomics approaches that could improve outcomes in CM.
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Affiliation(s)
- Karim Amrane
- Department of Oncology, Regional Hospital of Morlaix, Morlaix, 29600, France.
- Lymphocytes B et Autoimmunité, Inserm, UMR1227, Univ Brest, Inserm, LabEx IGO, Brest, France.
| | - Coline Le Meur
- Department of Radiotherapy, University Hospital of Brest, Brest, France
| | - Philippe Thuillier
- Department of Endocrinology, University Hospital of Brest, Brest, France
- UMR Inserm 1304 GETBO, University of Western Brittany, Brest, IFR 148, France
| | - Christian Berthou
- Lymphocytes B et Autoimmunité, Inserm, UMR1227, Univ Brest, Inserm, LabEx IGO, Brest, France
- Department of Hematology, University Hospital of Brest, Brest, France
| | - Arnaud Uguen
- Lymphocytes B et Autoimmunité, Inserm, UMR1227, Univ Brest, Inserm, LabEx IGO, Brest, France
- Department of Pathology, University Hospital of Brest, Brest, France
| | - Désirée Deandreis
- Department of Nuclear Medicine, Gustave Roussy Institute, University of Paris Saclay, Paris, France
| | - David Bourhis
- UMR Inserm 1304 GETBO, University of Western Brittany, Brest, IFR 148, France
- Department of Nuclear Medicine, University Hospital of Brest, Brest, France
| | - Vincent Bourbonne
- Department of Radiotherapy, University Hospital of Brest, Brest, France
- Inserm, UMR1101, LaTIM, University of Western Brittany, Brest, France
| | - Ronan Abgral
- UMR Inserm 1304 GETBO, University of Western Brittany, Brest, IFR 148, France
- Department of Nuclear Medicine, University Hospital of Brest, Brest, France
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Mao J, Ye W, Ma W, Liu J, Zhong W, Yuan H, Li T, Guan L, Wu D. Prediction by a multiparametric magnetic resonance imaging-based radiomics signature model of disease-free survival in patients with rectal cancer treated by surgery. Front Oncol 2024; 14:1255438. [PMID: 38454930 PMCID: PMC10917947 DOI: 10.3389/fonc.2024.1255438] [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: 07/08/2023] [Accepted: 01/31/2024] [Indexed: 03/09/2024] Open
Abstract
Objective The aim of this study was to assess the ability of a multiparametric magnetic resonance imaging (MRI)-based radiomics signature model to predict disease-free survival (DFS) in patients with rectal cancer treated by surgery. Materials and methods We evaluated data of 194 patients with rectal cancer who had undergone radical surgery between April 2016 and September 2021. The mean age of all patients was 62.6 ± 9.7 years (range: 37-86 years). The study endpoint was DFS and 1132 radiomic features were extracted from preoperative MRIs, including contrast-enhanced T1- and T2-weighted imaging and apparent diffusion coefficient values. The study patients were randomly allocated to training (n=97) and validation cohorts (n=97) in a ratio of 5:5. A multivariable Cox regression model was used to generate a radiomics signature (rad score). The associations of rad score with DFS were evaluated using Kaplan-Meier analysis. Three models, namely a radiomics nomogram, radiomics signature, and clinical model, were compared using the Akaike information criterion. Result The rad score, which was composed of four MRI features, stratified rectal cancer patients into low- and high-risk groups and was associated with DFS in both the training (p = 0.0026) and validation sets (p = 0.036). Moreover, a radiomics nomogram model that combined rad score and independent clinical risk factors performed better (Harrell concordance index [C-index] =0.77) than a purely radiomics signature (C-index=0.73) or clinical model (C-index=0.70). Conclusion An MRI radiomics model that incorporates a radiomics signature and clinicopathological factors more accurately predicts DFS than does a clinical model in patients with rectal cancer.
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Affiliation(s)
- Jiwei Mao
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Wanli Ye
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Weili Ma
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Jianjiang Liu
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Wangyan Zhong
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Hang Yuan
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Ting Li
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
| | - Le Guan
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Dongping Wu
- Department of Radiation Oncology, Shaoxing People’s Hospital, Shaoxing, China
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Xu C, Feng J, Yue Y, Cheng W, He D, Qi S, Zhang G. A hybrid few-shot multiple-instance learning model predicting the aggressiveness of lymphoma in PET/CT images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107872. [PMID: 37922655 DOI: 10.1016/j.cmpb.2023.107872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 09/29/2023] [Accepted: 10/16/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Patients with aggressive non-Hodgkin lymphoma (NHL) undergo distinct therapy strategies compared with indolent NHL patients. However, it is challenging to estimate NHL aggressiveness based on visual inspection of positron emission tomography (PET) or computed tomography (CT) images. Since diffuse large B-cell lymphoma (DLBCL) and Follicular lymphoma (FL) are the most typical and dominant aggressive and indolent NHL, respectively, this study aims to develop an artificial-intelligence-enabled model to distinguish DLBCL from FL in PET/CT images as the first step to tackle this challenge. METHODS We propose a hybrid few-shot multiple-instance learning model to predict the aggressiveness of the NHL. First, rotation-based self-supervision learning (SSL) has been employed to train the encoder on a large-scale, publicly available CT image dataset. Second, hybrid instance-level features are obtained for each NHL lesion by combining deep features with the radiomics features from both PET and CT modalities. Third, instance-level features are transformed into bag-level (or patient-level) representations. Finally, bag-level representations are fed into a distance-based classifier through few-shot learning to predict NHL aggressiveness. RESULTS Our model achieves an accuracy of 0.751 ± 0.008, a sensitivity of 0.787 ± 0.012, a specificity of 0.715 ± 0.013, an F1-score of 0.753 ± 0.009, and an area under the curve (AUC) of 0.795 ± 0.009 at the bag level. It outperforms the typical counterparts that use the radiomic features, random forest for feature selection, and support vector machines (SVMs) as classifiers. The three counterparts yield accuracies of 0.714 ± 0.023, 0.705 ± 0.008, and 0.698 ± 0.008, respectively. Moreover, settings of the SSL training dataset (Deep lesion) and task (rotation), hybrid CT and radiomic PET features, the pool-layer strategy of maximum, and distance-based classifier generate the best model. CONCLUSIONS A hybrid few-shot multiple-instance learning model can predict lymphoma aggressiveness in PET/CT images and could be a potential tool for determining therapy strategies. Hybrid features and the combination of SSL, few-shot learning, and weakly supervised learning are the two powerful pillars of the model, and these can be expanded to other medical applications with limited samples and incomplete annotations.
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Affiliation(s)
- Caiwen Xu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Jie Feng
- School of Chemical Equipment, Shenyang University of Technology, Liaoyang, China
| | - Yong Yue
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Wanjun Cheng
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Dianning He
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Guojun Zhang
- Department of Hematology, Shengjing Hospital of China Medical University, Shenyang, China.
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Bomhals B, Cossement L, Maes A, Sathekge M, Mokoala KMG, Sathekge C, Ghysen K, Van de Wiele C. Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules. J Clin Med 2023; 12:7731. [PMID: 38137800 PMCID: PMC10743692 DOI: 10.3390/jcm12247731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/11/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
Here, we report on the added value of principal component analysis applied to a dataset of texture features derived from 39 solitary pulmonary lung nodule (SPN) lesions for the purpose of differentiating benign from malignant lesions, as compared to the use of SUVmax alone. Texture features were derived using the LIFEx software. The eight best-performing first-, second-, and higher-order features for separating benign from malignant nodules, in addition to SUVmax (MaximumGreyLevelSUVbwIBSI184IY), were included for PCA. Two principal components (PCs) were retained, of which the contributions to the total variance were, respectively, 87.6% and 10.8%. When included in a logistic binomial regression analysis, including age and gender as covariates, both PCs proved to be significant predictors for the underlying benign or malignant character of the lesions under study (p = 0.009 for the first PC and 0.020 for the second PC). As opposed to SUVmax alone, which allowed for the accurate classification of 69% of the lesions, the regression model including both PCs allowed for the accurate classification of 77% of the lesions. PCs derived from PCA applied on selected texture features may allow for more accurate characterization of SPN when compared to SUVmax alone.
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Affiliation(s)
- Birte Bomhals
- Department of Diagnostic Sciences, University Ghent, 9000 Ghent, Belgium; (B.B.); (L.C.)
| | - Lara Cossement
- Department of Diagnostic Sciences, University Ghent, 9000 Ghent, Belgium; (B.B.); (L.C.)
| | - Alex Maes
- Department of Morphology and Functional Imaging, University Hospital Leuven, 3000 Leuven, Belgium;
- Department of Nuclear Medicine, Katholieke University Leuven, AZ Groeninge, President Kennedylaan 4, 8500 Kortrijk, Belgium
| | - Mike Sathekge
- Department of Nuclear Medicine, Steve Biko Academic Hospital and Nuclear Medicine Research Infrastructure (NuMeRi), University of Pretoria, Pretoria 0002, South Africa
| | - Kgomotso M. G. Mokoala
- Department of Nuclear Medicine, Steve Biko Academic Hospital and Nuclear Medicine Research Infrastructure (NuMeRi), University of Pretoria, Pretoria 0002, South Africa
| | - Chabi Sathekge
- Department of Nuclear Medicine, Steve Biko Academic Hospital and Nuclear Medicine Research Infrastructure (NuMeRi), University of Pretoria, Pretoria 0002, South Africa
| | - Katrien Ghysen
- Department of Pneumology, AZ Groeninge, 8500 Kortrijk, Belgium
| | - Christophe Van de Wiele
- Department of Diagnostic Sciences, University Ghent, 9000 Ghent, Belgium; (B.B.); (L.C.)
- Department of Nuclear Medicine, Katholieke University Leuven, AZ Groeninge, President Kennedylaan 4, 8500 Kortrijk, Belgium
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9
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Rogasch JMM, Shi K, Kersting D, Seifert R. Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET). Nuklearmedizin 2023; 62:361-369. [PMID: 37995708 PMCID: PMC10667066 DOI: 10.1055/a-2198-0545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 10/25/2023] [Indexed: 11/25/2023]
Abstract
AIM Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described in publications on radiomics and machine learning for PET-based outcome prediction. METHODS A systematic search for original articles was run on PubMed. All articles were rated according to 17 criteria proposed by the authors. Criteria with >2 rating categories were binarized into "adequate" or "inadequate". The association between the number of "adequate" criteria per article and the date of publication was examined. RESULTS One hundred articles were identified (published between 07/2017 and 09/2023). The median proportion of articles per criterion that were rated "adequate" was 65% (range: 23-98%). Nineteen articles (19%) mentioned neither a test cohort nor cross-validation to separate training from testing. The median number of criteria with an "adequate" rating per article was 12.5 out of 17 (range, 4-17), and this did not increase with later dates of publication (Spearman's rho, 0.094; p = 0.35). In 22 articles (22%), less than half of the items were rated "adequate". Only 8% of articles published the source code, and 10% made the dataset openly available. CONCLUSION Among the articles investigated, methodological weaknesses have been identified, and the degree of compliance with recommendations on methodological quality and reporting shows potential for improvement. Better adherence to established guidelines could increase the clinical significance of radiomics and machine learning for PET-based outcome prediction and finally lead to the widespread use in routine clinical practice.
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Affiliation(s)
- Julian Manuel Michael Rogasch
- Department of Nuclear Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital University Hospital Bern, Bern, Switzerland
| | - David Kersting
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Robert Seifert
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
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Amintas S, Giraud N, Fernandez B, Dupin C, Denost Q, Garant A, Frulio N, Smith D, Rullier A, Rullier E, Vuong T, Dabernat S, Vendrely V. The Crying Need for a Better Response Assessment in Rectal Cancer. Curr Treat Options Oncol 2023; 24:1507-1523. [PMID: 37702885 PMCID: PMC10643426 DOI: 10.1007/s11864-023-01125-9] [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] [Accepted: 07/09/2023] [Indexed: 09/14/2023]
Abstract
OPINION STATEMENT Since total neoadjuvant treatment achieves almost 30% pathologic complete response, organ preservation has been increasingly debated for good responders after neoadjuvant treatment for patients diagnosed with rectal cancer. Two organ preservation strategies are available: a watch and wait strategy and a local excision strategy including patients with a near clinical complete response. A major issue is the selection of patients according to the initial tumor staging or the response assessment. Despite modern imaging improvement, identifying complete response remains challenging. A better selection could be possible by radiomics analyses, exploiting numerous image features to feed data characterization algorithms. The subsequent step is to include baseline and/or pre-therapeutic MRI, PET-CT, and CT radiomics added to the patients' clinicopathological data, inside machine learning (ML) prediction models, with predictive or prognostic purposes. These models could be further improved by the addition of new biomarkers such as circulating tumor biomarkers, molecular profiling, or pathological immune biomarkers.
