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Ma J, Nie X, Kong X, Xiao L, Liu H, Shi S, Wu Y, Li N, Hu L, Li X. MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer. BMC Med Imaging 2024; 24:262. [PMID: 39367333 PMCID: PMC11453062 DOI: 10.1186/s12880-024-01439-6] [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: 05/31/2024] [Accepted: 09/24/2024] [Indexed: 10/06/2024] Open
Abstract
BACKGROUND The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making. METHODS A retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set (n = 136) and validation set (n = 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared. RESULTS KRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471-19.830; p < 0.001). 5 radiomics features will be used to construct radiomics model. The combined model was built by integrating radiomics model with clinical model. In both the training set (AUC:0.842, 95%CI: 0.778-0.907) and the validation set (AUC: 0.805; 95%CI: 0.692-0.918), the AUCs for the combined model surpassed those of the radiomics and clinical models. CONCLUSIONS Our study reveals that KRAS mutation stands as an independent predictor of RCLM. The radiomics features based on MR play a crucial role in the evaluation of RCLM. The combined model exhibits superior performance in the prediction of liver metastasis. CLINICAL TRIAL NUMBER Not applicable.
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Affiliation(s)
- Jiaqi Ma
- Department of Magnetic Resonance Imaging Diagnostic, The 2nd Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, 150086, China
| | - Xinsheng Nie
- Medical Imaging Center, the Xinjiang Production and Construction Corps Tenth Division Beitun Hospital, Beitun, 836099, China
| | - Xiangjiang Kong
- Medical Imaging Center, the Xinjiang Production and Construction Corps Tenth Division Beitun Hospital, Beitun, 836099, China
| | - Lingqing Xiao
- Medical Imaging Center, the Xinjiang Production and Construction Corps Tenth Division Beitun Hospital, Beitun, 836099, China
| | - Han Liu
- Department of Magnetic Resonance Imaging Diagnostic, The 2nd Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, 150086, China
| | - Shengming Shi
- Department of Magnetic Resonance Imaging Diagnostic, The 2nd Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, 150086, China
| | - Yupeng Wu
- Department of Magnetic Resonance Imaging Diagnostic, The 2nd Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, 150086, China
| | - Na Li
- Medical Imaging Center, the Xinjiang Production and Construction Corps Tenth Division Beitun Hospital, Beitun, 836099, China
| | - Linlin Hu
- Medical Imaging Center, the Xinjiang Production and Construction Corps Tenth Division Beitun Hospital, Beitun, 836099, China
| | - Xiaofu Li
- Department of Magnetic Resonance Imaging Diagnostic, The 2nd Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, 150086, China.
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Montagnon E, Cerny M, Hamilton V, Derennes T, Ilinca A, Elforaici MEA, Jabbour G, Rafie E, Wu A, Perdigon Romero F, Cadrin-Chênevert A, Kadoury S, Turcotte S, Tang A. Radiomics analysis of baseline computed tomography to predict oncological outcomes in patients treated for resectable colorectal cancer liver metastasis. PLoS One 2024; 19:e0307815. [PMID: 39259736 PMCID: PMC11389941 DOI: 10.1371/journal.pone.0307815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 07/11/2024] [Indexed: 09/13/2024] Open
Abstract
OBJECTIVE The purpose of this study was to determine and compare the performance of pre-treatment clinical risk score (CRS), radiomics models based on computed (CT), and their combination for predicting time to recurrence (TTR) and disease-specific survival (DSS) in patients with colorectal cancer liver metastases. METHODS We retrospectively analyzed a prospectively maintained registry of 241 patients treated with systemic chemotherapy and surgery for colorectal cancer liver metastases. Radiomics features were extracted from baseline, pre-treatment, contrast-enhanced CT images. Multiple aggregation strategies were investigated for cases with multiple metastases. Radiomics signatures were derived using feature selection methods. Random survival forests (RSF) and neural network survival models (DeepSurv) based on radiomics features, alone or combined with CRS, were developed to predict TTR and DSS. Leveraging survival models predictions, classification models were trained to predict TTR within 18 months and DSS within 3 years. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on the test set. RESULTS For TTR prediction, the concordance index (95% confidence interval) was 0.57 (0.57-0.57) for CRS, 0.61 (0.60-0.61) for RSF in combination with CRS, and 0.70 (0.68-0.73) for DeepSurv in combination with CRS. For DSS prediction, the concordance index was 0.59 (0.59-0.59) for CRS, 0.57 (0.56-0.57) for RSF in combination with CRS, and 0.60 (0.58-0.61) for DeepSurv in combination with CRS. For TTR classification, the AUC was 0.33 (0.33-0.33) for CRS, 0.77 (0.75-0.78) for radiomics signature alone, and 0.58 (0.57-0.59) for DeepSurv score alone. For DSS classification, the AUC was 0.61 (0.61-0.61) for CRS, 0.57 (0.56-0.57) for radiomics signature, and 0.75 (0.74-0.76) for DeepSurv score alone. CONCLUSION Radiomics-based survival models outperformed CRS for TTR prediction. More accurate, noninvasive, and early prediction of patient outcome may help reduce exposure to ineffective yet toxic chemotherapy or high-risk major hepatectomies.
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Affiliation(s)
- Emmanuel Montagnon
- Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
| | - Milena Cerny
- Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
- Department of Radiology, CISSS des Laurentides, Hôpital de Saint-Eustache, Saint-Eustache, QC, Canada
| | - Vincent Hamilton
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, QC, Canada
| | - Thomas Derennes
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, QC, Canada
| | - André Ilinca
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, QC, Canada
| | - Mohamed El Amine Elforaici
- Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
- MedICAL Laboratory, Polytechnique Montréal, Montréal, QC, Canada
| | - Gilbert Jabbour
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, QC, Canada
- Division of Internal Medicine, Department of Medicine, Hôpital du Sacré-Cœur-de-Montréal, Montréal, QC, Canada
| | - Edmond Rafie
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, QC, Canada
| | - Anni Wu
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, QC, Canada
| | | | | | - Samuel Kadoury
- Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
- MedICAL Laboratory, Polytechnique Montréal, Montréal, QC, Canada
| | - Simon Turcotte
- Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
- Hepatopancreatobiliary and Liver Transplantation Division, Department of Surgery, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, QC, Canada
| | - An Tang
- Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, QC, Canada
- Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, QC, Canada
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Cox V, Javle M, Sun J, Kang H. Radiogenomics of Intrahepatic Cholangiocarcinoma: Correlation of Imaging Features With BAP1 and FGFR Molecular Subtypes. J Comput Assist Tomogr 2024:00004728-990000000-00336. [PMID: 38968316 DOI: 10.1097/rct.0000000000001638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2024]
Abstract
PURPOSE Clinical research has shown unique tumor behavioral characteristics of BRCA-associated protein-1- (BAP1-) and fibroblast growth factor receptor (FGFR)-mutated intrahepatic cholangiocarcinomas (CCAs), with BAP1-mutated tumors demonstrating more aggressive forms of disease and FGFR-altered CCAs showing more indolent behavior. We performed a retrospective case-control study to evaluate for unique imaging features associated with BAP1 and FGFR genomic markers in intrahepatic CCA (iCCA). METHODS Multiple imaging features of iCCA at first staging were analyzed by 2 abdominal radiologists blinded to genomic data. Growth and development of metastases at available follow-up imaging were also recorded, as were basic clinical cohort data. Types of iCCA analyzed included those with BAP1, FGFR, or both alterations, as well as cases with low mutational burden or mutations with low clinical impact, which served as a control or "wild-type" group. There were 18 cases in the FGFR group, 10 with BAP1 mutations, and 31 wild types (controls). RESULTS Cases with BAP1 mutations showed significantly larger growth at first year of follow-up (P = 0.03) and more frequent tumor-associated biliary ductal dilatation (P = 0.04) compared with controls. FGFR-altered cases showed more infiltrative margins compared with controls (P = 0.047) and demonstrated less enhancement between arterial to portal venous phases (P = 0.02). BAP1 and FGFR groups had more cases with stage IV disease at presentation than controls (P = 0.025, P = 0.006). CONCLUSION Compared with wild-type iCCAs, FGFR-mutated tumors often demonstrate infiltrative margins, and BAP1 tumors show increased biliary ductal dilatation at presentation. BAP1-mutated cases had significantly larger growth at first-year restaging.
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Affiliation(s)
| | - Milind Javle
- Division of Cancer Medicine, Department of Gastrointestinal Medical Oncology
| | - Jia Sun
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
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Granata V, Fusco R, Setola SV, Brunese MC, Di Mauro A, Avallone A, Ottaiano A, Normanno N, Petrillo A, Izzo F. Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction. LA RADIOLOGIA MEDICA 2024; 129:957-966. [PMID: 38761342 DOI: 10.1007/s11547-024-01828-5] [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: 01/17/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024]
Abstract
PURPOSE To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases. METHODS Patient selection in a retrospective study was carried out from January 2018 to May 2021 considering the following inclusion criteria: patients subjected to surgical resection for liver metastases; proven pathological liver metastases; patients subjected to enhanced CT examination in the presurgical setting with a good quality of images; and RAS assessment as standard reference. A total of 851 radiomics features were extracted using the PyRadiomics Python package from the Slicer 3D image computing platform after slice-by-slice segmentation on CT portal phase by two expert radiologists of each individual liver metastasis performed first independently by the individual reader and then in consensus. Balancing technique was performed, and inter- and intraclass correlation coefficients were calculated to assess the between-observer and within-observer reproducibility of features. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers were considered. Moreover, features selection was performed before and after a normalized procedure using two different methods (3-sigma and z-score). RESULTS Seventy-seven liver metastases in 28 patients with a mean age of 60 years (range 40-80 years) were analyzed. The best predictors, at univariate analysis for both normalized procedures, were original_shape_Maximum2DDiameter and wavelet_HLL_glcm_InverseVariance that reached an accuracy of 80%, an AUC ≥ 0.75, a sensitivity ≥ 80% and a specificity ≥ 70% (p value < < 0.01). However, a multivariate analysis significantly increased the accuracy in RAS prediction when a linear regression model (LRM) was used. The best performance was obtained using a LRM combining linearly 12 robust features after a z-score normalization procedure: AUC of 0.953, accuracy 98%, sensitivity 96%, specificity of 100%, PPV 100% and NPV 96% (p value < < 0.01). No statistically significant increase was obtained considering the tested machine learning both without normalization and with normalization methods. CONCLUSIONS Normalized approach in CT radiomics analysis allows to predict RAS mutational status in colorectal liver metastases patients.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, Italy.
| | | | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Annabella Di Mauro
- Pathological Anatomy and Cytopathology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Antonio Avallone
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Alessandro Ottaiano
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Nicola Normanno
- IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", 47014, Mendola, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
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Martinelli E, Ciardiello D, Martini G, Napolitano S, Del Tufo S, D'Ambrosio L, De Chiara M, Famiglietti V, Nacca V, Cardone C, Avallone A, Cremolini C, Pietrantonio F, Maiello E, Granata V, Troiani T, Cappabianca S, Ciardiello F, Nardone V, Reginelli A. Radiomic Parameters for the Evaluation of Response to Treatment in Metastatic Colorectal Cancer Patients with Liver Metastasis: Findings from the CAVE-GOIM mCRC Phase 2 Trial. Clin Drug Investig 2024; 44:541-548. [PMID: 38886336 DOI: 10.1007/s40261-024-01372-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/26/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND CAVE is a single arm, Phase 2 trial, that demonstrated anti-tumor activity of cetuximab rechallenge plus avelumab in patients with RAS wild type (wt) metastatic colorectal cancer (mCRC). OBJECTIVE We conducted a post hoc analysis to identify potential radiomic biomarkers for patients with CRC liver metastasis (LM). PATIENTS AND METHODS Patients with LM that could be measured by enhanced contrast phase computed tomography (CT) imaging at baseline and at first response evaluation were included. Multiple texture parameters were extracted with the LifeX Software. Delta-texture (D-TA) variations were calculated by comparing data at baseline and after treatment. RESULTS Overall, 55/77 patients (71%) had LM; 39 met the inclusion criteria for the current analysis. The D-TA parameters that significantly correlated at univariate analysis with median progression-free survival (mPFS) were EntropyHistogram (p = 0.021), HomogeneityGLCM (p < 0.001) and Dissimilarity GLCM (p = 0.002). At multivariate analysis, only HomogeneityGLCM resulted significant for PFS (p = 0.001). Patients (19/39, 48.7%) with reduction of HomogeneityGLCM experienced better mPFS (4.6 vs 2.9 months; HR 0.45; 95% CI 0.23-0.88; p = 0.021) and median overall survival (mOS) (17.3 vs 6.8 months; HR 0.40, 95% CI 0.21-0.80; p = 0.010). A trend to better mPFS, was also observed in patients with RAS/BRAF wt circulating tumor DNA and reduction of HomogeneityGLCM. Overall survival was significantly better in this subgroup of patients with low HomogeneityGLCM: mOS was 17.8 (95% CI 15.5-20.2) versus 6.8 months (95% CI 3.6-10.0) (HR 0.34, 95% CI 0.14-0.81; p = 0.016). CONCLUSION Reduction in the D-TA parameter HomogeneityGLCM by radiomic analysis correlates with improved outcomes in patients with LM receiving cetuximab rechallenge plus avelumab therapy. Larger prospective studies are needed to validate and confirm these findings.
