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Maino C, Vernuccio F, Cannella R, Franco PN, Giannini V, Dezio M, Pisani AR, Blandino AA, Faletti R, De Bernardi E, Ippolito D, Gatti M, Inchingolo R. Radiomics and liver: Where we are and where we are headed? Eur J Radiol 2024; 171:111297. [PMID: 38237517 DOI: 10.1016/j.ejrad.2024.111297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/03/2024] [Accepted: 01/07/2024] [Indexed: 02/10/2024]
Abstract
Hepatic diffuse conditions and focal liver lesions represent two of the most common scenarios to face in everyday radiological clinical practice. Thanks to the advances in technology, radiology has gained a central role in the management of patients with liver disease, especially due to its high sensitivity and specificity. Since the introduction of computed tomography (CT) and magnetic resonance imaging (MRI), radiology has been considered the non-invasive reference modality to assess and characterize liver pathologies. In recent years, clinical practice has moved forward to a quantitative approach to better evaluate and manage each patient with a more fitted approach. In this setting, radiomics has gained an important role in helping radiologists and clinicians characterize hepatic pathological entities, in managing patients, and in determining prognosis. Radiomics can extract a large amount of data from radiological images, which can be associated with different liver scenarios. Thanks to its wide applications in ultrasonography (US), CT, and MRI, different studies were focused on specific aspects related to liver diseases. Even if broadly applied, radiomics has some advantages and different pitfalls. This review aims to summarize the most important and robust studies published in the field of liver radiomics, underlying their main limitations and issues, and what they can add to the current and future clinical practice and literature.
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Affiliation(s)
- Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy.
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Paolo Niccolò Franco
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Michele Dezio
- Department of Radiology, Miulli Hospital, Acquaviva delle Fonti 70021, Bari, Italy
| | - Antonio Rosario Pisani
- Nuclear Medicine Unit, Interdisciplinary Department of Medicine, University of Bari, Bari 70121, Italy
| | - Antonino Andrea Blandino
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Elisabetta De Bernardi
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, University of Milano Bicocca, Milano 20100, Italy; School of Medicine, University of Milano Bicocca, Milano 20100, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy; School of Medicine, University of Milano Bicocca, Milano 20100, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
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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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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 2023:10.1007/s00330-023-10316-9. [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] [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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Geady C, Abbas-Aghababazadeh F, Kohan A, Schuetze S, Shultz D, Haibe-Kains B. Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.22.23294942. [PMID: 37873411 PMCID: PMC10593058 DOI: 10.1101/2023.09.22.23294942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.79 for the most precise model (FDR = 0.01). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.
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Affiliation(s)
- Caryn Geady
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
- Medical Biophysics, University of Toronto, Toronto, Canada
| | | | - Andres Kohan
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Scott Schuetze
- Department of Medicine, University of Michigan, Ann Arbor, MI, USA
| | - David Shultz
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
- Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Medicine, University of Michigan, Ann Arbor, MI, USA
- Vector Institute for Artificial Intelligence, Toronto, Canada
- Ontario Institute for Cancer Research, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
- Department of Biostatistics, Dalla Lana School of Public Health, Toronto, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
- Medical Biophysics, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Canada
- Ontario Institute for Cancer Research, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
- Department of Biostatistics, Dalla Lana School of Public Health, Toronto, Canada
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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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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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Li ZF, Kang LQ, Liu FH, Zhao M, Guo SY, Lu S, Quan S. Radiomics based on preoperative rectal cancer MRI to predict the metachronous liver metastasis. Abdom Radiol (NY) 2023; 48:833-843. [PMID: 36529807 DOI: 10.1007/s00261-022-03773-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/06/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022]
Abstract
PURPOSE At present, there are few effective method to predict metachronous liver metastasis (MLM) from rectal cancer. We aim to investigate the efficacy of radiomics based on multiparametric MRI of first diagnosed rectal cancer in predicting MLM from rectal cancer. METHODS From 301 consecutive histopathologically confirmed rectal cancer patients, 130 patients who have no distant metastasis detected at the time of diagnosis were enrolled and divided into MLM group (n = 49) and non-MLM group (n = 81) according to whether liver metastasis be detected later than 6 month after the first diagnosis of rectal cancer within 3 years' follow-up. The 130 patients were divided into a training set (n = 91) and a testing set (n = 39) at a ratio of 7:3 by stratified sampling using SPSS 24.0 software. The DWI model, HD T2WI model, and DWI + HD T2WI model were constructed respectively. The best performing model was selected and combined with the screened clinical features (including non-radiomics MRI features) to construct a fusion model. The testing set was used to evaluate the performance of the models, and the area under the curve (AUC) of receiver operating characteristics (ROC) was calculated for both the training set and the testing set. RESULTS The AUC of the DWI + HD T2WI model in the testing set was higher than that of the DWI or the HD T2 model alone with statistically significance (P < 0.05). The screened clinical features were extramural vascular invasion (EMVI), T and N stages in MRI (mrT, mrN), and the distance from the lower edge of the tumor to the anal verge. The AUC of the fusion model in the testing set was 0.911. Decision curves and nomogram also showed that the fusion model had excellent clinical performance. CONCLUSION The fusion model of primary rectal cancer MRI based radiomics combing clinical features can effectively predict MLM from rectal cancer, which may assist clinicians in formulating individualized monitoring and treatment plans.
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Affiliation(s)
- Zhuo-Fu Li
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, No. 16 Xinhua Western Road, Yunhe District, Cangzhou, 061000, China
| | - Li-Qing Kang
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, No. 16 Xinhua Western Road, Yunhe District, Cangzhou, 061000, China.
| | - Feng-Hai Liu
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, No. 16 Xinhua Western Road, Yunhe District, Cangzhou, 061000, China
| | - Meng Zhao
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, No. 16 Xinhua Western Road, Yunhe District, Cangzhou, 061000, China
| | - Su-Yin Guo
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, No. 16 Xinhua Western Road, Yunhe District, Cangzhou, 061000, China
| | - Shan Lu
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, No. 16 Xinhua Western Road, Yunhe District, Cangzhou, 061000, China
| | - Shuai Quan
- GE HealthCare China (Shanghai), Shanghai, 210000, 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: 4] [Impact Index Per Article: 4.0] [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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9
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Reginelli A, Del Canto M, Clemente A, Gragnano E, Cioce F, Urraro F, Martinelli E, Cappabianca S. The Role of Dual-Energy CT for the Assessment of Liver Metastasis Response to Treatment: Above the RECIST 1.1 Criteria. J Clin Med 2023; 12:jcm12030879. [PMID: 36769527 PMCID: PMC9917684 DOI: 10.3390/jcm12030879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/18/2023] [Accepted: 01/19/2023] [Indexed: 01/25/2023] Open
Abstract
Imaging assessment of liver lesions is fundamental to predict therapeutic response and improve patient survival rates. Dual-Energy Computed Tomography (DECT) is an increasingly used technique in the oncologic field with many emerging applications. The assessment of iodine concentration within a liver lesion reflects the biological properties of the tumor and provides additional information to radiologists that is normally invisible to the human eye. The possibility to predict tumor aggressiveness and therapeutic response based on quantitative and reproducible parameters obtainable from DECT images could improve clinical decisions and drive oncologists to choose the best therapy according to metastasis biological features. Moreover, in comparison with standard dimensional criteria, DECT provides further data on the cancer microenvironment, especially for patients treated with antiangiogenic-based drugs, in which tumor shrinkage is a late parameter of response. We investigated the predictive role of DECT in the early assessment of liver metastasis response to treatment in comparison with standard dimensional criteria during antiangiogenetic-based therapy.
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Affiliation(s)
- Alfonso Reginelli
- Radiology and Radiotherapy Unit, Department of Precision Medicine, University of Campania “L. Vanvitelli”, Piazza Miraglia 2, 80138 Naples, Italy
| | - Mariateresa Del Canto
- Radiology and Radiotherapy Unit, Department of Precision Medicine, University of Campania “L. Vanvitelli”, Piazza Miraglia 2, 80138 Naples, Italy
| | - Alfredo Clemente
- Radiology and Radiotherapy Unit, Department of Precision Medicine, University of Campania “L. Vanvitelli”, Piazza Miraglia 2, 80138 Naples, Italy
- Correspondence: ; Tel.: +39-08-1566-5200
| | - Eduardo Gragnano
- Radiology and Radiotherapy Unit, Department of Precision Medicine, University of Campania “L. Vanvitelli”, Piazza Miraglia 2, 80138 Naples, Italy
| | - Fabrizio Cioce
- Radiology and Radiotherapy Unit, Department of Precision Medicine, University of Campania “L. Vanvitelli”, Piazza Miraglia 2, 80138 Naples, Italy
| | - Fabrizio Urraro
- Radiology and Radiotherapy Unit, Department of Precision Medicine, University of Campania “L. Vanvitelli”, Piazza Miraglia 2, 80138 Naples, Italy
| | - Erika Martinelli
- Medical Oncology, Department of Precision Medicine, University of Campania “L. Vanvitelli”, Piazza Miraglia 2, 80138 Naples, Italy
| | - Salvatore Cappabianca
- Radiology and Radiotherapy Unit, Department of Precision Medicine, University of Campania “L. Vanvitelli”, Piazza Miraglia 2, 80138 Naples, Italy
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10
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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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11
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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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12
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Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Dell'Aversana F, Grassi F, Belli A, Silvestro L, Ottaiano A, Nasti G, Avallone A, Flammia F, Miele V, Tatangelo F, Izzo F, Petrillo A. Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases. Radiol Med 2022; 127:763-772. [PMID: 35653011 DOI: 10.1007/s11547-022-01501-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/27/2022] [Indexed: 12/11/2022]
Abstract
PURPOSE The purpose of this study is to evaluate the Radiomics and Machine Learning Analysis based on MRI in the assessment of Liver Mucinous Colorectal Metastases.Query METHODS: The cohort of patients included a training set (121 cases) and an external validation set (30 cases) with colorectal liver metastases with pathological proof and MRI study enrolled in this approved study retrospectively. About 851 radiomics features were extracted as median values by means of the PyRadiomics tool on volume on interest segmented manually by two expert radiologists. Univariate analysis, linear regression modelling and pattern recognition methods were used as statistical and classification procedures. RESULTS The best results at univariate analysis were reached by the wavelet_LLH_glcm_JointEntropy extracted by T2W SPACE sequence with accuracy of 92%. Linear regression model increased the performance obtained respect to the univariate analysis. The best results were obtained by a linear regression model of 15 significant features extracted by the T2W SPACE sequence with accuracy of 94%, a sensitivity of 92% and a specificity of 95%. The best classifier among the tested pattern recognition approaches was k-nearest neighbours (KNN); however, KNN achieved lower precision than the best linear regression model. CONCLUSIONS Radiomics metrics allow the mucinous subtype lesion characterization, in order to obtain a more personalized approach. We demonstrated that the best performance was obtained by T2-W extracted textural metrics.