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Affiliation(s)
- Samuel Amintas
- Tumor Biology and Tumor Bank Laboratory, CHU Bordeaux, F-33600, Pessac, France.
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France.
| | - Nicolas Giraud
- Department of Radiation Oncology, CHU Bordeaux, F-33000, Bordeaux, France
| | | | - Charles Dupin
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Department of Radiation Oncology, CHU Bordeaux, F-33000, Bordeaux, France
| | - Quentin Denost
- Bordeaux Colorectal Institute, F-33000, Bordeaux, France
| | - Aurelie Garant
- UT Southwestern Department of Radiation Oncology, Dallas, USA
| | - Nora Frulio
- Radiology Department, CHU Bordeaux, F-33600, Pessac, France
| | - Denis Smith
- Department of Digestive Oncology, CHU Bordeaux, F-33600, Pessac, France
| | - Anne Rullier
- Histology Department, CHU Bordeaux, F-33000, Bordeaux, France
| | - Eric Rullier
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Surgery Department, CHU Bordeaux, F-33600, Pessac, France
| | - Te Vuong
- Department of Radiation Oncology, McGill University, Jewish General Hospital, Montreal, Canada
| | - Sandrine Dabernat
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Biochemistry Department, CHU Bordeaux, F-33000, Bordeaux, France
| | - Véronique Vendrely
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Department of Radiation Oncology, CHU Bordeaux, F-33000, Bordeaux, France
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11
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Cao Y, Zhang J, Huang L, Zhao Z, Zhang G, Ren J, Li H, Zhang H, Guo B, Wang Z, Xing Y, Zhou J. Construction of prediction model for KRAS mutation status of colorectal cancer based on CT radiomics. Jpn J Radiol 2023; 41:1236-1246. [PMID: 37311935 PMCID: PMC10613595 DOI: 10.1007/s11604-023-01458-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 06/04/2023] [Indexed: 06/15/2023]
Abstract
BACKGROUND In this study, we used computed tomography (CT)-based radiomics signatures to predict the mutation status of KRAS in patients with colorectal cancer (CRC) and to identify the phase of radiomics signature with the most robust and high performance from triphasic enhanced CT. METHODS This study involved 447 patients who underwent KRAS mutation testing and preoperative triphasic enhanced CT. They were categorized into training (n = 313) and validation cohorts (n = 134) in a 7:3 ratio. Radiomics features were extracted using triphasic enhanced CT imaging. The Boruta algorithm was used to retain the features closely associated with KRAS mutations. The Random Forest (RF) algorithm was used to develop radiomics, clinical, and combined clinical-radiomics models for KRAS mutations. The receiver operating characteristic curve, calibration curve, and decision curve were used to evaluate the predictive performance and clinical usefulness of each model. RESULTS Age, CEA level, and clinical T stage were independent predictors of KRAS mutation status. After rigorous feature screening, four arterial phase (AP), three venous phase (VP), and seven delayed phase (DP) radiomics features were retained as the final signatures for predicting KRAS mutations. The DP models showed superior predictive performance compared to AP or VP models. The clinical-radiomics fusion model showed excellent performance, with an AUC, sensitivity, and specificity of 0.772, 0.792, and 0.646 in the training cohort, and 0.755, 0.724, and 0.684 in the validation cohort, respectively. The decision curve showed that the clinical-radiomics fusion model had more clinical practicality than the single clinical or radiomics model in predicting KRAS mutation status. CONCLUSION The clinical-radiomics fusion model, which combines the clinical and DP radiomics model, has the best predictive performance for predicting the mutation status of KRAS in CRC, and the constructed model has been effectively verified by an internal validation cohort.
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Affiliation(s)
- Yuntai Cao
- Department of Radiology, Affiliated Hospital of Qinghai University, Tongren Road No. 29, Xining, 810001, People's Republic of China.
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, People's Republic of China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, People's Republic of China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, People's Republic of China.
| | - Jing Zhang
- The Fifth Affiliated Hospital of Zunyi Medical University, Zunyi, 519100, People's Republic of China
| | - Lele Huang
- Department of Nuclear Medicine, Lanzhou University Second Hospital, Lanzhou, China
| | - Zhiyong Zhao
- Department of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, China
| | - Guojin Zhang
- Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Beijing, China
| | - Hailong Li
- Affiliated Hospital of Qinghai University, Xining, China
| | - Hongqian Zhang
- Affiliated Hospital of Qinghai University, Xining, China
| | - Bin Guo
- Affiliated Hospital of Qinghai University, Xining, China
| | - Zhan Wang
- Affiliated Hospital of Qinghai University, Xining, China
| | - Yue Xing
- Xinxiang Medical University, Henan, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, People's Republic of China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, People's Republic of China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, People's Republic of China.
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12
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Lucia F, Lovinfosse P, Schick U, Le Pennec R, Pradier O, Salaun PY, Hustinx R, Bourbonne V. Radiotherapy modification based on artificial intelligence and radiomics applied to ( 18F)-fluorodeoxyglucose positron emission tomography/computed tomography. Cancer Radiother 2023; 27:542-547. [PMID: 37481344 DOI: 10.1016/j.canrad.2023.06.001] [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: 05/30/2023] [Revised: 06/06/2023] [Accepted: 06/07/2023] [Indexed: 07/24/2023]
Abstract
Over the last decades, the refinement of radiation therapy techniques has been associated with an increasing interest for individualized radiation therapy with the aim of increasing or maintaining tumor control and reducing radiation toxicity. Developments in artificial intelligence (AI), particularly machine learning and deep learning, in imaging sciences, including nuclear medecine, have led to significant enthusiasm for the concept of "rapid learning health system". AI combined with radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]-FDG PET/CT) offers a unique opportunity for the development of predictive models that can help stratify each patient's risk and guide treatment decisions for optimal outcomes and quality of life of patients treated with radiation therapy. Here we present an overview of the current contribution of AI and radiomics-based machine learning models applied to (18F)-FDG PET/CT in the management of cancer treated by radiation therapy.
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Affiliation(s)
- F Lucia
- Radiation Oncology Department, CHU de Brest, 29200 Brest, France; LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France; Division of Nuclear Medicine and Oncological Imaging, centre hospitalier universitaire de Liège, Liège, Belgium.
| | - P Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, centre hospitalier universitaire de Liège, Liège, Belgium
| | - U Schick
- Radiation Oncology Department, CHU de Brest, 29200 Brest, France; LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France
| | - R Le Pennec
- Service de médecine nucléaire, CHU de Brest, Inserm UMR 1304 (Getbo), université de Bretagne Occidentale, Brest, France
| | - O Pradier
- Radiation Oncology Department, CHU de Brest, 29200 Brest, France; LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France
| | - P-Y Salaun
- Service de médecine nucléaire, CHU de Brest, Inserm UMR 1304 (Getbo), université de Bretagne Occidentale, Brest, France
| | - R Hustinx
- Division of Nuclear Medicine and Oncological Imaging, centre hospitalier universitaire de Liège, Liège, Belgium
| | - V Bourbonne
- Radiation Oncology Department, CHU de Brest, 29200 Brest, France; LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France
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13
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Inchingolo R, Maino C, Cannella R, Vernuccio F, Cortese F, Dezio M, Pisani AR, Giandola T, Gatti M, Giannini V, Ippolito D, Faletti R. Radiomics in colorectal cancer patients. World J Gastroenterol 2023; 29:2888-2904. [PMID: 37274803 PMCID: PMC10237092 DOI: 10.3748/wjg.v29.i19.2888] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/07/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
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Affiliation(s)
- Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Francesco Cortese
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Michele Dezio
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Antonio Rosario Pisani
- Interdisciplinary Department of Medicine, Section of Nuclear Medicine, University of Bari “Aldo Moro”, Bari 70121, Italy
| | - Teresa Giandola
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
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14
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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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15
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Agüloğlu N, Aksu A. Evaluation of survival of the patients with metastatic rectal cancer by staging 18F-FDG PET/CT radiomic and volumetric parameters. Rev Esp Med Nucl Imagen Mol 2023; 42:122-128. [PMID: 36162744 DOI: 10.1016/j.remnie.2022.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 09/06/2022] [Accepted: 09/07/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVE The aim of this study is to predict the prognosis in patients with metastatic rectal cancer (mRC) by obtaining a model with machine learning (ML) algorithms through volumetric and radiomic data obtained from baseline 18-Fluorine Fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) images. METHODS Sixty-two patients with mRC who underwent 18F-FDG PET/CT imaging for staging between January 2015 and January 2021 were evaluated using LIFEx software. The volume of interest (VOI) of the primary tumor was generated and volumetric and textural features were obtained from this VOI. In addition, metabolic tumor volume (tMTV) and total lesion glycolysis (tTLG) values of tumor foci in the whole body. Clinical and radiomic data were evaluated with ML algorithms to create a model that predicts survival. Significant associations between these features and 1-year and 2-year survival were investigated. RESULTS Random forest algorithm was the most successful algorithm in predicting 2-year survival (AUC: 0.843, PRC: 0.822, and MCC: 0.583). The model obtained with this algorithm was able to predict 49 patients with 79.03% accuracy. While tMTV and tTLG values were successful in predicting 1-year survival (p: 0.002 and 0.007, respectively), texture characteristics from the primary tumor did not show a significant relationship with 1-year survival. CONCLUSIONS In addition to the important role of 18F-FDG PET/CT in staging patients with mRC, this study shows that it is possible to predict survival with ML methods, with parameters obtained using texture analysis from the primary tumor and whole body volumetric parameters.
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Affiliation(s)
- Nurşin Agüloğlu
- The University of Health Sciences, Dr. Suat Seren Chest Diseases and Surgery Training and Research Hospital, Department of Nuclear Medicine, İzmir, Turkey.
| | - Ayşegül Aksu
- İzmir Katip Çelebi University, Atatürk Training and Research Hospital, Department of Nuclear Medicine, İzmir, Turkey
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16
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Heterogeneidad del tumor primario en la18F-FDG PET/TC pretratamiento para predecir el pronóstico en pacientes con cáncer de recto sometidos a cirugía tras terapia neoadyuvante. Rev Esp Med Nucl Imagen Mol 2023. [DOI: 10.1016/j.remn.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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17
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Zhang S, Mu W, Dong D, Wei J, Fang M, Shao L, Zhou Y, He B, Zhang S, Liu Z, Liu J, Tian J. The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review. HEALTH DATA SCIENCE 2023; 3:0005. [PMID: 38487199 PMCID: PMC10877701 DOI: 10.34133/hds.0005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/05/2022] [Indexed: 03/17/2024]
Abstract
Importance Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity. Highlights We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma. Conclusion AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.
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Affiliation(s)
- Shuaitong Zhang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Wei Mu
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yu Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bingxi He
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Song Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jianhua Liu
- Department of Oncology, Guangdong Provincial People's Hospital/Second Clinical Medical College of Southern Medical University/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Meng Y, Sun J, Zhang G, Yu T, Piao H. Imaging glucose metabolism to reveal tumor progression. Front Physiol 2023; 14:1103354. [PMID: 36818450 PMCID: PMC9932271 DOI: 10.3389/fphys.2023.1103354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 01/20/2023] [Indexed: 02/05/2023] Open
Abstract
Purpose: To analyze and review the progress of glucose metabolism-based molecular imaging in detecting tumors to guide clinicians for new management strategies. Summary: When metabolic abnormalities occur, termed the Warburg effect, it simultaneously enables excessive cell proliferation and inhibits cell apoptosis. Molecular imaging technology combines molecular biology and cell probe technology to visualize, characterize, and quantify processes at cellular and subcellular levels in vivo. Modern instruments, including molecular biochemistry, data processing, nanotechnology, and image processing, use molecular probes to perform real-time, non-invasive imaging of molecular and cellular events in living organisms. Conclusion: Molecular imaging is a non-invasive method for live detection, dynamic observation, and quantitative assessment of tumor glucose metabolism. It enables in-depth examination of the connection between the tumor microenvironment and tumor growth, providing a reliable assessment technique for scientific and clinical research. This new technique will facilitate the translation of fundamental research into clinical practice.