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Affiliation(s)
- Erika Martinelli
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy.
| | - Davide Ciardiello
- Division of Gastrointestinal Medical Oncology and Neuroendocrine Tumors, European Institute of Oncology, IEO, IRCCS, Milan, Italy.
| | - Giulia Martini
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Stefania Napolitano
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Sara Del Tufo
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Luca D'Ambrosio
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Marco De Chiara
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Vincenzo Famiglietti
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Valeria Nacca
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Claudia Cardone
- Medical Oncology, Istituto Nazionale per lo Studio e la Cura dei Tumori "Fondazione Giovanni Pascale", IRCCS, Naples, Italy
| | - Antonio Avallone
- Medical Oncology, Istituto Nazionale per lo Studio e la Cura dei Tumori "Fondazione Giovanni Pascale", IRCCS, Naples, Italy
| | - Chiara Cremolini
- Unit of Medical Oncology 2, University Hospital of Pisa, Pisa, Italy
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Filippo Pietrantonio
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Evaristo Maiello
- Oncology Unit, IRCCS Foundation Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, Italy
| | - Teresa Troiani
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Fortunato Ciardiello
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Valerio Nardone
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
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Wiedeman C, Lorraine P, Wang G, Do R, Simpson A, Peoples J, De Man B. Simulated deep CT characterization of liver metastases with high-resolution filtered back projection reconstruction. Vis Comput Ind Biomed Art 2024; 7:13. [PMID: 38861067 PMCID: PMC11166620 DOI: 10.1186/s42492-024-00161-y] [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: 12/02/2023] [Accepted: 04/14/2024] [Indexed: 06/12/2024] Open
Abstract
Early diagnosis and accurate prognosis of colorectal cancer is critical for determining optimal treatment plans and maximizing patient outcomes, especially as the disease progresses into liver metastases. Computed tomography (CT) is a frontline tool for this task; however, the preservation of predictive radiomic features is highly dependent on the scanning protocol and reconstruction algorithm. We hypothesized that image reconstruction with a high-frequency kernel could result in a better characterization of liver metastases features via deep neural networks. This kernel produces images that appear noisier but preserve more sinogram information. A simulation pipeline was developed to study the effects of imaging parameters on the ability to characterize the features of liver metastases. This pipeline utilizes a fractal approach to generate a diverse population of shapes representing virtual metastases, and then it superimposes them on a realistic CT liver region to perform a virtual CT scan using CatSim. Datasets of 10,000 liver metastases were generated, scanned, and reconstructed using either standard or high-frequency kernels. These data were used to train and validate deep neural networks to recover crafted metastases characteristics, such as internal heterogeneity, edge sharpness, and edge fractal dimension. In the absence of noise, models scored, on average, 12.2% ( α = 0.012 ) and 7.5% ( α = 0.049 ) lower squared error for characterizing edge sharpness and fractal dimension, respectively, when using high-frequency reconstructions compared to standard. However, the differences in performance were statistically insignificant when a typical level of CT noise was simulated in the clinical scan. Our results suggest that high-frequency reconstruction kernels can better preserve information for downstream artificial intelligence-based radiomic characterization, provided that noise is limited. Future work should investigate the information-preserving kernels in datasets with clinical labels.
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Affiliation(s)
- Christopher Wiedeman
- Department of Electrical and Computer Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | | | - Ge Wang
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - Richard Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Amber Simpson
- Biomedical Computing and Informatics, Queen's University, Kingston, ON, K7L 3N6, Canada
| | - Jacob Peoples
- Biomedical Computing and Informatics, Queen's University, Kingston, ON, K7L 3N6, Canada
| | - Bruno De Man
- GE Research - Healthcare, Niskayuna, NY, 12309, USA.
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Zhou L, Ji Q, Peng H, Chen F, Zheng Y, Jiao Z, Gong J, Li W. Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning. Eur Radiol 2024; 34:3644-3655. [PMID: 37994966 DOI: 10.1007/s00330-023-10316-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 08/19/2023] [Accepted: 08/26/2023] [Indexed: 11/24/2023]
Abstract
OBJECTIVES To develop a dynamic nomogram containing radiomics signature and clinical features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and design an automatic image segmentation model to reduce labor and time costs. METHODS Data from 217 medulloblastoma (MB) patients over the past 4 years were collected and separated into a training set and a test set. Intraclass correlation coefficient (ICC), random survival forest (RSF), and least absolute shrinkage and selection operator (LASSO) regression methods were employed to select variables in the training set. Univariate and multivariate Cox proportional hazard models, as well as Kaplan-Meier analysis, were utilized to determine the relationship among the radiomics signature, clinical features, and overall survival. A dynamic nomogram was developed. Additionally, a 3D-Unet deep learning model was used to train the automatic tumor delineation model. RESULTS Higher Rad-scores were significantly associated with worse OS in both the training and validation sets (p < 0.001 and p = 0.047, respectively). The Cox model combined clinical and radiomics signatures ([IBS = 0.079], [C-index = 0.747, SE = 0.045]) outperformed either radiomics signatures alone ([IBS = 0.081], [C-index = 0.738, SE = 0.041]) or clinical features alone ([IBS = 0.085], [C-index = 0.565, SE = 0.041]). The segmentation model had mean Dice coefficients of 0.80, 0.82, and 0.78 in the training, validation, and test sets respectively. A deep learning-based tumor segmentation model was built with Dice coefficients of 0.8372, 0.8017, and 0.7673 on the training set, validation set, and test set, respectively. CONCLUSIONS A combination of radiomics features and clinical characteristics enhances the accuracy of OS prediction in medulloblastoma patients. Additionally, building an MRI image automatic segmentation model reduces labor and time costs. CLINICAL RELEVANCE STATEMENT A survival prognosis model based on radiomics and clinical characteristics could improve the accuracy of prognosis estimation for medulloblastoma patients, and an MRI-based automatic tumor segmentation model could reduce the cost of time. KEY POINTS • A model that combines radiomics and clinical features can predict the survival prognosis of patients with medulloblastoma. • Online nomogram and image automatic segmentation model can help doctors better judge the prognosis of medulloblastoma and save working time. • The developed AI system can help doctors judge the prognosis of diseases and promote the development of precision medicine.
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Affiliation(s)
- Lili Zhou
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | - Qiang Ji
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | - Hong Peng
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No. 639 Zhizaoju Road, Huangpu District, Shanghai, 20011, China
| | - Feng Chen
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | - Yi Zheng
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | | | - Jian Gong
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical Unversity, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China.
| | - Wenbin Li
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China.
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8
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Wang K, Karalis JD, Elamir A, Bifolco A, Wachsmann M, Capretti G, Spaggiari P, Enrico S, Balasubramanian K, Fatimah N, Pontecorvi G, Nebbia M, Yopp A, Kaza R, Pedrosa I, Zeh H, Polanco P, Zerbi A, Wang J, Aguilera T, Ligorio M. Delta Radiomic Features Predict Resection Margin Status and Overall Survival in Neoadjuvant-Treated Pancreatic Cancer Patients. Ann Surg Oncol 2024; 31:2608-2620. [PMID: 38151623 PMCID: PMC10908610 DOI: 10.1245/s10434-023-14805-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 12/06/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND Neoadjuvant therapy (NAT) emerged as the standard of care for patients with pancreatic ductal adenocarcinoma (PDAC) who undergo surgery; however, surgery is morbid, and tools to predict resection margin status (RMS) and prognosis in the preoperative setting are needed. Radiomic models, specifically delta radiomic features (DRFs), may provide insight into treatment dynamics to improve preoperative predictions. METHODS We retrospectively collected clinical, pathological, and surgical data (patients with resectable, borderline, locally advanced, and metastatic disease), and pre/post-NAT contrast-enhanced computed tomography (CT) scans from PDAC patients at the University of Texas Southwestern Medical Center (UTSW; discovery) and Humanitas Hospital (validation cohort). Gross tumor volume was contoured from CT scans, and 257 radiomics features were extracted. DRFs were calculated by direct subtraction of pre/post-NAT radiomic features. Cox proportional models and binary prediction models, including/excluding clinical variables, were constructed to predict overall survival (OS), disease-free survival (DFS), and RMS. RESULTS The discovery and validation cohorts comprised 58 and 31 patients, respectively. Both cohorts had similar clinical characteristics, apart from differences in NAT (FOLFIRINOX vs. gemcitabine/nab-paclitaxel; p < 0.05) and type of surgery resections (pancreatoduodenectomy, distal or total pancreatectomy; p < 0.05). The model that combined clinical variables (pre-NAT carbohydrate antigen (CA) 19-9, the change in CA19-9 after NAT (∆CA19-9), and resectability status) and DRFs outperformed the clinical feature-based models and other radiomics feature-based models in predicting OS (UTSW: 0.73; Humanitas: 0.66), DFS (UTSW: 0.75; Humanitas: 0.64), and RMS (UTSW 0.73; Humanitas: 0.69). CONCLUSIONS Our externally validated, predictive/prognostic delta-radiomics models, which incorporate clinical variables, show promise in predicting the risk of predicting RMS in NAT-treated PDAC patients and their OS or DFS.
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Affiliation(s)
- Kai Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - John D Karalis
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ahmed Elamir
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Alessandro Bifolco
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Megan Wachsmann
- Department of Pathology, Veterans Affairs North Texas Health Care System, Dallas, TX, USA
| | - Giovanni Capretti
- Pancreatic Surgery Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Paola Spaggiari
- Department of Pathology, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Sebastian Enrico
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Nafeesah Fatimah
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Giada Pontecorvi
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Martina Nebbia
- Pancreatic Surgery Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Adam Yopp
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ravi Kaza
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ivan Pedrosa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Herbert Zeh
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Patricio Polanco
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Alessandro Zerbi
- Pancreatic Surgery Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Todd Aguilera
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Matteo Ligorio
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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9
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Bülbül HM, Burakgazi G, Kesimal U. Preoperative assessment of grade, T stage, and lymph node involvement: machine learning-based CT texture analysis in colon cancer. Jpn J Radiol 2024; 42:300-307. [PMID: 37874525 DOI: 10.1007/s11604-023-01502-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 10/01/2023] [Indexed: 10/25/2023]
Abstract
PURPOSE To investigate whether texture analysis of primary colonic mass in preoperative abdominal computed tomography (CT) scans of patients diagnosed with colon cancer could predict tumor grade, T stage, and lymph node involvement using machine learning (ML) algorithms. MATERIALS AND METHODS This retrospective study included 73 patients diagnosed with colon cancer. Texture features were extracted from contrast-enhanced CT images using LifeX software. First, feature reduction was performed by two radiologists through reproducibility analysis. Using the analysis of variance method, the parameters that best predicted lymph node involvement, grade, and T stage were determined. The predictive performance of these parameters was assessed using Orange software with the k-nearest neighbor (kNN), random forest, gradient boosting, and neural network models, and their area under the curve values were calculated. RESULTS There was excellent reproducibility between the two radiologists in terms of 49 of the 58 texture parameters that were subsequently subject to further analysis. Considering all four ML algorithms, the mean AUC and accuracy ranges were 0.557-0.800 and 47-76%, respectively, for the prediction of lymph node involvement; 0.666-0.846 and 68-77%, respectively, for the prediction of grade; and 0.768-0.962 and 81-88%, respectively, for the prediction of T stage. The best performance was achieved with the random forest model in the prediction of LN involvement, the kNN model for the prediction of grade, and the gradient boosting model for the prediction of T stage. CONCLUSION The results of this study suggest that the texture analysis of preoperative CT scans obtained for staging purposes in colon cancer can predict the presence of advanced-stage tumors, high tumor grade, and lymph node involvement with moderate specificity and sensitivity rates when evaluated using ML models.
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Affiliation(s)
- Hande Melike Bülbül
- Department of Radiology, Ministry of Health Recep Tayyip Erdoğan University Training and Research Hospital, Rize, Turkey.
| | - Gülen Burakgazi
- Department of Radiology, Ministry of Health Recep Tayyip Erdoğan University Training and Research Hospital, Rize, Turkey
| | - Uğur Kesimal
- Department of Radiology, Ministry of Health Ankara Training and Research Hospital, Ankara, Turkey
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10
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Granata V, Fusco R, Brunese MC, Di Mauro A, Avallone A, Ottaiano A, Izzo F, Normanno N, Petrillo A. Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging. LA RADIOLOGIA MEDICA 2024; 129:420-428. [PMID: 38308061 DOI: 10.1007/s11547-024-01779-x] [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: 11/25/2023] [Accepted: 01/04/2024] [Indexed: 02/04/2024]
Abstract
PURPOSE To assess the efficacy of radiomics features, obtained by magnetic resonance imaging (MRI) with hepatospecific contrast agent, in pre-surgical setting, to predict RAS mutational status in liver metastases. METHODS Patients with MRI in pre-surgical setting were enrolled in a retrospective study. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. The features were extracted considering the agreement with the Imaging Biomarker Standardization Initiative (IBSI). Balancing was performed through synthesis of samples for the underrepresented classes using the self-adaptive synthetic oversampling (SASYNO) approach. Inter- and intraclass correlation coefficients (ICC) were calculated to assess the between-observer and within-observer reproducibility of all radiomics characteristics. For continuous variables, nonparametric Wilcoxon-Mann-Whitney test was utilized. Benjamini and Hochberg's false discovery rate (FDR) adjustment for multiple testing was used. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Moreover, features selection were performed before and after a normalized procedure using two different methods (3-sigma and z-score). McNemar test was used to assess differences statistically significant between dichotomic tables. All statistical procedures were done using MATLAB R2021b Statistics and Machine Toolbox (MathWorks, Natick, MA, USA). RESULTS Seven normalized radiomics features, extracted from arterial phase, 11 normalized radiomics features, from portal phase, 12 normalized radiomics features from hepatobiliary phase and 12 normalized features from T2-W SPACE sequence were robust predictors of RAS mutational status. The multivariate analysis increased significantly the accuracy in RAS prediction when a LRM was used, combining 12 robust normalized features extracted by VIBE hepatobiliary phase reaching an accuracy of 99%, a sensitivity 97%, a specificity of 100%, a PPV of 100% and a NPV of 98%. No statistically significant increase was obtained, considering the tested classifiers DT, KNN and SVM, both without normalization and with normalization methods. CONCLUSIONS Normalized approach in MRI radiomics analysis allows to predict RAS mutational status.
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Affiliation(s)
- Vincenza Granata
- Radiology Unit, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, Italy.
| | | | - Maria Chiara Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Annabella Di Mauro
- Pathological Anatomy and Cytopathology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Antonio Avallone
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Alessandro Ottaiano
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Francesco Izzo
- Epatobiliary Surgical Oncology Unit, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Nicola Normanno
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, 80131, Naples, Italy
| | - Antonella Petrillo
- Radiology Unit, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, Italy
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11
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Simpson AL, Peoples J, Creasy JM, Fichtinger G, Gangai N, Keshavamurthy KN, Lasso A, Shia J, D'Angelica MI, Do RKG. Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases. Sci Data 2024; 11:172. [PMID: 38321027 PMCID: PMC10847495 DOI: 10.1038/s41597-024-02981-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 01/17/2024] [Indexed: 02/08/2024] Open
Abstract
The liver is a common site for the development of metastases in colorectal cancer. Treatment selection for patients with colorectal liver metastases (CRLM) is difficult; although hepatic resection will cure a minority of CRLM patients, recurrence is common. Reliable preoperative prediction of recurrence could therefore be a valuable tool for physicians in selecting the best candidates for hepatic resection in the treatment of CRLM. It has been hypothesized that evidence for recurrence could be found via quantitative image analysis on preoperative CT imaging of the future liver remnant before resection. To investigate this hypothesis, we have collected preoperative hepatic CT scans, clinicopathologic data, and recurrence/survival data, from a large, single-institution series of patients (n = 197) who underwent hepatic resection of CRLM. For each patient, we also created segmentations of the liver, vessels, tumors, and future liver remnant. The largest of its kind, this dataset is a resource that may aid in the development of quantitative imaging biomarkers and machine learning models for the prediction of post-resection hepatic recurrence of CRLM.