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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
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084, Fisciano, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, Naples, Italy
| | - Federica Dell'Aversana
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, Naples, Italy
| | - Francesca Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, Naples, Italy
| | - Andrea Belli
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, Naples, Italy
| | - Lucrezia Silvestro
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, Naples, Italy
| | - Alessandro Ottaiano
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, Naples, Italy
| | - Guglielmo Nasti
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, Naples, Italy
| | - Antonio Avallone
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, Naples, Italy
| | - Federica Flammia
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134, Florence, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134, Florence, Italy
| | - Fabiana Tatangelo
- Division of Pathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, 80131, Naples, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, Naples, Italy
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13
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Radiomics and Machine Learning Analysis Based on Magnetic Resonance Imaging in the Assessment of Colorectal Liver Metastases Growth Pattern. Diagnostics (Basel) 2022; 12:diagnostics12051115. [PMID: 35626271 PMCID: PMC9140199 DOI: 10.3390/diagnostics12051115] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/11/2022] [Accepted: 04/27/2022] [Indexed: 02/07/2023] Open
Abstract
To assess Radiomics and Machine Learning Analysis in Liver Colon and Rectal Cancer Metastases (CRLM) Growth Pattern, we evaluated, retrospectively, a training set of 51 patients with 121 liver metastases and an external validation set of 30 patients with a single lesion. All patients were subjected to MRI studies in pre-surgical setting. For each segmented volume of interest (VOI), 851 radiomics features were extracted using PyRadiomics package. Nonparametric test, univariate, linear regression analysis and patter recognition approaches were performed. The best results to discriminate expansive versus infiltrative front of tumor growth with the highest accuracy and AUC at univariate analysis were obtained by the wavelet_LHH_glrlm_ShortRunLowGray Level Emphasis from portal phase of contrast study. With regard to linear regression model, this increased the performance obtained respect to the univariate analysis for each sequence except that for EOB-phase sequence. The best results were obtained by a linear regression model of 15 significant features extracted by the T2-W SPACE sequence. Furthermore, using pattern recognition approaches, the diagnostic performance to discriminate the expansive versus infiltrative front of tumor growth increased again and the best classifier was a weighted KNN trained with the 9 significant metrics extracted from the portal phase of contrast study, with an accuracy of 92% on training set and of 91% on validation set. In the present study, we have demonstrated as Radiomics and Machine Learning Analysis, based on EOB-MRI study, allow to identify several biomarkers that permit to recognise the different Growth Patterns in CRLM.
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14
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Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Grassi R, Grassi F, Ottaiano A, Nasti G, Tatangelo F, Pilone V, Miele V, Brunese MC, Izzo F, Petrillo A. Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases. Radiol Med 2022; 127:461-470. [PMID: 35347583 DOI: 10.1007/s11547-022-01477-6] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 02/25/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To assess the efficacy of radiomics features obtained by T2-weighted sequences to predict clinical outcomes following liver resection in colorectal liver metastases patients. METHODS This retrospective analysis was approved by the local Ethical Committee board and radiological databases were interrogated, from January 2018 to May 2021, to select patients with liver metastases with pathological proof and MRI study in pre-surgical setting. The cohort of patients included a training set and an external validation set. The internal training set included 51 patients with 61 years of median age and 121 liver metastases. The validation cohort consisted a total of 30 patients with single lesion with 60 years of median age. For each volume of interest, 851 radiomics features were extracted as median values using PyRadiomics. Nonparametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbours (KNN), artificial neural network (NNET) and decision tree (DT)) were considered. RESULTS The best predictor to discriminate expansive versus infiltrative front of tumour growth was obtained by wavelet_LHL_gldm_DependenceNonUniformityNormalized with an accuracy of 82%; to discriminate high grade versus low grade or absent was the wavelet_LLH_glcm_Imc1 with accuracy of 88%; to differentiate the mucinous type of tumour was the wavelet_LLH_glcm_JointEntropy with accuracy of 92% while to identify tumour recurrence was the wavelet_LLL_glcm_Correlation with accuracy of 85%. Linear regression model increased the performance obtained with respect to the univariate analysis exclusively in the discrimination of expansive versus infiltrative front of tumour growth reaching an accuracy of 90%, a sensitivity of 95% and a specificity of 80%. Considering significant texture metrics tested with pattern recognition approaches, the best performance was reached by the KNN in the discrimination of the tumour budding considering the four textural predictors obtaining an accuracy of 93%, a sensitivity of 81% and a specificity of 97%. CONCLUSIONS Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.
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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
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Roberta Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, Naples, Italy
| | - Francesca Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, Naples, Italy
| | - Alessandro Ottaiano
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Guglielmo Nasti
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Fabiana Tatangelo
- Division of Pathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Vincenzo Pilone
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Vittorio Miele
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences "V. Tiberio", University of Molise, 86100, Campobasso, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
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15
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CT-Based Radiomics Analysis to Predict Histopathological Outcomes Following Liver Resection in Colorectal Liver Metastases. Cancers (Basel) 2022; 14:cancers14071648. [PMID: 35406419 PMCID: PMC8996874 DOI: 10.3390/cancers14071648] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/17/2022] [Accepted: 03/21/2022] [Indexed: 02/06/2023] Open
Abstract
Simple Summary The objective of the study was to assess the radiomic features obtained by computed tomography (CT) examination as prognostic biomarkers in patients with colorectal liver metastases, in order to predict histopathological outcomes following liver resection. We obtained good performance considering the single significant textural metric in the identification of the front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type, and in the detection of recurrences. Abstract Purpose: We aimed to assess the efficacy of radiomic features extracted by computed tomography (CT) in predicting histopathological outcomes following liver resection in colorectal liver metastases patients, evaluating recurrence, mutational status, histopathological characteristics (mucinous), and surgical resection margin. Methods: This retrospectively approved study included a training set and an external validation set. The internal training set included 49 patients with a median age of 60 years and 119 liver colorectal metastases. The validation cohort consisted of 28 patients with single liver colorectal metastasis and a median age of 61 years. Radiomic features were extracted using PyRadiomics on CT portal phase. Nonparametric Kruskal–Wallis tests, intraclass correlation, receiver operating characteristic (ROC) analyses, linear regression modeling, and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The median value of intraclass correlation coefficients for the features was 0.92 (range 0.87–0.96). The best performance in discriminating expansive versus infiltrative front of tumor growth was wavelet_HHL_glcm_Imc2, with an accuracy of 79%, a sensitivity of 84%, and a specificity of 67%. The best performance in discriminating expansive versus tumor budding was wavelet_LLL_firstorder_Mean, with an accuracy of 86%, a sensitivity of 91%, and a specificity of 65%. The best performance in differentiating the mucinous type of tumor was original_firstorder_RobustMeanAbsoluteDeviation, with an accuracy of 88%, a sensitivity of 42%, and a specificity of 100%. The best performance in identifying tumor recurrence was the wavelet_HLH_glcm_Idmn, with an accuracy of 85%, a sensitivity of 81%, and a specificity of 88%. The best linear regression model was obtained with the identification of recurrence considering the linear combination of the 16 significant textural metrics (accuracy of 97%, sensitivity of 94%, and specificity of 98%). The best performance for each outcome was reached using KNN as a classifier with an accuracy greater than 86% in the training and validation sets for each classification problem; the best results were obtained with the identification of tumor front growth considering the seven significant textural features (accuracy of 97%, sensitivity of 90%, and specificity of 100%). Conclusions: This study confirmed the capacity of radiomics data to identify several prognostic features that may affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.
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16
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Germani MM, Borelli B, Boraschi P, Antoniotti C, Ugolini C, Urbani L, Morelli L, Fontanini G, Masi G, Cremolini C, Moretto R. The management of colorectal liver metastases amenable of surgical resection: How to shape treatment strategies according to clinical, radiological, pathological and molecular features. Cancer Treat Rev 2022; 106:102382. [PMID: 35334281 DOI: 10.1016/j.ctrv.2022.102382] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/16/2022] [Accepted: 03/17/2022] [Indexed: 02/05/2023]
Abstract
Metastatic colorectal cancer (mCRC) patients have poor chances of long term survival, being < 15% of them still alive after 5 years from diagnosis. Nonetheless, patients with colorectal liver metastases (CRLM) may be eligible for metastases resection thus being able to achieve long-term disease remission and survival. The likelihood for patients with CRLM of being or becoming eligible for liver metastasectomy is increasing, thanks to the evolution of surgical techniques, the availability of active systemic treatments and the widespread diffusion of experienced multidisciplinary boards to manage these patients. However, disease relapse after liver surgery is common and occurs in two-thirds of resected patients. Therefore, adequate radiological staging and risk stratification is crucial for the optimal selection of patients candidate to surgery in order to maximize the benefit-risk ratio of liver metastasectomy and to individualize the treatment strategy. Based on the multidimensional assessment, three possible approaches are available: upfront liver surgery followed by adjuvant chemotherapy, perioperative chemotherapy preceding and following liver surgery, and an upfront systemic treatment including chemotherapy plus a targeted agent, both chosen according to patients' and tumours' characteristics, then followed by liver surgery if indicated. In this review, we describe the most important factors impacting the therapeutic choices in patients with resectable and potentially resectable CRLM, and we discuss the most promising factors that may reshape the future decision-making process of these patients.