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Affiliation(s)
- Yiming Meng
- Central Laboratory, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang, China
| | - Jing Sun
- Central Laboratory, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang, China
| | - Guirong Zhang
- Central Laboratory, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang, China
| | - Tao Yu
- Department of Medical Image, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang, China,*Correspondence: Tao Yu, ; Haozhe Piao,
| | - Haozhe Piao
- Department of Neurosurgery, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang, China,*Correspondence: Tao Yu, ; Haozhe Piao,
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19
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Gülbahar Ateş S, Bilir Dilek G, Uçmak G. Primary tumor heterogeneity on pretreatment 18F-FDG PET/CT to predict outcome in patients with rectal cancer who underwent surgery after neoadjuvant therapy. Rev Esp Med Nucl Imagen Mol 2023:S2253-8089(23)00001-0. [PMID: 36690032 DOI: 10.1016/j.remnie.2023.01.001] [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] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 01/21/2023]
Abstract
PURPOSE This retrospective study aimed to investigate the value of texture features of primary tumors in pretreatment 18F-FDG PET/CT in the prediction of response to treatment, progression, and overall survival in patients with rectal cancer who underwent surgery after neoadjuvant therapy(NAT). METHODS Patients with rectal cancer who had pretreatment 18F-FDG PET/CT, and underwent surgery after NAT were included in this study. Clinicopathologic features, date of last follow-up, progression, and death were recorded. Textural and conventional PET parameters(maximum standardized uptake value-SUVmax, metabolic tumor volume-MTV, total lesion glycolysis-TLG) were obtained from PET/CT images using LifeX program. Parameters were grouped using Youden index in ROC analysis. Factors predicting the pathological response to treatment, progression, and overall survival were determined using logistic regression and Cox regression analyses. RESULTS Forty-four patients (26(59%) male, 18(41%) female; 60.1±11.4 years) with rectal cancer were included in this study. The numbers of patients with responders and non-responders to NAT were 15(34.9%) and 28(65.1%), respectively. One patient' pathology report did not contain the response status to NAT. The median of follow-up duration was 29.9 months. 9(20.5%) showed disease progression, and 8(18.2%) died during the follow-up period. Difference entropyGLCM and correlationGLCM parameters were found as independent predictors for response to NAT. The positivity of surgical margin, intensity interquartile rangeCONV and AUC-CSHDISC texture parameters were independent predictors of progression, while normalized inverse differenceGLCM and LZLGEGLZLM parameters were independent predictors of mortality. CONCLUSION The texture parameters obtained from pretreatment 18F-FDG PET/CT have presented a more robust predictive value than conventional parameters in patients with rectal cancer who underwent surgery after NAT.
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Affiliation(s)
- Seda Gülbahar Ateş
- Department of Nuclear Medicine, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey.
| | - Gülay Bilir Dilek
- Department of Pathology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey
| | - Gülin Uçmak
- Department of Nuclear Medicine, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey
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20
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Radiomics Approaches for the Prediction of Pathological Complete Response after Neoadjuvant Treatment in Locally Advanced Rectal Cancer: Ready for Prime Time? Cancers (Basel) 2023; 15:cancers15020432. [PMID: 36672381 PMCID: PMC9857080 DOI: 10.3390/cancers15020432] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/03/2023] [Accepted: 01/05/2023] [Indexed: 01/12/2023] Open
Abstract
In recent years, neoadjuvant therapy of locally advanced rectal cancer has seen tremendous modifications. Adding neoadjuvant chemotherapy before or after chemoradiotherapy significantly increases loco-regional disease-free survival, negative surgical margin rates, and complete response rates. The higher complete rate is particularly clinically meaningful given the possibility of organ preservation in this specific sub-population, without compromising overall survival. However, all locally advanced rectal cancer most likely does not benefit from total neoadjuvant therapy (TNT), but experiences higher toxicity rates. Diagnosis of complete response after neoadjuvant therapy is a real challenge, with a risk of false negatives and possible under-treatment. These new therapeutic approaches thus raise the need for better selection tools, enabling a personalized therapeutic approach for each patient. These tools mostly focus on the prediction of the pathological complete response given the clinical impact. In this article, we review the place of different biomarkers (clinical, biological, genomics, transcriptomics, proteomics, and radiomics) as well as their clinical implementation and discuss the most recent trends for future steps in prediction modeling in patients with locally advanced rectal cancer.
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He W, Li Q, Li X. Changing patterns of neoadjuvant therapy for locally advanced rectal cancer: A narrative review. Crit Rev Oncol Hematol 2023; 181:103885. [PMID: 36464124 DOI: 10.1016/j.critrevonc.2022.103885] [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: 08/10/2022] [Revised: 10/25/2022] [Accepted: 11/21/2022] [Indexed: 12/03/2022] Open
Abstract
Standard treatment for patients with locally advanced rectal cancer has been the multidisciplinary approach of neoadjuvant chemoradiotherapy, followed by total mesorectal excision (TME) and postoperative adjuvant chemotherapy. This reduces the local recurrence rate, but the challenge of distant metastasis still persists. The improvement in treatment approach has always been the focus of clinical research and studies have been conducted worldwide in recent years. On one hand, evidence suggests that increasing the intensity of treatment can result in better tumor regression, for example by adding a second drug to the neoadjuvant chemoradiotherapy, or extending the interval between neoadjuvant therapy and surgery, or incorporating chemotherapy and chemoradiotherapy in the neoadjuvant setting. On the other hand, neoadjuvant immunotherapy and selective omission of neoadjuvant radiotherapy may improve the quality of life of patients. In this article, we review the key clinical research progresses in neoadjuvant therapy for locally advanced rectal cancer, hoping to provide some valuable views on the individualized treatment for rectal cancer.
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Affiliation(s)
- Weijing He
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Qingguo Li
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Xinxiang Li
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
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Ebinç S, Güzel Y, Oruç Z, Kömek H, Kalkan Z, Can C, Taşdemir B, Urakçi Z, Kaplan MA, Küçüköner M, Işikdoğan A. 18 F-FDG PET/CT parameters for prediction of response to neoadjuvant therapy and prognosis in rectal cancer. Nucl Med Commun 2023; 44:81-90. [PMID: 36437550 DOI: 10.1097/mnm.0000000000001638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
OBJECTIVE This study aims to investigate the role of F-18 fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) parameters in the prediction of treatment response and the prognosis in locally advanced rectal cancer. METHODS We investigated the relationship of 18F-FDG PET/CT parameters [rectal metabolic tumor volume (MTV), rectal total lesion glycolysis (TLG), rectal standard uptake value (SUV) max, rectal highest peak SUV, lymph node MTV, lymph node TLG, lymph node highest peak SUV] with the pathological response and disease-free survival (DFS) in 60 patients who received neoadjuvant therapy for a diagnosis of locally advanced rectal cancer. Patients with a total score of 0 were assigned to the low-risk group, patients with a score of 1 were assigned to the intermediate-risk group and patients with a score of 2 were assigned to the high-risk group. RESULTS The multivariate analysis revealed that, from baseline PET CT parameters, lymph node highest peak SUV strongly predicted the pathological response at a cutoff value of 2.23. DFS was predicted by the lymph node highest peak SUV at a cutoff value of 3.13 and by the MTV value at a cutoff value of 27 cm 3 . The risk scoring performed with regard to rectal MTV and lymph node highest peak SUV values determined a median DFS of 19 months in patients with a risk score of 2, whereas the median DFS was not reached in patients with risk scores of 0 and 1 (P < 0.001). CONCLUSION This study determined that rectal MTV and lymph node highest peak SUV predicted the response to neoadjuvant therapy and DFS.
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Affiliation(s)
- Senar Ebinç
- Department of Medical Oncology, Gazi Yasargil Training and Research Hospital
| | - Yunus Güzel
- Department of Nuclear Medicine, Gazi Yasargil Training and Research Hospital
| | - Zeynep Oruç
- Department of Medical Oncology, Dicle University Faculty of Medicine
| | - Halil Kömek
- Department of Nuclear Medicine, Gazi Yasargil Training and Research Hospital
| | - Ziya Kalkan
- Department of Medical Oncology, Dicle University Faculty of Medicine
| | - Canan Can
- Department of Nuclear Medicine, Gazi Yasargil Training and Research Hospital
| | - Bekir Taşdemir
- Department of Nuclear Medicine, Dicle University Faculty of Medicine, Diyarbakir, Turkey
| | - Zuhat Urakçi
- Department of Medical Oncology, Dicle University Faculty of Medicine
| | | | - Mehmet Küçüköner
- Department of Medical Oncology, Dicle University Faculty of Medicine
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Kong Y, Xu M, Wei X, Qian D, Yin Y, Huang Z, Gu W, Zhou L. CT imaging-based radiomics signatures improve prognosis prediction in postoperative colorectal cancer. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:1281-1294. [PMID: 37638470 DOI: 10.3233/xst-230090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
OBJECTIVE To investigate the use of non-contrast-enhanced (NCE) and contrast-enhanced (CE) CT radiomics signatures (Rad-scores) as prognostic factors to help improve the prediction of the overall survival (OS) of postoperative colorectal cancer (CRC) patients. METHODS A retrospective analysis was performed on 65 CRC patients who underwent surgical resection in our hospital as the training set, and 19 patient images retrieved from The Cancer Imaging Archive (TCIA) as the external validation set. In training, radiomics features were extracted from the preoperative NCE/CE-CT, then selected through 5-fold cross validation LASSO Cox method and used to construct Rad-scores. Models derived from Rad-scores and clinical factors were constructed and compared. Kaplan-Meier analyses were also used to compare the survival probability between the high- and low-risk Rad-score groups. Finally, a nomogram was developed to predict the OS. RESULTS In training, a clinical model achieved a C-index of 0.796 (95% CI: 0.722-0.870), while clinical and two Rad-scores combined model performed the best, achieving a C-index of 0.821 (95% CI: 0.743-0.899). Furthermore, the models with the CE-CT Rad-score yielded slightly better performance than that of NCE-CT in training. For the combined model with CE-CT Rad-scores, a C-index of 0.818 (95% CI: 0.742-0.894) and 0.774 (95% CI: 0.556-0.992) were achieved in both the training and validation sets. Kaplan-Meier analysis demonstrated a significant difference in survival probability between the high- and low-risk groups. Finally, the areas under the receiver operating characteristics (ROC) curves for the model were 0.904, 0.777, and 0.843 for 1, 3, and 5-year survival, respectively. CONCLUSION NCE-CT or CE-CT radiomics and clinical combined models can predict the OS for CRC patients, and both Rad-scores are recommended to be included when available.
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Affiliation(s)
- Yan Kong
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Muchen Xu
- Department of Radiation Oncology, Dushu Lake Hospital Affiliated to Soochow University, Medical Center of Soochow University, Suzhou, Jiangsu, China
| | - Xianding Wei
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Danqi Qian
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Yuan Yin
- Wuxi Cancer Institute, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Zhaohui Huang
- Wuxi Cancer Institute, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, Ibaraki, Japan
| | - Leyuan Zhou
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
- Department of Radiation Oncology, Dushu Lake Hospital Affiliated to Soochow University, Medical Center of Soochow University, Suzhou, Jiangsu, China
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Zhong ME, Duan X, Ni-jia-ti MYDL, Qi H, Xu D, Cai D, Li C, Huang Z, Zhu Q, Gao F, Wu X. CT-based radiogenomic analysis dissects intratumor heterogeneity and predicts prognosis of colorectal cancer: a multi-institutional retrospective study. J Transl Med 2022; 20:574. [PMID: 36482390 PMCID: PMC9730572 DOI: 10.1186/s12967-022-03788-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND This study aimed to develop a radiogenomic prognostic prediction model for colorectal cancer (CRC) by investigating the biological and clinical relevance of intratumoural heterogeneity. METHODS This retrospective multi-cohort study was conducted in three steps. First, we identified genomic subclones using unsupervised deconvolution analysis. Second, we established radiogenomic signatures to link radiomic features with prognostic subclone compositions in an independent radiogenomic dataset containing matched imaging and gene expression data. Finally, the prognostic value of the identified radiogenomic signatures was validated using two testing datasets containing imaging and survival information collected from separate medical centres. RESULTS This multi-institutional retrospective study included 1601 patients (714 females and 887 males; mean age, 65 years ± 14 [standard deviation]) with CRC from 5 datasets. Molecular heterogeneity was identified using unsupervised deconvolution analysis of gene expression data. The relative prevalence of the two subclones associated with cell cycle and extracellular matrix pathways identified patients with significantly different survival outcomes. A radiogenomic signature-based predictive model significantly stratified patients into high- and low-risk groups with disparate disease-free survival (HR = 1.74, P = 0.003). Radiogenomic signatures were revealed as an independent predictive factor for CRC by multivariable analysis (HR = 1.59, 95% CI:1.03-2.45, P = 0.034). Functional analysis demonstrated that the 11 radiogenomic signatures were predominantly associated with extracellular matrix and immune-related pathways. CONCLUSIONS The identified radiogenomic signatures might be a surrogate for genomic signatures and could complement the current prognostic strategies.