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Affiliation(s)
- Amber L Simpson
- School of Computing, Queen's University, Kingston, Ontario, Canada.
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada.
| | - Jacob Peoples
- School of Computing, Queen's University, Kingston, Ontario, Canada
| | | | - Gabor Fichtinger
- School of Computing, Queen's University, Kingston, Ontario, Canada
| | - Natalie Gangai
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Andras Lasso
- School of Computing, Queen's University, Kingston, Ontario, Canada
| | - Jinru Shia
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michael I D'Angelica
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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12
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Triggiani S, Contaldo MT, Mastellone G, Cè M, Ierardi AM, Carrafiello G, Cellina M. The Role of Artificial Intelligence and Texture Analysis in Interventional Radiological Treatments of Liver Masses: A Narrative Review. Crit Rev Oncog 2024; 29:37-52. [PMID: 38505880 DOI: 10.1615/critrevoncog.2023049855] [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: 03/21/2024]
Abstract
Liver lesions, including both benign and malignant tumors, pose significant challenges in interventional radiological treatment planning and prognostication. The emerging field of artificial intelligence (AI) and its integration with texture analysis techniques have shown promising potential in predicting treatment outcomes, enhancing precision, and aiding clinical decision-making. This comprehensive review aims to summarize the current state-of-the-art research on the application of AI and texture analysis in determining treatment response, recurrence rates, and overall survival outcomes for patients undergoing interventional radiological treatment for liver lesions. Furthermore, the review addresses the challenges associated with the implementation of AI and texture analysis in clinical practice, including data acquisition, standardization of imaging protocols, and model validation. Future directions and potential advancements in this field are discussed. Integration of multi-modal imaging data, incorporation of genomics and clinical data, and the development of predictive models with enhanced interpretability are proposed as potential avenues for further research. In conclusion, the application of AI and texture analysis in predicting outcomes of interventional radiological treatment for liver lesions shows great promise in augmenting clinical decision-making and improving patient care. By leveraging these technologies, clinicians can potentially enhance treatment planning, optimize intervention strategies, and ultimately improve patient outcomes in the management of liver lesions.
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Affiliation(s)
- Sonia Triggiani
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maria T Contaldo
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Giulia Mastellone
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Anna M Ierardi
- Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, 20122 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
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Chen Q, Fu C, Qiu X, He J, Zhao T, Zhang Q, Hu X, Hu H. Machine-learning-based performance comparison of two-dimensional (2D) and three-dimensional (3D) CT radiomics features for intracerebral haemorrhage expansion. Clin Radiol 2024; 79:e26-e33. [PMID: 37926647 DOI: 10.1016/j.crad.2023.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/07/2023] [Accepted: 10/02/2023] [Indexed: 11/07/2023]
Abstract
AIM To investigate the value of non-contrast CT (NCCT)-based two-dimensional (2D) radiomics features in predicting haematoma expansion (HE) after spontaneous intracerebral haemorrhage (ICH) and compare its predictive ability with the three-dimensional (3D) signature. MATERIALS AND METHODS Three hundred and seven ICH patients who received baseline NCCT within 6 h of ictus from two stroke centres were analysed retrospectively. 2D and 3D radiomics features were extracted in the manner of one-to-one correspondence. The 2D and 3D models were generated by four different machine-learning algorithms (regularised L1 logistic regression, decision tree, support vector machine and AdaBoost), and the receiver operating characteristic (ROC) curve was used to compare their predictive performance. A robustness analysis was performed according to baseline haematoma volume. RESULTS Each feature type of 2D and 3D modalities used for subsequent analyses had excellent consistency (mean ICC >0.9). Among the different machine-learning algorithms, pairwise comparison showed no significant difference in both the training (mean area under the ROC curve [AUC] 0.858 versus 0.802, all p>0.05) and validation datasets (mean AUC 0.725 versus 0.678, all p>0.05), and the 10-fold cross-validation evaluation yielded similar results. The AUCs of the 2D and 3D models were comparable either in the binary or tertile volume analysis (all p>0.5). CONCLUSION NCCT-derived 2D radiomics features exhibited acceptable and similar performance to the 3D features in predicting HE, and this comparability seemed unaffected by initial haematoma volume. The 2D signature may be preferred in future HE-related radiomic works given its compatibility with emergency condition of ICH.
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Affiliation(s)
- Q Chen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - C Fu
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - X Qiu
- Department of Radiology, Qian Tang District of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - J He
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - T Zhao
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Q Zhang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - X Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - H Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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14
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Berbís MÁ, Godino FP, Rodríguez-Comas J, Nava E, García-Figueiras R, Baleato-González S, Luna A. Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application. Abdom Radiol (NY) 2024; 49:322-340. [PMID: 37889265 DOI: 10.1007/s00261-023-04071-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 10/28/2023]
Abstract
Radiomics allows the extraction of quantitative imaging features from clinical magnetic resonance imaging (MRI) and computerized tomography (CT) studies. The advantages of radiomics have primarily been exploited in oncological applications, including better characterization and staging of oncological lesions and prediction of patient outcomes and treatment response. The potential introduction of radiomics in the clinical setting requires the establishment of a standardized radiomics pipeline and a quality assurance program. Radiomics and texture analysis of the liver have improved the differentiation of hypervascular lesions such as adenomas, focal nodular hyperplasia, and hepatocellular carcinoma (HCC) during the arterial phase, and in the pretreatment determination of HCC prognostic factors (e.g., tumor grade, microvascular invasion, Ki-67 proliferation index). Radiomics of pancreatic CT and MR images has enhanced pancreatic ductal adenocarcinoma detection and its differentiation from pancreatic neuroendocrine tumors, mass-forming chronic pancreatitis, or autoimmune pancreatitis. Radiomics can further help to better characterize incidental pancreatic cystic lesions, accurately discriminating benign from malignant intrapancreatic mucinous neoplasms. Nonetheless, despite their encouraging results and exciting potential, these tools have yet to be implemented in the clinical setting. This non-systematic review will describe the essential steps in the implementation of the radiomics and feature extraction workflow from liver and pancreas CT and MRI studies for their potential clinical application. A succinct overview of reported radiomics applications in the liver and pancreas and the challenges and limitations of their implementation in the clinical setting is also discussed, concluding with a brief exploration of the future perspectives of radiomics in the gastroenterology field.
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Affiliation(s)
- M Álvaro Berbís
- Department of Radiology, HT Médica, San Juan de Dios Hospital, 14960, Córdoba, Spain.
- Department of Radiology, HT Médica, San Juan de Dios Hospital, Av. del Brillante, 106, 14012, Córdoba, Spain.
| | | | | | - Enrique Nava
- Department of Communications Engineering, University of Málaga, 29016, Málaga, Spain
| | - Roberto García-Figueiras
- Abdominal Imaging Section, University Clinical Hospital of Santiago, 15706, Santiago de Compostela, A Coruña, Spain
| | - Sandra Baleato-González
- Abdominal Imaging Section, University Clinical Hospital of Santiago, 15706, Santiago de Compostela, A Coruña, Spain
| | - Antonio Luna
- Department of Radiology, HT Médica, Clínica las Nieves, 23007, Jaén, Spain
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Jovanovic MM, Stefanovic AD, Sarac D, Kovac J, Jankovic A, Saponjski DJ, Tadic B, Kostadinovic M, Veselinovic M, Sljukic V, Skrobic O, Micev M, Masulovic D, Pesko P, Ebrahimi K. Possibility of Using Conventional Computed Tomography Features and Histogram Texture Analysis Parameters as Imaging Biomarkers for Preoperative Prediction of High-Risk Gastrointestinal Stromal Tumors of the Stomach. Cancers (Basel) 2023; 15:5840. [PMID: 38136387 PMCID: PMC10742259 DOI: 10.3390/cancers15245840] [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: 10/30/2023] [Revised: 12/06/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
BACKGROUND The objective of this study is to determine the morphological computed tomography features of the tumor and texture analysis parameters, which may be a useful diagnostic tool for the preoperative prediction of high-risk gastrointestinal stromal tumors (HR GISTs). METHODS This is a prospective cohort study that was carried out in the period from 2019 to 2022. The study included 79 patients who underwent CT examination, texture analysis, surgical resection of a lesion that was suspicious for GIST as well as pathohistological and immunohistochemical analysis. RESULTS Textural analysis pointed out min norm (p = 0.032) as a histogram parameter that significantly differed between HR and LR GISTs, while min norm (p = 0.007), skewness (p = 0.035) and kurtosis (p = 0.003) showed significant differences between high-grade and low-grade tumors. Univariate regression analysis identified tumor diameter, margin appearance, growth pattern, lesion shape, structure, mucosal continuity, enlarged peri- and intra-tumoral feeding or draining vessel (EFDV) and max norm as significant predictive factors for HR GISTs. Interrupted mucosa (p < 0.001) and presence of EFDV (p < 0.001) were obtained by multivariate regression analysis as independent predictive factors of high-risk GISTs with an AUC of 0.878 (CI: 0.797-0.959), sensitivity of 94%, specificity of 77% and accuracy of 88%. CONCLUSION This result shows that morphological CT features of GIST are of great importance in the prediction of non-invasive preoperative metastatic risk. The incorporation of texture analysis into basic imaging protocols may further improve the preoperative assessment of risk stratification.
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Affiliation(s)
- Milica Mitrovic Jovanovic
- Center for Radiology and Magnetic Resonance Imaging, University Clinical Centre of Serbia, Pasterova No. 2, 11000 Belgrade, Serbia; (M.M.J.)
- Department for Radiology, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
| | - Aleksandra Djuric Stefanovic
- Center for Radiology and Magnetic Resonance Imaging, University Clinical Centre of Serbia, Pasterova No. 2, 11000 Belgrade, Serbia; (M.M.J.)
- Department for Radiology, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
| | - Dimitrije Sarac
- Center for Radiology and Magnetic Resonance Imaging, University Clinical Centre of Serbia, Pasterova No. 2, 11000 Belgrade, Serbia; (M.M.J.)
| | - Jelena Kovac
- Center for Radiology and Magnetic Resonance Imaging, University Clinical Centre of Serbia, Pasterova No. 2, 11000 Belgrade, Serbia; (M.M.J.)
- Department for Radiology, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
| | - Aleksandra Jankovic
- Center for Radiology and Magnetic Resonance Imaging, University Clinical Centre of Serbia, Pasterova No. 2, 11000 Belgrade, Serbia; (M.M.J.)
- Department for Radiology, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
| | - Dusan J. Saponjski
- Center for Radiology and Magnetic Resonance Imaging, University Clinical Centre of Serbia, Pasterova No. 2, 11000 Belgrade, Serbia; (M.M.J.)
- Department for Radiology, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
| | - Boris Tadic
- Department for HBP Surgery, Clinic for Digestive Surgery, University Clinical Centre of Serbia, Koste Todorovica Street, No. 6, 11000 Belgrade, Serbia
- Department for Surgery, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
| | - Milena Kostadinovic
- Center for Physical Medicine and Rehabilitation, University Clinical Centre of Serbia, Pasterova Street, No. 2, 11000 Beograd, Serbia
| | - Milan Veselinovic
- Department for Surgery, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
- Department of Stomach and Esophageal Surgery, Clinic for Digestive Surgery, University Clinical Centre of Serbia, Koste Todorovica Street No. 6, 11000 Belgrade, Serbia
| | - Vladimir Sljukic
- Department for Surgery, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
- Department of Stomach and Esophageal Surgery, Clinic for Digestive Surgery, University Clinical Centre of Serbia, Koste Todorovica Street No. 6, 11000 Belgrade, Serbia
| | - Ognjan Skrobic
- Department for Surgery, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
- Department of Stomach and Esophageal Surgery, Clinic for Digestive Surgery, University Clinical Centre of Serbia, Koste Todorovica Street No. 6, 11000 Belgrade, Serbia
| | - Marjan Micev
- Department for Pathology, Clinic for Digestive Surgery, University Clinical Centre of Serbia, Koste Todorovica Street, No. 6, 11000 Belgrade, Serbia
| | - Dragan Masulovic
- Center for Radiology and Magnetic Resonance Imaging, University Clinical Centre of Serbia, Pasterova No. 2, 11000 Belgrade, Serbia; (M.M.J.)
- Department for Radiology, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
| | - Predrag Pesko
- Department for Surgery, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
- Department of Stomach and Esophageal Surgery, Clinic for Digestive Surgery, University Clinical Centre of Serbia, Koste Todorovica Street No. 6, 11000 Belgrade, Serbia
| | - Keramatollah Ebrahimi
- Department for Surgery, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
- Department of Stomach and Esophageal Surgery, Clinic for Digestive Surgery, University Clinical Centre of Serbia, Koste Todorovica Street No. 6, 11000 Belgrade, Serbia
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Ricci Lara MA, Esposito MI, Aineseder M, López Grove R, Cerini MA, Verzura MA, Luna DR, Benítez SE, Spina JC. Radiomics and Machine Learning for prediction of two-year disease-specific mortality and KRAS mutation status in metastatic colorectal cancer. Surg Oncol 2023; 51:101986. [PMID: 37729816 DOI: 10.1016/j.suronc.2023.101986] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/23/2023] [Accepted: 09/07/2023] [Indexed: 09/22/2023]
Abstract
PURPOSE Colorectal cancer is usually accompanied by liver metastases. The prediction of patient evolution is essential for the choice of the appropriate therapy. The aim of this study is to develop and evaluate machine learning models to predict KRAS gene mutations and 2-year disease-specific mortality from medical images. METHODS Clinical and follow-up information was collected from patients with metastatic colorectal cancer who had undergone computed tomography prior to liver resection. The dominant liver lesion was segmented in each scan and radiomic features were extracted from the volumes of interest. The 65% of the cases were employed to perform feature selection and to train machine learning algorithms through cross-validation. The best performing models were assembled and evaluated in the remaining cases of the cohort. RESULTS For the mortality model development, 101 cases were used as training set (64 alive, 37 deceased) and 35 as test set (22 alive, 13 deceased); while for KRAS mutation models, 55 cases were used for training (31 wild-type, 24 mutated) and 30 for testing (17 wild-type, 13 mutated). The ensemble of top performing models resulted in an area under the receiver operating characteristic curve of 0.878 for mortality and 0.905 for KRAS prediction. CONCLUSIONS Predicting the prognosis of patients with metastatic colorectal cancer is useful for making timely decisions about the best treatment options. This study presents a noninvasive method based on quantitative analysis of baseline images to identify factors influencing patient outcomes, with the aim of incorporating these tools as support systems.