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Affiliation(s)
- Marco Maria Germani
- Unit of Medical Oncology 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Beatrice Borelli
- Unit of Medical Oncology 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Piero Boraschi
- Department of Diagnostic and Interventional Radiology, and Nuclear Medicine, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Carlotta Antoniotti
- Unit of Medical Oncology 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Clara Ugolini
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
| | - Lucio Urbani
- Unit of General Surgery, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Luca Morelli
- General Surgery, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Gabriella Fontanini
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
| | - Gianluca Masi
- Unit of Medical Oncology 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Chiara Cremolini
- Unit of Medical Oncology 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Roberto Moretto
- Unit of Medical Oncology 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy.
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17
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Granata V, Fusco R, Setola SV, Simonetti I, Cozzi D, Grazzini G, Grassi F, Belli A, Miele V, Izzo F, Petrillo A. An update on radiomics techniques in primary liver cancers. Infect Agent Cancer 2022; 17:6. [PMID: 35246207 PMCID: PMC8897888 DOI: 10.1186/s13027-022-00422-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 02/28/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting. METHODS This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed. RESULTS In oncology, assessment of tissue heterogeneity is of particular interest: genomic analysis have demonstrated that the degree of tumour heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status in hepatocellular carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) patients. Radiomic analysis is a convenient radiological image analysis technique used to support clinical decisions as it is able to provide prognostic and / or predictive biomarkers that allow a fast, objective and repeatable tool for disease monitoring. CONCLUSIONS Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model. TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy.
| | | | - Sergio Venazio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Igino Simonetti
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Diletta Cozzi
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Francesca Grassi
- Division of Radiology, "Università Degli Studi Della Campania Luigi Vanvitelli", Naples, Italy
| | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, 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", Via Mariano Semmola 80131, Naples, Italy
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Radiofrequency ablation of hepatocellular carcinoma: CT texture analysis of the ablated area to predict local recurrence. Eur J Radiol 2022; 150:110250. [DOI: 10.1016/j.ejrad.2022.110250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 02/25/2022] [Accepted: 03/07/2022] [Indexed: 11/22/2022]
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EOB-MR Based Radiomics Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases. Cancers (Basel) 2022; 14:cancers14051239. [PMID: 35267544 PMCID: PMC8909637 DOI: 10.3390/cancers14051239] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/24/2022] [Accepted: 02/25/2022] [Indexed: 02/07/2023] Open
Abstract
Simple Summary The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. Ours results confirmed the capacity of radiomics to identify, as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach. These results were confirmed by external validation dataset. We obtained a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences. Abstract The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. This retrospective analysis was approved by the local Ethical Committee board of National Cancer of Naples, IRCCS “Fondazione Pascale”. Radiological databases were interrogated from January 2018 to May 2021 in order to select patients with liver metastases with pathological proof and EOB-MRI study in pre-surgical setting. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest by 2 expert radiologists, 851 radiomics features were extracted as median values using PyRadiomics. non-parametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. The best predictor to discriminate expansive versus infiltrative front of tumor growth was HLH_glcm_MaximumProbability extraxted on VIBE_FA30 with an accuracy of 84%, a sensitivity of 83%, and a specificity of 82%. The best predictor to discriminate tumor budding was Inverse Variance obtained by the original GLCM matrix extraxted on VIBE_FA30 with an accuracy of 89%, a sensitivity of 96% and a specificity of 65%. The best predictor to differentiate the mucinous type of tumor was the HHL_glszm_ZoneVariance extraxted on VIBE_FA30 with an accuracy of 85%, a sensitivity of 46% and a specificity of 95%. The best predictor to identify tumor recurrence was the LHL_glcm_Correlation extraxted on VIBE_FA30 with an accuracy of 86%, a sensitivity of 52% and a specificity of 97%. The best linear regression model was obtained in the identification of the tumor growth front considering the height textural significant metrics by VIBE_FA10 (an accuracy of 89%; sensitivity of 93% and a specificity of 82%). Considering significant texture metrics tested with pattern recognition approaches, the best performance for each outcome was reached by a KNN in the identification of recurrence with the 3 textural significant features extracted by VIBE_FA10 (AUC of 91%, an accuracy of 93%; sensitivity of 99% and a specificity of 77%). Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.
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Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, dell’ Aversana F, Ottaiano A, Avallone A, Nasti G, Grassi F, Pilone V, Miele V, Brunese L, Izzo F, Petrillo A. Contrast MR-Based Radiomics and Machine Learning Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases: A Preliminary Study. Cancers (Basel) 2022; 14:cancers14051110. [PMID: 35267418 PMCID: PMC8909569 DOI: 10.3390/cancers14051110] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 02/17/2022] [Accepted: 02/21/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary The objective of the study was to evaluate the radiomics features obtained by contrast MRI studies as prognostic biomarkers in colorectal liver metastases patients to predict clinical outcomes following liver resection. We demonstrated a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences. Moreover, considering linear regression models or neural network classifiers in a multivariate approach was possible to increase the performance in terms of accuracy, sensitivity, and specificity. Abstract Purpose: To assess radiomics features efficacy obtained by arterial and portal MRI phase in the prediction of clinical outcomes in the colorectal liver metastases patients, evaluating recurrence, mutational status, pathological characteristic (mucinous and tumor budding) and surgical resection margin. Methods: This retrospective analysis was approved by the local Ethical Committee board, and radiological databases were used to select patients with colorectal liver metastases with pathological proof and MRI study in a pre-surgical setting after neoadjuvant chemotherapy. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest on MRI by two expert radiologists, 851 radiomics features were extracted as median values using the PyRadiomics tool. Non-parametric Kruskal-Wallis test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The best predictor to discriminate expansive versus infiltrative tumor growth front was wavelet_LHH_glrlm_ShortRunLowGrayLevelEmphasis extracted on portal phase with accuracy of 82%, sensitivity of 84%, and specificity of 77%. The best predictor to discriminate tumor budding was wavelet_LLH_firstorder_10Percentile extracted on portal phase with accuracy of 92%, a sensitivity of 96%, and a specificity of 81%. The best predictor to differentiate the mucinous type of tumor was the wavelet_LLL_glcm_ClusterTendency extracted on portal phase with accuracy of 88%, a sensitivity of 38%, and a specificity of 100%. The best predictor to identify the recurrence was the wavelet_HLH_ngtdm_Complexity extracted on arterial phase with accuracy of 90%, a sensitivity of 71%, and a specificity of 95%. The best linear regression model was obtained in the identification of mucinous type considering the 13 textural significant metrics extracted by arterial phase (accuracy of 94%, sensitivity of 77% and a specificity of 99%). The best results were obtained in the identification of tumor budding with the eleven textural significant features extracted by arterial phase using a KNN (accuracy of 95%, sensitivity of 84%, and a specificity of 99%). Conclusions: Our results confirmed the capacity of radiomics to identify as biomarkers and several prognostic features that could affect the treatment choice in patients with liver metastases in order to obtain a more personalized approach.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
- Correspondence:
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy;
| | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy; (F.D.M.); (L.B.)
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Salerno, Italy; (C.C.); (V.P.)
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
| | - Federica dell’ Aversana
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy; (F.d.A.); (F.G.)
| | - Alessandro Ottaiano
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli, 80131 Naples, Italy; (A.O.); (A.A.); (G.N.)
| | - Antonio Avallone
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli, 80131 Naples, Italy; (A.O.); (A.A.); (G.N.)
| | - Guglielmo Nasti
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli, 80131 Naples, Italy; (A.O.); (A.A.); (G.N.)
| | - Francesca Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy; (F.d.A.); (F.G.)
| | - Vincenzo Pilone
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Salerno, Italy; (C.C.); (V.P.)
| | - Vittorio Miele
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy;
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Luca Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy; (F.D.M.); (L.B.)
| | - Francesco Izzo
- Division of Epatobiliary 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, 80131 Naples, Italy; (S.V.S.); (A.P.)
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Viganò L, Jayakody Arachchige VS, Fiz F. Is precision medicine for colorectal liver metastases still a utopia? New perspectives by modern biomarkers, radiomics, and artificial intelligence. World J Gastroenterol 2022; 28:608-623. [PMID: 35317421 PMCID: PMC8900542 DOI: 10.3748/wjg.v28.i6.608] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/29/2021] [Accepted: 01/20/2022] [Indexed: 02/06/2023] Open
Abstract
The management of patients with liver metastases from colorectal cancer is still debated. Several therapeutic options and treatment strategies are available for an extremely heterogeneous clinical scenario. Adequate prediction of patients’ outcomes and of the effectiveness of chemotherapy and loco-regional treatments are crucial to reach a precision medicine approach. This has been an unmet need for a long time, but recent studies have opened new perspectives. New morphological biomarkers have been identified. The dynamic evaluation of the metastases across a time interval, with or without chemotherapy, provided a reliable assessment of the tumor biology. Genetics have been explored and, thanks to their strong association with prognosis, have the potential to drive treatment planning. The liver-tumor interface has been identified as one of the main determinants of tumor progression, and its components, in particular the immune infiltrate, are the focus of major research. Image mining and analyses provided new insights on tumor biology and are expected to have a relevant impact on clinical practice. Artificial intelligence is a further step forward. The present paper depicts the evolution of clinical decision-making for patients affected by colorectal liver metastases, facing modern biomarkers and innovative opportunities that will characterize the evolution of clinical research and practice in the next few years.
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Affiliation(s)
- Luca Viganò
- Department of Hepatobiliary and General Surgery, IRCCS Humanitas Research Hospital, Rozzano 20089, MI, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele 20072, MI, Italy
| | - Visala S Jayakody Arachchige
- Department of Hepatobiliary and General Surgery, IRCCS Humanitas Research Hospital, Rozzano 20089, MI, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele 20072, MI, Italy
| | - Francesco Fiz
- Nuclear Medicine, IRCCS Humanitas Research Hospital, Rozzano 20089, MI, Italy
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Rabe E, Cioni D, Baglietto L, Fornili M, Gabelloni M, Neri E. Can the computed tomography texture analysis of colorectal liver metastases predict the response to first-line cytotoxic chemotherapy? World J Hepatol 2022; 14:244-259. [PMID: 35126852 PMCID: PMC8790398 DOI: 10.4254/wjh.v14.i1.244] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 07/04/2021] [Accepted: 12/02/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Artificial intelligence in radiology has the potential to assist with the diagnosis, prognostication and therapeutic response prediction of various cancers. A few studies have reported that texture analysis can be helpful in predicting the response to chemotherapy for colorectal liver metastases, however, the results have varied. Necrotic metastases were not clearly excluded in these studies and in most studies the full range of texture analysis features were not evaluated. This study was designed to determine if the computed tomography (CT) texture analysis results of non-necrotic colorectal liver metastases differ from previous reports. A larger range of texture features were also evaluated to identify potential new biomarkers.
