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Tang Y, Su YX, Zheng JM, Zhuo ML, Qian QF, Shen QL, Lin P, Chen ZK. Radiogenomic analysis for predicting lymph node metastasis and molecular annotation of radiomic features in pancreatic cancer. J Transl Med 2024; 22:690. [PMID: 39075486 PMCID: PMC11288107 DOI: 10.1186/s12967-024-05479-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 07/03/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND To provide a preoperative prediction model for lymph node metastasis in pancreatic cancer patients and provide molecular information of key radiomic features. METHODS Two cohorts comprising 151 and 54 pancreatic cancer patients were included in the analysis. Radiomic features from the tumor region of interests were extracted by using PyRadiomics software. We used a framework that incorporated 10 machine learning algorithms and generated 77 combinations to construct radiomics-based models for lymph node metastasis prediction. Weighted gene coexpression network analysis (WGCNA) was subsequently performed to determine the relationships between gene expression levels and radiomic features. Molecular pathways enrichment analysis was performed to uncover the underlying molecular features. RESULTS Patients in the in-house cohort (mean age, 61.3 years ± 9.6 [SD]; 91 men [60%]) were separated into training (n = 105, 70%) and validation (n = 46, 30%) cohorts. A total of 1,239 features were extracted and subjected to machine learning algorithms. The 77 radiomic models showed moderate performance for predicting lymph node metastasis, and the combination of the StepGBM and Enet algorithms had the best performance in the training (AUC = 0.84, 95% CI = 0.77-0.91) and validation (AUC = 0.85, 95% CI = 0.73-0.98) cohorts. We determined that 15 features were core variables for lymph node metastasis. Proliferation-related processes may respond to the main molecular alterations underlying these features. CONCLUSIONS Machine learning-based radiomics could predict the status of lymph node metastasis in pancreatic cancer, which is associated with proliferation-related alterations.
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Affiliation(s)
- Yi Tang
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Yi-Xi Su
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Jin-Mei Zheng
- Department of Radiology, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Min-Ling Zhuo
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Qing-Fu Qian
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Qing-Ling Shen
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China
| | - Peng Lin
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China.
| | - Zhi-Kui Chen
- Department of Medical Ultrasound, Fujian Medical University Union Hospital, 29 Xinquan road, Fuzhou, China.
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Zheng D, Grandgenett PM, Zhang Q, Baine M, Shi Y, Du Q, Liang X, Wong J, Iqbal S, Preuss K, Kamal A, Yu H, Du H, Hollingsworth MA, Zhang C. radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer. Sci Rep 2024; 14:12316. [PMID: 38811597 PMCID: PMC11137018 DOI: 10.1038/s41598-024-62741-5] [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: 11/17/2023] [Accepted: 05/21/2024] [Indexed: 05/31/2024] Open
Abstract
Addressing the significant level of variability exhibited by pancreatic cancer necessitates the adoption of a systems biology approach that integrates molecular data, biological properties of the tumors, medical images, and clinical features of the patients. In this study, a comprehensive multi-omics methodology was employed to examine a distinctive collection of patient dataset containing rapid autopsy tumor and normal tissue samples as well as longitudinal imaging with a focus on pancreatic cancer. By performing a whole exome sequencing analysis on tumor and normal tissues to identify somatic gene variants and a radiomic feature analysis to tumor CT images, the genome-wide association approach established a connection between pancreatic cancer driver genes and relevant radiomic features, enabling a thorough and quantitative assessment of the heterogeneity of pancreatic tumors. The significant association between sets of genes and radiomic features revealed the involvement of genes in shaping tumor morphological heterogeneity. Some results of the association established a connection between the molecular level mechanism and their outcomes at the level of tumor structural heterogeneity. Because tumor structure and tumor structural heterogeneity are related to the patients' overall survival, patients who had pancreatic cancer driver gene mutations with an association to a certain radiomic feature have been observed to experience worse survival rates than cases without these somatic mutations. Furthermore, the association analysis has revealed potential gene mutations and radiomic feature candidates that warrant further investigation in future research endeavors.
