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Arefan D, D'Ardenne NM, Iranpour N, Catania R, Yousef J, Chupetlovska K, Moghe A, Sholosh B, Thangasamy S, Borhani AA, Singhi AD, Monga SP, Furlan A, Wu S. Quantitative radiomics and qualitative LI-RADS imaging descriptors for non-invasive assessment of β-catenin mutation status in hepatocellular carcinoma. Abdom Radiol (NY) 2024; 49:2220-2230. [PMID: 38782785 DOI: 10.1007/s00261-024-04344-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 04/09/2024] [Accepted: 04/16/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE Gain-of-function mutations in CTNNB1, gene encoding for β-catenin, are observed in 25-30% of hepatocellular carcinomas (HCCs). Recent studies have shown β-catenin activation to have distinct roles in HCC susceptibility to mTOR inhibitors and resistance to immunotherapy. Our goal was to develop and test a computational imaging-based model to non-invasively assess β-catenin activation in HCC, since liver biopsies are often not done due to risk of complications. METHODS This IRB-approved retrospective study included 134 subjects with pathologically proven HCC and available β-catenin activation status, who also had either CT or MR imaging of the liver performed within 1 year of histological assessment. For qualitative descriptors, experienced radiologists assessed the presence of imaging features listed in LI-RADS v2018. For quantitative analysis, a single biopsy proven tumor underwent a 3D segmentation and radiomics features were extracted. We developed prediction models to assess the β-catenin activation in HCC using both qualitative and quantitative descriptors. RESULTS There were 41 cases (31%) with β-catenin mutation and 93 cases (69%) without. The model's AUC was 0.70 (95% CI 0.60, 0.79) using radiomics features and 0.64 (0.52, 0.74; p = 0.468) using qualitative descriptors. However, when combined, the AUC increased to 0.88 (0.80, 0.92; p = 0.009). Among the LI-RADS descriptors, the presence of a nodule-in-nodule showed a significant association with β-catenin mutations (p = 0.015). Additionally, 88 radiomics features exhibited a significant association (p < 0.05) with β-catenin mutations. CONCLUSION Combination of LI-RADS descriptors and CT/MRI-derived radiomics determine β-catenin activation status in HCC with high confidence, making precision medicine a possibility.
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Affiliation(s)
- Dooman Arefan
- Department of Radiology, University of Pittsburgh Medical Center, 200 Lothrop Street, Suite 200, Pittsburgh, PA, 15213, USA
- Pittsburgh Liver Research Center, University of Pittsburgh Medical Center and University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Nicholas M D'Ardenne
- Department of Radiology, University of Pittsburgh Medical Center, 200 Lothrop Street, Suite 200, Pittsburgh, PA, 15213, USA
| | - Negaur Iranpour
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Roberta Catania
- Department of Radiology, Northwestern University Feinberg School of Medicine, 676 N. Saint Clair Street, Suite 800, Chicago, IL, 60611, USA
| | - Jacob Yousef
- Department of Radiology, University of Pittsburgh Medical Center, 200 Lothrop Street, Suite 200, Pittsburgh, PA, 15213, USA
| | - Kalina Chupetlovska
- Diagnostic Imaging Department, University Hospital "Saint Ivan Rilski", Sofia, Bulgaria
| | - Akshata Moghe
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Texas Medical Branch, Galveston, TX, USA
| | - Biatta Sholosh
- Department of Radiology, University of Pittsburgh Medical Center, 200 Lothrop Street, Suite 200, Pittsburgh, PA, 15213, USA
| | - Senthur Thangasamy
- Department of Radiology, University of Pittsburgh Medical Center, 200 Lothrop Street, Suite 200, Pittsburgh, PA, 15213, USA
| | - Amir A Borhani
- Department of Radiology, Northwestern University Feinberg School of Medicine, 676 N. Saint Clair Street, Suite 800, Chicago, IL, 60611, USA
| | - Aatur D Singhi
- Pittsburgh Liver Research Center, University of Pittsburgh Medical Center and University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Pathology, University of Pittsburgh Medical Center, S405A-BST, 200 Lothrop Street, Pittsburgh, PA, 15261, USA
| | - Satdarshan P Monga
- Pittsburgh Liver Research Center, University of Pittsburgh Medical Center and University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Pathology, University of Pittsburgh Medical Center, S405A-BST, 200 Lothrop Street, Pittsburgh, PA, 15261, USA
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Alessandro Furlan
- Department of Radiology, University of Pittsburgh Medical Center, 200 Lothrop Street, Suite 200, Pittsburgh, PA, 15213, USA
- Pittsburgh Liver Research Center, University of Pittsburgh Medical Center and University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Shandong Wu
- Department of Radiology, University of Pittsburgh Medical Center, 200 Lothrop Street, Suite 200, Pittsburgh, PA, 15213, USA.