AIM To identify potential new imaging biomarkers with CT texture analysis which can predict the response to first-line cytotoxic chemotherapy in non-necrotic colorectal liver metastases (CRLMs).
METHODS Patients who presented with CRLMs from 2012 to 2020 were retrospectively selected on the institutional radiology information system of our private radiology practice. The inclusion criteria were non-necrotic CRLMs with a minimum size of 10 mm (diagnosed on archived 1.25 mm portal venous phase CT scans) which were treated with standard first-line cytotoxic chemotherapy (FOLFOX, FOLFIRI, FOLFOXIRI, CAPE-OX, CAPE-IRI or capecitabine). The final study cohort consisted of 29 patients. The treatment response of the CRLMs was classified according to the RECIST 1.1 criteria. By means of CT texture analysis, various first and second order texture features were extracted from a single non-necrotic target CRLM in each responding and non-responding patient. Associations between features and response to chemotherapy were assessed by logistic regression models. The prognostic accuracy of selected features was evaluated by using the area under the curve.
RESULTS There were 15 responders (partial response) and 14 non-responders (7 stable and 7 with progressive disease). The responders presented with a higher number of CRLMs (P = 0.05). In univariable analysis, eight texture features of the responding CRLMs were associated with treatment response, but due to strong correlations among some of the features, only two features, namely minimum histogram gradient intensity and long run low grey level emphasis, were included in the multiple analysis. The area under the receiver operating characteristic curve of the multiple model was 0.80 (95%CI: 0.64 to 0.96), with a sensitivity of 0.73 (95%CI: 0.48 to 0.89) and a specificity of 0.79 (95%CI: 0.52 to 0.92).
CONCLUSION Eight first and second order texture features, but particularly minimum histogram gradient intensity and long run low grey level emphasis are significantly correlated with treatment response in non-necrotic CRLMs.
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Affiliation(s)
- Etienne Rabe
- Academic Radiology, Master in Oncologic Imaging, Department of Translational Research, University of Pisa, Pisa 56126, Italy
- Bay Radiology-Cancercare Oncology Centre, Bay Radiology, Port Elizabeth 6001, Eastern Cape, South Africa
| | - Dania Cioni
- Academic Radiology, Master in Oncologic Imaging, Department of Translational Research, University of Pisa, Pisa 56126, Italy
| | - Laura Baglietto
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa 56126, Italy
| | - Marco Fornili
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa 56126, Italy
| | - Michela Gabelloni
- Academic Radiology, Master in Oncologic Imaging, Department of Translational Research, University of Pisa, Pisa 56126, Italy
| | - Emanuele Neri
- Academic Radiology, Master in Oncologic Imaging, Department of Translational Research, University of Pisa, Pisa 56126, Italy
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Jin Z, Zhang F, Wang Y, Tian A, Zhang J, Chen M, Yu J. Single-Photon Emission Computed Tomography/Computed Tomography Image-Based Radiomics for Discriminating Vertebral Bone Metastases From Benign Bone Lesions in Patients With Tumors. Front Med (Lausanne) 2022; 8:792581. [PMID: 35059418 PMCID: PMC8764284 DOI: 10.3389/fmed.2021.792581] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 11/22/2021] [Indexed: 12/31/2022] Open
Abstract
Purpose: The purpose of this study was to investigate the feasibility of Single-Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) image-based radiomics in differentiating bone metastases from benign bone lesions in patients with tumors. Methods: A total of 192 lesions from 132 patients (134 in the training group, 58 in the validation group) diagnosed with vertebral bone metastases or benign bone lesions were enrolled. All images were evaluated and diagnosed independently by two physicians with more than 20 years of diagnostic experience for qualitative classification, the images were imported into MaZda software in Bitmap (BMP) format for feature extraction. All radiomics features were selected by least absolute shrinkage and selection operator (LASSO) regression and 10-fold cross-validation algorithms after the process of normalization and correlation analysis. Based on these selected features, two models were established: The CT model and SPECT model (radiomics features were derived from CT and SPECT images, respectively). In addition, a combination model (ComModel) combined CT and SPECT features was developed in order to better evaluate the predictive performance of radiomics models. Subsequently, the diagnostic performance between each model was separately evaluated by a confusion matrix. Results: There were 12, 13, and 18 features contained within the CT, SPECT, and ComModel, respectively. The constructed radiomics models based on SPECT/CT images to discriminate between bone metastases and benign bone lesions not only had high diagnostic efficacy in the training group (AUC of 0.894, 0.914, 0.951 for CT model, SPECT model, and ComModel, respectively), but also performed well in the validation group (AUC; 0.844, 0.871, 0.926). The AUC value of the human experts was 0.849 and 0.839 in the training and validation groups, respectively. Furthermore, both SPECT model and ComModel show higher classification performance than human experts in the training group (P = 0.021 and P = 0.001, respectively) and the validation group (P = 0.037 and P = 0.007, respectively). All models showed better diagnostic accuracy than human experts in the training group and the validation group. Conclusion: Radiomics derived from SPECT/CT images could effectively discriminate between bone metastases and benign bone lesions. This technique may be a new non-invasive way to help prevent unnecessary delays in diagnosis and a potential contribution in disease staging and treatment planning.
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Affiliation(s)
- Zhicheng Jin
- Department of Nuclear Medicine, The Second Hospital of Dalian Medical University, Dalian, China
| | - Fang Zhang
- Department of Nuclear Medicine, The Second Hospital of Dalian Medical University, Dalian, China
| | - Yizhen Wang
- Department of Nuclear Medicine, The Second Hospital of Dalian Medical University, Dalian, China
| | - Aijuan Tian
- Department of Nuclear Medicine, The Second Hospital of Dalian Medical University, Dalian, China
| | - Jianan Zhang
- Department of Nuclear Medicine, The Second Hospital of Dalian Medical University, Dalian, China
| | - Meiyan Chen
- Department of Nuclear Medicine, The Second Hospital of Dalian Medical University, Dalian, China
| | - Jing Yu
- Department of Nuclear Medicine, The Second Hospital of Dalian Medical University, Dalian, China
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Wang Y, Ma LY, Yin XP, Gao BL. Radiomics and Radiogenomics in Evaluation of Colorectal Cancer Liver Metastasis. Front Oncol 2022; 11:689509. [PMID: 35070948 PMCID: PMC8776634 DOI: 10.3389/fonc.2021.689509] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 12/03/2021] [Indexed: 12/12/2022] Open
Abstract
Colorectal cancer is one common digestive malignancy, and the most common approach of blood metastasis of colorectal cancer is through the portal vein system to the liver. Early detection and treatment of liver metastasis is the key to improving the prognosis of the patients. Radiomics and radiogenomics use non-invasive methods to evaluate the biological properties of tumors by deeply mining the texture features of images and quantifying the heterogeneity of metastatic tumors. Radiomics and radiogenomics have been applied widely in the detection, treatment, and prognostic evaluation of colorectal cancer liver metastases. Based on the imaging features of the liver, this paper reviews the current application of radiomics and radiogenomics in the diagnosis, treatment, monitor of disease progression, and prognosis of patients with colorectal cancer liver metastases.
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Affiliation(s)
| | | | - Xiao-Ping Yin
- CT-MRI Room, Affiliated Hospital of Hebei University, Baoding, China
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25
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Wang F, Tan R, Feng K, Hu J, Zhuang Z, Wang C, Hou J, Liu X. Magnetic Resonance Imaging-Based Radiomics Features Associated with Depth of Invasion Predicted Lymph Node Metastasis and Prognosis in Tongue Cancer. J Magn Reson Imaging 2021; 56:196-209. [PMID: 34888985 PMCID: PMC9299921 DOI: 10.1002/jmri.28019] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/19/2021] [Accepted: 11/23/2021] [Indexed: 12/18/2022] Open
Abstract
Background Adequate safe margin in tongue cancer radical surgery is one of the most important prognostic factors. However, the role of peritumoral tissues in predicting lymph node metastasis (LNM) and prognosis using radiomics analysis remains unclear. Purpose To investigate whether magnetic resonance imaging (MRI)‐based radiomics analysis with peritumoral extensions contributes toward the prediction of LNM and prognosis in tongue cancer. Study type Retrospective. Population Two hundred and thirty‐six patients (38.56% female) with tongue cancer (training set, N = 157; testing set, N = 79; 37.58% and 40.51% female for each). Field Strength/Sequence 1.5 T; T2‐weighted turbo spin‐echo images. Assessment Radiomics models (Rprim, Rprim+3, Rprim+5, Rprim+10, Rprim+15) were developed with features extracted from the primary tumor without or with peritumoral extensions (3, 5, 10, and 15 mm, respectively). Clinicopathological characteristics selected from univariate analysis, including MRI‐reported LN status, radiological extrinsic lingual muscle invasion, and pathological depth of invasion (DOI) were further incorporated into radiomics models to develop combined radiomics models (CRprim, CRprim+3, CRprim+5, CRprim+10, CRprim+15). Finally, the model performance was validated in the testing set. DOI was measured from the adjacent normal mucosa to the deepest point of tumor invasion. Statistical Tests Chi‐square test, regression analysis, receiver operating characteristic curve (ROC) analysis, decision analysis, spearman correlation analysis. The Delong test was used to compare area under the ROC (AUC). P < 0.05 was considered statistically significant. Results Of all the models, the CRprim+10 reached the highest AUC of 0.995 in the training set and 0.872 in the testing set. Radiomics features were significantly correlated with pathological DOI (correlation coefficients, −0.157 to −0.336). The CRprim+10 was an independent indicator for poor disease‐free survival (hazard ratio, 5.250) and overall survival (hazard ratio, 17.464) in the testing set. Data Conclusion Radiomics analysis with a 10‐mm peritumoral extension had excellent power to predict LNM and prognosis in tongue cancer.