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Affiliation(s)
- María Agustina Ricci Lara
- Health Informatics Department, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de Buenos Aires, Argentina; Universidad Tecnológica Nacional, Av. Medrano 951, 1179, Ciudad Autónoma de Buenos Aires, Argentina.
| | - Marco Iván Esposito
- Health Informatics Department, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de Buenos Aires, Argentina; Instituto Tecnológico de Buenos Aires, Iguazú 341, 1437, Ciudad Autónoma de Buenos Aires, Argentina.
| | - Martina Aineseder
- Radiology Department, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de Buenos Aires, Argentina.
| | - Roy López Grove
- Radiology Department, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de Buenos Aires, Argentina.
| | - Matías Alejandro Cerini
- Oncology Department, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de Buenos Aires, Argentina.
| | - María Alicia Verzura
- Oncology Department, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de Buenos Aires, Argentina.
| | - Daniel Roberto Luna
- Health Informatics Department, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de Buenos Aires, Argentina; Instituto de Medicina Traslacional e Ingeniería Biomédica (IMTIB), UE de triple dependencia CONICET- Instituto Universitario del Hospital Italiano (IUHI) - Hospital ITaliano (HIBA), Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de Buenos Aires, Argentina.
| | - Sonia Elizabeth Benítez
- Health Informatics Department, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de Buenos Aires, Argentina; Instituto Universitario del Hospital Italiano, Potosí 4265, 1199, Ciudad Autónoma de Buenos Aires, Argentina.
| | - Juan Carlos Spina
- Radiology Department, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de Buenos Aires, Argentina.
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Saleh M, Virarkar M, Mahmoud HS, Wong VK, Gonzalez Baerga CI, Parikh M, Elsherif SB, Bhosale PR. Radiomics analysis with three-dimensional and two-dimensional segmentation to predict survival outcomes in pancreatic cancer. World J Radiol 2023; 15:304-314. [PMID: 38058604 PMCID: PMC10696186 DOI: 10.4329/wjr.v15.i11.304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/20/2023] [Accepted: 10/23/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND Radiomics can assess prognostic factors in several types of tumors, but considering its prognostic ability in pancreatic cancer has been lacking. AIM To evaluate the performance of two different radiomics software in assessing survival outcomes in pancreatic cancer patients. METHODS We retrospectively reviewed pretreatment contrast-enhanced dual-energy computed tomography images from 48 patients with biopsy-confirmed pancreatic ductal adenocarcinoma who later underwent neoadjuvant chemoradiation and surgery. Tumors were segmented using TexRad software for 2-dimensional (2D) analysis and MIM software for 3D analysis, followed by radiomic feature extraction. Cox proportional hazard modeling correlated texture features with overall survival (OS) and progression-free survival (PFS). Cox regression was used to detect differences in OS related to pretreatment tumor size and residual tumor following treatment. The Wilcoxon test was used to show the relationship between tumor volume and the percent of residual tumor. Kaplan-Meier analysis was used to compare survival in patients with different tumor densities in Hounsfield units for both 2D and 3D analysis. RESULTS 3D analysis showed that higher mean tumor density [hazard ratio (HR) = 0.971, P = 0.041)] and higher median tumor density (HR = 0.970, P = 0.037) correlated with better OS. 2D analysis showed that higher mean tumor density (HR = 0.963, P = 0.014) and higher mean positive pixels (HR = 0.962, P = 0.014) correlated with better OS; higher skewness (HR = 3.067, P = 0.008) and higher kurtosis (HR = 1.176, P = 0.029) correlated with worse OS. Higher entropy correlated with better PFS (HR = 0.056, P = 0.036). Models determined that patients with increased tumor size greater than 1.35 cm were likely to have a higher percentage of residual tumors of over 10%. CONCLUSION Several radiomics features can be used as prognostic tools for pancreatic cancer. However, results vary between 2D and 3D analyses. Mean tumor density was the only variable that could reliably predict OS, irrespective of the analysis used.
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Affiliation(s)
- Mohammed Saleh
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Mayur Virarkar
- Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
| | - Hagar S Mahmoud
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Vincenzo K Wong
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Carlos Ignacio Gonzalez Baerga
- Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
| | - Miti Parikh
- Keck School of Medicine, University of South California, Los Angeles, CA 90033, United States
| | - Sherif B Elsherif
- Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
| | - Priya R Bhosale
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
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Jia LL, Lei J. Response to letter by Fusco Roberta & Vincenza Granata-Re: Comments on "Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta‑analysis". Eur J Radiol 2023; 175:111195. [PMID: 38669754 DOI: 10.1016/j.ejrad.2023.111195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 11/11/2023] [Indexed: 04/28/2024]
Affiliation(s)
- Lu-Lu Jia
- The First Clinical Medical College of Lanzhou University, Lanzhou City, Gansu Province, China
| | - Junqiang Lei
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou City, Gansu Province, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou City, Gansu Province, China.
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19
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Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. LA RADIOLOGIA MEDICA 2023; 128:1310-1332. [PMID: 37697033 DOI: 10.1007/s11547-023-01710-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 08/22/2023] [Indexed: 09/13/2023]
Abstract
OBJECTIVE The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated. METHODS The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed. RESULTS The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence. CONCLUSIONS The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy.
| | | | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Alessandro Ottaiano
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Claudia Cardone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Antonio Avallone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Renato Patrone
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Fabiana Tatangelo
- Division of Pathological Anatomy and Cytopathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084, Salerno, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", 70124, Bari, Italy
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Radiology Unit-Sant'Andrea University Hospital, Sapienza-University of Rome, 00189, Rome, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
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Huang S, Xu F, Zhu W, Xie D, Lou K, Huang D, Hu H. Multi-dimensional radiomics analysis to predict visceral pleural invasion in lung adenocarcinoma of ≤3 cm maximum diameter. Clin Radiol 2023; 78:e847-e855. [PMID: 37607844 DOI: 10.1016/j.crad.2023.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 06/20/2023] [Accepted: 07/21/2023] [Indexed: 08/24/2023]
Abstract
AIM To explore the value of radiomics analysis in preoperatively predicting visceral pleural invasion (VPI) of lung adenocarcinoma (LAC) with ≤3 cm maximum diameter and to compare the performance of two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) radiomics models. MATERIALS AND METHODS A total of 391 LAC patients were enrolled retrospectively, of whom 142 were VPI (+) and 249 were VPI (-). Radiomics features were extracted from 2D and 3D regions of interest (ROIs) of tumours in CT images. 2D and 3D radiomics models were developed combining the optimal radiomics features by using the logistic regression machine-learning method and radiomics scores (rad-scores) were calculated. Nomograms were constructed by integrating independent risk factors and rad-scores. The performance of each model was evaluated by using the receiver operator characteristic (ROC) curve, decision curve analysis (DCA), clinical impact curve (CIC), and calculating the area under the curve (AUC). RESULTS There was no difference in the VPI prediction between 2D and 3D radiomics models (training group: 2D AUC=0.835, 3D AUC=0.836, p=0.896; validation group: 2D AUC=0.803, 3D AUC=0.794, p=0.567). The 2D and 3D nomograms performed similarly regarding discrimination (training group: 2D AUC=0.867, 3D AUC=0.862, p=0.409, validation group: 2D AUC=0.835, 3D AUC=0.827, p=0.558), and outperformed their corresponding radiomics models and the clinical model. DCA and CIC revealed that the 2D nomogram had slightly better clinical utility. CONCLUSION The 2D radiomics model has a similar discrimination capability compared with the 3D radiomics model. The 2D nomogram performs slightly better for individual VPI prediction in LAC.
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Affiliation(s)
- S Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Radiology, Ningbo Medical Center LiHuili Hospital, Ningbo, Zhejiang, China
| | - F Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - W Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - D Xie
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Radiology, Shaoxing Second Hospital, Shaoxing, Zhejiang, China
| | - K Lou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - D Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - H Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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Wang X, Zhou D, Kong Y, Cheng N, Gao M, Zhang G, Ma J, Chen Y, Ge S. Value of 18F-FDG-PET/CT radiomics combined with clinical variables in the differential diagnosis of malignant and benign vertebral compression fractures. EJNMMI Res 2023; 13:89. [PMID: 37819414 PMCID: PMC10567613 DOI: 10.1186/s13550-023-01038-6] [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: 06/15/2023] [Accepted: 09/20/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Vertebral compression fractures (VCFs) are common clinical problems that arise from various reasons. The differential diagnosis of benign and malignant VCFs is challenging. This study was designed to develop and validate a radiomics model to predict benign and malignant VCFs with 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT). RESULTS Twenty-six features (9 PET features and 17 CT features) and eight clinical variables (age, SUVmax, SUVpeak, SULmax, SULpeak, osteolytic destruction, fracture line, and appendices/posterior vertebrae involvement) were ultimately selected. The area under the curve (AUCs) of the radiomics and clinical-radiomics models were significantly different from that of the clinical model in both the training group (0.986, 0.987 vs. 0.884, p < 0.05) and test group (0.962, 0.948 vs. 0.858, p < 0.05), while there was no significant difference between the radiomics model and clinical-radiomics model (p > 0.05). The accuracies of the radiomics and clinical-radiomics models were 94.0% and 95.0% in the training group and 93.2% and 93.2% in the test group, respectively. The three models all showed good calibration (Hosmer-Lemeshow test, p > 0.05). According to the decision curve analysis (DCA), the radiomics model and clinical-radiomics model exhibited higher overall net benefit than the clinical model. CONCLUSIONS The PET/CT-based radiomics and clinical-radiomics models showed good performance in distinguishing between malignant and benign VCFs. The radiomics method may be valuable for treatment decision-making.
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Affiliation(s)
- Xun Wang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Dandan Zhou
- Big Data and Artificial Intelligence, Jining Polytechnic, Jinyu Road, Jining, Shandong, China
| | - Yu Kong
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Nan Cheng
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Ming Gao
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Guqing Zhang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Junli Ma
- Department of Radiation Oncology, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Yueqin Chen
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China.
| | - Shuang Ge
- Department of Radiation Oncology, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China.
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Li Y, Li J, Meng M, Duan S, Shi H, Hang J. Development and Validation of a Radiomics Nomogram for Liver Metastases Originating from Gastric and Colorectal Cancer. Diagnostics (Basel) 2023; 13:2937. [PMID: 37761304 PMCID: PMC10528017 DOI: 10.3390/diagnostics13182937] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/04/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
The origin of metastatic liver tumours (arising from gastric or colorectal sources) is closely linked to treatment choices and survival prospects. However, in some instances, the primary lesion remains elusive even after an exhaustive diagnostic investigation. Consequently, we have devised and validated a radiomics nomogram for ascertaining the primary origin of liver metastases stemming from gastric cancer (GCLMs) and colorectal cancer (CCLMs). This retrospective study encompassed patients diagnosed with either GCLMs or CCLMs, comprising a total of 277 GCLM cases and 278 CCLM cases. Radiomic characteristics were derived from venous phase computed tomography (CT) scans, and a radiomics signature (RS) was computed. Multivariable regression analysis demonstrated that gender (OR = 3.457; 95% CI: 2.102-5.684; p < 0.001), haemoglobin levels (OR = 0.976; 95% CI: 0.967-0.986; p < 0.001), carcinoembryonic antigen (CEA) levels (OR = 0.500; 95% CI: 0.307-0.814; p = 0.005), and RS (OR = 2.147; 95% CI: 1.127-4.091; p = 0.020) exhibited independent associations with GCLMs as compared to CCLMs. The nomogram, combining RS with clinical variables, demonstrated strong discriminatory power in both the training (AUC = 0.71) and validation (AUC = 0.78) cohorts. The calibration curve, decision curve analysis, and clinical impact curves revealed the clinical utility of this nomogram and substantiated its enhanced diagnostic performance.
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Affiliation(s)
- Yuying Li
- Department of Radiology, Changzhou Second People’s Hospital Affiliated with Nanjing Medical University, Changzhou 213000, China; (Y.L.); (J.L.); (M.M.)
- Graduate College, Dalian Medical University, Dalian 116000, China
| | - Jingjing Li
- Department of Radiology, Changzhou Second People’s Hospital Affiliated with Nanjing Medical University, Changzhou 213000, China; (Y.L.); (J.L.); (M.M.)
- Graduate College, Dalian Medical University, Dalian 116000, China
| | - Mingzhu Meng
- Department of Radiology, Changzhou Second People’s Hospital Affiliated with Nanjing Medical University, Changzhou 213000, China; (Y.L.); (J.L.); (M.M.)
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai 201100, China;
| | - Haifeng Shi
- Department of Radiology, Changzhou Second People’s Hospital Affiliated with Nanjing Medical University, Changzhou 213000, China; (Y.L.); (J.L.); (M.M.)
| | - Junjie Hang
- Department of Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
- Department of Oncology, Changzhou Second People’s Hospital Affiliated with Nanjing Medical University, Changzhou 213000, China
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Mervak BM, Fried JG, Wasnik AP. A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging. Diagnostics (Basel) 2023; 13:2889. [PMID: 37761253 PMCID: PMC10529018 DOI: 10.3390/diagnostics13182889] [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: 05/25/2023] [Revised: 08/23/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
Artificial intelligence (AI) has been a topic of substantial interest for radiologists in recent years. Although many of the first clinical applications were in the neuro, cardiothoracic, and breast imaging subspecialties, the number of investigated and real-world applications of body imaging has been increasing, with more than 30 FDA-approved algorithms now available for applications in the abdomen and pelvis. In this manuscript, we explore some of the fundamentals of artificial intelligence and machine learning, review major functions that AI algorithms may perform, introduce current and potential future applications of AI in abdominal imaging, provide a basic understanding of the pathways by which AI algorithms can receive FDA approval, and explore some of the challenges with the implementation of AI in clinical practice.