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Affiliation(s)
- Min-Er Zhong
- grid.488525.6Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655 China ,grid.413405.70000 0004 1808 0686Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China ,grid.488525.6Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xin Duan
- grid.12981.330000 0001 2360 039XSchool of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Ma-yi-di-li Ni-jia-ti
- Department of Radiology, The First People’s Hospital of Kashi Prefecture, Kashi, Xinjiang China
| | - Haoning Qi
- grid.488525.6Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655 China ,grid.488525.6Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Dongwei Xu
- grid.488525.6Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655 China ,grid.488525.6Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Du Cai
- grid.488525.6Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655 China ,grid.488525.6Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Chenghang Li
- grid.488525.6Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655 China ,grid.488525.6Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Zeping Huang
- grid.488525.6Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655 China ,grid.488525.6Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Qiqi Zhu
- grid.488525.6Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655 China ,grid.507012.10000 0004 1798 304XDepartment of Colorectal Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo, China
| | - Feng Gao
- grid.488525.6Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655 China ,grid.488525.6Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China ,Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Xiaojian Wu
- grid.488525.6Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655 China ,grid.488525.6Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
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Agüloğlu N, Aksu A. Evaluación de la supervivencia de los pacientes con cáncer de recto metastásico mediante parámetros radiómicos y volumétricos de la PET/TC con [18F]FDG de estadificación. Rev Esp Med Nucl Imagen Mol 2022. [DOI: 10.1016/j.remn.2022.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Chen R, Fu Y, Yi X, Pei Q, Zai H, Chen BT. Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges. JOURNAL OF ONCOLOGY 2022; 2022:1590620. [PMID: 36471884 PMCID: PMC9719428 DOI: 10.1155/2022/1590620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 10/30/2022] [Accepted: 11/09/2022] [Indexed: 08/01/2023]
Abstract
Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is the standard treatment for locally advanced rectal cancer (LARC). A noninvasive preoperative prediction method should greatly assist in the evaluation of response to nCRT and for the development of a personalized strategy for patients with LARC. Assessment of nCRT relies on imaging and radiomics can extract valuable quantitative data from medical images. In this review, we examined the status of radiomic application for assessing response to nCRT in patients with LARC and indicated a potential direction for future research.
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Affiliation(s)
- Rui Chen
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Yan Fu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Qian Pei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Hongyan Zai
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Bihong T. Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA
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Chen W, Wang R, Ma Z, Hua Y, Mao D, Wu H, Yang Y, Li C, Li M. A delta-radiomics model for preoperative prediction of invasive lung adenocarcinomas manifesting as radiological part-solid nodules. Front Oncol 2022; 12:927974. [DOI: 10.3389/fonc.2022.927974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 10/24/2022] [Indexed: 11/17/2022] Open
Abstract
PurposeThis study aims to explore the value of the delta-radiomics (DelRADx) model in predicting the invasiveness of lung adenocarcinoma manifesting as radiological part-solid nodules (PSNs).MethodsA total of 299 PSNs histopathologically confirmed as lung adenocarcinoma (training set, n = 209; validation set, n = 90) in our hospital were retrospectively analyzed from January 2017 to December 2021. All patients underwent diagnostic noncontrast-enhanced CT (NCECT) and contrast-enhanced CT (CECT) before surgery. After image preprocessing and ROI segmentation, 740 radiomic features were extracted from NCECT and CECT, respectively, resulting in 740 DelRADx. A DelRADx model was constructed using the least absolute shrinkage and selection operator logistic (LASSO-logistic) algorithm based on the training cohort. The conventional radiomics model based on NCECT was also constructed following the same process for comparison purposes. The prediction performance was assessed using area under the ROC curve (AUC). To provide an easy-to-use tool, a radiomics-based integrated nomogram was constructed and evaluated by integrated discrimination increment (IDI), calibration curves, decision curve analysis (DCA), and clinical impact plot.ResultsThe DelRADx signature, which consisted of nine robust selected features, showed significant differences between the AIS/MIA group and IAC group (p < 0.05) in both training and validation sets. The DelRADx signature showed a significantly higher AUC (0.902) compared to the conventional radiomics model based on NCECT (AUC = 0.856) in the validation set. The IDI was significant at 0.0769 for the integrated nomogram compared with the DelRADx signature. The calibration curve of the integrated nomogram demonstrated favorable agreement both in the training set and validation set with a mean absolute error of 0.001 and 0.019, respectively. Decision curve analysis and clinical impact plot indicated that if the threshold probability was within 90%, the integrated nomogram showed a high clinical application value.ConclusionThe DelRADx method has the potential to assist doctors in predicting the invasiveness for patients with PSNs. The integrated nomogram incorporating the DelRADx signature with the radiographic features could facilitate the performance and serve as an alternative way for determining management.
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Xie F, Zhao Q, Li S, Wu S, Li J, Li H, Chen S, Jiang W, Dong A, Wu L, Liu L, Huang H, Xu S, Shao Y, Liu L, Li L, Cai P. Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer. Front Oncol 2022; 12:901287. [PMID: 36408187 PMCID: PMC9669703 DOI: 10.3389/fonc.2022.901287] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 10/20/2022] [Indexed: 04/17/2024] Open
Abstract
In locally advanced rectal cancer (LARC), an improved ability to predict prognosis before and after treatment is needed for individualized treatment. We aimed to utilize pre- and post-treatment clinical predictors and baseline magnetic resonance imaging (MRI) radiomic features for establishing prognostic models to predict progression-free survival (PFS) in patients with LARC. Patients with LARC diagnosed between March 2014 and May 2016 were included in this retrospective study. A radiomic signature based on extracted MRI features and clinical prognostic models based on clinical features were constructed in the training cohort to predict 3-year PFS. C-indices were used to evaluate the predictive accuracies of the radiomic signature, clinical prognostic models, and integrated prognostic model (iPostM). In total, 166 consecutive patients were included (110 vs. 56 for training vs. validation). Eleven radiomic features were filtered out to construct the radiomic signature, which was significantly related to PFS. The MRI feature-derived radiomic signature exhibited better prognostic performance than the clinical prognostic models (P = 0.007 vs. 0.077). Then, we proposed an iPostM that combined the radiomic signature with tumor regression grade. The iPostM achieved the highest C-indices in the training and validation cohorts (0.942 and 0.752, respectively), outperforming other models in predicting PFS (all P < 0.05). Decision curve analysis and survival curves of the validation cohort verified that iPostM demonstrated the best performance and facilitated risk stratification. Therefore, iPostM provided the most reliable prognostic prediction for PFS in patients with LARC.
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Affiliation(s)
- Fei Xie
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Qin Zhao
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Shuqi Li
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Shuangshuang Wu
- School of Physics, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou, China
| | - Jinli Li
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Haojiang Li
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Shenghuan Chen
- Department of Radiology, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Qingyuan, China
| | - Wu Jiang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Annan Dong
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Liqing Wu
- Department of Radiology, Guangzhou Concord Cancer Center, Guangzhou, China
| | - Long Liu
- Department of Radiology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Huabin Huang
- Department of Radiology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Shuoyu Xu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Yuanzhi Shao
- School of Physics, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou, China
| | - Lizhi Liu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
- Department of Radiology, The Third People’s Hospital of Shenzhen, Shenzhen, China
| | - Li Li
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Peiqiang Cai
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
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Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3645-3659. [PMID: 35951085 DOI: 10.1007/s00261-022-03625-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/13/2022] [Accepted: 07/15/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE The current study aimed to evaluate the association of endorectal ultrasound (EUS) radiomics features at different denoising filters based on machine learning algorithms and to predict radiotherapy response in locally advanced rectal cancer (LARC) patients. METHODS The EUS images of forty-three LARC patients, as a predictive biomarker for predicting the treatment response of neoadjuvant chemoradiotherapy (NCRT), were investigated. For despeckling, the EUS images were preprocessed by traditional filters (bilateral, wiener, lee, frost, median, and wavelet filters). The rectal tumors were delineated by two readers separately, and radiomics features were extracted. The least absolute shrinkage and selection operator were used for feature selection. Classifiers including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), random forest, naive Bayes, and decision tree were trained using stratified fivefold cross-validation for model development. The area under the curve (AUC) of the receiver operating characteristic curve followed by accuracy, precision, sensitivity, and specificity were obtained for model performance assessment. RESULTS The wavelet filter had the best results with means of AUC: 0.83, accuracy: 77.41%, precision: 82.15%, and sensitivity: 79.41%. LR and SVM by having AUC: 0.71 and 0.76; accuracy: 70.0% and 71.5%; precision: 75.0% and 73.0%; sensitivity: 69.8% and 80.2%; and specificity: 70.0% and 60.9% had the highest model's performance, respectively. CONCLUSION This study demonstrated that the EUS-based radiomics model could serve as pretreatment biomarkers in predicting pathologic features of rectal cancer. The wavelet filter and machine learning methods (LR and SVM) had good results on the EUS images of rectal cancer.
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Ma Y, Lin C, Liu S, Wei Y, Ji C, Shi F, Lin F, Zhou Z. Radiomics features based on internal and marginal areas of the tumor for the preoperative prediction of microsatellite instability status in colorectal cancer. Front Oncol 2022; 12:1020349. [PMID: 36276101 PMCID: PMC9583004 DOI: 10.3389/fonc.2022.1020349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 09/20/2022] [Indexed: 11/21/2022] Open
Abstract
Objectives To explore whether the preoperative CT radiomics can predict the status of microsatellite instability (MSI) in colorectal cancer (CRC) patients and identify the region with the most stable and high-efficiency radiomics features. Methods This retrospective study involved 230 CRC patients with preoperative computed tomography scans and available MSI status between December 2019 and October 2021. Image segmentation and radiomic feature extraction were performed as follows. First, slices with the maximum tumor area (region of interest, ROI) were manually contoured. Subsequently, each ROI was shrunk inward by 1, 2, and 3 mm, respectively, where the remaining ROIs were considered as the internal region of the tumor (named as IROI1, IROI2, and IROI3), and the shrunk regions were considered as marginal regions of the tumor (named as MROI1, MROI2, and MROI3). Finally, radiomics features were extracted from each of the ROI. The intraclass correlation coefficient and least absolute shrinkage and selection operator method were used to choose the most reliable and relevant features of MSI status. Clinical, radiomics, and combined clinical radiomics models have been established. Calibration curve and decision curve analyses (DCA) were generated to explore the correction effect and assess the clinical applicability of the above models, respectively. Results In the testing cohort, the radiomics model based on IROI3 yielded the highest average area under the curve (AUC) value of 0.908, compared with the remaining radiomics models. Additionally, hypertension and N stage were considered as clinically independent factors of MSI status. The combined clinical radiomics model achieved excellent diagnostic efficacy (AUC: 0.928; sensitivity: 0.840; specificity: 0.867) in the testing cohort, as well as favorable calibration and clinical utility by calibration curve and DCA analyses. Conclusions The IROI3 model, which is based on a 3-mm shrink in the largest areas of the tumor, could noninvasively reflect the heterogeneity and genetic instability within the tumor. This suggests that it is an important biomarker for the preoperative prediction of MSI status. The model can extract more robust and effective radiomics features, which lays a foundation for the radiomics study of hollow organs, such as in CRC.
