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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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Mirza-Aghazadeh-Attari M, Madani SP, Shahbazian H, Ansari G, Mohseni A, Borhani A, Afyouni S, Kamel IR. Predictive role of radiomics features extracted from preoperative cross-sectional imaging of pancreatic ductal adenocarcinoma in detecting lymph node metastasis: a systemic review and meta-analysis. Abdom Radiol (NY) 2023; 48:2570-2584. [PMID: 37202642 DOI: 10.1007/s00261-023-03940-y] [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: 12/20/2022] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 05/20/2023]
Abstract
Lymph node metastases are associated with poor clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). In preoperative imaging, conventional diagnostic modalities do not provide the desired accuracy in diagnosing lymph node metastasis. The current review aims to determine the pooled diagnostic profile of studies examining the role of radiomics features in detecting lymph node metastasis in PDAC. PubMed, Google Scholar, and Embase databases were searched for relevant articles. The quality of the studies was examined using the Radiomics Quality Score and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tools. Pooled results for sensitivity, specificity, likelihood, and odds ratios with the corresponding 95% confidence intervals (CIs) were calculated using a random-effect model (DerSimonian-Liard method). No significant publication bias was detected among the studies included in this meta-analysis. The pooled sensitivity of the validation datasets included in the study was 77.4% (72.7%, 81.5%) and pooled specificity was 72.4% (63.8, 79.6%). The diagnostic odds ratio of the validation datasets was 9.6 (6.0, 15.2). No statistically significant heterogeneity was detected for sensitivity and odds ratio (P values of 0.3 and 0.08, respectively). However, there was significant heterogeneity concerning specificity (P = 0.003). The pretest probability of having lymph node metastasis in the pooled databases was 52% and a positive post-test probability was 76% after the radiomics features were used, showing a net benefit of 24%. Classifiers trained on radiomics features extracted from preoperative images can improve the sensitivity and specificity of conventional cross-sectional imaging in detecting lymph node metastasis in PDAC.
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Affiliation(s)
- Mohammad Mirza-Aghazadeh-Attari
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Seyedeh Panid Madani
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Haneyeh Shahbazian
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Golnoosh Ansari
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Alireza Mohseni
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Ali Borhani
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Shadi Afyouni
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Ihab R Kamel
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA.
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Chu LC, Fishman EK. Artificial Intelligence Outperforms Radiologists for Pancreatic Cancer Lymph Node Metastasis Prediction at CT. Radiology 2023; 306:170-171. [PMID: 36066370 PMCID: PMC9844535 DOI: 10.1148/radiol.222012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Linda C Chu
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Hal B168, 600 N Wolfe St, Baltimore, MD 21287
| | - Elliot K Fishman
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Hal B168, 600 N Wolfe St, Baltimore, MD 21287
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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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Huang B, Huang H, Zhang S, Zhang D, Shi Q, Liu J, Guo J. Artificial intelligence in pancreatic cancer. Am J Cancer Res 2022; 12:6931-6954. [PMID: 36276650 PMCID: PMC9576619 DOI: 10.7150/thno.77949] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/24/2022] [Indexed: 11/30/2022] Open
Abstract
Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.
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Affiliation(s)
- Bowen Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Haoran Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Shuting Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Dingyue Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Qingya Shi
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Jianzhou Liu
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Junchao Guo
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
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