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Affiliation(s)
| | | | - Ashish P. Wasnik
- Department of Radiology, University of Michigan—Michigan Medicine, 1500 E. Medical Center Dr., Ann Arbor, MI 48109, USA; (B.M.M.); (J.G.F.)
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24
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Liu Z, Duan T, Zhang Y, Weng S, Xu H, Ren Y, Zhang Z, Han X. Radiogenomics: a key component of precision cancer medicine. Br J Cancer 2023; 129:741-753. [PMID: 37414827 PMCID: PMC10449908 DOI: 10.1038/s41416-023-02317-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 05/02/2023] [Accepted: 06/12/2023] [Indexed: 07/08/2023] Open
Abstract
Radiogenomics, focusing on the relationship between genomics and imaging phenotypes, has been widely applied to address tumour heterogeneity and predict immune responsiveness and progression. It is an inevitable consequence of current trends in precision medicine, as radiogenomics costs less than traditional genetic sequencing and provides access to whole-tumour information rather than limited biopsy specimens. By providing voxel-by-voxel genetic information, radiogenomics can allow tailored therapy targeting a complete, heterogeneous tumour or set of tumours. In addition to quantifying lesion characteristics, radiogenomics can also be used to distinguish benign from malignant entities, as well as patient characteristics, to better stratify patients according to disease risk, thereby enabling more precise imaging and screening. Here, we have characterised the radiogenomic application in precision medicine using a multi-omic approach. we outline the main applications of radiogenomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalised medicine. Finally, we discuss the challenges in the field of radiogenomics and the scope and clinical applicability of these methods.
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Affiliation(s)
- Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
- Interventional Institute of Zhengzhou University, 450052, Zhengzhou, Henan, China
- Interventional Treatment and Clinical Research Center of Henan Province, 450052, Zhengzhou, Henan, China
| | - Tian Duan
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Hui Xu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Yuqing Ren
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Zhenyu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China.
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China.
- Interventional Institute of Zhengzhou University, 450052, Zhengzhou, Henan, China.
- Interventional Treatment and Clinical Research Center of Henan Province, 450052, Zhengzhou, Henan, China.
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Anichini M, Galluzzo A, Danti G, Grazzini G, Pradella S, Treballi F, Bicci E. Focal Lesions of the Liver and Radiomics: What Do We Know? Diagnostics (Basel) 2023; 13:2591. [PMID: 37568954 PMCID: PMC10417608 DOI: 10.3390/diagnostics13152591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/14/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Despite differences in pathological analysis, focal liver lesions are not always distinguishable in contrast-enhanced magnetic resonance imaging (MRI), contrast-enhanced computed tomography (CT), and positron emission tomography (PET). This issue can cause problems of differential diagnosis, treatment, and follow-up, especially in patients affected by HBV/HCV chronic liver disease or fatty liver disease. Radiomics is an innovative imaging approach that extracts and analyzes non-visible quantitative imaging features, supporting the radiologist in the most challenging differential diagnosis when the best-known methods are not conclusive. The purpose of this review is to evaluate the most significant CT and MRI texture features, which can discriminate between the main benign and malignant focal liver lesions and can be helpful to predict the response to pharmacological or surgical therapy and the patient's prognosis.
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Affiliation(s)
| | | | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy; (M.A.); (A.G.); (G.G.); (S.P.); (F.T.); (E.B.)
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Boutin L, Morisson L, Riché F, Barthélémy R, Mebazaa A, Soyer P, Gallix B, Dohan A, Chousterman BG. Radiomic analysis of abdominal organs during sepsis of digestive origin in a French intensive care unit. Acute Crit Care 2023; 38:343-352. [PMID: 37652864 PMCID: PMC10497895 DOI: 10.4266/acc.2023.00136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 06/12/2023] [Accepted: 06/15/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND Sepsis is a severe and common cause of admission to the intensive care unit (ICU). Radiomic analysis (RA) may predict organ failure and patient outcomes. The objective of this study was to assess a model of RA and to evaluate its performance in predicting in-ICU mortality and acute kidney injury (AKI) during abdominal sepsis. METHODS This single-center, retrospective study included patients admitted to the ICU for abdominal sepsis. To predict in-ICU mortality or AKI, elastic net regularized logistic regression and the random forest algorithm were used in a five-fold cross-validation set repeated 10 times. RESULTS Fifty-five patients were included. In-ICU mortality was 25.5%, and 76.4% of patients developed AKI. To predict in-ICU mortality, elastic net and random forest models, respectively, achieved areas under the curve (AUCs) of 0.48 (95% confidence interval [CI], 0.43-0.54) and 0.51 (95% CI, 0.46-0.57) and were not improved combined with Simplified Acute Physiology Score (SAPS) II. To predict AKI with RA, the AUC was 0.71 (95% CI, 0.66-0.77) for elastic net and 0.69 (95% CI, 0.64-0.74) for random forest, and these were improved combined with SAPS II, respectively; AUC of 0.94 (95% CI, 0.91-0.96) and 0.75 (95% CI, 0.70-0.80) for elastic net and random forest, respectively. CONCLUSIONS This study suggests that RA has poor predictive performance for in-ICU mortality but good predictive performance for AKI in patients with abdominal sepsis. A secondary validation cohort is needed to confirm these results and the assessed model.
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Affiliation(s)
- Louis Boutin
- Department of Anesthesiology and Critical Care, Hôpital Lariboisière, AP-HP, Paris, France
- INSERM UMR-S 942, MASCOT, Université Paris Cité, Paris, France
| | - Louis Morisson
- Department of Anesthesiology and Critical Care, Hôpital Lariboisière, AP-HP, Paris, France
| | - Florence Riché
- Department of Anesthesiology and Critical Care, Hôpital Lariboisière, AP-HP, Paris, France
| | - Romain Barthélémy
- Department of Anesthesiology and Critical Care, Hôpital Lariboisière, AP-HP, Paris, France
| | - Alexandre Mebazaa
- Department of Anesthesiology and Critical Care, Hôpital Lariboisière, AP-HP, Paris, France
- INSERM UMR-S 942, MASCOT, Université Paris Cité, Paris, France
| | - Philippe Soyer
- INSERM UMR-S 942, MASCOT, Université Paris Cité, Paris, France
- Department of Radiology, Cochin Hospital, AP-HP, Paris, France
| | - Benoit Gallix
- IHU Strasbourg, Strasbourg, France
- Icube Laboratory and Faculty of Medicine, University of Strasbourg, Strasbourg, France
- Department of Radiology, McGill University, Montreal, QC, Canada
| | - Anthony Dohan
- INSERM UMR-S 942, MASCOT, Université Paris Cité, Paris, France
- Department of Radiology, Cochin Hospital, AP-HP, Paris, France
| | - Benjamin G Chousterman
- Department of Anesthesiology and Critical Care, Hôpital Lariboisière, AP-HP, Paris, France
- INSERM UMR-S 942, MASCOT, Université Paris Cité, Paris, France
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Marmorino F, Faggioni L, Rossini D, Gabelloni M, Goddi A, Ferrer L, Conca V, Vargas J, Biagiarelli F, Daniel F, Carullo M, Vetere G, Granetto C, Boccaccio C, Cioni D, Antonuzzo L, Bergamo F, Pietrantonio F, Cremolini C, Neri E. The prognostic value of radiomic features in liver-limited metastatic colorectal cancer patients from the TRIBE2 study. Future Oncol 2023; 19:1601-1611. [PMID: 37577810 DOI: 10.2217/fon-2023-0406] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2023] Open
Abstract
Aims: Evaluating the prognostic role of radiomic features in liver-limited metastatic colorectal cancer treated with first-line therapy at baseline and best response among patients undergoing resection. Patients & methods: Among patients enrolled in TRIBE2 (NCT02339116), the association of clinical and radiomic data, extracted by SOPHiA-DDM™ with progression-free and overall survival (OS) in the overall population and with disease-free survival/postresection OS in those undergoing resection was investigated. Results: Among 98 patients, radiomic parameters improved the prediction accuracy of our model for OS (area under the curve: 0.83; sensitivity: 0.85; specificity: 0.73; accuracy: 0.78), but not progression-free survival. Of 46 resected patients, small-distance high gray-level emphasis was associated with shorter disease-free survival and high gray-level zone emphasis/higher kurtosis with shorter postresection OS. Conclusion: Radiomic features should be implemented as tools of outcome prediction for liver-limited metastatic colorectal cancer.
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Affiliation(s)
- Federica Marmorino
- Unit of Oncology, University Hospital of Pisa, Pisa, Italy & Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Lorenzo Faggioni
- Academic Radiology, Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Daniele Rossini
- Unit of Oncology, University Hospital of Pisa, Pisa, Italy & Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Antonio Goddi
- Academic Radiology, Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Loïc Ferrer
- SOPHiA GENETICS, Multimodal Research team, Cité de la Photonique, 11 avenue de Canteranne, 33600, PESSAC, France
| | - Veronica Conca
- Unit of Oncology, University Hospital of Pisa, Pisa, Italy & Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Jennifer Vargas
- SOPHiA GENETICS, Multimodal Research team, Cité de la Photonique, 11 avenue de Canteranne, 33600, PESSAC, France
| | | | - Francesca Daniel
- Oncology Unit 1, Veneto Institute of Oncology IOV - IRCCS, 35128, Padova, Italy
| | - Martina Carullo
- Unit of Oncology, University Hospital of Pisa, Pisa, Italy & Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Guglielmo Vetere
- Unit of Oncology, University Hospital of Pisa, Pisa, Italy & Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Cristina Granetto
- SC Oncologia AO S. Croce & Carle, University Teaching Hospital, Via A. Carle 25, 12100, Cuneo, Italy
| | - Chiara Boccaccio
- Unit of Oncology, University Hospital of Pisa, Pisa, Italy & Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Dania Cioni
- Academic Radiology, Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Lorenzo Antonuzzo
- Clinical Oncology Unit, Careggi University Hospital, Department of Experimental & Clinical Medicine, University of Florence, Viale Pieraccini 6, 50139, Firenze, Italy
| | - Francesca Bergamo
- Oncology Unit 1, Veneto Institute of Oncology IOV - IRCCS, 35128, Padova, Italy
| | - Filippo Pietrantonio
- Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Via Giacomo Venezian 1, 20133, Milano, Italy
| | - Chiara Cremolini
- Unit of Oncology, University Hospital of Pisa, Pisa, Italy & Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
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Li L, Wang L, Jia Q. Analysis of HER2 protein expression and clinical prognosis based on CT texture analysis in the arterial phase of gastric cancer. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2023. [DOI: 10.1016/j.jrras.2023.100567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Costa G, Cavinato L, Fiz F, Sollini M, Chiti A, Torzilli G, Ieva F, Viganò L. Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible. J Digit Imaging 2023; 36:1038-1048. [PMID: 36849835 PMCID: PMC10287605 DOI: 10.1007/s10278-023-00799-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 03/01/2023] Open
Abstract
Advanced imaging and analysis improve prediction of pathology data and outcomes in several tumors, with entropy-based measures being among the most promising biomarkers. However, entropy is often perceived as statistical data lacking clinical significance. We aimed to generate a voxel-by-voxel visual map of local tumor entropy, thus allowing to (1) make entropy explainable and accessible to clinicians; (2) disclose and quantitively characterize any intra-tumoral entropy heterogeneity; (3) evaluate associations between entropy and pathology data. We analyzed the portal phase of preoperative CT of 20 patients undergoing liver surgery for colorectal metastases. A three-dimensional core kernel (5 × 5 × 5 voxels) was created and used to compute the local entropy value for each voxel of the tumor. The map was encoded with a color palette. We performed two analyses: (a) qualitative assessment of tumors' detectability and pattern of entropy distribution; (b) quantitative analysis of the entropy values distribution. The latter data were compared with standard Hounsfield data as predictors of post-chemotherapy tumor regression grade (TRG). Entropy maps were successfully built for all tumors. Metastases were qualitatively hyper-entropic compared to surrounding parenchyma. In four cases hyper-entropic areas exceeded the tumor margin visible at CT. We identified four "entropic" patterns: homogeneous, inhomogeneous, peripheral rim, and mixed. At quantitative analysis, entropy-derived data (percentiles/mean/median/root mean square) predicted TRG (p < 0.05) better than Hounsfield-derived ones (p = n.s.). We present a standardized imaging technique to visualize tumor heterogeneity built on a voxel-by-voxel entropy assessment. The association of local entropy with pathology data supports its role as a biomarker.
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Affiliation(s)
- Guido Costa
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Lara Cavinato
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Francesco Fiz
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Guido Torzilli
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Francesca Ieva
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy.
- CHDS - Center for Health Data Science, Human Technopole, Milan, Italy.
| | - Luca Viganò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
- Hepatobiliary Unit, Department of Minimally Invasive General & Oncologic Surgery, Humanitas Gavazzeni University Hospital, Via M. Gavazzeni 21, 24125, Bergamo, Italy.
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Duan S, Hua Y, Cao G, Hu J, Cui W, Zhang D, Xu S, Rong T, Liu B. Differential diagnosis of benign and malignant vertebral compression fractures: Comparison and correlation of radiomics and deep learning frameworks based on spinal CT and clinical characteristics. Eur J Radiol 2023; 165:110899. [PMID: 37300935 DOI: 10.1016/j.ejrad.2023.110899] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 04/28/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
PURPOSE Differentiating benign from malignant vertebral compression fractures (VCFs) is a diagnostic dilemma in clinical practice. To improve the accuracy and efficiency of diagnosis, we evaluated the performance of deep learning and radiomics methods based on computed tomography (CT) and clinical characteristics in differentiating between Osteoporosis VCFs (OVCFs) and malignant VCFs (MVCFs). METHODS We enrolled a total of 280 patients (155 with OVCFs and 125 with MVCFs) and randomly divided them into a training set (80%, n = 224) and a validation set (20%, n = 56). We developed three predictive models: a deep learning (DL) model, a radiomics (Rad) model, and a combined DL_Rad model, using CT and clinical characteristics data. The Inception_V3 served as the backbone of the DL model. The input data for the DL_Rad model consisted of the combined features of Rad and DCNN features. We calculated the receiver operating characteristic curve, area under the curve (AUC), and accuracy (ACC) to assess the performance of the models. Additionally, we calculated the correlation between Rad features and DCNN features. RESULTS For the training set, the DL_Rad model achieved the best results, with an AUC of 0.99 and ACC of 0.99, followed by the Rad model (AUC: 0.99, ACC: 0.97) and DL model (AUC: 0.99, ACC: 0.94). For the validation set, the DL_Rad model (with an AUC of 0.97 and ACC of 0.93) outperformed the Rad model (with an AUC: 0.93 and ACC: 0.91) and the DL model (with an AUC: 0.89 and ACC: 0.88). Rad features achieved better classifier performance than the DCNN features, and their general correlations were weak. CONCLUSIONS The Deep learnig model, Radiomics model, and Deep learning Radiomics model achieved promising results in discriminating MVCFs from OVCFs, and the DL_Rad model performed the best.