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Affiliation(s)
- Dandan Zheng
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA.
| | - Paul M Grandgenett
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA
| | - Qi Zhang
- Department of Mathematics and Statistics, University of New Hampshire, Durham, NH, USA
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Yu Shi
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA
| | - Qian Du
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL, USA
| | - Jeffrey Wong
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Subhan Iqbal
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA
| | - Kiersten Preuss
- Department of Nutrition and Health Sciences, University of Nebraska, Lincoln, NE, USA
| | - Ahsan Kamal
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Hongfeng Yu
- School of Computing, University of Nebraska, Lincoln, NE, USA
| | - Huijing Du
- Department of Mathematics, University of Nebraska, Lincoln, NE, USA
| | - Michael A Hollingsworth
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA.
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA.
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Anghel C, Grasu MC, Anghel DA, Rusu-Munteanu GI, Dumitru RL, Lupescu IG. Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images. Diagnostics (Basel) 2024; 14:438. [PMID: 38396476 PMCID: PMC10887967 DOI: 10.3390/diagnostics14040438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/10/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentation of the lesion, and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show a lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.
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Affiliation(s)
- Cristian Anghel
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Mugur Cristian Grasu
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Denisa Andreea Anghel
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Gina-Ionela Rusu-Munteanu
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Radu Lucian Dumitru
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Ioana Gabriela Lupescu
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
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Fusco R, Granata V. Comments on "Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta-analysis". Eur J Radiol 2023; 169:111192. [PMID: 37976763 DOI: 10.1016/j.ejrad.2023.111192] [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: 10/08/2023] [Accepted: 11/05/2023] [Indexed: 11/19/2023]
Abstract
We read with interest the article from Dr Jia LL and colleagues in Eur J Radiol in which they assessed the methodological quality of radiomics-based studies for non-invasive preoperative prediction of Kirsten rat sarcoma (KRAS) mutations in patients with colorectal cancer. They systematically evaluated the prediction models diagnostic accuracy of twenty-nine studies between February 2014 and March 2022 and we congratulate the Authors on their accuracy in reporting recent published manuscript about radiomics-based studies to predict KRAS mutations in patients with colorectal cancer however they did not report the impact of contrast administration and the different phases of the contrast study (arterial, portal and transient phase) compared to the EOB phase in this research field.
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Affiliation(s)
- Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples I-80131, Italy.
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Zheng D, Grandgenett PM, Zhang Q, Baine M, Shi Y, Du Q, Liang X, Wong J, Iqbal S, Preuss K, Kamal A, Yu H, Du H, Hollingsworth MA, Zhang C. radioGWAS: link radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.02.23297995. [PMID: 37961101 PMCID: PMC10635263 DOI: 10.1101/2023.11.02.23297995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Addressing the significant level of variability exhibited by pancreatic cancer necessitates the adoption of a systems biology approach that integrates molecular data, biological properties of the tumors, and clinical features of the patients. In this study, a comprehensive multi-omics methodology was employed to examine a distinctive collection patient dataset containing rapid autopsy tumor and normal tissue samples as well as longitudinal imaging with a focus on pancreatic cancer. By performing a whole exome sequencing analysis on tumor and normal tissues to identify somatic gene variants and a radiomics feature analysis to tumor CT images, the genome-wide association approach established a connection between pancreatic cancer driver genes and relevant radiomics features, enabling a thorough and quantitative assessment of the heterogeneity of pancreatic tumors. The significant association between sets of genes and radiomics features revealed the involvement of genes in shaping tumor morphological heterogeneity. Some results of the association established a connection between the molecular level mechanism and their outcomes at the level of tumor structural heterogeneity. Because tumor structure and tumor structural heterogeneity are related to the patients' overall survival, patients who had pancreatic cancer driver gene mutations with an association to a certain radiomics feature have been observed to experience worse survival rates than cases without these somatic mutations. Furthermore, the outcome of the association analysis has revealed potential gene mutations and radiomics feature candidates that warrant further investigation in future research endeavors.
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