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Affiliation(s)
- Fei Wang
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Rukeng Tan
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Kun Feng
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Jing Hu
- Department of Oral and Maxillofacial Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zehang Zhuang
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Cheng Wang
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Jinsong Hou
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Xiqiang Liu
- Department of Oral and Maxillofacial Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Cheng S, Jin Z, Xue H. Assessment of Response to Chemotherapy in Pancreatic Cancer with Liver Metastasis: CT Texture as a Predictive Biomarker. Diagnostics (Basel) 2021; 11:diagnostics11122252. [PMID: 34943489 PMCID: PMC8700536 DOI: 10.3390/diagnostics11122252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/21/2021] [Accepted: 11/26/2021] [Indexed: 11/16/2022] Open
Abstract
In this paper, we assess changes in CT texture of metastatic liver lesions after treatment with chemotherapy in patients with pancreatic cancer and determine if texture parameters correlate with measured time to progression (TTP). This retrospective study included 110 patients with pancreatic cancer with liver metastasis, and mean, entropy, kurtosis, skewness, mean of positive pixels, and standard deviation (SD) values were extracted during texture analysis. Response assessment was also obtained by using RECIST 1.1, Choi and modified Choi criteria, respectively. The correlation of texture parameters and existing assessment criteria with TTP were evaluated using Kaplan-Meier and Cox regression analyses in the training cohort. Kaplan-Meier curves of the proportion of patients without disease progression were significantly different for several texture parameters, and were better than those for RECIST 1.1-, Choi-, and modified Choi-defined response (p < 0.05 vs. p = 0.398, p = 0.142, and p = 0.536, respectively). Cox regression analysis showed that percentage change in SD was an independent predictor of TTP (p = 0.016) and confirmed in the validation cohort (p = 0.019). In conclusion, CT texture parameters have the potential to become predictive imaging biomarkers for response evaluation in pancreatic cancer with liver metastasis.
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Radiogenomics: Hunting Down Liver Metastasis in Colorectal Cancer Patients. Cancers (Basel) 2021; 13:cancers13215547. [PMID: 34771709 PMCID: PMC8582778 DOI: 10.3390/cancers13215547] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/02/2021] [Accepted: 11/03/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Colorectal cancer (CRC) is the third leading cause of cancer and the second most deadly tumor type in the world. The liver is the most common site of metastasis in CRC patients. The conversion of new imaging biomarkers into diagnostic, prognostic and predictive signatures, by the development of radiomics and radiogenomics, is an important potential new tool for the clinical management of cancer patients. In this review, we assess the knowledge gained from radiomics and radiogenomics studies in liver metastatic colorectal cancer patients and their possible use for early diagnosis, response assessment and treatment decisions. Abstract Radiomics is a developing new discipline that analyzes conventional medical images to extract quantifiable data that can be mined for new biomarkers that show the biology of pathological processes at microscopic levels. These data can be converted into image-based signatures to improve diagnostic, prognostic and predictive accuracy in cancer patients. The combination of radiomics and molecular data, called radiogenomics, has clear implications for cancer patients’ management. Though some studies have focused on radiogenomics signatures in hepatocellular carcinoma patients, only a few have examined colorectal cancer metastatic lesions in the liver. Moreover, the need to differentiate between liver lesions is fundamental for accurate diagnosis and treatment. In this review, we summarize the knowledge gained from radiomics and radiogenomics studies in hepatic metastatic colorectal cancer patients and their use in early diagnosis, response assessment and treatment decisions. We also investigate their value as possible prognostic biomarkers. In addition, the great potential of image mining to provide a comprehensive view of liver niche formation is examined thoroughly. Finally, new challenges and current limitations for the early detection of the liver premetastatic niche, based on radiomics and radiogenomics, are also discussed.
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Defeudis A, Cefaloni L, Giannetto G, Cappello G, Rizzetto F, Panic J, Barra D, Nicoletti G, Mazzetti S, Vanzulli A, Regge D, Giannini V. Comparison of radiomics approaches to predict resistance to 1st line chemotherapy in liver metastatic colorectal cancer. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3305-3308. [PMID: 34891947 DOI: 10.1109/embc46164.2021.9630316] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Colorectal cancer (CRC) has the second-highest tumor incidence and is a leading cause of death by cancer. Nearly 20% of patients with CRC will have metastases (mts) at the time of diagnosis, and more than 50% of patients with CRC develop metastases during their disease. Unfortunately, only 45% of patients after a chemotherapy will respond to treatment. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts, using CT scans. Understanding which mts will respond or not will help clinicians in providing a more efficient per-lesion treatment based on patient specific response and not only following a standard treatment. A group of 92 patients was enrolled from two Italian institutions. CT scans were collected, and the portal venous phase was manually segmented by an expert radiologist. Then, 75 radiomics features were extracted both from 7x7 ROIs that moved across the image and from the whole 3D mts. Feature selection was performed using a genetic algorithm. Results are presented as a comparison of the two different approaches of features extraction and different classification algorithms. Accuracy (ACC), sensitivity (SE), specificity (SP), negative and positive predictive values (NPV and PPV) were evaluated for all lesions (per-lesion analysis) and patients (per-patient analysis) in the construction and validation sets. Best results were obtained in the per-lesion analysis from the 3D approach using a Support Vector Machine as classifier. We reached on the training set an ACC of 81%, while on test set, we obtained SE of 76%, SP of 67%, PPV of 69% and NPV of 75%. On the validation set a SE of 61%, SP of 60%, PPV of 57% and NPV of 64% were reached. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance- to develop a radiomics signatures predicting single liver mts response to therapy. A personalized mts approach is important to avoid unnecessary toxicity offering more suitable treatments and a better quality of life to oncological patients.
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Wang X, Wang Y, Zhang Z, Zhou M, Zhou X, Zhao H, Xing J, Zhou Y. Rim enhancement on hepatobiliary phase of pre-treatment 3.0 T MRI: A potential marker for early chemotherapy response in colorectal liver metastases treated with XELOX. Eur J Radiol 2021; 143:109887. [PMID: 34454297 DOI: 10.1016/j.ejrad.2021.109887] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 07/25/2021] [Accepted: 07/27/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE To assess the value of the enhanced features on the hepatobiliary phase (HBP) of pre-treatment Gd-EOB-DTPA MRI in evaluating response to chemotherapy in colorectal liver metastases (CRLMs). METHODS We retrospectively studied 65 patients with CRLMs who underwent Gd-EOB-DTPA enhanced MRI before chemotherapy from October 2015 to November 2017. The diagnosis of liver metastasis was established on the basis of imaging findings. Two radiologists evaluated the size, contrast-enhanced (CE) patterns of the maximum lesion on the HBP. According to the different CE patterns, we quantified area signal intensity (SI) by applying SI ratio (such as SIcenter/outer and SIrim/center). All of the above parameters were analyzed in terms of chemotherapy response. RESULTS Rim enhancement on the HBP was more frequent in the responding group of 28 patients (72%) than in the non-responding group of eight patients (31%). Additionally, there was a significant association between chemotherapy response and quantitative parameters: including diameter (P = 0.04), SIcenter/outer (P = 0.047) and SIrim/center (P = 0.012). The HBP CE pattern (P = 0.007) and SIcenter/outer (P = 0.022) were independent factors for chemotherapy response. The areas under the curve (AUCs) of the above-mentioned parameters were significant associated with response to chemotherapy, in which diameter, HBP CE patterns, SIcenter/outer, and SIrim/center were 0.638, 0.706, 0.712, and 0.673, respectively. Moreover, the combination of these parameters obtained the largest AUC of 0.821. CONCLUSION The CE patterns, in particular with rim enhancement, and SI ratio parameters on the HBP are useful indicators for early evaluation of therapeutic response after chemotherapy in patients with CRLMs.
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Affiliation(s)
- Xinxin Wang
- Department of Radiology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin 150010, Heilongjiang, China
| | - Yu Wang
- Department of Radiology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin 150010, Heilongjiang, China
| | - Ziqian Zhang
- Department of Radiology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin 150010, Heilongjiang, China
| | - Meng Zhou
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin 150010, Heilongjiang, China
| | - Xueyan Zhou
- School of Technology, Harbin University, 109 Zhongxing Street, Harbin 150010, Heilongjiang, China.
| | - Hongxin Zhao
- Department of Radiology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin 150010, Heilongjiang, China
| | - JiQing Xing
- Harbin Engineering University, Harbin 150001, Heilongjiang Province, China.
| | - Yang Zhou
- Department of Radiology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin 150010, Heilongjiang, China.
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Wesdorp NJ, van Goor VJ, Kemna R, Jansma EP, van Waesberghe JHTM, Swijnenburg RJ, Punt CJA, Huiskens J, Kazemier G. Advanced image analytics predicting clinical outcomes in patients with colorectal liver metastases: A systematic review of the literature. Surg Oncol 2021; 38:101578. [PMID: 33866191 DOI: 10.1016/j.suronc.2021.101578] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 03/30/2021] [Accepted: 04/01/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND To better select patients with colorectal liver metastases (CRLM) for an optimal selection of treatment strategy (i.e. local, systemic or combined treatment) new prognostic models are warranted. In the last decade, radiomics has emerged as a field to create predictive models based on imaging features. This systematic review aims to investigate the current state and potential of radiomics to predict clinical outcomes in patients with CRLM. METHODS A comprehensive literature search was conducted in the electronic databases of PubMed, Embase, and Cochrane Library, according to PRISMA guidelines. Original studies reporting on radiomics predicting clinical outcome in patients diagnosed with CRLM were included. Clinical outcomes were defined as response to systemic treatment, recurrence of disease, and survival (overall, progression-free, disease-free). Primary outcome was the predictive performance of radiomics. A narrative synthesis of the results was made. Methodological quality was assessed using the radiomics quality score. RESULTS In 11 out of 14 included studies, radiomics was predictive for response to treatment, recurrence of disease, survival, or a combination of outcomes. Combining clinical parameters and radiomic features in multivariate modelling often improved the predictive performance. Different types of individual features were found prognostic. Noticeable were the contrary levels of heterogeneous and homogeneous features in patients with good response. The methodological quality as assessed by the radiomics quality score varied considerably between studies. CONCLUSION Radiomics appears a promising non-invasive method to predict clinical outcome and improve personalized decision-making in patients with CRLM. However, results were contradictory and difficult to compare. Standardized prospective studies are warranted to establish the added value of radiomics in patients with CRLM.