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Affiliation(s)
- Yi Ma
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Changsong Lin
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China
| | - Song Liu
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Changfeng Ji
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Fan Lin
- Department of Cell Biology, Nanjing Medical University, Nanjing, China
- *Correspondence: Fan Lin, ; Zhengyang Zhou,
| | - Zhengyang Zhou
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- *Correspondence: Fan Lin, ; Zhengyang Zhou,
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Liu X, Hu X, Yu X, Li P, Gu C, Liu G, Wu Y, Li D, Wang P, Cai J. Frontiers and hotspots of 18F-FDG PET/CT radiomics: A bibliometric analysis of the published literature. Front Oncol 2022; 12:965773. [PMID: 36176388 PMCID: PMC9513237 DOI: 10.3389/fonc.2022.965773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 08/12/2022] [Indexed: 11/23/2022] Open
Abstract
Objective To illustrate the knowledge hotspots and cutting-edge research trends of 18F-FDG PET/CT radiomics, the knowledge structure of was systematically explored and the visualization map was analyzed. Methods Studies related to 18F-FDG PET/CT radiomics from 2013 to 2021 were identified and selected from the Web of Science Core Collection (WoSCC) using retrieval formula based on an interview. Bibliometric methods are mainly performed by CiteSpace 5.8.R3, which we use to build knowledge structures including publications, collaborative and co-cited studies, burst analysis, and so on. The performance and relevance of countries, institutions, authors, and journals were measured by knowledge maps. The research foci were analyzed through research of keywords, as well as literature co-citation analysis. Predicting trends of 18F-FDG PET/CT radiomics in this field utilizes a citation burst detection method. Results Through a systematic literature search, 457 articles, which were mainly published in the United States (120 articles) and China (83 articles), were finally included in this study for analysis. Memorial Sloan-Kettering Cancer Center and Southern Medical University are the most productive institutions, both with a frequency of 17. 18F-FDG PET/CT radiomics–related literature was frequently published with high citation in European Journal of Nuclear Medicine and Molecular Imaging (IF9.236, 2020), Frontiers in Oncology (IF6.244, 2020), and Cancers (IF6.639, 2020). Further cluster profile of keywords and literature revealed that the research hotspots were primarily concentrated in the fields of image, textural feature, and positron emission tomography, and the hot research disease is a malignant tumor. Document co-citation analysis suggested that many scholars have a co-citation relationship in studies related to imaging biomarkers, texture analysis, and immunotherapy simultaneously. Burst detection suggests that adenocarcinoma studies are frontiers in 18F-FDG PET/CT radiomics, and the landmark literature put emphasis on the reproducibility of 18F-FDG PET/CT radiomics features. Conclusion First, this bibliometric study provides a new perspective on 18F-FDG PET/CT radiomics research, especially for clinicians and researchers providing scientific quantitative analysis to measure the performance and correlation of countries, institutions, authors, and journals. Above all, there will be a continuing growth in the number of publications and citations in the field of 18F-FDG PET/CT. Second, the international research frontiers lie in applying 18F-FDG PET/CT radiomics to oncology research. Furthermore, new insights for researchers in future studies will be adenocarcinoma-related analyses. Moreover, our findings also offer suggestions for scholars to give attention to maintaining the reproducibility of 18F-FDG PET/CT radiomics features.
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Affiliation(s)
- Xinghai Liu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- The First Clinical College, Zunyi Medical University, Zunyi, China
| | - Xianwen Hu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Xiao Yu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- The First Clinical College, Zunyi Medical University, Zunyi, China
| | - Pujiao Li
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- The First Clinical College, Zunyi Medical University, Zunyi, China
| | - Cheng Gu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- The First Clinical College, Zunyi Medical University, Zunyi, China
| | - Guosheng Liu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- The First Clinical College, Zunyi Medical University, Zunyi, China
| | - Yan Wu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Dandan Li
- Department of Obstetrics, Zunyi Hospital of Traditional Chinese Medicine, Zunyi, China
- *Correspondence: Jiong Cai, ; Pan Wang, ; Dandan Li,
| | - Pan Wang
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- *Correspondence: Jiong Cai, ; Pan Wang, ; Dandan Li,
| | - Jiong Cai
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- *Correspondence: Jiong Cai, ; Pan Wang, ; Dandan Li,
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Ritter Z, Papp L, Zámbó K, Tóth Z, Dezső D, Veres DS, Máthé D, Budán F, Karádi É, Balikó A, Pajor L, Szomor Á, Schmidt E, Alizadeh H. Two-Year Event-Free Survival Prediction in DLBCL Patients Based on In Vivo Radiomics and Clinical Parameters. Front Oncol 2022; 12:820136. [PMID: 35756658 PMCID: PMC9216187 DOI: 10.3389/fonc.2022.820136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 05/18/2022] [Indexed: 12/11/2022] Open
Abstract
Purpose For the identification of high-risk patients in diffuse large B-cell lymphoma (DLBCL), we investigated the prognostic significance of in vivo radiomics derived from baseline [18F]FDG PET/CT and clinical parameters. Methods Pre-treatment [18F]FDG PET/CT scans of 85 patients diagnosed with DLBCL were assessed. The scans were carried out in two clinical centers. Two-year event-free survival (EFS) was defined. After delineation of lymphoma lesions, conventional PET parameters and in vivo radiomics were extracted. For 2-year EFS prognosis assessment, the Center 1 dataset was utilized as the training set and underwent automated machine learning analysis. The dataset of Center 2 was utilized as an independent test set to validate the established predictive model built by the dataset of Center 1. Results The automated machine learning analysis of the Center 1 dataset revealed that the most important features for building 2-year EFS are as follows: max diameter, neighbor gray tone difference matrix (NGTDM) busyness, total lesion glycolysis, total metabolic tumor volume, and NGTDM coarseness. The predictive model built on the Center 1 dataset yielded 79% sensitivity, 83% specificity, 69% positive predictive value, 89% negative predictive value, and 0.85 AUC by evaluating the Center 2 dataset. Conclusion Based on our dual-center retrospective analysis, predicting 2-year EFS built on imaging features is feasible by utilizing high-performance automated machine learning.
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Affiliation(s)
- Zsombor Ritter
- Department of Medical Imaging, Medical School, University of Pécs, Pécs, Hungary
| | - László Papp
- Medical University of Vienna, Center for Medical Physics and Biomedical Engineering, Vienna, Austria
| | - Katalin Zámbó
- Department of Medical Imaging, Medical School, University of Pécs, Pécs, Hungary
| | - Zoltán Tóth
- University of Kaposvár, PET Medicopus Nonprofit Ltd., Kaposvár, Hungary
| | - Dániel Dezső
- Department of Medical Imaging, Medical School, University of Pécs, Pécs, Hungary
| | - Dániel Sándor Veres
- Department of Biophysics and Radiation Biology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Domokos Máthé
- Department of Biophysics and Radiation Biology, Faculty of Medicine, Semmelweis University, Budapest, Hungary.,In Vivo Imaging Advanced Core Facility, Hungarian Centre of Excellence for Molecular Medicine, Budapest, Hungary
| | - Ferenc Budán
- Institute of Transdisciplinary Discoveries, Medical School, University of Pécs, Pécs, Hungary.,Institute of Physiology, Medical School, University of Pécs, Pécs, Hungary
| | - Éva Karádi
- Department of Hematology, University of Kaposvár, Kaposvár, Hungary
| | - Anett Balikó
- County Hospital Tolna, János Balassa Hospital, Szekszárd, Hungary
| | - László Pajor
- Department of Pathology, Medical School, University of Pécs, Pécs, Hungary
| | - Árpád Szomor
- 1st Department of Internal Medicine, Medical School, University of Pécs, Pécs, Hungary
| | - Erzsébet Schmidt
- Department of Medical Imaging, Medical School, University of Pécs, Pécs, Hungary
| | - Hussain Alizadeh
- 1st Department of Internal Medicine, Medical School, University of Pécs, Pécs, Hungary
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Li H, Xie Y, Liu H, Wang X. Non-Contrast CT-Based Radiomics Score for Predicting Hematoma Enlargement in Spontaneous Intracerebral Hemorrhage. Clin Neuroradiol 2022; 32:517-528. [PMID: 34324004 DOI: 10.1007/s00062-021-01062-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/28/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE To develop a non-contrast computed tomography-(CT)-based radiomics score for predicting the risk of hematoma early enlargement in spontaneous intracerebral hemorrhage. METHODS A total of 258 patients from a single-center database with acute spontaneous intracerebral parenchymal hemorrhage were collected. Radiomics software was explored to segment hematomas on baseline non-contrast CT images, and the texture features were extracted. Minimal Redundancy and Maximal Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO), were used to select optimized subset of features and radiomics score was calculated. The radiomics model (radiomics score-based), radiomics nomogram (radiomics score combined with clinical factors-based) and clinical model (clinical factors-based) were built in a training cohort and validated in a test cohort. The discrimination, calibration, and clinical usefulness of the models were evaluated. Finally, a subgroup analysis was performed to assess the predictive value of radiomics score in specific hemorrhage location. RESULTS Radiomics score was composed of 12 radiomics features. The radiomics model and radiomics nomogram both showed good performance in predicting hematoma enlargement (area under the curve, AUC 0.83 [0.71-0.95], AUC 0.82 [0.72, 0.93]), and were both better than clinical model (AUC 0.66 [0.54-0.79]). The radiomics model and radiomics nomogram showed satisfactory calibration and clinical usefulness for detecting hematoma enlargement. For subgroup analysis, radiomics score also showed good predictive value for hematoma enlargement in different locations (AUC were 0.828, 0.940, 0.836 and 0.904, respectively, for supratentorial, subtentorial, deep and lobes). CONCLUSION A radiomics score based on non-contrast CT may be considered as a potential biomarker for prediction of hematoma enlargement in patients with spontaneous intracerebral hemorrhage (SICH), and it presented a high incremental value to clinical factors for hematoma enlargement prediction.
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Affiliation(s)
- Hui Li
- Department of Radiology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, No. 26 Shengli Street, Jiangan District, 430014, Wuhan City, Hubei Province, China
| | - Yuanliang Xie
- Department of Radiology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, No. 26 Shengli Street, Jiangan District, 430014, Wuhan City, Hubei Province, China
| | - Huan Liu
- GE Healthcare, 201203, Shanghai, China
| | - Xiang Wang
- Department of Radiology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, No. 26 Shengli Street, Jiangan District, 430014, Wuhan City, Hubei Province, China.
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061330. [PMID: 35741139 PMCID: PMC9222024 DOI: 10.3390/diagnostics12061330] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 12/04/2022] Open
Abstract
The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.
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Affiliation(s)
- David Morland
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
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Marr L, Haller B, Pyka T, Peeken JC, Jesinghaus M, Scheidhauer K, Friess H, Combs SE, Münch S. Predictive value of clinical and 18F-FDG-PET/CT derived imaging parameters in patients undergoing neoadjuvant chemoradiation for esophageal squamous cell carcinoma. Sci Rep 2022; 12:7148. [PMID: 35504955 PMCID: PMC9065158 DOI: 10.1038/s41598-022-11076-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 04/12/2022] [Indexed: 12/24/2022] Open
Abstract
Aim of this study was to validate the prognostic impact of clinical parameters and baseline 18F-FDG-PET/CT derived textural features to predict histopathologic response and survival in patients with esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiation (nCRT) and surgery. Between 2005 and 2014, 38 ESCC were treated with nCRT and surgery. For all patients, the 18F-FDG-PET-derived parameters metabolic tumor volume (MTV), SUVmax, contrast and busyness were calculated for the primary tumor using a SUV-threshold of 3. The parameter uniformity was calculated using contrast-enhanced computed tomography. Based on histopathological response to nCRT, patients were classified as good responders (< 10% residual tumor) (R) or non-responders (≥ 10% residual tumor) (NR). Regression analyses were used to analyse the association of clinical parameters and imaging parameters with treatment response and overall survival (OS). Good response to nCRT was seen in 27 patients (71.1%) and non-response was seen in 11 patients (28.9%). Grading was the only parameter predicting response to nCRT (Odds Ratio (OR) = 0.188, 95% CI: 0.040–0.883; p = 0.034). No association with histopathologic treatment response was seen for any of the evaluated imaging parameters including SUVmax, MTV, busyness, contrast and uniformity. Using multivariate Cox-regression analysis, the heterogeneity parameters busyness (Hazard Ratio (HR) = 1.424, 95% CI: 1.044–1.943; p = 0.026) and contrast (HR = 6.678, 95% CI: 1.969–22.643;p = 0.002) were independently associated with OS, while no independent association with OS was seen for SUVmax and MTV. In patients with ESCC undergoing nCRT and surgery, baseline 18F-FDG-PET/CT derived parameters could not predict histopathologic response to nCRT. However, the PET/CT derived features busyness and contrast were independently associated with OS and should be further investigated.
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Affiliation(s)
- Lisa Marr
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Bernhard Haller
- Institute of Medical Informatics, Statistics and Epidemiology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Thomas Pyka
- Departement of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany.,Department of Nuclear Medicine, Inselspital Bern, Freiburgstr. 18, 3010, Bern, Switzerland
| | - Jan C Peeken
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU), Ingolstädter Landstraße 1, 85764, Oberschleißheim, Germany
| | - Moritz Jesinghaus
- Institute of Pathology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Klemens Scheidhauer
- Departement of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Helmut Friess
- Department of Surgery, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU), Ingolstädter Landstraße 1, 85764, Oberschleißheim, Germany
| | - Stefan Münch
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany. .,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.