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Affiliation(s)
- Shuo Duan
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China
| | - Yichun Hua
- Department of Medical Oncology, Beijing Tiantan Hospital, Capital Medical University, China
| | - Guanmei Cao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, China
| | - Junnan Hu
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China
| | - Wei Cui
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China
| | - Duo Zhang
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China
| | - Shuai Xu
- Department of Spinal Surgery, Peking University People's Hospital, Peking University, China
| | - Tianhua Rong
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China
| | - Baoge Liu
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, China; China National Clinical Research Center for Neurological Diseases, Beijing, China.
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Kim HY, Bae MS, Seo BK, Lee JY, Cho KR, Woo OH, Song SE, Cha J. Comparison of CT- and MRI-Based Quantification of Tumor Heterogeneity and Vascularity for Correlations with Prognostic Biomarkers and Survival Outcomes: A Single-Center Prospective Cohort Study. Bioengineering (Basel) 2023; 10:bioengineering10050504. [PMID: 37237574 DOI: 10.3390/bioengineering10050504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 04/17/2023] [Accepted: 04/20/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Tumor heterogeneity and vascularity can be noninvasively quantified using histogram and perfusion analyses on computed tomography (CT) and magnetic resonance imaging (MRI). We compared the association of histogram and perfusion features with histological prognostic factors and progression-free survival (PFS) in breast cancer patients on low-dose CT and MRI. METHODS This prospective study enrolled 147 women diagnosed with invasive breast cancer who simultaneously underwent contrast-enhanced MRI and CT before treatment. We extracted histogram and perfusion parameters from each tumor on MRI and CT, assessed associations between imaging features and histological biomarkers, and estimated PFS using the Kaplan-Meier analysis. RESULTS Out of 54 histogram and perfusion parameters, entropy on T2- and postcontrast T1-weighted MRI and postcontrast CT, and perfusion (blood flow) on CT were significantly associated with the status of subtypes, hormone receptors, and human epidermal growth factor receptor 2 (p < 0.05). Patients with high entropy on postcontrast CT showed worse PFS than patients with low entropy (p = 0.053) and high entropy on postcontrast CT negatively affected PFS in the Ki67-positive group (p = 0.046). CONCLUSIONS Low-dose CT histogram and perfusion analysis were comparable to MRI, and the entropy of postcontrast CT could be a feasible parameter to predict PFS in breast cancer patients.
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Affiliation(s)
- Hyo-Young Kim
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City 15355, Republic of Korea
| | - Min-Sun Bae
- Department of Radiology, Inha University Hospital, Inha University College of Medicine, Inhang-ro 27, Jung-gu, Incheon 22332, Republic of Korea
| | - Bo-Kyoung Seo
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City 15355, Republic of Korea
| | - Ji-Young Lee
- Department of Radiology, Ilsan Paik Hospital, Inje University College of Medicine, 170 Juhwa-ro, Ilsanseo-gu, Goyang 10380, Republic of Korea
| | - Kyu-Ran Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Ok-Hee Woo
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul 08308, Republic of Korea
| | - Sung-Eun Song
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Jaehyung Cha
- Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City 15355, Republic of Korea
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Peng J, Wang W, Jin H, Qin X, Hou J, Yang Z, Shu Z. Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learning. BMC Cancer 2023; 23:365. [PMID: 37085830 PMCID: PMC10120125 DOI: 10.1186/s12885-023-10855-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 04/17/2023] [Indexed: 04/23/2023] Open
Abstract
OBJECTIVE In this study, we aimed to investigate the predictive efficacy of magnetic resonance imaging (MRI) radiomics features at different time points of neoadjuvant therapy for rectal cancer in patients with pathological complete response (pCR). Furthermore, we aimed to develop and validate a radiomics space-time model (RSTM) using machine learning for artificial intelligence interventions in predicting pCR in patients. METHODS Clinical and imaging data of 83 rectal cancer patients were retrospectively analyzed, and the patients were classified as pCR and non-pCR patients according to their postoperative pathological results. All patients received one MRI examination before and after neoadjuvant therapy to extract radiomics features, including pre-treatment, post-treatment, and delta features. Delta features were defined by the ratio of the difference between the pre- and the post-treatment features to the pre-treatment feature. After feature dimensionality reduction based on the above three feature types, the RSTM was constructed using machine learning methods, and its performance was evaluated using the area under the curve (AUC). RESULTS The AUC values of the individual basic models constructed by pre-treatment, post-treatment, and delta features were 0.771, 0.681, and 0.871, respectively. Their sensitivity values were 0.727, 0.864, and 0.909, respectively, and their specificity values were 0.803, 0.492, and 0.656, respectively. The AUC, sensitivity, and specificity values of the combined basic model constructed by combining pre-treatment, post-treatment, and delta features were 0.901, 0.909, and 0.803, respectively. The AUC, sensitivity, and specificity values of the RSTM constructed using the K-Nearest Neighbor (KNN) classifier on the basis of the combined basic model were 0.944, 0.871, and 0.983, respectively. The Delong test showed that the performance of RSTM was significantly different from that of pre-treatment, post-treatment, and delta models (P < 0.05) but not significantly different from the combined basic model of the three (P > 0.05). CONCLUSIONS The RSTM constructed using the KNN classifier based on the combined features of before and after neoadjuvant therapy and delta features had the best predictive efficacy for pCR of neoadjuvant therapy. It may emerge as a new clinical tool to assist with individualized management of rectal cancer patients.
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Affiliation(s)
- Jiaxuan Peng
- Jinzhou medical university, Jinzhou, Liaoning Province, China
| | - Wei Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical and Pharmaceutical College, Chongqing, China
| | - Hui Jin
- Bengbu medical college, Bengbu, China
| | - Xue Qin
- Bengbu medical college, Bengbu, China
| | - Jie Hou
- Jinzhou medical university, Jinzhou, Liaoning Province, China
| | - Zhang Yang
- Center for General Practice Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhenyu Shu
- Center for General Practice Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China.
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Granata V, Fusco R, Setola SV, Galdiero R, Maggialetti N, Patrone R, Ottaiano A, Nasti G, Silvestro L, Cassata A, Grassi F, Avallone A, Izzo F, Petrillo A. Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics. Infect Agent Cancer 2023; 18:18. [PMID: 36927442 PMCID: PMC10018963 DOI: 10.1186/s13027-023-00495-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/07/2023] [Indexed: 03/18/2023] Open
Abstract
In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy.
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, Napoli, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, Milan, 20122, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Roberta Galdiero
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", Bari, 70124, Italy
| | - Renato Patrone
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Alessandro Ottaiano
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Guglielmo Nasti
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Lucrezia Silvestro
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Antonio Cassata
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Francesca Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, 80138, Italy
| | - Antonio Avallone
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
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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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Keyl J, Hosch R, Berger A, Ester O, Greiner T, Bogner S, Treckmann J, Ting S, Schumacher B, Albers D, Markus P, Wiesweg M, Forsting M, Nensa F, Schuler M, Kasper S, Kleesiek J. Deep learning-based assessment of body composition and liver tumour burden for survival modelling in advanced colorectal cancer. J Cachexia Sarcopenia Muscle 2023; 14:545-552. [PMID: 36544260 PMCID: PMC9891942 DOI: 10.1002/jcsm.13158] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 11/16/2022] [Accepted: 11/25/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Personalized therapy planning remains a significant challenge in advanced colorectal cancer care, despite extensive research on prognostic and predictive markers. A strong correlation of sarcopenia or overall body composition and survival has been described. Here, we explore whether automated assessment of body composition and liver metastases from standard of care CT images can add to clinical parameters in personalized survival risk prognostication. METHODS We retrospectively analysed clinical imaging data from 85 patients (50.6% female, mean age 58.9 SD 12.2 years) with colorectal cancer and synchronous liver metastases. Pretrained deep learning models were used to assess body composition and liver metastasis geometry from abdominal CT images before the initiation of systemic treatment. Abdominal muscle-to-bone ratio (MBR) was calculated by dividing abdominal muscle volume by abdominal bone volume. MBR was compared with body mass index (BMI), abdominal muscle volume, and abdominal muscle volume divided by height squared. Differences in overall survival based on body composition and liver metastasis parameters were compared using Kaplan-Meier survival curves. Results were correlated with clinical and biomarker data to develop a machine learning model for survival risk prognostication. RESULTS The MBR, unlike abdominal muscle volume or BMI, was significantly associated with overall survival (HR 0.39, 95% CI: 0.19-0.80, P = 0.009). The MBR (P = 0.022), liver metastasis surface area (P = 0.01) and primary tumour sidedness (P = 0.007) were independently associated with overall survival in multivariate analysis. Body composition parameters did not correlate with KRAS mutational status or primary tumour sidedness. A prediction model based on MBR, liver metastasis surface area and primary tumour sidedness achieved a concordance index of 0.69. CONCLUSIONS Automated segmentation enables to extract prognostic parameters from routine imaging data for personalized survival modelling in advanced colorectal cancer patients.
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Affiliation(s)
- Julius Keyl
- Department of Medical Oncology, West German Cancer CenterUniversity Hospital Essen (AöR)EssenGermany
- Institute for Artificial Intelligence in MedicineUniversity Hospital Essen (AöR)EssenGermany
- German Cancer Consortium (DKTK)Partner site University Hospital Essen (AöR)EssenGermany
| | - René Hosch
- Institute for Artificial Intelligence in MedicineUniversity Hospital Essen (AöR)EssenGermany
- Department of Diagnostic and Interventional Radiology and NeuroradiologyUniversity Hospital Essen (AöR)EssenGermany
| | - Aaron Berger
- Institute for Artificial Intelligence in MedicineUniversity Hospital Essen (AöR)EssenGermany
| | - Oliver Ester
- Institute for Artificial Intelligence in MedicineUniversity Hospital Essen (AöR)EssenGermany
| | | | - Simon Bogner
- Department of Medical Oncology, West German Cancer CenterUniversity Hospital Essen (AöR)EssenGermany
| | - Jürgen Treckmann
- Department of General, Visceral and Transplant Surgery, West German Cancer CenterUniversity Hospital Essen (AöR)EssenGermany
| | - Saskia Ting
- Institute of Pathology EssenWest German Cancer Center, University Hospital Essen (AöR)EssenGermany
| | | | - David Albers
- Department of GastroenterologyElisabeth Hospital EssenEssenGermany
| | - Peter Markus
- Department of General Surgery and TraumatologyElisabeth Hospital EssenEssenGermany
| | - Marcel Wiesweg
- Department of Medical Oncology, West German Cancer CenterUniversity Hospital Essen (AöR)EssenGermany
- Medical FacultyUniversity of Duisburg‐EssenEssenGermany
| | - Michael Forsting
- Department of Diagnostic and Interventional Radiology and NeuroradiologyUniversity Hospital Essen (AöR)EssenGermany
| | - Felix Nensa
- Institute for Artificial Intelligence in MedicineUniversity Hospital Essen (AöR)EssenGermany
- Department of Diagnostic and Interventional Radiology and NeuroradiologyUniversity Hospital Essen (AöR)EssenGermany
| | - Martin Schuler
- Department of Medical Oncology, West German Cancer CenterUniversity Hospital Essen (AöR)EssenGermany
- German Cancer Consortium (DKTK)Partner site University Hospital Essen (AöR)EssenGermany
- Medical FacultyUniversity of Duisburg‐EssenEssenGermany
| | - Stefan Kasper
- Department of Medical Oncology, West German Cancer CenterUniversity Hospital Essen (AöR)EssenGermany
- German Cancer Consortium (DKTK)Partner site University Hospital Essen (AöR)EssenGermany
- Medical FacultyUniversity of Duisburg‐EssenEssenGermany
| | - Jens Kleesiek
- Institute for Artificial Intelligence in MedicineUniversity Hospital Essen (AöR)EssenGermany
- Medical FacultyUniversity of Duisburg‐EssenEssenGermany
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Jia LL, Zhao JX, Zhao LP, Tian JH, Huang G. Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta‑analysis. Eur J Radiol 2023; 158:110640. [PMID: 36525703 DOI: 10.1016/j.ejrad.2022.110640] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 11/13/2022] [Accepted: 11/28/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE The purpose of this study was to evaluate the methodological quality of radiomics-based studies for noninvasive, preoperative prediction of Kirsten rat sarcoma (KRAS) mutations in patients with colorectal cancer; furthermore, we systematically evaluate the diagnostic accuracy of predicting models. METHODS We systematically searched PubMed, Embase, Cochrane Library and Web of Science databases up to 20 April 2022 for eligible studies. The methodological quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. A meta-analysis of studies on the prediction of KRAS status in colorectal cancer patients was performed. RESULT Twenty-nine studies were identified in the systematic review, including three studies on the prediction of KRAS status in colorectal cancer liver metastases. All studies had an average RQS score of 9.55 (26.5% of the total score), ranging from 3 to 17. Most studies demonstrated a low or unclear risk of bias in the domains of QUADAS-2. Nineteen studies were included in the meta-analysis, mostly imaged with magnetic resonance imaging (MRI), followed by computed tomography (CT), positron emission tomography-CT (PET/CT). With pooled sensitivity, specificity and area under the curve (AUC) of the training cohorts were 0.80(95% confidence interval(CI), 0.75-0.84), 0.80(95% CI, 0.74-0.85) and 0.87(95% CI, 0.84-0.90),respectively. The pooled sensitivity, specificity, and AUC for the validation cohorts (13 studies) were 0.78(95% CI, 0.71-0.84), 0.84(95% CI, 0.74-0.90), and 0.86(95% CI, 0.83-0.89), respectively. CONCLUSION Radiomics is a potential noninvasive technology that has a moderate preoperative diagnosis and prediction effect on KRAS mutations. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the methodological quality of the study and further externally validate the model using multicenter datasets.