- Pittsburgh Liver Research Center, University of Pittsburgh Medical Center and University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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Liu HF, Wang M, Lu YJ, Wang Q, Lu Y, Xing F, Xing W. CEMRI-Based Quantification of Intratumoral Heterogeneity for Predicting Aggressive Characteristics of Hepatocellular Carcinoma Using Habitat Analysis: Comparison and Combination of Deep Learning. Acad Radiol 2024; 31:2346-2355. [PMID: 38057182 DOI: 10.1016/j.acra.2023.11.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 12/08/2023]
Abstract
RATIONALE AND OBJECTIVES To explore both an intratumoral heterogeneity (ITH) model based on habitat analysis and a deep learning (DL) model based on contrast-enhanced magnetic resonance imaging (CEMRI) and validate its efficiency for predicting microvascular invasion (MVI) and pathological differentiation in hepatocellular carcinoma (HCC). METHODS CEMRI images were retrospectively obtained from 277 HCCs in 265 patients. Habitat analysis and DL features were extracted from the CEMRI images and selected with the least absolute shrinkage and selection operator approach to develop ITH and DL models, respectively, and these robust features were then integrated to design a fusion model for predicting MVI and poorly differentiated HCC (pHCC). The predictive value of the three models was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS The training and validation sets comprised 221 HCCs and 56 HCCs, respectively. The ITH and DL models presented AUC values of (0.90 vs. 0.87) for predicting MVI in the training set, with AUC values of 0.86 and 0.83 in the validation set. The AUC values of the ITH model to predict pHCC were 0.90 and 0.86 in the two sets, respectively; they were 0.84 and 0.80 for the DL model. The fusion model yielded the best performance for predicting MVI and pHCC in the training set (AUC=0.95, 0.90) and in the validation set (AUC=0.89, 0.87), respectively. CONCLUSION A fusion model integrating ITH and DL features derived from CEMRI images can serve as an excellent imaging biomarker for predicting aggressive characteristics in HCC.
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Affiliation(s)
- Hai-Feng Liu
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Min Wang
- Department of Anesthesiology, The Second People's Hospital of Changzhou, Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu, China (M.W.)
| | - Yu-Jie Lu
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Qing Wang
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Yang Lu
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Fei Xing
- Department of Radiology, Nantong Third People's Hospital, Nantong, Jiangsu, China (F.X.)
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.).