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Affiliation(s)
- N J Wesdorp
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Surgery, Cancer Center Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands.
| | - V J van Goor
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Surgery, Cancer Center Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - R Kemna
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Surgery, Cancer Center Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - E P Jansma
- Amsterdam UMC, University of Amsterdam, Department of Epidemiology and Biostatistics, Cancer Center Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - J H T M van Waesberghe
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Molecular Imaging, Cancer Center Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - R J Swijnenburg
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Surgery, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, Netherlands
| | - C J A Punt
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Department of Epidemiology, Utrecht, the Netherlands
| | - J Huiskens
- SAS Institute B.V., Flevolaan 69, Huizen, the Netherlands
| | - G Kazemier
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Surgery, Cancer Center Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
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Sartoretti E, Sartoretti T, Wyss M, Reischauer C, van Smoorenburg L, Binkert CA, Sartoretti-Schefer S, Mannil M. Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases. Sci Rep 2021; 11:5506. [PMID: 33750899 PMCID: PMC7943598 DOI: 10.1038/s41598-021-85168-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 02/24/2021] [Indexed: 12/13/2022] Open
Abstract
We sought to evaluate the utility of radiomics for Amide Proton Transfer weighted (APTw) imaging by assessing its value in differentiating brain metastases from high- and low grade glial brain tumors. We retrospectively identified 48 treatment-naïve patients (10 WHO grade 2, 1 WHO grade 3, 10 WHO grade 4 primary glial brain tumors and 27 metastases) with either primary glial brain tumors or metastases who had undergone APTw MR imaging. After image analysis with radiomics feature extraction and post-processing, machine learning algorithms (multilayer perceptron machine learning algorithm; random forest classifier) with stratified tenfold cross validation were trained on features and were used to differentiate the brain neoplasms. The multilayer perceptron achieved an AUC of 0.836 (receiver operating characteristic curve) in differentiating primary glial brain tumors from metastases. The random forest classifier achieved an AUC of 0.868 in differentiating WHO grade 4 from WHO grade 2/3 primary glial brain tumors. For the differentiation of WHO grade 4 tumors from grade 2/3 tumors and metastases an average AUC of 0.797 was achieved. Our results indicate that the use of radiomics for APTw imaging is feasible and the differentiation of primary glial brain tumors from metastases is achievable with a high degree of accuracy.
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Affiliation(s)
- Elisabeth Sartoretti
- Institute of Radiology, Kantonsspital Winterthur, Winterthur, Switzerland.,Faculty of Medicine, University of Zürich, Zürich, Switzerland
| | - Thomas Sartoretti
- Institute of Radiology, Kantonsspital Winterthur, Winterthur, Switzerland.,Faculty of Medicine, University of Zürich, Zürich, Switzerland
| | - Michael Wyss
- Institute of Radiology, Kantonsspital Winterthur, Winterthur, Switzerland.,Philips Healthsystems, Zürich, Switzerland
| | - Carolin Reischauer
- Department of Medicine, University of Fribourg, Fribourg, Switzerland.,Department of Radiology, HFR Fribourg-Hôpital Cantonal, Fribourg, Switzerland
| | | | | | | | - Manoj Mannil
- Department of Neuroradiology, Kantonsspital Aarau, Aarau, Switzerland. .,Institute of Clinical Radiology, University Hospital Münster, University of Münster, Albrecht-Schweitzer-Campus 1, E48149, Münster, Germany.
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Badic B, Tixier F, Cheze Le Rest C, Hatt M, Visvikis D. Radiogenomics in Colorectal Cancer. Cancers (Basel) 2021; 13:cancers13050973. [PMID: 33652647 PMCID: PMC7956421 DOI: 10.3390/cancers13050973] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 02/07/2021] [Accepted: 02/20/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Colorectal carcinoma is characterized by intratumoral heterogeneity that can be assessed by radiogenomics. Radiomics, high-throughput quantitative data extracted from medical imaging, combined with molecular analysis, through genomic and transcriptomic data, is expected to lead to significant advances in personalized medicine. However, a radiogenomics approach in colorectal cancer is still in its early stages and many problems remain to be solved. Here we review the progress and challenges in this field at its current stage, as well as future developments. Abstract The steady improvement of high-throughput technologies greatly facilitates the implementation of personalized precision medicine. Characterization of tumor heterogeneity through image-derived features—radiomics and genetic profile modifications—genomics, is a rapidly evolving field known as radiogenomics. Various radiogenomics studies have been dedicated to colorectal cancer so far, highlighting the potential of these approaches to enhance clinical decision-making. In this review, a general outline of colorectal radiogenomics literature is provided, discussing the current limitations and suggested further developments.
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Affiliation(s)
- Bogdan Badic
- National Institute of Health and Medical Research, LaTIM—Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France; (F.T.); (C.C.L.R.); (M.H.); (D.V.)
- Correspondence: ; Tel.: +33-298-347-215
| | - Florent Tixier
- National Institute of Health and Medical Research, LaTIM—Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France; (F.T.); (C.C.L.R.); (M.H.); (D.V.)
| | - Catherine Cheze Le Rest
- National Institute of Health and Medical Research, LaTIM—Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France; (F.T.); (C.C.L.R.); (M.H.); (D.V.)
- Department of Nuclear Medicine, University Hospital of Poitiers, 86021 Poitiers, France
| | - Mathieu Hatt
- National Institute of Health and Medical Research, LaTIM—Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France; (F.T.); (C.C.L.R.); (M.H.); (D.V.)
| | - Dimitris Visvikis
- National Institute of Health and Medical Research, LaTIM—Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France; (F.T.); (C.C.L.R.); (M.H.); (D.V.)
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Beaumont H, Iannessi A, Bertrand AS, Cucchi JM, Lucidarme O. Harmonization of radiomic feature distributions: impact on classification of hepatic tissue in CT imaging. Eur Radiol 2021; 31:6059-6068. [PMID: 33459855 DOI: 10.1007/s00330-020-07641-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/23/2020] [Accepted: 12/17/2020] [Indexed: 12/15/2022]
Abstract
OBJECTIVES Following the craze for radiomic features (RF), their lack of reliability raised the question of the generalizability of classification models. Inter-site harmonization of images therefore becomes a central issue. We compared RF harmonization processing designed to detect liver diseases in CT images. METHODS We retrospectively analyzed 76 multi-center portal CT series of non-diseased (NDL) and diseased liver (DL) patients. In each series, we positioned volumes of interest in spleen and liver, then extracted 9 RF (histogram and texture). We evaluated two RF harmonization approaches. First, in each series, we computed the Z-score of liver measurements based on those computed in the spleen. Second, we evaluated the ComBat method according to each imaging center; parameters were computed in the spleen and applied to the liver. We compared RF distributions and classification performances before/after harmonization. We classified NDL versus spleen and versus DL tissues. RESULTS The RF distributions were all different between liver and spleen (p < 0.05). The Z-score harmonization outperformed for the detection of liver versus spleen: AUC = 93.1% (p < 0.001). For the detection of DL versus NDL, in a case/control setting, we found no differences between the harmonizations: mean AUC = 73.6% (p = 0.49). Using the whole datasets, the performances were improved using ComBat (p = 0.05) AUC = 82.4% and degraded with Z-score AUC = 67.4% (p = 0.008). CONCLUSIONS Data harmonization requires to first focus on data structuring to not degrade the performances of subsequent classifications. Liver tissue classification after harmonization of spleen-based RF is a promising strategy for improving the detection of DL tissue. KEY POINTS • Variability of acquisition parameter makes radiomics of CT features non-reproducible. • Data harmonization can help circumvent the inter-site variability of acquisition protocols. • Inter-site harmonization must be carefully implemented and requires designing consistent data sets.
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Affiliation(s)
| | | | | | - Jean Michel Cucchi
- Centre Hospitalier Princesse Grâce, Avenue Pasteur, 98000, Monaco, Monaco
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Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment. Eur J Nucl Med Mol Imaging 2020; 48:1785-1794. [PMID: 33326049 PMCID: PMC8113210 DOI: 10.1007/s00259-020-05142-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 11/29/2020] [Indexed: 02/08/2023]
Abstract
Purpose Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors. Methods A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score. Results The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice. Conclusion Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-020-05142-w.
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Wei J, Cheng J, Gu D, Chai F, Hong N, Wang Y, Tian J. Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases. Med Phys 2020; 48:513-522. [PMID: 33119899 DOI: 10.1002/mp.14563] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 10/13/2020] [Accepted: 10/14/2020] [Indexed: 02/06/2023] Open
Abstract
PURPOSE The purpose of this study was to develop and validate a deep learning (DL)-based radiomics model to predict the response to chemotherapy in colorectal liver metastases (CRLM). METHODS In this retrospective study, we enrolled 192 patients diagnosed with CRLM who received first-line chemotherapy and were followed by response assessment. Tumor response was identified according to the Response Evaluation Criteria in Solid Tumors (RECIST). Contrast-enhanced multidetector computed tomography (MDCT) images were fed as inputs of the ResNet10-based DL radiomics model, and the possibility of response was predicted as the output. The final combined DL radiomics model was constructed by integrating the response-related clinical factors and the developed DL radiomics signature. A time-independent validation cohort (n = 48) was extracted from the 192 patients to evaluate the DL model with area under the receiver operating characteristic curve (AUC), specificity, and sensitivity. Meanwhile, a traditional radiomics model was constructed using least absolute shrinkage and selection operator (lasso) as comparisons with the DL-based model. RESULTS According to RECIST criteria, 131 patients were identified as responders with complete response, partial response, and stable disease, while 61 patients were nonresponders with progression disease. The selected predictive clinical factor turned out to be the carcinoembryonic antigen (CEA) level with AUC of 0.489 (95% confidence interval [CI], 0.380-0.599) and 0.558 (95% CI, 0.374-0.741) in the training and validation cohorts, respectively. The DL-based model provided better performance than the traditional classifier-based radiomics model with significantly higher AUC (training: 0.903 [95% CI, 0.851-0.955] vs 0.745 [95% CI, 0.659-0.831]; validation: 0.820 [95% CI, 0.681-0.959] vs 0.598 [95% CI, 0.422-0.774]). The combination of DL-based model with the CEA level provided slightly increased performance with AUC of 0.935 [95% CI, 0.897-0.973] in the training cohort and 0.830 [95% CI, 0.688-0.973] in the validation cohort. CONCLUSIONS The developed DL-based radiomics model could improve the efficiency to predict the response to chemotherapy in CRLM, which may assist in subsequent personalized treatment decision-making in CRLM management.