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Chuanji Z, Zheng W, Shaolv L, Linghou M, Yixin L, Xinhui L, Ling L, Yunjing T, Shilai Z, Shaozhou M, Boyang Z. Comparative study of radiomics, tumor morphology, and clinicopathological factors in predicting overall survival of patients with rectal cancer before surgery. Transl Oncol 2022; 18:101352. [PMID: 35144092 PMCID: PMC8844801 DOI: 10.1016/j.tranon.2022.101352] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 12/26/2021] [Accepted: 01/19/2022] [Indexed: 02/07/2023] Open
Abstract
Radiomics analysis of pretreatment MR images could predict overall survival (OS) in patients. Clinical, pathological and MRI imaging indexes were included and models were established. Tumor morphological model, clinicopathological model, radiomics model and comprehensive model were used to evaluate the prognosis of patients with rectal cancer. It can explore the influence of the above factors on the prognosis of rectal cancer from multi-level and multi-angle. The proposed radiomics nomogram showed better prognostic performance than the clinicopathological and imaging model in risk stratification and can classify patients into high- and low-risk groups with significant differences in OS.
We compared the ability of a radiomics model, morphological imaging model, and clinicopathological risk model to predict 3-year overall survival (OS) in 206 patients with rectal cancer who underwent radical surgery and had magnetic resonance imaging, clinicopathological, and OS data available. The patients were randomized to a training cohort (n = 146) and a verification cohort (n = 60). Radiomics features were extracted from preoperative T2-weighted images, and a radiomics score model was constructed. Factors that were significant in the Cox multivariate analysis were used to construct the final morphological tumor model and clinicopathological model. A comprehensive model in the form of a line chart was established by combining the three models. Ten radiomics features significantly related to OS were selected to construct the radiomics feature model and calculate the radiomics score. In the morphological model, mesorectal extension depth and distance between the lower tumor margin and the anal margin were significant prognostic factors. N stage was the only significant clinicopathological factor. The comprehensive model combined with the above factors had the best prediction performance for OS. The C-index had a predictive performance of 0.872 (95% confidence interval [CI]: 0.832–0.912) in the training cohort and 0.944 (95% CI: 0.890–0.990) in the verification cohort, which was better than for any single model. The comprehensive model was divided into high-risk and low-risk groups. Kaplan-Meier curve analysis showed that all factors were significantly correlated with poor OS in the high-risk group. A comprehensive nomogram based on multi-model radiomics features can predict 3-year OS after rectal cancer surgery.
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Tang B, Lenkowicz J, Peng Q, Boldrini L, Hou Q, Dinapoli N, Valentini V, Diao P, Yin G, Orlandini LC. Local tuning of radiomics-based model for predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. BMC Med Imaging 2022; 22:44. [PMID: 35287607 PMCID: PMC8919611 DOI: 10.1186/s12880-022-00773-x] [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: 01/18/2022] [Accepted: 03/04/2022] [Indexed: 11/10/2022] Open
Abstract
PURPOSE This study aims to further enhance a validated radiomics-based model for predicting pathologic complete response (pCR) after chemo‑radiotherapy in locally advanced rectal cancer (LARC) for use in clinical practice. METHODS A generalized linear model (GLM) to predict pCR in LARC patients previously trained in Europe and validated with an external inter-continental cohort (59 patients), was first examined with further 88 intercontinental patient datasets to assess its reproducibility; then new radiomics and clinical features, and validation methods were investigated to build a new model for enhancing the pCR prediction for patients admitted to our department. The patients were divided into training group (75%) and validation group (25%) according to their demographic. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to reduce the dimensionality of the extracted features of the training group and select the optimal ones; the performance of the reference GLM and enhanced models was compared through the area under curve (AUC) of the receiver operating characteristics. RESULTS The value of AUC of the reference model was 0.831 (95% CI, 0.701-0.961), and 0.828 (95% CI, 0.700-0.956) in the original and new validation cohorts, respectively, showing a reproducibility in the applicability of the GLM model. Eight features were found to be significant with LASSO and used to establish an enhanced model. The AUC of the enhanced model of 0.926 (95% CI, 0.859-0.993) for training, and 0.926 (95% CI, 0.767-1.00) for the validation group shows better performance than the reference model. CONCLUSIONS The GLM model shows good reproducibility in predicting pCR in LARC; the enhanced model has the potential to improve prediction accuracy and may be a candidate in clinical practice.
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Affiliation(s)
- Bin Tang
- Key Laboratory of Radiation Physics and Technology of the Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, Chengdu, China.,Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital and Institute, Chengdu, China
| | - Jacopo Lenkowicz
- Dipartimento Scienze Radiologiche, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Qian Peng
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital and Institute, Chengdu, China.
| | - Luca Boldrini
- Dipartimento Scienze Radiologiche, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Qing Hou
- Key Laboratory of Radiation Physics and Technology of the Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, Chengdu, China.
| | - Nicola Dinapoli
- Dipartimento Scienze Radiologiche, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Vincenzo Valentini
- Dipartimento Scienze Radiologiche, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Peng Diao
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital and Institute, Chengdu, China
| | - Gang Yin
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital and Institute, Chengdu, China
| | - Lucia Clara Orlandini
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital and Institute, Chengdu, China
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Artificial Intelligence Applications on Restaging [18F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomics Classification for Prediction of Disease Outcome. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12062941] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The aim of this study was to investigate the application of [18F]FDG PET/CT images-based textural features analysis to propose radiomics models able to early predict disease progression (PD) and survival outcome in metastatic colorectal cancer (MCC) patients after first adjuvant therapy. For this purpose, 52 MCC patients who underwent [18F]FDGPET/CT during the disease restaging process after the first adjuvant therapy were analyzed. Follow-up data were recorded for a minimum of 12 months after PET/CT. Radiomics features from each avid lesion in PET and low-dose CT images were extracted. A hybrid descriptive-inferential method and the discriminant analysis (DA) were used for feature selection and for predictive model implementation, respectively. The performance of the features in predicting PD was performed for per-lesion analysis, per-patient analysis, and liver lesions analysis. All lesions were again considered to assess the diagnostic performance of the features in discriminating liver lesions. In predicting PD in the whole group of patients, on PET features radiomics analysis, among per-lesion analysis, only the GLZLM_GLNU feature was selected, while three features were selected from PET/CT images data set. The same features resulted more accurately by associating CT features with PET features (AUROC 65.22%). In per-patient analysis, three features for stand-alone PET images and one feature (i.e., HUKurtosis) for the PET/CT data set were selected. Focusing on liver metastasis, in per-lesion analysis, the same analysis recognized one PET feature (GLZLM_GLNU) from PET images and three features from PET/CT data set. Similarly, in liver lesions per-patient analysis, we found three PET features and a PET/CT feature (HUKurtosis). In discrimination of liver metastasis from the rest of the other lesions, optimal results of stand-alone PET imaging were found for one feature (SUVbwmin; AUROC 88.91%) and two features for merged PET/CT features analysis (AUROC 95.33%). In conclusion, our machine learning model on restaging [18F]FDGPET/CT was demonstrated to be feasible and potentially useful in the predictive evaluation of disease progression in MCC.
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Lv L, Xin B, Hao Y, Yang Z, Xu J, Wang L, Wang X, Song S, Guo X. Radiomic analysis for predicting prognosis of colorectal cancer from preoperative 18F-FDG PET/CT. J Transl Med 2022; 20:66. [PMID: 35109864 PMCID: PMC8812058 DOI: 10.1186/s12967-022-03262-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 01/17/2022] [Indexed: 12/23/2022] Open
Abstract
Background To develop and validate a survival model with clinico-biological features and 18F- FDG PET/CT radiomic features via machine learning, and for predicting the prognosis from the primary tumor of colorectal cancer. Methods A total of 196 pathologically confirmed patients with colorectal cancer (stage I to stage IV) were included. Preoperative clinical factors, serum tumor markers, and PET/CT radiomic features were included for the recurrence-free survival analysis. For the modeling and validation, patients were randomly divided into the training (n = 137) and validation (n = 59) set, while the 78 stage III patients [training (n = 55), and validation (n = 23)] was divided for the further experiment. After selecting features by the log-rank test and variable-hunting methods, random survival forest (RSF) models were built on the training set to analyze the prognostic value of selected features. The performance of models was measured by C-index and was tested on the validation set with bootstrapping. Feature importance and the Pearson correlation were also analyzed. Results Radiomics signature (containing four PET/CT features and four clinical factors) achieved the best result for prognostic prediction of 196 patients (C-index 0.780, 95% CI 0.634–0.877). Moreover, four features (including two clinical features and two radiomics features) were selected for prognostic prediction of the 78 stage III patients (C-index was 0.820, 95% CI 0.676–0.900). K–M curves of both models significantly stratified low-risk and high-risk groups (P < 0.0001). Pearson correlation analysis demonstrated that selected radiomics features were correlated with tumor metabolic factors, such as SUVmean, SUVmax. Conclusion This study presents integrated clinico-biological-radiological models that can accurately predict the prognosis in colorectal cancer using the preoperative 18F-FDG PET/CT radiomics in colorectal cancer. It is of potential value in assisting the management and decision making for precision treatment in colorectal cancer. Trial registration The retrospectively registered study was approved by the Ethics Committee of Fudan University Shanghai Cancer Center (No. 1909207-14-1910) and the data were analyzed anonymously. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03262-5.
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Affiliation(s)
- Lilang Lv
- Department of Radiotherapy, Fudan University Shanghai Cancer Center, No.270 Dong'an Road, Xuhui district, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bowen Xin
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Yichao Hao
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Ziyi Yang
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, No.270 Dong'an Road, Xuhui district, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Center for Biomedical Imaging, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
| | - Junyan Xu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, No.270 Dong'an Road, Xuhui district, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Center for Biomedical Imaging, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
| | - Lisheng Wang
- Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Shaoli Song
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, No.270 Dong'an Road, Xuhui district, Shanghai, 200032, China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. .,Center for Biomedical Imaging, Fudan University, Shanghai, China. .,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China.
| | - Xiaomao Guo
- Department of Radiotherapy, Fudan University Shanghai Cancer Center, No.270 Dong'an Road, Xuhui district, Shanghai, 200032, China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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Saad M, He S, Thorstad W, Gay H, Barnett D, Zhao Y, Ruan S, Wang X, Li H. Learning-based Cancer Treatment Outcome Prognosis using Multimodal Biomarkers. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022; 6:231-244. [PMID: 35520102 PMCID: PMC9066560 DOI: 10.1109/trpms.2021.3104297] [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] [Indexed: 02/03/2023]
Abstract
Predicting early in treatment whether a tumor is likely to be responsive is a difficult yet important task to support clinical decision-making. Studies have shown that multimodal biomarkers could provide complementary information and lead to more accurate treatment outcome prognosis than unimodal biomarkers. However, the prognosis accuracy could be affected by multimodal data heterogeneity and incompleteness. The small-sized and imbalance datasets also bring additional challenges for training a designed prognosis model. In this study, a modular framework employing multimodal biomarkers for cancer treatment outcome prediction was proposed. It includes four modules of synthetic data generation, deep feature extraction, multimodal feature fusion, and classification to address the challenges described above. The feasibility and advantages of the designed framework were demonstrated through an example study, in which the goal was to stratify oropharyngeal squamous cell carcinoma (OPSCC) patients with low- and high-risks of treatment failures by use of positron emission tomography (PET) image data and microRNA (miRNA) biomarkers. The superior prognosis performance and the comparison with other methods demonstrated the efficiency of the proposed framework and its ability of enabling seamless integration, validation and comparison of various algorithms in each module of the framework. The limitation and future work was discussed as well.