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Wang P, Wang X, Zhang M, Li G, Zhao N, Qiao Q. Combining the radiomics signature and HPV status for the risk stratification of patients with OPC. Oral Dis 2024; 30:272-280. [PMID: 36135344 DOI: 10.1111/odi.14386] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/01/2022] [Accepted: 09/08/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVE The objective was to perform risk stratification of oropharyngeal cancer (OPC) for treatment de-escalation based on the radiomics analysis and human papillomavirus (HPV) status. METHODS A total of 265 patients with OPC who underwent baseline contrast-enhanced computed tomography were analyzed, and the patients were grouped into the training (n = 133) and test (n = 132) cohorts at a ratio of 1:1. Intratumoral and peritumoral radiomics features were extracted, and the radiomics signature (Rscore) was calculated using least absolute shrinkage and selection operator regression (LASSO) and Cox regression analyses. RESULTS Twelve features were selected to establish the radiomics signature (Rscore) of intratumoral regions +10-mm peritumoral regions, which yielded maximum AUCs of 0.835, 0.798, and 0.784 in the training, test, and validation cohorts, respectively. Patients with OPC were divided into the high-risk group, intermediate-risk group, and low-risk group based on the Rscore and HPV status and had different prognoses. Patients in the low-risk group benefit from radiotherapy alone, and patients in the intermediate-risk group only benefitted from chemoradiotherapy. CONCLUSION The radiomics signature appears to improve the predictive performance of clinical characteristics for oropharyngeal cancer. The combined stratification of the radiomics signature and HPV status might be preferred to select patients for de-escalated treatment.
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Affiliation(s)
- Ping Wang
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Xuan Wang
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Miao Zhang
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Guang Li
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Ning Zhao
- Department of Otolaryngology, The First Hospital of China Medical University, Shenyang, China
| | - Qiao Qiao
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
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Brancato V, Cerrone M, Garbino N, Salvatore M, Cavaliere C. Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score. World J Gastroenterol 2024; 30:381-417. [PMID: 38313230 PMCID: PMC10835534 DOI: 10.3748/wjg.v30.i4.381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/05/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies. AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS). METHODS A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05. RESULTS One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072). CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.
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Affiliation(s)
- Valentina Brancato
- Department of Information Technology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Cerrone
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Nunzia Garbino
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Salvatore
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Carlo Cavaliere
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
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Liu HF, Lu Y, Wang Q, Lu YJ, Xing W. Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation. J Hepatocell Carcinoma 2023; 10:2103-2115. [PMID: 38050577 PMCID: PMC10693828 DOI: 10.2147/jhc.s434895] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 11/22/2023] [Indexed: 12/06/2023] Open
Abstract
Purpose To develop and compare various machine learning (ML) classifiers that employ radiomics extracted from contrast-enhanced magnetic resonance imaging (CEMRI) for diagnosing pathological differentiation of hepatocellular carcinoma (HCC), and validate the performance of the best model. Methods A total of 251 patients with HCCs (n = 262) were assigned to a training (n = 200) cohort and a validation (n = 62) cohort. A collection of 5502 radiomics signatures were extracted from the CEMRI images for each HCC nodule. To reduce redundancy and dimensionality, Spearman rank correlation, minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) approach were employed. Eight ML classifiers were trained to obtain the best radiomics model. The performance of each model was evaluated based on the area under the receiver operating characteristic curve (AUC). The radiomics model was integrated with liver imaging reporting and data system (LI-RADS) features to design a combined model. Results The eXtreme Gradient Boosting (XGBoost)-based radiomics model outperformed other ML classifiers in evaluating pHCC, achieving an AUC of 1.00 and accuracy of 1.00 in the training cohort. The LI-RADS model demonstrated an AUC value of 0.77 and 0.82 in the training and validation cohorts. The combined model exhibited best performance in both the training and validation cohorts, with AUCs of 1.00 and 0.86 for evaluating HCC differentiation, respectively. Conclusion CEMRI radiomics integrating LI-RADS features demonstrated excellent performance in evaluating HCC differentiation, suggesting an optimal clinical decision tool for individualized diagnosis of HCC differentiation.