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Affiliation(s)
- Lu-Lu Jia
- First Clinical School of Medicine, Gansu University of Chinese Medicine, Lanzhou 73000, China.
| | - Jian-Xin Zhao
- First Clinical School of Medicine, Gansu University of Chinese Medicine, Lanzhou 73000, China.
| | - Lian-Ping Zhao
- Department of Radiology, Gansu Provincial Hospital, Lanzhou 730000, China.
| | - Jin-Hui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China.
| | - Gang Huang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou 730000, China.
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Tang G, Wang K, Peng Y, Sun M, Deng W, Guan J. Adrenal heterogeneity in the arterial phase of contrast-enhanced CT predicts prognosis in septic shock: comparison with hollow adrenal gland sign. Jpn J Radiol 2023; 41:92-97. [PMID: 35943685 DOI: 10.1007/s11604-022-01324-8] [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: 05/31/2022] [Accepted: 08/01/2022] [Indexed: 01/07/2023]
Abstract
PURPOSE To evaluate the prognostic value of adrenal heterogeneity in the arterial phase in patients with septic shock, comparatively to the hollow adrenal gland sign (HAGS). METHODS Totally 84 consecutive patients with septic shock (group S) were assessed retrospectively, and abdominal dual-phase contrast-enhanced CT was performed after the diagnosis of septic shock within one week. The patients were divided into two groups according to clinical outcome, including the survivor (group A, 41 cases) and death (group B, 43 cases) groups. Fifty negative cases were matched as the control (group C). The incidence of the HAGS in patients with septic shock (group S) was statistically analyzed. The average values of left adrenal density (Den-A and Den-V) and their standard deviations (SDDen-A and SDDen-V) in dual phases were measured. The above parameters were compared between groups A and B as well as with group C. The parameters were assessed for their predictive values of mortality in septic shock, comparatively to the HAGS. RESULTS Compared with group C, group S presented significantly higher Den-A (P = 0.003) and SDDen-A (P < 0.001). There were significantly higher SDDen-A (P < 0.001) in group B compared with group A. The incidence of the HAGS was about 27.4% (23/84) in group S. The sensitivity and specificity in predicting poor prognosis in patients with septic shock were 78% and 85% with SDDen-A, at a cut-off value of 28.64, respectively. The sensitivity and specificity were 41% and 88% for the HAGS, respectively. The area under ROC curve (AUC) was significantly greater for SDDen-A compared with the HAGS (0.820 vs. 0.670, P < 0.001). CONCLUSION Adrenal heterogeneity in the arterial phase can predict prognosis in patients with septic shock; the larger the SDDen-A, the poorer the prognosis. The predictive efficiency of adrenal heterogeneity in the arterial phase is better than the HAGS.
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Affiliation(s)
- Guanglei Tang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshanerlu Road, Guangzhou, Guangdong, People's Republic of China
| | - Ke Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshanerlu Road, Guangzhou, Guangdong, People's Republic of China
| | - Yang Peng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshanerlu Road, Guangzhou, Guangdong, People's Republic of China
| | - Mengya Sun
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshanerlu Road, Guangzhou, Guangdong, People's Republic of China
| | - Weiwei Deng
- Clinical Science, Philips Healthcare China, No. 718 Lingshi Road, Shanghai, People's Republic of China
| | - Jian Guan
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshanerlu Road, Guangzhou, Guangdong, People's Republic of China.
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Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 15:cancers15010063. [PMID: 36612061 PMCID: PMC9817513 DOI: 10.3390/cancers15010063] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.
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Affiliation(s)
- Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Correspondence:
| | - Shin Mei Chan
- Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06510, USA
| | - Omar Mustafa Fathy Omar
- Center for Vascular Biology, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - Shams I. Iqbal
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael S. Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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Gu XL, Cui Y, Wang K, Xing Q, Li XT, Zhu HT, Li ZW, Sun YS. Qualitative and quantitative parameters on hepatobiliary phase of gadoxetic acid-enhanced MR imaging for predicting pathological response to preoperative systemic therapy in colorectal liver metastases. Eur J Radiol 2022; 157:110572. [DOI: 10.1016/j.ejrad.2022.110572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 09/26/2022] [Accepted: 10/23/2022] [Indexed: 11/03/2022]
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Xie C, Hu Y, Han L, Fu J, Vardhanabhuti V, Yang H. Prediction of Individual Lymph Node Metastatic Status in Esophageal Squamous Cell Carcinoma Using Routine Computed Tomography Imaging: Comparison of Size-Based Measurements and Radiomics-Based Models. Ann Surg Oncol 2022; 29:8117-8126. [PMID: 36018524 DOI: 10.1245/s10434-022-12207-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 06/08/2022] [Indexed: 12/29/2022]
Abstract
BACKGROUND Lymph node status is vital for prognosis and treatment decisions for esophageal squamous cell carcinoma (ESCC). This study aimed to construct and evaluate an optimal radiomics-based method for a more accurate evaluation of individual regional lymph node status in ESCC and to compare it with traditional size-based measurements. METHODS The study consecutively collected 3225 regional lymph nodes from 530 ESCC patients receiving upfront surgery from January 2011 to October 2015. Computed tomography (CT) scans for individual lymph nodes were analyzed. The study evaluated the predictive performance of machine-learning models trained on features extracted from two-dimensional (2D) and three-dimensional (3D) radiomics by different contouring methods. Robust and important radiomics features were selected, and classification models were further established and validated. RESULTS The lymph node metastasis rate was 13.2% (427/3225). The average short-axis diameter was 6.4 mm for benign lymph nodes and 7.9 mm for metastatic lymph nodes. The division of lymph node stations into five regions according to anatomic lymph node drainage (cervical, upper mediastinal, middle mediastinal, lower mediastinal, and abdominal regions) improved the predictive performance. The 2D radiomics method showed optimal diagnostic results, with more efficient segmentation of nodal lesions. In the test set, this optimal model achieved an area under the receiver operating characteristic curve of 0.841-0.891, an accuracy of 84.2-94.7%, a sensitivity of 65.7-83.3%, and a specificity of 84.4-96.7%. CONCLUSIONS The 2D radiomics-based models noninvasively predicted the metastatic status of an individual lymph node in ESCC and outperformed the conventional size-based measurement. The 2D radiomics-based model could be incorporated into the current clinical workflow to enable better decision-making for treatment strategies.
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Affiliation(s)
- Chenyi Xie
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Yihuai Hu
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Esophageal Cancer Institute, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Thoracic Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Lujun Han
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jianhua Fu
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Esophageal Cancer Institute, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China.
| | - Hong Yang
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Esophageal Cancer Institute, Sun Yat-sen University Cancer Center, Guangzhou, China.
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Xue T, Peng H, Chen Q, Li M, Duan S, Feng F. A CT-Based Radiomics Nomogram in Predicting the Postoperative Prognosis of Colorectal Cancer: A Two-center Study. Acad Radiol 2022; 29:1647-1660. [PMID: 35346564 DOI: 10.1016/j.acra.2022.02.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/25/2022] [Accepted: 02/06/2022] [Indexed: 01/26/2023]
Abstract
RATIONALE AND OBJECTIVES This retrospective study aimed to develop a practical model to determine overall survival after surgery in patients with colorectal cancer according to radiomics signatures based on computed tomography (CT) images and clinical predictors. MATERIALS AND METHODS A total of 121 colorectal cancer (CRC) patients were selected to construct the model, and 51 patients and 114 patients were selected for internal validation and external testing. The radiomics features were extracted from each patient's CT images. Univariable Cox regression and least absolute shrinkage and selection operator regression were used to select radiomics features. The performance of the nomogram was evaluated by calibration curves and the c-index. Kaplan-Meier analysis was used to compare the overall survival between these subgroups. RESULTS The radiomics features of the CRC patients were significantly correlated with survival time. The c-indexes of the nomogram in the training cohort, internal validation cohort and external test cohort were 0.782, 0.721, and 0.677. Our nomogram integrated the optimal radiomics signature with clinical predictors showed a significant improvement in the prediction of CRC patients' overall survival. The calibration curves showed that the predicted survival time was close to the actual survival time. According to Kaplan-Meier analysis, the 1-, 2-, and 3-year survival rates of the low-risk group were higher than those of the high-risk group. CONCLUSION The nomogram combining the optimal radiomics signature and clinical predictors further improved the predicted accuracy of survival prognosis for CRC patients. These findings might affect treatment strategies and enable a step forward for precise medicine.
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Affiliation(s)
- Ting Xue
- Nantong University, Nantong, Jiangsu, PR China
| | - Hui Peng
- Nantong University, Nantong, Jiangsu, PR China
| | | | - Manman Li
- Nantong University, Nantong, Jiangsu, PR China
| | | | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu 226361, PR China.
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A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques. Healthcare (Basel) 2022; 10:healthcare10102075. [DOI: 10.3390/healthcare10102075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/17/2022] Open
Abstract
An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics’ potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy.
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Hewitt DB, Brown ZJ, Pawlik TM. The Role of Biomarkers in the Management of Colorectal Liver Metastases. Cancers (Basel) 2022; 14:cancers14194602. [PMID: 36230522 PMCID: PMC9559307 DOI: 10.3390/cancers14194602] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 09/17/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Colorectal cancer remains one of the most significant sources of cancer-related morbidity and mortality worldwide. The liver is the most common site of metastatic spread. Multiple modalities exist to manage and potentially cure patients with metastatic colorectal cancer. However, reliable biomarkers to assist with clinical decision-making are limited. Recent advances in genomic sequencing technology have greatly expanded our knowledge of colorectal cancer carcinogenesis and significantly reduced the cost and timing of the investigation. In this article, we discuss the current utility of biomarkers in the management of colorectal cancer liver metastases. Abstract Surgical management combined with improved systemic therapies have extended 5-year overall survival beyond 50% among patients with colorectal liver metastases (CRLM). Furthermore, a multitude of liver-directed therapies has improved local disease control for patients with unresectable CRLM. Unfortunately, a significant portion of patients treated with curative-intent hepatectomy develops disease recurrence. Traditional markers fail to risk-stratify and prognosticate patients with CRLM appropriately. Over the last few decades, advances in molecular sequencing technology have greatly expanded our knowledge of the pathophysiology and tumor microenvironment characteristics of CRLM. These investigations have revealed biomarkers with the potential to better inform management decisions in patients with CRLM. Actionable biomarkers such as RAS and BRAF mutations, microsatellite instability/mismatch repair status, and tumor mutational burden have been incorporated into national and societal guidelines. Other biomarkers, including circulating tumor DNA and radiomic features, are under active investigation to evaluate their clinical utility. Given the plethora of therapeutic modalities and lack of evidence on timing and sequence, reliable biomarkers are needed to assist clinicians with the development of patient-tailored management plans. In this review, we discuss the current evidence regarding biomarkers for patients with CRLM.
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Sheng W, Xia S, Wang Y, Yan L, Ke S, Mellisa E, Gong F, Zheng Y, Tang T. Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning. Front Oncol 2022; 12:964605. [PMID: 36172153 PMCID: PMC9510620 DOI: 10.3389/fonc.2022.964605] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundMost studies of molecular subtype prediction in breast cancer were mainly based on two-dimensional MRI images, the predictive value of three-dimensional volumetric features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting breast cancer molecular subtypes has not been thoroughly investigated. This study aimed to look into the role of features derived from DCE-MRI and how they could be combined with clinical data to predict invasive ductal breast cancer molecular subtypes.MethodsFrom January 2019 to December 2021, 190 Chinese women with invasive ductal breast cancer were studied (32 triple-negative, 59 HER2-enriched, and 99 luminal lesions) in this institutional review board-approved retrospective cohort study. The image processing software extracted 1130 quantitative radiomic features from the segmented lesion area, including shape-based, first-order statistical, texture, and wavelet features. Three binary classifications of the subtypes were performed: triple-negative vs. non-triple-negative, HER2-overexpressed vs. non-HER2-overexpressed, and luminal (A + B) vs. non-luminal. For the classification, five machine learning methods (random forest, logistic regression, support vector machine, naïve Bayes, and eXtreme Gradient Boosting) were employed. The classifiers were chosen using the least absolute shrinkage and selection operator method. The area evaluated classification performance under the receiver operating characteristic curve, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean.ResultsEXtreme Gradient Boosting model showed the best performance in luminal and non-luminal groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8282, 0.7524, 0.6542, 0.6964, 0.6086, 0.3458, 0.8524 and 0.7016, respectively. Meanwhile, the random forest model showed the best performance in HER2-overexpressed and non-HER2-overexpressed groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8054, 0.2941, 0.9744, 0.7679, 0.4348, 0.0256, 0.8333 and 0.5353, respectively. Furthermore, eXtreme Gradient Boosting model showed the best performance in the triple-negative and non-triple-negative groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.9031, 0.9362, 0.4444, 0.8571, 0.9167, 0.5556, 0.8980 and 0.6450.ConclusionClinical data and three-dimension imaging features from DCE-MRI were identified as potential biomarkers for distinguishing between three molecular subtypes of invasive ductal carcinomas breast cancer. In the future, more extensive studies will be required to evaluate the findings.
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Affiliation(s)
- Weiyong Sheng
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Shouli Xia
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yaru Wang
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Lizhao Yan
- Department of Hand Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Songqing Ke
- Department of Science and Technology Research Management, Wuhan Blood Center, Wuhan, China
| | - Evelyn Mellisa
- Department of Emergency Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fen Gong
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yun Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Yun Zheng, ; Tiansheng Tang,
| | - Tiansheng Tang
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
- *Correspondence: Yun Zheng, ; Tiansheng Tang,
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Wang S, Liu H, Yang T, Huang M, Zheng B, Wu T, Han L, Zhang Y, Ren J. Machine learning based on automated breast volume scanner ( ABVS) radiomics for differential diagnosis of benign and malignant BI‐RADS 4 lesions. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022; 32:1577-1587. [DOI: 10.1002/ima.22724] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 02/25/2022] [Indexed: 09/11/2023]
Abstract
AbstractBI‐RADS category 4 represents possibly malignant lesions and biopsy is recommended to distinguish benign and malignant. However, studies revealed that up to 67%–78% of BI‐RADS 4 lesions proved to be benign, but received unnecessary biopsies, which may cause unnecessary anxiety and discomfort to patients and increase the burden on the healthcare system. In this prospective study, machine learning (ML) based on the emerging breast ultrasound technology‐automated breast volume scanner (ABVS) was constructed to distinguish benign and malignant BI‐RADS 4 lesions and compared with different experienced radiologists. A total of 223 pathologically confirmed BI‐RADS 4 lesions were recruited and divided into training and testing cohorts. Radiomics features were extracted from axial, sagittal, and coronal ABVS images for each lesion. Seven feature selection methods and 13 ML algorithms were used to construct different ML pipelines, of which the DNN‐RFE (combination of recursive feature elimination and deep neural networks) had the best performance in both training and testing cohorts. The AUC value of the DNN‐RFE was significantly higher than less experienced radiologist at Delong's test (0.954 vs. 0.776, p = 0.004). Additionally, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the DNN‐RFE were 88.9%, 83.3%, 95.2%, 83.3%, and 95.2%, which also significantly better than less experienced radiologist at McNemar's test (p = 0.043). Therefore, ML based on ABVS radiomics may be a potential method to non‐invasively distinguish benign and malignant BI‐RADS 4 lesions.