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Affiliation(s)
- Jingwei Wei
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jin Cheng
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Dongsheng Gu
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Fan Chai
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Yi Wang
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, China.,Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
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Rizzetto F, Calderoni F, De Mattia C, Defeudis A, Giannini V, Mazzetti S, Vassallo L, Ghezzi S, Sartore-Bianchi A, Marsoni S, Siena S, Regge D, Torresin A, Vanzulli A. Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases. Eur Radiol Exp 2020; 4:62. [PMID: 33169295 PMCID: PMC7652946 DOI: 10.1186/s41747-020-00189-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 10/13/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Radiomics is expected to improve the management of metastatic colorectal cancer (CRC). We aimed at evaluating the impact of liver lesion contouring as a source of variability on radiomic features (RFs). METHODS After Ethics Committee approval, 70 liver metastases in 17 CRC patients were segmented on contrast-enhanced computed tomography scans by two residents and checked by experienced radiologists. RFs from grey level co-occurrence and run length matrices were extracted from three-dimensional (3D) regions of interest (ROIs) and the largest two-dimensional (2D) ROIs. Inter-reader variability was evaluated with Dice coefficient and Hausdorff distance, whilst its impact on RFs was assessed using mean relative change (MRC) and intraclass correlation coefficient (ICC). For the main lesion of each patient, one reader also segmented a circular ROI on the same image used for the 2D ROI. RESULTS The best inter-reader contouring agreement was observed for 2D ROIs according to both Dice coefficient (median 0.85, interquartile range 0.78-0.89) and Hausdorff distance (0.21 mm, 0.14-0.31 mm). Comparing RF values, MRC ranged 0-752% for 2D and 0-1567% for 3D. For 24/32 RFs (75%), MRC was lower for 2D than for 3D. An ICC > 0.90 was observed for more RFs for 2D (53%) than for 3D (34%). Only 2/32 RFs (6%) showed a variability between 2D and circular ROIs higher than inter-reader variability. CONCLUSIONS A 2D contouring approach may help mitigate overall inter-reader variability, albeit stable RFs can be extracted from both 3D and 2D segmentations of CRC liver metastases.
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Affiliation(s)
- Francesco Rizzetto
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Francesca Calderoni
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Cristina De Mattia
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Arianna Defeudis
- Department of Surgical Sciences, University of Turin, via Verdi 8, 10124, Turin, Italy
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, Turin, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, via Verdi 8, 10124, Turin, Italy
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, Turin, Italy
| | - Simone Mazzetti
- Department of Surgical Sciences, University of Turin, via Verdi 8, 10124, Turin, Italy
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, Turin, Italy
| | - Lorenzo Vassallo
- Radiology Unit, SS Annunziata Hospital ASLCN1 Cuneo, via Ospedali 14, 12038, Cuneo, Savigliano, Italy
| | - Silvia Ghezzi
- Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Andrea Sartore-Bianchi
- Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
| | - Silvia Marsoni
- Precision Oncology, IFOM - The FIRC Institute of Molecular Oncology, via Adamello 16, 20139, Milan, Italy
| | - Salvatore Siena
- Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
| | - Daniele Regge
- Department of Surgical Sciences, University of Turin, via Verdi 8, 10124, Turin, Italy
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, Turin, Italy
| | - Alberto Torresin
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
| | - Angelo Vanzulli
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy.
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy.
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Staal FCR, van der Reijd DJ, Taghavi M, Lambregts DMJ, Beets-Tan RGH, Maas M. Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review. Clin Colorectal Cancer 2020; 20:52-71. [PMID: 33349519 DOI: 10.1016/j.clcc.2020.11.001] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 09/03/2020] [Accepted: 11/02/2020] [Indexed: 02/07/2023]
Abstract
Prediction of outcome in patients with colorectal cancer (CRC) is challenging as a result of lack of a robust biomarker and heterogeneity between and within tumors. The aim of this review was to assess the current possibilities and limitations of radiomics (on computed tomography [CT], magnetic resonance imaging [MRI], and positron emission tomography [PET]) for the prediction of treatment outcome and long-term outcome in CRC. Medline/PubMed was searched up to August 2020 for studies that used radiomics for the prediction of response to treatment and survival in patients with CRC (based on pretreatment imaging). The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool and Radiomics Quality Score (RQS) were used for quality assessment. A total of 76 studies met the inclusion criteria and were included for further analysis. Radiomics analyses were performed on MRI in 41 studies, on CT in 30 studies, and on 18F-FDG-PET/CT in 10 studies. Heterogeneous results were reported regarding radiomics methods and included features. High-quality studies (n = 13), consisting mainly of MRI-based radiomics to predict response in rectal cancer, were able to predict response with good performance. Radiomics literature in CRC is highly heterogeneous, but it nonetheless holds promise for the prediction of outcome. The most evidence is available for MRI-based radiomics in rectal cancer. Future radiomics research in CRC should focus on independent validation of existing models rather than on developing new models.
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Affiliation(s)
- Femke C R Staal
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Denise J van der Reijd
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Marjaneh Taghavi
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Doenja M J Lambregts
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands; Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Monique Maas
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
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38
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Radiomics of Liver Metastases: A Systematic Review. Cancers (Basel) 2020; 12:cancers12102881. [PMID: 33036490 PMCID: PMC7600822 DOI: 10.3390/cancers12102881] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 10/03/2020] [Accepted: 10/05/2020] [Indexed: 12/15/2022] Open
Abstract
Simple Summary Patients with liver metastases can be scheduled for different therapies (e.g., chemotherapy, surgery, radiotherapy, and ablation). The choice of the most appropriate treatment should rely on adequate understanding of tumor biology and prediction of survival, but reliable biomarkers are lacking. Radiomics is an innovative approach to medical imaging: it identifies invisible-to-the-human-eye radiological patterns that can predict tumor aggressiveness and patients outcome. We reviewed the available literature to elucidate the role of radiomics in patients with liver metastases. Thirty-two papers were analyzed, mostly (56%) concerning metastases from colorectal cancer. Even if available studies are still preliminary, radiomics provided effective prediction of response to chemotherapy and of survival, allowing more accurate and earlier prediction than standard predictors. Entropy and homogeneity were the radiomic features with the strongest clinical impact. In the next few years, radiomics is expected to give a consistent contribution to the precision medicine approach to patients with liver metastases. Abstract Multidisciplinary management of patients with liver metastases (LM) requires a precision medicine approach, based on adequate profiling of tumor biology and robust biomarkers. Radiomics, defined as the high-throughput identification, analysis, and translational applications of radiological textural features, could fulfill this need. The present review aims to elucidate the contribution of radiomic analyses to the management of patients with LM. We performed a systematic review of the literature through the most relevant databases and web sources. English language original articles published before June 2020 and concerning radiomics of LM extracted from CT, MRI, or PET-CT were considered. Thirty-two papers were identified. Baseline higher entropy and lower homogeneity of LM were associated with better survival and higher chemotherapy response rates. A decrease in entropy and an increase in homogeneity after chemotherapy correlated with radiological tumor response. Entropy and homogeneity were also highly predictive of tumor regression grade. In comparison with RECIST criteria, radiomic features provided an earlier prediction of response to chemotherapy. Lastly, texture analyses could differentiate LM from other liver tumors. The commonest limitations of studies were small sample size, retrospective design, lack of validation datasets, and unavailability of univocal cut-off values of radiomic features. In conclusion, radiomics can potentially contribute to the precision medicine approach to patients with LM, but interdisciplinarity, standardization, and adequate software tools are needed to translate the anticipated potentialities into clinical practice.
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Giannini V, Defeudis A, Rosati S, Cappello G, Mazzetti S, Panic J, Regge D, Balestra G. An innovative radiomics approach to predict response to chemotherapy of liver metastases based on CT images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1339-1342. [PMID: 33018236 DOI: 10.1109/embc44109.2020.9176627] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Liver metastases (mts) from colorectal cancer (CRC) can have different responses to chemotherapy in the same patient. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts. 22 radiomic features (RF) were computed on pretreatment portal CT scans following a manual segmentation of mts. RFs were extracted from 7x7 Region of Interests (ROIs) that moved across the image by step of 2 pixels. Liver mts were classified as non-responder (R-) if their largest diameter increased more than 3 mm after 3 months of treatment and responder (R+), otherwise. Features selection (FS) was performed by a genetic algorithm and classification by a Support Vector Machine (SVM) classifier. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values were evaluated for all lesions in the training and validation sets, separately. On the training set, we obtained sensitivity of 86%, specificity of 67%, PPV of 89% and NPV of 61%, while, on the validation set, we reached a sensitivity of 73%, specificity of 47%, PPV of 64% and NPV of 57%. Specificity was biased by the low number of R- lesions on the validation set. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance- to personalize treatment of patients with metastastic colorectal cancer, based on the likelihood of response to chemotherapy of each liver metastasis.