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Affiliation(s)
- Maliazurina Saad
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA. She is now with the MD Anderson Cancer Center, Houston, TX, USA
| | - Shenghua He
- Department of Computer Science and Engineering, Washington University, Saint louis, MO, USA
| | - Wade Thorstad
- Department of Radiation Oncology, Washington University School of Medicine, Saint louis, MO, USA
| | - Hiram Gay
- Department of Radiation Oncology, Washington University School of Medicine, Saint louis, MO, USA
| | - Daniel Barnett
- Carle Cancer Center, Carle Foundation Hospital, Urbana, IL, USA
| | - Yujie Zhao
- Mao Clinic at Florida, Jacksonville, FL, USA
| | - Su Ruan
- Laboratoire LITIS (EA 4108), Equipe Quantif, University of Rouen, France
| | - Xiaowei Wang
- Department of Pharmacology and Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Hua Li
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Cancer Center at Illinois, and Carle Foundation Hospital, Urbana, IL, USA
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Lee SW, Park HL, Yoon N, Kim JH, Oh JK, Buyn JH, Choi EK, Hong JH. Prognostic Impact of Total Lesion Glycolysis (TLG) from Preoperative 18F-FDG PET/CT in Stage II/III Colorectal Adenocarcinoma: Extending the Value of PET/CT for Resectable Disease. Cancers (Basel) 2022; 14:cancers14030582. [PMID: 35158851 PMCID: PMC8833504 DOI: 10.3390/cancers14030582] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/18/2022] [Accepted: 01/21/2022] [Indexed: 01/27/2023] Open
Abstract
We investigated the prognostic role of metabolic parameters from preoperative 18F-FDG PET/CT in stage II/III colorectal adenocarcinoma. A total of 327 stage II/III colorectal adenocarcinoma patients who underwent curative resection were included. The maximal standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were analyzed for optimal cut-offs and their effect on DFS. Differences in DFS rates and hazard ratios for DFS between cut-offs were statistically significant in SUVmax, MTV2.5, MTV3, TLG 2.5, TLG3, and TLG30%. Factors significantly related to DFS in univariate Cox regression were age, sex, stage, preoperative CEA, SUVmax, MTV2.5, MTV3, TLG2.5, TLG3, and TLG30%. Age, sex, preoperative CEA, and TLG2.5 (p = 0.009) sustained statistically significant difference in multivariate analysis. The 1-, 3-, and 5-year DFS rates for TLG2.5 ≤ 448.5 were 98.1%, 79.6%, and 74.8%, significantly higher than 78.4%, 68.5%, and 61.1% of TLG2.5 > 448.5, respectively (p = 0.012). TLG, a parameter indicating both the metabolic activity and metabolic volume, was the strongest predictor independently associated with DFS, among several PET parameters with statistical significance. These results suggest the potential prognostic value of preoperative 18F-FDG PET/CT in stage II/III resectable colorectal cancer.
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Affiliation(s)
- Sea-Won Lee
- Department of Radiation Oncology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, Korea;
| | - Hye Lim Park
- Division of Nuclear Medicine, Department of Radiology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, Korea;
| | - Nara Yoon
- Department of Pathology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Incheon 21431, Korea;
| | - Ji Hoon Kim
- Department of General Surgery, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Incheon 21431, Korea;
| | - Jin Kyoung Oh
- Department of Radiology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Incheon 21431, Korea;
| | - Jae Ho Buyn
- Department of Internal Medicine, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Incheon 21431, Korea;
| | - Eun Kyoung Choi
- Department of Radiology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Incheon 21431, Korea;
- Correspondence: (E.K.C.); (J.H.H.); Tel.: +82-32-280-5242 (E.K.C.); +82-2-2030-4361 (J.H.H.)
| | - Ji Hyung Hong
- Department of Internal Medicine, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, Korea
- Correspondence: (E.K.C.); (J.H.H.); Tel.: +82-32-280-5242 (E.K.C.); +82-2-2030-4361 (J.H.H.)
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Tsuruta C, Hirata K, Kudo K, Masumori N, Hatakenaka M. DWI-related texture analysis for prostate cancer: differences in correlation with histological aggressiveness and data repeatability between peripheral and transition zones. Eur Radiol Exp 2022; 6:1. [PMID: 35018507 PMCID: PMC8752657 DOI: 10.1186/s41747-021-00252-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 10/25/2021] [Indexed: 11/10/2022] Open
Abstract
Background We investigated the correlation between texture features extracted from apparent diffusion coefficient (ADC) maps or diffusion-weighted images (DWIs), and grade group (GG) in the prostate peripheral zone (PZ) and transition zone (TZ), and assessed reliability in repeated examinations. Methods Patients underwent 3-T pelvic magnetic resonance imaging (MRI) before radical prostatectomy with repeated DWI using b-values of 0, 100, 1,000, and 1,500 s/mm2. Region of interest (ROI) for cancer was assigned to the first and second DWI acquisition separately. Texture features of ROIs were extracted from comma-separated values (CSV) data of ADC maps generated from several sets of two b-value combinations and DWIs, and correlation with GG, discrimination ability between GG of 1–2 versus 3–5, and data repeatability were evaluated in PZ and TZ. Results Forty-four patients with 49 prostate cancers met the eligibility criteria. In PZ, ADC 10% and 25% based on ADC map of two b-value combinations of 100 and 1,500 s/mm2 and 10% based on ADC map with b-value of 0 and 1,500 s/mm2 showed significant correlation with GG, acceptable discrimination ability, and good repeatability. In TZ, higher-order texture feature of busyness extracted from ADC map of 100 and 1,500 s/mm2, and high gray-level run emphasis, short-run high gray-level emphasis, and high gray-level zone emphasis from DWI with b-value of 100 s/mm2 demonstrated significant correlation, excellent discrimination ability, but moderate repeatability. Conclusions Some DWI-related features showed significant correlation with GG, acceptable to excellent discrimination ability, and moderate to good data repeatability in prostate cancer, and differed between PZ and TZ. Supplementary Information The online version contains supplementary material available at 10.1186/s41747-021-00252-y.
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Affiliation(s)
- Chie Tsuruta
- Department of Diagnostic Radiology, School of Medicine, Sapporo Medical University, South 1, West 17, Chuo-ku, Sapporo, 060-8556, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Japan.,Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | - Naoya Masumori
- Department of Urology, School of Medicine, Sapporo Medical University, Sapporo, Japan
| | - Masamitsu Hatakenaka
- Department of Diagnostic Radiology, School of Medicine, Sapporo Medical University, South 1, West 17, Chuo-ku, Sapporo, 060-8556, Japan.
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Analysis of KRAS Mutation Status Prediction Model for Colorectal Cancer Based on Medical Imaging. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2021:3953442. [PMID: 34976107 PMCID: PMC8716224 DOI: 10.1155/2021/3953442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/02/2021] [Accepted: 11/09/2021] [Indexed: 12/09/2022]
Abstract
This study retrospectively included some patients with colorectal cancer diagnosed by histopathology, to explore the feasibility of CT medical image texture analysis in predicting KRAS gene mutations in patients with colorectal cancer. Before any surgical procedure, all patients received an enhanced CT scan of the abdomen and pelvis, as well as genetic testing. To define patient groups, divide all patients into test and validation sets based on the order of patient enrollment. A radiologist took a look at the plain axial CT image of the tumor, as well as the portal vein CT image, at the corresponding level. The physician points the computer's cursor to the relevant area in the image, and TexRAD software programs together texture parameters based on various spatial scale factors, also known as total mean, total variance, statistical entropy, overall total average, mean total, positive mean, skewness value, kurtosis value, and general skewness. Using the same method again two weeks later, the observer and another physician measured the image of each patient again to see if the method was consistent between observers. With regard to clinical information, the KRAS gene mutation group and the wild group of participants in the test set and validation set each had values for the texture parameter. In a study of patients with colorectal cancer, the results demonstrated that CT texture parameters were correlated with the presence of the KRAS gene mutation. The best CT prediction model includes the values of the medium texture image's slope and the other CT fine texture image's value of entropy, the medium texture image's slope and kurtosis, and the medium texture image's mean and the other CT fine texture image's value of entropy. Regardless of the training set or the validation set, patients with and without KRAS gene mutations did not differ significantly in clinical characteristics. This method can be used to identify mutations in the KRAS gene in patients with colorectal cancer, making it practical to implement CT medical image texture analysis technology for that purpose.
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44
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Shariaty F, Orooji M, Velichko EN, Zavjalov SV. Texture appearance model, a new model-based segmentation paradigm, application on the segmentation of lung nodule in the CT scan of the chest. Comput Biol Med 2022; 140:105086. [PMID: 34861641 DOI: 10.1016/j.compbiomed.2021.105086] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 11/24/2021] [Accepted: 11/25/2021] [Indexed: 11/03/2022]
Abstract
Lung cancer causes more than one million deaths worldwide each year. Averages of 5-year survival rate of patients with Non-small cell lung cancer (NSCLC), which is the most common type of lung cancer, is 15%. Computer-Aided Detection (CAD) is a very important tool for identifying lung lesions in medical imaging. In general, the process line of a CAD system can be divided into four main stages: preprocessing, localization, feature extraction, and classification. As segmentation is required for localization in computer vision and medical image analysis, this step has become a major and challenging problem, and much research has been done on new segmentation techniques. In recent years, interest in model-based segmentation methods has increased, and the reason for this is even if some object information is lost, such gaps can be filled by using the previous information in the model. This paper proposed Texture Appearance Model (TAM), which is a new model-based method and segments all types of nodule areas accurately and efficiently, including juxta-pleural nodules, without separating the lung from the surrounding area in a CT scan of the lung. In this method, Texture Representation of Image (TRI) is obtained using tissue texture feature extraction and feature selection algorithms. The proposed method has been evaluated in 85 nodules of the dataset, received from the Iranian hospital, in which the ground-truth annotation by physicians and CT imaging data were provided. The results show that the proposed algorithm has an encouraging performance for distinguishing different types of nodules, including pleural, cavity and non-solid nodules, achieving an average dice similarity coefficient (DSC) of 84.75%.
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Affiliation(s)
- Faridoddin Shariaty
- Institute of Electronics and Telecommunications, Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia.
| | - Mahdi Orooji
- Department of Electrical and Computer Engineering, University of California, Davis, United States
| | - Elena N Velichko
- Institute of Electronics and Telecommunications, Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia
| | - Sergey V Zavjalov
- Institute of Electronics and Telecommunications, Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia
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Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net. Comput Biol Med 2021; 141:105172. [PMID: 34973585 PMCID: PMC8712746 DOI: 10.1016/j.compbiomed.2021.105172] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 12/21/2021] [Accepted: 12/21/2021] [Indexed: 01/08/2023]
Abstract
The efforts made to prevent the spread of COVID-19 face specific challenges in diagnosing COVID-19 patients and differentiating them from patients with pulmonary edema. Although systemically administered pulmonary vasodilators and acetazolamide are of great benefit for treating pulmonary edema, they should not be used to treat COVID-19 as they carry the risk of several adverse consequences, including worsening the matching of ventilation and perfusion, impaired carbon dioxide transport, systemic hypotension, and increased work of breathing. This study proposes a machine learning-based method (EDECOVID-net) that automatically differentiates the COVID-19 symptoms from pulmonary edema in lung CT scans using radiomic features. To the best of our knowledge, EDECOVID-net is the first method to differentiate COVID-19 from pulmonary edema and a helpful tool for diagnosing COVID-19 at early stages. The EDECOVID-net has been proposed as a new machine learning-based method with some advantages, such as having simple structure and few mathematical calculations. In total, 13 717 imaging patches, including 5759 COVID-19 and 7958 edema images, were extracted using a CT incision by a specialist radiologist. The EDECOVID-net can distinguish the patients with COVID-19 from those with pulmonary edema with an accuracy of 0.98. In addition, the accuracy of the EDECOVID-net algorithm is compared with other machine learning methods, such as VGG-16 (Acc = 0.94), VGG-19 (Acc = 0.96), Xception (Acc = 0.95), ResNet101 (Acc = 0.97), and DenseNet20l (Acc = 0.97).
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Yin YX, Xie MZ, Liang XQ, Ye ML, Li JL, Hu BL. Clinical Significance and Prognostic Value of the Maximum Standardized Uptake Value of 18F-Flurodeoxyglucose Positron Emission Tomography-Computed Tomography in Colorectal Cancer. Front Oncol 2021; 11:741612. [PMID: 34956868 PMCID: PMC8695495 DOI: 10.3389/fonc.2021.741612] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 11/15/2021] [Indexed: 01/05/2023] Open
Abstract
Background The role of 18F-flurodeoxyglucose (18F-FDG) positron emission tomography–computed tomography (PET/CT) in colorectal cancer (CRC) remains unclear. This study aimed to explore the association of the maximum standardized uptake value (SUVmax), a parameter of 18F-FDG PET/CT, with KRAS mutation, the Ki-67 index, and survival in patients with CRC. Methods Data of 66 patients with CRC who underwent 18F-FDG PET/CT was retrospectively collected in our center. The clinical significance of the SUVmax in CRC and the association of the SUVmax with KRAS mutation and the Ki-67 index were determined. A meta-analysis was conducted by a systematic search of PubMed, Web of Science, and CNKI databases, and the data from published articles were combined with that of our study. The association of the SUVmax with KRAS mutation and the Ki-67 index was determined using the odds ratio to estimate the pooled results. The hazard ratio was used to quantitatively evaluate the prognosis of the SUVmax in CRC. Results By analyzing the data of 66 patients with CRC, the SUVmax was found not to be related to the tumor-node-metastasis stage, clinical stage, sex, and KRAS mutation but was related to the tumor location and nerve invasion. The SUVmax had no significant correlation with the tumor biomarkers and the Ki-67 index. Data of 17 studies indicated that the SUVmax was significantly increased in the mutated type compared with the wild type of KRAS in CRC; four studies showed that there was no remarkable difference between patients with a high and low Ki-67 index score regarding the SUVmax. Twelve studies revealed that the SUVmax had no significant association with overall survival and disease-free survival in CRC patients. Conclusions Based on the combined data, this study demonstrated that the SUVmax of 18F-FDG PET/CT was different between colon and rectal cancers and associated with KRAS mutation but not the Ki-67 index; there was no significant association between the SUVmax and survival of patients with CRC.