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Affiliation(s)
- Shi‐jie Wang
- Department of Medical Ultrasonics The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Hua‐qing Liu
- Artificial Intelligence Innovation Center Research Institute of Tsinghua Guangzhou China
| | - Tao Yang
- Department of Ultrasound The Affiliated Hospital of Southwest Medical University Sichuan China
| | - Ming‐quan Huang
- Department of Breast Surgery The Affiliated Hospital of Southwest Medical University Sichuan China
| | - Bo‐wen Zheng
- Department of Medical Ultrasonics The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Tao Wu
- Department of Medical Ultrasonics The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Lan‐qing Han
- Artificial Intelligence Innovation Center Research Institute of Tsinghua Guangzhou China
| | - Yong Zhang
- Department of Nuclear Medicine The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Jie Ren
- Department of Medical Ultrasonics The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
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Li J, Zhang Y, Yin D, Shang H, Li K, Jiao T, Fang C, Cui Y, Liu M, Pan J, Zeng Q. CT perfusion-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease. Front Neurosci 2022; 16:974096. [PMID: 36033623 PMCID: PMC9403315 DOI: 10.3389/fnins.2022.974096] [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: 06/20/2022] [Accepted: 07/20/2022] [Indexed: 11/15/2022] Open
Abstract
Purpose To build CT perfusion (CTP)-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease (MMD). Methods Fifty-three MMD patients who underwent CTP and digital subtraction angiography (DSA) examination were retrospectively enrolled. Patients were divided into good and poor groups based on postoperative DSA. CTP parameters, such as mean transit time (MTT), time to drain (TTD), time to maximal plasma concentration (Tmax), and flow extraction product (FE), were obtained. CTP efficacy in evaluating surgical treatment were compared between the good and poor groups. The changes in the relative CTP parameters (ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE) were calculated to evaluate the differences between pre- and postoperative CTP values. CTP parameters were selected to build delta-radiomics models for identifying collateral vessel formation. The identification performance of machine learning classifiers was assessed using area under the receiver operating characteristic curve (AUC). Results Of the 53 patients, 36 (67.9%) and 17 (32.1%) were divided into the good and poor groups, respectively. The postoperative changes of ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE in the good group were significantly better than the poor group (p < 0.05). Among all CTP parameters in the perfusion improvement evaluation, the ΔrTTD had the largest AUC (0.873). Eleven features were selected from the TTD parameter to build the delta-radiomics model. The classifiers of the support vector machine and k-nearest neighbors showed good diagnostic performance with AUC values of 0.933 and 0.867, respectively. Conclusion The TTD-based delta-radiomics model has the potential to identify collateral vessel formation after the operation.
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Affiliation(s)
- Jizhen Li
- Department of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
- Department of Radiology, Shandong Mental Health Center Affiliated to Shandong University, Jinan, China
| | - Yan Zhang
- Department of Radiology, Shandong Mental Health Center Affiliated to Shandong University, Jinan, China
| | - Di Yin
- Department of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
| | - Hui Shang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Kejian Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Tianyu Jiao
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Caiyun Fang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Yi Cui
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Ming Liu
- Department of Neurosurgery, Qilu Hospital of Shandong University, Jinan, China
| | - Jun Pan
- Department of Radiology, Shandong Mental Health Center Affiliated to Shandong University, Jinan, China
| | - Qingshi Zeng
- Department of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
- *Correspondence: Qingshi Zeng,
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Bao J, Feng X, Ma Y, Wang Y, Qi J, Qin C, Tan X, Tian Y. The latest application progress of radiomics in prediction and diagnosis of liver diseases. Expert Rev Gastroenterol Hepatol 2022; 16:707-719. [PMID: 35880549 DOI: 10.1080/17474124.2022.2104711] [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] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Early detection and individualized treatment of patients with liver disease is the key to survival. Radiomics can extract high-throughput quantitative features by multimode imaging, which has good application prospects for the diagnosis, staging and prognosis of benign and malignant liver diseases. Therefore, this paper summarizes the current research status in the field of liver disease, in order to help these patients achieve personalized and precision medical care. AREAS COVERED This paper uses several keywords on the PubMed database to search the references, and reviews the workflow of traditional radiomics, as well as the characteristics and influencing factors of different imaging modes. At the same time, the references on the application of imaging in different benign and malignant liver diseases were also summarized. EXPERT OPINION For patients with liver disease, the traditional imaging evaluation can only provide limited information. Radiomics exploits the characteristics of high-throughput and high-dimensional extraction, enabling liver imaging capabilities far beyond the scope of traditional visual image analysis. Recent studies have demonstrated the prospect of this technology in personalized diagnosis and treatment decision in various fields of the liver. However, further clinical validation is needed in its application and practice.
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Affiliation(s)
- Jiaying Bao
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Xiao Feng
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Yan Ma
- Department of Ultrasound, Zibo Central Hospital, Zibo, P.R. China
| | - Yanyan Wang
- Departments of Emergency Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Jianni Qi
- Central Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Chengyong Qin
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Xu Tan
- Department of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Yongmei Tian
- Department of Geriatrics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
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Zhang Z, Yi X, Pei Q, Fu Y, Li B, Liu H, Han Z, Chen C, Pang P, Lin H, Gong G, Yin H, Zai H, Chen BT. CT radiomics identifying non-responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer. Cancer Med 2022; 12:2463-2473. [PMID: 35912919 PMCID: PMC9939108 DOI: 10.1002/cam4.5086] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/05/2022] [Accepted: 05/07/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND AND PURPOSE Early detection of non-response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT. MATERIALS AND METHODS Patients with LARC who were treated with nCRT were retrospectively enrolled between March 2009 and March 2019. Traditional radiological characteristics were analyzed by visual inspection and radiomic features were analyzed through computational methods from the pretreatment radiotherapy planning CT images. Differentiation models were constructed using radiomic methods and clinicopathological characteristics for predicting non-response to nCRT. Model performance was assessed for classification efficiency, calibration, discrimination, and clinical application. RESULTS This study enrolled a total of 215 patients, including 151 patients in the training cohort (50 non-responders and 101 responders) and 64 patients in the validation cohort (21 non-responders and 43 responders). For predicting non-response, the model constructed with an ensemble machine learning method had higher performance with area under the curve (AUC) values of 0.92 and 0.89 as compared to the model constructed with the logistic regression method (AUC: 0.72 and 0.71 for the training and validation cohorts, respectively). Both decision curve and calibration curve analyses confirmed that the ensemble machine learning model had higher prediction performance. CONCLUSION Pretreatment CT radiomics achieved satisfying performance in predicting non-response to nCRT and could be helpful to assist in treatment planning for patients with LARC.
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Affiliation(s)
- Zinan Zhang
- Department of Radiology (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China,Department of Gastroenterology (The Third Xiangya Hospital)Central South UniversityChangshaHunanP.R. China
| | - Xiaoping Yi
- Department of Radiology (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China,National Engineering Research Center of Personalized Diagnostic and Therapeutic TechnologyXiangya HospitalChangshaHunanP.R. China,National Clinical Research Center for Geriatric Disorders (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China,Hunan Key Laboratory of Skin Cancer and PsoriasisChangshaHunanP.R. China,Hunan Engineering Research Center of Skin Health and DiseaseChangshaHunanP.R. China
| | - Qian Pei
- Department of General Surgery (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China
| | - Yan Fu
- Department of Radiology (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China,National Engineering Research Center of Personalized Diagnostic and Therapeutic TechnologyXiangya HospitalChangshaHunanP.R. China
| | - Bin Li
- Department of Oncology (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China
| | - Haipeng Liu
- Department of Radiology (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China
| | - Zaide Han
- Department of Radiology (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China
| | - Changyong Chen
- Department of Radiology (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China
| | - Peipei Pang
- Department of Pharmaceuticals and DiagnosisGE HealthcareChangshaP.R. China
| | - Huashan Lin
- Department of Pharmaceuticals and DiagnosisGE HealthcareChangshaP.R. China
| | - Guanghui Gong
- Department of Pathology, Xiangya HospitalCentral South UniversityChangshaHunanP.R. China
| | - Hongling Yin
- Department of Pathology, Xiangya HospitalCentral South UniversityChangshaHunanP.R. China
| | - Hongyan Zai
- Department of General Surgery (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China
| | - Bihong T. Chen
- Department of Diagnostic RadiologyCity of Hope National Medical CenterDuarteCaliforniaUSA
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Zhou L, Feng F, Yang Y, Zheng X, Yang Y. Prognostic predictors of non-small cell lung cancer treated with curative resection: the role of preoperative CT texture features, clinical features, and laboratory parameters. Clin Radiol 2022; 77:e765-e770. [PMID: 35843728 DOI: 10.1016/j.crad.2022.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 06/11/2022] [Accepted: 06/15/2022] [Indexed: 11/03/2022]
Abstract
AIM To explore the value of preoperative contrast-enhanced computed tomography (CT) tumour texture characteristics, and clinical and laboratory parameters on the prognosis of curative resection for non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS This retrospective study included 64 patients (34 men and 30 women) with NSCLC who underwent curative resection and were then followed up for 5 years or until death. Preoperative contrast-enhanced CT images, clinical features, and laboratory parameters were collected for these patients. CT texture features of the primary tumour before surgery were extracted from the contrast-enhanced CT images using ImageJ software. Based on the cut-off values determined by X-tile software, the preoperative CT texture features, clinical features, and laboratory parameters were divided into two groups. Kaplan-Meier survival curves and log-rank tests were used to compare the 5-year overall survival (OS) of patients. Multivariate Cox regression analysis was used to determine the independent factors influencing the prognosis. RESULTS The mean survival was 51.5 months. Tumour volume, entropy, platelet-to-lymphocyte ratio (PLR), prognostic nutritional index (PNI), and albumin-to-globulin ratio (AGR) were shown to be significantly associated with 5-year OS (p<0.05). Multivariate Cox regression analysis revealed that entropy was the independent factor of prognosis (hazard ratio 4.375, 95% confidence interval [CI]: 1.646-11.620, p=0.003). CONCLUSION Entropy is an important and potentially non-invasive imaging biomarker for predicting the prognosis of NSCLC undergoing curative resection.
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Affiliation(s)
- L Zhou
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong, Jiangsu 226361, PR China.
| | - F Feng
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong, Jiangsu 226361, PR China
| | - Y Yang
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong, Jiangsu 226361, PR China
| | - X Zheng
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong, Jiangsu 226361, PR China
| | - Y Yang
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong, Jiangsu 226361, PR China
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Hu J, Xia X, Wang P, Peng Y, Liu J, Xie X, Liao Y, Wan Q, Li X. Predicting Kirsten Rat Sarcoma Virus Gene Mutation Status in Patients With Colorectal Cancer by Radiomics Models Based on Multiphasic CT. Front Oncol 2022; 12:848798. [PMID: 35814386 PMCID: PMC9263192 DOI: 10.3389/fonc.2022.848798] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveTo develop and validate radiomics models based on multiphasic CT in predicting Kirsten rat sarcoma virus (KRAS) gene mutation status in patients with colorectal cancer (CRC).Materials and MethodsA total of 231 patients with pathologically confirmed CRC were retrospectively enrolled and randomly divided into training(n=184) and test groups(n=47) in a ratio of 4:1. A total of 1316 quantitative radiomics features were extracted from non-contrast phase (NCP), arterial-phase (AP) and venous-phase (VP) CT for each patient. Four steps were applied for feature selection including Spearman correlation analysis, variance threshold, least absolute contraction and selection operator, and multivariate stepwise regression analysis. Clinical and pathological characteristics were also assessed. Subsequently, three classification methods, logistic regression (LR), support vector machine (SVM) and random tree (RT) algorithm, were applied to develop seven groups of prediction models (NCP, AP, VP, AP+VP, AP+VP+NCP, AP&VP, AP&VP&NCP) for KRAS mutation prediction. The performance of these models was evaluated by receiver operating characteristics curve (ROC) analysis.ResultsAmong the three groups of single-phase models, the AP model, developed by LR algorithm, showed the best prediction performance with an AUC value of 0.811 (95% CI:0.685–0.938) in the test cohort. Compared with the single-phase models, the dual-phase (AP+VP) model with the LR algorithm showed better prediction performance (AUC=0.826, 95% CI:0.700-0.952). The performance of multiphasic (AP+VP+NCP) model with the LR algorithm (AUC=0.811, 95%CI: 0.679-0.944) is comparable to the model with the SVM algorithm (AUC=0.811, 95%CI: 0.695-0.918) in the test cohort, but the sensitivity, specificity, and accuracy of the multiphasic (AP+VP+NCP) model with the LR algorithm were 0.810, 0.808, 0.809 respectively, which were highest among these seven groups of prediction models in the test cohort.ConclusionThe CT radiomics models have the potential to predict KRAS mutation in patients with CRC; different phases may affect the predictive efficacy of radiomics model, of which arterial-phase CT is more informative. The combination of multiphasic CT images can further improve the performance of radiomics model.
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Affiliation(s)
- Jianfeng Hu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaoying Xia
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Peng Wang
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yu Peng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jieqiong Liu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaobin Xie
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yuting Liao
- Department of Pharmaceutical Diagnostics, GE Healthcare, Shanghai, China
| | - Qi Wan
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- *Correspondence: Qi Wan, ; Xinchun Li,
| | - Xinchun Li
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- *Correspondence: Qi Wan, ; Xinchun Li,
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