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Affiliation(s)
- Hai-Feng Liu
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, People’s Republic of China
| | - Yang Lu
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, People’s Republic of China
| | - Qing Wang
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, People’s Republic of China
| | - Yu-Jie Lu
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, People’s Republic of China
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, People’s Republic of China
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Bernatz S, Elenberger O, Ackermann J, Lenga L, Martin SS, Scholtz JE, Koch V, Grünewald LD, Herrmann Y, Kinzler MN, Stehle A, Koch I, Zeuzem S, Bankov K, Doering C, Reis H, Flinner N, Schulze F, Wild PJ, Hammerstingl R, Eichler K, Gruber-Rouh T, Vogl TJ, dos Santos DP, Mahmoudi S. CT-radiomics and clinical risk scores for response and overall survival prognostication in TACE HCC patients. Sci Rep 2023; 13:533. [PMID: 36631548 PMCID: PMC9834236 DOI: 10.1038/s41598-023-27714-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 01/06/2023] [Indexed: 01/12/2023] Open
Abstract
We aimed to identify hepatocellular carcinoma (HCC) patients who will respond to repetitive transarterial chemoembolization (TACE) to improve the treatment algorithm. Retrospectively, 61 patients (mean age, 65.3 years ± 10.0 [SD]; 49 men) with 94 HCC mRECIST target-lesions who had three consecutive TACE between 01/2012 and 01/2020 were included. Robust and non-redundant radiomics features were extracted from the 24 h post-embolization CT. Five different clinical TACE-scores were assessed. Seven different feature selection methods and machine learning models were used. Radiomics, clinical and combined models were built to predict response to TACE on a lesion-wise and patient-wise level as well as its impact on overall-survival prognostication. 29 target-lesions of 19 patients were evaluated in the test set. Response rates were 37.9% (11/29) on the lesion-level and 42.1% (8/19) on the patient-level. Radiomics top lesion-wise response prognostications was AUC 0.55-0.67. Clinical scores revealed top AUCs of 0.65-0.69. The best working model combined the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical score mHAP_II_score_group with AUC = 0.70, accuracy = 0.72. We transferred this model on a patient-level to achieve AUC = 0.62, CI = 0.41-0.83. The two radiomics-clinical features revealed overall-survival prognostication of C-index = 0.67. In conclusion, a random forest model using the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical mHAP-II-score-group seems promising for TACE response prognostication.
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Affiliation(s)
- Simon Bernatz
- University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany. .,Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590, Frankfurt am Main, Germany. .,Frankfurt Cancer Institute (FCI), 60590, Frankfurt am Main, Germany.
| | - Oleg Elenberger
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Jörg Ackermann
- grid.7839.50000 0004 1936 9721Department of Molecular Bioinformatics, Institute of Computer Science, Goethe University Frankfurt am Main, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main, Germany
| | - Lukas Lenga
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Simon S. Martin
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Jan-Erik Scholtz
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Vitali Koch
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Leon D. Grünewald
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Yannis Herrmann
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Maximilian N. Kinzler
- grid.411088.40000 0004 0578 8220Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Angelika Stehle
- grid.411088.40000 0004 0578 8220Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Ina Koch
- grid.7839.50000 0004 1936 9721Department of Molecular Bioinformatics, Institute of Computer Science, Goethe University Frankfurt am Main, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main, Germany
| | - Stefan Zeuzem
- grid.411088.40000 0004 0578 8220Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Katrin Bankov
- grid.7839.50000 0004 1936 9721Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590 Frankfurt am Main, Germany
| | - Claudia Doering
- grid.7839.50000 0004 1936 9721Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590 Frankfurt am Main, Germany
| | - Henning Reis
- grid.7839.50000 0004 1936 9721Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590 Frankfurt am Main, Germany
| | - Nadine Flinner
- grid.7839.50000 0004 1936 9721Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590 Frankfurt am Main, Germany
| | - Falko Schulze
- grid.7839.50000 0004 1936 9721Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590 Frankfurt am Main, Germany
| | - Peter J. Wild
- grid.7839.50000 0004 1936 9721Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590 Frankfurt am Main, Germany ,grid.511198.5Frankfurt Cancer Institute (FCI), 60590 Frankfurt am Main, Germany ,grid.417999.b0000 0000 9260 4223Frankfurt Institute for Advanced Studies (FIAS), 60438 Frankfurt am Main, Germany
| | - Renate Hammerstingl
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Katrin Eichler
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Tatjana Gruber-Rouh
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Thomas J. Vogl
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Daniel Pinto dos Santos