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Affiliation(s)
- Yi-Xin Yin
- Department of Research, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Ming-Zhi Xie
- Department of Chemotherapy, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Xin-Qiang Liang
- Department of Research, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Meng-Ling Ye
- Department of Research, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Ji-Lin Li
- Department of Research, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Bang-Li Hu
- Department of Research, Guangxi Medical University Cancer Hospital, Nanning, China
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Cui Y, Wang G, Ren J, Hou L, Li D, Wen Q, Xi Y, Yang X. Radiomics Features at Multiparametric MRI Predict Disease-Free Survival in Patients With Locally Advanced Rectal Cancer. Acad Radiol 2021; 29:e128-e138. [PMID: 34961658 DOI: 10.1016/j.acra.2021.11.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/24/2021] [Accepted: 11/26/2021] [Indexed: 01/04/2023]
Abstract
OBJECTIVE To investigate the potential value of radiomics features based on preoperative multiparameter MRI in predicting disease-free survival (DFS) in patients with local advanced rectal cancer (LARC). METHODS We identified 234 patients with LARC who underwent preoperative MRI, including T2-weighted, diffusion kurtosis imaging, and contrast enhanced T1-weighted. All patients were randomly divided into the training (n = 164) and validation (n = 70) cohorts. 414 features were extracted from the tumor from above sequences and the radiomics signature was then generated, mainly based on feature stability and Cox proportional hazards model. Two models, integrating pre- and postoperative variables, were constructed to validate the radiomics signatures for DFS estimation. RESULTS The radiomics signature, composed of six DFS-related features, was significantly associated with DFS in the training and validation cohorts (both p < 0.001). The radiomics signature and MR-defined extramural venous invasion (mrEMVI) were identified as the independent predictor of DFS both in the pre- and postoperative models. In both cohorts, the two radiomics-based models exhibited better prediction performance (C-index ≥0.77, all p < 0.05) than the corresponding clinical models, with positive net reclassification improvement and lower Akaike information criterion (AIC). Decision curve analysis also confirmed their clinical usefulness. The radiomics-based models could categorize LARC patients into high- and low-risk groups with distinct profiles of DFS (all p < 0.05). CONCLUSION The proposed radiomics models with pre- and postoperative features have the potential to predict DFS, and may provide valuable guidance for the future individualized management in patients with LARC.
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Wu KC, Chen SW, Hsieh TC, Yen KY, Law KM, Kuo YC, Chang RF, Kao CH. Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer with Metric Learning Using Pretreatment 18F-Fluorodeoxyglucose Positron Emission Tomography. Cancers (Basel) 2021; 13:cancers13246350. [PMID: 34944970 PMCID: PMC8699508 DOI: 10.3390/cancers13246350] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/12/2021] [Accepted: 12/14/2021] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVES Neoadjuvant chemoradiotherapy (NCRT) followed by surgery is the mainstay of treatment for patients with locally advanced rectal cancer. Based on baseline 18F-fluorodeoxyglucose ([18F]-FDG)-positron emission tomography (PET)/computed tomography (CT), a new artificial intelligence model using metric learning (ML) was introduced to predict responses to NCRT. PATIENTS AND METHODS This study used the data of 236 patients with newly diagnosed rectal cancer; the data of 202 and 34 patients were for training and validation, respectively. All patients received pretreatment [18F]FDG-PET/CT, NCRT, and surgery. The treatment response was scored by Dworak tumor regression grade (TRG); TRG3 and TRG4 indicated favorable responses. The model employed ML combined with the Uniform Manifold Approximation and Projection for dimensionality reduction. A receiver operating characteristic (ROC) curve analysis was performed to assess the model's predictive performance. RESULTS In the training cohort, 115 patients (57%) achieved TRG3 or TRG4 responses. The area under the ROC curve was 0.96 for the prediction of a favorable response. The sensitivity, specificity, and accuracy were 98.3%, 96.5%, and 97.5%, respectively. The sensitivity, specificity, and accuracy for the validation cohort were 95.0%, 100%, and 98.8%, respectively. CONCLUSIONS The new ML model presented herein was used to determined that baseline 18F[FDG]-PET/CT images could predict a favorable response to NCRT in patients with rectal cancer. External validation is required to verify the model's predictive value.
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Affiliation(s)
- Kuo-Chen Wu
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan;
- Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung 404, Taiwan; (S.-W.C.); (K.-M.L.); (Y.-C.K.)
| | - Shang-Wen Chen
- Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung 404, Taiwan; (S.-W.C.); (K.-M.L.); (Y.-C.K.)
- School of Medicine, College of Medicine, China Medical University, Taichung 404, Taiwan
- School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Department of Radiation Oncology, China Medical University Hospital, Taichung 404, Taiwan
| | - Te-Chun Hsieh
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 404, Taiwan; (T.-C.H.); (K.-Y.Y.)
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 404, Taiwan
| | - Kuo-Yang Yen
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 404, Taiwan; (T.-C.H.); (K.-Y.Y.)
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 404, Taiwan
| | - Kin-Man Law
- Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung 404, Taiwan; (S.-W.C.); (K.-M.L.); (Y.-C.K.)
- Department of Computer Science and Engineering, National Chung Hsing University, Taichung 402, Taiwan
| | - Yu-Chieh Kuo
- Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung 404, Taiwan; (S.-W.C.); (K.-M.L.); (Y.-C.K.)
| | - Ruey-Feng Chang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan;
- Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung 404, Taiwan; (S.-W.C.); (K.-M.L.); (Y.-C.K.)
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan
- Correspondence: (R.-F.C.); or (C.-H.K.); Tel.: +886-2-33664888 (ext. 331) (R.-F.C.); +886-4-22052121 (C.-H.K.)
| | - Chia-Hung Kao
- Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung 404, Taiwan; (S.-W.C.); (K.-M.L.); (Y.-C.K.)
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 404, Taiwan; (T.-C.H.); (K.-Y.Y.)
- Graduate Institute of Biomedical Sciences, School of Medicine, College of Medicine, China Medical University, Taichung 404, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413, Taiwan
- Correspondence: (R.-F.C.); or (C.-H.K.); Tel.: +886-2-33664888 (ext. 331) (R.-F.C.); +886-4-22052121 (C.-H.K.)
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Uchiyama Y, Hirata K, Watanabe S, Okamoto S, Shiga T, Okada K, Ito YM, Kudo K. Development and validation of a prediction model based on the organ-based metabolic tumor volume on FDG-PET in patients with differentiated thyroid carcinoma. Ann Nucl Med 2021; 35:1223-1231. [PMID: 34379284 DOI: 10.1007/s12149-021-01664-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 07/29/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Although patients with differentiated thyroid cancer (DTC) generally have a good prognosis, patients with a large metabolic tumor volume (MTV) on FDG-PET may experience poor clinical courses. We measured organ-based MTVs and tested its prognostic performance in comparison to conventional MTV (cMTV). METHODS We retrospectively analyzed the cases of 280 patients who received their first I-131 therapy in 2003-2014 at our hospital and showed an FDG-avid metastatic lesion. We randomly divided the patients into training (n = 190) and validation (n = 90) datasets. We classified the MTVs as MTVneck-node, MTVdistant-node, MTVlung, MTVbone, and MTVother-organs and tested with/without dichotomization vis-à-vis overall survival (OS). Based on the estimated weighting coefficients of the organ-based MTVs, we propose a new index: the adjusted whole-body MTV (aMTV). Using the validation dataset, we compared the aMTV with cMTV for predicting OS. RESULTS In a univariate analysis, MTVdistant-node and MTVother-organs were more strongly correlated with the OS than the dichotomized forms, whereas the dichotomized forms of MTVneck-node, MTVlung, and MTVbone were more strongly correlated with OS than the continuous variables. The aMTV was thus expressed as 0.69 × dic(MTVneck-node) + 0.02 × MTVdistant-node + 1.05 × dic(MTVlung) + 1.58 × dic(MTVbone) + 0.01 × MTVother-organs, where dic(x) represents 0 or 1 based on the optimized cut-off. In the model evaluation using the validation group, aMTV was a significant predictor of OS with a higher c-index (0.7676) than cMTV (0.7218). CONCLUSION In DTC patients with FDG-avid metastasis before I-131 therapy, all organ-based MTVs were significant predictors of prognosis. As the aMTV outperformed the cMTV for predicting prognoses, we recommend measuring the MTV on an organ basis.
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Affiliation(s)
- Yuko Uchiyama
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Department of Nuclear Medicine, Hokkaido University Hospital, Sapporo, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan.
- Department of Nuclear Medicine, Hokkaido University Hospital, Sapporo, Japan.
| | - Shiro Watanabe
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Department of Nuclear Medicine, Hokkaido University Hospital, Sapporo, Japan
| | - Shozo Okamoto
- Department of Radiology, Obihiro-Kosei General Hospital, Obihiro, Japan
| | - Tohru Shiga
- Advanced Clinical Research Center, Fukushima Global Medical Science Center, Fukushima, Japan
| | - Kazufumi Okada
- Data Science Center, Promotion Unit, Institute of Health Science Innovation for Medical Care, Hokkaido University Hospital, Sapporo, Japan
| | - Yoichi M Ito
- Data Science Center, Promotion Unit, Institute of Health Science Innovation for Medical Care, Hokkaido University Hospital, Sapporo, Japan
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Department of Diagnostic Imaging and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
- Faculty of Medicine, Global Center for Biomedical Science and Engineering, Hokkaido University, Sapporo, Japan
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Chen L, Liu K, Zhao X, Shen H, Zhao K, Zhu W. Habitat Imaging-Based 18F-FDG PET/CT Radiomics for the Preoperative Discrimination of Non-small Cell Lung Cancer and Benign Inflammatory Diseases. Front Oncol 2021; 11:759897. [PMID: 34692548 PMCID: PMC8526895 DOI: 10.3389/fonc.2021.759897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 09/14/2021] [Indexed: 12/25/2022] Open
Abstract
Purpose To propose and evaluate habitat imaging-based 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomics for preoperatively discriminating non-small cell lung cancer (NSCLC) and benign inflammatory diseases (BIDs). Methods Three hundred seventeen 18F-FDG PET/CT scans were acquired from patients who underwent aspiration biopsy or surgical resection. All volumes of interest (VOIs) were semiautomatically segmented. Each VOI was separated into variant subregions, namely, habitat imaging, based on our adapted clustering-based habitat generation method. Radiomics features were extracted from these subregions. Three feature selection methods and six classifiers were applied to construct the habitat imaging-based radiomics models for fivefold cross-validation. The radiomics models whose features extracted by conventional habitat-based methods and nonhabitat method were also constructed. For comparison, the performances were evaluated in the validation set in terms of the area under the receiver operating characteristic curve (AUC). Pairwise t-test was applied to test the significant improvement between the adapted habitat-based method and the conventional methods. Results A total of 1,858 radiomics features were extracted. After feature selection, habitat imaging-based 18F-FDG PET/CT radiomics models were constructed. The AUC of the adapted clustering-based habitat radiomics was 0.7270 ± 0.0147, which showed significantly improved discrimination performance compared to the conventional methods (p <.001). Furthermore, the combination of features extracted by our adaptive habitat imaging-based method and non-habitat method showed the best performance than the other combinations. Conclusion Habitat imaging-based 18F-FDG PET/CT radiomics shows potential as a biomarker for discriminating NSCLC and BIDs, which indicates that the microenvironmental variations in NSCLC and BID can be captured by PET/CT.
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Affiliation(s)
- Ling Chen
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Kanfeng Liu
- Positron Emission Tomography (PET) Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xin Zhao
- Positron Emission Tomography (PET) Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Hui Shen
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Kui Zhao
- Positron Emission Tomography (PET) Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Wentao Zhu
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
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