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40
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Wei J, Jiang H, Gu D, Niu M, Fu F, Han Y, Song B, Tian J. Radiomics in liver diseases: Current progress and future opportunities. Liver Int 2020; 40:2050-2063. [PMID: 32515148 PMCID: PMC7496410 DOI: 10.1111/liv.14555] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/28/2020] [Accepted: 05/29/2020] [Indexed: 02/05/2023]
Abstract
Liver diseases, a wide spectrum of pathologies from inflammation to neoplasm, have become an increasingly significant health problem worldwide. Noninvasive imaging plays a critical role in the clinical workflow of liver diseases, but conventional imaging assessment may provide limited information. Accurate detection, characterization and monitoring remain challenging. With progress in quantitative imaging analysis techniques, radiomics emerged as an efficient tool that shows promise to aid in personalized diagnosis and treatment decision-making. Radiomics could reflect the heterogeneity of liver lesions via extracting high-throughput and high-dimensional features from multi-modality imaging. Machine learning algorithms are then used to construct clinical target-oriented imaging biomarkers to assist disease management. Here, we review the methodological process in liver disease radiomics studies in a stepwise fashion from data acquisition and curation, region of interest segmentation, liver-specific feature extraction, to task-oriented modelling. Furthermore, the applications of radiomics in liver diseases are outlined in aspects of diagnosis and staging, evaluation of liver tumour biological behaviours, and prognosis according to different disease type. Finally, we discuss the current limitations of radiomics in liver disease studies and explore its future opportunities.
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Affiliation(s)
- Jingwei Wei
- Key Laboratory of Molecular ImagingInstitute of AutomationChinese Academy of SciencesBeijingChina
- Beijing Key Laboratory of Molecular ImagingBeijingChina
| | - Hanyu Jiang
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
| | - Dongsheng Gu
- Key Laboratory of Molecular ImagingInstitute of AutomationChinese Academy of SciencesBeijingChina
- Beijing Key Laboratory of Molecular ImagingBeijingChina
| | - Meng Niu
- Department of Interventional RadiologyThe First Affiliated Hospital of China Medical UniversityShenyangChina
| | - Fangfang Fu
- Department of Medical ImagingHenan Provincial People’s HospitalZhengzhouHenanChina
- Department of Medical ImagingPeople’s Hospital of Zhengzhou University. ZhengzhouHenanChina
| | - Yuqi Han
- Key Laboratory of Molecular ImagingInstitute of AutomationChinese Academy of SciencesBeijingChina
- Beijing Key Laboratory of Molecular ImagingBeijingChina
| | - Bin Song
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
| | - Jie Tian
- Key Laboratory of Molecular ImagingInstitute of AutomationChinese Academy of SciencesBeijingChina
- Beijing Key Laboratory of Molecular ImagingBeijingChina
- Beijing Advanced Innovation Center for Big Data‐Based Precision MedicineSchool of MedicineBeihang UniversityBeijingChina
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of EducationSchool of Life Science and TechnologyXidian UniversityXi’anShaanxiChina
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41
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Mühlberg A, Holch JW, Heinemann V, Huber T, Moltz J, Maurus S, Jäger N, Liu L, Froelich MF, Katzmann A, Gresser E, Taubmann O, Sühling M, Nörenberg D. The relevance of CT-based geometric and radiomics analysis of whole liver tumor burden to predict survival of patients with metastatic colorectal cancer. Eur Radiol 2020; 31:834-846. [PMID: 32851450 DOI: 10.1007/s00330-020-07192-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 07/02/2020] [Accepted: 08/13/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES To investigate the prediction of 1-year survival (1-YS) in patients with metastatic colorectal cancer with use of a systematic comparative analysis of quantitative imaging biomarkers (QIBs) based on the geometric and radiomics analysis of whole liver tumor burden (WLTB) in comparison to predictions based on the tumor burden score (TBS), WLTB volume alone, and a clinical model. METHODS A total of 103 patients (mean age: 61.0 ± 11.2 years) with colorectal liver metastases were analyzed in this retrospective study. Automatic segmentations of WLTB from baseline contrast-enhanced CT images were used. Established biomarkers as well as a standard radiomics model building were used to derive 3 prognostic models. The benefits of a geometric metastatic spread (GMS) model, the Aerts radiomics prior model of the WLTB, and the performance of TBS and WLTB volume alone were assessed. All models were analyzed in both statistical and predictive machine learning settings in terms of AUC. RESULTS TBS showed the best discriminative performance in a statistical setting to discriminate 1-YS (AUC = 0.70, CI: [0.56, 0.90]). For the machine learning-based prediction for unseen patients, both a model of the GMS of WLTB (0.73, CI: [0.60, 0.84]) and the Aerts radiomics prior model (0.76, CI: [0.65, 0.86]) applied on the WLTB showed a numerically higher predictive performance than TBS (0.68, CI: [0.54, 0.79]), radiomics (0.65, CI: [0.55, 0.78]), WLTB volume alone (0.53, CI: [0.40. 0.66]), or the clinical model (0.56, CI: [0.43, 0.67]). CONCLUSIONS The imaging-based GMS model may be a first step towards a more fine-grained machine learning extension of the TBS concept for risk stratification in mCRC patients without the vulnerability to technical variance of radiomics. KEY POINTS • CT-based geometric distribution and radiomics analysis of whole liver tumor burden in metastatic colorectal cancer patients yield prognostic information. • Differences in survival are possibly attributable to the spatial distribution of metastatic lesions and the geometric metastatic spread analysis of all liver metastases may serve as robust imaging biomarker invariant to technical variation. • Imaging-based prediction models outperform clinical models for 1-year survival prediction in metastatic colorectal cancer patients with liver metastases.
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Affiliation(s)
| | - Julian W Holch
- Comprehensive Cancer Center Munich, University Hospital, LMU Munich, Munich, Germany
| | - Volker Heinemann
- Comprehensive Cancer Center Munich, University Hospital, LMU Munich, Munich, Germany
| | - Thomas Huber
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Department of Radiology, Munich University Hospitals, Munich, Germany
| | - Jan Moltz
- Fraunhofer Institute for Medical Image Computing MEVIS, Bremen, Germany
| | - Stefan Maurus
- Department of Radiology, Munich University Hospitals, Munich, Germany
| | - Nils Jäger
- Department of Radiology, Munich University Hospitals, Munich, Germany
| | - Lian Liu
- Comprehensive Cancer Center Munich, University Hospital, LMU Munich, Munich, Germany
| | - Matthias F Froelich
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Department of Radiology, Munich University Hospitals, Munich, Germany
| | | | - Eva Gresser
- Department of Radiology, Munich University Hospitals, Munich, Germany
| | - Oliver Taubmann
- CT R&D Image Analytics, Siemens Healthineers, Forchheim, Germany
| | - Michael Sühling
- CT R&D Image Analytics, Siemens Healthineers, Forchheim, Germany
| | - Dominik Nörenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany. .,Department of Radiology, Munich University Hospitals, Munich, Germany.
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42
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Diagnostic Accuracy of Single-Phase Computed Tomography Texture Analysis for Prediction of LI-RADS v2018 Category. J Comput Assist Tomogr 2020; 44:188-192. [PMID: 32195797 DOI: 10.1097/rct.0000000000001003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE The aim of this study was to determine if texture analysis can classify liver observations likely to be hepatocellular carcinoma based on the Liver Imaging Reporting and Data System (LI-RADS) using single portal venous phase computed tomography. METHODS This research ethics board-approved retrospective cohort study included 64 consecutive LI-RADS observations. Individual observation texture analysis features were compared using Kruskal-Wallis and 2 sample t tests. Logistic regression was used for prediction of LI-RADS group. Diagnostic accuracy was assessed using receiver operating characteristic curves and Youden method. RESULTS Multiple texture features were associated with LI-RADS including the mean HU (P = 0.003), median (P = 0.002), minimum (P = 0.010), maximum (P = 0.013), standard deviation (P = 0.009), skewness (P = 0.007), and entropy (P < 0.001). On logistic regression, LI-RADS group could be predicted with area under the curve, sensitivity, and specificity of 0.98, 96%, and 100%, respectively. CONCLUSIONS Texture analysis features on portal venous phase computed tomography can identify liver observations likely to be hepatocellular carcinoma, which may preclude the need to recall some patients for additional multiphase imaging.
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43
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Hazhirkarzar B, Khoshpouri P, Shaghaghi M, Ghasabeh MA, Pawlik TM, Kamel IR. Current state of the art imaging approaches for colorectal liver metastasis. Hepatobiliary Surg Nutr 2020; 9:35-48. [PMID: 32140477 DOI: 10.21037/hbsn.2019.05.11] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
One of the most common cancers worldwide, colorectal cancer (CRC) has been associated with significant morbidity and mortality and therefore represents an enormous burden to the health care system. Recent advances in CRC treatments have provided patients with primary and metastatic CRC a better long-term prognosis. The presence of synchronous or metachronous metastasis has been associated, however, with worse survival. The most common site of metastatic disease is the liver. A variety of treatment modalities aimed at targeting colorectal liver metastases (CRLM) has been demonstrated to improve the prognosis of these patients. Loco-regional approaches such as surgical resection and tumor ablation (operative and percutaneous) can provide patients with a chance at long-term disease control and even cure in select populations. Patient selection is important in defining the most suitable treatment option for CRLM in order to provide the best possible survival benefit while avoiding unnecessary interventions and adverse events. Medical imaging plays a crucial role in evaluating the characteristics of CRLMs and disease resectability. Size of tumors, proximity to adjacent anatomical structures, and volume of the unaffected liver are among the most important imaging parameters to determine the suitability of patients for surgical management or other appropriate treatment approaches. We herein provide a comprehensive overview of current-state-of-the-art imaging in the management of CRLM, including staging, treatment planning, response and survival assessment, and post-treatment surveillance. Computed tomography (CT) scan and magnetic resonance imaging (MRI) are two most commonly used techniques, which can be used solely or in combination with functional imaging modalities such as positron emission tomography (PET) and diffusion weighted imaging (DWI). Providing up-to-date evidence on advantages and disadvantages of imaging modalities and tumor assessment criteria, the current review offers a practice guide to assist providers in choosing the most suitable imaging approach for patients with CRLM.
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Affiliation(s)
- Bita Hazhirkarzar
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Pegah Khoshpouri
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mohammadreza Shaghaghi
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mounes Aliyari Ghasabeh
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Timothy M Pawlik
- Department of Surgery, The Ohio State University, Wexner Medical Center, Columbus, OH, USA
| | - Ihab R Kamel
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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