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany ,grid.6190.e0000 0000 8580 3777Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Str. 62, 50937 Cologne, Germany
| | - Scherwin Mahmoudi
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
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Miranda J, Horvat N, Fonseca GM, Araujo-Filho JDAB, Fernandes MC, Charbel C, Chakraborty J, Coelho FF, Nomura CH, Herman P. Current status and future perspectives of radiomics in hepatocellular carcinoma. World J Gastroenterol 2023; 29:43-60. [PMID: 36683711 PMCID: PMC9850949 DOI: 10.3748/wjg.v29.i1.43] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/27/2022] [Accepted: 12/14/2022] [Indexed: 01/04/2023] Open
Abstract
Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging. The main potential applications of radiomic models in HCC are to predict histology, response to treatment, genetic signature, recurrence, and survival. Despite the encouraging results to date, there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice. The purpose of this article is to review the main concepts and challenges pertaining to radiomics, and to review recent studies and potential applications of radiomics in HCC.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-010, Brazil
| | - Natally Horvat
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | | | - Maria Clara Fernandes
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Charlotte Charbel
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Jayasree Chakraborty
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
| | - Paulo Herman
- Department of Gastroenterology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
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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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9
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Mao Q, Zhou MT, Zhao ZP, Liu N, Yang L, Zhang XM. Role of radiomics in the diagnosis and treatment of gastrointestinal cancer. World J Gastroenterol 2022; 28:6002-6016. [PMID: 36405385 PMCID: PMC9669820 DOI: 10.3748/wjg.v28.i42.6002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/24/2022] [Accepted: 10/27/2022] [Indexed: 11/10/2022] Open
Abstract
Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.
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Affiliation(s)
- Qi Mao
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Mao-Ting Zhou
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Zhang-Ping Zhao
- Department of Radiology, Panzhihua Central Hospital, Panzhihua 617000, Sichuan Province, China
| | - Ning Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Lin Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xiao-Ming Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
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10
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Lipkova J, Chen RJ, Chen B, Lu MY, Barbieri M, Shao D, Vaidya AJ, Chen C, Zhuang L, Williamson DFK, Shaban M, Chen TY, Mahmood F. Artificial intelligence for multimodal data integration in oncology. Cancer Cell 2022; 40:1095-1110. [PMID: 36220072 PMCID: PMC10655164 DOI: 10.1016/j.ccell.2022.09.012] [Citation(s) in RCA: 112] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 07/12/2022] [Accepted: 09/15/2022] [Indexed: 02/07/2023]
Abstract
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging from radiology, histology, and genomics to electronic health records. Current artificial intelligence (AI) models operate mainly in the realm of a single modality, neglecting the broader clinical context, which inevitably diminishes their potential. Integration of different data modalities provides opportunities to increase robustness and accuracy of diagnostic and prognostic models, bringing AI closer to clinical practice. AI models are also capable of discovering novel patterns within and across modalities suitable for explaining differences in patient outcomes or treatment resistance. The insights gleaned from such models can guide exploration studies and contribute to the discovery of novel biomarkers and therapeutic targets. To support these advances, here we present a synopsis of AI methods and strategies for multimodal data fusion and association discovery. We outline approaches for AI interpretability and directions for AI-driven exploration through multimodal data interconnections. We examine challenges in clinical adoption and discuss emerging solutions.
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Affiliation(s)
- Jana Lipkova
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Richard J Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Bowen Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Computer Science, Harvard University, Cambridge, MA, USA
| | - Ming Y Lu
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Matteo Barbieri
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel Shao
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Harvard-MIT Health Sciences and Technology (HST), Cambridge, MA, USA
| | - Anurag J Vaidya
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Harvard-MIT Health Sciences and Technology (HST), Cambridge, MA, USA
| | - Chengkuan Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Luoting Zhuang
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Drew F K Williamson
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Muhammad Shaban
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Tiffany Y Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA; Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA.
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